Search Results (4,140)

Background: The conventional definition of diabetes mellitus is based on the presence of hyperglycemia. However, growing evidence indicates that key pathophysiological processes—particularly β-cell dysfunction—occur well before any measurable increase in blood glucose. These early changes remain undetected by standard diagnostics but are clinically significant. Objective: This work introduces the proposed concept of normoglycemic diabetes, defined as a pathophysiological state in which conventional glucose criteria remain normal while measurable evidence of early β-cell dysfunction is already present. Particular emphasis is placed on intact proinsulin as a marker of impaired proinsulin processing and β-cell secretory stress. Methods: A staged model of declining functional β-cell mass is presented, ranging from compensated β-cell stress during normoglycemia to overt secretory failure and dysglycemia, incorporating genetic, epigenetic, and environmental influences. Diagnostic strategies, including extended OGTT assessment and intact proinsulin quantification, and their potential preventive implications are discussed. Results: Early impairment of functional β-cell mass can be detectable while fasting glucose, HbA1c, and conventional glucose tolerance criteria remain normal. Intact proinsulin and proinsulin-related ratios may provide information on impaired hormone processing and secretory stress before overt hyperglycemia develops. The proposed concept therefore identifies a biological state that may become a target for earlier, marker-guided prevention. Conclusions: Normoglycemic diabetes is proposed as an early pathophysiological disease concept complementary to established glucose-defined categories. It is distinct from diabetes prevention itself: the former describes measurable β-cell pathology during normoglycemia, whereas prevention describes interventions intended to delay or avoid progression to dysglycemia and overt type 2 diabetes. Prospective studies are required before this concept can be considered an established diagnostic entity. Full article

The management of suspected ischemic stroke has evolved significantly, largely due to advancements in imaging technologies such as Computed Tomography Perfusion (CTP). This narrative review aims to delineate the practical applications and limitations of CTP in acute stroke evaluation, particularly in challenging scenarios. It is organized around illustrative clinical and imaging cases regarding small subcortical infarctions, posterior fossa ischemia, abnormal reference hemispheres, chronic steno-occlusive disease, the simultaneous involvement of multiple vascular territories, luxury perfusion, diaschisis, and systemic hemodynamic abnormalities. We discuss the core–penumbra paradigm, emphasizing the need for accurate interpretation of perfusion maps to guide therapeutic decisions. While automated CTP analyses enhance diagnostic efficiency, they can overlook critical patient-specific factors and exhibit variability in performance across different software platforms. Limitations in detecting small infarcts, particularly in lacunar strokes, highlight the necessity for qualitative visual assessments alongside quantitative data. Individualized case evaluations are crucial, as discrepancies between automated readings and qualitative interpretations can influence the management of single cases. Furthermore, we propose an integrated approach that combines automated assessments with human expertise to improve diagnostic precision and treatment efficacy. Future research should focus on refining CTP methodologies and enhancing their applicability in diverse clinical contexts to optimize patient management in acute ischemic stroke situations. Full article

Background: Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are progressive neurodegenerative disorders affecting over 100 million people worldwide. Effective connectivity (EC), which quantifies directional influence between brain regions, offers insight into disrupted neural circuit dynamics. This study aimed to identify neurophysiological biomarkers of AD and FTD and elucidate mechanisms distinguishing them from cognitively normal (CN) controls. Event-related EEG signals from 88 subjects (36 AD, 23 FTD, 29 CN) were analysed. Methods: Node-level graph-theoretical features were derived from three EC measures (phase slope index (PSI), transfer entropy (TE), and a linear state-space model (LSS) inspired by dynamic causal modelling) across five frequency bands. Results: Classification performance was strongest in the delta and beta bands, with hyperlink-induced topic search (HITS) authority and eigenvector centrality emerging as the most discriminative features, respectively. Quadratic discriminant analysis using HITS authority features from LSS in the delta band performed best, achieving 74.1% accuracy, 76.4% precision, 74.1% recall, 74.3% F1-score, and an AUC of 0.82. Conclusion: These preliminary findings suggest that altered nonlinear EC may be associated with AD and FTD pathophysiology, and highlight EEG-based graph-theoretical features as a promising direction for diagnostic decision-support research, pending validation in larger, independent cohorts. Full article

Background: Comorbidities such as anxiety and challenging behaviours are common in children with autism spectrum disorder (ASD). These comorbidities are often linked to emotional dysregulation (ED) and are frequently accompanied by motor stereotypies (MS). Objective: This study aimed primarily to examine the association between lifetime motor stereotypies (LMS) and both emotional dysregulation (ED) and anxiety/depression symptoms (ADS) in children with ASD. Additionally, a secondary objective was to explore the association between ED and the lifetime broader spectrum of restricted and repetitive behaviours (RRBs), and their specific subtypes. Methods: The study included 151 children with ASD (ages 3–11). ADS and ED were assessed using the Child Behaviour Checklist (CBCL) and the Child Behaviour Dysregulation Profile (CBCL-DP), respectively, while LMS and lifetime RRBs were evaluated via the Autism Diagnostic Interview-Revised (ADI-R). Data were analyzed using regression models. Results: Frequent LMS were significantly associated with ED and ADS. Gender, intellectual disability (ID), and ASD severity did not significantly influence these relationships. Age had a significant effect on the association between frequent LMS and ADS. No significant correspondence was found between ED and lifetime RRBs in general or with subtypes of RRBs. Conclusions: Our findings suggest that frequent LMS are associated with ED and ADS in children with ASD. Identifying ASD subgroups based on the presence of LMS may contribute to the development of detection methods and targeted and effective interventions for ED. Differences observed between LMS and other RRBs in relation to ED support the notion that low- and high-level repetitive behaviours involve distinct clinical features and should be studied independently. Full article

Zoonotic viral diseases continue to pose major challenges to global health, particularly as environmental, social and economic changes increasingly influence interactions among humans, animals, vectors, and ecosystems. This review explores Rift Valley fever virus (RVFV), Crimean-Congo hemorrhagic fever virus (CCHFV), and hantaviruses (HTVs) within the framework of One Health, emphasizing the ecological and epidemiological factors that govern their emergence and transmission. Despite their distinct transmission cycles, these viruses share important determinants of zoonotic risk. RVFV is primarily maintained through interactions between mosquitoes, livestock, wildlife, and humans; CCHFV is sustained within complex tick–vertebrate systems involving Hyalomma ticks, domestic animals, and wildlife, while HTVs are predominantly maintained through persistent infections in mammalian reservoir hosts, particularly rodents. Climate variability, habitat alteration, agricultural expansion, livestock movement, urbanization, and other anthropogenic pressures can modify these transmission systems and create new opportunities for pathogen spillover. Effective prevention and control therefore require integrated surveillance that encompasses human disease, animal populations, vectors or wildlife reservoirs, and environmental conditions. Advances in molecular diagnostics, genomic epidemiology, ecological modeling, remote sensing, and climate-based forecasting provide valuable opportunities for improving early detection and outbreak preparedness. To reduce the burden of these zoonotic viral diseases, a sustained collaboration among human and veterinary health professionals, ecologists, entomologists, environmental scientists, and public health authorities is required. Therefore, a coordinated One Health approach is essential for anticipating emerging threats, strengthening preparedness, and protecting human, animal, and ecosystem health. Full article

Eco-labels are important information cues for evaluating the environmental attributes of food products online, yet their effectiveness depends on whether consumers perceive these claims as credible and decision-relevant. This study examines how perceived food eco-label credibility shapes sustainable food purchase intention through two complementary cognitive mechanisms—perceived diagnosticity and perceived value—and whether these processes are contingent on food traceability and price sensitivity. Survey data were collected from 496 consumers of Hema Fresh, a major Chinese online grocery retail platform, and analyzed using structural equation modeling, bootstrap mediation analysis, and moderation analysis. Eco-label credibility positively influenced perceived diagnosticity (β = 0.415, p < 0.001) and perceived value (β = 0.417, p < 0.001), which subsequently increased sustainable food purchase intention (β = 0.372 and 0.295, respectively; both p < 0.001). Bootstrap results confirmed significant indirect effects through perceived diagnosticity (effect = 0.154, 95% CI [0.099, 0.215]) and perceived value (effect = 0.123, 95% CI [0.075, 0.179]). Food traceability strengthened the effects of eco-label credibility on both perceived diagnosticity (β = 0.215, p < 0.001) and perceived value (β = 0.103, p < 0.001). Price sensitivity weakened the perceived diagnosticity–purchase intention relationship, whereas its moderation of the perceived value–intention relationship was not significant. These findings reveal how credible eco-label information is translated into sustainable food choice through complementary information-diagnostic and value-evaluation processes, while identifying traceability and price sensitivity as distinct boundary conditions in online grocery retail. Full article

This study examines the predictive performance of machine learning models in classifying accounting manipulation risk among listed non-financial firms in the Middle East and North Africa (MENA) using the Beneish M-Score. Drawing on 8978 firm-year observations from 1165 firms across 16 countries over 2013–2024, the study evaluates whether machine learning models capture information beyond the accounting variables embedded in the outcome definition. Eight supervised algorithms are estimated, including Logistic Regression, Random Forest, Extra Trees, XGBoost, LightGBM, CatBoost, Support Vector Machine, and Multilayer Perceptron, using a chronological train-validation-test framework. Predictor variables are classified by their relationship to the Beneish components, allowing model performance to be examined under progressively restrictive information sets. The results show substantial differences across predictor specifications. The full predictor set achieves a test ROC-AUC of 0.9024, whereas removing direct Beneish inputs reduces the best performance to 0.7965. Further removal of close accounting proxies reduces ROC-AUC to 0.6017. A forward-looking specification yields lower but economically meaningful discrimination, with ROC-AUCs of 0.7095 and 0.6863 for the full and restricted sets, respectively. Panel-econometric diagnostics further examine heterogeneity, cross-sectional dependence, and parameter stability. The findings indicate that reported predictive performance is strongly influenced by accounting information embedded in the Beneish classification rule and highlight the importance of leakage-aware evaluation in machine learning applications to accounting risk. Full article

Background/Objectives: Large language models (LLMs) are increasingly used as decision-support tools in radiology, yet their influence on clinical decision-making remains a significant concern. This study evaluates how artificial intelligence (AI)-generated malignancy risk estimates influence clinical decisions in breast imaging and assesses automation bias (AB) among radiology residents at different training levels. Methods: In this simulation-based study, 20 residents reviewed radiology reports for 15 synthetic breast imaging cases generated by ChatGPT-4 Turbo, each presented in either a concordant or discordant format. Participants were stratified into two groups based on their completion of a breast imaging rotation and evaluated each case by assigning a BI-RADS score, recommending management, and rating their confidence in the AI risk estimate. Diagnostic accuracy and independent predictors of AB were analyzed using Generalized Estimating Equations (GEE) to account for reader-level clustering. Results: Overall management accuracy was significantly lower in cases with discordant AI outputs (p = 0.008). A similar, albeit non-significant, decreasing trend was observed for BI-RADS accuracy. While advanced residents maintained high management accuracy regardless of AI concordance, early-stage residents showed a sharp decline in discordant cases (77.3% vs. 45.3%; p = 0.006). This decline was most critical in malignant cases for early-stage residents, where accuracy dropped from 92.0% to 48.0% (p < 0.001). In the multivariable GEE logistic regression model, training level was the only independent predictor of AB (OR: 0.09, 95% CI: 0.03–0.28, p < 0.001). Conclusions: In this simulation study, advanced clinical training was associated with lower susceptibility to AB in breast imaging, although training level alone is unlikely to be a sufficient safeguard. Safe integration of AI tools will require caution among junior trainees alongside system-level measures such as model transparency and institutional oversight. Full article

Antimicrobial resistance (AMR) is commonly framed as a consequence of antimicrobial use, yet many conditions that shape resistance arise before treatment is considered and outside clinical care. This review examines contemporary determinants of AMR across human health, animal health, food production, environmental systems, and wider social and institutional contexts. Determinants are organized around common pathways, such as infection burden, antimicrobial exposure, selection pressure, transmission, and systems’ capacity to prevent and control resistance. This pathway-based perspective helps identify critical intervention points in diagnostics, infection prevention, vaccination, water and sanitation, biosecurity, wastewater management, governance, and emergency preparedness. It also shows why surveillance conducted within separate sectors may miss interactions at human, animal, and environmental interfaces. The synthesis supports a broader understanding of AMR literacy that extends beyond knowledge and individual antibiotic behavior to include recognition of interacting determinants, opportunities for prevention, and the different capacities of actors to influence them. Shared responsibility should therefore be understood as differentiated responsibility among individuals, professionals, institutions, industry, and governments. By linking determinants with the pathways through which they act, potential points for intervention, and different levels of responsibility, this review provides a framework for more coherent One Health surveillance, prevention, preparedness, and policy responses. Full article

Background: Artificial intelligence (AI) and large language models (LLMs) may make surgical information easier to access and understand. However, these tools cannot replace the clinician’s role in shared decision-making or the requirements for valid informed consent. Methods: This narrative review follows the surgical patient pathway from diagnosis and explanation to option comparison, shared decision-making, and informed consent. It examines conventional digital tools, LLM-assisted communication, personalized prediction, direct consent studies, patient and professional perspectives and legal, ethical, and governance issues. A formal meta-analysis and study-level risk-of-bias assessment were not performed. Results: AI may support several stages of surgical care. Diagnostic and predictive systems can assist clinicians. LLMs can help explain approved information, prepare patients for consultation, and improve consent materials. Conventional interactive tools have the strongest evidence for better comprehension. Evidence for LLM-assisted consent is still based mainly on simulations and document analyses. Patient-facing use requires source control, privacy protection, and visible clinical oversight. AI cannot establish capacity, voluntariness, or final authorization. We propose a five-level framework in which safeguards increase with clinical influence. Conclusions: AI may support the patient’s path from diagnosis to an informed surgical choice when it uses approved information, serves a defined task, and remains under clinical oversight. Current evidence does not support autonomous AI-mediated consent or AI-directed treatment choice. The aim should be a better-informed, preference-sensitive decision, not an automated signature. Full article

The conceptualization of Parkinson’s disease (PD) has shifted from a strictly central neurodegenerative disorder to a systemic synucleinopathy that may, in some patients, originate in the enteric nervous system. This review synthesizes recent evidence highlighting the potential role of sulfate-reducing Desulfovibrio (DSV) bacteria as candidate pathobionts in the proposed “gut-first” pathogenic cascade. By colonizing anaerobic niches within the colon and metabolizing dietary sulfur compounds, DSV generate hydrogen sulfide (H₂S), a gaseous metabolite that may compromise intestinal barrier integrity and impair mitochondrial function through the inhibition of cytochrome c oxidase. These processes have been associated with ATP depletion, cytochrome c release, and iron-dependent oxidative stress, creating conditions that may favor α-synuclein misfolding and aggregation within enteroendocrine cells. We further examine evidence supporting the retrograde propagation of α-synuclein pathology from the gut to the brain via the vagus nerve—a hypothesis strengthened by experimental findings and epidemiological observations, including studies reporting reduced PD risk following truncal vagotomy. Although the increased prevalence of DSV in PD patients suggests potential applications in prodromal diagnostics and microbiome-based risk stratification, a definitive causal relationship has yet to be established. We therefore discuss key methodological limitations, including biases associated with 16S rRNA sequencing and the current lack of robust in vivo metabolomic evidence. Finally, we consider whether targeted modulation of the intestinal environment, including DSV-specific bacteriophages and [FeFe]-hydrogenase inhibitors, could emerge as a future strategy for influencing early disease processes. Full article

Leishmaniasis continues to be a major neglected tropical disease, particularly in endemic areas where reliable diagnosis remains challenging. Many existing diagnostic methods are invasive, lack sufficient sensitivity, or require equipment and expertise that may not be available in resource-limited settings. Parasite-derived spliced leader (SL) RNA has recently gained attention as a promising molecular marker because it may enable the sensitive detection of viable Leishmania parasites and help track changes in infection over time. Extracellular vesicles (EVs) are also of growing interest because they protect and transport stable biomolecules that may provide valuable diagnostic information and influence interactions between the parasite and its host. In this proof-of-concept study, we evaluated the feasibility of detecting Leishmania-derived SL-RNA in EV-enriched preparations from experimental leishmaniasis model systems. EV-enriched preparations were isolated from parasite-conditioned media (PCM), infected THP-1 macrophage (I-MP) cell-conditioned medium (CCM), and serum from experimentally infected mice using a precipitation method. As a complementary isolation approach, additional preparations were isolated from PCM by ultracentrifugation. An aliquot of each preparation was analyzed by nanoparticle tracking analysis to determine particle size and concentration, and the remaining portion was used for RNA extraction. RNA was reverse transcribed into cDNA and analyzed for SL-RNA amplification using quantitative polymerase chain reaction (qPCR). SL-RNA amplification was successfully detected in PCM, I-MP CCM, and serum from experimentally infected mice, while uninfected controls showed no specific amplification. These findings were independently confirmed via ddPCR analysis. Notably, EV-enriched preparations from infected mice yielded a stronger SL-RNA signal than serum alone, highlighting the diagnostic potential of targeting SL-RNA in EV-enriched human clinical samples, particularly in low-burden infections. Although these findings support the feasibility of EV-associated SL-RNA as a molecular biomarker for Leishmania infection, clinical validation with human samples is required. Full article

Background: Growth hormone (GH) stimulation testing remains the diagnostic gold standard for GH deficiency (GHD), but body mass index (BMI) inversely influences peak GH levels, creating a risk of overdiagnosis. We evaluated the association between BMI standard deviation score (SDS) and peak GH response in children undergoing diagnostic work-up for suspected GHD. Methods: This is a retrospective single-center study of 298 children (55.4% male, mean age 9.5 ± 2.9 years) evaluated for growth failure with arginine or clonidine stimulation testing. GHD was confirmed in 114 patients (38.3%). Multivariable linear and logistic regression models evaluated associations after adjustment for age, sex, and pubertal status. Results: BMI SDS negatively correlated with peak GH levels in the overall cohort (r = −0.299, p < 0.001) and in GHD patients (r = −0.322, p < 0.001), but not in non-GHD controls (r = −0.102, p = 0.167; Fisher’s z p = 0.050). On multivariable analysis, each 1-SDS increase in BMI SDS was independently associated with a 1.63 ng/mL reduction in peak GH (β = −1.63, 95% CI −2.20 to −1.07; p < 0.001) and 2.10-fold higher odds of GHD diagnosis (OR 2.10, 95% CI 1.60–2.74; p < 0.001). Across BMI SDS quartiles (Q1 to Q4), mean peak GH progressively decreased from 11.14 to 6.93 ng/mL (p < 0.0001), while GHD prevalence increased from 22.4% to 62.7% (p < 0.0001). Conclusions: BMI SDS significantly blunts peak GH response and increases the likelihood of a GHD diagnosis. This inverse relationship was evident primarily in children with confirmed GHD, suggesting that adiposity predominantly impairs an already compromised somatotropic axis. Adiposity should be accounted for when interpreting GH stimulation tests. Full article

Vulnerable employment remains one of the most persistent obstacles to inclusive development in emerging economies, yet the joint influence of globalization, migration, and remittances on employment quality has received limited attention in second-generation panel econometrics. This study investigates the heterogeneous effects of globalization-driven migration and remittance inflows on vulnerable employment, and the moderating role of institutional quality, across 30 emerging economies over the period 1996–2024. The empirical strategy accounts for cross-sectional dependence and slope heterogeneity through a comprehensive set of diagnostics, including the Pesaran CD test, the Pesaran and Yamagata slope homogeneity test, CIPS panel unit root tests, and Westerlund Durbin-Hausman cointegration tests, followed by Common Correlated Effects Mean Group and Augmented Mean Group estimation of the long-run parameters and Method of Moments Quantile Regression for distributional heterogeneity. The long-run estimates show that globalization, remittances, institutional quality, and urbanization are associated with lower vulnerable employment, while net migration is associated with higher vulnerable employment. Globalization weakens the positive migration association, whereas institutional quality strengthens the negative globalization association. MMQR estimates show stronger associations at higher quantiles, with cross-quantile tests confirming significant heterogeneity. Extensive robustness checks consistently support these findings. The findings support governance-centred labour market strategies that combine openness, remittance channelling, and institutional reform to accelerate progress toward Sustainable Development Goal 8. Full article

Background: Artificial-intelligence-based diagnostic systems increasingly influence healthcare delivery, yet strong aggregate performance can coexist with clinically meaningful disparities across demographic subgroups. Such disparities raise not only model-development concerns but also health-equity, implementation, and governance challenges for healthcare organizations. Methods: We conducted a systematic review reported in accordance with PRISMA 2020 of literature published from January 2019 through August 2026. The search identified 1842 records; 87 records were retained in the overall evidence corpus and 46 underwent structured quantitative extraction. The corpus included primary empirical/model-evaluation evidence together with secondary or adjacent methodological evidence used for qualitative contextualization. We also developed FRCS-10, a ten-item descriptive reporting-completeness index derived from established reporting and governance guidance, and applied it to the full corpus. Results: Peer-reviewed primary sources reported baseline true-positive-rate gaps of 0.062–0.281 across sex, age, and race on CheXpert and MIMIC-CXR, while a contextual dermatology meta-analysis reported AUROC values of 0.89 for Fitzpatrick I–III and 0.82 for Fitzpatrick IV–VI. These values were synthesized as source-reported estimates and were not independently re-analysed from patient-level data. Data representation was the most frequently coded bias source (38%), followed by algorithmic design (21%) and label or annotation bias (19%). The descriptive FRCS-10 mean was 4.6/10; only 7 of 87 records described post-deployment fairness monitoring. Five primary empirical studies with sufficiently explicit estimands were assessed for compatibility; their disparity definitions were not commensurable, so no pooled effect size was computed. Conclusions: We propose the Equity-Aware Bias Detection and Mitigation Framework (EA-BDMF), a literature-derived, implementation-oriented lifecycle specification for healthcare AI. It links data auditing, prevalence-aware preprocessing, fairness-constrained training, calibration and threshold governance, and post-deployment monitoring. The framework is accompanied by a Lifecycle Equity Debt (LED) accounting measure, checkpoint documentation, a Bias Provenance–Mitigation Traceability Matrix, and descriptive regulatory mapping. The complete framework has not yet been validated end to end, and its clinical and organizational utility requires prospective evaluation in real-world healthcare settings. Full article