1
|
Park SH, Pinto-Powell R, Thesen T, Lindqwister A, Levy J, Chacko R, Gonzalez D, Bridges C, Schwendt A, Byrum T, Fong J, Shasavari S, Hassanpour S. Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. Med Educ Online 2024; 29:2315684. [PMID: 38351737 PMCID: PMC10868429 DOI: 10.1080/10872981.2024.2315684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.
Collapse
Affiliation(s)
- Soo Hwan Park
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Thomas Thesen
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Joshua Levy
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Rachael Chacko
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Connor Bridges
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Adam Schwendt
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Travis Byrum
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Justin Fong
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | | |
Collapse
|
2
|
Zhao K, Seeliger E, Niendorf T, Liu Z. Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope? J Magn Reson Imaging 2024; 59:1494-1513. [PMID: 37675919 DOI: 10.1002/jmri.29000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Kaixuan Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Erdmann Seeliger
- Institute of Translational Physiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| |
Collapse
|
3
|
Jackson S, Freeman R, Noronha A, Jamil H, Chavez E, Carmichael J, Ruiz KM, Miller C, Benke S, Perrot R, Hockley M, Murphy K, Casillan A, Radanovich L, Deforest R, Nunes ME, Galarreta-Aima C, Sidlow R, Einhorn Y, Woods J. Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data. Am J Med Genet A 2024; 194:e63505. [PMID: 38168469 DOI: 10.1002/ajmg.a.63505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 01/05/2024]
Abstract
Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.
Collapse
Affiliation(s)
| | - Rebecca Freeman
- Valley Children's Hospital, Madera, California, USA
- UCSF Benioff Children's Hospital Oakland, Oakland, California, USA
| | | | - Hafsah Jamil
- Valley Children's Hospital, Madera, California, USA
| | - Eric Chavez
- Valley Children's Hospital, Madera, California, USA
| | | | | | | | - Sarah Benke
- Valley Children's Hospital, Madera, California, USA
| | | | | | - Kady Murphy
- Valley Children's Hospital, Madera, California, USA
| | | | | | | | - Mark E Nunes
- Valley Children's Hospital, Madera, California, USA
| | | | | | | | - Jeremy Woods
- Valley Children's Hospital, Madera, California, USA
- Stanford University, Palo Alto, California, USA
- Eureka Institute for Translational Medicine, Siracusa, Italy
- Translation Science Foundation, Fresno, California, USA
| |
Collapse
|
4
|
Sigmund LM, S SS, Albers A, Erdmann P, Paton RS, Greb L. Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of p-Block-Atom-Based Molecules. Angew Chem Int Ed Engl 2024; 63:e202401084. [PMID: 38452299 DOI: 10.1002/anie.202401084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
"How strong is this Lewis acid?" is a question researchers often approach by calculating its fluoride ion affinity (FIA) with quantum chemistry. Here, we present FIA49k, an extensive FIA dataset with 48,986 data points calculated at the RI-DSD-BLYP-D3(BJ)/def2-QZVPP//PBEh-3c level of theory, including 13 different p-block atoms as the fluoride accepting site. The FIA49k dataset was used to train FIA-GNN, two message-passing graph neural networks, which predict gas and solution phase FIA values of molecules excluded from training with a mean absolute error of 14 kJ mol-1 (r2=0.93) from the SMILES string of the Lewis acid as the only input. The level of accuracy is notable, given the wide energetic range of 750 kJ mol-1 spanned by FIA49k. The model's value was demonstrated with four case studies, including predictions for molecules extracted from the Cambridge Structural Database and by reproducing results from catalysis research available in the literature. Weaknesses of the model are evaluated and interpreted chemically. FIA-GNN and the FIA49k dataset can be reached via a free web app (www.grebgroup.de/fia-gnn).
Collapse
Affiliation(s)
- Lukas M Sigmund
- Anorganisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120, Heidelberg, Germany
- Department of Chemistry, Colorado State University, 1301 Center Avenue, Fort Collins, CO, 80523, USA
| | - Shree Sowndarya S
- Department of Chemistry, Colorado State University, 1301 Center Avenue, Fort Collins, CO, 80523, USA
| | - Andreas Albers
- Anorganisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120, Heidelberg, Germany
| | - Philipp Erdmann
- Anorganisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120, Heidelberg, Germany
| | - Robert S Paton
- Department of Chemistry, Colorado State University, 1301 Center Avenue, Fort Collins, CO, 80523, USA
| | - Lutz Greb
- Anorganisch-Chemisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 270, 69120, Heidelberg, Germany
| |
Collapse
|
5
|
Grünwald NJ, Bock CH, Chang JH, De Souza AA, Ponte EMD, du Toit LJ, Dorrance AE, Dung J, Gent D, Goss EM, Lowe-Power TM, Madden LV, Martin FN, McDowell J, Naegele RP, Potnis N, Quesada-Ocampo LM, Sundin GW, Thiessen L, Vinatzer BA, Zeng Q. Open Access and Reproducibility in Plant Pathology Research: Guidelines and Best Practices. Phytopathology 2024:PHYTO12230483IA. [PMID: 38330057 DOI: 10.1094/phyto-12-23-0483-ia] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
The landscape of scientific publishing is experiencing a transformative shift toward open access, a paradigm that mandates the availability of research outputs such as data, code, materials, and publications. Open access provides increased reproducibility and allows for reuse of these resources. This article provides guidance for best publishing practices of scientific research, data, and associated resources, including code, in The American Phytopathological Society journals. Key areas such as diagnostic assays, experimental design, data sharing, and code deposition are explored in detail. This guidance aligns with that observed by other leading journals. We hope the information assembled in this paper will raise awareness of best practices and enable greater appraisal of the true effects of biological phenomena in plant pathology.
Collapse
Affiliation(s)
- Niklaus J Grünwald
- U.S. Department of Agriculture-Agricultural Research Service, Horticultural Crops Disease and Pest Management Research Unit, Corvallis, OR 97331, U.S.A
| | - Clive H Bock
- U.S. Department of Agriculture-Agricultural Research Service, Southeastern Fruit and Tree Nut Research Station, Byron, GA 31008, U.S.A
| | - Jeff H Chang
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, U.S.A
| | | | - Emerson M Del Ponte
- Departamento de Fitopatologia, Universidade Federal de Viçosa, Viçosa, MG, 36570-900, Brazil
| | - Lindsey J du Toit
- Department of Plant Pathology, Washington State University, Mount Vernon, WA 98273, U.S.A
| | - Anne E Dorrance
- Department of Plant Pathology, College of Food, Agricultural and Environmental Sciences, The Ohio State University, Wooster, OH 44691, U.S.A
| | - Jeremiah Dung
- Department of Botany and Plant Pathology, Central Oregon Agricultural Research and Extension Center, Oregon State University, Madras, OR 97741, U.S.A
| | - David Gent
- U.S. Department of Agriculture-Agricultural Research Service, Forage Seed and Cereal Research Unit, Corvallis, OR 97331, U.S.A
| | - Erica M Goss
- Department of Plant Pathology and Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, U.S.A
| | - Tiffany M Lowe-Power
- Department of Plant Pathology, University of California Davis, Davis, CA 95616, U.S.A
| | - Laurence V Madden
- Department of Plant Pathology, College of Food, Agricultural and Environmental Sciences, The Ohio State University, Wooster, OH 44691, U.S.A
| | - Frank N Martin
- U.S. Department of Agriculture-Agricultural Research Service, Crop Protection and Improvement Research Center, Salinas, CA 93905, U.S.A
| | - John McDowell
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, U.S.A
| | - Rachel P Naegele
- U.S. Department of Agriculture-Agricultural Research Service, Sugarbeet and Bean Research Unit, East Lansing, MI 48824, U.S.A
| | - Neha Potnis
- Department of Entomology and Plant Pathology, Auburn University, Auburn, AL 36849, U.S.A
| | - Lina M Quesada-Ocampo
- Department of Entomology and Plant Pathology and NC Plant Sciences Initiative, North Carolina State University, Raleigh, NC 27606, U.S.A
| | - George W Sundin
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, U.S.A
| | - Lindsey Thiessen
- Domestic and Emergency Scientific Support, U.S. Department of Agriculture-Animal & Plant Health Inspection Service-Plant Protection and Quarantine, Raleigh, NC 27606, U.S.A
| | - Boris A Vinatzer
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, U.S.A
| | - Quan Zeng
- Department of Plant Pathology and Ecology, The Connecticut Agricultural Experiment Station, New Haven, CT 06511, U.S.A
| |
Collapse
|
6
|
Harandi AA, McPherson K, Lo Y, Gutiérrez R, Chao JY. A pragmatic methodology to extract anesthetic and physiological data from the electronic health record. Paediatr Anaesth 2024; 34:318-323. [PMID: 38055618 PMCID: PMC10922302 DOI: 10.1111/pan.14817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/03/2023] [Accepted: 11/19/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND/AIMS Traditional manual methods of extracting anesthetic and physiological data from the electronic health record rely upon visual transcription by a human analyst that can be labor-intensive and prone to error. Technical complexity, relative inexperience in computer coding, and decreased access to data warehouses can deter investigators from obtaining valuable electronic health record data for research studies, especially in under-resourced settings. We therefore aimed to develop, pilot, and demonstrate the effectiveness and utility of a pragmatic data extraction methodology. METHODS Expired sevoflurane concentration data from the electronic health record transcribed by eye was compared to an intermediate preprocessing method in which the entire anesthetic flowsheet narrative report was selected, copy-pasted, and processed using only Microsoft Word and Excel software to generate a comma-delimited (.csv) file. A step-by-step presentation of this method is presented. Concordance rates, Pearson correlation coefficients, and scatterplots with lines of best fit were used to compare the two methods of data extraction. RESULTS A total of 1132 datapoints across eight subjects were analyzed, accounting for 18.9 h of anesthesia time. There was a high concordance rate of data extracted using the two methods (median concordance rate 100% range [96%, 100%]). The median time required to complete manual data extraction was significantly longer compared to the time required using the intermediate method (240 IQR [199, 482.5] seconds vs 92.5 IQR [69, 99] seconds, p = .01) and was linearly associated with the number of datapoints (rmanual = .97, p < .0001), whereas time required to complete data extraction using the intermediate approach was independent of the number of datapoints (rintermediate = -.02, p = .99). CONCLUSIONS We describe a pragmatic data extraction methodology that does not require additional software or coding skills intended to enhance the ease, speed, and accuracy of data collection that could assist in clinician investigator-initiated research and quality/process improvement projects.
Collapse
Affiliation(s)
- Arshia Aalami Harandi
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
| | - Katherine McPherson
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
| | - Yungtai Lo
- Department of Epidemiology & Population Health (Biostatistics), Albert Einstein College of Medicine, Bronx, New York, USA
| | - Rodrigo Gutiérrez
- Department of Anesthesiology and Perioperative Medicine, Center of Advanced Clinical Research, University of Chile, Santiago, Chile
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jerry Y. Chao
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| |
Collapse
|
7
|
Chattrattrai T, Aarab G, Blanken TF, Pires GN, Herrero Babiloni A, Dal Fabbro C, van Someren E, Lavigne G, Maluly M, Andersen ML, Tufik S, Lobbezoo F. Network analysis of sleep bruxism in the EPISONO adult general population. J Sleep Res 2024; 33:e13957. [PMID: 37246335 DOI: 10.1111/jsr.13957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/13/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Sleep bruxism (SB) has been associated with biological and psychosocial factors. The assessment of SB includes self-report, clinical evaluation, and polysomnography. This study aimed to investigate the associations of self-reported SB with other sleep disorders and demographic, psychological, and lifestyle factors in the adult general population, and to investigate whether self-reported SB and polysomnographically (PSG) confirmed SB provide similar outcomes in terms of their associated factors. We recruited 915 adults from the general population in Sao Paulo, Brazil. All participants underwent a one-night PSG recording and answered questions about sex, age, BMI, insomnia, OSA risk, anxiety, depression, average caffeine consumption, smoking frequency, and alcohol consumption frequency. We investigated the link between SB and the other variables in univariate, multivariate, and network models, and we repeated each model once with self-reported SB and once with PSG-confirmed SB. Self-reported SB was only significantly associated with sex (p = 0.042), anxiety (p = 0.002), and depression (p = 0.03) in the univariate analysis, and was associated with insomnia in the univariate (p < 0.001) and multivariate (β = 1.054, 95%CI 1.018-1.092, p = 0.003) analyses. Network analysis showed that self-reported SB had a direct positive edge to insomnia, while PSG-confirmed SB was not significantly associated with any of the other variables. Thus, sleep bruxism was positively associated with insomnia only when self-reported, while PSG-confirmed SB was not associated with any of the included factors.
Collapse
Affiliation(s)
- Thiprawee Chattrattrai
- Department of Orofacial Pain and Dysfunction, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Masticatory Science, Faculty of Dentistry, Mahidol University, Bangkok, Thailand
| | - Ghizlane Aarab
- Department of Orofacial Pain and Dysfunction, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Gabriel N Pires
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Instituto do Sono, Sao Paulo, Brazil
| | - Alberto Herrero Babiloni
- Center for Advanced Research in Sleep Medicine, Research Center of CIUSSS NIM and CHUM, Faculty of Dental Medicine, University of Montreal, Montreal, Quebec, Canada
- Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada
| | - Cibele Dal Fabbro
- Instituto do Sono, Sao Paulo, Brazil
- Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada
| | - Eus van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gilles Lavigne
- Center for Advanced Research in Sleep Medicine, Research Center of CIUSSS NIM and CHUM, Faculty of Dental Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Milton Maluly
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Instituto do Sono, Sao Paulo, Brazil
| | - Monica L Andersen
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Instituto do Sono, Sao Paulo, Brazil
| | - Sergio Tufik
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Instituto do Sono, Sao Paulo, Brazil
| | - Frank Lobbezoo
- Department of Orofacial Pain and Dysfunction, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
8
|
Baek J, Lawson J, Rahimzadeh V. Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey. JMIR Form Res 2024; 8:e49822. [PMID: 38506894 PMCID: PMC10993121 DOI: 10.2196/49822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. OBJECTIVE The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health-funded research in the United States. METHODS We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. RESULTS In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into "a National Institutes of Health-funded repository" to meet the Data Management and Sharing requirements but also within the "institution's own repository" or a third-party repository. CONCLUSIONS Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections.
Collapse
Affiliation(s)
| | | | - Vasiliki Rahimzadeh
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| |
Collapse
|
9
|
Kanjilal S. The modern alchemy of clinical pathology: turning the output of microbiology laboratory operations into gold. J Clin Microbiol 2024:e0170922. [PMID: 38506516 DOI: 10.1128/jcm.01709-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024] Open
Abstract
The clinical microbiology laboratory generates a huge amount of high-quality data that play a vital role in clinical care. With proper extraction, cleaning, analysis, and validation pipelines, these data can serve multiple other purposes that include supporting laboratory operations, understanding local epidemiology, informing hospital-specific policies, and public health surveillance. In this review, I use one of the core activities of the microbiology laboratory, antimicrobial susceptibility testing (AST), to illustrate several potential applications of next-generation data analytics. The first involves continuous monitoring of commercial AST systems using comparisons of minimum inhibitory concentration (MIC) distributions over time to trigger re-verification when statistically significant differences are detected. An extension of this is temporal analysis of joint MIC distributions to understand performance for multidrug-resistant organisms. More sophisticated analyses involve linking microbiologic data to clinical metadata to gain insight into the clinical validity of AST data and to inform treatment policies. The elements of a robust, validated analysis engine using routine data streams already exist, but numerous challenges must be overcome to make it a reality. Most importantly, it will require the sustained collaboration and advocacy of hospital leadership, microbiologists, clinicians, antimicrobial stewardship, data scientists, and regulatory agencies. Though no small feat, achieving this vision would provide an important resource for microbiology laboratories facing a rapidly evolving practice landscape and further cement its role as an integral part of a learning health system.
Collapse
Affiliation(s)
- Sanjat Kanjilal
- Department of Population Medicine, Harvard Pilgrim Healthcare Institute and Harvard Medical School, Boston, Massachusetts, USA
- Division of Infectious Diseases, Brigham & Women's Hospital, Boston, Massachusetts, USA
| |
Collapse
|
10
|
Bullock GS, Ward P, Kluzek S, Hughes T, Shanley E, Arundale AJH, Ranson C, Nimphius S, Riley RD, Collins GS, Impellizzeri FM. Paving the way for greater open science in sports and exercise medicine: navigating the barriers to adopting open and accessible data practices. Br J Sports Med 2024; 58:293-295. [PMID: 38135463 DOI: 10.1136/bjsports-2023-107225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Affiliation(s)
- Garrett S Bullock
- Orthopaedic Surgery & Rehabilitation, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
- Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | | | - Stefan Kluzek
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Tom Hughes
- Department of Health Professions, Manchester Metropolitan University, Manchester, UK
| | - Ellen Shanley
- Clinical Excellence, ATI Physical Therapy, Greer, South Carolina, USA
- Arnold School of Public Health, University of South Carolina System, Columbia, South Carolina, USA
| | | | | | - Sophia Nimphius
- School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
| | - Richard D Riley
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Oxford University, Oxford, UK
| | - Franco M Impellizzeri
- School of Sport, Exercise, and Rehabilitation, University of Technology Sydney, Broadway, New South Wales, Australia
| |
Collapse
|
11
|
Emmert-Streib F, Bottini S, Franco L. Editorial: AI and multi-omics for rare diseases: challenges, advances and perspectives, Volume III. Front Mol Biosci 2024; 11:1392943. [PMID: 38533312 PMCID: PMC10963626 DOI: 10.3389/fmolb.2024.1392943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/28/2024] Open
Affiliation(s)
- Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Silvia Bottini
- Center of Modeling, Simulation and Interactions, Université Côte d’Azur, Nice, France
| | - Leonardo Franco
- Computer Science Department, Malaga University, Malaga, Spain
| |
Collapse
|
12
|
Garma LD, Osório NS. Demystifying dimensionality reduction techniques in the 'omics' era: A practical approach for biological science students. Biochem Mol Biol Educ 2024; 52:165-178. [PMID: 37937712 DOI: 10.1002/bmb.21800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.
Collapse
Affiliation(s)
- Leonardo D Garma
- Breast Cancer Clinical Research Unit, Centro Nacional de Investigaciones Oncológicas - CNIO, Madrid, Spain
| | - Nuno S Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's -PT Government Associate Laboratory, Braga, Portugal
| |
Collapse
|
13
|
Bertulfo MCP, Kirkcaldy RD, Franzke LH, Papagari Sangareddy SR, Reza F. Advancing Data Science Among the Federal Public Health Workforce: The Data Science Upskilling Program, Centers for Disease Control and Prevention. J Public Health Manag Pract 2024; 30:E41-E46. [PMID: 38271110 PMCID: PMC10860639 DOI: 10.1097/phh.0000000000001865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
CONTEXT Data can guide decision-making to improve the health of communities, but potential for use can only be realized if public health professionals have data science skills. However, not enough public health professionals possess the quantitative data skills to meet growing data science needs, including at the Centers for Disease Control and Prevention (CDC). PROGRAM The Data Science Upskilling (DSU) program increases data science literacy among staff and fellows working and training at CDC. The DSU program was established in 2019 as a team-based, project-driven, on-the-job applied upskilling program. Learners, within interdisciplinary teams, use curated learning resources to advance their CDC projects. The program has rapidly expanded from upskilling 13 teams of 31 learners during 2019-2020 to upskilling 36 teams of 143 learners during 2022-2023. EVALUATION All 2022-2023 cohort respondents to the end-of-project survey reported the program increased their data science knowledge. In addition, 90% agreed DSU improved their data science skills, 93% agreed it improved their confidence making data science decisions, and 96% agreed it improved their ability to perform data science work that benefits CDC. DISCUSSION DSU is an innovative, inclusive, and successful approach to improving data science literacy at CDC. DSU may serve as an upskilling model for other organizations.
Collapse
Affiliation(s)
- Mary Catherine P Bertulfo
- Public Health Workforce Branch, Division of Workforce Development, National Center for State, Territorial, Local, and Tribal Public Health Infrastructure and Workforce (Ms Bertulfo and Drs Kirkcaldy and Franzke); Office of Science (Dr Papagari Sangareddy); and Informatics and Data Analytics Branch, Immunization Services Division, National Center for Immunization and Respiratory Diseases (Dr Reza), Centers for Disease Control and Prevention, Atlanta, Georgia
| | | | | | | | | |
Collapse
|
14
|
Schloss PD. Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses. mSphere 2024; 9:e0035423. [PMID: 38251877 PMCID: PMC10900887 DOI: 10.1128/msphere.00354-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
Considering it is common to find as much as 100-fold variation in the number of 16S rRNA gene sequences across samples in a study, researchers need to control for the effect of uneven sequencing effort. How to do this has become a contentious question. Some have argued that rarefying or rarefaction is "inadmissible" because it omits valid data. A number of alternative approaches have been developed to normalize and rescale the data that purport to be invariant to the number of observations. I generated community distributions based on 12 published data sets where I was able to assess the ability of multiple methods to control for uneven sequencing effort. Rarefaction was the only method that could control for variation in uneven sequencing effort when measuring commonly used alpha and beta diversity metrics. Next, I compared the false detection rate and power to detect true differences between simulated communities with a known effect size using various alpha and beta diversity metrics. Although all methods of controlling for uneven sequencing effort had an acceptable false detection rate when samples were randomly assigned to two treatment groups, rarefaction was consistently able to control for differences in sequencing effort when sequencing depth was confounded with treatment group. Finally, the statistical power to detect differences in alpha and beta diversity metrics was consistently the highest when using rarefaction. These simulations underscore the importance of using rarefaction to normalize the number of sequences across samples in amplicon sequencing analyses. IMPORTANCE Sequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.
Collapse
Affiliation(s)
- Patrick D. Schloss
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, USA
| |
Collapse
|
15
|
Ritto AP, de Araujo AL, de Carvalho CRR, De Souza HP, Favaretto PMES, Saboya VRB, Garcia ML, Kulikowski LD, Kallás EG, Pereira AJR, Cobello Junior V, Silva KR, Abdalla ERF, Segurado AAC, Sabino EC, Ribeiro Junior U, Francisco RPV, Miethke-Morais A, Levin ASS, Sawamura MVY, Ferreira JC, Silva CA, Mauad T, Gouveia NDC, Letaif LSH, Bego MA, Battistella LR, Duarte AJDS, Seelaender MCL, Marchini J, Forlenza OV, Rocha VG, Mendes-Correa MC, Costa SF, Cerri GG, Bonfá ESDDO, Chammas R, de Barros Filho TEP, Busatto Filho G. Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic. Front Public Health 2024; 12:1369129. [PMID: 38476486 PMCID: PMC10927964 DOI: 10.3389/fpubh.2024.1369129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/13/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction The COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency. Methods At the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output. Results Over the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020-2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19. Discussion Research is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.
Collapse
Affiliation(s)
- Ana Paula Ritto
- Faculdade de Medicina, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | | | | | - Heraldo Possolo De Souza
- Departamento de Emergências Médicas, Faculdade de Medicina, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Patricia Manga e Silva Favaretto
- Diretoria Executiva dos Laboratórios de Investigação Médica, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Vivian Renata Boldrim Saboya
- Diretoria Executiva dos Laboratórios de Investigação Médica, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Michelle Louvaes Garcia
- Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | | | - Esper Georges Kallás
- Departamento de Moléstias Infecciosas e Parasitárias, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | | | - Vilson Cobello Junior
- Núcleo Especializado em Tecnologia da Informação, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Katia Regina Silva
- Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Eidi Raquel Franco Abdalla
- Divisão de Biblioteca e Documentação, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Aluisio Augusto Cotrim Segurado
- Departamento de Moléstias Infecciosas e Parasitárias, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ester Cerdeira Sabino
- Departamento de Moléstias Infecciosas e Parasitárias, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ulysses Ribeiro Junior
- Departamento de Gastroenterologia, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Rossana Pulcineli Vieira Francisco
- Departamento de Obstetrícia e Ginecologia, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Anna Miethke-Morais
- Diretoria Clínica, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Anna Sara Shafferman Levin
- Departamento de Moléstias Infecciosas e Parasitárias, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Marcio Valente Yamada Sawamura
- Faculdade de Medicina, Instituto de Radiologia, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Juliana Carvalho Ferreira
- Faculdade de Medicina, Instituto do Coração, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Clovis Artur Silva
- Instituto da Criança e do Adolescente, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Thais Mauad
- Departamento de Patologia, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Nelson da Cruz Gouveia
- Departamento de Medicina Preventiva, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Leila Suemi Harima Letaif
- Diretoria Clínica, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Marco Antonio Bego
- Faculdade de Medicina, Instituto de Radiologia, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Linamara Rizzo Battistella
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Alberto José da Silva Duarte
- Divisão de Laboratório Central, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | | | - Julio Marchini
- Departamento de Emergências Médicas, Faculdade de Medicina, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Orestes Vicente Forlenza
- Departamento e Instituto de Psiquiatria, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Vanderson Geraldo Rocha
- Departamento de Clínica Médica, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Maria Cassia Mendes-Correa
- Departamento de Moléstias Infecciosas e Parasitárias, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Silvia Figueiredo Costa
- Departamento de Moléstias Infecciosas e Parasitárias, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Giovanni Guido Cerri
- Faculdade de Medicina, Instituto de Radiologia, Hospital das Clínicas HC-FMUSP, Universidade de São Paulo, São Paulo, SP, Brazil
| | | | - Roger Chammas
- Departamento de Radiologia e Oncologia, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | | | - Geraldo Busatto Filho
- Departamento e Instituto de Psiquiatria, Hospital das Clínicas HC-FMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| |
Collapse
|
16
|
Paiste HJ, Godwin RC, Smith AD, Berkowitz DE, Melvin RL. Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine. Front Digit Health 2024; 6:1316931. [PMID: 38444721 PMCID: PMC10912557 DOI: 10.3389/fdgth.2024.1316931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/01/2024] [Indexed: 03/07/2024] Open
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.
Collapse
Affiliation(s)
- Henry J. Paiste
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Ryan C. Godwin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
- Department of Radiology, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| | - Andrew D. Smith
- Department of Radiology, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| | - Dan E. Berkowitz
- Department of Anesthesiology and Perioperative Medicine, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| | - Ryan L. Melvin
- Department of Anesthesiology and Perioperative Medicine, University of Alabama Birmingham School of Medicine, Birmingham, AL, United States
| |
Collapse
|
17
|
Nelson SD. Artificial intelligence and the future of pharmacy. Am J Health Syst Pharm 2024; 81:83-84. [PMID: 38141260 DOI: 10.1093/ajhp/zxad316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Indexed: 12/25/2023] Open
Affiliation(s)
- Scott D Nelson
- Department of Biomedical Informatics Vanderbilt University Medical Center, Nashville, TN, USA
| |
Collapse
|
18
|
Bornkamp B, Zaoli S, Azzarito M, Martin R, Müller CP, Moloney C, Capestro G, Ohlssen D, Baillie M. Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation. Pharm Stat 2024. [PMID: 38326967 DOI: 10.1002/pst.2368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 12/01/2023] [Accepted: 01/21/2024] [Indexed: 02/09/2024]
Abstract
We present the motivation, experience, and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. A total of 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organization. We outline the motivation for running the challenge, the challenge rules, and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings. We also present our view on the implications of the results on exploratory analyses related to treatment effect heterogeneity.
Collapse
Affiliation(s)
- Björn Bornkamp
- Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | - Silvia Zaoli
- Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | | | - Ruvie Martin
- Global Drug Development, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | | | - Conor Moloney
- Global Drug Development, Novartis Pharma AG, Dublin, Ireland
| | - Giulia Capestro
- Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| | - David Ohlssen
- Global Drug Development, Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
| | - Mark Baillie
- Global Drug Development, Novartis Pharma AG, Basel, Switzerland
| |
Collapse
|
19
|
Chiang KS, Chang YM, Liu HI, Lee JY, Jarroudi ME, Bock CH. Survival Analysis as a Basis for Testing Hypotheses when Using Quantitative Ordinal Scale Disease Severity Data. Phytopathology 2024; 114:378-392. [PMID: 37606348 DOI: 10.1094/phyto-02-23-0055-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
Disease severity in plant pathology is often measured by the amount of a plant or plant part that exhibits disease symptoms. This is typically assessed using a numerical scale, which allows a standardized, convenient, and quick method of rating. These scales, known as quantitative ordinal scales (QOS), divide the percentage scale into a predetermined number of intervals. There are various ways to analyze these ordinal data, with traditional methods involving the use of midpoint conversion to represent the interval. However, this may not be precise enough, as it is only an estimate of the true value. In this case, the data may be considered interval-censored, meaning that we have some knowledge of the value but not an exact measurement. This type of uncertainty is known as censoring, and techniques that address censoring, such as survival analysis (SA), use all available information and account for this uncertainty. To investigate the pros and cons of using SA with QOS measurements, we conducted a simulation based on three pathosystems. The results showed that SA almost always outperformed midpoint conversion with data analyzed using a t test, particularly when data were not normally distributed. Midpoint conversion is currently a standard procedure. In certain cases, the midpoint approach required a 400% increase in sample size to achieve the same power as the SA method. However, as the mean severity increases, fewer additional samples are needed (approximately an additional 100%), regardless of the assessment method used. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.
Collapse
Affiliation(s)
- K S Chiang
- Division of Biometrics, Department of Agronomy, National Chung Hsing University, Taichung, Taiwan
| | - Y M Chang
- Department of Statistics, Tunghai University, Taichung 407, Taiwan
| | - H I Liu
- Bachelor Program in Industrial Artificial Intelligence, Ming Chi University of Technology, New Taipei City 243, Taiwan
| | - J Y Lee
- Department of Statistics, Feng Chia University, Taichung 407, Taiwan
| | - M El Jarroudi
- University of Liège, Department of Environmental Sciences and Management, SPHERES Research Unit, Arlon, Belgium
| | - C H Bock
- U.S. Department of Agriculture-Agricultural Research Service-SEFTNRL, Byron, GA 31008, U.S.A
| |
Collapse
|
20
|
Tanweer A, Steinhoff J. Academic data science: Transdisciplinary and extradisciplinary visions. Soc Stud Sci 2024; 54:133-160. [PMID: 37417195 PMCID: PMC10832338 DOI: 10.1177/03063127231184443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
As a nascent field within the academy, the contours, attributes, and bounties of data science are still indeterminate and contested. We studied how participants in an initiative to establish data science at a large American research university defined data science and articulated their relationships to the field. We discuss two contrasting visions for data science among our research participants. One vision is a transdisciplinary view portraying data science as a phenomenon with transcendent, appropriative, and impositional qualities that sits apart from academic domains. Another view of data science-one that was far more prevalent among our research subjects-casts data science as grounded, relational, and adaptive, emerging from crosspollination of numerous academic domains. We argue that this latter formulation represents a more quotidian reality of data science and positions the field as an extradiscipline, defined as a field that exists to facilitate the exchange of knowledge, skills, tools, and methods from an indeterminate and fluctuating set of disciplinary perspectives while conserving the boundaries of those disciplines. We argue that the dueling transdisciplinary and extradisciplinary visions for data science have important implications for how the field will mature, and that the extradiscipline concept opens novel directions for studying academic knowledge production in STS, contributing additional precision to the literature on disciplinarity and its permutations.
Collapse
|
21
|
Javor D, Bennani-Baiti BI, Clauser P, Kifjak D, Baltzer PAT. Automated analysis of the total choline resonance peak in breast proton magnetic resonance spectroscopy. NMR Biomed 2024; 37:e5054. [PMID: 37794648 DOI: 10.1002/nbm.5054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 10/06/2023]
Abstract
The aim of the current study was to compare the performance of fully automated software with human expert interpretation of single-voxel proton magnetic resonance spectroscopy (1H-MRS) spectra in the assessment of breast lesions. Breast magnetic resonance imaging (MRI) (including contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted imaging) and 1H-MRS images of 74 consecutive patients were acquired on a 3-T positron emission tomography-MRI scanner then automatically imported into and analyzed by SpecTec-ULR 1.1 software (LifeTec Solutions GmbH). All ensuing 117 spectra were additionally independently analyzed and interpreted by two blinded radiologists. Histopathology of at least 24 months of imaging follow-up served as the reference standard. Nonparametric Spearman's correlation coefficients for all measured parameters (signal-to-noise ratio [SNR] and integral of total choline [tCho]), Passing and Bablok regression, and receiver operating characteristic analysis, were calculated to assess test diagnostic performance, as well as to compare automated with manual reading. Based on 117 spectra of 74 patients, the area under the curve for tCho SNR and integrals ranged from 0.768 to 0.814 and from 0.721 to 0.784 to distinguish benign from malignant tissue, respectively. Neither method displayed significant differences between measurements (automated vs. human expert readers, p > 0.05), in line with the results from the univariate Spearman's rank correlation coefficients, as well as the Passing and Bablok regression analysis. It was concluded that this pilot study demonstrates that 1H-MRS data from breast MRI can be automatically exported and interpreted by SpecTec-ULR 1.1 software. The diagnostic performance of this software was not inferior to human expert readers.
Collapse
Affiliation(s)
- Domagoj Javor
- Division of Cardiovascular and Interventional Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Radiology, University Hospital Krems, Krems, Austria
- Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Barbara I Bennani-Baiti
- Department of Radiology, University Hospital Krems, Krems, Austria
- Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Paola Clauser
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daria Kifjak
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Radiology, UMass Memorial Medical Center and University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Pascal A T Baltzer
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
22
|
Choon YW, Choon YF, Nasarudin NA, Al Jasmi F, Remli MA, Alkayali MH, Mohamad MS. Artificial intelligence and database for NGS-based diagnosis in rare disease. Front Genet 2024; 14:1258083. [PMID: 38371307 PMCID: PMC10870236 DOI: 10.3389/fgene.2023.1258083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/24/2023] [Indexed: 02/20/2024] Open
Abstract
Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.
Collapse
Affiliation(s)
- Yee Wen Choon
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, Malaysia
- Faculty of Data Science and Informatics, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, Malaysia
| | - Yee Fan Choon
- Faculty of Dentistry, Lincoln University College, Petaling Jaya, Selangor, Malaysia
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Muhamad Akmal Remli
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, Malaysia
- Faculty of Data Science and Informatics, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, Malaysia
| | | | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| |
Collapse
|
23
|
Goddard TR, Brookes KJ, Sharma R, Moemeni A, Rajkumar AP. Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells 2024; 13:223. [PMID: 38334615 PMCID: PMC10854541 DOI: 10.3390/cells13030223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.
Collapse
Affiliation(s)
- Thomas R. Goddard
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
| | - Keeley J. Brookes
- Department of Biosciences, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
| | - Riddhi Sharma
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
- UK Health Security Agency, Radiation Effects Department, Radiation Protection Science Division, Harwell Science Campus, Didcot, Oxfordshire OX11 0RQ, UK
| | - Armaghan Moemeni
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Anto P. Rajkumar
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
| |
Collapse
|
24
|
Dushyanthen S, Choo D, Perrier M, Gray K, Capurro D, Pires D, Chapman BE, Hart GK, Huckvale K, Chapman WW, Lyons K. Designing an Interprofessional Online Course to Foster Learning Health Systems. Stud Health Technol Inform 2024; 310:1241-1245. [PMID: 38270013 DOI: 10.3233/shti231163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.
Collapse
Affiliation(s)
- Sathana Dushyanthen
- Centre for Digital Transformation of Health, University of Melbourne, Australia
| | - Dawn Choo
- Melbourne School of Population and Global Health, University of Melbourne, Australia
| | - Meg Perrier
- Centre for Digital Transformation of Health, University of Melbourne, Australia
| | - Kathleen Gray
- Centre for Digital Transformation of Health, University of Melbourne, Australia
- School of Computing and Information Systems, University of Melbourne, Australia
| | - Daniel Capurro
- Centre for Digital Transformation of Health, University of Melbourne, Australia
- School of Computing and Information Systems, University of Melbourne, Australia
| | - Douglas Pires
- Centre for Digital Transformation of Health, University of Melbourne, Australia
- School of Computing and Information Systems, University of Melbourne, Australia
| | - Brian E Chapman
- School of Computing and Information Systems, University of Melbourne, Australia
| | - Graeme K Hart
- Centre for Digital Transformation of Health, University of Melbourne, Australia
| | - Kit Huckvale
- Centre for Digital Transformation of Health, University of Melbourne, Australia
| | - Wendy W Chapman
- Centre for Digital Transformation of Health, University of Melbourne, Australia
| | - Kayley Lyons
- Centre for Digital Transformation of Health, University of Melbourne, Australia
| |
Collapse
|
25
|
Ratti MFG, Martingano I, Otero PD, Otero CM, Farina JM, Rubin L, Luna D, Esteban JA, Pedretti AS, Rodríguez MDLP, Cid MSD, Martínez BJ. Unscheduled Emergency Department Revisits Within 48 Hours of Discharge. Stud Health Technol Inform 2024; 310:304-308. [PMID: 38269814 DOI: 10.3233/shti230976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
This study aimed to analyze early revisits (within 48 hours of discharge) in an Emergency Department. Among the 178,295 visits, 11,686 were revisits, resulting in a rate of 6.55% (95%CI 6.43-6.67). A total of 1,410 revisits required hospitalization, and 252 were due to preventable errors (17.87%). These errors were mainly related to an inadequate therapeutic plan at discharge (47.22%), an incomplete diagnostic process (29.37%), and misdiagnoses (13.10%). These findings represent a technology-enabled clinical audit tool. Electronic Healthcare Records have the potential to: provide quality metrics of hospital performance, help to keep revisit rates updated (assessment through a real-time dashboard), and improve clinical management (by transparency initiatives about errors, and a supportive learning environment regarding lessons learned).
Collapse
Affiliation(s)
| | - Ignacio Martingano
- Department of Internal Medicine, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Paula Daniela Otero
- Department of Health Informatics, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Carlos Martin Otero
- Department of Health Informatics, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Luciana Rubin
- Department of Health Informatics, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Daniel Luna
- Department of Health Informatics, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Jorge Ariel Esteban
- Emergency Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Ana Soledad Pedretti
- Emergency Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | | | | |
Collapse
|
26
|
Yoon J, Boyle J. A New Statistical Method to Detect Disease Outbreaks from Hospital Emergency Department Data. Stud Health Technol Inform 2024; 310:886-890. [PMID: 38269936 DOI: 10.3233/shti231092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Early detection and prediction of disease outbreaks are crucial for public health service delivery, containment response, saving patient lives, and reducing costs. We propose a new data-driven statistical methodology for outbreak detection and prediction based on routinely collected hospital Emergency Department data. The time between consecutive ED presentations matching a diagnosis of interest forms the basis of a novel index measure to signal that an outbreak has occurred. We validate the method using historical presentations of influenza-like illness made to a large sample of public hospital EDs in 2020 and compare outbreaks identified by the method with the start of the first wave of COVID-19. The method shows promise within the field of disease outbreak detection.
Collapse
Affiliation(s)
- Jin Yoon
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Australia
| | - Justin Boyle
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Australia
| |
Collapse
|
27
|
Saint-Sardos A, Aish A, Tchakarov N, Bourgoin T, Petit LM, Sun JS, Vignes-Lebbe R. Bioinspire-Explore: Taxonomy-Driven Exploration of Biodiversity Data for Bioinspired Innovation. Biomimetics (Basel) 2024; 9:63. [PMID: 38392109 PMCID: PMC10886457 DOI: 10.3390/biomimetics9020063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/24/2024] Open
Abstract
Successful bioinspired design depends on practitioners' access to biological data in a relevant form. Although multiple open-access biodiversity databases exist, their presentation is often adapted to life scientists, rather than bioinspired designers. In this paper, we present a new tool, "Bioinspire-Explore", for navigating biodiversity data in order to uncover biological systems of interest for a range of sectors. Bioinspire-Explore allows users to search for inspiring biological models via taxa (species, genera, etc.) as an entry point. It provides information on a taxon's position in the "tree of life", its distribution and climatic niche, as well as its appearance. Bioinspire-Explore also shows users connections in the bioinspiration literature between their taxon of interest and associated biological processes, habitats, and physical measurements by way of their semantic proximity. We believe Bioinspire-Explore has the potential to become an indispensable resource for both biologists and bioinspired designers in different fields.
Collapse
Affiliation(s)
- Adrien Saint-Sardos
- Centre d'Études et d'Expertises en Biomimétisme de Senlis (CEEBIOS), 62 Rue du Faubourg Saint-Martin, 60300 Senlis, France
| | - Annabelle Aish
- Bioinspire-Museum, Museum National d'Histoire Naturelle, 57 rue Cuvier, 75005 Paris, France
| | - Nikolay Tchakarov
- Centre d'Études et d'Expertises en Biomimétisme de Senlis (CEEBIOS), 62 Rue du Faubourg Saint-Martin, 60300 Senlis, France
| | - Thierry Bourgoin
- Sorbonne Université, Muséum National d'Histoire Naturelle, CNRS, EPHE, Université des Antilles, Institut de Systématique Évolution Biodiversité, ISYEB, CP 48, 57 Rue Cuvier, 75005 Paris, France
| | - Luce-Marie Petit
- Centre d'Études et d'Expertises en Biomimétisme de Senlis (CEEBIOS), 62 Rue du Faubourg Saint-Martin, 60300 Senlis, France
| | - Jian-Sheng Sun
- Bioinspire-Museum, Museum National d'Histoire Naturelle, 57 rue Cuvier, 75005 Paris, France
| | - Régine Vignes-Lebbe
- Sorbonne Université, Muséum National d'Histoire Naturelle, CNRS, EPHE, Université des Antilles, Institut de Systématique Évolution Biodiversité, ISYEB, CP 48, 57 Rue Cuvier, 75005 Paris, France
| |
Collapse
|
28
|
Bhuvaneshwar K, Gusev Y. Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review. Brief Bioinform 2024; 25:bbae098. [PMID: 38493340 PMCID: PMC10944574 DOI: 10.1093/bib/bbae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/23/2024] [Accepted: 02/18/2024] [Indexed: 03/18/2024] Open
Abstract
Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.
Collapse
Affiliation(s)
- Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington DC, 20007, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington DC, 20007, USA
| |
Collapse
|
29
|
Li S, Du Y, Meireles C, Song D, Sharma K, Yin Z, Brimhall B, Wang J. Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study. Res Sq 2024:rs.3.rs-3854650. [PMID: 38313251 PMCID: PMC10836100 DOI: 10.21203/rs.3.rs-3854650/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months. Methods Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach. Results Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app. Conclusions This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions. Trial registration The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.
Collapse
Affiliation(s)
- Shiyu Li
- Department of Kinesiology, Pennsylvania State University
| | - Yan Du
- School of Nursing, UT Health San Antonio
| | | | - Dan Song
- College of Nursing, Florida State University
| | | | - Zenong Yin
- Department of Public Health, The University of Texas at San Antonio
| | | | - Jing Wang
- College of Nursing, Florida State University
| |
Collapse
|
30
|
Liptovszky M. Advancing zoo animal welfare through data science: scaling up continuous improvement efforts. Front Vet Sci 2024; 11:1313182. [PMID: 38298448 PMCID: PMC10827962 DOI: 10.3389/fvets.2024.1313182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Affiliation(s)
- Matyas Liptovszky
- Perth Zoo, South Perth, WA, Australia
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, United Kingdom
| |
Collapse
|
31
|
Fredston AL, Lowndes JSS. Welcoming More Participation in Open Data Science for the Oceans. Ann Rev Mar Sci 2024; 16:537-549. [PMID: 37418835 DOI: 10.1146/annurev-marine-041723-094741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Open science is a global movement happening across all research fields. Enabled by technology and the open web, it builds on years of efforts by individuals, grassroots organizations, institutions, and agencies. The goal is to share knowledge and broaden participation in science, from early ideation to making research outputs openly accessible to all (open access). With an emphasis on transparency and collaboration, the open science movement dovetails with efforts to increase diversity, equity, inclusion, and belonging in science and society. The US Biden-Harris Administration and many other US government agencies have declared 2023 the Year of Open Science, providing a great opportunity to boost participation in open science for the oceans. For researchers day-to-day, open science is a critical piece of modern analytical workflows with increasing amounts of data. Therefore, we focus this article on open data science-the tooling and people enabling reproducible, transparent, inclusive practices for data-intensive research-and its intersection with the marine sciences. We discuss the state of various dimensions of open science and argue that technical advancements have outpaced our field's culture change to incorporate them. Increasing inclusivity and technical skill building are interlinked and must be prioritized within the marine science community to find collaborative solutions for responding to climate change and other threats to marine biodiversity and society.
Collapse
Affiliation(s)
- Alexa L Fredston
- Department of Ocean Sciences, University of California, Santa Cruz, California, USA;
| | - Julia S Stewart Lowndes
- National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, California, USA
| |
Collapse
|
32
|
Doğan RS, Yılmaz B. Histopathology image classification: highlighting the gap between manual analysis and AI automation. Front Oncol 2024; 13:1325271. [PMID: 38298445 PMCID: PMC10827850 DOI: 10.3389/fonc.2023.1325271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024] Open
Abstract
The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.
Collapse
Affiliation(s)
- Refika Sultan Doğan
- Department of Bioengineering, Abdullah Gül University, Kayseri, Türkiye
- Biomedical Instrumentation and Signal Analysis Laboratory, Abdullah Gül University, Kayseri, Türkiye
| | - Bülent Yılmaz
- Biomedical Instrumentation and Signal Analysis Laboratory, Abdullah Gül University, Kayseri, Türkiye
- Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Türkiye
- Department of Electrical Engineering, Gulf University for Science and Technology, Mishref, Kuwait
| |
Collapse
|
33
|
Chung MK, House JS, Akhtari FS, Makris KC, Langston MA, Islam KT, Holmes P, Chadeau-Hyam M, Smirnov AI, Du X, Thessen AE, Cui Y, Zhang K, Manrai AK, Motsinger-Reif A, Patel CJ. Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs). Exposome 2024; 4:osae001. [PMID: 38344436 PMCID: PMC10857773 DOI: 10.1093/exposome/osae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/16/2023] [Accepted: 11/20/2023] [Indexed: 03/07/2024]
Abstract
This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.
Collapse
Affiliation(s)
- Ming Kei Chung
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Konstantinos C Makris
- Cyprus International Institute for Environmental and Public Health, School of Health Sciences, Cyprus University of Technology, Limassol, Cyprus
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of TN, Knoxville, TN, USA
| | - Khandaker Talat Islam
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern CA, Los Angeles, CA, USA
| | - Philip Holmes
- Department of Physics, Villanova University, Villanova, Philadelphia, USA
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Alex I Smirnov
- Department of Chemistry, NC State University, Raleigh, NC, USA
| | - Xiuxia Du
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of NC at Charlotte, Charlotte, NC, USA
| | - Anne E Thessen
- Department of Biomedical Informatics, University of CO Anschutz Medical Campus, Aurora, CO, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of NY, Rensselaer, NY, USA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
34
|
Kuo NIH, Perez-Concha O, Hanly M, Mnatzaganian E, Hao B, Di Sipio M, Yu G, Vanjara J, Valerie IC, de Oliveira Costa J, Churches T, Lujic S, Hegarty J, Jorm L, Barbieri S. Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project. JMIR Med Educ 2024; 10:e51388. [PMID: 38227356 PMCID: PMC10828942 DOI: 10.2196/51388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 10/20/2023] [Accepted: 11/08/2023] [Indexed: 01/17/2024]
Abstract
Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.
Collapse
Affiliation(s)
- Nicholas I-Hsien Kuo
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia
| | - Oscar Perez-Concha
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia
| | - Mark Hanly
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia
| | | | - Brandon Hao
- The University of New South Wales, Sydney, Australia
| | | | - Guolin Yu
- The University of New South Wales, Sydney, Australia
| | - Jash Vanjara
- The University of New South Wales, Sydney, Australia
| | | | - Juliana de Oliveira Costa
- Medicines Intelligence Research Program, School of Population Health, The University of New South Wales, Sydney, Australia
| | - Timothy Churches
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
- Ingham Institute of Applied Medical Research, Liverpool, Sydney, Australia
| | - Sanja Lujic
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia
| | - Jo Hegarty
- Sydney Local Health District, Sydney, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia
| | - Sebastiano Barbieri
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia
| |
Collapse
|
35
|
Nan X, Kuru Çolak T, Akçay B, Xie H, Zhao L, Borysov M. Results of Gensingen Bracing in Patients With Adolescent Idiopathic Scoliosis: Retrospective Cross-Sectional Feasibility Study. JMIR Rehabil Assist Technol 2024; 11:e50299. [PMID: 38198197 PMCID: PMC10809064 DOI: 10.2196/50299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/10/2023] [Accepted: 12/11/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Bracing is an essential part of scoliosis treatment. The standard of brace treatment for patients with scoliosis today is still very variable in terms of brace quality and outcome. The Gensingen brace is a further developed Chêneau brace derivative with individual design, which can be adapted through computer-aided design. OBJECTIVE This study aims to generate a template to obtain a database for prospective multicenter studies study to analyze the results of high-corrective asymmetric Gensingen brace treatment for patients with adolescent idiopathic scoliosis (AIS). METHODS A template for the database was created, which contains the patients' basic data (age, menarcheal status, Risser Sign, curve pattern, and daily brace wearing time), the Cobb angles of curvature, and the cosmetically relevant angles of trunk rotation (ATR). A retrospective review of medical records of patients with AIS, who met the Scoliosis Research Society's inclusion criteria for brace studies, was performed to test the feasibility of the template. Template items were filled in by the researchers. RESULTS Out of 115 patients between 2014 and 2018, the complete data of 33 patients followed up at least 3 months after complete Gensingen brace weaning could be analyzed. The mean age was 12 years, the mean Cobb angle was 33.6°, and the mean Risser value was 0.7 at the beginning of the treatment. The mean improvement in the Cobb angle on in-brace x-ray imaging was -26.1० (80% of in-brace correction). The Cobb angle of the major curvature changed as follows: curve stabilization was achieved in 7 (21.2%) cases, and curve improvement was achieved in 26 (78.8%) cases. None of the patients showed a curve progression. The Cobb angle was significantly reduced in the brace at the end of treatment and at follow-up evaluation (P<.001). ATR improved significantly for thoracic (P<.001) and lumbar curves (P<.001). CONCLUSIONS The database proved to be informative in the assessment of radiological and clinical outcome parameters. The example data set we have generated can be a helpful tool for professionals who work in clinics but do not store regular patient data. Especially with regard to different patient collectives worldwide, different results may be achieved with the same standards of care. In addition, the results of this study suggest that above-average correction effects with a full-time brace application lead to significant improvements in the Cobb angle after brace treatment has been completed.
Collapse
Affiliation(s)
- Xiaofeng Nan
- Nan Xiaofeng's Spinal Orthopedic Workshop, Xi'an Shaanxi, China
| | - Tuğba Kuru Çolak
- Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Marmara University, İstanbul, Turkey
| | - Burçin Akçay
- Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Bandırma Onyedi Eylül University, Balıkesir, Turkey
| | - Hua Xie
- Schroth Health Technology, Chongqing, China
| | - Liwei Zhao
- National Research Centre for Rehabilitation Technical Aids, Schroth Health Technology, Beijing, China
| | | |
Collapse
|
36
|
Souza L, Miller BR, Cammarota RC, Lo A, Lopez I, Shiue YS, Bergstrom BD, Dishman SN, Fettinger JC, Sigman MS, Shaw JT. Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools. ACS Catal 2024; 14:104-115. [PMID: 38205021 PMCID: PMC10775150 DOI: 10.1021/acscatal.3c04256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/10/2023] [Indexed: 01/12/2024]
Abstract
Interactions between catalysts and substrates can be highly complex and dynamic, often complicating the development of models to either predict or understand such processes. A dirhodium(II)-catalyzed C-H insertion of donor/donor carbenes into 2-alkoxybenzophenone substrates to form benzodihydrofurans was selected as a model system to explore nonlinear methods to achieve a mechanistic understanding. We found that the application of traditional methods of multivariate linear regression (MLR) correlating DFT-derived descriptors of catalysts and substrates leads to poorly performing models. This inspired the introduction of nonlinear descriptor relationships into modeling by applying the sure independence screening and sparsifying operator (SISSO) algorithm. Based on SISSO-generated descriptors, a high-performing MLR model was identified that predicts external validation points well. Mechanistic interpretation was aided by the deconstruction of feature relationships using chemical space maps, decision trees, and linear descriptors. Substrates were found to have a strong dependence on steric effects for determining their innate cyclization selectivity preferences. Catalyst reactive site features can then be matched to product features to tune or override the resultant diastereoselectivity within the substrate-dictated ranges. This case study presents a method for understanding complex interactions often encountered in catalysis by using nonlinear modeling methods and linear deconvolution by pattern recognition.
Collapse
Affiliation(s)
- Lucas
W. Souza
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Beck R. Miller
- Department
of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Ryan C. Cammarota
- Department
of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Anna Lo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ixchel Lopez
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Yuan-Shin Shiue
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Benjamin D. Bergstrom
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Sarah N. Dishman
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - James C. Fettinger
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Matthew S. Sigman
- Department
of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Jared T. Shaw
- Department
of Chemistry, University of California, Davis, California 95616, United States
| |
Collapse
|
37
|
Meyers EM. NeuroDecodeR: a package for neural decoding in R. Front Neuroinform 2024; 17:1275903. [PMID: 38235167 PMCID: PMC10791947 DOI: 10.3389/fninf.2023.1275903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/16/2023] [Indexed: 01/19/2024] Open
Abstract
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.
Collapse
Affiliation(s)
- Ethan M. Meyers
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
- School of Cognitive Science, Hampshire College, Amherst, MA, United States
- The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA, United States
| |
Collapse
|
38
|
Pearson TA, Vitalis D, Pratt C, Campo R, Armoundas AA, Au D, Beech B, Brazhnik O, Chute CG, Davidson KW, Diez-Roux AV, Fine LJ, Gabriel D, Groenveld P, Hall J, Hamilton AB, Hu H, Ji H, Kind A, Kraus WE, Krumholz H, Mensah GA, Merchant RM, Mozaffarian D, Murray DM, Neumark-Sztainer D, Petersen M, Goff D. The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities. JACC Adv 2024; 3:100759. [PMID: 38375059 PMCID: PMC10876066 DOI: 10.1016/j.jacadv.2023.100759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce.
Collapse
Affiliation(s)
- Thomas A. Pearson
- College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida, USA
| | - Debbie Vitalis
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Charlotte Pratt
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Rebecca Campo
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital and Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - David Au
- Center of Innovation for Veteran-Centered and Value-Driven Care, University of Washington, Seattle, Washington, USA
| | - Bettina Beech
- UH Population Health, University of Houston, Houston, Texas, USA
| | - Olga Brazhnik
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Christopher G. Chute
- Johns Hopkins Medicine, Institute for Clinical and Translational Research, Baltimore, Maryland, USA
| | - Karina W. Davidson
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, New York, USA
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA
| | - Ana V. Diez-Roux
- Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA
| | - Lawrence J. Fine
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Davera Gabriel
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peter Groenveld
- Center for Health Care Transformation and Innovation, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jaclyn Hall
- Department of Health Outcomes and Biomedical Informatics, Institute for Child Health Policy, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Alison B. Hamilton
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Hui Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Heng Ji
- Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, Illinois, USA
| | - Amy Kind
- Center for Health Disparities Research (CHDR), University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - William E. Kraus
- Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, North Carolina, USA
| | - Harlan Krumholz
- Institute for Social and Policy Studies, of Investigative Medicine and of Public Health (Health Policy), Yale University, New Haven, Connecticut, USA
| | - George A. Mensah
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Raina M. Merchant
- Center for Health Care Transformation and Innovation, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science & Policy, Tufts University, Medford, Massachusetts, USA
| | - David M. Murray
- Office of Disease Prevention, National Institutes of Health, Bethesda, Maryland, USA
| | - Dianne Neumark-Sztainer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Maya Petersen
- Division of Biostatistics, and UCSF-UC Berkeley Program in Computational Precision Health, School of Public Health, University of California-Berkeley, Berkeley, California, USA
- University of California-San Francisco, San Francisco, California, USA
| | - David Goff
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| |
Collapse
|
39
|
Lötsch J, Wolter A, Hähner A, Hummel T. Odor dilution sorting as a clinical test of olfactory function: normative values and reliability data. Chem Senses 2024; 49:bjae008. [PMID: 38401152 DOI: 10.1093/chemse/bjae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Indexed: 02/26/2024] Open
Abstract
Clinical assessment of an individual's sense of smell has gained prominence, but its resource-intensive nature necessitates the exploration of self-administered methods. In this study, a cohort of 68 patients with olfactory loss and 55 controls were assessed using a recently introduced olfactory test. This test involves sorting 2 odorants (eugenol and phenylethyl alcohol) in 5 dilutions according to odor intensity, with an average application time of 3.5 min. The sorting task score, calculated as the mean of Kendall's Tau between the assigned and true dilution orders and normalized to [0,1], identified a cutoff for anosmia at a score ≤ 0.7. This cutoff, which marks the 90th percentile of scores obtained with randomly ordered dilutions, had a balanced accuracy of 89% (78% to 97%) for detecting anosmia, comparable to traditional odor threshold assessments. Retest evaluations suggested a score difference of ±0.15 as a cutoff for clinically significant changes in olfactory function. In conclusion, the olfactory sorting test represents a simple, self-administered approach to the detection of anosmia or preserved olfactory function. With balanced accuracy similar to existing brief olfactory tests, this method offers a practical and user-friendly alternative for screening anosmia, addressing the need for resource-efficient assessments in clinical settings.
Collapse
Affiliation(s)
- Jörn Lötsch
- Goethe-University, Medical Faculty, Institute of Clinical Pharmacology, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - Anne Wolter
- Smell & Taste Clinic, Department of Otorhinolaryngology, Universitätsklinik Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Antje Hähner
- Smell & Taste Clinic, Department of Otorhinolaryngology, Universitätsklinik Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Thomas Hummel
- Smell & Taste Clinic, Department of Otorhinolaryngology, Universitätsklinik Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| |
Collapse
|
40
|
Mayer B, Kringel D, Lötsch J. Artificial intelligence and machine learning in clinical pharmacological research. Expert Rev Clin Pharmacol 2024; 17:79-91. [PMID: 38165148 DOI: 10.1080/17512433.2023.2294005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/08/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research. METHODS Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized. RESULTS ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers. CONCLUSIONS ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.
Collapse
Affiliation(s)
- Benjamin Mayer
- Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
| | - Dario Kringel
- Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
| | - Jörn Lötsch
- Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
| |
Collapse
|
41
|
Lötsch J, Brosig O, Slobodova J, Kringel D, Haehner A, Hummel T. Diagnosed and subjectively perceived long-term effects of COVID-19 infection on olfactory function assessed by supervised machine learning. Chem Senses 2024; 49:bjad051. [PMID: 38213039 DOI: 10.1093/chemse/bjad051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Indexed: 01/13/2024] Open
Abstract
Loss of olfactory function is a typical acute coronavirus disease 2019 (COVID-19) symptom, at least in early variants of SARS-CoV2. The time that has elapsed since the emergence of COVID-19 now allows for assessing the long-term prognosis of its olfactory impact. Participants (n = 722) of whom n = 464 reported having had COVID-19 dating back with a mode of 174 days were approached in a museum as a relatively unbiased environment. Olfactory function was diagnosed by assessing odor threshold and odor identification performance. Subjects also rated their actual olfactory function on an 11-point numerical scale [0,…10]. Neither the frequency of olfactory diagnostic categories nor olfactory test scores showed any COVID-19-related effects. Olfactory diagnostic categories (anosmia, hyposmia, or normosmia) were similarly distributed among former patients and controls (0.86%, 18.97%, and 80.17% for former patients and 1.17%, 17.51%, and 81.32% for controls). Former COVID-19 patients, however, showed differences in their subjective perception of their own olfactory function. The impact of this effect was substantial enough that supervised machine learning algorithms detected past COVID-19 infections in new subjects, based on reduced self-awareness of olfactory performance and parosmia, while the diagnosed olfactory function did not contribute any relevant information in this context. Based on diagnosed olfactory function, results suggest a positive prognosis for COVID-19-related olfactory loss in the long term. Traces of former infection are found in self-perceptions of olfaction, highlighting the importance of investigating the long-term effects of COVID-19 using reliable and validated diagnostic measures in olfactory testing.
Collapse
Affiliation(s)
- Jörn Lötsch
- Goethe-University, Institute of Clinical Pharmacology, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - Oskar Brosig
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Jana Slobodova
- Department of Otolaryngology, University of Pardubice, Faculty of Health Studies, Pardubice, Czech Republic
| | - Dario Kringel
- Goethe-University, Institute of Clinical Pharmacology, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
| | - Antje Haehner
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Thomas Hummel
- Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| |
Collapse
|
42
|
Stewart R, Chaturvedi J, Roberts A. Natural language processing - relevance to patient outcomes and real-world evidence. Expert Rev Pharmacoecon Outcomes Res 2024; 24:5-9. [PMID: 37874661 DOI: 10.1080/14737167.2023.2275670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/23/2023] [Indexed: 10/26/2023]
Affiliation(s)
- Robert Stewart
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Jaya Chaturvedi
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Angus Roberts
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| |
Collapse
|
43
|
Rasmussen J, Skejø S, Waagepetersen RP. Predicting Tissue Loads in Running from Inertial Measurement Units. Sensors (Basel) 2023; 23:9836. [PMID: 38139682 PMCID: PMC10747732 DOI: 10.3390/s23249836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. METHODS We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. RESULTS Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. CONCLUSIONS Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.
Collapse
Affiliation(s)
- John Rasmussen
- Department of Materials and Production, Aalborg University, Fibigerstraede 16, 9220 Aalborg East, Denmark
| | - Sebastian Skejø
- Department of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark;
- Research Unit for General Practice, Aarhus University, Bartholins Allé 2, 8000 Aarhus, Denmark
| | | |
Collapse
|
44
|
Lu W, Gauthier LD, Poterba T, Giacopuzzi E, Goodrich JK, Stevens CR, King D, Daly MJ, Neale BM, Karczewski KJ. CHARR efficiently estimates contamination from DNA sequencing data. Am J Hum Genet 2023; 110:2068-2076. [PMID: 38000370 PMCID: PMC10716339 DOI: 10.1016/j.ajhg.2023.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
DNA sample contamination is a major issue in clinical and research applications of whole-genome and -exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a metric to estimate DNA sample contamination from variant-level whole-genome and -exome sequence data called CHARR, contamination from homozygous alternate reference reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VariantDataset format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole-genome and exome sequencing datasets.
Collapse
Affiliation(s)
- Wenhan Lu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Laura D Gauthier
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Timothy Poterba
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Julia K Goodrich
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christine R Stevens
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Daniel King
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark J Daly
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| |
Collapse
|
45
|
Del Ben F, Da Col G, Cobârzan D, Turetta M, Rubin D, Buttazzi P, Antico A. A fully interpretable machine learning model for increasing the effectiveness of urine screening. Am J Clin Pathol 2023; 160:620-632. [PMID: 37658807 PMCID: PMC10691191 DOI: 10.1093/ajcp/aqad099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/17/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES This article addresses the need for effective screening methods to identify negative urine samples before urine culture, reducing the workload, cost, and release time of results in the microbiology laboratory. We try to overcome the limitations of current solutions, which are either too simple, limiting effectiveness (1 or 2 parameters), or too complex, limiting interpretation, trust, and real-world implementation ("black box" machine learning models). METHODS The study analyzed 15,312 samples from 10,534 patients with clinical features and the Sysmex Uf-1000i automated analyzer data. Decision tree (DT) models with or without lookahead strategy were used, as they offer a transparent set of logical rules that can be easily understood by medical professionals and implemented into automated analyzers. RESULTS The best model achieved a sensitivity of 94.5% and classified negative samples based on age, bacteria, mucus, and 2 scattering parameters. The model reduced the workload by an additional 16% compared to the current procedure in the laboratory, with an estimated financial impact of €40,000/y considering 15,000 samples/y. Identified logical rules have a scientific rationale matched to existing knowledge in the literature. CONCLUSIONS Overall, this study provides an effective and interpretable screening method for urine culture in microbiology laboratories, using data from the Sysmex UF-1000i automated analyzer. Unlike other machine learning models, our model is interpretable, generating trust and enabling real-world implementation.
Collapse
Affiliation(s)
- Fabio Del Ben
- CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | - Giacomo Da Col
- KI4LIFE, Fraunhofer Austria Research, Klagenfurt, Austria
| | | | - Matteo Turetta
- CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy
| | | | | | | |
Collapse
|
46
|
Szilagyi IS, Eggeling E, Bornemann-Cimenti H, Ullrich T. Impact of the pandemic and its containment measures in Europe upon aspects of affective impairments: a Google Trends informetrics study. Psychol Med 2023; 53:7685-7697. [PMID: 37357891 PMCID: PMC10755220 DOI: 10.1017/s0033291723001563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND In late 2019, a new virus began spreading in Wuhan, China. By the end of 2021, more than 260 million people worldwide had been infected and 5.2 million people had died because of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Various countermeasures have been implemented to contain the infections, depending on the country, infection prevalence, and political and infrastructural resources. The pandemic and the containment measures have induced diverse psychological burdens. Using internet queries as a proxy, this study examines the psychological consequences on a European level of SARS-CoV-2 containment measures. METHODS Using informetric analyses, this study reviews within 32 European countries a total of 28 search parameters derived from the International Statistical Classification of Diseases and Related Health Problems (ICD-10) as aspects of affective disorder. RESULTS Our results show that there are several psychological aspects which are significantly emphasized during the pandemic and its containment measures: 'anxiety', 'dejection', 'weariness', 'listlessness', 'loss of appetite', 'loss of libido', 'panic attack', and 'worthlessness'. These terms are significantly more frequently part of a search query during the pandemic than before the outbreak. Furthermore, our results revealed that search parameters such as 'psychologist', 'psychotherapist', 'psychotherapy' have increased highly significantly (p < 0.01) since the pandemic. CONCLUSIONS The psychological distress caused by the pandemic correlates significantly with the frequency of people searching for psychological and psychotherapeutic support on the Internet.
Collapse
Affiliation(s)
- Istvan-Szilard Szilagyi
- Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, 8036 Graz, Austria
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, 8036 Graz, Austria
| | - Eva Eggeling
- Fraunhofer Austria Research GmbH, 8010 Graz, Austria
| | - Helmar Bornemann-Cimenti
- Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, 8036 Graz, Austria
| | - Torsten Ullrich
- Fraunhofer Austria Research GmbH, 8010 Graz, Austria
- Institute of Computer Graphics and Knowledge Visualization, Graz University of Technology, 8010 Graz, Austria
| |
Collapse
|
47
|
Leaks K, Norden-Krichmar T, Brody JP. Predicting moderate drinking behaviors in National Health and Nutrition Examination Survey participants using biochemical and demographical factors with machine learning. Alcohol 2023; 113:1-10. [PMID: 37543050 DOI: 10.1016/j.alcohol.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 06/26/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023]
Abstract
Recent studies revealed that any amount of alcohol consumption is an overall health detriment to multiple populations, contrary to popular beliefs. In addition, very few alcohol use studies utilized machine learning methods to compare the biological health of moderate drinkers compared to those that abstain from alcohol consumption, opting instead to focus on binge drinking and heavy drinking. Using participant data of multiple factor types from the National Health and Nutrition Examination Survey, we created prediction models with stacked ensembles and gradient boosting models. Machine learning models were used to identify which factors most enabled the prediction of moderate drinking behaviors. Our combined factor runs produced a cross-validation area under the curve (AUC) of 0.929 and a validation area under the curve of 0.806. Runs that only included biochemical or demographical factors received cross-validation AUC values of 0.825 and 0.925, and validation AUC values of 0.757 and 0.783, respectively. The top predictive factors for our machine learning runs, including gamma glutamyl transferase, gender, iron levels, and cigarette and marijuana usage, corroborate past studies that link those factors to alcohol consumption. Our findings identified key differences in the biological health of moderate drinkers compared to those that abstain from drinking. These results reveal a need to further explore the health effects of moderate drinking, especially for vulnerable populations.
Collapse
Affiliation(s)
- Kalan Leaks
- Department of Biomedical Engineering, University of California, Irvine 3120 Natural Sciences II, Irvine, CA 92697-2715, United States
| | - Trina Norden-Krichmar
- Department of Epidemiology & Biostatistics, University of California, Irvine, 856 Health Sciences Quad, Suite 3400, Irvine, CA 92617, United States
| | - James P Brody
- Department of Biomedical Engineering, University of California, Irvine 3120 Natural Sciences II, Irvine, CA 92697-2715, United States.
| |
Collapse
|
48
|
Marshak A, Young H, Naumova EN. Data on Humanitarian Crises: Who and What Are We Missing? Food Nutr Bull 2023; 44:S124-S126. [PMID: 37021371 DOI: 10.1177/03795721231162429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Affiliation(s)
- Anastasia Marshak
- Feinstein International Center, Tufts University, Boston, MA, USA
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Helen Young
- Feinstein International Center, Tufts University, Boston, MA, USA
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Elena N Naumova
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| |
Collapse
|
49
|
Ahmad RM, Ali BR, Al-Jasmi F, Sinnott RO, Al Dhaheri N, Mohamad MS. A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer. Brief Bioinform 2023; 25:bbad479. [PMID: 38149678 PMCID: PMC10782903 DOI: 10.1093/bib/bbad479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/22/2023] [Accepted: 12/04/2023] [Indexed: 12/28/2023] Open
Abstract
Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.
Collapse
Affiliation(s)
- Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| | - Bassam R Ali
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| | - Fatma Al-Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Richard O Sinnott
- School of Computing and Information System, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Noura Al Dhaheri
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| |
Collapse
|
50
|
Dobson R, Stowell M, Warren J, Tane T, Ni L, Gu Y, McCool J, Whittaker R. Use of Consumer Wearables in Health Research: Issues and Considerations. J Med Internet Res 2023; 25:e52444. [PMID: 37988147 DOI: 10.2196/52444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023] Open
Abstract
As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.
Collapse
Affiliation(s)
- Rosie Dobson
- School of Population Health, University of Auckland, Auckland, New Zealand
- Institute for Innovation and Improvement, Te Whatu Ora Waitematā, Auckland, New Zealand
| | - Melanie Stowell
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Jim Warren
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Taria Tane
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Lin Ni
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Yulong Gu
- School of Health Sciences, Stockton University, Galloway, NJ, United States
| | - Judith McCool
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Robyn Whittaker
- School of Population Health, University of Auckland, Auckland, New Zealand
- Institute for Innovation and Improvement, Te Whatu Ora Waitematā, Auckland, New Zealand
| |
Collapse
|