ethicalabs/Research-Intent-Collab huggingface dataset:
From genomics to clinic: the transformative impact of AI in pharmacogenomics and personalized medicine

At the crossroads of genomics and pharmacology, pharmacogenomics is revolutionizing healthcare by tailoring drug therapies to individual genetic profiles thereby reducing the risk of adverse drug reactions and propelling the field of precision medicine forward. This review delves into the role of pharmacogenomics in uncovering genetic variations, including single nucleotide polymorphisms, that affects how drugs are metabolized and effective. Artificial intelligence (AI) has ameliorated the discovery of biomarkers and the drug development process; enabled real-time clinical decision-making, expanding the possibilities of personalized medicine. AI-powered models, especially in machine learning and deep learning have demonstrated potential in forecasting drug responses and enhancing the precision of genetic variant identification, exemplified by tools like DeepVariant and AlphaFold. However, the diversity of data, the clarity of model interpretations, and the ethical issues surrounding data privacy and genetic discrimination remain as major hurdles. Efforts are underway to address these challenges through multi-omics integration, federated learning, and explainable AI, all aimed at improving clinical translation and promoting fair access to personalized treatments. This review enunciates the existing applications, translational pathways, and prospects of AI in pharmacogenomics, its promise in achieving the goal of precision medicine ensuring the proper treatment of right patient at the right moment.
A Statistical Inference Framework for the Minimal Clinically Important Difference

In clinical research, the effect of a treatment or intervention is widely assessed through clinical importance, instead of statistical significance. In this paper, we propose a principled statistical inference framework to learning the minimal clinically important difference (MCID), a vital concept in assessing clinical importance. We formulate the scientific question into a novel statistical learning problem, develop an efficient algorithm for parameter estimation, and establish the asymptotic theory for the proposed estimator. We conduct comprehensive simulation studies to examine the finite sample performance of the proposed method. We also re-analyze the ChAMP (Chondral Lesions And Meniscus Procedures) trial, where the primary outcome is the patient-reported pain score and the ultimate goal is to determine whether there exists a significant difference in post-operative knee pain between patients undergoing debridement versus observation of chondral lesions during the surgery. Some previous analysis of this trial exhibited that the effect of debriding the chondral lesions does not reach a statistical significance. Our analysis reinforces this conclusion that the effect of debriding the chondral lesions is not only statistically non-significant, but also clinically un-important.
Democracy and UN Peace-Keeping ― Conflict Resolution through State-Building and Democracy Promotion in Haiti

This contribution analyses the relationship between the role of democracy in international law and UN Peacekeeping missions. It concludes that a right to democracy has not become a legal norm yet but is widely accepted as precondition for peace. It is therefore included in the mandate of robust, integrated UN peace-keeping missions. An analysis of the development and impact of five UN peace-keeping missions to Haiti informs about the UN peace-keepers relevance in the Haitian conflict resolution and regime change.
Life insurance and breast cancer risk assessment: Adverse selection, genetic testing decisions, and discrimination

AbstractLife insurance industry access to genetic information is controversial. Consumer groups argue that access will increase discrimination in life insurance premiums and discourage individuals from undergoing genetic testing that may provide health benefits. Conversely, life insurers argue that without access to risk information available to individuals, they face substantial financial risk from adverse selection. Given this controversy, we conducted a retrospective cohort study to evaluate the impact of breast cancer risk information on life insurance purchasing, the impact of concerns about life insurance discrimination on use of BRCA1/2 testing, and the incidence of life insurance discrimination following participation in breast cancer risk assessment and BRCA1/2 testing. Study participants were 636 women who participated in genetic counseling and/or genetic testing at a University based clinic offering breast cancer risk assessment, genetic counseling, and BRCA1/2 testing between January 1995 and May 2000. Twenty‐seven women (4%) had increased and six (1%) had decreased their life insurance since participation in breast cancer risk assessment. The decision to increase life insurance coverage was associated with predicted breast cancer risk (adjusted OR 1.03 for each 1% absolute increase in risk, 95% CI 1.01–1.10) and being found to carry a mutation in BRCA1/2 (OR 5.10, 95% CI 1.90–13.66). Concern about life insurance discrimination was inversely associated with the decision to undergo BRCA1/2 testing (RR 0.67, 95% CI 0.52–0.85). No respondent reported having life insurance denied or canceled. In this cohort of women, these results indicate that information about increased breast cancer risk is associated with increase in life insurance purchasing, raising the possibility of adverse selection. Although fear of insurance discrimination is associated with the decision not to undergo BRCA1/2 testing, there was no evidence of actual insurance discrimination from BRCA1/2 testing. © 2003 Wiley‐Liss, Inc.
Methodological trends in machine learning for sport

Since 2020, the field of machine learning research in sports has expanded. This includes predictive modelling for sports medicine and athlete monitoring using wearables and screening data, sequential modelling for trajectory prediction, and computer vision for broadcast understanding (event spotting, multi-object tracking, pose estimation). Peer-reviewed journal and major conference proceeding evidence published between 2020-01-01 and 2026-02-11 is integrated in this review (search date: 2026-02-11). After outlining repeatable search terms and inclusion criteria, we offer a thematic synthesis arranged by task family (recognition/segmentation, tracking and pose, tactical decision support, biomechanics/engineering prediction, and health/injury risk) and data modality (wearables, clinical/screening data, video, and tracking). Algorithm/sport frequencies throughout the extracted sample are reported in a quantitative synthesis that summarizes representative studies. The findings show that: (i) open benchmarks like SoccerNet-v2 (~300k annotations over 500 broadcast soccer videos) and FineGym (708 hours; 530 fine-grained elements) have fuelled the dominance of deep learning in video and pose pipelines; (ii) tree-based boosting and SVMs continue to be common for structured sports medicine data; and (iii) interpretability tooling (SHAP, permutation importance) is being used more and more to convert models into insights that coaches and clinicians can use. Restrictions include partial PRISMA count reconstruction and limited access to certain paywalled indexing services. We wrap up by discussing unresolved issues with deployment, fairness, domain shift, generalization, and label quality.
Efficient computational algorithm for optimal continuous experimental designs

A simple yet efficient computational algorithm for computing the continuous optimal experimental design for linear models is proposed. An alternative proof the monotonic convergence for $D$-optimal criterion on continuous design spaces are provided. We further show that the proposed algorithm converges to the $D$-optimal design. We also provide an algorithm for the $A$-optimality and conjecture that the algorithm convergence monotonically on continuous design spaces. Different numerical examples are used to demonstrated the usefulness and performance of the proposed algorithms.
QUANTITATIVE ANALYSIS OF STRATEGIC FINANCIAL PLANNING IN CORPORATE MANAGEMENT: A STUDY OF OPTIMIZATION MODELS, RISK ASSESSMENT FRAMEWORKS, AND DECISION-SUPPORT TOOLS

Strategic financial planning is more and more dependent upon data-driven decision support models in which profitability, risk analysis and capital allocation are integrated. This paper develops and uses a quantitative model based on fundamental analysis to discuss how firms can be systematically analyzed and selected for the deployment of capital for maximum returns. With a globally representative sample of 1,254 firms in 18 countries over the period 2013-2023, the analysis begins with the construction of simple financial ratios of return on assets (ROA), debt to equity and current ratio, which then are used in the measurement of profitability, leverage and liquidity. Risk is defined as a measure of volatility of earnings, which is defined as the standard deviation of the net income growth over time and it allows consistent and market-independent proxy for risk. Descriptive results indicate a right-skewed distribution of profitability, high dispersion of capital structures and the low correlation between liquidity and returns. It then utilizes a composite score heuristic optimization process to invest in companies with better risk-adjusted performance subject to leverage and liquidity constraints. The optimal portfolio is rewarded with a higher weighted-average ROA than the sample median while violating no risk constraint and essentially reallocates the portfolio to the empirical efficient frontier. The results stress the usefulness of transparent and reproducible heuristics to inform strategic financial planning and offer managerial insights that are relevant for balancing growth, profitability and stability in an uncertain environment.
The simulation of complete 11 and 12 year modulation cycles for cosmic rays in the heliosphere using a drift model with global merged interaction regions

Two-dimensional, time-dependent drift models have done exceptionally well in explaining major modulation features, especially during the A less than 0 magnetic polarity cycle of the heliospheric magnetic field when positively charge particles are drifting in along the heliospheric neutral sheet (HNS). These models were found to do well when the heliospheric 'tilt angles' alpha less than approximately 30 deg (le Roux & Potgeiter). However, they seem to do less well when alpha greater than approximately 30 deg during A less than 0 cycles seem to fail when this happens in A greater than 0 cycles. Progress was made in understanding these phases of the modulation cycle when merged interaction regions (MIRs) were incorporated in time-dependent drift models (Potgieter et al.). It was also explicitly shown that in obtaining large step decreases in cosmic rays, the MIRs had to be global, i.e., having a latitudinal extent of more than approximately 60 deg. Other classes of MIRs, such as local MIRs and co-rotating MIRs were found to be of secondary importance for establishing long-term modulation. In a previous paper we studied the effects of two consecutive, identical global MIRs, together with a changing wavy HNS, on long-term modulation (Potegieter & le Roux). This approach gave a very natural and convincing explanation for the observed step decreases in cosmic-ray modulation. Emphasis was placed in the declining and recovery phases of the 11 yr modulation cycle. In this paper, four consecutive, nonidentical global MIRs, in combination with a varying wavy HNS, were included in our time-dependent drift model in order to do simulations closer to what was observed between 1977 and 1987. By doing this we could model, for the first time, complete 11 and 22 yr cycles in the heliospheric modulation of galactic cosmic-rays, including the solar magnetic polarity reversals.
Statistical mechanics for organic mixed conductors: phase transitions in a lattice gas

Organic mixed conductors (OMCs) represent a promising class of materials for applications in bioelectronics, physical computing, and thermoelectrics. Rather unparalleled, OMCs feature dynamics spanning multiple length and time scales, involving an intricate coupling between electronic, ionic, and mass transport. These characteristics set them notably apart from traditional semiconductors and hinder the description by conventional semiconductor theory. In this work, we approach the charge carrier modulation of OMCs using statistical mechanics. We discuss OMCs from a thermodynamic perspective and contrast them with established semiconductor materials, highlighting key differences in their collective charge carrier dynamics. This motivates our description of OMCs as a lattice gas, which we analyze within the grand canonical ensemble. The model exhibits a first-order phase transition analogous to a classical vapor$\unicode{x2013}$liquid transition, governed by temperature and chemical potential. In doing so, it captures the formation of distinct low- and high-density carrier phases, consistent with recently reported experimental observations. It also illustrates how metastability near the phase boundary can give rise to history-dependent characteristics in device operation, a similarly well-reported effect in OMC transistors. This work is intended as a simple motivation for studying OMCs through the lens of statistical mechanics, offering a more natural description than traditional semiconductor models developed for materials of fundamentally distinct character.
Paradox between Policy and Practice: Implications of Land/Property Rights of South Asian Indigenous People on Current Development Agendas of Countries

Starting from the historical massacres of native tribes by the colonizers/settlers in different parts of the world, to the current appropriation of their land and the exploitation of their natural resources, indigenous people have suffered assault, discrimination and marginalization to a grievous extent. The Asian indigenous population that makes up seventy percent of a world community of approximately two hundred and fifty million of indigenous groups also intensely share in this suffering. The present study centred around the still unresolved grievances of the indigenous communities in South Asia, mainly with regard to land/property. In order to probe into this issue, the study employed the qualitative method of library research in synthesizing available secondary data pertaining to the social, anthropological and demographic indigenous information from South Asia. By employing this method, the study aimed to provide an overview of the situations in the countries examined, particularly in relation to the land issue. Although research is available on indigenous cultures and associated problems, indigenous information from Sri Lanka in comparison with the rest of South Asian indigenous contexts is an area that has not been adequately explored, which is the gap that this study attempted to address. The outcome of the study further ascertains that notwithstanding the previous and the latest conventions, treaties and constitutional reforms ratified by consecutive governments of the relevant countries, the land-related problems of indigenous groups keep aggravating almost on a daily basis. In the modern society where inclusivity and mutual respect are given importance, the present research can be deemed significant in recognizing the most recent indigenous information with a view to sensitizing the world to one of the continuing problems of a historically significant human enclave in a context of socio-economic development and sustainable investment.
Spotting Differences in Molecular Dynamic Simulations of Influenza A M2 Protein-Ligand Complexes by Varying M2 construct, Lipid Bilayer and Force Field

We studied by molecular dynamic (MD)  simulations systems including the inward                   closed                   state of influenza A  M2 protein in complex with aminoadamantane drugs in membrane bilayers. We  varied the M2 construct and performed MD simulations in M2TM or M2TM with  amphipathic helices (M2AH). We also varied the lipid bilayer by changing either  the lipid, DMPC or POPC, POPE or POPC/cholesterol (chol), or the lipids buffer  size, 10x10 Å                   2                   or 20x20 Å                   2                   . We aimed to suggest optimal  system conditions for the computational description of this ion channel and  related systems. Measures performed include quantities that are available experimentally  and include: (a) the position of ligand, waters and chlorine anion inside the M2  pore, (b) the passage of waters from the outward Val27 gate of M2 S31N in  complex with an aminoadamantane-aryl head blocker, (c) M2 orientation, (d) the  AHs conformation and structure which is affected from interactions with lipids  and chol and is important for membrane curvature and virus budding. In several  cases we tested OPLS2005, which is routinely applied to describe drug-protein  binding, and CHARMM36 which describes reliably protein conformation. We found  that for the description of the ligands position inside the M2 pore, a 10x10 Å                   2                   lipids buffer in DMPC is needed when M2TM is used but 20x20 Å                   2                   lipids buffer of the softer POPC; when M2AH is used all 10x10 Å                   2                   lipid  buffers with any of the tested lipids can be used. For the passage of waters at  least M2AH with a 10x10 Å                   2                   lipid buffer is needed. The folding  conformation of AHs which is defined from hydrogen bonding interactions with  the bilayer and the complex with chol is described well with a 10x10 Å                   2                   lipids buffer and CHARMM36.
Land use, biodiversity conservation, and rural development in the dehesas of Cuatro Lugares, Spain

Dehesas are an agrosilvopastoral system that has enhanced the maintenance of an extraordinarily high biodiversity. The traditional use is characterized by mixed livestock raising at low stocking densities, employment of hardy regional breeds and an elaborated maintenance and exploitation of holm oaks. Livestock production has traditionally been accompanied by arable systems with long rotations and closed nutrient cycles without external inputs of fodder, fertilizers and agro-chemicals. Modern trends are a specialization toward lamb and beef production and the employment of intensive techniques like free-range grazing at high stocking levels or crossbreeding with high-performance breeds. A model income statement shows that livestock create an income of 49.91 US$ per ewe per year on an average basis. The central problem for the continuity of the dehesas is the gradual decay of the tree canopy. Intensification of agricultural production and the abandonment of traditional grazing practices additionally threaten biodiversity within the dehesas. The authors suggest the foundation of a biosphere reserve in Cuatro Lugares as a framework for a sustainable development of the dehesas.
The use of surrogate outcomes in model-based cost-effectiveness analyses: a survey of UK Health Technology Assessment reports

To explore the use of surrogate outcomes in Health Technology Assessment (HTA) and provide a basis for guidance for their future use, validation and reporting. This report focuses on the role of surrogate outcomes in cost-effectiveness models (CEMs) within UK HTA Programme reports.Reports published in the UK HTA Programme monograph series in 2005 and 2006 formed the sampling frame for this study.Reports were selected on the basis that they addressed a treatment effectiveness/efficacy question, that they included a CEM and that the CEM was primarily based on a surrogate outcome. Reports addressing diagnostic, screening, aetiology, prognostic and methodological questions were excluded. Information was extracted from included reports by two reviewers using a standardised proforma. Surrogate outcomes were assessed according to two published validation frameworks [Journal of the American Medical Association (JAMA) criteria and Outcomes Measures in Rheumatology Clinical Trials (OMERACT) scoring schema]. A narrative synthesis of findings is presented in the form of tabular summaries and illustrative qualitative quotations.A total of 35 UK HTA reports published in 2005 and 2006 addressed an effectiveness/efficacy question and contained a CEM. Of these, four were found to have based their CEM on a surrogate outcome. All four reports sourced treatment-related changes in surrogate outcomes through a systematic review of the literature; however, there was some variability in the consistency and transparency by which these reports provided evidence of the validation for the surrogate-final outcome relationship. Only one of the reports undertook a systematic review to specifically seek the evidence base for the association between surrogate and final outcomes. Furthermore, this was the only report to provide level 1 surrogate-final outcome validation evidence, i.e. RCT data showing a strong association between the change in surrogate outcome (BPAR) and the change in final outcome (graft survival) at an individual patient level. This report met the JAMA criteria for acceptable evidence of a surrogate. Two reports provided level 2 evidence, i.e. observational study data showing the relationship between the surrogate and final outcome, and one report provided level 3 evidence, i.e. a review of disease natural history. None of the four reports achieved a sufficient score on the OMERACT schema to be judged to have acceptable evidence of a surrogate outcome by its authors.In this survey of UK HTA reports about 10% of the CEMs therein were explicitly based on surrogate outcomes. The strength of evidence for the surrogate-final outcome relationship, transparency of quantification and exploration of uncertainty of this relationship were found to vary considerably. Recommendations are made for the use of surrogate outcomes in future HTA reports.
ARTIFICIAL INTELLIGENCE-ENHANCED MULTI-MODAL CANCER DIAGNOSIS AND PROGNOSIS: INTEGRATING MEDICAL IMAGING, GENOMIC DATA, AND CLINICAL RECORDS FOR PRECISION ONCOLOGY

Multimodal artificial intelligence (AI) methods are now a paradigm-shifting approach for cancer prognosis and diagnosis, allowing the blending of heterogeneous data modalities including histopathology images, radiomics, genomics, and clinical data. In this systematic review, the existing evidence regarding the application, integration methods, and performance measures of multimodal AI models in oncology is compiled. According to the PRISMA 2020 guideline, a wide literature search was performed on Scopus, Web of Science, IEEE Xplore, and PubMed for literature from January 2019 to May 2025. There were 1,280 records found, out of which 50 were found to meet the inclusion criteria and were examined. Data retrieved consisted of study attributes, data types, fusion methods, evaluation measures, and achieved performance. Descriptive synthesis indicated a consistent increase in multimodal AI articles from 2021, with the most prevalent integration strategy being hybrid fusion (42% of research), then late fusion (26%), early fusion (18%), transformer-based models (8%), and graph neural networks (6%). Comparative analysis demonstrated that transformer-based and hybrid models offered the highest mean area under the curve (AUC) and concordance index measures (0.93 and 0.91, and 0.89 and 0.88, respectively). Despite standout performance, heterogeneity of the data set, reproducibility, and limited external validation concerns were widely reported. This review identifies the promise of multimodal AI to improve diagnostic accuracy and prognosis prediction in cancer, with cautionary notes on the necessary standardised datasets, clear reporting, and validation in clinical use for the translation to standard care.