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The article "Identification of LGR5 and TFF2 as Biomarkers in High-Risk Chronic Atrophic Gastritis: From Multi-Omics Mining to Clinical Validation" [Digestion. 2026; https://doi.org/10.1159/000549887] by Qingqing Zhang, Di Wu, Fengyun Guo, Shengnan Yang, Lijing Bao, Ruiying Zhang and Ping Wang has been retracted by the Publisher and the Editor.Post-publication concerns were raised regarding the integrity of the clinical cohort data reported in the article. The data presented in Table 1 shares the same subgroup size, gender distributions and H. pylori infection rates across all three subgroups to those reported in a previously published article by the same research group [1]. Additionally, the images in Figure 4b and d of this article are published as Figure 2a and b in [1]. The previous publication was not cited in this article.When asked about the similarities between the cohorts, the authors stated that they were two independent cohorts and that the similarities were coincidental. The Editors did not find the authors' explanation sufficient to resolve their concerns, and as a result have lost confidence in the reliability and originality of the reported cohort data.The authors did not respond to our correspondence about the retraction of this article within the timeframe specified.

Transforming growth factor-β1 (TGF-β1) is involved in airway remodeling in asthma and chronic obstructive pulmonary disease (COPD) through extracellular matrix deposition, pro-fibrotic signal transduction and structural changes. This study used bibliometric methods to draw the development context and current research trends of TGF-β1-related airway remodeling research. On July 18, 2024, we queried the Web of Science Core Collection (WoSCC) to retrieve relevant records. Subsequent bibliometric analyses were performed using VOSviewer, CiteSpace, and R package "bibliometrix" to map collaborative networks, keyword co-occurrences, and emerging research frontiers. A total of 920 articles published from 1994 to 2024 were included, involving 5398 authors from 2652 institutions in 215 countries/regions. China had the highest global productivity with 290 publications. The University of California System was the leading institution with 65 publications. At the level of the individual author, Gosens Reinoud was the most productive author (with 17 publications). At the journal level, articles were mostly published in the American Journal of Respiratory Cell and Molecular Biology. Keyword analysis identified "expression," "inflammation," "asthma," and "growth-factor-beta" as frequent terms. Finally, burst detection illustrated "epithelial-mesenchymal transition (EMT)" and "oxidative stress" as important topics that have emerged recently. This bibliometric analysis maps the main contributors, cooperation networks, and evolving research topics of TGF-β1-related airway remodeling. This field has been centered on cellular mechanisms and inflammatory pathways, while recent studies have increasingly focused on oxidative stress and epithelial-mesenchymal transition. These findings may help guide the prioritization of future airway remodeling research.

Respiratory infectious diseases impose a heavy burden on global health. Exercise-related interventions have attracted increasing research attention, but the knowledge landscape and emerging trends in this field have not yet been comprehensively mapped. This study aims to address this gap through a bibliometric and visualization analysis of the literature published between 2000 and 2025. A search was conducted in the Web of Science Core Collection and PubMed databases, incorporating relevant English-language literature published between January 1, 2000, and March 31, 2025. After deduplication and screening, 944 articles were ultimately included. Co-occurrence network, clustering, and emergence analyses were conducted using CiteSpace 6.4.R1 software. Descriptive statistical analyses of publication volume and country, institutional, and author distribution were performed using Microsoft Excel. Publication output in this field has increased significantly since 2020, peaking in 2022, a trend highly consistent with the progression of the coronavirus disease 2019 (COVID-19) outbreak. Arena Ross, the University of London (United Kingdom), and the United States emerged as the most prolific author, institution, and country, respectively. Frequent keywords include "Physical activity," "Pulmonary rehabilitation," "Exercise," "COVID-19," "Coronavirus disease," "Rehabilitation," and "Quality of life." Keywords exhibiting high burst strength include "COVID-19 (30.04)," "Coronavirus disease (10.19)," and "respiratory tract infections (7.95)." Emerging research hotspots concentrate primarily on 3 domains: "respiratory function," "Telerehabilitation," and "dyspnea." Research on physical activity interventions for respiratory infectious diseases has shown sustained growth over the past 25 years, undergoing significant structural shifts during the COVID-19 pandemic. This study provides a broad overview of the knowledge landscape and developmental trajectory in this field.

Bipolar disorder (BD) is a chronic psychiatric illness. Thyroid hormones play a crucial role in maintaining normal brain function and have a significant impact on mood regulation and neuroendocrine activity. Increasing evidence suggests that thyroid dysfunction is closely related to the onset, progression, and severity of BD. A systematic bibliometric analysis of this field is important for understanding its research trajectory and identifying emerging hotspots. Literature on BD and thyroid hormones published between 1998 and 2022 was retrieved from the Web of Science Core Collection. Data were analyzed and visualized using VOSviewer and CiteSpace software to explore publication trends, major contributing countries and institutions, authors, journals, and keyword characteristics. A total of 452 relevant publications were identified, covering 229 journals, 2318 authors, 1443 institutions, and 188 countries. The United States, China, and Germany were the most active countries in this field. Major contributing institutions included the University of California, Los Angeles, the University of Toronto, and the Technical University of Dresden. Leading scholars were Bauer M, Whybrow PC, and Rybakowski JK. The Journal of Affective Disorders published the most relevant papers. Keyword analysis revealed that research hotspots mainly focused on BD, thyroid function, and depression, particularly around themes such as "risk," "affective illness," and "bipolar affective disorder." The relationship between thyroid hormones and BD has become an important research focus in recent years, providing new directions for future studies in this field. This study systematically reveals the research landscape and developmental trends from 1998 to 2022, offering valuable references and guidance for future research and clinical practice.

Cognitive impairment and cerebral dysfunction are common among patients with end-stage kidney disease undergoing maintenance hemodialysis. Repeated intradialytic hemodynamic stress, including reductions in cerebral blood flow and episodes of intradialytic hypotension, has been associated with adverse neurological outcomes. Quantitative electroencephalography (qEEG) offers a noninvasive approach for continuous assessment of cerebral activity; however, its potential role in dialysis monitoring remains unclear because of methodological heterogeneity and implementation challenges. This scoping review aimed to synthesize current evidence regarding qEEG alterations across the hemodialysis cycle and to evaluate the technical requirements for translating qEEG-derived features into future dialysis-related cerebral monitoring systems. A structured literature search was conducted in PubMed/MEDLINE, Embase, and IEEE Xplore for studies published between January 2005 and February 2026. After removal of duplicates, 554 records were screened, and 70 full-text articles were assessed for eligibility. Sixty-two studies met the inclusion criteria and were included in the qualitative synthesis. Evidence was organized according to the 3 temporal domains of the hemodialysis cycle: predialysis baseline, intradialytic exposure, and postdialysis recovery. In addition to clinical findings, engineering-related evidence concerning electroencephalography hardware, electrode systems, signal processing pipelines, artifact mitigation, multimodal synchronization, edge computing, and digital biomarker validation was reviewed. Patients receiving maintenance hemodialysis consistently demonstrated baseline spectral slowing characterized by increased delta and theta activity, and reduced alpha power. During dialysis, multimodal imaging studies reported cerebral blood flow reductions of approximately 10% to 15%, accompanied by dynamic qEEG changes, including alterations in the alpha-delta ratio and slow wave activity. Connectivity and complexity measures provided complementary information regarding network-level and recovery-related responses but showed substantial variability across acquisition conditions and analytical pipelines. Major implementation challenges included electrical interference, motion artifacts, electrode instability, asynchronous physiological data streams, and limited standardization of preprocessing methods. Comparative analysis indicated that acquisition quality, artifact rejection performance, synchronization accuracy, and individualized baseline modeling are critical determinants of translational feasibility. Current evidence indicates that qEEG-derived measures have been reported in association with both chronic and intradialytic neurophysiological changes in patients undergoing hemodialysis. However, available evidence remains heterogeneous and insufficient to support routine clinical deployment. Future implementation will require standardized acquisition protocols, robust artifact mitigation, synchronized multimodal monitoring, and prospective validation studies. At present, qEEG-derived features should be regarded as candidate digital biomarkers and components of a proposed translational monitoring framework rather than clinically validated monitoring tools.

Approximately 39.9 million people are living with HIV worldwide, most in sub-Saharan Africa. Effective antiretroviral therapy has shifted attention to metabolic comorbidities, and emerging evidence links integrase inhibitors such as dolutegravir to elevated serum uric acid, yet the global burden of hyperuricemia and gout in this population is unquantified. This protocol describes a systematic review and meta-analysis of observational studies (cross-sectional, cohort, and case-control) published from inception to May 2026. We will search PubMed/MEDLINE, EMBASE, Web of Science, and CINAHL. Studies involving adults (≥18 years) living with HIV that report the prevalence or incidence of hyperuricemia or gout will be included. Two independent reviewers will screen studies, extract data, and assess risk of bias using the Joanna Briggs Institute Prevalence Critical Appraisal Checklist and the Newcastle-Ottawa Scale (NOS). Primary outcomes are the pooled prevalence of hyperuricemia and gout in PLHIV. Secondary outcomes include risk factors associated with urate dysregulation (ART regimen, Cluster of Differentiation 4 [CD4] count, viral load, metabolic comorbidities, and HIV duration), and gout as an immune reconstitution inflammatory syndrome (IRIS) manifestation. A random-effects meta-analysis using the DerSimonian-Laird method with Freeman-Tukey double-arcsine transformation for prevalence data will be performed in R software. Heterogeneity will be assessed using the I2 statistic and Cochran's Q test. Subgroup analyses will explore variation by ART class (pre-ART, PI, NNRTI, INSTI/dolutegravir era), geographic region, CD4 category, and study quality. CRD420261360734.

Pharmacists are expanding their participation in veterinary healthcare teams and assuming roles beyond traditional dispensing duties. However, the scope of these collaborative practices and the degree of mutual recognition vary substantially across regions. This scoping review aimed to organize the existing literature on collaboration between veterinarians and pharmacists, clarifying current roles, practical applications, and professional perceptions within the veterinary field. A scoping review was conducted to examine the roles, practices, and perceptions associated with veterinarian-pharmacist collaboration in veterinary medicine and related fields. Two researchers independently searched PubMed, Web of Science, the Cochrane Library, and Ichushi-Web, and additionally screened Google Scholar to identify gray literature (from database inception to August 2025, in English or Japanese). Records were independently screened at the title/abstract and full-text levels using predefined eligibility criteria, and relevant studies were identified. The search yielded 239 records, of which 16 studies published between 2007 and 2024 were included. Studies were conducted primarily in the United States (n = 7), New Zealand (n = 3), and Japan (n = 3); one study collected data from both Japan and Taiwan. Most studies employed cross-sectional survey designs. Pharmacist roles most frequently involved compounding and dispensing for animal patients (62.5%), followed by drug information (DI) and consultation (37.5%), inventory and supply management (25.0%), client education (18.8%), and safety and exposure control (12.5%). This scoping review demonstrates that veterinarian-pharmacist collaboration is described within a limited and regionally variable evidence base, with pharmacists most often contributing through compounding/dispensing and drug information support. Sustained and scalable implementation will require improved mutual understanding of professional roles, strengthened veterinary-specific education for pharmacists, and more robust empirical research to inform collaborative practice models.

Stress, as commonly recognized, is an integral part of modern life and can significantly affect both mental and physical health. While substantial advancements have been made in measuring physical fitness through wearable devices, the detection and assessment of mental stress remain in their early stages. The objective of this paper is to review recent studies of wearable-based stress detection in naturalistic settings, with a specific focus on characterizing machine learning frameworks inspired by the model card approach. This review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. A total of 353 articles were identified through searches in databases such as PubMed, MEDLINE, ScienceDirect, IEEE, ACM Digital Library, Web of Science, and Embase. Studies were considered eligible if they collected data from healthy adults in naturalistic settings using wearable devices and used machine learning models for stress detection. A total of 34 articles met the eligibility criteria, including 11 conference papers, 22 journal articles, and 1 preprint published between 2017 and 2024. From these studies, we analyzed key machine learning modeling decisions such as problem formulation, ground truth determination, and machine learning algorithms. Additionally, we examined the major contributions of each study, focusing on the challenges they addressed and the solutions they proposed. Based on these findings, we proposed a model card framework for reporting machine learning-based, wearable-based stress detection. This scoping review highlights recent trends in machine learning models for stress detection and measurement using wearable signals. It underscores the need for improved standardization in reporting practices for datasets and key machine learning decisions, as well as the importance of addressing critical challenges associated with data collection in real-world settings. We hope this review will support and strengthen ongoing research efforts, promote knowledge sharing, and promote collaboration among researchers-ultimately advancing the field as a community.

Impulsivity is a defining feature of borderline personality disorder (BPD), yet it remains unclear whether it reflects a stable deficit in inhibitory control or a context-dependent breakdown in behavioural regulation under conditions of emotional arousal. Existing models variably emphasise trait, cognitive or affective mechanisms, with limited integration of contextual influences. This scoping review synthesised empirical evidence examining affective mechanisms, neurobiological and methodological accounts of impulsivity in BPD. Following PRISMA-ScR guidelines, CINAHL, EMBASE, EMCARE, MEDLINE, and PsycINFO were searched for studies published between 1980 to November 2025. Forty-six studies meeting inclusion criteria were synthesised across affective, neurobiological, and methodological domains. Study designs included cross-sectional/correlational investigations (n = 17), experimental or task-based paradigms (n = 13), neuroimaging studies (n = 11), lesion or quasi-experimental designs (n = 3), and randomised controlled trials (n = 2). Impulsive behaviour in BPD was most consistently associated with affective instability, with negative urgency emerging as the most robust correlate. Self-report measures showed consistent elevations, whereas behavioural measures of inhibitory control under neutral conditions yielded heterogeneous findings, with several studies indicating preserved performance. Impairments were more reliably observed under emotionally salient or stress-inducing conditions. Neurobiological findings converged on dysregulation within frontolimbic and salience networks, particularly amygdala-prefrontal circuitry. Convergence across measurement modalities was limited, highlighting substantial heterogeneity. Evidence supports a model in which impulsivity in BPD reflects affect-modulated, context-dependent behavioural dyscontrol rather than a pervasive trait-level deficit. Heightened negative urgency and disrupted amygdala-prefrontal regulation appears central, linking affective instability to impulsive behaviour. These findings support mechanism-informed and context-sensitive approaches to assessment and intervention.

Magnaporthe oryzae is a devastating fungal pathogen causing blast disease in rice and other crops, threatening global grain production and food security. Phenamacril (PHA) effectively inhibits Fusarium graminearum by targeting F. graminearum myosin I (FgMyoI). However, PHA shows limited activity against M. oryzae, despite the high sequence similarity between FgMyoI and M. oryzae myosin I (MoMyoI). Using our published PHA-FgMyoI complex structure as a template, we identified K378 in MoMyoI as a key determinant of insensitivity to PHA. Substitution of K378 with methionine, the corresponding residue in FgMyoI, markedly enhanced PHA-mediated inhibition of MoMyoI ATPase activity and improved PHA efficacy against M. oryzae. Guided by structure-based design, we synthesized PHA derivatives targeting MoMyoI. Among them, NJY-10 showed improved MoMyoI binding, ATPase inhibition, and protective efficacy against rice blast disease.

Physical inactivity among children and adolescents remains a major global public health concern, yet the ways in which behavioral theory is applied to inform physical activity (PA) interventions are often insufficiently described. The Behavior Change Wheel (BCW) model provides a systematic framework for linking behavioral determinants to intervention content, but its application in PA promotion for this population has not been comprehensively mapped. This scoping review was conducted in accordance with the Joanna Briggs Institute methodology and reported following the PRISMA-ScR guideline. Seven electronic databases were searched for studies published since 2011 that explicitly applied the BCW to promote PA among children and adolescents. Data were charted on study characteristics, targeted components of the Capability, Opportunity, and Motivation-Behavior (COM-B) model, BCW intervention functions, behavior change techniques, and implementation outcomes. Fifteen studies met the inclusion criteria. Most applications focused on intervention development, co-design, or feasibility rather than effectiveness evaluation. Psychological capability and reflective motivation were the most frequently targeted COM-B components, and education, training, and enablement were the dominant intervention functions. Commonly used behavior change techniques included goal setting, action planning, self-monitoring, feedback, and social support. Functions involving coercion or restriction were rarely used. Reporting of implementation outcomes was inconsistent, with limited attention to reach, penetration, and sustainability. In studies using the BCW framework, PA interventions for children and adolescents predominantly emphasize individual-level self-regulation strategies, with less focus on opportunity-level change and implementation sustainability. Greater transparency in theoretical mapping and stronger integration of implementation considerations are needed to enhance the translation of theory-based interventions into practice.

The aging Canadian population has led to an increase in Canada's use of home and community care services. In this context, the healthcare system relies heavily on personal support workers who work in various healthcare settings. A significant proportion of these workers are immigrants, and many live in linguistic minorities. Despite their pivotal role, the experiences and working conditions of these personal support workers are underrepresented in the scientific literature, specifically in intersectional analyses. This scoping review aims to examine the work experiences, health conditions, and well-being of immigrant personal support workers in Canada who work in a minority language setting. Studies addressing the work experiences, health conditions, and well-being of immigrant personal support workers in Canada who work in a minority language setting, and those published in English or French. There will have no restrictions on publication date. This review will follow the JBI recommendations for scoping reviews and incorporate the PRISMA-ScR checklist. We will develop an adapted search strategy for five relevant databases (Medline [Ovide], Web of Science, Embase, CINAHL, and Google Scholar). Two independent reviewers will select full-text articles based on pre-established inclusion criteria and extracted relevant data. The results will be presented in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) guidelines. This scoping review contributes to expanding knowledge about the professional, health, and social realities of immigrant PSWs in Canada's linguistic minority communities.

Recent advances in large language models (LLMs) such as GPT-3/4 have spurred the development of artificial intelligence (AI) chatbots and advisory tools in medicine. These systems are posited to assist or augment physician-patient communication, potentially improving empathy, clarity, and responsiveness. However, their actual impact on communication outcomes remains uncertain. This study aimed to systematically review and meta-analyze peer-reviewed studies (2020-2025) evaluating how LLM-based interventions affect physician-patient communication, including empathy, clarity, trust, and patient understanding. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we searched PubMed/MEDLINE, Embase, Scopus, and Web of Science for studies published from 2020 to 2025 examining LLM or chatbot applications in clinical communication contexts. Eligible designs included randomized, observational, cross-sectional, and qualitative studies. Two reviewers (WHP and SR) independently screened titles or abstracts, assessed full texts, and extracted data on study design, population, LLM type, communication measures, and outcomes. We conducted a qualitative synthesis and random-effects meta-analysis, reporting pooled standardized mean differences or odds ratios with 95% CIs. From 312 records, 10 studies were included, all quantitative and predominantly cross-sectional. Populations ranged from patients with chronic conditions to health care professionals and laypersons. Outcomes assessed included empathy (8 studies), clarity or information quality (6 studies), satisfaction or usefulness (4 studies), and trust perceptions (2 studies). In 6 direct comparisons of AI- versus physician-generated responses, LLMs were rated significantly higher in empathy in 5 studies. One large study found that chatbot replies were judged empathetic in 45.1% of cases versus 4.6% for physician replies (odds ratio approximately 9.8, P<.001). Similarly, ChatGPT-4 answers scored higher in empathy on a 5-point scale than human-written responses (mean 4.18 vs 2.70, P<.001). One neurology study showed higher empathy scores (Consultation and Relational Empathy Scale +1.38, P<.01) for ChatGPT answers. Only 1 study found no significant empathy difference. LLM content was also longer and more information-rich, improving patient-perceived clarity and understanding. On the other hand, GPT-4 simplified pathology reports, increasing patient comprehension scores (7.98 vs 5.23/10, P<.001) and reducing consultation time by 70%. However, AI replies were sometimes less concise or less readable for low-literacy patients. In pooled analyses (k=4 studies; total evaluations N=2604), LLM assistance showed a large positive effect on empathy (standardized mean difference 1.02, 95% CI 0.44-1.60; random-effects model). Patient satisfaction results were mixed. No study directly assessed long-term trust. Current evidence suggests that LLM-based chatbots can enhance physician-patient communication by producing more empathetic, detailed, and understandable responses. These improvements may positively influence patient experience and engagement. However, LLMs may also generate overly lengthy or occasionally inaccurate advice, emphasizing the need for physician oversight. While meta-analytic findings are promising, robust randomized controlled trials, real-world and longitudinal studies are needed to confirm benefits, assess trust outcomes, and define optimal clinical integration strategies.

Although deep learning for periodontitis diagnosis on panoramic radiographs has advanced rapidly, most studies use single-vendor data and cross-vendor generalization is rarely evaluated. Therefore, we evaluate the cross-vendor robustness of a hybrid CNN-CAD framework for automated four-stage periodontitis classification. Five hundred panoramic radiographs were retrospectively collected from three vendors (Instrumentarium, n=400; Vatech, n=50; PointNix, n=50). The framework extends a previously published hybrid pipeline by adding a YOLO-based CNN for missing-teeth quantification, enabling four-stage classification according to the 2017 World Workshop criteria. Three dataset configurations were evaluated: pooled multi-vendor training and two leave-one-device-out (LODO) splits. We compared segmentation backbones (U-Net, Dense U-Net, SegNet, Mask R-CNN) and YOLO detector variants. Agreement with three oral and maxillofacial radiologists (3, 5 and 10 years of experience) was assessed using mean absolute difference (MAD), Pearson and intraclass correlation, Bland-Altman, and Passing-Bablok analyses. Under pooled multi-vendor training, Mask R-CNN achieved Dice coefficients of 0.96, 0.92 and 0.94 for periodontal bone level, cemento-enamel junction level and teeth/implants respectively; CNNv4-tiny reached a mean AP of 0.86 for missing teeth. The MAD between automated and expert staging was 0.31, overall image-level ICC was 0.93 (95% CI 0.86-0.97; p<0.01), and Bland-Altman bias against the most experienced radiologist was 0.007. Under LODO, Dice coefficient dropped to 0.75-0.86 (all p<0.001 versus pooled), quantifying substantial vendor-induced domain shift. The hybrid framework achieves expert-level agreement under pooled multi-vendor training but degrades in held-out vendors, providing a quantitative reference for vendor-induced domain shift in panoramic radiograph AI and motivating vendor-aware training or domain adaptation in future clinical deployments. This work provides, to our knowledge, the first cross-vendor benchmark for deep-learning-based periodontitis staging on panoramic radiographs and quantifies vendor-induced domain shift directly addressing the external-validation gap recently identified in this journal.

As an established driver of hypertension, obstructive sleep apnea (OSA) generates a significant cardiovascular burden when both disorders are present. This intersection not only compromises nocturnal hemodynamics but also hampers long-term clinical management. Yet, current health care delivery rarely integrates the simultaneous and continuous tracking of these dual burdens. While wearable technology provides a noninvasive, pragmatic toolset for synchronized physiological monitoring, research remains largely siloed within single-disease frameworks. Consequently, clinical evidence supporting wearable applications specifically for comorbid populations remains sparse. In this scoping review, we summarized the current applications of wearable technology for comorbid OSA and hypertension. The analysis primarily outlines device categories, monitored physiological indicators, prevalent clinical scenarios, and existing challenges. The search for relevant literature spanned PubMed, Web of Science, Embase, and IEEE Xplore, covering the period from January 2015 to February 2026. Eligible studies included adults and used wearable or portable technologies to objectively track sleep- or respiratory-related indicators and cardiovascular/blood pressure metrics. Study selection, data charting, and evidence synthesis were conducted using a 2-reviewer process and descriptive and narrative approaches. Our initial search yielded 739 records. Following title and abstract screening, we reviewed 54 full texts, ultimately finalizing a cohort of 13 eligible studies. Published between 2015 and 2026 across 9 countries, these articles capture data from 5596 participants. Most were observational studies and device validation studies. Evaluated device types included wrist-worn devices, fingertip contact devices, patch and single-lead devices, and multiparameter portable monitoring systems. The clinical applications mainly focused on screening and risk stratification for comorbid OSA and hypertension, monitoring of abnormal nocturnal blood pressure and hemodynamic changes, and cardiovascular risk assessment and remote longitudinal management. However, research remains sparse, and different devices vary greatly in reference standards, diagnostic thresholds, and validation pathways; therefore, their clinical translational value still requires further validation. Wearable devices may complement traditional assessment by providing continuous nocturnal and longitudinal data. However, at present, they are more suitable as auxiliary monitoring and risk warning tools in comorbidity management and cannot yet replace standard sleep studies or standard blood pressure monitoring. Future research should further shift from feasibility validation to large-sample, prospective, multicenter clinical studies in comorbid populations.

Aim: The aim of this review is to assess the role of neuromolecular biomarkers in post-traumatic stress disorder (PTSD) and their usefulness in improving diagnostic accuracy and supporting personalized treatment strategies. Materials and Methods: A narrative review of studies published between 2015 and 2025 was performed using databases such as PubMed, Scopus, and Web of Science. The analysis included clinical and experimental studies focusing on neuroinflammation, oxidative stress, endoplasmic reticulum stress, and markers of neuronal damage in PTSD. Special attention was given to associations between biomarkers and symptom severity, duration of illness, and treatment outcomes. Results: PTSD is associated with disturbances in neuroimmune and neuroendocrine pathways. Increased levels of pro-inflammatory cytokines, including interleukin-1 beta (IL-1&#x3b2;), interleukin-6 (IL-6), and interleukin-18 (IL-18), are frequently observed. Activation of the NLR family pyrin domain containing 3 inflammasome (NLRP3 inflammasome) and elevated oxidative stress markers, such as malondialdehyde (MDA), indicate ongoing inflammatory and oxidative processes. Changes in neuronal injury markers, including ubiquitin carboxyl-terminal hydrolase L1 (UCHL1), suggest neurodegenerative mechanisms. Altered chemokine signaling, particularly fractalkine (chemokine C-X3-C motif ligand 1, CX3CL1), and activation of endoplasmic reticulum stress pathways, such as inositol-requiring enzyme 1 (IRE1) and activating transcription factor 6 (ATF6), are linked to impaired neuronal function and reduced synaptic plasticity. These findings indicate biological heterogeneity within PTSD. Conclusions: Neuromolecular biomarkers may improve the current symptom-based diagnostic model of PTSD. Their integration with clinical assessment tools may support identification of biologically defined subgroups and enable more targeted treatment.

Aim: To analyze the availability of medicinal products for the Ukrainian population under martial law, identify key challenges in the pharmaceutical sector, and substantiate adaptive approaches for ensuring continuous pharmaceutical provision. Materials and Methods: A systematic analysis of regulatory, legal, and empirical data (2022-2025) was conducted using sources from the Ministry of Health of Ukraine, the Ministry of Digital Transformation of Ukraine, the State Service of Ukraine on Medicines and Drugs Control, and international organizations. The search covered the period from February 2022 to early 2026, corresponding to the duration of full-scale war and martial law in Ukraine. Sources were included if they addressed pharmaceutical provision, access to medicines, or pharmacy services; reflected the functioning of healthcare or pharmaceutical systems under crisis or wartime conditions; provided empirical data, regulatory information, or analytical insights; and were published in peer-reviewed journals, official reports, or credible professional and industry platforms in English or Ukrainian. Sources were excluded if they were not relevant to pharmaceutical accessibility or healthcare delivery, lacked analytical or factual content, duplicated previously identified sources, or originated from unverified platforms. The initial search and screening resulted in 68 sources. After preliminary screening and relevance assessment, 32 sources were selected for full-text review. Ultimately, 21 sources were included in the final analysis, forming the evidence base of this study. Conclusions: The pharmaceutical sector in Ukraine demonstrated high adaptive capacity under martial law. Despite infrastructural damage, workforce shortages, disrupted supply chains, and reduced affordability, the implementation of adaptive mechanisms improved access to essential medicines. Alternative delivery models (mobile pharmacies and "Ukrposhta. Pharmacy"), digital monitoring tools, regulatory simplification, and financial support programs enhanced both physical and economic accessibility. The integrated approach combining regulatory flexibility, digitalization, and alternative distribution systems ensures continuity of pharmaceutical provision and strengthens long-term resilience under crisis conditions.

Mild cognitive impairment (MCI) represents a clinically critical and heterogeneous syndrome characterized by cognitive decline exceeding normal aging expectations, yet insufficient to impair daily functioning. As the prodromal stage of dementia, MCI offers a crucial intervention window during which therapeutic strategies can modify disease trajectory. Therefore, accurate and early diagnosis is of paramount importance. Artificial intelligence (AI) systems have demonstrated promising performance across multiple diagnostic modalities, yet their performance relative to that of physicians remains incompletely characterized in the literature. A systematic search of 6 electronic databases (PubMed/MEDLINE, EMBASE, Web of Science, Scopus, PsycINFO, and CINAHL) was conducted, resulting in 1358 records. Following de-duplication, systematic evaluation of abstracts and full text applying prespecified eligibility criteria, and exclusion of systematic reviews and meta-analyses, 71 primary research studies were included for analysis. The included studies comprised 71 primary research studies published between 2008 and 2026. AI systems achieved diagnostic accuracy ranging from 62% to 100%, with neuroimaging-based and multimodal approaches often reporting the strongest performance (85-99%). Only 8 studies directly compared AI with physician diagnostic performance. In these limited and methodologically heterogeneous comparisons, AI systems generally matched or exceeded physician performance, although the available evidence base remains small. An artificial neural network achieved 90.0% sensitivity and 84.78% specificity compared to a panel of physicians collectively 46.66% sensitivity, while GPT-4 outperformed junior neurologists in 1 language-based study (81% vs 41-49%; P&#x2005;<&#x2005;.001). AI assistance improved neurologist diagnostic performance in 1 large study by approximately 26% in area under the receiver operating characteristic curve (AUROC) (P&#x2005;<&#x2005;.05). Most studies used internal validation without external datasets, limiting conclusions about real-world generalisability. AI approaches for early diagnosis of MCI appear promising and may support or enhance physician performance in selected settings, but widespread clinical adoption requires prospective validation, standardized physician benchmarking, and rigorous evaluation in real-world clinical environments.

Upper limb motor impairment is a major contributor to long-term disability after stroke. Mirror therapy (MT) promotes motor relearning through visuomotor feedback, whereas transcranial direct current stimulation (tDCS) may facilitate neuroplasticity by modulating cortical excitability. We conducted a systematic review and meta-analysis to evaluate whether MT combined with tDCS provides additional benefits for poststroke upper limb rehabilitation. We searched PubMed, Embase, Web of Science, the Cochrane Library, CNKI, and Wanfang for randomized controlled trials (RCTs) published from 2015 to November 2025. Eligible studies enrolled adults with stroke-related upper limb motor deficits and compared MT&#x2005;+&#x2005;tDCS with MT alone or tDCS alone. Twelve RCTs involving 1024 participants were included. For Fugl-Meyer Assessment for Upper Extremity, MT&#x2005;+&#x2005;tDCS showed superior improvement versus MT alone (3 studies; I-squared statistic (I2)&#x2005;=&#x2005;21.9%; MD&#x2005;=&#x2005;12.65, 95% confidence interval [CI] 10.07-15.23) and versus tDCS alone (7 studies; I2&#x2005;=&#x2005;93.3%; MD&#x2005;=&#x2005;7.03, 95% CI 3.76-10.31). For activities of daily living, the effect on Modified Barthel Index versus MT alone was highly heterogeneous and imprecise (2 studies; I2&#x2005;=&#x2005;94.7%; MD&#x2005;=&#x2005;24.19, 95% CI&#x2005;-&#x2005;144.99 to 193.38), whereas MT&#x2005;+&#x2005;tDCS significantly improved Modified Barthel Index versus tDCS alone (5 studies; I2&#x2005;=&#x2005;42.7%; MD&#x2005;=&#x2005;9.29, 95% CI 6.36-12.22). For hand function, MT&#x2005;+&#x2005;tDCS improved Wolf Motor Function Test versus tDCS alone (3 studies; I2&#x2005;=&#x2005;80.3%; MD&#x2005;=&#x2005;4.90, 95% CI 0.59-9.22). Reported adverse events were generally mild and transient. MT combined with tDCS appears to provide additional benefits for poststroke upper limb motor recovery, with consistent improvements in Fugl-Meyer Assessment for Upper Extremity and favorable effects on daily functioning and hand performance in key comparisons. However, heterogeneity across protocols and imprecision in some outcomes limit certainty. Larger, rigorously designed RCTs with standardized MT dosing and tDCS parameters and longer follow-up are warranted.