Clinical and Translational Science

GLP-1 receptor agonists (GLP-1 RAs) are effective in delaying progression of chronic kidney disease in individuals with type 2 diabetes mellitus (T2DM). We evaluated whether GLP-1 RA prescription is associated with reduced nephrotoxicity in adults receiving long-term lithium therapy. We conducted a retrospective, propensity score-matched cohort study using electronic health records from the TriNetX global network, which includes de-identified data from over 127 million patients across 109 healthcare organizations. The study population consisted of adults aged [≥]18 years with T2DM, with lithium exposure within the 2 years preceding the index date and at least one prescription for a GLP-1 RA. The primary efficacy outcome was the rate of renal nephrotoxicity in persons with T2DM prescribed lithium and a GLP-1 RA versus those with T2DM prescribed lithium but no GLP-1 RA or other antidiabetic agents. Nephrotoxicity was a composite of ICD-10 and CPT-coded renal disease. Incidence and time-to-event outcomes were assessed using Kaplan-Meier curves and Cox proportional hazards models. In our 24-month analysis, 462 matched patient pairs were included. Initiation of a GLP-1 RA during lithium therapy was associated with a lower incidence of renal events versus lithium alone (6{middle dot}1% vs 10{middle dot}4%), corresponding to a risk difference of -4.3% (95% CI -7{middle dot}86 to -0{middle dot}80), a risk ratio of 0{middle dot}58 (95% CI 0{middle dot}37-0{middle dot}91; p=0{middle dot}017), and higher event-free survival (89{middle dot}0% vs 83{middle dot}2%; log-rank p=0{middle dot}037). GLP-1 receptor agonist therapy was associated with a reduction in reports of lithium-associated nephrotoxicity. Our findings provide impetus to conduct mechanistic renal histopathologic studies combining GLP-1 RAs with lithium.

IntroductionAnti-vascular endothelial growth factor (anti-VEGF) therapies are standards of care for vision-threatening retinal diseases. This retrospective observational study describes demographics, utilization, best recorded visual acuity (BRVA), and safety among eyes with neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), diabetic macular edema (DME), or retinal vein occlusion (RVO) treated with the biosimilar aflibercept-ayyh (PAVBLU(R)) in routine clinical practice. MethodsElectronic medical records from the Retina Consultants of America database of patients receiving aflibercept-ayyh (12/1/2024-10/31/2025) were analyzed, focusing on eyes with [&ge;]84 days of follow-up. The index date was the first documented aflibercept-ayyh injection. Postindex data were used to assess treatment patterns, BRVA (Wilcoxon signed rank test), and adverse events of special interest (AESIs). ResultsA total of 1,000 consecutive eyes from 989 patients received 3,730 injections of aflibercept-ayyh; most (91%) switched from prior anti-VEGF therapy and 9% were anti-VEGF treatment-naive. Disease distribution was 58% nAMD, 19% RVO, 16% DME, and 7% DR. Among switchers, median (IQR) number of prior injections was 21 (8-46). Median (IQR) follow-up was 6.0 months (4.6-7.1). Median (IQR) number of aflibercept-ayyh injections per eye was 4 (3-5). Among eyes with [&ge;]84 days of follow-up (n=889), mean BRVA expressed as logarithm of minimum angle of resolution (logMAR) remained stable for switchers (0.4 to 0.4; P=0.96) and improved from baseline in anti-VEGF-naive eyes (0.5 to 0.4; P<0.01). Confirmed AESIs included iritis (n=2; 0.05% of injections), with no events of vitreous cells, endophthalmitis, retinal detachment, retinal vasculitis, or vitreous hemorrhage. ConclusionIn this descriptive real-world analysis, aflibercept-ayyh was associated with stable visual acuity in previously treated eyes and vision improvement in treatment-naive eyes, with no new or unexpected safety findings, consistent with expectations for aflibercept. These findings add real-world experience to preexisting evidence demonstrating no clinically meaningful differences between aflibercept-ayyh (PAVBLU(R)) and reference aflibercept (EYLEA(R)). KEY SUMMARY POINTSO_ST_ABSWhy carry out this study?C_ST_ABSO_LIThe anti-vascular endothelial growth factor (VEGF) drug aflibercept, approved in 2011 and marketed in the United States as EYLEA(R),* has demonstrated efficacy in treating retinal diseases such as neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), diabetic macular edema (DME), or retinal vein occlusion (RVO) and is a standard of care for these disorders. C_LIO_LIAflibercept-ayyh is a biosimilar to aflibercept that has demonstrated comparable efficacy and safety in the treatment of nAMD in a randomized controlled clinical trial. C_LIO_LIThis study describes the real-world use patterns, vision outcomes, and safety of aflibercept-ayyh in clinical settings in the United States for the treatment of nAMD, DR, DME, and RVO. C_LI What was learned from the study?O_LIIn this real-world study of 1,000 consecutive eyes treated with the biosimilar aflibercept-ayyh in patients with retinal diseases, we observed no new safety concerns and that aflibercept-ayyh maintained visual acuity in eyes switching anti-VEGF agents and improved vision in anti-VEGF-naive eyes, consistent with expected responses to aflibercept. C_LIO_LIThese findings support aflibercept-ayyh as a suitable treatment option when anti-VEGF therapy is indicated. *EYLEA(R) is a registered trademark of Regeneron Pharmaceuticals, Inc. PAVBLU(R) is a registered trademark of Amgen Inc. C_LI

Human genetics has become a cornerstone of drug target discovery, yet the value of Mendelian randomization (MR) for predicting clinical success remains uncertain. Here, we systematically evaluated MR across 11,482 target-indication pairs with documented Phase II clinical outcomes to assess its utility for drug development. We find that MR statistical significance alone does not enrich for Phase II success, in contrast to genome-wide association study (GWAS) support, which confers an increase in success probability. However, this apparent limitation reflects the heterogeneous nature of clinical failure and the fact that MR encodes information beyond P values. When MR-derived features, including instrument strength and explained variance, are integrated into machine learning models, predictive performance improves substantially. An MR-informed XGBoost classifier identifies target-indication pairs with a 55% overall approval rate, corresponding to a 6.4-fold enrichment over unstratified programs and a 2.8-fold improvement over GWAS- supported targets in Phase II. Notably, this enrichment is achieved without reliance on statistically significant MR results. Our findings demonstrate that MR is most informative when treated as a graded, context-dependent source of causal evidence rather than a binary hypothesis test, and that its integration with machine learning enables scalable, genetics-informed prioritization of drug targets across the clinical pipeline.

BackgroundBiomedical Large Language Models (LLMs) combined with prompt engineering offer domain-specific reasoning, yet their application to individual-level causality assessment remains unexplored. This study evaluated five combinations of biomedical LLMs, prompting strategies, and causality algorithms by comparing their agreement with two human expert evaluators. Research design and methodsA total of 150 Individual Case Safety Reports (ICSRs) were analyzed: 140 reports from Food and Drug Administration Adverse Event Reporting System (FAERS), and 10 myocarditis/pericarditis ICSRs from Vaccine AERS (VAERS). Assessments were conducted using the Naranjo and WHO-UMC algorithms. Biomedical LLMs tested included TinyLlama 1.1B, Medicine LLaMA-3 8B, and MedLLaMA v20, combined with Chain-of-Thought (CoT) or Decomposition prompting. Agreement was measured using Gwets Agreement Coefficient 1 (AC1) and percentage agreement, alongside performance metrics and qualitative error analysis. ResultsThe Medicine LLaMA-3 8B-Naranjo-CoT combination achieved the highest agreement with human assessors for the final classification of causality (64%). Biomedical LLMs demonstrated low inter-rater agreement on critical items of causality assessment such as identification of listed AE, temporal plausibility, alternative causes, and objective evidence of AEs. Frequent model failures included irrelevant responses. ConclusionsBiomedical LLMs showed improved performance over general purpose models previously tested but remain suboptimal for reliable causality assessment of ICSRs.

Artificial intelligence (AI), particularly large language models (LLMs), is increasingly explored in healthcare, yet its real-world usability and safety in high-risk clinical pharmacy tasks remain uncertain. Vancomycin therapeutic drug monitoring (TDM), which requires precise pharmacokinetic calculations and context-sensitive interpretation within a narrow therapeutic window, provides a stringent test case for AI-assisted decision support. This proof-of-concept study developed and evaluated a hybrid clinical decision support system (TDM-AID) integrating a validated deterministic pharmacokinetic calculation engine, GPT-4o-based structured clinical interpretation, and retrieval-augmented guideline support. Thirty retrospective adult vancomycin TDM cases were assessed using a weighted six-domain rubric covering pharmacokinetic accuracy, AUC estimation, prospective prediction, timing recommendations, clinical judgment, and documentation quality. Two independent expert pharmacists evaluated system outputs against benchmark consultations. The overall median performance was 78% (IQR 12%), classified as Acceptable, and 73% (IQR 14%) when deterministic calculations were excluded. Foundational pharmacokinetic calculations achieved 100% accuracy. Clinical judgment demonstrated Good performance (83%), whereas prospective prediction was limited (58%), and timing recommendations were absent in all cases. Safety violations occurred in 17% of cases, including dose recommendations exceeding 4 g/day. Inter-rater reliability was good (ICC 0.87). These findings suggest that hybrid AI-driven decision support is technically feasible and usable as a pharmacist-augmenting draft generator; however, limitations in predictive reasoning, timing logistics, and safety enforcement necessitate deterministic safeguards and mandatory expert oversight before clinical implementation.

BackgroundGLP-1 receptor agonists (GLP-1RAs) and SGLT2 inhibitors (SGLT2Is) have established cardiovascular benefits for patients with type 2 diabetes mellitus (T2DM), with similar class-level effectiveness found in previous studies. However, real-world comparative effectiveness assessments of individual agents remain limited. ObjectivesTo compare the cardiovascular effectiveness of individual GLP-1RAs and SGLT2Is. MethodsWe conducted a multi-national, retrospective, new-user active-comparator cohort study using 10 US and non-US administrative claims and electronic health record databases. The study included 1,245,211 adults with T2DM receiving metformin who initiated second-line therapy with one of six GLP-1RAs (albiglutide, dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide) or one of four SGLT2Is (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin). Empagliflozin (393,499; 31.6%), semaglutide (235,585; 18.9%), dapagliflozin (208,666; 16.8%), and dulaglutide (207,348; 16.8%) were most commonly used. A secondary subgroup analysis included 316,242 patients with established cardiovascular diseases (CVD). Primary outcomes were 3-point major adverse cardiovascular events (MACE: acute myocardial infarction, stroke, sudden cardiac death) and 4-point MACE (adding hospitalization/ER visit with heart failure). Secondary outcomes included the individual components. Hazard ratios (HRs) were estimated for pairwise agent comparisons while on-treatment (per-protocol) and over total follow-up using Cox proportional hazards models, with propensity score adjustments, negative control calibration, and pre-specified study diagnostics to guard against potential confounding. Random-effects meta-analysis produced summary HR estimates across data sources that passed diagnostics. ResultsAcross the study cohort, individual GLP-1RAs and SGLT2Is demonstrated broadly similar cardiovascular effectiveness, both within and across drug classes. For example, semaglutide and empagliflozin showed comparable risks for 3-point MACE (meta-analytic HR 1.05; 95% CI 0.79-1.39) and 4-point MACE (meta-analytic HR 0.95; 95% CI 0.81-1.12), with consistent findings in the CVD subgroup. Study diagnostics confirmed adequate equipoise, covariate balance and statistical power to detect similarity in HRs between 0.8 and 1.2 for commonly used agents. ConclusionsIn this large-scale real-world study, individual GLP-1RAs and SGLT2Is exhibited largely comparable cardiovascular benefits, including in patients with established CVD. These findings align with network meta-analytic estimates from major cardiovascular outcome trials and broadly support current treatment guidelines. Clinical choices should be guided by relevant factors such as safety, adherence, tolerability, cost, and patient preference, where further work is needed.

ObjectiveElectronic Health Record (EHR)-based trial emulation can support translation of randomized clinical trial (RCT) evidence into practice, yet emulations often diverge from published RCT results. We hypothesized that these discrepancies are structured and learnable properties of a health systems data-generating process, and that autonomous agentic workflows can generate discrepancies at the scale required for cumulative learning. Materials and MethodsWe developed an agentic trial emulation framework that (1) uses an autonomous LLM agent (Biomni) to execute an end-to-end, instruction-driven emulation pipeline against an OMOP CDM database and (2) calibrates EHR estimates to RCT results with a Bayesian hierarchical model. Biomni performed protocol parsing, OMOP concept set construction, cohort building, confounder adjustment, and treatment effect estimation; it also synthesized literature-derived, comparison-specific priors for expected EHR-RCT disagreement. Five atrial fibrillation anticoagulation trials were emulated using Mount Sinais OMOP-mapped EHR, with three independent runs per trial to quantify agent-induced analytic variability. Discrepancies between EHR-derived and published log-hazard ratios were modeled as the sum of a literature-informed reproducibility expectation, an institution-specific systematic shift, and residual heterogeneity. Performance was assessed using leave-one-out cross-validation across four in-domain DOAC-versus-warfarin trials, with one out-of-distribution evaluation (apixaban versus aspirin). ResultsIn pooled leave-one-out validation, calibration reduced mean absolute error from 0.567 to 0.224 log-hazard ratio (60.5% reduction) and achieved 100% empirical coverage of 95% posterior predictive intervals across held-out trials (4/4). The posterior institution-specific shift was consistently positive across folds (median 0.364-0.580), indicating systematic attenuation of DOAC benefit in the local EHR beyond literature-expected disagreement; residual heterogeneity was moderate (median 0.199-0.264). For the out-of-distribution AVERROES trial, calibrated error decreased from 0.379 to 0.051 (86.5% reduction), with the published effect within the 95% credible interval. Discussion and ConclusionAutonomous emulation with agents enables repeated, standardized trial replications that convert EHR-RCT disagreement into data for learning institution-level transport properties. Separating comparison-specific reproducibility expectations from system-level shifts yields calibrated, uncertainty-aware local interpretations of trial evidence.

Amatoxin-induced acute liver failure complicates misidentified foraged mushroom ingestion worldwide; abrupt multisystem collapse punctuates apparent improvement. Our prospective single-arm clinical trial investigated proactive toxicokinetic-based management to preserve elimination capacity: sustained enhanced hydration to maintain renal clearance; fasting plus octreotide to suppress meal-driven enterohepatic circulation; and intravenous silibinin to inhibit OATP1B3-mediated hepatic uptake, enabling safe passage and elimination of gallbladder-confined amatoxin-laden bile. Safety population (N=99) transplant-free recovery (TFR): 88.0% (87 recoveries, 6 transplants, 6 deaths). Protocol-adherent Efficacy population (n=86) TFR: 98.8% (85 recoveries, 1 transplant, 0 deaths). Multivariable analysis identified uninterrupted hydration as strongest TFR predictor (P<0.001), followed by earlier silibinin initiation (P=0.003); octreotide shortened INR recovery by 11 hours (P=0.033). These findings support a toxin elimination model in which preserved renal clearance and biliary sequestration are central recovery determinants. The kinetic balance between renal clearance and hepatic uptake governs both recovery and collapse.

BackgroundJournals may respond to integrity concerns by publishing an editorial response (editorial notice, expression of concern (EoC) or retraction). We investigated whether the type of editorial response affected citation rates. MethodsWe obtained citations for 172 randomised controlled trials (RCTs) with integrity concerns (41 had editorial notices, 38 EoCs and 23 retractions) and control RCTs from the same journal and year. Monthly citation rates up to 60 months before and after editorial responses were compared by editorial response type, and to citation decline in control RCTs. Results172 RCTs had 10,603 citations from 6,376 articles. 3,330 control trials were identified for 151/172 RCTs (15,948 citations, 87,811 articles). For both groups, citations increased steadily, peaking 45-65 months post-publication. There were no statistically significant differences in citation decline post-editorial response for trials receiving a retraction, EoC, or notice. Citations were lower in controls than index trials, so analyses were restricted to 1598 highly cited (>25) controls. The rate of decline for highly cited control trials was not statistically significantly different from the post-editorial response rate for index groups. ConclusionCitation rate decline after editorial responses did not differ by type of editorial response nor from the natural decline in control trials. HighlightsO_LIJournals may respond to integrity concerns by issuing an editorial notice. C_LIO_LIThe effect of expressions of concern or other editorial notices on citation patterns is unclear. C_LIO_LIEditorial notices did not accelerate citation decline compared with control trials. C_LIO_LIThe type of notice was not associated with differences in citation decline. C_LIO_LILate editorial notices appear ineffective in preventing continued citation. C_LI

PurposeTimely detection of disease activity in chronic retinal diseases improves visual outcomes but is limited by the lack of validated systems for continuous monitoring and care management. We evaluated the real-world performance of an integrated remote physiologic monitoring and principal care management program (RemoniHealth(R)) using a self-administered multimodal retinal function test (Macustat(R)) for home monitoring. MethodsThis single-arm real-world intervention study was conducted across 33 retina practices. A total of 2,216 adults with chronic retinal diseases performed weekly home retinal function testing with integrated care management support. Primary endpoints included the annualized rate of disease progression detection, time to intervention after first flag, true positive rate, and patient adherence. Descriptive statistics and data analyses were analyzed using chi-square tests and Clopper-Pearson confidence intervals. ResultsParticipants contributed 82,644 encounters and 16,805 patient-months of monitoring. The program generated 241 alerts, including 101 Macustat flags and 135 care management prompts. Among 73 adjudicated flags, 56 were true positives and 17 false positives (PPV 76.7%). The annualized detection rate was 4 per 100 patient-years. Of confirmed events, 93% led to intravitreal injection or other major management change. Mean adherence was 72.1%, and patients with [&ge;]80% adherence had higher odds of true positivity. DiscussionThis RPM-PCM model achieved high engagement and meaningful detection of asymptomatic progression between visits, supporting the value of home monitoring for timely intervention. Translational RelevanceThese findings support scalable integration of home vision testing and care management into routine retinal practice to enable earlier intervention and improved continuity of care.

Background Antibiotic pricing is a key determinant of access and stewardship in low- and middle-income countries (LMICs), yet empirical evidence on how prices are formed within pharmaceutical markets remains limited. However, there is little longitudinal evidence on how antibiotic prices behave within national pharmaceutical supply systems. This study evaluated the patterns and determinants of systemic antibiotic pricing in Tanzania using national regulatory import permit data. Methods We conducted a retrospective analysis of antibiotic importation records from the Tanzania Medicines and Medical Devices Authority for 2010-2016. Systemic antibiotics for human use imported via oral or parenteral routes were included. Unit prices (USD per smallest unit of measure) were summarized using the median and interquartile range (IQR). Prices were compared by route of administration, supplier country, and product naming practice (INN-named versus brand-named) using Mann-Whitney U and Kruskal-Wallis tests with false discovery rate adjustment. Results Of the 14,301 records, 10,894 (76.2%) met the inclusion criteria. Oral antibiotics predominated (89.6%). Although the median oral antibiotic prices declined over time, substantial price dispersion persisted across all study years. Parenteral antibiotics were consistently more expensive (USD 0.755-3.370) and more variable than oral antibiotics. Importation was concentrated in a few medicines, with amoxicillin-clavulanate (16.7%) and amoxicillin (11.4%) accounting for over one-quarter of records, and in a few supplier countries, with India representing 44.9% of the records. Significant price differences between INN-named and branded products were observed for amoxicillin (adjusted p<0.001) and ciprofloxacin (adjusted p=0.018), whereas prices differed significantly by supplier country across major medicines (adjusted p<0.05). Across medicines and years, wide within-product price distributions indicate persistent market segmentation rather than price convergence. Conclusions Antibiotic import prices in Tanzania exhibit systematic and reproducible variations associated with formulation type, supplier origin, and product naming practices. The findings indicate that procurement structure and supplier participation strongly influence pricing in the import-dependent pharmaceutical market. Monitoring import-level prices can serve as an upstream indicator of market conditions and support evidence-informed procurement, pricing regulations, and antimicrobial stewardship policies in LMIC settings.

The terms personalized, individualized and precision medicine are increasingly used to describe health interventions, yet their operational meaning in clinical research remains unclear. Despite extensive conceptual discussion, there is limited empirical evidence on how these labels are applied in randomized controlled trials (RCTs) and whether such trials meet standards of transparency and methodological rigor. We systematically examined 262 RCTs published between 2020 and 2022 that used the terms "personalized", "individualized", or "precision" in the title to describe an intervention. The term "personalized" was used most frequently (49.2%), followed by "individualized" (45.8%) and "precision" (5.0%). In most trials, personalization involved behavioral, digital, or pharmacological interventions, with few studies employing -omics approaches. Personalization was most often based on individual lifestyle factors, psychological characteristics, or disease classification. We also found that in most trials, personalization consisted of tailoring a single intervention to individuals (82.8%), often through individualized dosage (73.2%). Most included RCTs were judged to be at high risk of bias and showed limited transparency with respect to data and code sharing. Our study suggests that, in contemporary RCTs, the labels "personalized", "individualized", and "precision" are applied interchangeably to a wide range of heterogeneous interventions that are predominantly non-genomic. Greater conceptual clarity and stronger methodological standards are needed to ensure that claims of personalization in clinical research are empirically meaningful and reliable.

Topiramate (TPM) is approved for seizures and migraine prophylaxis and is used off-label for several neuropsychiatric conditions. The available dosage forms, including tablets and sprinkle capsules, are unsuitable for patients who may be unable to take medicine orally. The resulting potential treatment interruptions could have untoward consequences and underscores the importance of developing a parenteral formulation. In this study, we developed a population pharmacokinetic model of a novel, intravenous TPM formulation using data from a study in patients with epilepsy or migraine receiving a single intravenous dose of stable-labeled TPM. In total, 246 TPM concentrations from 20 adult patients were included for model development. A three-compartment pharmacokinetic model with linear elimination fit the concentration-time data best. Simulations for various loading and maintenance regimens for patients with and without enzyme-inducing comedications were performed. The final estimates(95% confidence interval (CI)) for CL (L/h), V1 (L), and the peripheral volumes, V2 and V3 for a 70 kg person were 1.31(1.01 - 1.53), 9.84 (8.49 - 11.0), 39.1 (36.5 - 41.8)L, and 9.01 (6.41 - 44.3) respectively. The use of enzyme-inducing co-medication was the only significant covariate, associated with a 63% increase in clearance .Goodness-of-fit plots and visual predictive checks indicate satisfactory model performance and prediction. The simulation results indicate that adjusting doses for patients receiving IV TPM can mitigate the changes in plasma TPM concentrations resulting from enzyme induction. This population pharmacokinetic model for intravenous topiramate can inform dosing decisions for patients with epilepsy when used as either initiation or bridging therapy.

Large language models (LLMs) are increasingly transforming scientific workflows, yet their application to rigorous evidence synthesis remains underexplored. Through the execution of a single Python script, we present a fully automated pipeline leveraging the Claude API to generate systematic reviews from literature search through manuscript completion without human intervention. Our pipeline processes hundreds of papers through iterative API calls for inclusion evaluation, information extraction, and synthesis, achieving citation accuracy rates of 95.87% through controlled text-restriction strategies that mitigate hallucination. In a blinded evaluation, six board-certified hematopathologists rated AI-generated systematic reviews (mean quality score: 3.4-3.66/5) higher than a published human-authored review (2.6/5) on the same topic, yet failed to reliably distinguish AI from human authorship. Notably, the human-written review was most frequently misidentified as AI-generated, revealing systematic biases in expert perception of AI capabilities. While demonstrating superior prose quality and topic coherence, AI-generated reviews exhibited increased repetition and had to be restricted to only referencing a select number of papers per section, highlighting fundamental trade-offs between automation scale and information breadth. Our findings establish both the technical feasibility and critical limitations of LLM-driven knowledge synthesis, raising urgent questions about verification standards, disclosure practices, and potential misuse in academic publishing. As automated high-quality scientific writing becomes computationally trivial, we argue for establishing transparent integration frameworks and enhanced AI literacy among domain experts to preserve scientific integrity while harnessing efficiency gains.

Background and aimsPopulation screening for liver disease in high-risk groups is recommended. Community diagnosis of liver disease is a challenge due to the asymptomatic nature of disease until very advanced stages. Moreover, regional variation in testing availability can result in people with clinically significant liver disease being missed. Machine learning (ML) has been proposed as a method to reduce diagnostic error and automate screening. We present a novel machine learning derived algorithm (ID LIVER-ML) designed to predict the risk of clinically significant liver disease in a high-risk community population to identify those needing further investigations or specialist referral. MethodsUsing data from 2039 patients recruited to two UK cohorts, we created a parsimonious model using investigations that would be available in primary care using liver stiffness measurement as reference standard. The performance of ID LIVER-ML was compared against FIB-4 score in a second unseen hold out cohort (n=327). ResultsID LIVER-ML performed well at identifying patients at risk of clinically significant liver fibrosis (sensitivity 0.90, Specificity 0.43, PPV 0.54, NPV 0.86, AUC 0.83) and outperformed conventional risk scoring systems (FIB-4: AUC 0.65; NAFLD Fibrosis Score: AUC 0.66; APRI: AUC 0.53; BARD: AUC 0.58). ConclusionMachine learning derived algorithms can help screen high risk populations in a community setting for liver fibrosis. ClinicalTrials.gov ID: NCT04666402 Impact and ImplicationsThe prevalence of steatotic liver disease is rising globally and is an increasingly significant challenge for healthcare systems. Existing risk stratification scores are not validated in a real-world cohort where patients have risk factors for multiple aetiologies of liver disease. Our work shows that a machine learning model can predict the risk of clinically significant liver disease using routine primary care data, better than existing non-invasive risk stratification tools in a real-world cohort. This highlights a potential role for machine learning in the automation of fibrosis risk assessment in primary care. Highlights- Machine learning derived algorithms can predict the risk of clinically significant liver disease in an at risk community population with a mixed aetiology of liver diseases. - The performance of the ML algorithm (ID LIVER-ML) is not affected by metabolic, alcohol, or mixed aetiologies. - ID LIVER-ML outperforms traditional risk stratification scoring systems such as FIB-4 and NAFLD fibrosis scores. - Compared to the FIB-4 score, the use of Machine Learning can reduce the need for secondary care investigations by 59%.

IntroductionPatients undergoing anterior cruciate ligament (ACL) reconstruction experience substantial postoperative pain, which delays recovery and leads to both immediate and long-term opioid use. In other knee procedures, infiltration between the popliteal artery and the capsule of the posterior knee (IPACK) has demonstrated analgesic and opioid reducing effects. However, the effect in patients undergoing ACL reconstruction has not been investigated. We aimed to investigate the real-world effect of IPACK in patients undergoing ACL reconstruction on immediate postoperative opioid consumption. ParticipantsIn this single-centre difference-in-differences cohort study, all patients who underwent ACL reconstruction surgery at Bispebjerg Hospital, Denmark, from 1 February 2024 to 30 June 2025 are included. The study further includes a similar reference cohort, comprising all patients who underwent trochleaplasty, Elmslie-Trillat, or medial patellofemoral ligament reconstruction during the same period, and at the same hospital. InterventionThe primary exposure is the implementation of IPACK as part of perioperative management for ACL reconstruction on 1 January 2025. The IPACK was performed under ultrasound guidance, immediately before surgery, administering 20 mL of ropivacaine 0.5% between the popliteal artery and the posterior knee capsule. OutcomesThe primary outcome is the cumulative opioid consumption from surgical incision to 2 hours postoperatively. Secondary outcomes include the cumulative opioid consumption from incision to 24 hours postoperatively, the worst reported pain score at 0-24h postoperatively, occurrence of postoperative nausea or vomiting (PONV) 0-24h postoperatively, length of PACU stay, length of hospital stay, and nerve injuries. As an exploratory outcome, carbon dioxide emissions will be investigated. Statistical analysisThe main analysis will be a standard two-way fixed effects DiD regression assessing the changes occurring at the time of implementation of IPACK in the ACL cohort, with adjustment for the underlying time trend. Continuous outcomes are reported as mean difference (95% confidence interval [CI]), and binary outcomes as absolute and relative risks (95% CI).

BackgroundPersonalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) data provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the diagnostic applicability of exome data for pharmacogenomics remains insufficiently studied. Materials and MethodsA retrospective analysis of 6,102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from PharmGKB (levels of evidence 1A-2B), CPIC, and PharmVar. Additionally, WES limitations and the feasibility of imputing non-coding pharmacogenetic variants were evaluated. ResultsPopulation frequencies of alleles and metabolic phenotypes were determined for 13 Very Important Pharmacogenes (VIPs), along with the distribution of HLA class I and II alleles. The highest allelic and phenotypic variability was observed in CYP family genes, particularly CYP2D6, CYP2C19, and CYP2B6. A total of 663 pharmacogenomic annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), including psychotropic agents, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. At least 32 drugs require pharmacogenetic testing based on variants located in non-coding regions, as well as accurate CYP2D6 copy number determination. Linkage disequilibrium analysis demonstrated the inability to reliably impute most non-coding pharmacogenetic variants from WES data. ConclusionThese findings represent one of the largest reference assessments to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm the utility of WES for population pharmacogenomic screening while simultaneously highlighting its fundamental limitations and the need for alternative genetic diagnostic methods in selected cases.

BackgroundPersistent Spinal Pain Syndrome (PSPS) type II represents a challenging clinical entity with limited therapeutic options. Various spinal cord stimulation (SCS) modalities have emerged as potential treatments, but their comparative effectiveness remains unclear. ObjectiveOur goal in this paper is to systematically evaluate and compare the efficacy of different SCS modalities in patients with PSPS type II through meta-analysis of available randomized controlled trials. Evidence ReviewWe conducted a systematic review following PRISMA guidelines, searching major databases for randomized controlled trials evaluating SCS modalities in PSPS type II patients until the end of May 2025(search updated on October 3rd). Primary outcomes included pain intensity (VAS) and functional disability (ODI) at 6 and 12 months. Subgroup analyses compared tonic versus burst stimulation and high-frequency versus low-frequency SCS. FindingsNine randomized controlled trials were included, encompassing 565 patients across different SCS modalities. For the primary outcome of clinically meaningful pain relief ([&ge;]50% reduction), pooled analysis demonstrated that 45% (95% CI: 18-75%, I{superscript 2} = 92.2%) of patients achieved this threshold for back pain and 55% (95% CI: 45-65%, I{superscript 2} = 0%) for leg pain. Subgroup analysis revealed significant differences in back pain responder rates by stimulation modality: High-frequency SCS demonstrated responder rates of 92% (95% CI: 79-98%) versus 28% (95% CI: 13-49%) for conventional frequencies (p < 0.001). For leg pain, no significant difference was observed between tonic (51%, 95% CI: 37-65%) and burst stimulation (60%, 95% CI: 45-74%, p = 0.36) and mean VAS scores demonstrated significantly lower pain with high-frequency SCS (13.30, 95% CI: 8.82-17.78) compared to conventional frequency (28.42, 95% CI: 24.02-32.88, p<0.0001). For back pain, mean VAS scores decreased from a baseline of 73.03 to 41.67 (95% CI: 36.12-47.22, I{superscript 2}=22.8%) at 6 months and remained stable at 35.66 (95% CI: 25.39-45.93, I{superscript 2}=75.0%) at 12 months. Leg pain showed more pronounced improvement, with VAS scores declining from a baseline of 61.81 to 23.75 (95% CI: 17.69-29.81, I{superscript 2}=78.8%) at 6 months and 29.16 (95% CI: 24.81-33.52, I{superscript 2}=0%) at 12 months). Meta-regression identified longer pain duration and older age as positive predictors of response, while higher baseline leg pain predicted lower responder rates. Serious adverse events occurred in 10%, with a 16% revision surgery rate. Only two studies demonstrated a low risk of bias across all domains. ConclusionsCurrent evidence demonstrates that various SCS modalities provide clinically meaningful pain relief in PSPS type II patients, with approximately half achieving [&ge;]50% pain reduction. High-frequency SCS shows significantly superior responder rates for back pain compared to conventional tonic stimulation, while burst stimulation yields significantly superior reductions in continuous pain intensity metrics. However, the limited number of studies, substantial heterogeneity, and lack of head-to-head comparisons prevent definitive recommendations regarding optimal stimulation parameters. Future large-scale randomized trials with standardized protocols and responder-based outcomes are needed to establish evidence-based treatment algorithms for PSPS type II patients.

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss in ageing populations, with oxidative stress recognised as a key pathogenic driver. The dietary antioxidant and cytoprotectant, L-ergothioneine (ET), is avidly accumulated in many tissues, especially the eye. However its relationship to AMD has not been investigated. Here, we examined ETs distribution in ocular tissue and assessed circulating and intraocular ET levels in patients with neovascular AMD. Compared with ocularly-normal age-matched individuals, AMD patients exhibited significantly lower serum ET; elevated levels of ET metabolites, hercynine and ETSO, which may be generated by oxidative stress; and elevated levels of serum allantoin, a product of oxidative damage to urate in humans. Levels of ET in aqueous humour in AMD patients were marginally lower than cataractous patients who are already known to have significantly lower ET levels than healthy eyes. High ET levels were seen in human ocular tissues concentrating in regions vulnerable to oxidative injury, including the lens, retina, retinal pigment epithelium, and choroid, supporting a physiological protective role of ET in the eye. These findings identify the strong association between low ET levels and AMD, warranting further studies to determine whether ET supplementation can modify AMD risk or progression.

BackgroundKetone bodies have shown potential to improve cardiac metabolism and function in patients with heart failure (HF). ObjectiveTo evaluate the effects of exogenous ketone-based interventions on cardiac function in patients with HF or related cardiometabolic risk factors. MethodsWe conducted a systematic review based on a search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Scopus from inception to January 2025. Eligible studies included randomized controlled trials evaluating exogenous ketones (oral ketones or ketone infusions) compared to placebo in adults with HF or patients with risk factors for HF including type 2 diabetes mellitus, hypertension, or coronary artery disease. Paired reviewers independently screened and identified hits at title-and-abstract and full-text levels to determine eligibility and extracted data from eligible studies. Random-effects meta-analysis was performed. Effects of interventions were summarized as mean differences (MD). Risk of bias was assessed using Cochrane RoB 2.0 tool. Certainty of evidence was evaluated using the GRADE (grading of recommendations assessment, development and evaluation) approach. ResultsOut of 565 unique records, 22 full-text articles were reviewed, and 8 studies met inclusion criteria. Exogenous ketone administration increased left ventricular ejection fraction (LVEF) (MD = 3.94, 95% CI 2.18-5.70, p = 0.001), cardiac output (CO) (MD = 1.11 L/min, 95% CI 0.55-1.67, p = 0.002), heart rate (4.85 bpm, 95% CI 2.24-7.46, p = 0.003), and stroke volume (SV) (MD = 10.21 mL, 95% CI 4.06-16.35, p = 0.005). Pulmonary capillary wedge pressure (PCWP) decreased (MD = -0.93 mmHg, 95% CI -1.44 to -0.43, p = 0.003), while mean arterial pressure showed no change (MD = -1.37 mmHg, 95% CI -3.53 to 0.79, p = 0.18). ConclusionsExogenous ketone-based therapies are associated with improvements in hemodynamic markers of cardiac function, including increases in LVEF, CO, and SV, along with a reduction in PCWP. These findings suggest that ketone supplementation may offer clinical benefits for patients with HF or vascular disease.