Search Results (619)

Metformin, widely prescribed for type 2 diabetes mellitus (T2D), has emerged as a systemic immunomodulator with effects that extend far beyond glycemic control. Recent advances in immunometabolism reveal that metformin modulates innate immune responses through coordinated cellular metabolic reprogramming and epigenetic modification, which collectively modulate the functional phenotype of innate immune cells. This narrative review summarizes current evidence regarding the immunomodulatory effects of metformin on the innate immune system, with a focus on immunometabolism and epigenetic regulation. It explores how metformin modulates innate immunity by altering cellular energy sensing, mitochondrial function, and nutrient utilization. Such metabolic changes and alterations further reshape chromatin structure and architecture, as well as transcriptional profiles and programs. Through the regulation of glycolysis, fatty acid oxidation, and histone modification landscapes, metformin regulates the phenotypes of innate immune cells, which can be pro-inflammatory, tolerogenic, or homeostatic. This conceptual framework presents a new understanding of metformin. As well as acting as an anti-inflammatory agent, it may regulate immune memory. Full article

Background/Objectives: Respiratory tract symptoms, urinary symptoms, and acute fevers frequently prompt emergency urgent care visits. Distinguishing bacterial from viral or non-bacterial etiology remains difficult because clinical features overlap and laboratory microbiological tests are often non-specific or delayed. The MeMed BV^® test is a rapid host-response assay that combines TRAIL, IP-10, and CRP into a composite score to differentiate between bacterial and viral/non-bacterial infections within 15 min. The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy and clinical utility of the MeMed BV test in adults and children with suspected respiratory tract infections, urinary tract infections, and undifferentiated fever. Methods: The review followed PRISMA-DTA guidelines. Medline, Embase, CINAHL, and the Cochrane Library databases were searched. The risk of bias was assessed using the QUADAS-2, Cochrane RoB 2.0, ROBINS-I, and JBI tools. Where appropriate, meta-analyses were performed using a bivariate random-effects or HSROC model. Results: Sixteen studies (12 diagnostic test accuracy (DTA) studies and four non-DTA studies) were included. The pooled sensitivity was 91% (95% CI: 86–94%), and specificity was 92% (95% CI: 91–93%), with consistent accuracy in adults (Sensitivity 93%/Specificity 91%) and children (Sensitivity 88%/Specificity 93%). The non-DTA studies demonstrated that MeMed BV-guided management improved antibiotic stewardship: antibiotics were prescribed in 20.6% of viral versus 73.2% of bacterial cases, and clinician adherence to MeMed BV results reached 75–80%. Conclusions: The MeMed BV test demonstrates consistently high diagnostic accuracy and is associated with improved antibiotic decision-making, supporting its integration into clinical workflows. Full article

Pediatric drug therapy remains fundamentally challenged by profound interindividual variability driven by dynamic development, genetic, and environmental factors. Although dosing strategies based on age, body weight, or body surface area remain important starting points in pediatric pharmacotherapy, they may not fully capture ontogeny-dependent variability in drug disposition and response. Consequently, clinically relevant differences in efficacy and toxicity may still occur among children receiving similar weight-adjusted doses. Pharmacogenomics offers a promising framework for individualized therapy; however, its clinical translation in pediatrics is limited by developmental variability in gene expression and enzyme activity. Emerging evidence highlights the pivotal role of epigenetic regulation, including DNA methylation, histone modifications, and microRNAs, in modulating pharmacogenetic expression across developmental stages, thereby reshaping drug response trajectories. Concurrently, advances in artificial intelligence and next-generation sequencing enable integration of multidimensional datasets, facilitating predictive modeling of drug efficacy and toxicity. This narrative review provides a comprehensive synthesis of developmental pharmacology, pharmacogenomics, and epigenetic mechanisms, while critically evaluating current translational gaps and implementation challenges. Importantly, it proposes an integrative precision framework that incorporates genetic, epigenetic, and computational insights to optimize pediatric pharmacotherapy. By bridging mechanistic biology with emerging digital health technologies, this work advances a paradigm shift from empirical prescribing toward predictive, adaptive, and individualized therapeutic strategies. The proposed approach holds significant potential to enhance clinical outcomes, minimize adverse effects, and accelerate the realization of precision medicine in pediatric populations. Full article

Background: Exercise prehabilitation may improve physiological resilience before colorectal cancer (CRC) surgery. However, patient acceptance, reflected by recruitment, retention and adherence, is variably reported. Understanding how acceptance is captured and described is essential for designing effective, equitable interventions. Objectives: Map how CRC prehabilitation programmes report recruitment, retention and adherence, and identify characteristics associated with high acceptance. Methods: A scoping review was conducted following published guidance. MEDLINE, Embase, CINAHL, PsycINFO and Cochrane databases were searched. Studies of unimodal or multimodal prehabilitation interventions including an exercise component were included. Data relating to recruitment processes, retention, adherence and engagement-enhancing strategies were extracted and summarised using descriptive and content analysis. Reporting quality and variation were mapped. Results: Thirty-four studies were included: 15 randomised controlled trials, 12 prospective cohorts, four retrospective comparative cohorts, two non-randomised trials, and one quality-improvement project. Recruitment rates varied widely (3.8% to >90%), with four studies not reporting the proportion of eligible patients who declined and no study providing demographic characterisation of patients not recruited. Retention was reported in 31 of 34 studies and was generally high, including seven studies reporting 100% retention, although no consistent definition was used. Adherence was not reported in nine studies; among those reporting it, supervised programmes achieved attendance rates of 68–100% and unsupervised programmes 78–98%. Only four studies quantified adherence to prescribed exercise intensity or volume. No consistent association emerged between programme format (location, supervision, and digital support) and patient acceptance. Conclusions: Substantial variability exists in how CRC prehabilitation studies report recruitment, retention and adherence, constraining understanding of acceptance. Future research should prioritise standardised, detailed acceptance reporting and consider behaviour change theory informed, patient-centred intervention design to ensure effective and equitable CRC prehabilitation. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in clinical decision-support systems, yet its adoption in low- and middle-income countries, including Jordan, remains limited and underexplored. Understanding how healthcare professionals perceive AI-assisted clinical decision-making is essential for safe and contextually appropriate implementation. This study explored healthcare professionals’ perceptions of AI-assisted clinical decision-making in Jordan, with particular attention to trust, accuracy, accountability, professional judgement, digital literacy, and health-system readiness. Medication-related safety and prescribing concerns were examined as secondary cross-cutting issues where they emerged from participants’ accounts. Methods: A qualitative study was conducted using semi-structured, in-depth interviews with 22 purposively sampled healthcare professionals from public, private, and university-affiliated healthcare institutions in Amman, Irbid, and Zarqa. Participants included physicians, nurses, pharmacists, and allied health professionals with varied specialties and levels of seniority. Data were analysed using Braun and Clarke’s reflexive thematic analysis. Member checking, peer debriefing, reflexive memos, and audit trails were used to enhance trustworthiness, and reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Results: Eight overarching themes were identified: conditional trust in AI-assisted clinical decision-making; concerns regarding accuracy and confident algorithmic errors; accountability and professional responsibility; AI as an adjunct rather than a substitute for clinical judgement; the influence of experience, specialty, and digital literacy on AI acceptance; Jordanian health-system readiness; privacy, confidentiality, and algorithmic bias; and training requirements for safe AI use. Medication-related safety emerged as a cross-cutting concern, particularly in relation to dosing, polypharmacy, drug–drug and drug–herb interactions, and the risk of over-reliance on AI-generated recommendations. Conclusions: Healthcare professionals in Jordan expressed cautious but constructive views toward AI-assisted clinical decision-making. AI was perceived as potentially useful when used to support, rather than replace, professional judgement. Participants’ accounts suggest that safe implementation depends on local validation, clear accountability frameworks, ethical data governance, interprofessional training, and careful consideration of medication-safety expertise where AI tools influence prescribing or therapeutic decisions. These findings highlight the importance of context-sensitive AI governance strategies that support trustworthy, accountable, and professionally supervised AI adoption in healthcare. Full article

Pediatric respiratory infections remain among the leading causes of emergency department visits, hospitalization and pediatric intensive care unit (PICU) admission. Although most acute respiratory infections in children are viral, clinical manifestations overlap substantially among viral, bacterial and atypical pathogens, creating diagnostic uncertainty and promoting empirical antimicrobial use. Rapid antigen tests, nucleic acid amplification tests, multiplex respiratory panels and metagenomic sequencing have expanded the ability to detect pathogens within clinically actionable timeframes. However, evidence from pediatric emergency trials indicates that rapid pathogen detection alone does not necessarily reduce antibiotic prescribing or healthcare costs. These findings suggest that the value of rapid diagnostics depends less on analytical breadth than on whether testing is applied to the right child, in the right clinical scenario and within a predefined decision pathway. This narrative review reorganizes the evidence around a scenario-driven top-pathogen framework. Top pathogens are defined as organisms that, in a specific age group, syndrome, season or care setting, have high prevalence, severe disease potential, transmissibility, treatment implications, antimicrobial resistance relevance or infection-control value. We discuss how top-pathogen testing should differ across emergency triage, inpatient ward management, severe pneumonia, PICU care, hospital-acquired pneumonia, ventilator-associated pneumonia and outbreak settings. We further examine the economic mechanisms through which rapid testing may generate value, including reduced unnecessary antibiotics, timely antiviral therapy, optimized isolation, shorter length of stay, reduced repeated testing and prevention of healthcare-associated transmission. Finally, we propose implementation principles centered on diagnostic stewardship, antimicrobial stewardship, local epidemiology and real-world cost-effectiveness evaluation. A scenario-driven top-pathogen strategy may provide a practical bridge between broad syndromic testing and precision infectious disease management in children. Full article

Background: The COVID-19 pandemic was associated with extensive empirical antibiotic exposure despite the predominantly viral etiology of the disease. Evaluating patterns of azithromycin use, prescribing determinants, and appropriateness provides insight into real-world antibiotic use under conditions of diagnostic uncertainty. Methods: We performed a retrospective cohort study including 3200 adult patients hospitalized with laboratory-confirmed COVID-19 at the General Hospital in Ferizaj between June 2020 and June 2022. Demographic characteristics, comorbidities, markers of disease severity, laboratory parameters, and antibiotic exposure were extracted from medical records. Azithromycin prescriptions were classified as appropriate, potentially appropriate, or inappropriate based on predefined clinical and laboratory indicators suggestive of bacterial co-infection. Drug utilization patterns, temporal trends, and independent determinants of inappropriate prescribing were assessed using descriptive analyses and multivariable logistic regression modeling. Results: Among 3200 hospitalized patients, 1968 (61.5%) received azithromycin. Of these prescriptions, 612 (31.1%) were classified as appropriate, 418 (21.3%) as potentially appropriate, and 938 (47.6%) as inappropriate. The proportion of inappropriate use decreased over time, from 52.4% in 2020–2021 to 38.7% in 2022 (p < 0.001). However, a substantial proportion of prescriptions remained inappropriate throughout the study period. In multivariable analysis, absence of laboratory markers suggestive of bacterial infection (OR 2.41; 95% CI 1.98–2.93), concomitant use of more than one antibiotic (OR 1.67; 95% CI 1.32–2.11), and lower clinical severity at admission (OR 1.54; 95% CI 1.21–1.95) were independently associated with inappropriate azithromycin prescribing. Conclusions: Azithromycin use was frequent among hospitalized adults with COVID-19, and a considerable proportion of prescriptions lacked clinical or laboratory justification. Although prescribing patterns changed over time, the persistence of inappropriate use highlights ongoing challenges in aligning antibiotic use with emerging evidence. These findings contribute to the understanding of antibiotic utilization patterns in acute care settings and underscore the importance of integrating objective diagnostic indicators into antimicrobial decision-making to strengthen stewardship practice. Full article

Approximately 10% of the general population reports a penicillin allergy, making it one of the most commonly documented drug allergies in clinical practice. Yet formal evaluation confirms true hypersensitivity in fewer than 10% of these cases. This gap has practical consequences. Patients who carry an inaccurate allergy label are more likely to receive broader-spectrum alternative antibiotics, with downstream effects on cost, adverse drug events, and antimicrobial resistance. Although primary care physicians are often the first to record these labels and the ones who face their consequences most often in daily prescribing, they have remained peripheral to most systematic delabeling efforts. In this narrative review, we examine how antibiotic allergy labels arise, why they persist, and what they cost—clinically, economically, and from a stewardship perspective. We also discuss emerging approaches to reassessment in primary care, including risk stratification tools and international guideline recommendations, along with the possible role of digital health tools and patient education in improving the accuracy of allergy documentation. Full article

Introduction: Musculoskeletal conditions impose a substantial economic burden on the United States (U.S.) healthcare system, but contemporary national estimates of condition-specific spending for common orthopaedic conditions remain limited. This study utilized the 2023 Medical Expenditure Panel Survey (MEPS) to estimate the national prevalence, condition-specific expenditures, and payer distribution for treated knee injuries and shoulder disorders. Methods: Adults with treated knee injuries or shoulder disorders were identified using ICD-10-CM codes from the MEPS Medical Conditions File. Condition-specific expenditures were estimated by linking diagnoses to medical events and payments using the MEPS Condition–Event Link File. Expenditures were aggregated across inpatient, outpatient, office-based, emergency, home health, and prescribed medicine categories. Survey-weighted analyses were used to estimate national prevalence, mean expenditures, service-level spending patterns, and payer distributions. Survey-weighted Gamma generalized linear models with log link were used to examine patient characteristics associated with expenditures among the U.S. civilian noninstitutionalized population with positive condition-specific spending. Results: The analysis identified 2.55 million adults with treated knee injuries and 2.58 million adults with treated shoulder disorders. Mean annual condition-specific expenditures per person were higher for knee injuries ($10,552; 95% CI: $6128–$14,975) than for shoulder disorders ($4310; 95% CI: $3337–$5283). Knee injury expenditures were concentrated in inpatient and home health care, whereas shoulder disorder expenditures were concentrated in outpatient and office-based care. Private insurance, Medicare, out-of-pocket payments, and Worker’s Compensation each contributed to the financial burden, with payer distributions varying by condition. In adjusted models, fair/poor self-rated health and female sex were associated with higher knee injury expenditures, while no covariates were statistically significant for shoulder disorder expenditures. Conclusions: Treated knee injuries and shoulder disorders showed distinct condition-specific expenditure profiles across care settings and payer sources. These findings provide contemporary national benchmarks for orthopaedic spending and may support future research, utilization monitoring, and value-based reimbursement planning. Full article

Parabolic solar dish–Stirling (PSDS) technologies are among the most efficient solar-to-electric conversion options, but their system-level modeling remains challenging because optical losses, receiver heat losses, package leakage, and Stirling engine non-idealities are strongly coupled under variable operating conditions. This study develops a modular, energy-consistent system-level framework that couples dish receiver optics and thermal behavior, hot-end package losses, and a non-ideal Stirling engine under variable charge (Qu-mode) control. The key novelty is a receiver engine heat-matching formulation in which receiver temperature, useful heat, working gas charge/mean pressure, and engine output emerge from a closed energy balance rather than from prescribed hot-side temperature, fixed heat input, or prescribed mean pressure. The framework was benchmarked in stages against the Mendoza receiver formulation, GPU-3/LeRC Stirling engine data, and EuroDish dispatch-level measurements. At the integrated EuroDish level, it reproduced heat input, cooler rejection, and net electrical output with mean absolute percentage errors of 2.90%, 4.07%, and 4.28%, respectively, while preserving explicit traceability of optical, receiver, package, engine, generator, and parasitic losses. A receiver formulation comparison showed that the final receiver treatment reduced the cooler rejection MAPE from 8.11% to 4.07% relative to the Mendoza-type receiver swap baseline. A limited-input transferability study for representative pressure-controlled dish–Stirling platforms retained peak power and efficiency within a ±10% envelope for the quantitatively assessed cases. Parametric studies further showed a broad engine speed optimum, a heat exchanger sizing trade-off governed by conductance and pumping/friction losses, stronger sensitivity to ambient temperature than wind over the tested EuroDish range, and cooling boundary effects that redirect fixed thermal input from electricity to rejected heat. The resulting framework provides a compact predictive basis for loss diagnosis, design studies, and control-oriented evaluation of PSDS units. Full article

Independent community pharmacies serve as critical healthcare access points in rural areas, yet they consistently underperform chain pharmacies on contraceptive access measures. This narrative commentary draws on mystery caller studies, implementation research, and policy analyses to examine pharmacy-based contraceptive access in the United States (US). Using emergency contraception (EC) as a case study, we show that independent pharmacies stock EC at dramatically lower rates than chains (e.g., 14.6% vs. 76.3% in West Virginia), provide less accurate information about purchase requirements and timing, and impose more barriers to access. Because independent pharmacies account for 76.5% of pharmacies in rural areas, this disparity concentrates contraceptive inaccessibility in communities already facing the highest rates of unintended pregnancy, maternal mortality, and maternity care deserts. This pattern extends beyond EC to pharmacist-prescribed contraception and over-the-counter daily oral contraceptives. These disparities reflect systemic barriers, such as inadequate reimbursement, limited training infrastructure, and absence of corporate support, rather than failures of individual pharmacies. Drawing on implementation research and the success of West Virginia’s COVID-19 vaccination model, this paper proposes coordinated, sector-specific strategies to transform independent pharmacies from barriers into bridges for rural women’s contraceptive access. Full article

Background: Antimicrobial resistance (AMR) is a growing global health threat, disproportionately affecting low- and middle-income countries such as Sudan. Conflict-related health system disruption has further intensified inappropriate antibiotic use and weakened stewardship capacity. Objective: This narrative review synthesizes contemporary evidence on antibiotic prescribing practices in Sudan, with emphasis on ambulatory care, and examines their implications for AMR control in the context of ongoing conflict. Methods: A non-systematic, structured narrative review was conducted successfully. PubMed, Google Scholar, WHO/EMRO databases, and Sudan’s National Action Plan (NAP) materials were examined for literature published between January 2010 and December 2025. Peer-reviewed research, government guidelines, surveillance reports, and gray literature were among the eligible sources. A total of 78 studies were included after titles, abstracts, and full texts were screened. Two reviewers independently confirmed the data extraction, and the synthesis aligned with SANRA guidelines. Results: Antibiotic prescribing in Sudan is frequently inappropriate, particularly in ambulatory and community settings, where empirical treatment, polypharmacy, and extensive use of Watch antibiotics are common. Alignment with WHO AWaRe recommendations remains suboptimal. Recent clinical and molecular evidence demonstrates increasing multidrug resistance among Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus, including ESBL production and emerging carbapenemase genes (e.g., NDM 1, IMP 1). Conflict-related disruptions—such as reduced laboratory capacity, supply chain breakdown, and unregulated community dispensing—have further accelerated AMR. Pilot stewardship interventions show promise but remain limited in scale. Conclusions: Inappropriate antibiotic use in Sudan is driven largely by ambulatory and community practices and has been exacerbated by conflict. Strengthening stewardship beyond hospitals, enforcing prescription-only regulations, operationalizing the AWaRe framework, and aligning empirical therapy with local resistance patterns are critical for mitigating AMR in Sudan and similar conflict-affected settings. Full article

Conversational AI agents are now a routine touchpoint in e-commerce customer service, and AI empathy has emerged as the headline humanization strategy for repairing relational damage during service failures. A growing evidence base reports that empathic AI often backfires, because consumers cannot reconcile felt warmth with their lay model of what an artificial agent is. This research asks under what conditions AI empathy can be made credible to consumers. We propose that mechanistic interpretability, operationalized in the present studies as a consumer-facing visualization of an AI agent’s internal emotion-vector activations designed in the style of mechanistic-interpretability research, operates as a costly authenticity signal that rehabilitates empathic AI by enabling an attributional shift along the experience dimension of mind perception. Signaling Theory carries the antecedent stage of the causal chain, where mechanistic interpretability serves as a verifiable cue of computational authenticity. Mind Perception Theory carries the downstream stage, where the authenticated empathy is converted into consumer-brand intimacy. Two between-subjects experiments preceded by a feasibility pilot tested the account on Mainland Chinese consumers recruited via the Credamo online panel. Study 1 used a single-factor design contrasting high versus low AI empathy. Study 2 used a two (AI empathy) by two (mechanistic interpretability) full factorial. Study 1 showed a pattern consistent with high (versus low) AI empathy lowering brand intimacy through reduced perceived authenticity. Study 2 replicated the AI-empathy backfire when interpretability was absent, reversed the sign of the AI-empathy slope on the perceived-authenticity mediator when interpretability was present, and neutralized the negative conditional indirect effect on brand intimacy through perceived authenticity. The findings introduce mechanistic interpretability to consumer-marketing scholarship as a manipulable signaling channel, document a structural reversal in the mediator-stage slope coupled with neutralization of the indirect effect on the relational outcome, and prescribe pairing empathic AI phrasing with mechanistic-transparency design rather than deploying empathy without an accompanying transparency cue. Full article

Background/Objectives: Antimicrobial resistance (AMR) represents one of the most pressing global public health challenges, with inappropriate antibiotic prescribing being a major contributor. In Hungary, general practitioners (GPs) account for over 70% of all antibiotic prescriptions, yet limited research has examined the complex relationship between patient expectations and physician prescribing behavior. This study explores general practitioners’ antibiotic prescribing practices and their perceptions of patient expectations. Methods: A cross-sectional study was conducted among 181 GPs in Hungary from March 2024 to April 2025. The sample is representative of the northeastern region of Hungary. Participants completed anonymous paper-based questionnaires assessing their self-reported professional knowledge, perceived patient expectations, prescribing behavior, and antibiotic stewardship practices. Results: Most respondents recognized antimicrobial resistance as a significant public health issue (81.7%, n = 147); however, only 52.2% (n = 94) felt capable of effectively taking action against it. While 80.6% (n = 145) reported that patients expect antibiotic prescriptions and 71.1% (n = 128) experienced conflicts over prescribing refusals at least once within the previous six months, 87.2% (n = 157) stated they do not yield to patient pressure. Concerning patterns emerged: 56.1% (n = 101) reported completing patient-initiated antibiotic courses, 36.1% (n = 65) admitted to “just-in-case” prescribing at least once within the past six months, and 38.9% (n = 70) encountered self-medication despite regulations restricting antibiotics to prescription-only use. Only 17.8% (n = 32) regularly monitored their antibiotic prescribing indicators. Physicians with multiple specialty qualifications reported less frequent patient education and more conflicts (p = 0.010). Conclusions: General practitioners demonstrate resilience despite substantial patient pressure; however, self-medication and defensive prescribing practices reveal important gaps in antimicrobial stewardship. Targeted, multifaceted interventions addressing both prescriber behavior and systemic vulnerabilities are needed to strengthen stewardship efforts. Full article

In the context of Kolmogorov complexity, the complexity of an object can be characterized by the length of the shortest algorithm required to describe or compute it. In condensed matter systems under equilibrium and nonequilibrium conditions, macroscopic properties distinct from elementary ones can emerge from large numbers of particles. Such a large system size allows system properties to change as control parameters change, producing phase transitions. Inspired by this analogy, it is natural to consider that an optimized computing system may undergo a transition from an initially random organization to a functionally organized state. In this paper, by adopting a specific multi-task setting based on a typical dynamical system, we propose an evolutionary echo state network that realizes the functional differentiation of a random neural network into two subnetworks—one specialized for memory and the other for program execution. The model suggests a minimal neural mechanism for dynamic processes that extract rules embedded in input sequences and store information over short or long time scales. Because the proposed evolutionary model is driven by constraints that jointly reduce task errors and structural redundancy, the resulting network can be regarded as an optimized descriptor of memory and program functions. To clarify the relationship between the proposed model and algorithmic complexity, we introduce Lempel–Ziv-based complexity indices for both network structure and node-wise reservoir dynamics. Although neither density, effective spectral radius, nor the Lempel–Ziv-based complexity indices were prescribed or optimized in the eESN, the evolved network exhibited structural complexity comparable to conventional ESNs in the corresponding region and slightly higher dynamical complexity. These results suggest that the advantage of eESN is not attributable to a direct maximization of complexity itself, but rather to the evolutionary organization of complexity into a functionally differentiated reservoir that supports both memory-like retention and program-like rule extraction. Full article