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To evaluate the performance of machine learning models in predicting liver metastasis in colorectal cancer (CRC) patients using the SEER database and external validation from Ningbo No.2 Hospital. The data on patients with colorectal cancer were obtained from Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2023. Patients were classified into training (n = 29017) and testing sets (n = 12437). The data were used to build eight machine learning models to predict liver metastasis in colorectal cancer patients. A total of 11 clinical variables were entered into these models. Model performance was measured with the area under the receiver operating characteristic curve (ROC) and area under precision-recall curve (AUPR). The models were visualized and interpreted using the SHAP method. In the SEER database cohort, the incidence of liver metastasis was 7.2% (2977/41,454). Of the eight machine learning models, Gradient Boosting (GB) had the best AUC (0.837) and AUPR (0.294). Upon external validation, the GB model achieved an AUC of 0.730 and an AUPR of 0.278. We explored the significance of features in the model through SHAP analysis. CEA, N stage and T stage were the heavily weighted factors used by the GB. An online calculator was developed for clinical use. The GB model demonstrates robust predictive performance for liver metastasis in CRC, validated internally and externally, and presents a potentially valuable tool for clinical decision-making.

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.

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.

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.

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.

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.

We aimed to estimate contraceptive claims prevalence among reproductive-aged women with selected autoimmune diseases compared with those without these conditions in 2019. Using IBM MarketScan Commercial Claims and Encounters and Multistate Medicaid databases, we analyzed permanent and prescription contraception claims prevalence among women aged 15-49 years with inflammatory bowel disease (IBD), multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and among women with these autoimmune diseases using selected fetotoxic medications. We calculated adjusted prevalence ratios (aPR) and 95% confidence intervals (CIs) using Poisson regression. In 2019, most (60-75%) insured women with selected autoimmune diseases did not have contraception claims. Among commercially insured women, those with SLE were less likely to have claims for any method assessed (aPR = 0.90, 95% CI: 0.86-0.95), less likely to have combined hormonal contraceptives (CHC) claims (aPR = 0.65, 95% CI: 0.60-0.70), and more likely to have long-acting reversible contraceptives (LARC) claims (aPR = 1.14, 95% CI: 1.05-1.23) than women without selected autoimmune diseases. Among Medicaid-insured women, those with IBD, MS, RA, and SLE were more likely to have claims for any method assessed (aPR = 1.23-1.31) and LARC (aPR = 1.23-1.47) than women without selected autoimmune diseases. Women with SLE with lupus nephritis were less likely to have CHC claims (aPR = 0.56, 95% CI: 0.35-0.89) than females without selected autoimmune diseases. Among those using selected fetotoxic medications, 70.4% of commercially insured women and 72.2% of Medicaid-insured women did not have contraceptive claims in 2019. Less than half of the insured women with selected autoimmune diseases had permanent or prescription contraception claims in 2019.

Tranexamic acid (TXA) is a widely used antifibrinolytic agent in surgical and trauma settings in adults. This study aimed to evaluate the efficacy and safety of TXA in pediatric trauma patients across various clinical outcomes. A comprehensive literature search was conducted across 4 databases. We included clinical trials and observational studies that reported the use of TXA in pediatric trauma patients (aged ≤18 years). Data extraction and risk-of-bias assessment were performed by independent reviewers. Meta-analyses were conducted with RStudio software. A total of 12 studies (2 randomized controlled trials [RCTs] and 10 observational) involving 66,398 pediatric trauma patients were included. Tranexamic acid was not significantly associated with reduction in hospital mortality (OR = 1.06; 95% CI, 0.32-3.45) but was associated with significantly shorter hospital stays (mean difference [MD] = -1.49; 95% CI, -2.43 to -0.56). The need for emergency mechanical ventilation was higher among the TXA group (OR = 4.29; 95% CI, 2.52-7.31), whereas the need for mechanical ventilation at discharge was lower (OR = 0.23; 95% CI, 0.08-0.64). Tranexamic acid use did not significantly alter the risk of thromboembolic events (OR = 0.72; 95% CI, 0.19-2.79) or poor neurological outcomes (OR = 2.51; 95% CI, 0.86-7.35). Tranexamic acid may reduce hospital length of stay in pediatric trauma patients, with inconsistent effects on mortality and adverse events. Its use should be individualized based on injury severity and resource availability. Further high-quality research is needed to confirm these findings and clarify the role of TXA in pediatric trauma care.

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 β-lactams, carbapenems, fluoroquinolones, and colistin (MIC 4 µg/mL), while remaining susceptible to aminoglycosides (amikacin and gentamicin) and tigecycline (MIC 2 µg/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.

Designing carbanions capable of efficient CO2 chemisorption is an important approach for advancing reactive capture and conversion technologies. In this work, we integrate a nucleophilicity prediction model, trained using directed message-passing neural networks on Mayr's Reactivity Database (a large, experimentally derived collection of nucleophilicity, electrophilicity, and sensitivity parameters for organic and inorganic molecules), with the Hierarchical Variational Autoencoder (HierVAE) to generate novel carbanions. Fine-tuning the pretrained latent space with predicted log k values yields structurally diverse carbanions with strong CO2 reactivity and high generation success. Analysis of the top candidates reveals consistent trends in α-substitution, electron-withdrawing groups, and synthetic accessibility. Density functional theory (DFT) validation of the highest-ranked reactive candidates reveals good agreement for electronically stabilized systems bearing strong electron-withdrawing substituents, while deviations increase for weakly stabilized or sterically distinct carbanions, defining a clear domain of applicability for the predictive model. This study demonstrates the promise of property-guided generative AI for discovering novel carbanions for room-temperature CO2 chemisorption.

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.

Hand surgery is a multidisciplinary field involving surgical and rehabilitation disciplines. Although clinical care often requires collaboration between specialties, the academic structure of hand surgery research and the relative contributions of different disciplines remain incompletely characterized. We studied specialty representation and patterns of interdisciplinary collaboration in hand surgery literature over four decades. A cross-sectional bibliometric analysis was done on publications indexed in PubMed from six dedicated hand surgery journals between 1980 and 2025. Bibliographic metadata were retrieved through automated database queries and author affiliations were analysed using computational text pattern matching to classify specialties represented in each publication. Specialty representation and interdisciplinary collaboration were assessed across the decades. A total of 22,021 publications were identified, of which 18,229 contained analysable affiliation data. Orthopaedic surgery represented the most frequent specialty involvement, followed by hand surgery units and plastic surgery. Plastic surgery maintained a relatively stable proportion across the decades despite reports of declining participation in hand surgery training. Interdisciplinary collaboration increased substantially over time, with a fivefold increase in multi-specialty publications across the decades. Hand therapy was the most frequent non-surgical collaborator and surgeon-hand therapist publications increased progressively over the study period. Hand surgery research has evolved toward greater interdisciplinary collaboration. Orthopaedic surgery remains the most represented specialty, while plastic surgery maintains a stable academic contribution. These findings support the concept of hand surgery as a shared academic domain in which collaboration between surgical and rehabilitation disciplines may drive future innovation and improvements in patient outcomes.

Alzheimer's disease (AD) is characterized by progressive cognitive decline accompanied by profound disturbances in cerebral energy metabolism. Mitochondrial dysfunction has long been implicated in AD pathophysiology; however, the specific contribution of mitochondrial enzymes in human disease remains fragmented across heterogeneous studies. Enzymes regulating carbon entry into the tricarboxylic acid cycle, oxidative phosphorylation, and redox balance represent key metabolic control points whose dysfunction may contribute to neuronal vulnerability. To systematically synthesize human evidence on mitochondrial enzyme alterations in Alzheimer's disease and to evaluate the feasibility of quantitative meta-analysis based on current reporting practices. A systematic literature search was conducted in PubMed, Scopus, and Web of Science from database inception through January 2026 in accordance with PRISMA 2020 guidelines. Studies were included if they investigated mitochondrial enzymes in human postmortem brain tissue, human-derived cellular models, or peripheral biospecimens. Risk of bias was assessed using the ROBINS-I tool. The feasibility of meta-analysis was evaluated based on the availability and comparability of group-level summary statistics. Fifteen studies met the eligibility criteria and were included in the final synthesis. Mitochondrial enzymes involved in carbon entry into the tricarboxylic acid cycle, oxidative phosphorylation, redox regulation, and neurotransmitter-linked mitochondrial metabolism were the most frequently investigated targets. Direct enzyme-activity evidence most consistently implicated selected metabolic control points, particularly PDHC and &#x3b1;KGDHC, whereas additional studies supported mitochondrial impairment through protein or post-translational modification changes, respiratory dysfunction, redox alterations, or RNA-regulatory mechanisms. Quantitative meta-analysis was not feasible due to heterogeneous assay methodologies, variable normalization strategies, and inconsistent reporting of group-level summary statistics. Human evidence consistently implicates mitochondrial enzyme dysfunction as a central metabolic feature of Alzheimer's disease. However, progress toward cumulative quantitative synthesis remains limited by methodological heterogeneity and incomplete reporting of enzyme activity outcomes. Standardized measurement and reporting of mitochondrial enzyme alterations will be essential to advance mechanistic understanding and enable future meta-analytic integration.

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.

Anxiety, which involves feelings of tension, worry, and physiological changes in the body, can have significant impacts on patients, including an increased risk of mortality. In ophthalmic surgeries, particularly cataract procedures, anxiety levels tend to be high, often stemming from fears of blindness or surgical failure. This study aimed to determine the best and most effective interventions to reduce anxiety in patients undergoing cataract surgery. Systematic reviews, with or without meta-analysis. Additionally, selected studies were required to meet two mandatory criteria from the Database of Abstracts of Reviewers of Effects and be English-language review articles published between January 2010 and 2025 that met these criteria and focused on anxiety reduction strategies in patients undergoing cataract surgery. Finally, out of 75 relevant papers, 5 review studies with 9638 patients were eligible and included in the study. (1) Non-pharmacological interventions (educational videos, patient education, aromatherapy, relaxation techniques, etc.) significantly reduced mean preoperative anxiety compared to the control group. (SMD: -2.14, 95% CI: -3.48 to -0.79; p&#x2009;<&#x2009;0.001). (2) Nursing techniques could reduce pain and anxiety during the operation (SMD&#x2009;=&#x2009;-&#x2009;1.19; 95% (CI): -1.96 to -0.43; p&#x2009;=&#x2009;0.002). (3) The use of anxiolytics (melatonin) could reduce postoperative anxiety in cataract patients. (SMD&#x2009;=&#x2009;-&#x2009;0.55; 95% CI: -0.95 to -0.15; p&#x2009;=&#x2009;0.007). (4) Music therapy. This review study identified techniques and strategies to reduce stress in patients undergoing cataract surgery. These strategies, tailored to patient needs, can be implemented individually or in combination, and prioritizing individual patient needs to enhance patient well-being and lead to several positive clinical outcomes and potentially decrease healthcare costs. Future clinical trials are essential to the integration of new technologies and identifying the most effective methods for widespread implementation.

Patients with eosinophilic asthma are at increased risk of poor asthma control despite optimized standard therapy. Depemokimab, a novel long-acting anti - interleukin-5 monoclonal antibody, has shown efficacy in reducing exacerbations; however, its effects across clinical outcomes have not been comprehensively synthesized. PubMed, Embase, Cochrane databases, and ClinicalTrials.gov were searched for randomized controlled trials (RCTs) comparing depemokimab with placebo. Outcomes included Asthma Control Questionnaire-5 (ACQ-5), asthma exacerbation rate, quality of life assessed by St. George's Respiratory Questionnaire (SGRQ), and adverse events. Results were reported as mean differences (MDs) or incidence rate ratios (IRRs) with 95% confidence intervals (CIs), and heterogeneity was assessed using the I2 statistic. Four RCTs (n&#x2009;=&#x2009;954) were included. Depemokimab did not significantly improve ACQ-5 (MD -0.30; 95% CI -0.92 to 0.32; I2&#x2009;=&#x2009;73.6%; p&#x2009;=&#x2009;0.23), but reduced exacerbation rates (IRR 0.47; 95% CI 0.36 to 0.59; I2&#x2009;=&#x2009;0%; p&#x2009;<&#x2009;0.001) and modestly improved SGRQ scores without reaching the MCID (MD -2.80; 95% CI -5.38 to -0.23; I2&#x2009;=&#x2009;0.0%; p&#x2009;=&#x2009;0.033). Safety was comparable to placebo. Depemokimab reduced exacerbations and had a comparable safety profile, but did not improve symptom control or achieve the MCID for health-related quality-of-life. The study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420261282685).

The application of machine learning (ML) models in healthcare management offers high potential. In particular, resource allocation and operational decision-making in intensive care units (ICUs) can benefit from ML predictions, leading to improvements in patient outcomes and operational efficiency. However, the generalizability of these models across diverse hospital settings with potentially different patient populations remains a critical challenge. This study examines the generalizability of ML-based ICU outcome prediction models built using external data. We utilize data from two sources: a European University Hospital (EUH) dataset from Universit&#xe4;tsklinikum Carl Gustav Carus Dresden, Germany and the Medical Information Mart for Intensive Care (MIMIC)-IV database, representing different healthcare systems and patient populations. Our approach evaluates multiple models of varying architectures and complexity across three common prediction tasks in ICU settings (mortality, length of stay, and readmission), analyzes the impact of data availability on model performance, and applies interpretability techniques to identify features and scenarios where models succeed or fail in new environments. We found that locally trained models generally outperform those using external data when sufficient local data is available. Low and medium complexity models, such as generalized additive models, demonstrate significantly superior generalizability compared to high complexity models and require substantially less local data for high-quality predictions, offering evidence-based guidance for healthcare managers dealing with limited data resources. Our results demonstrate how interpretability techniques can identify dataset differences that hinder generalizability, providing valuable insights for healthcare practitioners in implementing ML solutions across diverse hospitals. This research contributes to the development of more generalizable and interpretable ML models in healthcare.

Hepatocellular carcinoma (HCC) arises predominantly in cirrhotic livers and remains one of the leading causes of mortality related to chronic liver disease. Chronic inflammation, immune dysfunction, and tissue remodeling sustain hepatocarcinogenesis, making circulating cytokines promising candidates for clinical biomarkers. To critically synthesize evidence on the role of interleukin-6 (IL-6) and interleukin-10 (IL-10) as biomarkers associated with HCC occurrence, staging, therapeutic response, and prognosis in individuals with cirrhosis. A narrative review was performed based on a structured literature search in PubMed/MEDLINE, SciELO, Europe PMC, and publishers' databases (2010-2025), using descriptors related to hepatocellular carcinoma, cirrhosis, IL-6, IL-10, prognosis, and biomarkers. Clinical studies assessing serum or plasma cytokine levels, meta-analyses, mechanistic reviews, and contemporary clinical guidelines were prioritized. The majority of clinical studies indicate a consistent association between elevated IL-6 levels and poor prognosis, increased tumor burden, systemic inflammation, and inferior outcomes following both systemic and locoregional therapies. For IL-10, the evidence supports elevated levels in a substantial proportion of patients with HCC, with signals of association with tumor-related immunosuppression and worse outcomes in advanced disease, although some studies suggest a context- and disease-stage-dependent role. IL-6 demonstrates greater consistency as a biomarker of progression and prognosis in cirrhosis-associated HCC, whereas IL-10 emerges as an immune regulatory marker with heterogeneous behavior depending on disease etiology, tumor stage, and the tumor microenvironment. Standardization of assay methodologies, cutoff values, and multivariable prognostic models is essential for clinical implementation.

The aim of this review is to synthesize current evidence on the interaction between Hippo-YAP signaling and EMT in the malignant transformation of oral potentially malignant disorders (OPMDs) to oral squamous cell carcinoma (OSCC) and to examine their potential utility as biomarkers and therapeutic targets. Design: Following PRISMA 2020 guidelines, five electronic databases (PubMed, Web of Science, Scopus, LIVIVO, and Embase) were utilized for this search. Eligible studies included human tissue-based investigations and complementary in vitro experiments evaluating YAP/TAZ or EMT markers in OPMDs and OSCC. Risk of bias was assessed using QUIN, SYRCLE, and JBI tools. From 2,208 records, 12 studies (26 datasets) were included. Across study designs, Hippo-YAP dysregulation and EMT activation were consistently observed across the normal to OPMD to OSCC progression. YAP nuclear localization correlated with reduced E-cadherin and increased vimentin, N-cadherin, Snail, and Slug expression. Crosstalk between YAP and MAPK/ERK, PI3K/Akt/mTOR, and Wnt/&#x3b2;-catenin pathways further amplified EMT signaling. High YAP, hTERT, circEPSTI1, and SNAI2 expression, together with low KLK6, were associated with poor prognosis and increased malignant transformation risk. Pharmacologic inhibition of PI3&#x2005;K/mTOR, MEK/ERK, or LSD1 reversed EMT phenotypes in experimental models. Conclusion: Integrated activation of Hippo-YAP and EMT pathways is a pivotal event in OPMD-OSCC progression. YAP-centered EMT regulation shows promise as both a biomarker of malignant potential and a therapeutic target for chemoprevention and early intervention.