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Per- and polyfluoroalkyl substances (PFAS) represent a critical class of persistent environmental contaminants with significant ecological and human health implications. However, the rapid emergence of novel PFAS has far outpaced the development of reference mass spectral databases. Here, Neural Per- and Polyfluoroalkyl Substances Mass Spectrometry (NPFAS-MS), a transfer learning-based neural network model, was developed to predict PFAS-specific high-resolution mass spectra. NPFAS-MS was fine-tuned from a pretrained model using PFAS tandem mass (MS/MS) spectra. NPFAS-MS outperformed other in silico spectral prediction models for PFAS spectra prediction across multiple spectral similarity metrics. In library searching tasks, libraries generated by other spectral prediction models showed top-1 recall between 42.1% and 55.4%, while NPFAS-MS demonstrated 71.1%. Applying the virtual PFAS mass spectral library generated with NPFAS-MS using 10,553 PFAS structures from the U.S. EPA and NORMAN databases to groundwater and aqueous film-forming foam (AFFF) samples revealed more potential PFAS than other mass spectral databases. Specifically, 38 potential PFAS were annotated in AFFF products and 40 in groundwater samples. NPFAS-MS enabled characterization of emerging PFAS, including ultrashort-chain, unsaturated, and substituted derivatives in environmental matrices. This advancement enables comprehensive environmental monitoring of rapidly evolving PFAS contamination. NPFAS-MS and associated resources were deployed as a web-based tool at https://cosbi10.ee.ncku.edu.tw/NPFAS_MS/, enabling both structure-to-spectrum prediction and library searching against 31,659 predicted PFAS spectra.

A BigSMILES string encodes the structural connectivity of any polymer chemistry and topology as a linear string. However, multiple BigSMILES strings can encode the same ensemble, making string-based searches for polymers in digital databases challenging. This work presents a canonicalization algorithm that breaks the degeneracy of the BigSMILES language for both linear and branched polymers and can reverse-translate canonicalized structures back into BigSMILES. The algorithm was validated on broadly representative polymer chemistries and topologies from the literature. First, the BigSMILES string is mapped onto a tree automaton, a type of state machine that accommodates branch points and recognizes the same ensemble of molecules that BigSMILES encodes. The automaton can then be minimized into a unique graph with the fewest states through existing algorithms. Finally, a human-readable canonicalized BigSMILES is obtained upon translation of the state machine transition rules back into a string. This robust canonicalization algorithm allows polymers to be searched rapidly in large database systems, making data findable, accessible, interoperable, and reusable (FAIR) and enabling the development of novel data-driven approaches with BigSMILES.

Narrative medicine is defined as medicine practiced with the competence to absorb, interpret, and respond to narratives. We hereby present a resource compiling narrative medicine texts, aiming to make narratives created by patients and/or their families fully accessible to citizens, by developing a documentary database and describing its characteristics. Active bibliographic search, March-June 2022 for narratives in Spanish and/or Catalan written after the year 2000 by patients and/or their companions. Subsequently, narratives up to June 2024 were included. The compilation is available in a searchable and open-source web ( https://osf.io/pk9b3/ ). Three hundred seventeen narratives, 50.14% written by women, are showing an increase from 2020 onwards. Texts are related to cancer/hematological diseases (45.11%), mental illnesses (10.41%), neurodegenerative diseases (9.4%). Personal stories (28.7%), autobiographical (11.29%), companion stories (5%), children's or young adult stories/narratives (8.87%). There are studies, websites, and digital platforms that recognize the importance of narrative as part of the therapeutic process and how it improves the experience of illness (either one's own or that of a family member). Despite this, to date, no one had compiled a collection of patient texts in Spanish and Catalan. For this reason, we believe our database is innovative and can pave the way for improving the patient-professional relationship.

This study is to systematically evaluate the efficacy of acupuncture for PHN and provide a visual overview of treatment landscape. A systematic search was conducted in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP), and Wanfang Database for systematic reviews (SRs) on acupuncture for PHN up to Apr 18, 2025. Studies were included if they were SRs of randomized controlled trials (RCTs) assessing traditional Chinese acupuncture interventions for PHN, and excluded if they involved non-traditional acupuncture, herpes zoster, or PHN prevention research. Two independent reviewers utilized Excel, EndNote 20, and R software for data analysis and assessed the quality of included studies using the AMSTAR2 tool. Of 351 identified records, 40 SRs met inclusion criteria, encompassing 926 RCTs, 63,493 patients, 13 types of acupuncture interventions and 29 outcomes. Acupuncture interventions, particularly fire needling, CPBLC, Fu's subcutaneous needling, plum-blossom needle, multi-acupuncture and multi-acupuncture + pharmacotherapy, showed the most robust benefits in improving effective rate, reducing visual analog scale (VAS) scores, and decreasing adverse reactions in PHN treatment. Despite most SRs reporting positive outcomes, the quality was generally low by AMSTAR2. Acupuncture could be a valuable adjunct to standard PHN treatment, offering benefits in overall efficacy, pain management and treatment safety. However, high-quality clinical trials and systematic reviews are needed to confirm these preliminary results and guide clinical practice.

Ferroptosis plays a significant role in pulmonary arterial hypertension (PAH), although its underlying mechanisms and key pathogenic genes remain unclear. Transcriptomic data from human PAH and control lung tissue were obtained from the Gene Expression Omnibus (GEO) database, whereas ferroptosis-related genes (FRGs) were sourced from the MsigDb and FerrDb databases. Differentially expressed FRGs (DE-FRGs) were identified through the intersection of FRGs with differentially expressed genes (DEGs). Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Key hub genes were identified through Least Absolute Shrinkage and Selection Operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and weighted correlation network analysis (WGCNA). Gene set enrichment analysis (GSEA) was conducted to explore the functional roles and associated pathways of hub genes. The relationship between hub genes and immune infiltration was investigated. Expression levels of potential biomarkers were validated via Quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) in two PAH animal models (monocrotaline-induced and Sugen5416 plus hypoxia-induced PAH). Finally, molecular docking was employed to screen potential therapeutic compounds. A total of 133 DE-FRGs were identified, with KEGG and GO analyses highlighting their involvement in intracellular iron homeostasis and ferroptosis. Hub genes, notably FZD7 and NFE2, were identified using LASSO, SVM-RFE, and WGCNA. Immune infiltration analysis suggested that monocytes and neutrophils play key roles in PAH pathogenesis. Validation in PAH animal models showed significant upregulation of Fzd7 and downregulation of Nfe2 in lung tissues of both MCT- and SuHx-induced PAH models. Molecular docking identified tetrachlorodibenzodioxin (TCDD) has good binding affinity. In summary, we investigated two ferroptosis-related biomarkers, FZD7 and NFE2, in PAH using transcriptomics, offering new insights into molecular mechanisms and potential targeted therapies for the disease.

Venous thromboembolism (VTE), which encompasses deep venous thrombosis (DVT) and pulmonary embolism (PE), is a common preventable complication in hospitalized patients. Risk assessment tools allow for easy stratification of patient VTE risk and have been demonstrated to reduce incidence of VTE. However, risk assessment tools remain underutilized in clinical practice. This scoping review aims to explore barriers and facilitators to VTE risk assessment usage to improve rates of hospital-acquired VTE and provide recommendations for future implementation strategies. Four databases (PubMed/MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Database of Systematic Reviews) were searched from January 1990 through December 2025, and 59 studies were included after selection by three independent reviewers. Themes related to 'decreased provider compliance' and 'difficulty of use' were the most commonly cited barriers. For facilitators, the majority of themes surrounded 'electronic medical record integration,' 'forcing functions,' and 'education.' A prevalence of barriers and a paucity of facilitators contribute to decreased VTE risk assessment usage. Hospital administrators and clinicians should address current barriers and promote facilitators during VTE risk assessment initiatives to maximize patient quality improvement outcomes. (Prospero ID: CRD42022360033).

Peripheral arterial disease (PAD) is highly prevalent among patients requiring hemodialysis access and reflects advanced systemic atherosclerosis. We evaluated whether PAD severity is associated with increased postoperative complications and loss of primary patency following dialysis access creation and performed stratified analyses by access type: arteriovenous fistula (AVF) and arteriovenous graft (AVG). We used the Vascular Quality Initiative database from 2011 to 2024, including patients undergoing upper-extremity hemodialysis access creation. PAD status was categorized as no PAD, asymptomatic PAD, claudication, or chronic limb-threatening ischemia (CLTI). Postoperative outcomes included 30-day mortality, prolonged length of stay, bleeding, thrombosis, and steal syndrome. Six-month primary patency was defined as time to first intervention, thrombosis, failure, or abandonment. Multivariable logistic and Cox regression analyses were performed in the overall cohort, followed by subgroup analyses stratified by AVF and AVG. A total of 78,607 patients were included (25,718 [32.7%] no PAD; 47,771 [60.8%] asymptomatic PAD; 2079 [2.6%] claudication; and 3039 [3.9%] CLTI). In the overall cohort, increasing PAD severity was independently associated with worse postoperative outcomes. Compared with patients without PAD, asymptomatic PAD (adjusted odds ratio [aOR] 1.55; 95% confidence interval [CI], 1.27-1.90; P < .001), claudication (aOR 2.20; 95% CI, 1.52-3.18; P < .001), and CLTI (aOR 3.01; 95% CI, 2.24-4.06; P < .001) were associated with progressively higher odds of 30-day mortality. Asymptomatic PAD (aOR 1.41; 95% CI, 1.16-1.71; P < .001) and CLTI (aOR 2.27; 95% CI, 1.82-2.83; P < .001) were associated with prolonged length of stay. Asymptomatic PAD was also associated with increased odds of postoperative bleeding (aOR 1.30; 95% CI, 1.01-1.69; P = .043). Claudication (aOR 3.57; 95% CI, 1.33-9.59; P = .012) and CLTI (aOR 2.87; 95% CI, 1.21-6.82; P = .017) were associated with increased odds of steal syndrome, whereas PAD severity was not associated with postoperative thrombosis. At 6 months, all PAD stages were strongly associated with loss of primary patency: asymptomatic PAD (adjusted hazard ratio [aHR] 5.09; 95% CI, 3.15-8.22; P < .001), claudication (aHR 4.79; 95% CI, 2.86-8.02; P < .001), and CLTI (aHR 5.05; 95% CI, 3.14-8.10; P < .001). Findings were consistent in analyses stratified by AVF and AVG. To our knowledge, this is the first study to comprehensively evaluate the impact of PAD and PAD progression on outcomes and patency of dialysis access. PAD severity is independently associated with increased postoperative complications, 30-day mortality, and loss of primary patency following dialysis access creation. Notably, even asymptomatic PAD confers significantly increased risk of mortality and loss of primary patency at 6 months. These findings highlight the importance of incorporating PAD status into preoperative risk stratification and dialysis access planning.

Klebsiella quasipneumoniae is a Gram-negative, non-motile, capsulated, facultative anaerobic rod within the K. pneumoniae species complex (KpSC) and is increasingly recognized as an opportunistic pathogen associated with bloodstream infections, urinary tract infections (UTIs), and other clinically significant conditions. Because it shares many phenotypic characteristics with K. pneumoniae, accurate diagnosis remains challenging in routine clinical settings, particularly in low-resource laboratories lacking access to molecular identification tools. In this study, we characterized the antimicrobial resistance (AMR) profile of a K. quasipneumoniae subsp. quasipneumoniae isolate obtained from a UTI case in Peshawar, Pakistan. The isolate exhibited a multidrug resistant (MDR) phenotype, with resistance to &#x3b2;-lactams, carbapenems, fluoroquinolones, and colistin (MIC 4&#xa0;&#xb5;g/mL), while remaining susceptible to aminoglycosides (amikacin and gentamicin) and tigecycline (MIC 2&#xa0;&#xb5;g/mL). Whole-genome sequencing (WGS) identified chromosomally encoded AMR determinants, including blaOKP-A-8, oqxAB, and fosA6, with no identifiable plasmid replicons detected. Multiple nonsynonymous mutations were observed in mgrB, pmrA/B, phoP/Q, lpxM, ompK35/36, and gyrA/parC, which have been previously associated with resistance phenotypes; however, their functional contribution in this isolate was inferred from genomic data and not experimentally validated. Virulence-associated loci such as fim, ecp, entB, and fepC were present, consistent with a classical (non-hypervirulent) phenotype. Phylogenomic analysis positioned the isolate (Kq1223) on a distinct branch relative to publicly available genomes, but given that this study is based on a single isolate, no definitive conclusions regarding regional lineage or evolutionary patterns can be established. The allelic profile identified by multilocus sequence typing (MLST) has not been previously reported and may represent an unassigned sequence type pending formal database validation. The presence of chromosomally mediated MDR, including colistin resistance, highlights potential therapeutic challenges and underscores the importance of accurate species identification and expanded genomic surveillance of Klebsiella species in clinical microbiology, particularly in resource limited settings.

Breast cancer detection remains a significant challenge in medical diagnostics. Traditional diagnostic methods are time-consuming, unable to detect complex patterns in medical images, and achieve only moderate accuracy with high false-positive rates. The study aims to identify the most effective and efficient algorithm for clinical application that reduces the rates of false positives and false negatives Various image segmentation methods are available, including U-Net (Ronneberger et al. in International Conference on Medical image computing and computer-assisted intervention, Springer International Publishing, Cham, 2015), Mask R-CNN (He et al. in Proceedings of the IEEE international conference on computer vision, 2017), fully convolutional networks (FCNs) (Long et al. in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015), and SegNet (Badrinarayanan et al. in IEEE Trans Pattern Anal Mach Intell, 39:2481-2495, 2017). However, some of these have complex architectures, require extensive training time, and consume significant computational resources. Beyond these methods, other well-cited studies include CNN & context aggregation, transformer-based segmentation, promptable models, and medical-focused architectures. However, all of these still struggle with issues like long-range dependencies caused by the locality of convolutions, architecture design, high computational cost, require large amounts of pre-training data, not always being optimal for domain-specific tasks, high compute and memory demands, and the need for fine-tuning for specialised high-accuracy applications that are frequently trained on relatively small datasets. Therefore, this study employs YOLO, which is a DL-based one-step object detection algorithm that greatly enhances speed, accuracy, and recognition across various categories of image processing and video processing. It also lies in its ability to perform real-time object detection and its time-sensitive applications. This study assesses the effectiveness of two cutting-edge deep learning based object detection algorithms, YOLOv5 and YOLOv8, for early breast cancer screening and detection. A total of 2,620 digitised mammography images from film, including normal, benign, and malignant cases with verified pathology, are included in the original Digital Database for Screening Mammography (DDSM) dataset. Improving patient outcomes and early detection rates depends on the efficacy of these algorithms.YOLOv5 and YOLOv8 are used in this study to detect breast cancer lesions, and their performance metrics are compared. The results for YOLOv5 show an F1 score of 0.97, a precision-recall score of 0.97, and confidence scores for precision and recall of 0.67 and 0.98, respectively. The results for YOLOv5 show an F1 score of 0.97, a precision-recall score of 0.97, and confidence scores of 0.67 and 0.98 for precision and recall, respectively. The mAP of YOLOv8 is 0.99. Therefore, YOLOv8 outperforms YOLOv5 across all assessed parameters, indicating that it is more suitable for clinical use in automated breast cancer screening and detection. With YOLOv8's improved recall and precision, there may be a greater likelihood of accurate, early detection of breast cancer, thereby reducing false positives and negatives. Future research will refine these models for real-time use to improve comprehensive cancer screening programs and investigate their integration with other diagnostic techniques.

Robotic assisted minimally invasive direct coronary artery bypass grafting (RA-MIDCAB) represents an effective alternative to conventional coronary artery bypass grafting for revascularization of the left anterior descending artery (LAD) with the left internal thoracic artery (LITA). The first LITA-LAD bypass using a robot was performed in 1998. Since then, progress has been made, bringing the benefits of reduced surgical trauma and faster patient recovery. We report on our single-center initial experience of RA-MIDCAB surgery. This retrospective study included the first 27 patients who underwent RA-MIDCAB between July 2024 and September 2025. Baseline characteristics, perioperative and postoperative outcomes were collected from institutional databases and patient records. All RA-MIDCAB procedures were successfully performed by 2 expert surgeons. Intraoperative graft patency was measured. No mortality was registered, neither on 30-day nor long-term. Conversion to sternotomy never occurred. The mean surgery time was 245.7 minutes. Early outcomes included postoperative new onset atrial fibrillation (n = 2; 7.4%) and respiratory failure (n = 1; 3.7%). Mean length of intensive care unit stay and hospital stay were, respectively, 2.4 and 3.9 days. No patients underwent repeat revascularization or suffered major adverse cardiovascular events. RA-MIDCAB surgery can be performed safely with excellent results. Despite an initial investment, starting a RA-MIDCAB program improves quality of care.

Cancer diagnoses impact adherence to antidiabetic medications, but limited research has focused on patients with prostate cancer and type 2 diabetes (T2DM). We investigated adherence trajectories to oral antidiabetic medications one year before and after a prostate cancer diagnosis and identified risk factors. This retrospective cohort study used the 2011-2021 MarketScan Commercial and Medicare Supplemental databases. We included newly diagnosed prostate cancer patients with T2DM with continuous insurance enrollment. We applied group-based trajectory modeling with a beta distribution to evaluate adherence patterns before and after prostate cancer diagnosis. Model covariates included age, total number of medications, number of antidiabetic medications, the Charlson Comorbidity Index (CCI), cost, insurance type, and complicated diabetes from the year before diagnosis. Metastasis and cancer treatments were included in the model after diagnosis. The study included 7864 patients (mean age = 74.5 &#xb1; 7.1). Three adherence trajectories were identified before diagnosis: consistently high adherence, steady decliners, and consistently low adherence. After diagnosis, a fourth trajectory revealing a moderate decline emerged. Over half (61.2%) changed adherence patterns after diagnosis. Among those with consistently high adherence before diagnosis, 57.8% transitioned to a lower adherence trajectory. In contrast, 58.5% of steady decliners and 57.6% of consistently low adherents transitioned to a higher adherence trajectory after diagnosis. Predictors of high adherence included older age, fewer antidiabetic medications, lower CCI, and complicated diabetes before diagnosis. After diagnosis, fewer antidiabetic medications and complicated diabetes remained predictive of high adherence. Patterns of adherence to oral antidiabetic medications undergo substantial changes after a prostate cancer diagnosis. Targeted interventions are needed to support and facilitate effective diabetes management in this population.

Surgeon case volume has been linked with outcomes across many orthopaedic procedures, but its influence on distal radius fracture fixation remains uncertain. (1) For distal radius fracture surgery, at what surgeon annual case volume does the risk of complications plateau? (2) For distal radius fracture surgery, at what surgeon annual case volume does the risk of revision surgery plateau? A retrospective, population-based study was performed using administrative health databases in Ontario, Canada, accessed through the Institute for Clinical Evaluative Sciences, an independent, nonprofit research institute that houses linkable, individual-level health administrative data for Ontario's publicly funded healthcare system. Between 2010 and 2020, a total of 27,945 adult patients (&#x2265; 18 years of age) underwent surgical fixation for acute isolated distal radius fracture. After applying prespecified inclusion and exclusion criteria, including exclusion of patients with open fractures, polytrauma, compartment syndrome, neurovascular injury, emergent presentations, incomplete administrative records, or prior distal radius surgery, a final cohort of 13,389 patients (48% of the initial cohort) was included (71% [9533] females; mean &#xb1; SD age 56 &#xb1; 15 years). Surgeon annual case volume, defined as the number of distal radius fracture fixations performed in the preceding year, was the primary exposure. The primary outcome was a composite of complications, including postoperative complications or revision surgery up to 10 years after the index procedure; revision surgery was also analyzed separately. Cox proportional hazards models were adjusted for demographics, comorbidities, fracture type (intraarticular versus extraarticular), fixation method, and hospital type (teaching versus nonteaching). Restricted cubic spline models were used to assess nonlinearity and identify potential volume thresholds. Surgeons performing < 5 distal radius fracture fixations annually had the highest hazards of both composite complications and revision surgery. Complication hazards declined with increasing surgeon volume and stabilized after approximately 20 procedures per year; consistent with this threshold, surgeons performing 20 to 24 procedures annually demonstrated a 37% lower hazard of complications compared with surgeons performing < 5 procedures per year (HR 0.63 [95% confidence interval (CI) 0.49 to 0.81]; p = 0.004). Revision surgery hazards likewise declined with increasing surgeon volume but plateaued at a lower threshold of approximately 10 procedures per year; surgeons performing 10 to 14 procedures annually had a 56% lower hazard of revision surgery compared with surgeons performing < 5 procedures per year (HR 0.44 [95% CI 0.33 to 0.60]; p < 0.001). Surgeons who perform distal radius fracture fixation infrequently may benefit from focused strategies to support maintenance of procedural proficiency including continuing professional development and enhanced surgical training. At a systems level, the lower risk of complications observed among surgeons performing at least 20 procedures per year have implications for training programs, ongoing competence frameworks, and health-system planning, particularly in settings where referral options may be limited. Level III, prognostic study.

Background:The COVID-19 pandemic disrupted work environments worldwide, increasing productivity loss through absenteeism and presenteeism. Identifying key associated factors is essential for informing workplace health strategies during public health crises. Methods/Project: A systematic review and meta-analysis were conducted following PRISMA guidelines, using comprehensive searches of seven electronic databases from inception through January 2024. Studies were systematically selected based on predefined eligibility criteria, and 24 studies examining individual and work-related factors associated with work productivity loss were included. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal tools. Correlation coefficients were synthesized using a random-effects meta-analysis of correlations in STATA 17.0, and heterogeneity was evaluated using the I2 statistic and Cochran's Q test. Findings: Twenty-one factors were analyzed. Job stress, fear of COVID-19, mental health problems, job insecurity, turnover intention, exhaustion, and job demands exhibited moderate positive correlations with productivity loss during the COVID-19 pandemic. Fear of COVID-19 and mental health problems showed relatively large positive correlations with presenteeism. General health status was the factor most strongly associated with absenteeism, exhibiting a moderate negative correlation. Conclusions/Application to Practice: These findings identify key individual and work-related determinants of productivity loss during pandemics. The results support the development of targeted workplace health promotion, mental health support, and preparedness strategies to mitigate productivity loss during future public health emergencies.

To conduct a longitudinal bibliometric analysis of sleep-related research output across 6 major health disciplines and to identify the specific sleep variables most explored within these fields. Using the Journal Citation Reports (JCR), the top 10 high-impact journals from 6 categories were evaluated: Clinical Psychology, Nutrition & Dietetics, Pediatrics, Dentistry, Geriatrics & Gerontology, and Sports Sciences. Publication trends for the term "sleep" were analyzed in Web of Science Core Collection (WoS) database across 3 5-year timeframes: 2010-2014 (T1), 2015-2019 (T2), and 2020-2024 (T3). Total publication volume of each journal, relative percentage growth in term "sleep", and prevalence of specific sleep-related descriptors (e.g., insomnia, obstructive sleep apnea - OSA, sleep quality) were quantified. Sleep-related research accounted for 0.4% to 2.2% of the total scientific output across the analyzed categories. While total scientific publication volume increased across all fields over the 15-year period, the growth of sleep-related research outpaced general journal expansion by more than 1.5-fold in 4 of the 6 categories. The surge between T2 and T3 was most pronounced in Sport Sciences (140.6%) and Geriatrics (49.4%). Overall, the total relative increase from T1 to T3 was most substantial in Sport Sciences (305.3%) and Clinical Psychology (95.5%), followed by Dentistry (60.0%) and Geriatrics (51.2%). Insomnia, OSA and Sleep Quality were the most frequent descriptors across disciplines. Our findings reveal that diverse scientific disciplines are increasingly incorporating sleep as a key research variable, as evidenced by the significant growth in sleep-related publications within their specialized journals. This trend underscores a growing recognition of sleep's fundamental role in physiological mechanisms and its crucial influence on health. The broader dissemination of sleep science across these distinct fields enables researchers to develop specialized interventions aimed at reducing pathologies and enhancing quality of life through domain-specific practices. not applicable for this type of study.

Antipsychotic medications are associated with increased cardiovascular morbidity and mortality in patients with serious mental illness (SMI). Clozapine-induced myocarditis and cardiomyopathy are well established; however, emerging pharmacovigilance and observational data suggest that cardiotoxic risk extends beyond clozapine to other antipsychotic agents. We conducted a narrative review of the literature (2000-2026), including pharmacovigilance databases (VigiBase, FAERS), cohort studies, case series, and consensus guidelines. Emphasis was placed on epidemiology, mechanisms, comparative risk, clinical presentation, diagnosis, and practical management strategies relevant to psychiatric practice. Clozapine demonstrates the highest risk of myocarditis (estimated incidence 0.3-3%), typically within the first 4-8 weeks of treatment. Pharmacovigilance data consistently identify secondary signals for quetiapine and olanzapine, with weaker but present associations for risperidone, aripiprazole, and other agents. Proposed mechanisms include hypersensitivity myocarditis, inflammatory cytokine activation, oxidative stress, and mitochondrial dysfunction. Early recognition through symptom monitoring and selective biomarker use significantly improves outcomes. Cardiotoxicity associated with antipsychotics is not limited to clozapine. Psychiatrists should adopt a risk-stratified approach incorporating cautious titration, early symptom recognition, and targeted monitoring. Greater awareness and multidisciplinary collaboration are essential to optimize both psychiatric and cardiovascular outcomes.

Poor adherence to psychopharmacological treatment may contribute to relapses in bipolar disorder (BD). We performed a systematic review and meta-analysis to identify factors associated with poor adherence in BD. The protocol was registered in Open Science Framework Registries (https://doi.org/10.17605/OSF.IO/2KZFJ). We searched main electronic databases through March 2025. Random-effects meta-analyses were performed to obtain pooled odds ratios (ORs) and standardized mean differences (SMDs) for relevant correlates. We included 19 studies. Subjects with poor adherence were more likely to be younger (SMD&#x2009;=&#x2009;-0.22, 95% CI: -0.42--0.02) and to have lower education (SMD&#x2009;=&#x2009;-0.34, 95% CI: -0.55--0.12), and less likely to be in a relationship (OR&#x2009;=&#x2009;0.54, 95% CI: 0.34-0.86). Moreover, earlier age at onset (SMD&#x2009;=&#x2009;-0.29, 95% CI: -0.53--0.04), psychotic features (OR&#x2009;=&#x2009;1.58, 95% CI: 1.30-1.92), a history of suicide attempts (OR&#x2009;=&#x2009;1.36, 95% CI: 1.03-1.78), a higher number of manic (SMD&#x2009;=&#x2009;0.34, 95% CI: 0.08-0.61) and mixed (SMD&#x2009;=&#x2009;0.16, 95% CI: 0.03-0.28) episodes, and more hospitalizations (SMD&#x2009;=&#x2009;0.53, 95% CI: 0.32-0.73) all emerged as correlates of poor adherence. Also, cannabis (OR&#x2009;=&#x2009;2.34, 95% CI: 1.79-3.07) and alcohol use disorders (OR&#x2009;=&#x2009;1.71, 95% CI: 1.39-2.12), comorbid generalized anxiety disorder (OR&#x2009;=&#x2009;3.70, 95% CI: 1.90-7.22), and comorbid personality disorders (OR&#x2009;=&#x2009;5.54, 95% CI: 1.32-23.15) were associated with poor adherence. Finally, poorly adherent individuals had higher global severity (SMD&#x2009;=&#x2009;0.21, 95% CI: 0.01-0.41), lower insight (SMD&#x2009;=&#x2009;-0.74, 95% CI: -1.08--0.41), and lower global functioning (SMD&#x2009;=&#x2009;-0.60, 95% CI: -0.87--0.34). No differences were estimated for other variables. This meta-analysis showed that poor adherence in people with BD is associated with specific correlates. Although evidence was generally weak due to small effect sizes, imprecision, inconsistency, and potential publication bias, our findings highlight the importance of strategies to improve adherence.

Growing evidence supports the therapeutic role of nutraceuticals as complementary and alternative therapies in patients with type 2 diabetes mellitus (T2DM). Resveratrol, a natural polyphenol compound has been shown to modulate metabolically disturbances include insulin resistance and lipid profile disturbances. This umbrella review and meta-analysis study conducted to assess the effect of resveratrol on glycemic indices and lipid profile in T2DM. The study was performed by using the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines (PRISMA) checklist. The PubMed, Web of Sciences, and Google Scholar databases were used to search the published papers up to 2025. The AMSTAR questionnaire was used for assessing the quality of eligible studies. Additionally, The Cochran Q test and I2 statistics were used for examining heterogeneity. Of 10 meta-analyses evaluating the resveratrol effects on glycemic indices and lipid profile showed no significant effects on fasting blood sugar, glycosylated hemoglobin A1c (HbA1c), insulin, homeostatic model assessment for insulin resistance (HOMA-IR), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), triglyceride (TG), total cholesterol (TC), despite significant heterogeneity. Nevertheless, based on SMD analyses, resveratrol supplementation showed only significant effects on LDL-C reduction. Also, significant decline in serum insulin level was observed for sample size&#x2009;&#x2265;&#x2009;500 and study number&#x2009;&#x2265;&#x2009;10. Given the high heterogeneity and limitations attributed to the study, resveratrol supplementation was not considered as a beneficial agent in declining glycemic indices and lipid profile in patients with T2DM.

Safety-net hospital (SNH) status is associated with high perioperative morbidity in pulmonary resection. Multiple etiologies have been proposed, including delays in diagnosis and inefficient care pathways. In integrated health systems, surgical volume has been shown to improve outcomes in pulmonary resection. However, whether surgical volume can overcome the inherent challenges of SNHs is unclear. We hypothesize that surgical volume is associated with improved outcomes at SNHs. The 2016 to 2021 Nationwide Readmissions Database was queried for all adult (&#x2265;18 years) patients undergoing elective lobectomy for lung cancer. Centers in the top quartile of Medicaid or self-pay/uninsured admissions were defined as SNHs. SNHs were further stratified by lobectomy caseload as a low-volume hospital (<10 cases/y), medium-volume hospital (10-33 cases/y), or high-volume hospital (>33 cases/y). Multivariable regressions were built to consider the independent association of hospital volume on acute clinical and financial outcomes among patients treated at SNHs. Care at high-volume centers remained associated with significantly reduced likelihood of overall major morbidity (adjusted odds ratio [AOR], 0.81; 95% CI, 0.68-0.97), respiratory complications (AOR, 0.79; 95% CI, 0.65-0.96), need for blood transfusion (AOR, 0.67; 95% CI, 0.48-0.93), and nonhome discharge (AOR, 0.66; 95% CI, 0.48-0.88). Care at high-volume centers was also associated with a decrease in duration of hospitalization (&#x3b2; = -1.02 days; 95% CI, -1.48 to -0.54 days) and overall expenditures (&#x3b2; = -$4360; 95% CI, -$7020 to -$1700). Surgical volume is associated with improved outcomes in pulmonary resection at SNHs. Patients who are eligible for care only at SNHs can still benefit from undergoing pulmonary resection at a high-volume center.

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogenesis is complex and involves the interaction of multiple genes and multiple pathways. Recent epidemiological studies have shown that the risk of BLCA in patients with T2DM is significantly higher than that in non-diabetic people, suggesting that there may be a potential biological correlation between the two. Genomic studies have opened up new ways to reveal the common genetic characteristics of T2DM and BLCA. However, most of the current studies only focus on a single disease, and the comorbidity mechanism of these two diseases still needs to be further explored. Firstly, the datasets of BLCA and T2DM were downloaded from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. Differential expression genes (DEGs) were identified using the Limma package. Weighted gene co-expression network analysis (WGCNA) was employed to determine the co-expression modules related to BLCA and T2DM, and the common potential target genes were obtained. Correlation analysis and enrichment analysis were conducted on these target genes. Then, the best diagnostic biomarker - plasminogen activator (PLAU) was selected using machine learning algorithms. Additionally, the role of PLAU in the progression of T2DM and BLCA was confirmed through immunohistochemistry, Western Blot, and Edu experiments. Finally, small molecule compounds targeting PLAU were discovered through molecular docking and virtual screening, and the inhibitory effect of these small molecules on the progression of bladder urothelial carcinoma was verified through experiments. This study conducted a combined limma and WGCNA analysis on the T2DM and BLCA datasets to identify 42 common potential target genes, which were enriched in pathways such as innate immunity. Using machine learning algorithms such as LASSO and SVM, PLAU was identified as the best diagnostic marker for T2DM combined with BLCA. It was significantly highly expressed in both T2DM and BLCA samples, and high expression of PLAU predicted a shorter overall survival period for BLCA patients. Experimental results confirmed that PLAU was highly expressed in BLCA tissues and increased with the severity of malignancy. Knockdown (sh-PLAU) of PLAU could inhibit cancer cell proliferation and migration in a high-glucose environment, while overexpression (oe-PLAU) still promoted cancer cell progression in a low-glucose environment. Finally, molecular docking virtual screening revealed that the small molecule compound epigallocatechin gallate (EGCG) could target and inhibit PLAU, and effectively inhibited the proliferation and invasion of BLCA cells in experiments. The results of this study reveal the role of PLAU, a common characteristic gene of T2DM and BLCA, whose high expression drives tumor progression and poor prognosis. Moreover, small molecule drugs targeting PLAU, such as EGCG, have therapeutic potential. This study provides a new direction for accurate diagnosis and treatment of BLCA patients with T2DM.