Big Data in Medicine, the Present and Hopefully the Future

The risk of osteoporotic fracture in gastric cancer survivors: total gastrectomy versus subtotal gastrectomy versus endoscopic treatment

PURPOSES

Previous studies have suggested that there is an increased risk of osteoporotic fracture in gastric cancer survivors. However, the data was not classified according to surgery type. This study investigated the cumulative incidence osteoporotic fracture (OF) in gastric cancer survivors according to treatment modality.

METHODS

A total of 85,124 gastric cancer survivors during 2008-2016 were included. The type of surgery was classified as total gastrectomy (TG, n = 14,428)/subtotal gastrectomy (SG, n = 52,572)/endoscopic mucosal dissection and endoscopic mucosal resection (ESD/EMR, n = 18,125). The site of osteoporotic fractures included the spine, hip, wrist, and humerus. We examined cumulative incidence using Kaplan-Meier survivor analysis and cox proportional hazards regression analysis to determine the risk factor of OF.

RESULTS

The incidence of OF per 100,000 patient year was 2.6, 2.1, 1.8 in TG, SG, ESD/EMR group. The cumulative incidence rate was 2.3% at 3 years, 4.0% at 5 years, and 5.8% at 7 years in gastrectomy group, and 1.8% at 3 years, 3.3% at 5 years in the SG group, and 4.9% at 7 years postoperatively in ESD/EMR group. TG increased the risk of OF compared to patients who underwent SG (HR 1.75, 95% confidence interval [CI] 1.57-1.94), and ESD/EMR (hazard ratio [HR] 2.23, 95% CI 2.14-2.32).

CONCLUSION

Gastric cancer survivors who underwent TG had an increased osteoporotic fracture risk than did SG or ESD/EMR in these patients. The amount of gastric resection and accompanying metabolic changes seemed to mediate such risk. Additional research is needed to establish an optimal strategy for each type of surgery.

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Multimodal data fusion for cancer biomarker discovery with deep learning

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.

Medicine and health of 21st Century: Not just a high biotech-driven solution

Many biotechnological innovations have shaped the contemporary healthcare system (CHS) with significant progress to treat or cure several acute conditions and diseases of known causes (particularly infectious, trauma). Some have been successful while others have created additional health care challenges. For example, a reliance on drugs has not been a panacea to meet the challenges related to multifactorial noncommunicable diseases (NCDs)-the main health burden of the 21st century. In contrast, the advent of omics-based and big data technologies has raised global hope to predict, treat, and/or cure NCDs, effectively fight even the current COVID-19 pandemic, and improve overall healthcare outcomes. Although this digital revolution has introduced extensive changes on all aspects of contemporary society, economy, firms, job market, and healthcare management, it is facing and will face several intrinsic and extrinsic challenges, impacting precision medicine implementation, costs, possible outcomes, and managing expectations. With all of biotechnology's exciting promises, biological systems' complexity, unfortunately, continues to be underestimated since it cannot readily be compartmentalized as an independent and segregated set of problems, and therefore is, in a number of situations, not readily mimicable by the current algorithm-building proficiency tools. Although the potential of biotechnology is motivating, we should not lose sight of approaches that may not seem as glamorous but can have large impacts on the healthcare of many and across disparate population groups. A balanced approach of "omics and big data" solution in CHS along with a large scale, simpler, and suitable strategies should be defined with expectations properly managed.

Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis

BACKGROUND

A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.

METHODS

We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date - Modification]: "2021" [Date - Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs).

RESULTS

The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34).

CONCLUSION

Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.

The successful use of -omic technologies to achieve the 'One Health' concept in meat producing animals

Human health and wellbeing are closely linked to healthy domestic animals, a vital wildlife, and an intact ecosystem. This holistic concept is referred to as 'One Health'. In this review, we provide an overview of the potential and the challenges for the use of modern -omics technologies, especially transcriptomics and proteomics, to implement the 'One Health' idea for food-producing animals. These high-throughput studies offer opportunities to find new potential molecular biomarkers to monitor animal health, detect pharmacological interventions and evaluate the wellbeing of farm animals in modern intensive livestock systems.

Using sentiment analysis to identify similarities and differences in research topics and medical subject headings (MeSH terms) between Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) in 2020: A bibliometric study

Background:

Little systematic information has been collected about the nature and types of articles published in 2 journals by identifying the latent topics and analyzing the extracted research themes and sentiments using text mining and machine learning within the 2020 time frame. The goals of this study were to conduct a content analysis of articles published in 2 journals, describe the research type, identify possible gaps, and propose future agendas for readers.

Methods:

We downloaded 5610 abstracts in the journals of Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) from the PubMed library in 2020. Sentiment analysis (ie, opinion mining using a natural language processing technique) was performed to determine whether the article abstract was positive or negative toward sentiment to help readers capture article characteristics from journals. Cluster analysis was used to identify article topics based on medical subject headings (MeSH terms) using social network analysis (SNA). Forest plots were applied to distinguish the similarities and differences in article mood and MeSH terms between these 2 journals. The Q statistic and I 2 index were used to evaluate the difference in proportions of MeSH terms in journals.

Results:

The comparison of research topics between the 2 journals using the 737 cited articles was made and found that most authors are from mainland China and Taiwan in Medicine and JFMA, respectively, similarity is supported by observing the abstract mood (Q = 8.3, I 2 = 0, P = .68; Z = 0.46, P = .65), 2 journals are in a common cluster (named latent topic of patient and treatment) using SNA, and difference in overall effect was found by the odds ratios of MeSH terms (Q = 185.5 I 2 = 89.8, P < .001; Z = 5.93, P < .001) and a greater proportion of COVID-19 articles in JFMA.

Conclusions:

SNA and forest plots were provided to readers with deep insight into the relationships between journals in research topics using MeSH terms. The results of this research provide readers with a concept diagram for future submissions to a given journal.

Highlights

The main approaches frequently used in Meta-analysis for drawing forest plots contributed to the following:

Remodelling structure-based drug design using machine learning

To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of 'training data' as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

The use of forest plot to identify article similarity and differences in characteristics between journals using medical subject headings terms: A protocol for bibliometric study

BACKGROUND

Comparison of similarity and difference in research types among journals are concerned in literature. However, to date, none display the methodology seen in selecting similar journals related to the target journal, as similar articles did to a given article. Authors need 1 effective method not only to find similar journals for their studies but also to know the difference in methods. This study (1) shows the similar journals for the target journal online displayed, and (2) identifies the effect of similarity odds ratio compared to the counterparts using the forest plots in Meta-analysis and the major medical subject headings (MeSH terms).

METHODS

We downloaded 1000 recent top 20 most similar articles related to the Respiratory Care journal from the PubMed library, plotted the clusters of related journals using social network analysis (SNA), and compared the MeSH terms in differences in an odds ratio unit using the forest plot relevant to Respiratory Care and the most similar journals. Q statistic and I-square (I2) index were used to evaluate the difference in the proportion of events.

RESULTS

This study found that (1) the journals related to Respiratory Care are easily presented on Google Maps; (2) 10 journal clusters were identified using SNA; (3) the top 3 MeSH terms are methods, therapy, and physiopathology, and (4) the odds ratios of MeSH terms between journals associated with the Respiratory Care showing different from Int J Chron Obstruct Pulmori Dis and similar to Curr Opin Endocrinol Diabetes Obes within heterogeneity with I2 = 70.5% (P < 0.001) and 0% (P = 0.803), respectively.

CONCLUSIONS

SNA and forest plots provide deep insight into the relationships between journals in MeSH terms. The results of this research can provide readers with a concept diagram that can be used for future submissions to a given journal.

A multi-omics view of the complex mechanism of vascular calcification

Vascular calcification is a high incidence and high risk disease with increasing morbidity and high mortality, which is considered the consequence of smooth muscle cell transdifferentiation initiating the mechanism of accumulation of hydroxyl calcium phosphate. Vascular calcification is also thought to be strongly associated with poor outcomes in diabetes and chronic kidney disease. Numerous studies have been accomplished; however, the specific mechanism of the disease remains unclear. Development of the genome project enhanced the understanding of life science and has entered the post-genomic era resulting in a variety of omics techniques used in studies and a large amount of available data; thus, a new perspective on data analysis has been revealed. Omics has a broader perspective and is thus advantageous over a single pathway analysis in the study of complex vascular calcification mechanisms. This paper reviews in detail various omics studies including genomics, proteomics, transcriptomics, metabolomics and multiple group studies on vascular calcification. Advances and deficiencies in the use of omics to study vascular calcification are presented in a comprehensive view. We also review the methodology of the omics studies and omics data analysis and processing. In addition, the methodology and data processing presented here can be applied to other areas. An omics landscape perspective across the boundaries between genomics, transcriptomics, proteomics and metabolomics is used to examine the mechanisms of vascular calcification. The perspective combined with various technologies also provides a direction for the subsequent exploration of clinical significance.

[Resources for the evaluation of public policy on lung cancer in MexicoRecursos para avaliação da política pública de câncer de pulmão no México]

OBJECTIVES

To estimate lung cancer mortality rates adjusted by age distribution for the country's 32 states between 1998 and 2017; to contrast the territorial distribution of demand for oncological services with the availability of specialists to provide care; and to determine the predictive capacity of three different supervised classification algorithms in the context of automated learning techniques.

METHODS

An exploratory analysis and data modeling were conducted, considering death records from the national health information system.

RESULTS

Deaths from lung cancer in Mexico dropped by 14.5% between the period prior to implementation of the General Law on Tobacco Control and the subsequent period. A 22% reduction was observed in the male population by the end of the entire period. There is evidence of an imbalance between the demand for oncological services and the availability of specialists. The modeling phase demonstrated the usefulness of the country's electronic death records.

CONCLUSIONS

Despite reductions in lung cancer mortality patterns in Mexico in the last two decades, the analysis showed persistent areas of opportunity for improvement, mainly in the female population of Guerrero, Oaxaca, and Puebla states. Based on this research, the main recommendation for focusing efforts to manage this oncological disease in Mexico is to determine whether these patterns are associated with smoking habits or with other social determinants.

Isabl Platform, a digital biobank for processing multimodal patient data

BACKGROUND

The widespread adoption of high throughput technologies has democratized data generation. However, data processing in accordance with best practices remains challenging and the data capital often becomes siloed. This presents an opportunity to consolidate data assets into digital biobanks-ecosystems of readily accessible, structured, and annotated datasets that can be dynamically queried and analysed.

RESULTS

We present Isabl, a customizable plug-and-play platform for the processing of multimodal patient-centric data. Isabl's architecture consists of a relational database (Isabl DB), a command line client (Isabl CLI), a RESTful API (Isabl API) and a frontend web application (Isabl Web). Isabl supports automated deployment of user-validated pipelines across the entire data capital. A full audit trail is maintained to secure data provenance, governance and ensuring reproducibility of findings.

CONCLUSIONS

As a digital biobank, Isabl supports continuous data utilization and automated meta analyses at scale, and serves as a catalyst for research innovation, new discoveries, and clinical translation.