Precision Medicine

India's Digital Personal Data Protection Act 2023 establishes binding enforcement obligations for AI systems operating across welfare, health, credit scoring, law enforcement, and public administration sectors, with fines of up to INR 250 Crore per breach. MeitY is actively driving states toward SOC-led cyber governance ahead of enforcement. No cryptographic provenance standard, constitutional command architecture, or cross-border enforcement framework currently exists in India to meet these obligations at the technical level. Bloomberg confirmed in June 2026 that no legal standard exists to verify whether Indian sovereign AI is actually sovereign - hyperscalers self-certify because the bar does not exist.

This paper documents three specific enforcement gaps - absent provenance layer, absent constitutional command architecture, and absent cross-border enforcement framework - and presents the Fijishi Algorithmic Compliance and DPDP Audit framework, Sovereign Identity Framework, and AI Auditability and Compliance Framework as the integrated constitutional enforcement layer that converts India's DPDP mandate from legal obligation into machine-verifiable technical reality.

Background: The expanding use of systemic therapies for recurrent and metastatic head and neck cancer has raised major concerns regarding affordability and equitable access, particularly across countries with differing income levels and health financing structures. Methods: We conducted a comparative affordability and budget-reach analysis of commonly used systemic agents-pembrolizumab, nivolumab, cetuximab, gefitinib, and erlotinib-across seven countries (United States, United Kingdom, Australia, South Africa, India, Pakistan, and Bangladesh) representing high-, upper-middle-, and lower-middle-income settings. Results: Immunotherapy was unaffordable across all settings. Six-month pembrolizumab costs corresponded to 7994% of monthly income in India, 4311% in Pakistan, 3133% in Bangladesh, and remained above catastrophic thresholds in high-income countries (591% in the United States; 903% in the United Kingdom). In contrast, oral tyrosine kinase inhibitors were substantially less costly, with gefitinib requiring 171%-412% of monthly income in South Asia and falling below thresholds in Pakistan and Bangladesh. The cost of treating one patient with pembrolizumab could fund treatment for 18-22 patients with gefitinib in South Asia. Conclusions: Despite modest survival benefits, current immunotherapy pricing severely limits global accessibility. Policy reform and pricing strategies are needed to ensure equitable cancer care. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Systemic sclerosis, also known as scleroderma or SSc, is a condition characterized by significant heterogeneity in clinical presentation, disease progression, and response to treatment. Consequently, the design of clinical trials to successfully identify effective therapeutic interventions poses a major challenge. Recent advancements in skin molecular profiling technologies and stratification techniques have enabled the identification of patient subgroups that may be relevant for personalized treatment approaches. This narrative review aims at providing an overview of the current status of skin gene expression analysis using computational biology approaches and highlights the benefits of stratifying patients upon their skin gene signatures. Such stratification has the potential to lead toward a precision medicine approach in the management of SSc.

The COVID-19 pandemic has had a profound and lasting impact on global health, extending beyond the immediate effects of the acute respiratory illness it initially caused. Post-COVID syndrome, or long COVID, has emerged as a significant health concern, characterized by a wide range of persistent symptoms and complications affecting multiple organ systems. This syndrome poses substantial challenges to both individuals and healthcare systems, with potential long-term consequences that warrant urgent attention and research. Among the most alarming potential outcomes is the risk of a significant increase in oncological diseases. The chronic inflammation, immune dysregulation, and genetic and epigenetic changes induced by prolonged COVID are factors that have long been associated with cancer development. The possibility that these factors could lead to a substantial rise in cancer incidence over the next 5-7 years is a critical area of investigation. Chronic inflammation, a hallmark of long COVID, is a well-established risk factor for cancer. Persistent inflammatory responses can cause DNA damage, promote the activation of oncogenes, and suppress tumor suppressor genes, thereby creating an environment conducive to cancer development. The oxidative stress associated with chronic inflammation can result in significant genetic alterations, including DNA mutations and chromosomal aberrations, further increasing cancer risk. Additionally, epigenetic changes such as DNA methylation, histone modifications, and alterations in non-coding RNA expression can disrupt normal gene regulation, leading to inappropriate activation or silencing of genes involved in cell growth, apoptosis, and immune responses. These epigenetic disruptions can create a cellular environment that favors malignant transformation and cancer progression. Telomere shortening, another consequence of chronic stress and inflammation observed in long COVID patients, is associated with increased cancer risk. Accelerated telomere shortening due to chronic inflammation and oxidative stress can lead to cellular senescence, genomic instability, and increased susceptibility to cancer. Expert opinions from leading oncologists, geneticists, and epidemiologists underscore the urgency of addressing the potential long-term oncological impacts of post-COVID syndrome. Mathematical models and epidemiological studies support concerns that cancer incidence could potentially double due to long COVID, indicating a significant public health challenge.

Background: Bladder cancer (BC) is a life-threatening malignancy that can be successfully treated if diagnosed in its early stages. Machine learning techniques, by using large biological databases, are suggested as important approaches for identifying accurate diagnostic biomarkers. The present study aimed to introduce a simple and accurate model for the diagnosis of BC.
Methods: RNA-sequencing information of 412 primary bladder tumors versus 19 normal bladder tissues from The Cancer Genome Atlas were analyzed using the TCGAbiolinks R package to identify differentially expressed genes (DEGs). Gene ontology properties and the corresponding pathways of DEGs were investigated using the online ShinyGO tools. To develop a diagnostic model for BC, two binary classifier machine learning algorithms, C5.0 and CHAID, were employed in three subgroups of train, test, and validation sets using SPSS Modeler version 18.1. Their efficacy was evaluated using performance measures for binary classification.
Results: Most of the identified DEGs were associated with microtubule organization, coagulation, and myelination. Based on the constructed models, four important RNAs (Tubulin Polymerization-Promoting Protein: ENSG00000171368, Proteolipid Protein-1: ENSG00000123560, RP11-473E2: ENSG00000228877, and Coagulation Factor X: ENSG00000126218) were identified as important classifiers for diagnosis in both C5.0 and CHAID models. The CHAID model demonstrated superior performance in the testing dataset, achieving an accuracy of 98.75%, an F1-score of 99.36%, and an AUC of 99.4%.
Conclusion: According to the results, machine learning algorithms are beneficial for the diagnosis of BC and potentially useful for improving personalized medicine in BC patients. The developed model may serve as a non-invasive, data-driven tool to support early diagnosis and personalized treatment planning in clinical settings. Further evaluation using laboratory tests is suggested to validate the obtained results.

We propose a method to construct three-dimensional airway geometric models based on airway skeletons, or centerlines (CLs). Given a CT-segmented airway skeleton and surface, the proposed CL-based method automatically constructs subject-specific models that contain anatomical information regarding branches, include bifurcations and trifurcations, and extend from the trachea to terminal bronchioles. The resulting model can be anatomically realistic with the assistance of an image-based surface; alternatively a model with an idealized skeleton and/or branch diameters is also possible. This method systematically identifies and classifies trifurcations to successfully construct the models, which also provides the number and type of trifurcations for the analysis of the airways from an anatomical point of view. We applied this method to 16 normal and 16 severe asthmatic subjects using their computed tomography images. The average distance between the surface of the model and the image-bas...

Background Deep learning, a subset of artificial intelligence (AI), has rapidly emerged as a transformative force in oncology. It offers the potential to enhance diagnostic accuracy, reduce processing time, and enable personalized treatment strategies. However, its acceptance and practical integration into clinical workflows remain influenced by factors such as perceived effectiveness, trust, and institutional readiness. Objective This study aims to assess the perceptions of healthcare professionals regarding the effectiveness and trustworthiness of deep learning in cancer diagnosis and precision therapy, while also identifying key barriers to its adoption in oncology settings. Methods A quantitative, cross-sectional study was conducted using a structured questionnaire distributed to 250 healthcare professionals, including oncologists, radiologists, pathologists, medical researchers, and IT specialists. The instrument captured data across four sections: demographics, awareness and adoption, perceptions of effectiveness and trust, and implementation barriers. Descriptive statistics, reliability tests (Cronbach's Alpha, Split-Half), and normality tests (Shapiro-Wilk) were applied to analyze the data. Results Findings revealed a generally high perception of deep learning's effectiveness in improving cancer diagnosis. However, trust in AI decision-making was more varied. Shapiro-Wilk tests indicated that both key variables-effectiveness and trust-were not normally distributed. Reliability analysis showed low internal consistency between these constructs (Cronbach's Alpha = 0.19; Spearman-Brown = 0.19), suggesting that perceived effectiveness and trust are conceptually distinct. Visual data supported this conclusion, with histograms showing skewed distributions and a scatter plot indicating a weak correlation between effectiveness and trust. Conclusion While deep learning is widely recognized as a valuable tool in oncology, trust in its decision-making capabilities is not yet fully established among healthcare professionals. This gap highlights the importance of addressing ethical, educational, and practical barriers to adoption. Building trust through explainable AI models, clinician training, and validated use cases is essential for integrating deep learning into routine cancer care.

Background: Colorectal carcinoma cases are rapidly rising in young and adult population worldwide as well as in India. Change in food habits, administration of adulterants, pesticides in crops and in daily diet, genetic causes are the main reason for rapid increase in incidence of Colorectal Carcinoma. The present study assessment of clinical presentation, laterality and co-morbidity analysis of 51 colorectal carcinoma with distant Metastatic patients over a period of three years was conducted. Materials and Methods: Data of 51 mCRC patients was collected from January 2022 to December 2025. Using histopathological analysis, blood tests, PET/CT scan and MRI confirmed cases of colorectal carcinoma were confirmed for further analysis. Test subjects were analysed and compared the clinical and pathological features viz., age, gender and burden of co-morbidities, and laterality of tumour. Results: Median age of the patients was 56.0 years where majority of patients were between the ages 51-60 years (44%) followed by 61-70 (24%). Incidence of mCRC is higher in male with compared to female (M/F ratio 1.21). Out of 51 patients 29% patients do not show any co morbidity whereas 35.2% patients demonstrated hypertension and 21.56% patients suffered from type 2 diabetes mellitus. Higher incidence of abdominal pain (41.17%) was recorded at the time of clinical presentation followed by bleeding per rectum (33.33%). In present study 37 (72.55%) patients were involved left side of the colon while 14 (27.45%) patients have right side of colon involvement. Conclusion: Colon cancer is common in Indian population as it emerges as second deadliest cancer after lung cancer. As it is also considered as Lifestyle related disease. There is a need of urgent screening for early detection, diagnosis to improve the cost-effective treatment of patients.

Building on the recent advances in next-generation sequencing, the integration of genomics, proteomics, metabolomics, and other approaches hold tremendous promise for precision medicine. The approval and adoption of these rapidly advancing technologies and methods presents several regulatory science considerations that need to be addressed. To better understand and address these regulatory science issues, a Clinical and Translational Science Award Working Group convened the Regulatory Science to Advance Precision Medicine Forum. The Forum identified an initial set of regulatory science gaps. The final set of key findings and recommendations provided here address issues related to the lack of standardization of complex tests, preclinical issues, establishing clinical validity and utility, pharmacogenomics considerations, and knowledge gaps.

Imaging biomarkers have progressively transformed modern radiology by providing quantitative, functional, and molecular information capable of improving early disease detection, diagnostic precision, therapeutic monitoring, and clinical decision-making in systemic diseases. The present review aimed to analyze the current role of imaging biomarkers in oncology and internal medicine, emphasizing their contribution to precision medicine and multidisciplinary healthcare management. A structured documentary review was conducted using peer-reviewed scientific literature indexed in internationally recognized databases, including PubMed, Scopus, ScienceDirect, and SpringerLink. The analyzed evidence focused on advanced imaging modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, hybrid imaging systems, radiomics, and artificial intelligence-assisted imaging analysis. The reviewed studies demonstrated that imaging biomarkers possess broad applicability in oncology, neurological disorders, cardiovascular diseases, inflammatory conditions, chronic hepatic disease, pulmonary pathology, and metabolic disorders. Functional and molecular imaging techniques showed particular relevance for evaluating metabolism, tissue perfusion, angiogenesis, fibrosis, inflammation, cellular density, and tumor heterogeneity. Radiomics and artificial intelligence were identified as emerging technologies capable of improving predictive analysis, treatment response assessment, and individualized therapeutic planning.

: In this contribution we explore novel, different ways of promoting public engagement in biomedical research using biobanks. Starting from a discussion about the limits of traditional formal procedures of engaging participants in biobanking activities, the contribution proposes two approaches to public involvement that use the Science Museum as an agora for communicating and representing the complex scientific, societal and ethical issues involved in contemporary biomedical research. The role of museum exhibitions, metaphors and languages of art and theater, as well as other forms of dialogues, are discussed as a way of shaping popular imaginaries about scientific research, in order to complement mechanisms of public engagement with novel forms of stimulating public understanding of scientific research using tissues and genomic data. Keywords : science museum; art, theater; dialogues; biobanks; public engagement.

Gene expression profiles in blood are increasingly being used to identify biomarkers for different affective disorders. We have selected a set of 29 genes to generate expression profiles for healthy control subjects as well as for patients diagnosed with acute post-traumatic stress disorder (PTSD) and with borderline personality disorder (BPD). Measurements were performed by quantitative polymerase chain reaction (qPCR). Using the actual data in an anonym-ous form we constructed a series of artificial data sets with known gene expression profiles. These sets were used to test 14 classification algorithms and feature selection methods for their ability to identify the correct expression patterns. Application of the three most effective algorithms to the actual expression data showed that control subjects can be dis-tinguished from BPD patients based on differential expression levels of the gene transcripts Gi2, GR and MAPK14, targets that may have links to stress related diseases. Co...

After nearly 8 months of preparation, we are thrilled to finally share our seminar with you.
Through this meeting, we aim to honor Galen of Pergamon, widely regarded as the father of medicine and pharmacy, whose ideas shaped medical thought for more than 1600 years. Starting from ancient times and extending all the way to today’s modern healthcare systems, we invite you to join us on an intellectual journey across the history and future of medicine.
Asking the question “How would Galen think today?”, this event will bring together historical perspective and contemporary discussion through round table sessions with some of the world’s leading Galen scholars and experts from different disciplines.
Please mark Friday, June 5 in your calendars and don’t forget to register by scanning the QR code.
We look forward to welcoming everyone who believes in the power of transdisciplinary thinking, historical perspective, and the future of healthcare.

The first European Rhinology Research Forum organized by the European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA) was held in the Royal Academy of Medicine in Brussels on 17th and 18th November 2016, in collaboration with the European Rhinologic Society (ERS) and the Global Allergy and Asthma European Network (GA2LEN). One hundred and thirty participants (medical doctors from different specialties, researchers, as well as patients and industry representatives) from 27 countries took part in the multiple perspective discussions including brainstorming sessions on care pathways and research needs in rhinitis and rhinosinusitis. The debates started with an overview of the current state of the art, including weaknesses and strengths of the current practices, followed by the identification of essential research needs, thoroughly integrated in the context of Precision Medicine (PM), with personalized care, prediction of success of treatment, participation of ...

Artificial intelligence is no longer a technology of the future it is already inside the tools
gynecologists use every day. From ultrasound machines that measure fetal growth
automatically, to algorithms that flag high-risk pregnancies before symptoms appear, AI
is quietly reshaping how women's healthcare is delivered. The numbers tell a striking
story: fewer than ten scientific articles per year mentioned AI in medicine before 1971;
by 2023, that number had surpassed 37,000. Obstetrics and gynecology sit at the
center of this change, because the specialty already generates exactly the kind of data
AI thrives on imaging, lab results, vital signs, and long patient histories. This summary
walks through what the evidence actually shows across three areas: obstetrics,
gynecology, and fertility treatment separating what is confirmed from what is still being
tested

We present the infrastructure established for the Breast Cancer Now Tissue Bank centre at the Barts Cancer Institute as an exemplar of a dynamic biobank that supports precision medicine for breast cancer through providing access to integrated data and tools, and could serve as a blueprint for future cancer biobanks.

Longitudinal patient biospecimens and data advance breast cancer research through enabling precision medicine approaches for identifying risk, early diagnosis, improved disease management and targeted therapy. Cancer biobanks must evolve to provide not only access to high-quality annotated biospecimens and rich associated data, but also the tools required to harness these data. We present the Breast Cancer Now Tissue Bank centre at the Barts Cancer Institute as an exemplar of a dynamic biobanking ecosystem that hosts and links longitudinal biospecimens and multimodal data including electronic health records, genomic and imaging data, offered alongside integrated data sharing and analytics tools. We demonstrate how such an ecosystem can inform precision medicine efforts in breast cancer research.

Predictive multiscale cellular modeling is emerging as a consequential direction in precision medicine, converging hypothesis grammars, digital twins, and integrative genomics to interrogate tumor-immune dynamics, therapeutic resistance, and cellular plasticity. This perspective synthesizes recent progress across these domains and critically maps their translational potential alongside their current limitations. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE-PDE systems, enabling rapid in silico hypothesis testing while lowering the authoring barrier for domain scientists. Patient-specific digital twins, driven by multi-omics data, employ stochastic

Minimal residual disease (MRD) is an important prognostic marker in mature B-cell and plasma cell malignancies, providing sensitive assessment of treatment response and risk of relapse. Next-generation sequencing (NGS) enables highly sensitive detection of MRD through identification and tracking of immunoglobulin gene rearrangements associated with malignant clones of B-cell origin. This review outlines the principles of NGS-based MRD assessment and its application in mature B-cell and plasma cell malignancies including several mature B-cell lymphomas, chronic lymphocytic leukaemia (CLL), and multiple myeloma (MM). The advantages of NGS, such as high sensitivity and ability to use a range of sample types, are discussed alongside its limitations, including the requirement for baseline diagnostic samples and cost. Current evidence suggests that NGS is a powerful tool for MRD detection and is increasingly being incorporated into clinical trials as a response endpoint, with potential to guide treatment strategies such as escalation of therapy in routine clinical practice.

Precision medicine in neuro-oncology increasingly requires the integration of histopathological, molecular, clinical, and omics-derived data. However, these data sources are often managed separately, limiting their combined analytical value and the development of artificial intelligence-based research workflows. This study reports the technical design, implementation, and preliminary proof-of-concept validation of an integrated artificial intelligence-based research platform for multimodal brain tumor analysis, with emphasis on secure data governance, modular architecture, histopathological image processing, and future clinical validation. A modular cloud-based platform was developed to integrate digitized hematoxylin and eosin-stained histopathological images, structured clinical data, and omics-related information within a privacy-preserving environment. The platform was implemented on Microsoft Azure and incorporated secure data storage, image upload, metadata management, artificial intelligence model execution, and user-oriented result visualization. As a proof of concept, an IDH status classification workflow was implemented using a patch-based histopathological image analysis pipeline. The proof-of-concept task used 276 histopathological samples, including 229 IDH-negative and 47 IDH-positive cases, processed into 67,305 image patches distributed across training, validation, and test subsets. The implementation demonstrated the technical feasibility of deploying image-based molecular classification workflows within a secure and scalable research infrastructure. Although the current work represents an internal proof of concept rather than a clinically validated diagnostic system, it establishes a foundation for future integration of clinical, histopathological, transcriptomic, epigenomic, and genomic data. External validation, complete molecular integration, harmonized performance assessment, and clinical user evaluation will be required before any clinical deployment can be considered.

Cardio-metabolic diseases such as atherosclerotic cardiovascular disease, type 2 diabetes mellitus, metabolic dysfunction-associated steatotic liver disease, and hypertension continue to be the major causes of morbidity and mortality worldwide. Traditional preventive strategies are heavily dependent on static risk prediction models based on population means, which commonly neglect preclinical metabolic dysfunction at the systems level. Recent breakthroughs in artificial intelligence (AI), systems biology, and multi-modal biomarker fusion provide a new paradigm for precision prevention. Mechanistic metabolic network modeling, especially in the context of one-carbon metabolism, mitochondrial bioenergetics, inflammatory signaling, and lipid metabolism pathways, can now be coupled with high-dimensional data sources such as genomics, metabolomics, proteomics, radiomics, electronic health records, and wearable-derived physiological signals. AI-based architectures facilitate dynamic risk prediction, subclinical disease trajectory identification, and personalized preventive intervention mapping. This review article summarizes existing evidence on AI-assisted metabolic modeling for cardio-metabolic risk prediction, provides a translational framework for multi-modal biomarker fusion into preventive strategies, and explores challenges in the realms of interpretability and equity, culminating in a systems-informed roadmap for precision preventive medicine.

Recent advances in molecular technology have unraveled the complexity of leukemogenesis and provided the opportunity to design more personalized and pathophysiology-targeted therapeutic strategies. Despite the use of intensive chemotherapy, relapse remains the most common cause for therapeutic failure in acute myelogenous leukemia (AML). The interactions between leukemia stem cells (LSC) and marrow microenvironment appear to be critical in promoting therapeutic resistance through progressive acquisition of genetic and epigenetic changes within leukemia cells and immune evasion, resulting in leukemia cell survival. With advances in genomic-sequencing efforts, epigenetic and phenotypic characterization, personalized therapeutic strategies aimed at critical leukemia survival mechanisms may be feasible in the near future. Here, we review select novel approaches to therapy of AML such as targeting LSC, altering leukemia/marrow microenvironment interactions, inhibiting DNA repair or cell-cycle checkpoints, and augmenting immune-based antileukemia activity. Clin Cancer Res; 20(24); 6233-41. Ó2014 AACR.

Quantum technologies have emerged as one of the most transformative scientific frontiers of the twentyfirst century, encompassing quantum computing, quantum cryptography, and quantum sensing. This review provides a comprehensive survey of recent advances across these interconnected domains, examining how fundamental quantum mechanical principles are being harnessed to solve classically intractable problems, secure communications against computational attacks, and achieve unprecedented measurement precision. We discuss the current state of noisy intermediate-scale quantum (NISQ) devices and variational quantum algorithms that represent the frontier of practical quantum computation. The review explores quantum machine learning paradigms including quantum kernel methods, neural quantum states, and federated quantum learning, alongside their applications in complex systems. We examine post-quantum cryptographic schemes being standardized by NIST, quantum key distribution protocols, and advanced quantum security frameworks including the Quantum Permutation Pad. Quantum sensing technologies are surveyed with particular attention to quantum dot-based sensors for environmental and energy applications, nitrogen-vacancy center magnetometry, and atomic clock systems. The convergence of these fields through quantum networks and distributed quantum architectures is analyzed, along with the technical challenges of decoherence, scalability, and error correction that must be overcome. Finally, we discuss the future outlook for quantum technologies and their potential to reshape computing, security, and sensing landscapes across scientific and industrial domains.

Objectives Conduct a preliminary comparison of the bioavailability between two formulations: commercial grade coenzyme Q10 (CoQ10) powder (solid formulation) and a new oil-in-water liquid emulsion and their effect on other antioxidants. Methods Six healthy individuals participated in a randomized, crossover, open, consecutive design, with a 2-week washout period. Pharmacokinetic parameters were assessed after a single and multiple intakes of 250 mg CoQ10 given daily for 1 week. Key Findings The differences in the pharmacokinetic parameters of maximum plasma concentration, area under the curve between 0–360 and 0–4 h, elimination half-life were statistically significant with a relative bioavailability of 489% increase over solid CoQ10 formulation. A multiple dose supplementation increased plasma CoQ10 levels in both formulations, liquid emulsion performing better (2.4- vs 3.9-fold for solid and liquid formulation, respectively) without modifications on other antioxidants. Furthermore...

Future priorities for the management of hypoxemic respiratory failure (HRF) and pulmonary hypertension include primary prevention of neonatal lung diseases, 'precision medicine' and translating promising clinical and preclinical research into novel therapies. Promising areas of investigation include noninvasive ventilation strategies, emerging pulmonary vasodilators (for example, cinaciguat, intravenous bosentan, rho-kinase inhibitors, peroxisome proliferator-activated receptor-γ agonists) and hemodynamic support (arginine vasopressin). Research challenges include the optimal timing for primary prevention interventions and development of validated biomarkers that predict later disease or serve as surrogates for long-term respiratory outcomes. Differentiating respiratory disease endotypes using biomarkers and experimental therapies tailored to the underlying pathobiology are central to the concept of 'precision medicine' (that is, prevention and treatment strategies that take individual variability into account). The ideal biomarker should be expressed early in the neonatal course to offer an opportunity for effective and targeted interventions to modify outcomes. The feasibility of this approach will depend on the identification and validation of accurate, rapid and affordable point-of-care biomarker tests. Trials targeting patient-specific pathobiology may involve less risk than traditional randomized controlled trials that enroll all at-risk neonates. Such approaches would reduce trial costs, potentially with fewer negative trials and improved health outcomes. Initiatives such as the Prematurity and Respiratory Outcomes Program, supported by the National Heart, Lung, and Blood Institute, provide a framework to develop refined outcome measures and early biomarkers that will enhance our understanding of novel, mechanistic therapeutic targets that can be tested in clinical trials in neonates with HRF.

The emergence of automated acquisition of experimental data in genomics, such as high-throughput sequencing, serves as driving force for the development of complex genomic data infrastructure. Such systems support the seamless integration of large genomic data into clinical processes and provide an extensive service offering for biomedical research that  requires  large  scale  genomic  data.  Consequently,  these  infrastructure  enable  the triggering of large numbers of DNA sequencing requests from patients who will benefit from such tests.

Adaptive systems for genomic intelligence learn from experience, customize problem-solving methods to data and task characteristics, and autonomously change behaviour. Such systems are particularly well-suited for the analysis of evolving, high-dimensional, and heterogeneous genomic data, as well as for the identification of optimal solutions in noisy  environments  described  by  incomplete  information.  They  are  essential  for addressing  complex  scientific  questions  and  developing  adaptive  genomic  medicine solutions.

This paper applies the Recursive Harmonic Collapse Matrix (RHCM) framework to nutrigenomics, amino acid essentiality, and gene-nutrient interaction prediction. RHCM assigns each molecule a φ-logarithmic depth coordinate derived from mass. Within this framework, the φ²¹ wall at d = 29.9592 is proposed as a metabolic synthesis boundary: amino acids below this wall are generally accessible to human metabolism through short glycolytic or TCA-derived pathways, while essential amino acids cluster at or near/above the boundary and require longer biosynthetic pathways absent in humans.

The first major result is the φ²¹ synthesis boundary theorem. Glycine, alanine, serine, and proline sit below the boundary and are directly synthesizable. Most essential amino acids sit at or above the wall, while valine and threonine appear as meaningful borderline cases consistent with their glucogenic accessibility.

The second result is a first-principles gene-nutrient framework based on enzyme-substrate depth compatibility. RHCM derives genotype-specific predictions for MTHFR C677T, VDR Fok1, BCMO1 R267S, CYP1A2 slow metabolizer status, FADS1 rs174537, SOD2 Val16Ala, COMT Val158Met, TCN2 776C>G, GC Gc1F, and APOE E4/E4. Several predictions align with existing clinical recommendations, including 5-MTHF for MTHFR C677T, preformed retinol for BCMO1 variants, EPA/DHA for FADS1 variants, and mitochondrial support strategies for SOD2 variation.

The paper positions RHCM as a nutrigenomics engine capable of extending beyond empirical lookup tables by generating testable predictions for novel variants and compound genotypes. Falsification criteria are provided for amino acid synthesis classification, MTHFR folate response, BCMO1 retinol conversion, novel MTHFR missense variants, and SOD2-targeted mitochondrial antioxidant intervention.

Nanomaterial based biosensor have emerged apromising approach that demonstrates great progress in the diagnosis of breast cancer with increased sensitivity and selectivity compared to conventional detection methods. These nanomaterials may allow for combinatorial detection capabilities that could provide simultaneous identification of multiple biomarkers in order to reach an accurate tumor classification, by using quantum dot imaging and carbon nanotube electrical sensing. However, this field faces challenges that must be addressed before widespread application of clinical. Key among these is the need to standardize methods and protocols to ensure reproducibility and minimize nonspecific correlations. Furthermore, ensuring the nanomaterial , s safety and biocompatibility, as well as the toxicity of a diagnostic methods, are essential elements for their success in future patient care.

This paper applies the Recursive Harmonic Collapse Matrix (RHCM) framework to Alzheimer’s disease drug repurposing. RHCM assigns each molecule a φ-logarithmic depth coordinate based on mass. Within this framework, the Tau PHF6 hexapeptide sequence VQIVYK—the aggregation seed responsible for paired helical filament formation—falls at d=33.148, inside the oral drug-like well bounded by φ²¹ and φ³⁴. This makes Tau PHF6 a uniquely accessible Alzheimer’s target for oral small molecules, while amyloid-beta42 sits above the φ³⁴ oral-drug ceiling.

The paper shows that several drugs with reported or hypothesized Alzheimer’s relevance, including nilotinib, bosutinib, LMTM/TRx0237, and atorvastatin, cluster near the Tau PHF6 depth coordinate. RHCM therefore provides a single geometric explanation for multiple otherwise disconnected empirical findings.

The framework then generates novel repurposing predictions: approved HIV protease inhibitors—especially indinavir, followed by lopinavir, nelfinavir, and saquinavir—should be screened as Tau PHF6 aggregation inhibitor candidates. These drugs are peptidomimetics designed to disrupt peptide-protein interfaces, making them structurally plausible candidates for interfering with PHF6 beta-sheet aggregation.

The prediction is directly falsifiable using standard Thioflavin T aggregation assays. If confirmed, the result would support RHCM as a first-principles candidate-generation framework for Alzheimer’s drug repurposing and broader pharmacological screening.

Rapidly increasing amounts of genomic information have made computational genomics a core field in the mapping of the genetic etiology of human disease. The algorithmic evolution and hardware acceleration techniques driving the next generation of disease gene discovery are synthesized in this review. We critically examine the computational terrain according to three fundamental paradigms: Variant Prioritization, Polygenic Risk Analysis, AI-Powered Discovery, tackling head-on the central challenge of cross-ancestry portability. We then break down the computational complexity of these workflows and analyze how it can be implemented on the current hardware; we consider the GPUs as the leading hardware to accelerate them due to the high throughput and programmability. Lastly, we talk of how performance-portable, interpretable, and equitable computational frameworks need to be co-located in order to achieve the promise of precision medicine. The review summarizes the findings of 40 iconic publications to give an overview of the direction of computational problems in genomics in the future.

Diabetes mellitus (DM) is the most common endocrine disorder and among the top 10 leading diseases causing death worldwide. Coicis semen [CS] (Coix lacryma-jobi), also known as adlay have been reported to display anti-diabetic properties. Unfortunately, studies on the pharmacological mechanisms involving adlay for the treatment of diabetes are nil. Thus, this study was conducted to evaluate the interactions and mechanisms of the bioactive compound targets of adlay in the treatment of diabetes using network analysis. Adlay bioactive compounds and potential target genes were obtained from SymMap. Diabetes related target genes were collected from CTD. Protein-Protein Interaction Network was analyzed using the STRING database. GO and KEGG pathway enrichment analyses were performed using DAVID to further explore the mechanisms of adlay in treating diabetes. PPI and compound-target-pathway were visualized using Cytoscape. A total of 25 bioactive compounds, 201 corresponding targets, and 3...

The rapid evolution of artificial intelligence (AI) in healthcare has transformed biomedical data analysis, enabling the development of predictive systems capable of improving diagnosis, prognosis, and personalized treatment planning. Among the most promising advancements is the integration of multimodal biomedical data, particularly RNA sequencing (RNA-Seq) and neurophysiological signals such as electroencephalography (EEG) and electromyography (EMG). These modalities provide complementary insights into molecular, neurological, and muscular processes, creating opportunities for comprehensive predictive healthcare systems. However, the increasing complexity of deep learning models has introduced concerns regarding transparency, interpretability, and trustworthiness in clinical environments. Explainable Artificial Intelligence (XAI) has emerged as a critical solution to these concerns by enabling clinicians and researchers to understand how predictive models reach their conclusions. This article explores the role of explainable AI in multimodal biomedical systems that integrate RNA-Seq and EEG-EMG signals for predictive healthcare applications. The discussion covers the fundamentals of RNA-Seq analysis, EEG and EMG signal processing, multimodal data fusion strategies, and the application of machine learning and deep learning techniques in predictive healthcare. The article further examines the importance of explainability in medical AI systems, including model transparency, clinical trust, regulatory compliance, and ethical considerations. Different XAI methods such as SHAP, LIME, saliency maps, attention mechanisms, and rule-based explanations are analyzed in the context of biomedical systems. Additionally, the paper highlights current applications in neurological disease diagnosis, cancer prediction, rehabilitation medicine, and personalized healthcare. Major challenges such as data heterogeneity, dimensionality reduction, noise handling, computational complexity, and privacy concerns are discussed alongside future research opportunities involving federated learning, graph neural networks, edge AI, and hybrid neuro-symbolic systems. The study concludes that explainable multimodal AI systems integrating RNA-Seq and EEG-EMG signals have significant potential to revolutionize predictive healthcare by enhancing diagnostic accuracy, supporting clinical decision-making, and improving patient outcomes while maintaining transparency and accountability.

Hematological malignancies, including leukemias, lymphomas, myelomas, and myelodysplastic syndromes, impose a substantial global health burden, accounting for approximately 10% of all new cancer diagnoses worldwide. Early identification of pre-malignant blood cells a critical window for preventive intervention remains clinically challenging due to the limitations of conventional diagnostic tools such as light microscopy, flow cytometry, and next-generation sequencing, none of which is individually optimized for risk stratification prior to overt disease manifestation. This review examines machine learning (ML) approaches for classifying pre-malignant blood cells, synthesizing evidence from 25 studies encompassing 38,417 participants across diverse clinical settings. Ensemble methods and Random Forest algorithms demonstrated consistently strong discriminative performance, achieving AUC-ROC values ranging from 0.856 to 0.932. Multi-omics integration combining morphological, immunophenotypic, genetic, and epigenetic data systematically outperformed single-domain approaches, underscoring the biological complexity of premalignant transformation. Key predictive biomarkers identified across studies included CD34 expression levels, telomere length attrition, and TP53 mutation status, consistent with established pathways of clonal hematopoietic evolution. Despite these promising findings, significant methodological limitations were identified: external validation was reported in only 44% of studies, and open-source code availability was documented in just 40%, raising concerns about reproducibility and generalizability. Additionally, most training cohorts lacked demographic diversity, limiting applicability across varied populations. Successful translation of ML-based pre-malignant cell classification into routine clinical practice will require prospective validation trials, standardized reporting frameworks aligned with existing diagnostic criteria, and the development of ethnically and geographically diverse training datasets.

applied in medicine with the use of algorithms that are developed from data analysis to help in improving healthcare-related outcomes and experiences. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have paved the way for AI to play an integral role in modern healthcare . AI applications are being developed to support physicians in inpatient, outpatient, and surgical settings and to help deliver patient-centered precision medicine. AI has already been applied throughout many medical fields, with up to 12,517 articles published since the 2000s on the health care of patients, and only a few,

Hypoglycemia is a well-known risk associated with the use of sulfonylureas and insulin, often limiting achievement of glycemic goals. Recognizing the precipitants and recurrence patterns of hypoglycemic events, particularly major events, is therefore clinically important. The SAVOR-TIMI-53 trial was a cardiovascular outcome study of 16,492 patients allocated to saxagliptin vs. placebo added to conventional care for a median of 2.1 years. Hypoglycemic events were a pre-specified outcome in the study and were defined as a symptomatic episode which recovered with carbohydrates or any recorded blood glucose <54 mg/dL. A major event was defined as one which required third party assistance. Analyzing the features of the first hypoglycemic event for each patient revealed that a precipitant for the event was recognized by less than half of the patients, most often being a missed meal. In 40% of patients reporting major hypoglycemic events, no precipitating factor was recognized and in ov...

Novel treatment in multiple myeloma represented by proteasome inhibitors, immunomodulatory drugs and monoclonal antibodies have produced a deep response. However, relapses are possible, and all classes of drugs are refractory to patients. Next-generation sequencing has improved our understanding of the multiple myeloma genome related to drug resistance and has discovered many genomic variants. Therefore, this study was conducted to investigate new variants associated with drug resistance in MM patients who relapsed and refractory to bortezomib regimen and daratumumab treatment using nextgeneration sequencing for whole-exome sequencing. Peripheral blood samples were collected in EDTA tubes from six patients; four were in relapsed and refractory to bortezomib regimens and daratumumab; two patients responded to bortezomib regimens. Whole-exome sequencing was performed by the MGI-DNBSEQ-G400 instrument. We identified 21 variants in multiple myeloma patients. Seventeen variants were found in relapsed and refractory multiple myeloma in 11 genes (GNAQ, PMS1, CREB1, NSUNS2, PIK3CG, ROS1, PMS2, FIT4, KDM5A, STK11 and ZFHX3). And four variants were identified in two patients with response to bortezomib regimens in 4 genes (RAF1, CREB1, ZFHX3 and INSR). We have observed several genetic variants in many genes that may have been associated with the poor prognosis and poor response to treatment in these patients. These values should be further confirmed in large sample studies using the RNA-seq technique to identify genome expression.

The selection of medicine providers is a critical process, particularly in urgent situations such as responding to health crises or escalating demands. This study employs the COPRAS method to evaluate and prioritize five medicine providers based on essential criteria including reliability, quality, pricing, production capacity, technological expertise, unique medicine attributes, and delivery efficiency. Health tourism has seen a substantial surge, encompassing not only medical services but also accommodations, local attractions, transport, and dining options. Customers seek a method to choose healthcare destinations considering various factors. The focus on medical services, especially during emergencies like natural disasters or viral outbreaks, has prompted extensive research. This paper aims to use the COPRAS method, known for handling uncertain and incomplete data, to select medical providers for such critical situations. This method calculates criteria weights, manages uncertain evaluations from experts, and ranks feasible alternatives. By demonstrating its effectiveness through rankings, the paper applies this model to evaluate and select medical providers for New York-based patients, showcasing its practicality and computational process. The findings from this comprehensive evaluation framework offer a structured approach for decision-making, enabling stakeholders to select medicine providers tailored to meet specific priorities or demands, ensuring timely and reliable access to critical medical supplies, especially in emergency situations.

Obesity has emerged as one of the most pressing global public health challenges, driven by complex interactions among genetic, behavioural, environmental, and socioeconomic factors. Conventional approaches to obesity risk assessment and nutritional intervention often rely on population-based models that fail to account for individual variability. Recent advances in artificial intelligence (AI) and machine learning (ML) have transformed obesity research by enabling accurate prediction of obesity risk and facilitating the development of personalized, preventive nutrition therapies. This review explores the role of AI in obesity prediction, including the use of machine learning algorithms, deep learning models, and big data analytics to integrate anthropometric, dietary, lifestyle, genetic, and metabolic data. Furthermore, it highlights AI-driven approaches for designing personalized nutrition interventions, behaviour modification strategies, and digital health tools for obesity prevention. Ethical challenges, data limitations, and future research directions are also discussed. The review underscores the potential of AI-based systems to revolutionize preventive nutrition and support precision public health strategies for obesity management.

Biodegradable biosensor-integrated drug delivery systems represent an emerging class of smart therapeutics capable of real-time monitoring and on-demand drug release. These systems combine biodegradable polymers, nanomaterials, and bioresponsive sensors to detect physiological or pathological signals such as pH, enzymes, glucose, or inflammatory markers, enabling precision therapy with minimal invasiveness. Materials including polylactic-co-glycolic acid, chitosan, silk fibroin, and transient metals support controlled degradation and safe bioresorption, eliminating the need for device removal. Despite significant promise, challenges remain in achieving sensor stability, signal accuracy, scalable fabrication, and regulatory standardization. Future prospects focus on wireless integration, closed-loop feedback control, and personalized medicine applications. Overall, biodegradable biosensor-guided drug delivery platforms offer transformative potential for improving therapeutic efficacy, patient compliance, and long-term clinical outcomes across diverse diseases.

Cardio-metabolic diseases are the major cause of morbidity and mortality in the world, and this is attributed to the complex interplay of metabolic dysregulation, vascular dysfunction, inflammation, and epigenetic remodeling. B vitamins, such as folate (B9), cobalamin (B12), and pyridoxine (B6), are crucial in the regulation of one-carbon metabolism, the clearance of homocysteine, methylation reactions, redox reactions, and blood homeostasis. Abnormalities in the complex interrelationship of the aforementioned biochemical pathways can be reflected as measurable disturbances in the concentrations of circulating biomarkers such as homocysteine, s-adenosylmethionine/s-adenosylhomocysteine ratios, oxidative stress markers, inflammatory markers, and markers of endothelial dysfunction. Despite the plethora of biochemical research in the field, the translation to specific prediction of cardio-metabolic risk has not been consistent. However, the emerging field of artificial intelligence has the potential to integrate the complex interrelationship of the aforementioned biochemical pathways to quantify the specific relationship between the B vitamin-regulated networks in the blood and the individualized cardio-metabolic risk profile. In the following sections, the current mechanistic understanding of the B vitamin-dependent biochemical pathways will be discussed, followed by the proposal of the AI-integrated system in the prediction of cardio-metabolic risk.

Artificial intelligence (AI) and digital pathology (DP) are transforming the landscape of diagnostic histopathology. This review provides an overview of how traditional pathology workflows-long reliant on physical slides and subjective interpretation-are being reshaped by digital slide acquisition, machine learning algorithms, and multi-omics integration. The article outlines the historical foundations of diagnostic pathology and the shift toward digitization, followed by an exploration of the practical challenges in implementing AI tools, including infrastructure needs, training requirements, and regulatory considerations. The clinical applications of AI-such as automated cancer detection, mutation prediction, and deep learning-based classification-are discussed alongside recent developments in federated learning and explainable AI (XAI). Particular attention is given to the integration of genomics and digital morphology, which allows for more accurate prognostic and predictive modeling. While the implementation of AI raises concerns regarding cost, standardization, and interpretability, the conclusion emphasizes that AI will serve as a powerful tool to support-not replace-the pathologist. By enhancing diagnostic reproducibility, reducing workload, and expanding the analytical scope of histopathology, AI has the potential to advance precision medicine and improve patient care.

The Recursive Harmonic Collapse Matrix (RHCM) framework assigns a logarithmic depth parameter d = D_P + log_φ(m • 0.9946) to any physical system of rest mass m (Daltons). We apply this framework to three aspects of the genetic code. (1) All eight DNA and RNA nucleotide monophosphates cluster within the Fibonacci stability well [φ 21 , φ 34 ] at depths d = 31.74-32.09, a range of 0.35 depth units-a tight geometric constraint that we propose underlies the universality of the four-letter genetic alphabet. (2) The Hardy-Weinberg allele frequency equation p² + 2pq + q² = 1 is satisfied exactly at allele frequencies p = 1/φ = 0.61803... and q = 1/φ² = 0.38197..., following from the algebraic identity φ² = φ + 1. This constitutes a mathematically exact φ-quantization of population genetic equilibrium. (3) We derive a three-component SNP impact predictor from RHCM first principles: Component 1 = Δd (depth change from amino acid mass), Component 2 = Δχ (Randić graph-topology index, handling structural isomers), Component 3 = ΔZ•α (charge change times the fine structure constant, handling electrostatic variants). Applied to 16 clinically characterized pharmacogenomic variants from PharmGKB Levels 1A-2A, the three-component model achieves 100% accuracy on the validation set when combined with a binary expression model for splice variants and positional weighting from active-site crystal structure annotations. We compare this approach to existing pharmacogenomic platforms and identify the conditions under which RHCM provides predictions where empirical lookup tables cannot.