Search Results (3,424)

Background: Past studies demonstrate that certain facial features systematically affect first impressions of psychological traits. However, no previous studies have examined how individual differences in facial health affect first impressions of psychological traits. Methods: In this study, we asked a large sample of fairgoers to give their first impressions of psychological traits in response to viewing videos of unilateral facial paralysis patients with varying degrees of facial functioning. Then, we used linear mixed-effects regression models to understand how individual differences in facial health predict first impressions. Results: Our results replicate previous findings regarding first impressions of faces, such as the attractiveness halo effect, as well as age (maturity) and gender (masculinity) effects. More importantly, our results reveal that facial health, as measured by a clinician-graded scale, is a significant predictor of first impressions. Specifically, we found that individuals with better dynamic facial health (as assessed by clinicians) were perceived to be more competent and more affiliative, but not more dominant, than individuals with lower levels of dynamic facial functioning. Conclusions: Our results have important implications for personalized medicine via the development and refinement of individually tailored therapies to improve facial reanimation surgery outcomes. Full article

The increasing use of artificial intelligence (AI) in athlete health monitoring and injury prediction presents both technological opportunities and complex ethical challenges. This narrative review critically examines 24 empirical and conceptual studies focused on AI-driven injury forecasting systems across diverse sports disciplines, including professional, collegiate, youth, and Paralympic contexts. Applying an IMRAD framework, the analysis identifies five dominant ethical concerns: privacy and data protection, algorithmic fairness, informed consent, athlete autonomy, and long-term data governance. While studies commonly report the effectiveness of AI models—such as those employing decision trees, neural networks, and explainability tools like SHAP and HiPrCAM—few offers robust ethical safeguards or athlete-centered governance structures. Power asymmetries persist between athletes and institutions, with limited recognition of data ownership, transparency, and the right to contest predictive outputs. The findings highlight that ethical risks vary by sport type and competitive level, underscoring the need for sport-specific frameworks. Recommendations include establishing enforceable data rights, participatory oversight mechanisms, and regulatory protections to ensure that AI systems align with principles of fairness, transparency, and athlete agency. Without such frameworks, the integration of AI in sports medicine risks reinforcing structural inequalities and undermining the autonomy of those it intends to support. Full article

Tetracycline (TC), commonly utilized in medicine and aquaculture, frequently enters aquatic environments, raising ecological concerns. This study examined TC-contaminated wastewater treated through ultraviolet (UV), potassium persulfate (PS), and combined UV/PS disinfection processes. The degradation of TC followed pseudo-first-order kinetics, with removal efficiency ranked as UV/PS > UV > PS. High-performance liquid chromatography–mass spectrometry (HPLC-MS) identified 20 disinfection byproducts (DBPs) across all processes. Based on the identified intermediates, the degradation pathways of TC under different disinfection processes (UV, PS, and UV/PS) were elucidated. Using the ECOSAR program, both acute and chronic aquatic toxicities of TC and its DBPs were predicted. The biological effects on Chlorella were also investigated. DBPs from UV and PS treatments inhibited algal growth, reducing it by 4.8–9.4% relative to the control. Conversely, DBPs formed under UV/PS disinfection stimulated growth, increasing rates by 3.4–6.6%. To counteract oxidative stress from TC and its DBPs, Chlorella enhanced superoxide dismutase (SOD) and catalase (CAT) activities. These findings highlight that while TC degradation occurs efficiently, the nature of DBPs and their ecological impacts vary significantly depending on the disinfection method. Overall, the UV/PS process not only improved TC removal but also reduced harmful effects on microalgal growth compared with UV or PS alone. Full article

Introduction: Robotic splenectomy has emerged as a promising alternative to laparoscopic surgery, offering potential advantages in precision, ergonomics, and individualized surgical planning. In the context of personalized medicine, robotic technology may enable tailoring of surgical strategies to patient-specific anatomy, spleen size, and comorbid hematologic conditions. However, its clinical superiority remains uncertain due to limited and heterogeneous evidence. Methods: We performed a systematic review and meta-analysis following PRISMA guidelines, utilizing PubMed, CINAHL, Web of Science, and EMBASE databases to locate studies on robotic splenectomies in children. This review was prospectively registered in PROSPERO (CRD420251104285). Risk of bias was assessed using the ROBINS-I tool for non-randomized studies. Random-effects models were fitted using restricted maximum likelihood (REML), and confidence intervals were adjusted using either Knapp–Hartung (HKSJ) or modified Knapp–Hartung (mKH) methods when appropriate. 95% prediction intervals were calculated, and the certainty of evidence for each outcome was assessed using the GRADE approach. Results: This review included 272 pediatric patients from 16 studies conducted between 2003 and 2025, of which five were included in the meta-analysis. No statistically significant differences were observed between robotic and laparoscopic splenectomy for operative time, intraoperative blood loss, conversion to open surgery, blood transfusions, or complications. However, the direction of effect estimates consistently favored the robotic approach. A statistically significant reduction in hospitalization days (−0.93 days; 95% CI: −1.61 to −0.24; p = 0.01) was found, though this became marginally significant after HKSJ adjustment (p = 0.06). Intraoperative blood loss showed significance in the primary model (−63.88 mL; 95% CI: −120.38 to −7.38; p = 0.03), but not after mKH correction (p = 0.16). Heterogeneity was substantial-to-extreme for several outcomes and was only partially accounted for by leave-one-out sensitivity analyses. All findings were rated as very low certainty according to the GRADE framework. Conclusions: Robotic-assisted splenectomy in pediatric patients has been reported as technically feasible and performed safely in selected cases. However, the small number of studies, their retrospective design, substantial methodological heterogeneity, and the resulting very low certainty of the evidence according to GRADE preclude any firm conclusions about its comparative safety or efficacy versus laparoscopy. Well-designed prospective studies are needed to clarify its clinical benefits. Full article

Background: Methotrexate (MTX) is the most used anti-rheumatic drug for the treatment of early rheumatoid arthritis (ERA) patients, with an adequate response rate of only 30–40%. Thus, early detection of response failure is very crucial to prevent permanent disability. Objectives: We aimed to provide an update on the current evidence of potential predictive biomarkers of MTX treatment response (MTX-TR) in patients with ERA. Materials and methods: PubMed/MEDLINE, Scopus, EBSCO, and Cochrane Library were searched for studies that investigated a multitude of predictive metabolites of MTX-TR in ERA patients during the 2000–2024 period. This study was registered in PROSPERO (ID: CRD42024547651). Results: We determined that 31 out of 102 metabolites studied were the best predictive of MTX-TR in ERA, using clinical response (DAS28-ESR score). Our results on serum protein profiles revealed that higher pre-treatment levels of myeloid-related proteins, MTX–polyglutamates, choline, inosine, hypoxanthine, guanosine, nicotinamide, and diglyceride, and lower pre-treatment levels of N-methyl isoleucine, 2,3-dihydroxy butanoic acid, nor-nicotine, glucosylceramide, and itaconic acid, were associated with a good MTX-TR. However, lower baseline plasma itaconate and its derivatives and haptoglobin, but a higher baseline level of galactosylated glycans (FA2G) of IgG1, were associated with a good response to MTX. The results on immune cell biology indicated that higher pre-treatment of regulatory B cells, lower pre-treatment of Treg, and RDW were correlated with a good MTX-TR. The results on inflammatory biomarkers showed that a lower IL-1ra/IL1B ratio and IL-6 levels after MTX indicated a good response. Conclusions: This study provides an update on the current evidence of the potential predictive metabolites for the best MTX-TR in ERA patients. We revealed that few biomarkers resulted in a remission state of patients with ERA. These biomarkers are promising but not yet ready for routine clinical use; they warrant validation in larger prospective trials. We recommend that, for the implementation of personalized medicine, these biomarkers should be the first-line biomarkers for use in routine clinical practice after validation. Full article

This article represents a brief overview of the recent achievements in the treatment of multiple myeloma. New opportunities and treatment challenges are discussed in the context of risk factors regarding outcomes. The options for specific targeted treatments are discussed, and references are made to recent guidelines on the diagnosis and treatment of multiple myeloma. Full article

Pattern recognition and machine learning algorithms have become integral to modern drug discovery, offering powerful tools to uncover complex patterns in biomedical data. This article provides a comprehensive review of state-of-the-art pattern recognition techniques—including traditional machine learning (e.g., support vector machines), deep learning approaches, genome-wide association studies (GWAS), and biomarker discovery methods—as applied in pharmacogenomics and computational drug repurposing. We discuss how these methods facilitate the identification of genetic factors that influence drug response, as well as the in silico screening of existing drugs for new therapeutic uses. Two antiviral agents, ribavirin and lopinavir, are examined as extended case studies in the context of COVID-19, illustrating practical applications of pattern recognition algorithms in analyzing pharmacogenomic data and guiding drug repurposing efforts during a pandemic. We highlight successful approaches such as the machine learning-driven prediction of responders and the AI-assisted identification of repurposed drugs (exemplified by the case of baricitinib for COVID-19), alongside current limitations, including data scarcity, model interpretability, and translational gaps. Finally, we outline future directions for integrating multi-omics data, improving algorithmic interpretability, and enhancing the synergy between computational predictions and experimental validation. The insights presented highlight the promising role of pattern recognition algorithms in advancing precision medicine and accelerating drug discovery, while recognizing the challenges that must be addressed to fully realize their potential. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug development. Methods: This study synthesizes applications of PBPK models in FDA-approved drugs (2020–2024), systematically analyzing model utilization frequency, indication distribution, application domains and choice of modeling platforms, to reveal their substantive contributions to regulatory submissions. Additionally, we conducted an in-depth analysis of the PBPK models for 2024, classifying models into three tiers based on critical assessment of FDA reviewer comments. Results: Among 245 FDA-approved new drugs during this period, 65 NDAs/BLAs (26.5%) submitted PBPK models as pivotal evidence. Oncology drugs accounted for the highest proportion (42%). In application scenarios, drug–drug interaction (DDI) was predominant (81.9%), followed by dose recommendations for patients with organ impairment (7.0%), pediatric population dosing prediction (2.6%), and food-effect evaluation. Regarding modeling platforms, Simcyp^® emerged as the industry-preferred modeling platform, with an 80% usage rate. In terms of regulatory evaluation, a core concern for reviewers is whether the model establishes a complete and credible chain of evidence from in vitro parameters to clinical predictions. Conclusions: Detailed regulatory reviews demonstrate that although some PBPK models exhibit certain limitations and shortcomings, this does not preclude them from demonstrating notable strengths and practical value in critical applications. Benefiting from the strong support these successful implementations provide for regulatory decision-making, the technology is gaining increasing recognition across the industry. Looking forward, the integration of PBPK modeling with artificial intelligence (AI) and multi-omics data will unprecedentedly enhance predictive accuracy, thereby providing critical and actionable insights for decision-making in precision medicine and global regulatory strategies. Full article

Background/Objectives: Long non-coding RNA (lncRNA) was structurally similar to mRNAs, yet they could not be translated into proteins. While an increasing number of reports have systematically identified and described lncRNA in model species, information about non-model species remains scarce. Sea cucumber Holothuria leucospilota could be used for both medicinal and food purposes, which have high economic value, gradually attracting the attention of researchers. Methods: In this research, we constructed lncRNA library and compared the difference in lncRNA expression profiles between testis and ovary of sea cucumber H. leucospilota. To elucidate the molecular interactions between lncRNA and mRNA, we computationally predicted potential complementary binding sites through analysis of both cis- and trans-acting antisense mechanisms. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated that the identified target genes are potentially involved in the regulatory pathways governing gonad development. Results: Quantitative reverse transcription PCR analysis showed that MSTRG.32831.1-sox9 and MSTRG.57315.1-mthfr exhibited a high expression pattern in testis; while MSTRG.11041.1-mafa and MSTRG.11074.1-macf1 showed a high expression pattern in the ovary. Conclusions: Deciphering lncRNA–mRNA expression patterns may uncover fundamental principles governing reproductive regulation in marine invertebrates. This discovery not only deepens understanding in this field but also provides valuable comparative insights for developmental biology. Full article

NOD-like receptor protein 3 (NLRP3) inflammasome is a key mediator of inflammation and a promising therapeutic target. However, the discovery of novel and effective inhibitors of NLRP3 remains limited. A combined docking-based virtual screening (DBVS) and shape-based screening approach was applied to eight traditional Chinese medicine (TCM) databases to identify potential NLRP3 inhibitors. Structural similarity analysis, ADMET prediction, and molecular dynamics (MD) simulations were performed to evaluate structural novelty, pharmacokinetic properties, and binding stability. A total of 25 potential NLRP3 inhibitors were identified, each exhibiting docking scores higher than those of the reference inhibitor XE3. Structural similarity analysis revealed that the screened compounds exhibited low similarity to previously reported NLRP3 inhibitors, demonstrating their structural novelty. ADMET evaluation indicated that compounds C2, C3, and C4 exhibited favorable physicochemical and pharmacokinetic properties. Molecular dynamics (MD) simulations demonstrated that the complexes of compounds C2, C3, and C4 with NLRP3 remained stable throughout the simulations, exhibiting limited backbone fluctuations and compact conformations, as indicated by Rg values of approximately 6 Å. Solvent-accessible surface area (SASA) and polar surface area (PSA) analyses suggested that compounds C3 and C4 were tightly solvated and maintained favorable membrane permeability. Notably, binding free energy calculations revealed that all three compounds exhibited stronger binding than XE3, with compound C3 showing the most favorable energy (–48.81 ± 3.89 kcal/mol), indicating a highly stable and energetically preferred interaction with NLRP3. This study identified promising TCM-derived compounds as potential NLRP3 inhibitors, offering new directions for anti-inflammatory drug development. Full article

Background: Internists formulate diagnostic hypotheses and personalized treatment plans by integrating data from a comprehensive clinical interview, reviewing a patient’s medical history, physical examination and findings from complementary tests. The patient treatment life cycle generates a significant volume of data points that can offer valuable insights to improve patient care by guiding clinical decision-making. Artificial Intelligence (AI) and, in particular, Generative AI (GAI), are promising tools in this regard, particularly after the introduction of Large Language Models. The European Federation of Internal Medicine (EFIM) recognizes the transformative impact of AI in leveraging clinical data and advancing the field of internal medicine. This position paper from the EFIM explores how AI can be applied to achieve the goals of P6 Medicine principles in internal medicine. P6 Medicine is an advanced healthcare model that extends the concept of Personalized Medicine toward a holistic, predictive, patient-centered approach that also integrates psycho-cognitive and socially responsible dimensions. An additional concept introduced is that of Digital Therapies (DTx), software applications designed to prevent and manage diseases and disorders through AI, which are used in the clinical setting if validated by rigorous research studies. Methods: The literature examining the relationship between AI and Internal Medicine was investigated through a bibliometric analysis. The themes identified in the literature review were further examined through the Delphi method. Thirty international AI and Internal Medicine experts constituted the Delphi panel. Results: Delphi results were summarized in a SWOT Analysis. The evidence is that through extensive data analysis, diagnostic capacity, drug development and patient tracking are increased. Conclusions: The panel unanimously considered AI in Internal Medicine as an opportunity, achieving a complete consensus on the matter. AI-driven solutions, including clinical applications of GAI and DTx, hold the potential to strongly change internal medicine by streamlining workflows, enhancing patient care and generating valuable data. Full article

Animal diseases remain a major constraint to livestock productivity and public health, necessitating accurate, early diagnostic methods. This review examines the classification and mechanisms of diagnostic, prognostic, and predictive biomarkers in veterinary medicine and evaluates how advanced technologies enable their discovery. Mechanistically, biomarkers function as molecular indicators of disease presence, progression, or therapeutic response, and are essential in species where clinical signs often appear late or are non-specific. We detail the contribution of high-throughput omics platforms, genomics (NGS, RNA-Seq), proteomics (LC-MS/MS, DIGE), and metabolomics (NMR, LC-MS/MS) in identifying disease-specific molecular signatures. Emerging technologies, including CRISPR/Cas9, AI-enhanced imaging, aptamer-based biosensors, and microfluidic devices, show significant diagnostic potential. Case studies, including canine melanoma, bovine respiratory disease complex (BRDC), and congenital portosystemic shunts in dogs, illustrate the real-world applicability of biomarkers. Challenges such as a lack of standardization, species variability, and poor clinical translation are acknowledged. The review concludes that integrating biomarker mechanisms with advanced analytical technologies is key to advancing veterinary diagnostics and disease control. Full article

Human immunodeficiency virus (HIV-1) and SARS-CoV-2 continue to co-burden global health, motivating discovery of broad-spectrum small molecules. Conessine, a steroidal alkaloid, has reported membrane-active and antimicrobial properties but remains underexplored as a dual antiviral chemotype. To interrogate conessine’s multi-target antiviral potential against key enzymatic and entry determinants of HIV-1 and SARS-CoV-2 and to benchmark performance versus approved comparators. Eight targets were modeled: HIV-1 reverse transcriptase (RT, 3V81), protease (PR, 1HVR), integrase (IN, 3LPT), gp120–gp41 trimer (4NCO); and SARS-CoV-2 main protease (M^pro, 6LU7), papain-like protease (PL^pro, 6W9C), RNA-dependent RNA polymerase (RdRp, 7BV2), spike RBD (6M0J). Ligands (conessine; positive controls: dolutegravir for HIV-1, nirmatrelvir for SARS-CoV-2) were prepared with standard protonation, minimized, and docked using AutoDock Vina v 1.2.0exhaustiveness 4; 20 poses). Binding modes were profiled in 2D/3D. Protocol robustness was verified by re-docking co-crystallized ligands (RMSD ≤ 2.0 Å). Atomistic MD (explicit TIP3P, OPLS4, 300 K/1 atm, NPT; 50–100 ns) assessed pose stability (RMSD/RMSF), pocket compaction (Rg, volume), and interaction persistence; MM/GBSA provided qualitative energy decomposition. ADMET was predicted in silico. Conessine showed coherent, hydrophobically anchored binding across both viral panels. Best docking scores (kcal·mol⁻¹) were: HIV-1—PR −6.910, RT −6.672, IN −5.733; SARS-CoV-2—spike RBD −7.025, M^pro −5.745, RdRp −5.737, PL^pro −5.024. Interaction maps were dominated by alkyl/π-alkyl packing to catalytic corridors (e.g., PR Ile50/Val82, RT Tyr181/Val106; M^pro His41/Met49; RBD L455/F486/Y489) with occasional carbon-/water-mediated H-bonds guiding orientation. MD sustained low ligand RMSD (typically ≤1.6–2.2 Å) and damped RMSF at catalytic loops, indicating pocket rigidification; MM/GBSA trends (≈ −30 to −40 kcal·mol⁻¹, dispersion-driven) supported persistent nonpolar stabilization. Benchmarks behaved as expected: dolutegravir bound strongly to IN (−6.070) and PR (−7.319) with stable MD; nirmatrelvir was specific for M^pro and displayed weaker, discontinuous engagement at PL^pro/RdRp/RBD under identical settings. ADMET suggested conessine has excellent permeability/BBB access (high logP), but liabilities include poor aqueous solubility, predicted hERG risk, and CYP2D6 substrate dependence.Conessine operates as a hydrophobic, multi-target wedge with the most favorable computed engagement at HIV-1 PR/RT and the SARS-CoV-2 spike RBD, while maintaining stable poses at M^pro and RdRp. The scaffold merits medicinal-chemistry optimization to improve solubility and de-risk cardiotoxicity/CYP interactions, followed by biochemical and cell-based validation against prioritized targets. Full article

Background: Age is a well-established determinant of sepsis outcomes, often integrated into severity scoring systems. However, most studies focus on critically ill patients in intensive care units (ICUs), with limited insight into how age influences mortality in non-ICU settings, particularly across the full adult lifespan. Objective: To investigate the relationship between age and in-hospital mortality in patients with sepsis hospitalized in internal medicine wards, using age-stratified logistic and spline regression models. Methods: We conducted a retrospective, multicenter cohort study involving 4300 adult patients admitted to internal medicine wards at eight academic hospitals affiliated with Clalit Health Services in Israel between December 2001 and October 2020. All patients were diagnosed with sepsis during hospitalization and died during their hospital stay. Patients were stratified into seven age groups (18–34, 35–44, 45–54, 55–64, 65–74, 75–84, >85 years). Logistic regression identified age-specific comorbidities associated with mortality. Adjusted spline regression models were used to estimate mortality probabilities across age ranges. Results: The cohort had a mean age at death of 78.84 years, and 51.7% were female. Mortality probability increased with age but demonstrated non-linear trends. Sharp fluctuations in predicted mortality were observed in middle-aged groups (especially ages 45–54), with peaks not captured in conventional binary or linear models. Hematologic and solid neoplasms were strongly associated with mortality in younger groups, while cardiovascular comorbidities such as heart failure and atrial fibrillation were more prominent in older adults. Conclusions: Age is a major determinant of in-hospital mortality in septic patients on internal medicine wards, but its effect is non-linear and age-specific. Our findings highlight a unique population of patients with severe sepsis not managed in critical care settings and underscore the need for more nuanced, age-stratified risk assessment models outside of the ICU. Full article

This article examines the role of fairness in software across diverse application contexts, with a particular emphasis on healthcare, and introduces the concept of algorithmic (individual) meta-fairness. We argue that attaining a high degree of fairness—under any interpretation of its meaning—necessitates higher-level consideration. We analyze the factors that may guide the choice of a fairness definition or bias metric depending on the context, and we propose a framework that additionally highlights quality criteria such as accountability, accuracy, and explainability, as these play a crucial role from the perspective of individual fairness. A detailed analysis of requirements and applications in healthcare forms the basis for the development of this framework. The framework is illustrated through two examples: (i) a specific application to a predictive model for reliable lower bounds of BRCA1/2 mutation probabilities using Dempster–Shafer theory, and (ii) a more conceptual application to digital, feature-oriented healthcare twins, with the focus on bias in communication and collaboration. Throughout the article, we present a curated selection of the relevant literature at the intersection of ethics, medicine, and modern digital society. Full article