A real-world pharmacovigilance study of FDA adverse event reporting system events for sildenafil

BACKGROUND

Sildenafil, a selective inhibitor of phosphodiesterase type 5 (PDE5), is widely used for the treatment of erectile dysfunction (ED). However, the safety profile of sildenafil, including adverse event (AEs), requires comprehensive evaluation.

METHODS

This retrospective pharmacovigilance study aimed to evaluate AEs linked to sildenafil by analyzing data sourced from the FDA Adverse Event Reporting System (FAERS) database. A case/non-case design was utilized, and various algorithms including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multiitem gamma Poisson shrinker (MGPS) were employed to measure the signals indicating the presence of sildenafil-related AEs.

RESULTS

Among 339,230 reports, 33,692 specifically mentioned sildenafil use. Most of AEs occurred in males over 60 years old. The United States accounted for the highest proportion of reported AEs. Severe outcomes, including death, disability, and life-threatening events, were reported. Significant system organ class (SOC) included "Reproductive system and breast disorders" (SOC: 10038604), "Neoplasms benign, malignant and unspecified" (SOC: 10038738), "Vascular disorders" (SOC: 10047065), and "Blood and lymphatic system disorders" (SOC: 10005329). Noteworthy preferred terms (PTs) associated with sildenafil included "Vision blurred," "Flushing," "sudden hearing loss," "Painful erection," and "Priapism." Unexpected AEs, such as "Malignant melanoma," "Pulmonary hypertension," "Malignant melanoma in situ," "Pulmonary arterial hypertension," "Metastatic malignant melanoma," "Malignant melanoma stage III," "Malignant melanoma stage II," "Acquired hemophilia," "Aortic dissection rupture," and "Intracranial artery dissection" were also identified.

CONCLUSIONS

These findings emphasize the importance of monitoring and understanding the potential risks associated with sildenafil. Further investigation is warranted to validate these associations and address previously unrecognized safety concerns.

Potential routes of plastics biotransformation involving novel plastizymes revealed by global multi-omic analysis of plastic associated microbes

Despite increasing efforts across various disciplines, the fate, transport, and impact of synthetic plastics on the environment and public health remain poorly understood. To better elucidate the microbial ecology of plastic waste and its potential for biotransformation, we conducted a large-scale analysis of all publicly available meta-omic studies investigating plastics (n = 27) in the environment. Notably, we observed low prevalence of known plastic degraders throughout most environments, except for substantial enrichment in riverine systems. This indicates rivers may be a highly promising environment for discovery of novel plastic bioremediation products. Ocean samples associated with degrading plastics showed clear differentiation from non-degrading polymers, showing enrichment of novel putative biodegrading taxa in the degraded samples. Regarding plastisphere pathogenicity, we observed significant enrichment of antimicrobial resistance genes on plastics but not of virulence factors. Additionally, we report a co-occurrence network analysis of 10 + million proteins associated with the plastisphere. This analysis revealed a localized sub-region enriched with known and putative plastizymes-these may be useful for deeper investigation of nature's ability to biodegrade man-made plastics. Finally, the combined data from our meta-analysis was used to construct a publicly available database, the Plastics Meta-omic Database (PMDB)-accessible at plasticmdb.org. These data should aid in the integrated exploration of the microbial plastisphere and facilitate research efforts investigating the fate and bioremediation potential of environmental plastic waste.

Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques

The COVID-19 pandemic has profoundly reshaped human life. The development of COVID-19 vaccines has offered a semblance of normalcy. However, obstacles to vaccination have led to substantial loss of life and economic burdens. In this study, we analyze data from a prominent health insurance provider in the United States to uncover the underlying reasons behind the inability, refusal, or hesitancy to receive vaccinations. Our research proposes a methodology for pinpointing affected population groups and suggests strategies to mitigate vaccination barriers and hesitations. Furthermore, we estimate potential cost savings resulting from the implementation of these strategies. To achieve our objectives, we employed Bayesian data mining methods to streamline data dimensions and identify significant variables (features) influencing vaccination decisions. Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, demonstrating superior performance.

Post-marketing safety concerns with palbociclib: a disproportionality analysis of the FDA adverse event reporting system

OBJECTIVES

To explore the association between palbociclib and related adverse events (AEs) in the real world through U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database.

METHODS

The signal strength of palbociclib-related AEs was done by disproportionality analysis. Clinical priority of palbociclib-related AEs was scored and ranked by assessing five different features. Outcome analysis, time to onset analysis, dose-report /AEs number analysis, and stratification analysis were all performed.

RESULTS

There were 61,821 'primary suspected (PS)' reports of palbociclib and 195,616 AEs associated with palbociclib. The four algorithms simultaneously detected 18 positive signals at the SOC level, and 65 positive signals at the PT level. Bone marrow failure, neuropathy, peripheral, pleural effusion, myelosuppression, pulmonary edema, and pulmonary thrombosis were also found to have positive signals. Gender (female vs male, χ2 = 5.287, p = 0.022) and age showed significant differences in serious and non-serious reports. Palbociclib-related AEs had a median onset time of 79 days (interquartile range [IQR] 20-264 days).

CONCLUSIONS

The study identified potential Palbociclib-related AEs and offered warnings for special AEs, providing further data for palbociclib safety studies in breast cancer patients. Nonetheless, prospective clinical trials are needed to validate these results and explain their relationship.

A real‑world pharmacovigilance study of FDA adverse event reporting system events for daratumumab

BACKGROUND

Daratumumab, a first-in-class humanized IgG1κ monoclonal antibody that targets the CD38 epitope, has been approved for treatment of multiple myeloma by FDA. The current study was to evaluate daratumumab-related adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS).

RESEARCH DESIGN AND METHODS

Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were employed to quantify the signals of daratumumab-associated AEs.

RESULTS

Out of 10,378,816 reports collected from the FAERS database, 8727 reports of daratumumab-associated AEs were identified. A total of 183 significant disproportionality preferred terms (PTs) were retained. Unexpected significant AEs such as meningitis aseptic, leukoencephalopathy, tumor lysis syndrome, disseminated intravascular coagulation, hyperviscosity syndrome, sudden hearing loss, ileus and diverticular perforation were also detected. The median onset time of daratumumab-related AEs was 11 days (interquartile range [IQR] 0-76 days), and most of the cases occurred within 30 days.

CONCLUSION

Our study found potential new and unexpected AEs signals for daratumumab, suggesting prospective clinical studies are needed to confirm these results and illustrate their relationship.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Dynamic molecular network analysis of iPSC-Purkinje cells differentiation delineates roles of ISG15 in SCA1 at the earliest stage

Better understanding of the earliest molecular pathologies of all neurodegenerative diseases is expected to improve human therapeutics. We investigated the earliest molecular pathology of spinocerebellar ataxia type 1 (SCA1), a rare familial neurodegenerative disease that primarily induces death and dysfunction of cerebellum Purkinje cells. Extensive prior studies have identified involvement of transcription or RNA-splicing factors in the molecular pathology of SCA1. However, the regulatory network of SCA1 pathology, especially central regulators of the earliest developmental stages and inflammatory events, remains incompletely understood. Here, we elucidated the earliest developmental pathology of SCA1 using originally developed dynamic molecular network analyses of sequentially acquired RNA-seq data during differentiation of SCA1 patient-derived induced pluripotent stem cells (iPSCs) to Purkinje cells. Dynamic molecular network analysis implicated histone genes and cytokine-relevant immune response genes at the earliest stages of development, and revealed relevance of ISG15 to the following degradation and accumulation of mutant ataxin-1 in Purkinje cells of SCA1 model mice and human patients.

Analysis of Acupoint Selection Rules for Guasha Treatment of Primary Headaches Based on Data Mining

Objective

We aimed to understand the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches using data mining technology, providing a reference for the clinical application of Guasha therapy for primary headaches.

Methods

Literature related to Guasha therapy for primary headaches in PubMed, Web of Science, Chinese National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Chinese Biomedical Literature Database were searched, up until May 12, 2023. A database of acupoints for Guasha therapy for primary headaches was established in Excel. The frequency of the acupoints used for Guasha in therapy of primary headaches were calculated by SPSS 25.0. The association rules between the acupoints were further described using SPSS Modeler 18.0.

Results

A total of 67 papers were included, involving 51 acupoints for Guasha against primary headaches. The most commonly used acupoints were Fengchi, Baihui, Taiyang, Shuaigu, Tianzhu, and Hegu. The common acupoint combinations for Guasha therapy for primary headaches were Fengchi-Taiyang, Fengchi-Baihui, Fengchi-Taiyang-Baihui, Fengchi-Tianzhu-Baihui, and Fengchi-Shuaigu-Taiyang-Baihui.

Conclusion

Data mining can effectively analyze the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches, providing a reliable basis for clinical acupoint selection.

Unveiling the Negative Customer Experience in Diagnostic Centers: A Data Mining Approach

Introduction

This study aims to identify the negative customer experiences reflected in complaints against diagnostic centers using data mining tools.

Methods

Analyzing customer complaints from a consumer complaints website, the Apriori algorithm was employed to uncover frequent patterns and identify key areas of concern. The frequency and distribution of terms used in complaints were also analyzed, and word clouds were generated to visualize the findings.

Results

The study revealed that major areas of unfavorable customer experience included delayed test reports, erroneous test results, difficulties scheduling appointments, staff incivility, subpar service, and medical negligence.

Discussion

These findings and the proposed model can guide diagnostic centers in incorporating data mining tools for customer experience analysis, enabling managers to proactively address issues and view complaints as opportunities for service improvement rather than legal liabilities.

scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, broad applications of the methods still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly's superiority over baseline methods in preserving cellular heterogeneity while translating datasets of various contexts and in revealing cell type-specific biological insights. Besides, we demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Moreover, scButterfly can be generalized to unpaired data training, perturbation-response analysis, and consecutive translation.

Establishment-level safety analytics: a scoping review

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Exploring long-term breast cancer survivors' care trajectories using dynamic time warping-based unsupervised clustering

OBJECTIVES

Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary.

MATERIALS AND METHODS

A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female BCS of a large longitudinal retrospective cohort of Spain. The extracted care-transition patterns are graphically represented using directed network diagrams with aggregated patient and time information. A control group consisting of 12 412 females without breast cancer is also used for comparison.

RESULTS

The use of radiology and hospital admission are explored as patterns of special interest. In the generated networks, a more intense and complex use of certain healthcare services (eg, radiology, outpatient care, hospital admission) is shown and quantified for the BCS. Higher mortality rates and numbers of comorbidities are observed in various transitions and compared with non-breast cancer. It is also demonstrated how a wealth of patient and time information can be revealed from individual service transitions.

DISCUSSION

The presented methodology permits the identification and descriptive visualization of temporal patterns of the usage of healthcare services by the BCS, that otherwise would remain hidden in the trajectories.

CONCLUSION

The results could provide the basis for better understanding the BCS' circulation through the health system, with a view to more efficiently predicting their forthcoming needs and thus designing more effective personalized survivorship care plans.

Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium

OBJECTIVE

The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.

METHODS

The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events).

RESULTS

In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora.

CONCLUSION

To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer's Disease Neuroimaging Initiative cohort

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults

PURPOSE

The purpose of this study was to use data mining methods to establish a simple and reliable predictive model based on the risk factors related to gallbladder stones (GS) to assist in their diagnosis and reduce medical costs.

MATERIALS AND METHODS

This was a retrospective cross-sectional study. A total of 4215 participants underwent annual health examinations between January 2019 and December 2019 at the Physical Examination Center of Shengjing Hospital Affiliated to China Medical University. After rigorous data screening, the records of 2105 medical examiners were included for the construction of J48, multilayer perceptron (MLP), Bayes Net, and Naïve Bayes algorithms. A ten-fold cross-validation method was used to verify the recognition model and determine the best classification algorithm for GS.

RESULTS

The performance of these models was evaluated using metrics of accuracy, precision, recall, F-measure, and area under the receiver operating characteristic curve. Comparison of the F-measure for each algorithm revealed that the F-measure values for MLP and J48 (0.867 and 0.858, respectively) were not statistically significantly different (p>0.05), although they were significantly higher than the F-measure values for Bayes Net and Naïve Bayes (0.824 and 0.831, respectively; p<0.05).

CONCLUSION

The results of this study showed that MLP and J48 algorithms are effective at screening individuals for the risk of GS. The key attributes of data mining can further promote the prevention of GS through targeted community intervention, improve the outcome of GS, and reduce the burden on the medical system.

Classifying protein kinase conformations with machine learning

Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.

Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records

STUDY OBJECTIVES

The objectives of this study were to understand the relative comorbidity burden of obstructive sleep apnea (OSA), determine whether these relationships were modified by sex or age, and identify patient subtypes defined by common comorbidities.

METHODS

Cases with OSA and noncases (controls) were defined using a validated electronic health record (EHR)-based phenotype and matched for age, sex, and time period of follow-up in the EHR. We compared prevalence of the 20 most common comorbidities between matched cases and controls using conditional logistic regression with and without controlling for body mass index. Latent class analysis was used to identify subtypes of OSA cases defined by combinations of these comorbidities.

RESULTS

In total, 60,586 OSA cases were matched to 60,586 controls (from 1,226,755 total controls). Patients with OSA were more likely to have each of the 20 most common comorbidities compared with controls, with odds ratios ranging from 3.1 to 30.8 in the full matched set and 1.3 to 10.2 after body mass index adjustment. Associations between OSA and these comorbidities were generally stronger in females and patients with younger age at diagnosis. We identified 5 distinct subgroups based on EHR-defined comorbidities: High Comorbidity Burden, Low Comorbidity Burden, Cardiovascular Comorbidities, Inflammatory Conditions and Less Obesity, and Inflammatory Conditions and Obesity.

CONCLUSIONS

Our study demonstrates the power of leveraging the EHR to understand the relative health burden of OSA, as well as heterogeneity in these relationships based on age and sex. In addition to enrichment for comorbidities, we identified 5 novel OSA subtypes defined by combinations of comorbidities in the EHR, which may be informative for understanding disease outcomes and improving prevention and clinical care. Overall, this study adds more evidence that OSA is heterogeneous and requires personalized management.

CITATION

Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records. J Clin Sleep Med. 2024;20(4):521-533.

Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining

The surge in 'Big data' has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness. This definition should both reflect the attributes related to biocompatibility and be compatible with computational data-mining methods. Here, we discuss the need for a comprehensive and contemporary definition of biocompatibility and the challenges in developing one. We also identify the key elements that comprise biocompatibility, and propose an integrated biocompatibility definition that enables data-mining approaches.

Data mining and safety analysis of traditional immunosuppressive drugs: a pharmacovigilance investigation based on the FAERS database

OBJECTIVE

The purpose of this study aimed to explore the new and serious adverse events(AEs) of Tacrolimus(FK506), cyclosporine(CsA), azathioprine(AZA), mycophenolate mofetil(MMF), cyclophosphamide(CTX) and methotrexate(MTX), which have not been concerned.

METHODS

The FAERS data from January 2016 and December 2022 were selected for disproportionality analysis to discover the potential risks of traditional immunosuppressive drugs.

RESULTS

Compared with CsA, FK506 has more frequent transplant rejection, and is more related to renal impairment, COVID-19, cytomegalovirus infection and aspergillus infection. However, CsA has a high infection-related fatality rate. In addition, we also found some serious and rare AE in other drugs which were rarely reported in previous studies. For example, AZA is closely related to hepatosplenic T-cell lymphoma with high fatality rate and MTX is strongly related to hypofibrinogenemia.

CONCLUSION

The AEs report on this study confirmed that the results were basically consistent with the previous studies, but there were also some important safety signals that were inconsistent with or not mentioned in previous published studies.

EXPERT OPINION

The opinion section discusses some of the limitations and shortcomings, proposing the areas where more effort should be invested in order to improve the safety of immunosuppressive drugs.

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

Global Research Status Regarding Uveitis in the Last Decade

PURPOSE

To provide an overview on global uveitis research in the last decade in terms of countries/regions, organizations, scholars, journals, trending topics, and fundings.

METHODS

This cross-sectional bibliometric analysis yielded 10656 uveitis publications in English for subsequent bibliometric analysis.

RESULTS

In terms of the number of publications, the leading country/region was the USA (3007). The most productive organization was the University College London (420). The most productive research team was Professor Yang's group (146). A higher h-index was noted in University College London (48). Professor Rosenbaum was the first h-index holder (32). Keywords of interest included topics such as biologics, COVID and OCT. Publications by Ocular Immunology and Inflammation (968) ranked the first position.

CONCLUSIONS

The USA is the leading force in uveitis study. Asian countries/regions, such as China (mainland) and India, are exerting a substantial role worldwide. Trendy topics cover COVID-19, OCTA.

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context

OBJECTIVE

Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation.

METHODS

Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria.

RESULTS

There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93.

SIGNIFICANCE

This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral.

PLAIN LANGUAGE SUMMARY

Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia.

Information heterogeneity between progress notes by physicians and nurses for inpatients with digestive system diseases

This study focused on the heterogeneity in progress notes written by physicians or nurses. A total of 806 days of progress notes written by physicians or nurses from 83 randomly selected patients hospitalized in the Gastroenterology Department at Kagawa University Hospital from January to December 2021 were analyzed. We extracted symptoms as the International Classification of Diseases (ICD) Chapter 18 (R00-R99, hereinafter R codes) from each progress note using MedNER-J natural language processing software and counted the days one or more symptoms were extracted to calculate the extraction rate. The R-code extraction rate was significantly higher from progress notes by nurses than by physicians (physicians 68.5% vs. nurses 75.2%; p = 0.00112), regardless of specialty. By contrast, the R-code subcategory R10-R19 for digestive system symptoms (44.2 vs. 37.5%, respectively; p = 0.00299) and many chapters of ICD codes for disease names, as represented by Chapter 11 K00-K93 (68.4 vs. 30.9%, respectively; p < 0.001), were frequently extracted from the progress notes by physicians, reflecting their specialty. We believe that understanding the information heterogeneity of medical documents, which can be the basis of medical artificial intelligence, is crucial, and this study is a pioneering step in that direction.

Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence

Coronary artery calcium (CAC) is a powerful tool to refine atherosclerotic cardiovascular disease (ASCVD) risk assessment. Despite its growing interest, contemporary public attitudes around CAC are not well-described in literature and have important implications for shared decision-making around cardiovascular prevention. We used an artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model and unsupervised machine learning techniques to analyze 5,606 CAC-related discussions on Reddit. A total of 91 discussion topics were identified and were classified into 14 overarching thematic groups. These included the strong impact of CAC on therapeutic decision-making, ongoing non-evidence-based use of CAC testing, and the patient perceived downsides of CAC testing (e.g., radiation risk). Sentiment analysis also revealed that most discussions had a neutral (49.5%) or negative (48.4%) sentiment. The results of this study demonstrate the potential of an AI-based approach to analyze large, publicly available social media data to generate insights into public perceptions about CAC, which may help guide strategies to improve shared decision-making around ASCVD management and public health interventions.

What is new in FungiDB: a web-based bioinformatics platform for omics-scale data analysis for fungal and oomycete species

FungiDB (https://fungidb.org) serves as a valuable online resource that seamlessly integrates genomic and related large-scale data for a wide range of fungal and oomycete species. As an integral part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org), FungiDB continually integrates both published and unpublished data addressing various aspects of fungal biology. Established in early 2011, the database has evolved to support 674 datasets. The datasets include over 300 genomes spanning various taxa (e.g. Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, as well as Albuginales, Peronosporales, Pythiales, and Saprolegniales). In addition to genomic assemblies and annotation, over 300 extra datasets encompassing diverse information, such as expression and variation data, are also available. The resource also provides an intuitive web-based interface, facilitating comprehensive approaches to data mining and visualization. Users can test their hypotheses and navigate through omics-scale datasets using a built-in search strategy system. Moreover, FungiDB offers capabilities for private data analysis via the integrated VEuPathDB Galaxy platform. FungiDB also permits genome improvements by capturing expert knowledge through the User Comments system and the Apollo genome annotation editor for structural and functional gene curation. FungiDB facilitates data exploration and analysis and contributes to advancing research efforts by capturing expert knowledge for fungal and oomycete species.

Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers

We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F1-score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), autism spectrum disorders (1744 users), delayed speech (902 users), or asthma (1255 users), demonstrating the potential of Twitter as a complementary resource for assessing associations between pregnancy exposures and childhood health outcomes on a large scale.

InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data

Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Investigating acupoint selection and combinations of acupuncture for primary idiopathic tinnitus using data mining

BACKGROUND

Acupuncture is widely used in the treatment of tinnitus worldwide because of its good efficacy and safety. However, the criteria for selecting acupoint prescriptions and combinations have not been summarized. Therefore, data mining was used herein to determine the treatment principles and the most effective acupoint selection for the treatment of idiopathic tinnitus.

METHODS

The clinical research literature of acupuncture in the treatment of idiopathic tinnitus from the establishment of the database to September 1, 2023 in China National Knowledge Infrastructure, China Medical Journal Full-text Database, PubMed, Embase, Cochrane Library and Web of Science databases was retrieved and extracted. Microsoft Excel 2016 was used to establish the acupoint prescription database and the frequency statistics of acupoints, meridians and specific acupoints were carried out. IBM SPSS Statistics 25.0 software was used for cluster analysis of acupoints, and IBM SPSS Modeler18.0 software was used for association rule analysis of acupoints.

RESULTS

A total of 112 articles were included, involving 221 acupuncture prescriptions, including 99 acupoints, with a total frequency of 1786 times. The 5 most frequently used acupoints were Tinggong (SI19), Tinghui (GB2), Yifeng (TE17), Ermen (TE21), and Zhongzhu (TE3). The commonly used meridians were Sanjiao meridian of hand-shaoyang, Gallbladder meridian of foot-shaoyang and Small intestine meridian of hand-taiyang. The specific points are mostly Crossing point, Five-shu point and Yuan-primary point. The core acupoint combination of association rules was Ermen (TE21)-Tinggong (SI19)-Tinghui (GB2)-Yifeng (TE17), and 3 effective clustering groups were obtained by cluster analysis of high-frequency acupoints.

CONCLUSION

In this study, the published literature on acupuncture treatment of idiopathic tinnitus was analyzed by data mining, and the relationship between acupoints was explored, which provided a more wise choice for clinical acupuncture treatment of idiopathic tinnitus.

Computational Drug Discovery in Diaphragm dysfunction via Text Mining and Biomedical Database

The diaphragm, which is crucial for ventilation, is the primary muscle responsible for inspiration. Patients with severe burns who experience diaphragmatic dysfunction have an increased risk of mortality. Unfortunately, there are currently no effective medications available to prevent or treat this condition. The objective of our study is to utilize bioinformatics to identify potential genes and drugs associated with diaphragmatic dysfunction. In this study, text mining techniques were utilized to identify genes associated with diaphragmatic dysfunction and recovery. Common genes were then analyzed using GO and KEGG pathway analysis, as well as protein-protein interaction (PPI) network analysis. The obtained hub genes were processed using Cytoscape software, and their expression levels in diaphragmatic dysfunction were validated using quantitative real-time polymerase chain reaction (qRT-PCR) in severe burn rats. Genes that were confirmed were then examined in drug-gene interaction databases to identify potential drugs associated with these genes. Our analysis revealed 96 genes that were common to both the "Diaphragm dysfunction" and "Functional Recovery" text mining concepts. Gene enrichment analysis identified 19 genes representing ten pathways. qRT-PCR showed a significant increase in expression levels of 13 genes, including CCL2, CCL3, CD4, EGF, HGF, IFNG, IGF1, IL17A, IL6, LEP, PTGS2, TGFB1, and TNF, in samples with diaphragmatic dysfunction. Additionally, we found that a total of 56 drugs targeted 5 potential genes. These findings provide new insights into the development of more effective drugs for treating diaphragmatic dysfunction, and also present substantial opportunities for researching new target pharmacology and promoting drugs in the pharmaceutical industry.

Lactate dehydrogenase to albumin ratio is associated with in-hospital mortality in patients with acute heart failure: Data from the MIMIC-III database

The effect of the lactate dehydrogenase to albumin ratio (LAR) on the survival of patients with acute heart failure (AHF) is unclear. We aimed to analyze the impact of LAR on survival in patients with AHF. We retrieved eligible patients for our study from the Monitoring in Intensive Care Database III. For each patient in our study, we gathered clinical data and demographic information. We conducted multivariate logistic regression modeling and smooth curve fitting to assess whether the LAR score could be used as an independent indicator for predicting the prognosis of AHF patients. A total of 2,177 patients were extracted from the database. Survivors had an average age of 69.88, whereas nonsurvivors had an average age of 71.95. The survivor group had a mean LAR ratio of 13.44, and the nonsurvivor group had a value of 17.38. LAR and in-hospital mortality had a nearly linear correlation, according to smooth curve fitting (P < 0.001). According to multivariate logistic regression, the LAR may be an independent risk factor in predicting the prognosis of patients with AHF (odd ratio = 1.09; P < 0.001). The LAR ratio is an independent risk factor associated with increased in-hospital mortality rates in patients with AHF.

A Data Mining Study for Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Carpal Tunnel Syndrome

Background

Carpal tunnel syndrome (CTS) is the most prevalent upper limb compressive neuropathy. A considerable number of clinical trials and meta-analyses have provided evidence supporting the effectiveness of acupuncture in treating CTS. Nevertheless, the ideal choice of acupoints remains ambiguous.

Objective

A data mining analysis was conducted with the objective of determining the most effective acupoint combinations and selection for CTS.

Methods

A search was conducted across seven Chinese and English electronic bibliographic databases spanning from their inception to March 2023. Selected were clinical trials that evaluated the efficacy of acupuncture therapy for CTS, with or without randomised controlled methods. Data extraction mainly included acupoint prescriptions. Information such as first author, study design and study setting were also extracted. The principal outcomes comprised the clinical manifestations linked to CTS. Statistical descriptions were generated using Excel 2019. The analysis of association rules was conducted using SPSS Modeler 18.0. Using SPSS Statistics 26.0, exploratory factor analysis and cluster analysis were conducted.

Results

142 trials (including 86 RCTs and 56 non RCTs) were identified, and 193 groups of effective prescriptions involving 68 acupoints were extracted. The most frequently used acupoints were Da-ling (PC7), Nei-guan (PC6), He-gu (LI4), Wai-guan (TE5), and Yang-xi (LI5). The most frequently used meridians were the pericardial meridian and the large intestine meridian. The majority of special acupoints used were Five-shu points and Yuan-source points, with acupoints on the upper limbs being the most frequently used. The core acupoint groups were analyzed and 11 groups of association rules, 8 factors, and 5 effective cluster groups were obtained.

Conclusion

The evidence-based acupoint selection and combinations of acupuncture therapy for carpal tunnel syndrome were provided by the findings of this study.

Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains

To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k-mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains' evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.

Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women

Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection.

Exploration of the molecular mechanism guiding Xinfeng capsule regulatory mechanism for rheumatoid arthritis inflammation

OBJECTIVES

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joint synovium. The traditional Chinese medicine Xinfeng capsule (XFC) has a remarkable alleviating effect on inflammatory symptoms, such as joint pain and swelling, in patients with RA. However, the underlying mechanism of action remains to be elucidated. This study intended to conduct network pharmacology, animal experiments, data mining, and molecular docking to explore the molecular mechanism through which XFC can improve the inflammatory symptoms of RA.

METHODS

The Apriori association rules and a random walk model were employed to evaluate the effect of XFC on the clinical inflammatory indexes of RA. The active ingredients and the potential target genes of XFC were obtained from public databases. Based on the search tool for recurring instances of neighboring genes (STRING) database, the Database for Annotation, Visualization and Integrated Discovery (DAVID) database, Cytoscape software, and molecular docking method, the molecular mechanism by which XFC acts on RA was also analyzed. Finally, an adjuvant arthritis rat model was established to verify the effects of XFC on inflammation-related signaling pathways and inflammatory factors.

RESULTS

XFC significantly reduced the level of C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and the erythrocyte sedimentation rate (ESR). The docking space structures of the active ingredients in XFC, namely triptolide and quercetin, and the key targets were stable. Inflammation-related biological processes were identified as the key factors involved in the development of RA, and the regulation of the toll-like receptor (TLR) signaling pathway may be the key link for XFC toward improving the inflammatory state of RA. The expression levels of toll-like receptor 4 (TLR4), myeloid differentiation primary response protein MyD88 (MyD88), interleukin-1 receptor-associated kinase 1 (IRAK1), TNF receptor-associated factor 6 (TRAF6), TGF-beta-activated kinase 1 (TAK1), phospho-Inhibitor of NF-κB kinaseβ (p-IKKβ), phospho-Nuclear factor-k-gene binding (p-NF-κB), and interleukin-1β (IL-1β) can all be decreased by XFC. XFC improves joint inflammation symptoms by lowering pro-inflammatory factors tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interferon-γ (INF-γ) levels.

CONCLUSIONS

XFC could effectively improve the clinical inflammatory indexes of RA. The active ingredients of XFC improved the inflammatory state of RA by regulating the TLR-signaling pathway.

Acupoint selection rules of acupuncture for Tourette syndrome in children

OBJECTIVES

To explore the rules of acupoint selection and compatibility of acupuncture for Tourette syndrome(TS) in children.

METHODS

The relevant literature regarding acupuncture for Tourette syndrome in children included in CNKI, Wanfang, VIP, SinoMed, PubMed, Web of Science and Cochrane Library from the establishment of the database to March 31st, 2023 was retrieved.The information of acupuncture prescription, syndrome type, meridian affinity was extracted to set up database. The Microsoft Excel 2019 was used for descriptive statistical analysis, SPSS modeler18.0 was for association rule analysis, lantern5.0 was for latent structure analysis and comprehensive clustering.

RESULTS

①A total of 80 literature was included, and 112 acupuncture prescriptions were extracted, involving 104 acupoints, with a cumulative frequency of 859 times.②The acupoints with high use frequency were Taichong(LR 3), Baihui(GV 20), Fengchi(GB 20), Hegu(LI 4), Sishencong(EX-HN 1), Sanyinjiao(SP 6) and Zusanli(ST 36).③In the treatment of TS with acupuncture, the governor vessel acupoints were the most frequently used, the proportion of acupoints on the head, face, neck and lower limbs was higher. ④The association rule analysis showed that Fengchi(GB 20)-Hegu(LI 4) and Taichong(LR 3)-Hegu(LI 4) had the highest support degree, both were 47.32%.⑤Five comprehensive clustering models were obtained by analyzing the latent structure of high-frequency acupoints, corresponding to yin deficiency disturbing wind, liver hyperactivity and spleen deficiency, liver yang transforming into wind, phlegm-heat harassing the interior and qi stagnation transformed fire.

CONCLUSIONS

Acupuncture for TS in children is based on the principle of soothe the liver and extinguish the wind, regulating qi and blood, and paying attention to regulating spirit and qi. The core acupoints are Fengchi(GB 20), Hegu(LI 4), Taichong(LR 3), Baihui(GV 20), Sanyinjiao(SP 6) , Zusanli(ST 36), acupoints should be selected according to different syndrome in clinical.

A real-world disproportionality analysis of Everolimus: data mining of the public version of FDA adverse event reporting system

Background: Everolimus is an inhibitor of the mammalian target of rapamycin and is used to treat various tumors. The presented study aimed to evaluate the Everolimus-associated adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: The AE records were selected by searching the FDA Adverse Event Reporting System database from the first quarter of 2009 to the first quarter of 2022. Potential adverse event signals were mined using the disproportionality analysis, including reporting odds ratio the proportional reporting ratio the Bayesian confidence propagation neural network and the empirical Bayes geometric mean and MedDRA was used to systematically classify the results. Results: A total of 24,575 AE reports of Everolimus were obtained using data from the FAERS database, and Everolimus-induced AEs occurrence targeted 24 system organ classes after conforming to the four algorithms simultaneously. The common significant SOCs were identified, included benign, malignant and unspecified neoplasms, reproductive system and breast disorders, etc. The significant AEs were then mapped to preferred terms such as stomatitis, pneumonitis and impaired insulin secretion, which have emerged in the study usually reported in patients with Everolimus. Of note, unexpected significant AEs, including biliary ischaemia, angiofibroma, and tuberous sclerosis complex were uncovered in the label. Conclusion: This study provided novel insights into the monitoring, surveillance, and management of adverse drug reaction associated with Everolimus. The outcome of serious adverse events and the corresponding detection signals, as well as the unexpected significant adverse events signals are worthy of attention in order to improving clinical medication safety during treatment of Everolimus.

Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.

BioVDB: biological vector database for high-throughput gene expression meta-analysis

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

Multivariate analysis for data mining to characterize poultry house environment in winter

The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1-3, 4-6, and 7-9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing cyberbullying for a long time by applying machine learning and deep learning techniques. However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance. Consequently, finding generic algorithms that can work on dynamic data available across several platforms is critical. This study used a unique hybrid random forest-based CNN model for text classification, combining the strengths of both approaches. Real-time datasets from Twitter and Instagram were collected and annotated to demonstrate the effectiveness of the proposed technique. The performance of various ML and DL algorithms was compared, and the RF-based CNN model outperformed them in accuracy and execution speed. This is particularly important for timely detection of bullying episodes and providing assistance to victims. The model achieved an accuracy of 96% and delivered results 3.4 seconds faster than standard CNN models.

Acupoint Selection in Postoperative Ophthalmic Pain Management: A Data Mining Protocol

Background

Postoperative ophthalmic pain not only induces anxiety and depression among patients, but also prolongs the recovery cycle. However, the management of postoperative pain in ophthalmology is still not standardized. The effectiveness of acupuncture in treating postoperative pain has been validated based on numerous clinical trials and meta-analysis. Our study is to conduct the first data mining analysis to identify the most effective acupoints selection and combinations for treating postoperative ophthalmic pain, inform.

Methods

We will search bibliographic databases from inception to November 2023. Clinical trials evaluating the effectiveness of acupuncture therapy in the management of postoperative ophthalmic pain will be selected. Reviews, protocols, animal studies, case reports, systematic evaluations and Meta-analyses will be excluded. Primary outcome indicators will be clinical outcomes related to postoperative ophthalmic pain. Descriptive statistics will be performed in Excel 2019. Association rule analysis will be performed in SPSS Modeler 18.0. Exploratory factor analysis and cluster analysis will be performed in SPSS Statistics 25.0.

Results

This study will investigate the most effective point selection and combination of acupuncture points for the treatment of postoperative ophthalmic pain.

Conclusion

Our findings will provide evidence for the effectiveness and potential therapeutic prescription of acupuncture for postoperative ophthalmologic pain, helping clinicians and patients work together to make more informed decisions.

Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer

It is vital to investigate the complex mechanisms underlying tumors to better understand cancer and develop effective treatments. Metabolic abnormalities and clinical phenotypes can serve as essential biomarkers for diagnosing this challenging disease. Additionally, genetic alterations provide profound insights into the fundamental aspects of cancer. This study introduces Cancer-Alterome, a literature-mined dataset that focuses on the regulatory events of an organism's biological processes or clinical phenotypes caused by genetic alterations. By proposing and leveraging a text-mining pipeline, we identify 16,681 thousand of regulatory events records encompassing 21K genes, 157K genetic alterations and 154K downstream bio-concepts, extracted from 4,354K pan-cancer literature. The resulting dataset empowers a multifaceted investigation of cancer pathology, enabling the meticulous tracking of relevant literature support. Its potential applications extend to evidence-based medicine and precision medicine, yielding valuable insights for further advancements in cancer research.

Drugs associated with a risk of supraventricular tachycardia: analysis using the OpenVigil database

OBJECTIVE

The OpenVigil database can be used to assess medications that may cause supraventricular tachycardia (SVT) and to produce a reference for their safe use in clinical settings.

METHODS

We analyzed first-quarter data from 2004 to 2023, obtained by searching the OpenVigil database using the keyword "supraventricular tachycardia." Trade names and generic names were obtained by querying the RxNav database, and the proportions were summarized. The proportionate reporting ratio (PRR), reporting odds ratio, and chi-square values were also summarized. We created Asahi diagrams and set the screening criteria to drug events ≥30, PRR >2, and chi-square >4. Outcomes were evaluated using the Side Effect Resource database, several scientific literature databases, and the Hangzhou Yiyao Rational Medication System.

RESULTS

A total of 2435 distinct medications were found to induce SVT between the first quarter of 2004 and 2023, leading to 22,375 documented adverse events related to SVT. Further investigation revealed that salbutamol, paroxetine, formoterol, paclitaxel, venlafaxine, and theophylline were most likely to cause SVT.

CONCLUSION

We conducted signal mining of adverse drug events using the OpenVigil database and evaluated the six drugs most likely to cause SVT. The results of this research can serve as a drug safety reference in the clinic.

Text mining applications to support health library practice: A case study on marijuana legalization Twitter analytics

BACKGROUND

Twitter is rich in data for text and data analytics research, with the ability to capture trends.

OBJECTIVES

This study examines Canadian tweets on marijuana legalization and terminology used. Presented as a case study, Twitter analytics will demonstrate the varied applications of how this kind of research method may be used to inform library practice.

METHODS

Twitter API was used to extract a subset of tweets using seven relevant hashtags. Using open-source programming tools, the sampled tweets were analysed between September to November 2018, identifying themes, frequently used terms, sentiment, and co-occurring hashtags.

RESULTS

More than 1,176,000 tweets were collected. The most popular hashtag co-occurrence, two hashtags appearing together, was #cannabis and #CdnPoli. There was a high variance in the sentiment analysis of all collected tweets but most scores had neutral sentiment.

DISCUSSION

The case study presents text-mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis.

CONCLUSIONS

Findings from sentiment analysis may determine usage patterns from users. There are several ways in which libraries may use text mining to make evidence-informed decisions such as examining all possible terminologies used by the public to help inform comprehensive evidence synthesis projects and build taxonomies for digital libraries and repositories.

Cardiovascular toxicities following the use of tyrosine kinase inhibitors in hepatocellular cancer patients: a retrospective, pharmacovigilance study

BACKGROUND

Cardiac adverse events (AEs) are common in tyrosine kinase inhibitors(TKIs). This study explored the cardiac AEs of TKIs through the Food and Drug Administration's Adverse Event Reporting System (FAERS).

METHODS

Disproportionality analysis and Bayesian analysis were utilized for data mining of the suspected cardiac AEs of TKIs, based on FAERS data from January 2004 to December 2021.

RESULTS

A total of 4708 cardiac AEs reports of sorafenib, regorafenib, lenvatinib, and cabozantinib were identified. Hypertension accounts for the most reported cardiac AE. Lenvatinib appears to induce cardiac failure with the highest signals strength [ROR = 7.7 (3.46,17.17)]. Acute myocardial infarction was detected in lenvatinib [ROR = 7.91 (5.64,11.09)] and sorafenib [ROR = 2.22 (1.74, 2.84)]. Acute coronary syndrome was detected in lenvatinib [ROR = 11.57 (6.84, 19.58)] and sorafenib [ROR = 2.81 (1.87,4.24)]. Atrial fibrillation was detected in sorafenib [ROR = 1.82 (1.55,2.14)] and regorafenib [ROR = 1.36 (1.03,1.81)]. Meanwhile, aortic dissections were detected in sorafenib [ROR = 5.08 (3.31,7.8)] and regorafenib [ROR = 3.39 (1.52,7.56)]. Most patients developed hypertension and cardiac failure within 30 days of initiating TKI treatments. Patients taking lenvatinib had an increased incidence of developing acute coronary syndrome after 180 days of treatment.

CONCLUSION

Analysis of FAERS data provides a precise profile on the characteristics of cardiac AEs associated with different TKI regimens. Distinct monitoring and appropriate management are needed in the care of TKI recipients.

An open-source implementation of tree-based scan statistics

PURPOSE

We develop an open-source R package to implement tree-based scan statistics (TBSS) analyses.

METHODS

TBSS are data mining methods used by the United States Food and Drug Administration and the Centers for Disease Control. They simultaneously screen thousands of hierarchically aggregated outcomes to identify unsuspected adverse effects of drugs or vaccines, accounting for multiple comparisons. The general structure of TBSS is highly adaptable, with four essential components: (1) a hierarchical outcome structure, (2) a test statistic to be computed for each element of the hierarchy, (3) an algorithm to generate data replicates under a null distribution, and (4) observed outcomes at the lower level of the hierarchy. We encode the general TBSS framework in a convenient R package that offers user-friendly functions for the most used TBSS methods. To illustrate the performance of our software, we evaluated two examples of archetypical TBSS analyses previously analyzed using proprietary, closed-source TreeScan™ software. The first considers the risk of congenital malformations associated with first-trimester exposure to valproate, and the second compares exposure to newly prescribed canagliflozin with a dipeptidyl peptidase 4 inhibitor in adults affected by type 2 diabetes.

RESULTS

The results of the original studies are replicated.

CONCLUSIONS

The diffusion of an open-source implementation of TBSS can enhance innovation of TBSS methods and foster collaborations. We offer an intuitive R package implementing standard TBSS methods with accompanying tutorials. Our unified object-oriented implementation allows expert users to extend the framework, introduce new features, or enhance existing ones.

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.

Real-world safety of icosapent ethyl: analysis based on spontaneous reports in FAERS database

BACKGROUND

The triglyceride-lowering drug, icosapent ethyl (IPE), was granted a new indication for the reduction of atherosclerotic cardiovascular disease risk in 2019. This study aimed to investigate the safety profile of IPE by mining the FDA Adverse Event Reporting System (FAERS) database.

METHODS

The reporting odds ratio was used to analyze IPE's adverse events (AEs) based on the FAERS data from July 2012 to December 2022. We described the characteristics of AE reports and evaluated the clinical prioritization of AEs. Then we defined and analyzed nine interested adverse drug reactions (ADRs) in both overall and subgroups, and investigated the times to onset.

RESULTS

The findings of our study strengthen the evidence for an increased risk of atrial fibrillation using IPE. IPE alone may not increase the risk of bleeding unless combined with antithrombotic drugs. Similar to statins, IPE alone can increase the risk of musculoskeletal pain, drug-related hepatic disorders, and hyperglycemia, but the risk could not double when IPE was combined with statins. Most ADRs occur in the early stage of treatment.

CONCLUSIONS

This study provides a comprehensive real-world safety profile of IPE, which indicates that IPE is well-tolerated.