Precision medicine in the era of artificial intelligence: implications in chronic disease management

Facing stress and inflammation: From the cell to the planet

As identified in 1936 by Hans Selye, stress is shaping diseases through the induction of inflammation. But inflammation display some yin yang properties. On one hand inflammation is merging with the innate immune response aimed to fight infectious or sterile insults, on the other hand inflammation favors chronic physical or psychological disorders. Nature has equipped the cells, the organs, and the individuals with mediators and mechanisms that allow them to deal with stress, and even a good stress (eustress) has been associated with homeostasis. Likewise, societies and the planet are exposed to stressful settings, but wars and global warming suggest that the regulatory mechanisms are poorly efficient. In this review we list some inducers of the physiological stress, psychologic stress, societal stress, and planetary stress, and mention some of the great number of parameters which affect and modulate the response to stress and render it different from an individual to another, from the cellular level to the societal one. The cell, the organ, the individual, the society, and the planet share many stressors of which the consequences are extremely interconnected ending in the domino effect and the butterfly effect.

Lifestyle factors in the biomedical literature: an ontology and comprehensive resources for named entity recognition

MOTIVATION

Despite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly in chronic diseases, they have still not been systematically described in the biomedical literature. This is in part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs in text. The task is challenging due to their inherent diversity, lack of a comprehensive LSF classification for dictionary-based NER, and lack of a corpus for deep learning-based NER.

RESULTS

We present a novel lifestyle factor ontology (LSFO), which we used to develop a dictionary-based system for recognition and normalization of LSFs. Additionally, we introduce a manually annotated corpus for LSFs (LSF200) suitable for training and evaluation of NER systems, and use it to train a transformer-based system. Evaluating the performance of both NER systems on the corpus revealed an F-score of 64% for the dictionary-based system and 76% for the transformer-based system. Large-scale application of these systems on PubMed abstracts and PMC Open Access articles identified over 300 million mentions of LSF in the biomedical literature.

AVAILABILITY AND IMPLEMENTATION

LSFO, the annotated LSF200 corpus, and the detected LSFs in PubMed and PMC-OA articles using both NER systems, are available under open licenses via the following GitHub repository: https://github.com/EsmaeilNourani/LSFO-expansion. This repository contains links to two associated GitHub repositories and a Zenodo project related to the study. LSFO is also available at BioPortal: https://bioportal.bioontology.org/ontologies/LSFO.

Current Trends in Chronic Non-Communicable Disease Management: A Bibliometric Analysis of the Past Two Decades

Background

In recent years, there has been a growing focus on chronic non-communicable diseases (NCD) and their impact on personal and social health. Effective management of NCD is essential for their prevention and treatment. This study aims to utilize bibliometric methods to analyze and summarize the current development and emerging trends in NCD management.

Methods

A literature search and screening were conducted on the Web of Science Core Collection database from January 1, 2004, to December 31, 2023. VOSviewer and Citespace software was performed to examine publication volume, authors, institutions, countries, journals, citation frequencies, keywords, clustering, and burst terms, and to create a visual map.

Results

A total of 996 valid publications from 464 journals were included in the study. The number of publications exhibited a gradual growth trend over the years. The United States was the most productive and influential country, contributing the highest proportion of both publications and total citations. BMC Health Services Research, Toronto University, and Marshall, Bruce C. were identified as the most productive journal, institution, and author, respectively. Further analysis of keyword co-occurrence and burst detection revealed that the most prevalent keywords were "improving primary care" and "integrated care".

Conclusion

This bibliometric analysis provides a comprehensive overview of the current status and trends in NCD management over the past two decades, providing valuable insights for future research directions. It indicates a potential shift towards enhancing primary healthy care, integrated care, and digital health.

[Feasibility, Reliability, and Validity of the VIG-Express Questionnaire as an Instrument for Rapid Multidimensional Geriatric Assessment: A Multicenter Study]

OBJECTIVE

To evaluate the feasibility, reliability and validity of the VIG-express questionnaire.

DESIGN

Descriptive, observational, cross-sectional and multicenter study.

SETTING

Catalonia.

PARTICIPANTS

24 professionals from 18 centers: 10 from Primary Care, 5 from hospitals acute, 2 intermediate care and 3 residential.

MAIN MEASUREMENTS

For the feasibility analysis, the administration time -mean and standard deviation (SD)-. The questionnaire was administered twice to the same patient by the same professional (intraobserver agreement), or by two different professionals (interobserver agreement), evaluating the intraclass correlation coefficient (ICC). Discriminant validity was calculated by comparing the responses of subgroup with higher fragility (percentile >75) and subgroup with lower fragility (percentile <25), for each item of the questionnaire.

RESULTS

195 questionnaires were administered, 59 repeatedly, in a group of elderly (mean age of 79 years) and fragile (mean score of 0.33 in the Fragile Index-VIG). The average administration time was 6.52minutes (DE: 6.02). The concordance in the degree of fragility score obtained a ICC of 0.95 (intraobserver) and 0.72 (interobserver). In discriminant validity, the differences in response frequencies between the two subgroups ranged from 1.7 (oncological disease) to 67.1 (medication management), all of which were statistically significant (p<0.05), with the sole exceptions of the presence of oncological and neurological diseases.

CONCLUSIONS

The VIG-express questionnaire appears to be a feasible, reliable and valid tool for rapid multidimensional/geriatric assessment.

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives

Artificial intelligence is constantly revolutionizing biomedical research and healthcare management. Disease comorbidity is a major threat to the quality of life for susceptible groups, especially middle-aged and elderly patients. The presence of multiple chronic diseases makes precision diagnosis challenging to realize and imposes a heavy burden on the healthcare system and economy. Given an enormous amount of accumulated health data, machine learning techniques show their capability in handling this puzzle. The present study conducts a review to uncover current research efforts in applying these methods to understanding comorbidity mechanisms and making clinical predictions considering these complex patterns. A descriptive metadata analysis of 791 unique publications aims to capture the overall research progression between January 2012 and June 2023. To delve into comorbidity-focused research, 61 of these scientific papers are systematically assessed. Four predictive analytics of tasks are detected: disease comorbidity data extraction, clustering, network, and risk prediction. It is observed that some machine learning-driven applications address inherent data deficiencies in healthcare datasets and provide a model interpretation that identifies significant risk factors of comorbidity development. Based on insights, both technical and practical, gained from relevant literature, this study intends to guide future interests in comorbidity research and draw conclusions about chronic disease prevention and diagnosis with managerial implications.

Disulfidptosis: A New Target for Parkinson's Disease and Cancer

Recent studies have uncovered intriguing connections between Parkinson's disease (PD) and cancer, two seemingly distinct disease categories. Disulfidptosis has garnered attention as a novel form of regulated cell death that is implicated in various pathological conditions, including neurodegenerative disorders and cancer. Disulfidptosis involves the dysregulation of intracellular redox homeostasis, leading to the accumulation of disulfide bonds and subsequent cell demise. This has sparked our interest in exploring common molecular mechanisms and genetic factors that may be involved in the relationship between neurodegenerative diseases and tumorigenesis. The Gene4PD database was used to retrieve PD differentially expressed genes (DEGs), the biological functions of differential expression disulfidptosis-related genes (DEDRGs) were analyzed, the ROCs of DEDRGs were analyzed using the GEO database, and the expression of DEDRGs was verified by an MPTP-induced PD mouse model in vivo. Then, the DEDRGs in more than 9000 samples of more than 30 cancers were comprehensively and systematically characterized by using multi-omics analysis data. In PD, we obtained a total of four DEDRGs, including ACTB, ACTN4, INF2, and MYL6. The enriched biological functions include the regulation of the NF-κB signaling pathway, mitochondrial function, apoptosis, and tumor necrosis factor, and these genes are rich in different brain regions. In the MPTP-induced PD mouse model, the expression of ACTB was decreased, while the expression of ACTN4, INF2, and MYL6 was increased. In pan-cancer, the high expression of ACTB, ACTN4, and MYL6 in GBMLGG, LGG, MESO, and LAML had a poor prognosis, and the high expression of INF2 in LIHC, LUAD, UVM, HNSC, GBM, LAML, and KIPAN had a poor prognosis. Our study showed that these genes were more highly infiltrated in Macrophages, NK cells, Neutrophils, Eosinophils, CD8 T cells, T cells, T helper cells, B cells, dendritic cells, and mast cells in pan-cancer patients. Most substitution mutations were G-to-A transitions and C-to-T transitions. We also found that miR-4298, miR-296-3p, miR-150-3p, miR-493-5p, and miR-6742-5p play important roles in cancer and PD. Cyclophosphamide and ethinyl estradiol may be potential drugs affected by DEDRGs for future research. This study found that ACTB, ACTN4, INF2, and MYL6 are closely related to PD and pan-cancer and can be used as candidate genes for the diagnosis, prognosis, and therapeutic biomarkers of neurodegenerative diseases and cancers.

Eating for Optimization: Unraveling the Dietary Patterns and Nutritional Strategies in Endometriosis Management

Endometriosis is a chronic gynecological disorder affecting millions of women worldwide, causing chronic pelvic pain, dyspareunia, dysmenorrhea, and infertility, and severely impacting their quality of life. Treatment primarily involves hormonal therapies and surgical excision, but high recurrence rates and the economic burden are substantial. With these challenges, significant discussion surrounds the potential role of dietary patterns in managing endometriosis, making it necessary to bridge this critical gap. This review investigates the current scientific evidence on the dietary patterns (eg, Mediterranean, vegetarian, anti-inflammatory, low-fermentable oligosaccharides, disaccharides, monosaccharides, and polyols [low-FODMAP], and Western-style diets) associated with endometriosis and provides a concise, yet thorough, overview on the subject. In addition, antioxidants, microbiota, and artificial intelligence (AI) and their potential roles were also evaluated as future directions. An electronic-based search was performed in MEDLINE, Embase, Cochrane Library, CINAHL, ClinicalTrials.gov, Scopus, and Web of Science. The current data on the topic indicate that a diet based on the Mediterranean and anti-inflammatory diet pattern, rich in dietary fiber, omega-3 fatty acids, plant-based protein, and vitamins and minerals, has a positive influence on endometriosis, yielding a promising improvement in patient symptoms. Preclinical investigations and clinical trials indicate that dietary antioxidants and gut microbiota modulation present potential new approaches in managing endometriosis. Also, AI may offer a promising avenue to explore how dietary components interact with endometriosis. Ultimately, considering genetic and lifestyle factors, a healthy, balanced, personalized approach to diet may offer valuable insights on the role of diet as a means of symptom improvement, facilitating the utilization of nutrition for the management of endometriosis.

Artificial intelligence in autoimmune bullous dermatoses

Dermatologists treating patients with autoimmune bullous dermatoses (AIBDs), as well as the patients themselves, encounter challenges at every stage of their interaction, including dermatologic and comorbidities assessment, diagnosis, prognosis evaluation, treatment, and follow-up monitoring. We summarize the current and potential future clinical applications of artificial intelligence (AI) in the field of AIBDs. Recent research and AI models have demonstrated their potential to enhance or may already be contributing to advancements in every phase of the comprehensive diagnosis and personalized treatment process in AIBDs, providing patients, clinicians, and administrators with valuable support. Image recognition AI systems might assist precise clinical diagnoses of various diseases, including AIBDs, and could offer consistent and reliable scoring of disease severity. Automated and standardized AI-assisted laboratory methods could improve the accuracy and decrease the time and cost of gold-standard tests such as direct and indirect immunofluorescence. The studies and tools discussed in this contribution, although in the early stages, might be a small precursor to a transformative shift in the way we take care of patients with chronic skin diseases, including AIBDs.

Knowledge and perception of mHealth medication adherence applications among pharmacists and pharmacy students in Jazan, Kingdom of Saudi Arabia

The advances in digital health, including mobile healthcare (mHealth) medication adherence applications (MApps), have been demonstrated to support medication adherence and improve health outcomes. This study aims to evaluate the knowledge and perception of the MApps among pharmacists and pharmacy students. An online cross-sectional survey was conducted among 223 pharmacists and pharmacy students in the Jazan region of Saudi Arabia between 1st and 30th April 2023. The survey collected information about the participants' socio-demographics, knowledge, and perception of the MApps. Among the 223 participants included in the study, 105 (47.1%) were pharmacists and 118 (52.9%) were pharmacy students. Most participants were females (72.6%) and aged 18-30 (70.4%). About half of the participants had poor knowledge of the MApps [pharmacists (48.0%) and students (42.0%)] and mainly encountered Medisafe (18.1%) or Pills (17.0%) MApps, respectively. Pharmacy students showed significantly higher knowledge of MApps (p = 0.048), especially the Pills (p = 0.022) than pharmacists. However, the pharmacists had significantly higher knowledge of MyMeds (p = 0.001) than pharmacy students. Most participants had a positive perception of the usefulness of the MApps (pharmacists, 79.0%; students 80.0%). Notably, over 85% of the participants expressed willingness to know and provide guidance on MApps, with over 50% willing to recommend it to the patients. There was no significant difference in perception between the pharmacists and pharmacy students (p>0.05). In conclusion, the study demonstrates limited knowledge with a positive perception of mHealth medication adherence applications among pharmacists and pharmacy students. Integrating digital adherence tools like the MApps into pharmacy training could significantly improve professional practice mHealth competencies, and optimize healthcare delivery and patient outcomes.

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning

Background

Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.

Methods

We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS.

Results

Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4.

Conclusion

In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Burden of Caregivers of Patients with Chronic Diseases in Primary Health Care: A Cross-Sectional Study in Greece

BACKGROUND

In the world of elderly people and people with chronic diseases, caregivers give a solution to caring at home. This study aimed to evaluate the burden of caregivers of patients with chronic diseases in primary health care and identify possible demographic and other determinants of it.

METHODS

This was a cross-sectional study with a convenience sample, which was conducted in two health centers. The sample comprised 291 caregivers who visited the aforementioned health centers in Patra, Greece. A composite questionnaire was utilized: the first part included demographic data and care-related information and the second included the Zarit Burden Interview and the Depression, Anxiety, and Stress Scale-21 (DASS-21).

RESULTS

The highest mean score in the DASS was recorded in the depression subscale and the lowest in the stress subscale. Concerning the Zarit Burden Interview, the highest mean score was recorded in the personal strain subscale and the lowest in the management of care subscale. The highest correlation was recorded between role strain and anxiety and the lowest was between management of care and stress. Similarly, the total score in the Zarit Burden Interview correlated significantly (in a positive direction) with depression, anxiety, and stress.

CONCLUSIONS

Most of the caregivers of patients with chronic diseases in primary health care experienced a moderate to severe burden (especially in the dimension of personal strain) and moderate depression. The experienced burden was positively associated with depression, anxiety, and stress. There were significant differences in the caregivers' burden according to several demographic and care-related characteristics.

Shining a spotlight on sarcopenia and myosteatosis in liver disease and liver transplantation: Potentially modifiable risk factors with major clinical impact

Muscle-wasting and disease-related malnutrition are highly prevalent in patients with chronic liver diseases (CLD) as well as in liver transplant (LT) candidates. Alterations of body composition (BC) such as sarcopenia, myosteatosis and sarcopenic obesity and associated clinical frailty were tied to inferior clinical outcomes including hospital admissions, length of stay, complications, mortality and healthcare costs in various patient cohorts and clinical scenarios. In contrast to other inherent detrimental individual characteristics often observed in these complex patients, such as comorbidities or genetic risk, alterations of the skeletal muscle and malnutrition are considered as potentially modifiable risk factors with a major clinical impact. Even so, there is only limited high-level evidence to show how these pathologies should be addressed in the clinical setting. This review discusses the current state-of-the-art on the role of BC assessment in clinical outcomes in the setting of CLD and LT focusing mainly on sarcopenia and myosteatosis. We focus on the disease-related pathophysiology of BC alterations. Based on these, we address potential therapeutic interventions including nutritional regimens, physical activity, hormone and targeted therapies. In addition to summarizing existing knowledge, this review highlights novel trends, and future perspectives and identifies persisting challenges in addressing BC pathologies in a holistic way, aiming to improve outcomes and quality of life of patients with CLD awaiting or undergoing LT.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment

Qure.AI, a leading company in artificial intelligence (AI) applied to healthcare, has developed a suite of innovative solutions to revolutionize medical diagnosis and treatment. With a plethora of FDA-approved tools for clinical use, Qure.AI continually strives for innovation in integrating AI into healthcare systems. This article delves into the efficacy of Qure.AI's chest X-ray interpretation tool, "qXR," in medicine, drawing from a comprehensive review of clinical trials conducted by various institutions. Key applications of AI in healthcare include machine learning, deep learning, and natural language processing (NLP), all of which contribute to enhanced diagnostic accuracy, efficiency, and speed. Through the analysis of vast datasets, AI algorithms assist physicians in interpreting medical data and making informed decisions, thereby improving patient care outcomes. Illustrative examples highlight AI's impact on medical imaging, particularly in the diagnosis of conditions such as breast cancer, heart failure, and pulmonary nodules. AI can significantly reduce diagnostic errors and expedite the interpretation of medical images, leading to more timely interventions and treatments. Furthermore, AI-powered predictive analytics enable early detection of diseases and facilitate personalized treatment plans, thereby reducing healthcare costs and improving patient outcomes. The efficacy of AI in healthcare is underscored by its ability to complement traditional diagnostic methods, providing physicians with valuable insights and support in clinical decision-making. As AI continues to evolve, its role in patient care and medical research is poised to expand, promising further advancements in diagnostic accuracy and treatment efficacy.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions

OBJECTIVES

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome.

METHODS

Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions.

RESULTS

Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players.

CONCLUSION

This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Importance-aware adaptive dataset distillation

Herein, we propose a novel dataset distillation method for constructing small informative datasets that preserve the information of the large original datasets. The development of deep learning models is enabled by the availability of large-scale datasets. Despite unprecedented success, large-scale datasets considerably increase the storage and transmission costs, resulting in a cumbersome model training process. Moreover, using raw data for training raises privacy and copyright concerns. To address these issues, a new task named dataset distillation has been introduced, aiming to synthesize a compact dataset that retains the essential information from the large original dataset. State-of-the-art (SOTA) dataset distillation methods have been proposed by matching gradients or network parameters obtained during training on real and synthetic datasets. The contribution of different network parameters to the distillation process varies, and uniformly treating them leads to degraded distillation performance. Based on this observation, we propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance by automatically assigning importance weights to different network parameters during distillation, thereby synthesizing more robust distilled datasets. IADD demonstrates superior performance over other SOTA dataset distillation methods based on parameter matching on multiple benchmark datasets and outperforms them in terms of cross-architecture generalization. In addition, the analysis of self-adaptive weights demonstrates the effectiveness of IADD. Furthermore, the effectiveness of IADD is validated in a real-world medical application such as COVID-19 detection.

Revitalizing Postoperative Pain Management in Enhanced Recovery After Surgery via Inter-departmental Collaboration Toward Precision Medicine: A Narrative Review

This narrative review explores the crucial aspects of postoperative pain management within the framework of Enhanced Recovery After Surgery (ERAS). It emphasizes the significance of effective and secure pain management, highlighting its impact on patient well-being, surgical outcomes, and hospital stays. The inadequacy of perioperative pain relief increases the risk of persistent postoperative pain, emphasizing the need to challenge the notion that pain is expected after surgery. The goals of postoperative pain management extend beyond mere relief, encompassing comfortable sleep, pain-free rest, and liberation from pain during initial recovery. Inadequate pain management can lead to complications such as heightened postoperative bleeding and an increased risk of thrombosis. The review delves into various analgesic methods, their complications, and safety measures. ERAS programs, focused on reducing complications and medical costs, emphasize the importance of judicious postoperative pain management and active rehabilitation. The review discusses complications associated with analgesic methods like opioids, epidural analgesia, and adjuvant analgesics. Collaboration within the perioperative management team is crucial for effective postoperative pain relief. Interdepartmental collaboration is essential for evaluating surgical procedures, analgesic methods, and crisis management strategies. The review concludes by integrating precision medicine into postoperative pain management, emphasizing the potential of genetic information in assessing pain sensitivity. It underscores the importance of inter-departmental collaboration and information gathering for the successful implementation of precision medicine tailored to each facility's perioperative management systems. Additionally, the impact of artificial intelligence (AI) on preoperative risk assessment and innovative monitoring techniques is discussed, paving the way for the advancement of precision medicine in postoperative pain management.

Molecular World Today and Tomorrow: Recent Trends in Biological Sciences 2.0

Molecular techniques have become influential instruments in biological study, transforming our comprehension of life at the cellular and genetic levels [...].

The declining insulinogenic index correlates with inflammation and metabolic dysregulation in non-obese individuals assessed by blood gene expression

AIMS

Diabetes onset is difficult to predict. Since decreased insulinogenic index (IGI) is observed in prediabetes, and blood gene expression correlates with insulin secretion, candidate biomarkers can be identified.

METHODS

We collected blood from 96 participants (54 males, 42 females) in 2008 (age: 52.5 years) and 2016 for clinical and gene expression analyses. IGI was derived from values of insulin and glucose at fasting and at 30 min post-OGTT. Two subgroups were identified based on IGI variation: "Minor change in IGI" group with absolute value variation between -0.05 and +0.05, and "Decrease in IGI" group with a variation between -20 and -0.05.

RESULTS

Following the comparison of "Minor change in IGI" and "Decrease in IGI" groups at time 0 (2008), we identified 77 genes correlating with declining IGI, related to response to lipid, carbohydrate, and hormone metabolism, response to stress and DNA metabolic processes. Over the eight years, genes correlating to declining IGI were related to inflammation, metabolic and hormonal dysregulation. Individuals with minor change in IGI, instead, featured homeostatic and regenerative responses.

CONCLUSIONS

By blood gene expression analysis of non-obese individuals, we identified potential gene biomarkers correlating to declining IGI, associated to a pathophysiology of inflammation and metabolic dysregulation.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup

OBJECTIVE

Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS

A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS

Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

Introduction

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.

Methods

Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.

Results

With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.

Discussion

Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

Advances in Developing Small Molecule Drugs for Alzheimer's Disease

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; the future of AD drug development.

Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, ‎we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases.

Clinical Perspectives, Eligibility, and Success Criteria for Bariatric/Metabolic Surgery

Obesity is a worldwide chronic, complex, and progressive disease that poses a challenge for physicians to pursue optimal therapeutic decision making. This chapter focuses on the definition of obesity, based on excessive fat accumulation, and thus underscores the importance of body composition, and the clinical tools used to diagnose it in the context of excess weight, metabolic alteration, and obesity-associated comorbidity development. Additionally, it addresses the indications for surgery that are currently applicable and the description of the different types of patients who could benefit the most from the surgical management of excessive body fat and its associated metabolic derangements and quality of life improvement. Furthermore, it also highlights plausible underlying mechanisms of action for the beneficial effects following bariatric/metabolic surgery.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI.

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

Perspectives of Patients With Chronic Diseases on Future Acceptance of AI-Based Home Care Systems: Cross-Sectional Web-Based Survey Study

BACKGROUND

Artificial intelligence (AI)-based home care systems and devices are being gradually integrated into health care delivery to benefit patients with chronic diseases. However, existing research mainly focuses on the technical and clinical aspects of AI application, with an insufficient investigation of patients' motivation and intention to adopt such systems.

OBJECTIVE

This study aimed to examine the factors that affect the motivation of patients with chronic diseases to adopt AI-based home care systems and provide empirical evidence for the proposed research hypotheses.

METHODS

We conducted a cross-sectional web-based survey with 222 patients with chronic diseases based on a hypothetical scenario.

RESULTS

The results indicated that patients have an overall positive perception of AI-based home care systems. Their attitudes toward the technology, perceived usefulness, and comfortability were found to be significant factors encouraging adoption, with a clear understanding of accountability being a particularly influential factor in shaping patients' attitudes toward their motivation to use these systems. However, privacy concerns persist as an indirect factor, affecting the perceived usefulness and comfortability, hence influencing patients' attitudes.

CONCLUSIONS

This study is one of the first to examine the motivation of patients with chronic diseases to adopt AI-based home care systems, offering practical insights for policy makers, care or technology providers, and patients. This understanding can facilitate effective policy formulation, product design, and informed patient decision-making, potentially improving the overall health status of patients with chronic diseases.

Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review

This review proposes and explores the significance of "experience-based medical education" (EXPBME) in the context of an artificial intelligence (AI)-driven society. The rapid advancements in AI, particularly driven by deep learning, have revolutionized medical practices by replicating human cognitive functions, such as image analysis and data interpretation, significantly enhancing efficiency and precision across medical settings. The integration of AI into healthcare presents substantial potential, ranging from precise diagnostics to streamlined data management. However, non-technical skills, such as situational awareness on recognizing AI's fallibility or inherent risks, are critical for future healthcare professionals. EXPBME in a clinical or simulation environment plays a vital role, allowing medical practitioners to navigate AI failures through sufficient reflections. As AI continues to evolve, aligning educational frameworks to nurture these fundamental non-technical skills is paramount to adequately prepare healthcare professionals. Learner-centered EXPBME, combined with the AI literacy acquirement, stands as a key pillar in shaping the future of medical education.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

INTRODUCTION

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Impact of ChatGPT on Teleconsultants in Healthcare: Perceptions of Healthcare Experts in Saudi Arabia

Purpose

This study aims to investigate the impact of ChatGPT on teleconsultants in managing their operations and services.

Methods

A qualitative approach with focus groups is adopted in this study. A total of 54 participants with varying degrees of experience using AI such as ChatGPT in healthcare, including 11 physicians, 24 nurses, eight dieticians, six pharmacists, and five physiotherapists providing teleconsultations participated in this study.

Results

Twelve themes including informational support, diagnostic assistance, communication, enhancing efficiency, cost and time saving, personalizing care, multilingual support, assisting in medical research, decision-making, documentation, continuing education, and enhanced team collaboration reflecting positive impact were identified from the data analysis of seven focus groups. In addition, six themes including misdiagnosis and errors, issues in personalized care, ethical and legal issues, limited medical context/knowledge, communication challenges, and increased dependency reflecting negative impact were identified.

Conclusion

Although ChatGPT has several advantages for teleconsultants in the healthcare sector, it is associated with ethical issues.

How will the identification and therapeutic intervention of genetic targets in oncology evolve for future therapy?

INTRODUCTION

Mapping of the human genome, together with the broad understanding of new biomolecular pathways involved in cancer development, represents a huge dividing line for advances in cancer treatment. This special article aims to express the next evolution of cancer therapy, while also considering the challenges and uncertainties facing future directions.

AREA COVERED

The recent achievements of medical science in the oncology field concern both new diagnostic techniques, such as liquid biopsy, and therapeutic strategies with innovative anticancer drugs. Although several molecular characteristics of tumors are linked to the tissue of origin, some mutations are shared by multiple tumor histologies, thus paving the way for what is called 'precision oncology.' The article highlights the importance of identifying new mutations and biomolecular pathways that can be pursued with new anticancer drugs.

EXPERT OPINION

Oncology and medical science have made great progress in understanding new molecular targets; being able to early identify tumor markers that are not confined to a single organ through minimally invasive diagnostic techniques allows us to design new effective therapeutic strategies. Multidisciplinary teams now aim to evaluate the most appropriate and personalized diagnostic/therapeutic approach for the individual patient.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

INTRODUCTION

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

AREAS COVERED

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

EXPERT OPINION

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

Personalised management and supporting individuals to live with their asthma in a primary care setting

INTRODUCTION

Complementing recognition of biomedical phenotypes, a primary care approach to asthma care recognizes diversity of disease, health beliefs, and lifestyle at a population and individual level.

AREAS COVERED

We review six aspects of personalized care particularly pertinent to primary care management of asthma: personalizing support for individuals living with asthma; targeting asthma care within populations; managing phenotypes of wheezy pre-school children; personalizing management to the individual; meeting individual preferences for provision of asthma care; optimizing digital approaches to enhance personalized care.

EXPERT OPINION

In a primary care setting, personalized management and supporting individuals to live with asthma extend beyond the contemporary concepts of biological phenotypes and pharmacological 'treatable traits' to encompass evidence-based tailored support for self-management, and delivery of patient-centered care including motivational interviewing. It extends to how we organize clinical practiceand the choices provided in mode of consultation. Diagnostic uncertainty due to recognition of phenotypes of pre-school wheeze remains a challenge for primary care. Digital health can support personalized management, but there are concerns about increasing inequities. This broad approach reflects the traditionally holistic ethos of primary care ('knowing their patients and understanding their communities'), but the core concepts resonate with all healthcare.

Precision Medicine 2.0: How Digital Health and AI Are Changing the Game

In the era of rapid IT developments, the health domain is undergoing a considerable transformation [...].

Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity.

Methods

The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT).

Results

Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records).

Conclusions

AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.

An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series

Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.

Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy

RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. RhoB expression is known to have implications in pathologic processes of diseases. Investigation in the regulation and communication of this protein, detected by immunohistochemical staining on the microscope, is worth exploring to gain insightful information that may lead to identifying optimal disease treatment options. In particular, the role of RhoB in rectal cancer is not well discovered. Here, we report that methods of deep learning-based image analysis and the decomposition of multiway arrays discover the predictive factor of RhoB in two cohorts of patients with rectal cancer having survival rates of <5 and >5 years. The analysis results show distinctions between the tensor decomposition factors of the two cohorts.

Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections

With the exception of an extremely small number of cases caused by single gene mutations, most autoimmune diseases result from the complex interplay between environmental and genetic factors. In a nutshell, etiology of the common autoimmune disorders is unknown in spite of progress elucidating certain effector cells and molecules responsible for pathologies associated with inflammatory and tissue damage. In recent years, population genetics approaches have greatly enriched our knowledge regarding genetic susceptibility of autoimmunity, providing us with a window of opportunities to comprehensively re-examine autoimmunity-associated genes and possible pathways. In this review, we aim to discuss etiology and pathogenesis of common autoimmune disorders from the perspective of human genetics. An overview of the genetic basis of autoimmunity is followed by 3 chapters detailing susceptibility genes involved in innate immunity, adaptive immunity and inflammatory cell death processes respectively. With such attempts, we hope to expand the scope of thinking and bring attention to lesser appreciated molecules and pathways as important contributors of autoimmunity beyond the 'usual suspects' of a limited subset of validated therapeutic targets.

Emerging Hallmarks of Metabolic Reprogramming in Prostate Cancer

Prostate cancer (PCa) is the most common male malignancy and the fifth leading cause of cancer death in men worldwide. Prostate cancer cells are characterized by a hybrid glycolytic/oxidative phosphorylation phenotype determined by androgen receptor signaling. An increased lipogenesis and cholesterogenesis have been described in PCa cells. Many studies have shown that enzymes involved in these pathways are overexpressed in PCa. Glutamine becomes an essential amino acid for PCa cells, and its metabolism is thought to become an attractive therapeutic target. A crosstalk between cancer and stromal cells occurs in the tumor microenvironment because of the release of different cytokines and growth factors and due to changes in the extracellular matrix. A deeper insight into the metabolic changes may be obtained by a multi-omic approach integrating genomics, transcriptomics, metabolomics, lipidomics, and radiomics data.

How do nurses work in chronic management in the age of artificial intelligence? development and future prospects

AI is undeniably revolutionizing medical research and patient care across diverse fields. Chronic disease nursing care, a pivotal aspect of clinical management, has significantly reaped the benefits of AI across numerous dimensions. Understanding the operational principles of artificial intelligence before implementation is crucial, avoiding indiscriminate replacement of all tasks with AI. Nurses serve as the primary force in symptom group research, expanding beyond diabetes to encompass various chronic diseases; their primary responsibility involves recording patients' daily symptoms and vital signs. However, a substantial portion of current AI research excludes nurses from the developmental phase, encompassing them solely in user and feedback populations. The comprehensive design of the symptom analysis and long-term management approach necessitates the guidance and oversight of nurses; however, their current insufficient involvement might stem from nursing staff's comparatively limited comprehension of AI and their ambiguous perception of their role's value in AI. Therefore, an imperative exploration of nurses' roles in symptom analysis and long-term management, leveraging the latest research in these areas, is vital to pinpoint breakthroughs in nurses' AI involvement in the future.

Endometriosis-associated infertility diagnosis based on saliva microRNA signatures

RESEARCH QUESTION

Can a saliva-based miRNA signature for endometriosis-associated infertility be designed and validated by analysing the human miRNome?

DESIGN

The prospective ENDOmiARN study (NCT04728152) included 200 saliva samples obtained between January 2021 and June 2021 from women with pelvic pain suggestive of endometriosis. All patients underwent either laparoscopy, magnetic resonance imaging, or both. Patients diagnosed with endometriosis were allocated to one of two groups according to their fertility status. Data analysis consisted of identifying a set of miRNA biomarkers using next-generation sequencing, and development of a saliva-based miRNA signature of infertility among patients with endometriosis based on a random forest model.

RESULTS

Among the 153 patients diagnosed with endometriosis, 24% (n = 36) were infertile and 76% (n = 117) were fertile. Small RNA-sequencing of the 153 saliva samples yielded approximately 3712 M raw sequencing reads (from ∼13.7 M to ∼39.3 M reads/sample). Of the 2561 known miRNAs, the feature selection method generated a signature of 34 miRNAs linked to endometriosis-associated infertility. After validation, the most accurate signature model had a sensitivity, specificity and area under the curve of 100%.

CONCLUSION

A saliva-based miRNA signature for endometriosis-associated infertility is reported. Although the results still require external validation before using the signature in routine practice, this non-invasive tool is likely to have a major effect on care provided to women with endometriosis.

Short Chain Fatty Acid Metabolism in Relation to Gut Microbiota and Genetic Variability

It is widely accepted that the gut microbiota plays a significant role in modulating inflammatory and immune responses of their host. In recent years, the host-microbiota interface has gained relevance in understanding the development of many non-communicable chronic conditions, including cardiovascular disease, cancer, autoimmunity and neurodegeneration. Importantly, dietary fibre (DF) and associated compounds digested by the microbiota and their resulting metabolites, especially short-chain fatty acids (SCFA), were significantly associated with health beneficial effects, such as via proposed anti-inflammatory mechanisms. However, SCFA metabolic pathways are not fully understood. Major steps include production of SCFA by microbiota, uptake in the colonic epithelium, first-pass effects at the liver, followed by biodistribution and metabolism at the host's cellular level. As dietary patterns do not affect all individuals equally, the host genetic makeup may play a role in the metabolic fate of these metabolites, in addition to other factors that might influence the microbiota, such as age, birth through caesarean, medication intake, alcohol and tobacco consumption, pathogen exposure and physical activity. In this article, we review the metabolic pathways of DF, from intake to the intracellular metabolism of fibre-derived products, and identify possible sources of inter-individual variability related to genetic variation. Such variability may be indicative of the phenotypic flexibility in response to diet, and may be predictive of long-term adaptations to dietary factors, including maladaptation and tissue damage, which may develop into disease in individuals with specific predispositions, thus allowing for a better prediction of potential health effects following personalized intervention with DF.

Artificial Intelligence in Breast Cancer Screening and Diagnosis

Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.