Journal of Personalized Medicine

Objectives: Older adults with comorbidities and polypharmacy have disproportionately high risk of hospitalization as well as readmission from adverse drug events (ADEs), of which 28%-71% are preventable (pADEs). This paper introduces an LLM application, CommunicADE, designed to support risk-mitigation of pADE-related readmission for the aforementioned population. We aim to evaluate CommunicADE's technical performance with OpenAI's HealthBench criteria: accuracy, completeness, communication quality, context awareness, and instruction following. Materials and Methods: Our technical validation study used an LLM (KimiK2.5) to simulate interviews between CommunicADE and nine high-fidelity synthetic patients hospitalized and at increased risk for pADE-related readmission (65+ years, comorbidities, 5+ medications). Some pADE risk mechanisms clues were visible to CommunicADE in patient H&Ps, but most mechanisms were solely discoverable in interviews. Two pharmacists evaluated CommunicADE's interview questions and EHR notes with HealthBench-informed variables. Analyzes used descriptive statistics. Results: For 35 mechanisms across 9 patients (avg=3.89 mechanisms/patient), CommunicADE's precision and recall were 0.92 and 0.63, respectively. Hallucinations were absent. Coherence and person-centeredness scored 4.28 and 4.44 on a 5-point scale (5=highest). On average, communication was at a 5th grade level and objective for 78% of patients. Most patient-reported quotes included in notes (92%) supported detected mechanisms. CommunicADE followed all instructions regarding interview length and patient approvals. Discussion: CommunicADE's strongest performance was in accuracy (precision, hallucinations), communication quality (coherence, readability), context awareness (person-centeredness). Completeness (recall) and instruction following (objectivity, pADE mechanism/quote alignment) show room for improvement. Conclusion: Findings suggest technical readiness for a feasibility pilot with real-world patients, and key areas for performance improvement.

Background: Obesity is globally recognized as a complex, multifactorial chronic disease, with biological, psychological, environmental and behavioural factors involved in both disease pathogenesis and maintenance. Although previous group-based studies demonstrated involvement of each of these factors, there is large inter-individual variability in the factors contributing to disease development as well as intervention outcomes, causing limited translatability to the individual level. This heterogeneity in treatment effectiveness might be due to differential causal and maintenance factors of obesity. To enable the transition from a one-size-fits-all approach to a more personalized approach for individuals with overweight or obesity, this study aims to investigate if and how the degree of weight loss and changes in daily life behaviour after a combined lifestyle intervention depend on individual baseline profiles comprising of person characteristics, biological, psychological, environmental and behavioural factors. Methods: This study will include 600 individuals varying in BMI, 200 participants with a healthy BMI (18.5-24.9kg/m2), 200 with overweight (BMI 25.0-29.9kg/m2), and 200 with obesity (BMI [≥]30.0kg/m2). For all participants, a comprehensive individual baseline profile is created, including person characteristics, biological, psychological, environmental and behavioural factors. A clustering method is applied to identify clusters of participants with similar characteristics. Next, we examine if and how these clusters are linked to bodyweight indicators measured at baseline, and how they relate to daily lifestyle behaviour, as measured by ecological momentary assessment (EMA) using a smartphone app and sensor technology (3-week measurements). Individuals with overweight or obesity will be randomized to the intensive lifestyle intervention or a lifestyle information condition, to determine if treatment response can be predicted based on cluster characteristics, how daily lifestyle behaviour changes after an intervention, and how changes in daily lifestyle behaviour relate to treatment response. Discussion: The End of Average study aims to characterize a large set of individuals varying in body weight to predict intervention effectiveness measured as changes in body weight indicators and in daily lifestyle behaviours. If reliable predictors of treatment success can be identified, these can be applied in personalized lifestyle interventions to improve lifestyle behaviour, body weight management and overall health.

Background Cardiovascular-kidney-metabolic (CKM) syndrome integrates adiposity, metabolic risk, kidney dysfunction, and cardiovascular disease in a prevention-oriented framework. National estimates across 1999-2023 NHANES and future burden remain limited. Methods We analyzed US adults aged 20 years from 11 NHANES cycles, 1999-2000 through August 2021-August 2023. CKM stage 0-4 was assigned using harmonized examination, laboratory, medication, and questionnaire data. Prevalence was survey-weighted and standardized to the 2010 US Census adult population. Decade trends used survey-weighted logistic regression adjusted for age, sex, and race and ethnicity. Exploratory 2040 and 2050 projections combined NHANES prevalence models with US Census projections under population-aging-only, trend-continuation, and risk-improvement scenarios. Results Among 62,890 eligible adults, 62,888 had sufficient CKM data. In 2021-2023, age-standardized prevalence was 87.9% (95% CI, 86.5%-89.4%) for CKM stage 1 and 62.0% (95% CI, 60.1%-63.8%) for stages 2-4. Stage 2 accounted for 50.1% (95% CI, 48.2%-51.9%) and stages 3-4 for 11.9% (95% CI, 11.0%-12.7%). From 1999-2000 to 2021-2023, any CKM increased by 4.6 percentage points (95% CI, 2.4 to 6.9; P<0.001), whereas stages 2-4 changed by 2.1 percentage points (95% CI, 5.1 to 0.8; P=0.156). In adjusted decade models, any CKM increased (OR, 1.28; 95% CI, 1.19-1.38; P<0.001), while stages 2-4 showed no significant linear trend (OR, 0.95; 95% CI, 0.89-1.01; P=0.084). Excess adiposity and diabetes increased, dyslipidemia declined, and hypertension, chronic kidney disease, and clinical cardiovascular disease were stable. With population aging alone, projected stages 2-4 burden rose from 164.8 million adults in 2023 to 193.7 million in 2050; under risk improvement, it was 147.7 million. Conclusions CKM syndrome remained highly prevalent among US adults. Although later stages did not increase significantly, population aging may expand the absolute care burden unless broad risk improvement occurs.

Gray-market retatrutide use is increasing, but patient safety experiences remain poorly characterized. This cross-sectional analysis examined Reddit posts and comments from retatrutide-specific and broader peptide or weight-management communities through December 2025. A validated large language model classified self-reported retatrutide use and extracted author-attributed symptoms mapped to MedDRA Preferred Terms. Among 13,589 users reporting current use, 7,823 had at least one mapped symptom after exclusions. Unlike phase 2 trial findings dominated by gastrointestinal events, Reddit reports most often described appetite increase, fatigue, increased energy, nausea, food craving, insomnia, and elevated heart rate. Findings are hypothesis-generating and warrant pharmacovigilance attention.

Background: Dihydropyridine calcium channel blockers (DHP-CCB) are widely prescribed antihypertensives whose adverse effects may trigger unnecessary prescribing of additional medications, termed prescribing cascades (PC). We aimed to identify potential DHP-CCB-induced PCs using high-throughput sequence symmetry analysis (HTSSA). Methods: Using Medicare claims data (2011-2020), we identified new users aged [&ge;]66 years with continuous enrollment [&ge;]360 days before and [&ge;]180 days after DHP-CCB initiation. We screened for initiation of 446 "marker" drug classes within {+/-}90 days of DHP-CCB initiation. Sequence ratios compared marker drug initiation after versus before DHP-CCB initiation. Adjusted sequence ratios (aSR), accounting for prescribing trends over time, were calculated with 95% CIs >1 considered statistically significant. Clinical experts classified statistically significant signals as potential PCs through consensus. Results: Among 388,862 DHP-CCB initiators (mean age 76.6 {+/-} 7.5 years; 62.5% women, 92.3% with hypertension), 82 of 446 marker drug classes had significantly elevated aSRs, of which 24 were classified as potential PCs. Strongest signals ranked by highest aSR included other systemic hemostatics (aSR 2.99; 95% CI, 1.10-8.16), other nasal preparations (aSR 1.99; 95% CI, 1.47-2.70), and drugs used in erectile dysfunction (aSR 1.85; 95% CI, 1.27-2.70). Other clinically relevant signals, ranked by number needed to harm (lowest to highest), included sulfonamides (NNTH 104; 95% CI, 98-111), electrolyte solutions (NNTH 216; 95% CI, 196-241), and osmotically acting laxatives (NNTH 710; 95% CI, 540-1056). Conclusion: Potential PCs identified in this Medicare cohort reflected known and underrecognized adverse effects of DHP-CCBs. Further studies are needed to evaluate the clinical consequences of these PCs.

Background Gastric cancer management is heterogeneous, and although the treating surgeon leads decisions across the pathway, surgeon level outcome variation remains poorly quantified. This study assessed surgeon identity as an independent predictor of survival after risk adjustment, introducing the Surgical Assessment and Healthcare (SAH) Index. Methods This single institution retrospective study (Ruijin Hospital, Shanghai Jiao Tong University; NCT07180966) included 692 patients undergoing curative-intent resection for gastric adenocarcinoma (pStage I ,II, III) in 2019 by eight consultant surgeons. Overall survival was modelled by multivariable Cox regression (primary model, 199 events, EPV 16.6; complete-case sensitivity model, N = 647). The SAH Index expressed surgeon * stage observed-to-expected ratios for five-year mortality and major morbidity (Clavien Dindo [&ge;] IIIa). Median follow up was 74.3 months. Results Independent predictors of survival were tumour stage (HR 2.979/step), age (HR 1.030/year), and non-distal gastrectomy (HR 1.498; all p [&le;] .006). After full adjustment, surgeon identity remained significant (Wald = 14.58, df = 7, p = .042): two surgeons carried roughly double the reference hazard S6 (HR 2.219, p = .003) and S8 (HR 2.034, p = .031) both with the cohort's lowest neoadjuvant chemotherapy rates (3.0% and 7.0% versus 17.6%), implicating pre-operative pathway decisions. The effect persisted in the sensitivity model (MSI also prognostic, HR 3.162, p = .007). Morbidity benchmarking flagged no surgeon for excess complications (no Tier 2 flags) and one survival-outlier cell (S6, Stage II; Tier 3). Conclusion Surgeon identity is independently associated with survival in gastric cancer beyond measurable case mix. The SAH Index offers a reproducible tool for institutional and inter-hospital benchmarking, with tier assignments stable across all four prespecified weighting scenarios confirming tier classification is independent of weight specification.

The significant challenges associated with rare diseases in the medical and research domains include the scarcity of information, which is often confined to unstructured formats. Although existing approaches provide valuable insights, there is a need to develop effective methods to identify information pertinent to rare diseases for advancing rare disease research. We identified mentions of rare diseases in relevant texts and assessed their relevance using derived scores, the confidence score and semantic similarity from a fine-tuned BioMedBERT encoder. This encoder was fine-tuned using rare disease related text from Online Mendelian Inheritance in Man (OMIM), Orphanet, a manually validated dataset, and STS benchmark datasets. The process of identifying meaningful rare disease mentioned was presented through two case studies that retrieved relevant NIH-funded projects, utilizing a generated knowledge graph in Neo4j to host data on 2,067 GARD diseases with over 320,000 NIH funded projects. Through various case studies with NIH-funded projects related to rare diseases, we demonstrated the effectiveness of our approach in systematically providing rare disease related data to enhance our understanding of rare diseases for future investigations.

Background: The All of Us Research Program represents a rich resource for cancer epidemiology research, with over 400,000 participants with whole genome sequences linked to electronic health records (EHR). Large cancer datasets often focus exclusively on cases without controls and neglect pre-diagnosis healthcare occurrences. Here, we perform a phenome-wide association study (PheWAS) of EHR data at least 1 year pre-diagnosis between cancer cases and matched controls, revealing co-occurring and mutually exclusive phenotypes. Methods: We identified 55,000+ cancer cases across 21 cancer types in All of Us version 8. To eliminate age-related confounding, we implemented a two-stage matching and censoring strategy: loose matching on demographics to establish index dates and cohort comparability, followed by right-censoring of EHR data (excluding 1 year pre-diagnosis/index), then 1:2 matching to address residual demographic imbalance. We tested associations between 23,193 cancer cases, 46,386 matched controls and approximately 1,600 clinical phenotypes using logistic regression adjusted for sex at birth, self-reported race, age at diagnosis/index date, and two censored EHR metrics: observation window and unique condition count, with Bonferroni correction for multiple testing. Results: Our analysis identified 232 significantly associated phenotypes, confirming established cancer risk factors including elevated prostate specific antigen (OR = 2.92, 95% CI: 2.65-3.23; p-value=1.8x10-101) and multinodular goiter (OR = 1.73, 95% CI: 1.56-1.91; p-value=6.7x10-27). Further investigation into the relationship between several phenotypes with seeming inverse effects is warranted. Conclusions: This PheWAS of EHR data at least 1 year pre-diagnosis leveraged the diversity of All of Us to examine how clinical phenotypes prior to cancer diagnosis vary across cancer types and racial groups. Our findings validate All of Us as a robust platform for cancer epidemiology research, confirming established risk factors at scale across diverse populations. This work provides methodological insights for EHR-based susceptibility analyses and demonstrates the value of agnostic phenome-wide approaches for generating hypotheses in precision medicine.

BACKGROUND & AIMS Conventional reusable endoscopes incur significant expenses in the form of purchase, maintenance, reprocessing, and disinfection. Reprocessing is frequently ineffective even following the use of high-level disinfectants (HLDs). Disposable gastroscopy might be a strategy to decrease infectious outbreaks associated with reusable endoscope. The aim of this study was to analyze and evaluate the performance, efficiency and safety in gastroscopy observation and subsequent potential EMR procedure via the disposable gastroscope in a clinical setting. METHODS Patients who required gastroscopies and met the criteria were recruited to this prospective, open-label, non-inferiority study. After obtaining the written informed content, the enrolled subjects selected themselves independently to the disposable group or reusable group. The primary measure was to evaluate the acceptable image quality and whether the disposable endoscope devices could meet the basic clinical demands with a noninferiority margin of -8%. The second measures were to analyze and evaluate the image conditions, accepted endoscopic maneuverability, efficiency and safety of observation and advanced potential EMR procedure. Appropriate statistical methods were conducted via PASS software and SAS 9.4. A two-tailed P value < 0.05 was considered statistically significant. RESULTS A total of 90 individuals (the number of those in disposable group and reusable group was both 45) were recruited to this study. The success rate of acceptable image quality via photographing iconic anatomical sites between two groups was 100.0% (45/45, 95% confidence interval (CI): 0.9213,1.0000) and the lower limit of the 95%CI (-7.8654%, 7.8654%) was larger than the noninferiority margin of -8% (Newcombe-Wilson score method). Significant differences were showed in the measures of image conditions (image acquisition, image quality, brightness, contrast and sharpness) and accepted endoscopic maneuverability (endoscopy body rigidity). No significant differences were observed in the field of knob operation, sharp angle adaptability, and the auxiliary features including air supply, water supply and suction. In terms of efficiency, the total operating time, insertion time and withdrawal time were longer in the disposable group. The En-bloc resection rate of those observed polyps and required to EMR procedure due to relatively larger diameter (5mm-15mm) was the same 100% in both groups (26/26 vs 23/23, 95%CI: 0.8713,1.0000). Nevertheless, the procedure time of EMR for each polyp was significantly longer in the disposable group. This study showed no intraoperative bleeding, delayed bleeding, perforation or other study-related adverse events among 90 patients. No dramatic fluctuations in vital signs were showed in perioperative period. CONCLUSIONS In consideration of the efficiency, efficacy and safety evaluation, the disposable gastroscopes might represent an alternative to conventional reusable gastroscopes in routine examination and endoscopic mucosal resection.

Introduction Coding tumor diagnoses from free-text clinical documentation currently requires substantial manual effort. Promising approaches for automating this process include large language mod-els (LLMs), embedding models, and retrieval-augmented generation (RAG). While previous studies often focus on a single method, we directly compare these approaches on a real-world dataset of tumor diagnosis descriptions to assess their strengths and limitations. Methods We evaluated nine different embedding models using similarity search and embedding-based classification, as well as LLM-based coding, with and without RAG, on a real-world dataset of 2,024 unique German tumor diagnosis descriptions labeled with ICD-10 and ICD-O topography codes. The retrieval knowledge base was constructed exclusively from stand-ardized Alpha-ID, ICD-10-GM, and ICD-O-3 classifications. Performance was assessed for exact (full-code) and partial (three-character) code prediction. For RAG, we evaluated base and fine-tuned versions of Llama 3.1 8B and Llama 3.3 70B. Results Qwen3-Embedding-8B, the largest embedding model, yielded the best results. It achieved 47.8% exact-match and 72.1% partial-match accuracy for ICD-10 coding with classification, and 42.7% exact-match and 73.5% partial-match accuracy for ICD-O coding with similarity search. The other embedding models, including medically specialized ones, showed varied but lower performance. RAG improved base LLM perfor-mance and outperformed embedding-based approaches on partial-match accura-cy (80.6% partial-match accuracy for ICD-10 and 75.0% for ICD-O with Llama 3.3 70B), but not on exact-match accuracy. Conclusion A direct comparison with embedding-based approaches is essential to determine whether the additional effort of RAG is justified. The strong variation in performance also highlights the importance of model selection. Further advances in embedding-based methods, potential-ly supported by larger and more diverse training data, may offer a promising direction for future work.

Multimorbidity, the co-existence of two or more long-term conditions, is up to three times more prevalent among people with cancer than in the general population and is associated with poorer survival, particularly for cancers with a more favourable prognosis such as colorectal cancer. In Scotland, multimorbidity is the norm among older adults, emerges earlier in socioeconomically deprived populations, and may contribute to comparatively low cancer survival rates. Despite this, the influence of multimorbidity on the colorectal cancer pathway remains poorly understood. We conducted a Scottish data-linkage study of adults diagnosed with colorectal cancer between 2010 and 2014, linking the Scottish Cancer Registry to national prescribing, hospital admissions, death registration, and bowel screening datasets. Prescribing data were used to derive overall and system-specific comorbidity measures as a proxy for multimorbidity and active disease burden. Associations with stage at diagnosis, treatment, survival, and screening uptake were examined using logistic regression and Cox proportional hazards models adjusted for demographic and clinical covariates. Among 19,043 patients, 87% had at least one prescribing-based comorbidity, most commonly cardiovascular, nervous system, and gastrointestinal conditions. Overall comorbidity burden was not associated with stage at diagnosis, although laxative-related prescribing was associated with later-stage disease. Increasing comorbidity burden reduced the likelihood of receiving any treatment and surgery, while associations varied across system-specific comorbidities. Higher comorbidity burden was also associated with increased all-cause and colorectal cancer-specific mortality, particularly among patients with respiratory, nervous system, and haematological/nutritional conditions. Screening uptake was not associated with overall comorbidity burden but did differ by system-specific comorbidity. Prescribing-based multimorbidity was highly prevalent and strongly associated with treatment patterns and mortality among patients with colorectal cancer. System-specific multimorbidity measures provided greater discrimination than overall morbidity counts, highlighting the importance of considering distinct multimorbidity profiles when assessing cancer pathways and designing targeted interventions for optimising treatment and survival. Keywords (primary health care, general practice, multimorbidity, comorbidity, colorectal cancer, early diagnosis, cancer treatment, survival)

Objective To determine if it is possible to assess individual patient risk of the development of colorectal cancer (CRC) in people in high-risk groups due to their family history. Design/Method Retrospective observational study of prospectively collected data from consecutive patients referred for a colonoscopy. 2,478 consecutive patients were referred to a single colorectal surgical practice in Sydney, Australia between 1977 and 2018 for a colonoscopy because of a family history of CRC. Of these, 1,963 have been followed for more than 10 years and are the subject of this paper. Histopathological findings categorised as normal (N), non-advanced adenoma (NAA) or advanced neoplasia (AN) with AN proven to be the precursor to CRC. Intervention Colonoscopic screening on the basis of contemporary practice to 2006 and subsequently according to Australian National Health and Medical Research Council guidelines. Results Participants with normal or low-risk findings in the first decade remain at lower risk of CRC for 30 years from the commencement of screening. Conclusion It is possible to stratify individual patients in a high relative risk cohort into those with high or low personal risk of CRC based on colonoscopic findings in the first 10 years of surveillance. Those with no AN in the first ten years have a lower 30-year risk of developing AN than the general community. This offers the possibility of structuring surveillance programs around individual risk rather than group risk, lessening the need for multiple surveillance colonoscopies in the majority of such patients and improving the cost effectiveness of CRC screening at the population level.

Background: African Americans (AA) experience disproportionate burden of colorectal cancer (CRC). Dysregulation of the Wingless-related integration site (WNT) pathways contributes to tumor progression, yet their prognostic roles in FOLFOX-treated CRC among AA patients remain understudied. Methods: We analyzed 2,562 CRC cases stratified by ancestry, age at onset, and FOLFOX treatment using Fisher's exact, chi-square, and Kaplan-Meier analyses from AACR Project GENIE and cBioPortal databases. To enhance data integration and interpretation, we applied AI-HOPE and AI-HOPE-WNT, conversational artificial intelligence (AI) platforms designed to integrate clinical, genomic, and treatment data through natural language-driven queries. Results: Overall survival analyses showed that early-onset CRC (EOCRC) AA patients treated with FOLFOX who had WNT pathway alterations experienced significantly better survival (p = 0.035). WNT pathway alterations were less frequent in late-onset AA patients treated with FOLFOX compared to those not treated (80% vs. 92%; p = 0.05). Conclusions: Chemotherapy exposure may influence pathway-specific mutation frequencies across ancestry and disease stage. AI-enabled integrative analyses highlight the potential of conversational AI platforms to accelerate biomarker discovery and reveal ancestry- and treatment-specific vulnerabilities in CRC.

Background Liver fibrosis (LF) represents a pivotal pathological phase in the advancement of chronic liver disorders toward cirrhosis. Amino acid metabolism reprogramming plays a pivotal role in its pathogenesis, yet the underlying molecular mechanisms remain incompletely understood. Methods Integrating three public datasets (GSE14323, GSE84044, and GSE136103) with amino acid metabolism-related gene sets, we performed consensus clustering, machine learning algorithms, functional enrichment analysis, immune microenvironment composition, regulatory network construction, and drug prediction. Results Fibrotic samples were classified into two amino acid metabolism-related subtypes with distinct immune landscapes and functional phenotypes. Through integrated analysis of differentially expressed genes (DEGs) common to both subtypes, fibrotic versus control comparisons, and amino acid metabolism-related gene sets, four biomarkers, GSTP1, LDHB, OXCT1, and PTGDS, were identified. These biomarkers were enriched in pathways related to epithelial-mesenchymal transition, interferon responses, and TNF/NF-{kappa}B signaling. Notably, GSTP1 and LDHB positively correlated with M1 macrophage infiltration and negatively with regulatory T cell abundance. Single-cell transcriptomic analysis revealed that cholangiocytes expressed all four biomarkers with elevated levels in fibrosis and interacted with macrophages/mesenchymal cells via MIF-CD74/CXCR4. Regulatory network analysis highlighted key modulators, including MALAT1, hsa-miR-3163, OXCT1, SMAD4, and RELA. Furthermore, 5-fluorouracil was predicted as a multi-target compound, with the strongest predicted binding affinity for OXCT1. In vitro validation confirmed the upregulation of GSTP1 and LDHB, aligning with the bioinformatics findings. Conclusion This study identified four amino acid metabolism-related biomarkers, revealing immune heterogeneity and cholangiocyte-centered intercellular communication in LF. These findings establish a foundation for biomarker-based diagnosis, subtype-guided patient stratification, and the development of cell-type-specific therapeutic strategies in LF.

BackgroundRheumatoid arthritis (RA) is a chronic immune-mediated inflammatory disease characterized by a heterogeneous clinical course with periods of remission and flare. Although biologic DMARDs (bDMARDs) have revolutionized RA treatment by enabling sustained disease control, their long-term use is associated with adverse effects and high costs, making dose tapering an attractive but clinically challenging strategy. The lack of reliable biomarkers to predict flare risk limits safe implementation of treatment de-escalation. This study aimed to identify novel circulating protein biomarkers associated with flare risk in RA patients undergoing bDMARDs tapering, useful to enable biomarker-guided treatment optimization strategies. MethodsA discovery proteomic analysis using mass spectrometry was performed on baseline serum samples from a subset of the OPTIBIO clinical trial (n=44), followed by validation in the full cohort (n=194) using ELISA. Functional pathway analysis explored biological processes associated with candidate biomarkers. In parallel, anti-cytokine autoantibodies were profiled using multiplex immunoassays. Logistic and Cox regression models were used to assess associations with flare risk. Predictive models integrating biomarkers and clinical variables were evaluated using receiver operating characteristic (ROC) analysis, sensitivity and specificity metrics, and decision curve analysis to assess clinical utility. ResultsMass spectrometry identified 806 proteins, of which 87 were differentially expressed at baseline between patients who flared and those who maintained remission during follow-up within the intervention (tapering) arm. Functional enrichment analysis highlighted immune-regulatory and innate immune pathways. Among the candidates, V-set immunoglobulin-domain-containing 4 (VSIG4) was validated as a biomarker associated with increased flare risk. Anti-interferon-{gamma} (anti-IFN{gamma}) autoantibodies were also associated with flare. A combined model including VSIG4, anti-IFN{gamma}, and the clinical variable DAS28-CRP improved predictive performance compared with clinical variables alone (AUC 0.76 vs 0.66), achieving significantly higher sensitivity. Decision curve analysis demonstrated higher net benefit of the combined model, indicating improved clinical decision-making. In a secondary analysis focused on patients with prolonged remission, representing the most suitable candidates for safe treatment tapering, the model performance further improved (AUC 0.84). ConclusionIntegration of novel serum proteomic and autoantibody biomarkers with clinical parameters improves prediction of flare during biologic tapering in RA and provides clinically relevant benefit for patient stratification. These findings support further development of biomarker-driven approaches for personalized treatment optimization strategies.

BackgroundLymphangiogenesis plays a critical role in various liver diseases, yet its function in liver fibrosis remains controversial. This study aimed to explore the role of lymphangiogenesis in liver fibrogenesis and its underlying regulatory mechanisms. MethodsLiver fibrotic mice were established by carbon tetrachloride (CCl4) or Thioacetamide (TAA)-induced injection or bile duct ligation. Lymphatic vessels were marked by podoplain (Pdpn) staining in mice and D2-40 staining in clinical samples. Lymphatic vessels area and density were measured to indicate lymphangiogenesis. Multiplexing immunohistochemistry was used to detect co-localization of proteins. ResultsIn the present study, we first verified increased lymphangiogenesis in human and murine fibrotic livers. Afterwards, we identified VEGFC rather than VEGFD as the primary driver of lymphangiogenesis in liver fibrosis. Furthermore, we demonstrated that M1 macrophages serve as the major source of VEGFC. Founctional studies revealed that VEGFC-mediated lymphangiogenesis exacerbates hepatic fibrosis, while its inhibition alleviated fibrosis. Bioinformatic analysis uncovered Midkine (MDK) as a key downstream of lymphangiogenesis. Both in vivo and in vitro studies confirmed that exogenous MDK promotes liver fibrosis via activating hepatic stellate cells (HSCs), whereas MDK inhibition counteracts the profibrotic effects of VEGFC-induced lymphangiogenesis. Importantly, we discovered that MDK activates HSCs through the Hippo/YAP signaling pathway. ConclusionsM1 macrophage-mediated lymphangiogenesis aggravates liver fibrosis via MDK secretion, which activates HSCs. These findings provide novel insights into coordinated crosstalk between macrophages, lymphatic endothelial cells and HSCs in liver fibrosis and suggest lymphangiogenesis and MDK as potential therapeutic targets for fibrotic liver diseases.

BACKGROUND: Critical care pharmacists (CCPs) reduce adverse drug events (ADEs) and mortality in the intensive care unit (ICU). Board certification is the established professional standard for CCPs but its impact on ICU patient outcomes, including its relationship between CCP characteristics and workload, remain unclear. The purpose of this study was to evaluate the association between pharmacist board certification, CCP workload characteristics, and patient outcomes. METHODS: This was a pre-planned analysis of the multicenter, observational Optimizing Pharmacist Team Integration for ICU Patient Management (OPTIM) study, including adult ICU patients cared for by CCPs. Patients cared for exclusively by board certified pharmacists on every ICU day were categorized as the BCP group; those with at least one day of care from a non board certified pharmacist comprised the non BCP group. The primary outcome was hospital mortality; secondary outcomes included the hazard of discharge alive (HDA) from the ICU and hospital. Multivariable logistic regression was used to evaluate the association between BCP and mortality; Fine-Gray competing risk models were used to assess the relationship between BCP and ICU and hospital HDA. RESULTS: A total of 201 pharmacists (184 BCPs; 17 non BCPs) from 63 institutions caring for 20,537 ICU patients were included. Care provided exclusively by a BCP (vs. >/= 1 day by a non-BCP) was associated with lower mortality (OR 0.80, 95% CI 0.69 to 0.92, p=0.002) and both a higher ICU HDA (HR 1.08, 95% CI 1.03 to 1.13, p<0.001) and hospital HDA (HR 1.19, 95% CI 1.13 to 1.26, p<0.001). CONCLUSION: Daily ICU care delivered by pharmacists with board certification was independently associated with reduced mortality and improved hazard of discharge alive from the ICU. Board-certified pharmacists may enhance the quality and/or efficiency of critical care pharmacy services. These findings support the role of board certification as a modifiable factor to improve patient outcomes and optimize workload in the ICU.

PurposeIn this study, we aimed to develop and evaluate an artificial intelligence-based diagnostic model for the diagnosis of acute cholecystitis (AC) using non-contrast CT images and clinical data. Materials and MethodsThis retrospective study included 199 patients (100 AC, 99 non-AC) treated between January 2016 and December 2025 at a single center. Patients were randomly divided into training (n=139) and test (n=60) datasets. Three models were constructed: an imaging-based deep learning model, a clinical data-based machine learning model, and a hybrid machine learning model integrating deep learning-derived imaging features with clinical data. CT images were preprocessed, and gallbladder regions were segmented. Clinical variables included white blood cell counts and levels of C-reactive protein and liver function markers. Model performance was evaluated using accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Statistical comparisons were performed using Welchs t-test and Chi-square test. ResultsThe imaging-based model achieved accuracy 0.883, precision 0.848, recall 0.933, specificity 0.833, and AUC 0.916. The blood-based model achieved accuracy 0.917, precision 0.931, recall 0.900, specificity 0.933, and AUC 0.949. The hybrid model showed the highest performance, with accuracy 0.950, precision 0.909, recall 1.000, specificity 0.900, F1 score 0.952, and AUC 0.986. ConclusionA hybrid model integrating CT imaging and clinical data improved diagnostic performance for AC compared with single-modality models.

Background and Objective: Access to real-world electronic health records (EHRs) remains limited by privacy, governance and annotation constraints, hindering the development of clinical natural language processing models. Realistic synthetic progress notes may provide EHR-like corpora that preserve clinically rigorous information on diagnoses, treatments, symptoms, imaging, laboratory findings and therapeutic trajectories without relying directly on sensitive patient records. This study evaluates whether large language models (LLMs) can generate realistic Spanish prostate cancer progress notes from published case reports, preserving clinical content, temporality and hospital-style conventions.

Background: Alzheimers disease (AD) is a multifactorial neurodegenerative disorder in which copper dyshomeostasis, mitochondrial stress, oxidative injury and immune dysregulation may contribute to pathogenesis. Cuproptosis, a copper-triggered regulated cell death pathway, has emerged as a potential mechanistic link to AD, but its therapeutic and biomarker implications remain incompletely defined. Methods: We integrated transcriptomic, machine learning, immune infiltration, QSFR, molecular docking, docking validation and ADME analyses using GEO blood- and brain-based AD cohorts. Differentially expressed genes were intersected with curated cuproptosis-related genes, followed by pathway enrichment, construction and validation of a hybrid ensemble classifier, CIBERSORT-based immune correlation analysis, QSFR-driven target prioritization, ligand docking, consensus docking validation and SwissADME profiling. Results: The transcriptomic analyses revealed reproducible AD associated signatures enriched in neurodegenerative, oxidative stress, mitochondrial and inflammatory pathways. Across multiple machine learning models, FDX1, PDHB, PDHA1, DLAT and DLD consistently emerged as the most important cuproptosis-related genes, with the hybrid ensemble achieving the best diagnostic performance. Immune profiling suggested that these genes are linked to distinct immune infiltration patterns. QSFR and docking prioritized FDX1 as a key target and Clioquinol, PBT2 and Ebselen showed the strongest and most consistent binding behavior. Docking validation confirmed reliable pose reproduction and enrichment over decoys, while ADME analysis supported Clioquinol, PBT2 and Ebselen as the most balanced candidates for further consideration. Conclusion: This integrated workflow identifies a cuproptosis-centered mitochondrial gene module as a robust AD signature and highlights Clioquinol, PBT2 and Ebselen as promising repurposing candidates. The findings provide a prioritized computational framework for future experimental validation of copper-linked therapeutic strategies in AD.