Pharmacology Research & Perspectives

Nicotinamide riboside (NR), a precursor to the essential coenzyme nicotinamide adenine dinucleotide (NAD+), has been extensively studied following oral administration, while the longer-term safety and physiological effects of injectable NR remain less characterized. Following an initial in-clinic phase evaluating repeat intramuscular (IM) and subcutaneous (SC) administration of pharmaceutical-grade NR, participants transitioned to an at-home phase designed to assess the safety, feasibility, and exploratory physiological effects of repeated self-administered SC administration. Participants continued their assigned 50 or 100 mg dose group and self-administered NR subcutaneously 3 times weekly for approximately 90 days. Safety was assessed through adverse event (AE) monitoring, vital signs, and clinical laboratory health measures. Exploratory assessments included markers of mitochondrial bioenergetics, urinary biomarkers of oxidative stress, and participant-reported outcomes related to fatigue, sleep, pain, and quality of life. At-home administration of NR was generally well-tolerated, with no clear dose-dependent safety concerns identified over the study period. Further, participants were able to successfully self-administer repeated SC injections over the study period, demonstrating positive feasibility. Whole-blood NAD+ concentrations remained largely stable over time in both dose groups. MeScreen analysis demonstrated a reduction in GlycoATP at Day 30 and increases in the MitoATP/GlycoATP ratio at Day 30 and Day 90, signifying a preferential shift toward oxidative metabolism. These findings may be indicative of positive metabolic health shifts. Urinary oxidative stress profiling demonstrated heterogeneous temporal responses. Several lipid oxidation markers significantly declined at Day 30 but returned toward baseline at Day 90. Select markers of nucleic acid, nitrative stress, and advanced glycation end-product oxidation demonstrated more sustained reductions. These results for which the majority of measured oxidative stress markers improved suggest that NR injections may exhibit anti-oxidative characteristics. Validated participant-reported sleep and fatigue measures showed improvements. Self-reported facial skin improvement increased descriptively from 11.4% at baseline to 27.8% at Day 30 and 36.8% at Day 90; improvements noticed by others increased from 9.1% to 23.7%, while reported body-skin improvement increased more modestly, from 8.8% to 16.7%. As these outcomes were not designed or powered to establish efficacy or definitive clinical claims, the findings should be considered hypothesis generating and provide preliminary data to inform the selection of outcomes and design considerations for future randomized, placebo-controlled, adequately powered trials of SC NR administration.

Aims Rosuvastatin is commonly co-administered with ticagrelor in patients with acute coronary syndrome. Ticagrelor inhibits breast cancer resistance protein (BCRP/ABCG2), increasing rosuvastatin exposure and potentially the risk of rhabdomyolysis. This risk may be further influenced by age and the ABCG2 c.421C>A variant. We aimed to predict optimal rosuvastatin dosing with ticagrelor in elderly patients stratified by ABCG2 genotype using physiologically based pharmacokinetic (PBPK) modelling Methods PBPK models for rosuvastatin and ticagrelor were developed and validated in PK-Sim using clinical data from healthy adults, elderly patients, and individuals with wild-type or homozygous ABCG2 c.421C>A genotypes. Key parameters, including BCRP catalytic activity and age-dependent declines in CYP2C9 and CYP3A4 activity, were optimized. The drug-drug interaction model incorporated competitive BCRP inhibition by ticagrelor. Rosuvastatin steady-state exposure was simulated with half or quarter standard doses during ticagrelor co-administration, and exposure matching approach was used to identify optimal dosing. Results The PBPK models demonstrated good predictive performance across populations and genotypes. In young adults with the wild-type genotype, halving the rosuvastatin dose resulted in an AUC fold change of 1.44, supporting a half-dose strategy. In young homozygous variant carriers, half-dose co-administration produced comparable exposure (AUC fold change 0.78). In elderly wild-type individuals, a four-fold dose reduction achieved comparable exposure (AUC fold change 1.06), whereas elderly variant carriers required only a half-dose reduction (1.12). Conclusions PBPK modelling supported age- and ABCG2 genotype-specific rosuvastatin dosing with ticagrelor. Elderly wild-type patients may require a further dose reduction to minimise toxicity while maintaining therapeutic exposure.

Background: Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for EGFR-mutated non-small cell lung cancer (NSCLC) but have distinct toxicity profiles across generations. Aims: To investigate relationships between EGFR-TKI physicochemical, pharmacokinetic and pharmacological properties and drug-specific adverse drug reactions (ADRs) using national and global pharmacovigilance data. Methods: ADRs for erlotinib, afatinib and osimertinib were extracted from the UK MHRA Yellow Card scheme and WHO VigiAccess. Disproportionality analysis used reporting odds ratios (RORs). Signals were defined as ROR >2, lower 95% confidence interval >1 and >3 cases. Drug properties were curated from ChEMBL, emc, SmPC and EMA sources. Results: VigiAccess contained 88,662 individual case safety reports (163,619 ADRs) and Yellow Card contained 752 reports (1,814 ADRs). Four System Organ Class (SOC) signals were identified in VigiAccess and three in Yellow Card, with five additional Yellow Card Preferred Term signals. Eye disorders showed the strongest consistent association with erlotinib (VigiAccess ROR 4.33; Yellow Card ROR 4.18). Osimertinib was associated with pneumonitis (ROR 7.67) and pulmonary embolism (ROR 3.94). Afatinib showed psychiatric disorder signals in both databases (ROR 3.2-3.3) and diarrhoea (ROR 2.79). Neoplasm-related signals likely reflected confounding by indication. Conclusions: EGFR-TKIs demonstrate generation-specific pharmacovigilance profiles. Integration with drug properties supports mechanistic plausibility for several signals, while the atypical afatinib psychiatric association warrants further investigation. Findings are hypothesis-generating and require confirmation in controlled observational or mechanistic studies.

Purpose Prescribing to lactating women is constrained by limited evidence. Published compatibility classifications provide clinical guidance and a discrete research comparator. How often prescribing diverges from that guidance, and what characteristics are associated with divergence, is unknown at national scale. Methods Retrospective analysis of prescriptions filled during lactation, identified in the 2012-2014 MarketScan Commercial Claims Database. Prescriptions were classified as concordant (Hale's lactation risk category L1-L3) or discordant (L4-L5). Multivariable logistic regression identified factors associated with concordance. Results Of 96,484 new prescriptions, 95,710 (99.2%) were concordant and 774 (0.8%) were discordant. Discordant prescriptions were filled later after delivery (median 59 vs 33 days) and had a day supply three times longer (30 vs 10 days). In multivariable analysis (N=28,732), concordant prescriptions were associated with maternal age under 35 (adjusted odds ratio (aOR)=1.34, 95% confidence interval (CI): 1.02-1.75) and chronic-condition medications (aOR=10.94, 95% CI: 6.47-18.50), and were less likely within 14 days of delivery (aOR=0.53, 95% CI: 0.38-0.73) and with each additional day of supply (aOR=0.96, 95% CI: 0.96-0.96). Compared with family practice, concordance did not differ for obstetric, internal medicine, or emergency medicine prescriptions, but prescriptions from other provider types had lower odds of concordance (aOR=0.57, 95% CI: 0.41-0.79). Conclusions Prescribing during lactation was overwhelmingly concordant with published compatibility guidance; the rare divergence was patterned by prescription and prescriber characteristics. Because the categories are an expert classification rather than consensus guidance, divergence should not be read as inappropriate prescribing.

The aim of this study was to develop a framework for assessing the incremental value of decisions on drug amount, timing, input structure, cessation and restart. The LOGOS (Longitudinal Optimization of Global Outcomes and Strategies) approach combines matched nested regimen classes, mechanism-specific benchmarks and evidence requirements in a single longitudinal comparison. Thus, the contribution of additional regimen freedom is assessed separately from improvements due to different resources, constraints or evaluation horizons. Nested feasible classes have nondecreasing objective suprema, whereas a strict gain requires a feasible witness above a valid bound for the smaller class. The approach is illustrated through analytical examples of periodic smoothing, matched desensitization, relaxed exposure allocation, finite-course loading and safety after cessation. In a normalized finite Emax model, integrated benefit increased from 0.629606 for three equal boluses to 0.672945 after optimization of amounts, 0.677043 for an amount-and-time candidate and 0.679553 for a globally certified loading-maintenance-coast solution. The adjacent observed gains were 6.88%, 0.61% and 0.37%, respectively, and all schedules satisfied the full-tail injury bound. In addition, a periodic factorial example separated response curvature from a joint recovery and repair process, including a null case and results dependent on feasibility constraints. The LOGOS framework allows assessment of whether additional regimen flexibility improves benefit, provides no added value or restores feasibility. These dimensionless examples support conditional mathematical and model-based conclusions; clinical superiority and safe restart in patients have not been established.

Background. Regulatory pressure to avoid confusingly similar drug names appears to have pushed manufacturers to coin more distinctive proprietary names, but no one has quantitatively analyzed this trend. Objective. To determine whether proprietary drug names became more orthographically distinctive (unusual in spelling) over time. Methods. We examined 1,321 US Food and Drug Administration (FDA) new molecular entity (NME) names (1985-2024), 2,769 FDA Orange Book trade names (1982-2026), 177,337 US pharmaceutical trademarks (1980-2026), and 1,273 generic names (1982-2025). Compared to prior names (including those already marketed) and ordinary English, we scored each name's character trigram (three-letter sequence) surprisal (statistical unexpectedness) and its edit-distance similarity to the closest existing names. Linear regression tested time trends, controlling for name length. Results. Mean NME trigram surprisal rose in every decade between the 1980s and 2020s, from 7.21 to 8.50 nats (natural-logarithm units, higher meaning less predictable), an 18% increase (b = 0.038, p < .001, R2 = 0.34). Half of 2020s names (7% in the 1980s) contained a letter sequence found in no common English word (e.g., Jesduvroq, Omvoh). Nearest-neighbor similarity fell after the early 1990s. Trademark data showed the same trend (b = 0.016, p < .001). Generic names showed a weaker trend (b = 0.011, R2 = 0.063). Conclusions. Over four decades, drug names became more orthographically distinctive, consistent with namespace crowding, deliberate efforts to avoid confusing names, and branding strategy. Because unfamiliar letter sequences can introduce other error pathways, increased distinctiveness does not necessarily imply improved safety.

Study Objectives: Glucagon-like peptide-1 (GLP-1) receptor agonists raise resting heart rate. We asked whether, after semaglutide initiation, the increase is larger during rapid eye movement (REM) sleep than during non-rapid eye movement (NREM) sleep. Methods: In the Human Phenotype Project, an Israeli cohort of generally healthy adults aged 40 to 70 years, 96 of 494 self-reported semaglutide initiators had a home sleep study (WatchPAT; sleep stages estimated from peripheral arterial tone and actigraphy) before and at least three months after starting. Each was propensity-matched 1:1 to a never-user of GLP-1 drugs measured at the same visits. We estimated the initiators' change minus their controls' change (difference-in-differences). The REM minus NREM contrast was designated primary after an exploratory analysis. Results: Relative to controls (90 to 94 pairs), heart rate rose during nocturnal wake (+3.89 beats per minute [bpm], 95% confidence interval 2.35 to 5.44), NREM sleep (+4.82 bpm, 3.12 to 6.52) and REM sleep (+6.25 bpm, 4.42 to 7.91); the REM increase exceeded the NREM increase by 1.42 bpm (0.98 to 1.89). The excess persisted across matching designs and on a relative scale; the proportion of REM sleep did not change. Never-users with similar weight loss showed no REM sleep heart-rate rise (+0.40 bpm, -0.63 to 1.50). Heart-rate variability decreases disappeared after adjustment for heart rate. Conclusions: Semaglutide initiation was associated with higher heart rate throughout sleep, most during REM sleep, unexplained by weight loss. Clinic measurements may understate this effect. Confirmation with polysomnographic staging is needed.

OBJECTIVE: To describe (1) US emergency department-treated pickleball injury burden and the injury-to-participation measure among total players, with burden among adults aged 60 to 79 years as a descriptive subgroup, and (2) determine whether general internet search interest for 'pickleball' is also associated with total pickleball-related injuries. DESIGN: Ecological time series study, United States, 2014 to 2023. DATA SOURCES: National Electronic Injury Surveillance System (NEISS) emergency department microdata yielded 1,738 adjudicated pickleball cases, 921 of them orthopedic, weighted to an estimated 101,450 injuries nationally. Google Trends, English Wikipedia pageviews, and GDELT news volume served as general pickleball attention signals, with the Trends index for 'sourdough' as a negative control. Sports and Fitness Industry Association (SFIA) participation estimates supplied a total player denominator. MAIN OUTCOME MEASURES: Annual national injury estimates with design-based 95 percent confidence intervals; injury-to-participation measures; and correlations between total pickleball-related injuries and general pickleball attention signals before and after removing trend and seasonality. RESULTS: Estimated emergency department-treated pickleball injuries rose 17.5-fold, from 1,391 in 2014 (highly unstable) to 24,294 in 2023. 70 percent of adjudicated cases involved adults aged 60 to 79. During 2017-2023, injury estimates rose 4.0-fold and participation rose about 4.4-fold. No temporal trend was detected in the injury-to-participation measure (P=0.59). Search interest looked like an excellent proxy: in annual levels it correlated with total pickleball-related injuries at R=0.96, and Pickleball Wikipedia pageviews at R=0.98. However, so did searches for "sourdough", at R=0.76. Removing trend and seasonality left little (monthly residual R=0.084; year-on-year R=0.28 over 96 months, 2015-2023), and the seasons ran opposite, with injuries peaking in February and searches in July, such that, on 12-month mean profiles, the sourdough negative control fitted the injury season (R=0.96) while pickleball ran against it (R=-0.40). General pickleball search interest and pickleball news coverage remained correlated in year-on-year changes in 2017-2023 (R=0.64), whereas their corresponding correlations with total pickleball-related injuries in 2017-2023 were 0.25 and 0.20. CONCLUSIONS: The absolute burden of emergency department-treated pickleball injury has grown substantially, but the available surveillance data cannot resolve whether the sport has become more hazardous per participant. The observed rise is consistent with growth in exposure alone. The strong correlation between search interest and injury counts was not specific, the association was substantially attenuated after detrending, and the seasonal patterns were opposed. These descriptive results do not establish that search interest can substitute for injury surveillance, and series that rise together should not be assumed to be related.

Background: Opioids remain the cornerstone of acute postoperative pain management but are associated with potentially serious adverse effects. We aimed to investigate the effect of oxycodone patient-controlled analgesia with adjunctive S-ketamine on pain after lumbar fusion surgery. Methods: In this secondary analysis, we examined oxycodone and S-ketamine exposure and PCA behaviour during the first 24 h. We assessed current pain versus subsequent PCA activity and whether the association between plasma oxycodone and adequate analgesia changed with postoperative time. Results: Plasma concentrations of administered drugs rose rapidly after PCA initiation, but variation was large. Metabolite concentrations accumulated over time. PCA attempt and delivered-dose rates decreased over postoperative time (RR 0.965 and 0.966 per hour, respectively; both P<0.001). Older age was associated with a lower delivered-dose rate (RR 0.905 per 10 years, 95% CI 0.829-0.987). Each 1-point higher current pain score was associated with more subsequent PCA attempts (RR 1.09, 95% CI 1.04-1.14) and delivered doses (RR 1.07, 95% CI 1.03-1.11). The oxycodone-analgesia association weakened over time [interaction P <0.001; odds ratios (Ors) per 10 ng/mL 1.16, 1.13, and 1.00 at 1, 4, and 24 h, respectively]. In active S-ketamine groups, including S-ketamine, norketamine, and 6-hydroxynorketamine concentrations left the pain-PCA association unchanged (RR 1.09 and 1.07) and preserved the time-dependent oxycodone association (ORs 1.30, 1.26, and 1.02). Conclusions: During PCA, drug exposure develops from patient-generated dosing histories while pain and recovery change. Current pain was associated with subsequent PCA activity, and the oxycodone-analgesia association was time-dependent.

Purpose: Accurately capturing a patient's health journey is essential for ensuring the validity of scientific and public health studies. Electronic health records (EHR) and administrative claims are commonly used in healthcare research, albeit separately. Previous research has demonstrated that EHR and claims capture different aspects of patient history and care, potentially introducing bias when used alone. In this study, we leveraged the largest public US EHR resource, the National Clinical Cohort Collaborative (N3C), alongside Centers for Medicare & Medicaid Services (CMS) claims data to compare their information coverage and agreement. Methods: We integrated N3C EHR and CMS data to create one cohesive dataset and quantified the proportion of key variables present in EHR alone, CMS alone, and the overlap present in both data sources. Overlap in information coverage was quantified pre- and post-landmark diagnosis of COVID-19. To evaluate the impact of data coverage differences, we conducted a case study evaluating the effect of COVID-19 vaccination on ischemic stroke using both data sources alone and in combination. Results: CMS data helped identify many individuals that were duplicated among EHR identifiers. Examination of the overlap showed that CMS data more completely captured conditions, while EHR data more completely captured medications after COVID-19 diagnosis. Some variables exhibited marked differences over time after landmark COVID-19 diagnosis. Using target trial emulation and self-controlled case series methods, we found that data capture variations altered effect estimates: EHR data alone yielded lower risk of ischemic stroke after vaccination in the target trial emulation (odds ratio 0.47, 95% CI 0.28-0.79), with a smaller, non-significant reduction in the self-controlled case series (incidence rate ratio 0.81, 95% CI 0.64-1.02), whereas analyses using CMS data alone or both sources combined yielded estimates close to the null in both designs. Conclusions: Results indicated that when using either a national database combining multiple EHR sources, or CMS claims data alone, patients' medical histories are less completely captured compared to when these data sources are combined. Case study results highlighted the value of combining data sources for not only yielding fuller patient health histories and valid scientific results, but for identifying and explaining biases that may emerge in research studies that rely on a single data source.

Background: The relationship between recent viral infection and subsequent susceptibility to, or protection against, other viruses remains unclear, particularly across ages and symptom status. Methods: A prospective cohort study enrolled participants aged 6 months to 49 years in Washington and Oregon, USA, 2022-2024. Weekly nasal swabs were tested for SARS-CoV-2, respiratory syncytial virus (RSV), and influenza A/B; additional swabs were collected during symptomatic illness and were also tested for parainfluenza (PIV) 1-4, endemic human coronaviruses (hCOV), adenovirus, human metapneumovirus, human rhinovirus (hRV), and enteroviruses. Proportional hazards regression models were used to characterize associations between incident symptomatic infection (illness) and history of recent illness due to other viruses (within 1-7, 8-14, 15-30, 31-60, 61-90, 91-180 days), adjusted for geographic region, age, sex, daycare/school/work attendance, household size, and number of children in the household. For SARS-CoV-2, influenza, and RSV, these analyses were repeated to study incidence of infection (including asymptomatic cases). Findings: Of 3,560 participants, 2,969 (83%) had [&ge;]1 illness over 4,299 person-years. Adjusted hazard ratios (aHRs) in the short term ([&le;]2 weeks) after an illness were predominantly below 1, indicating that recent viral illness was often associated with lower risk of subsequent illness with other viruses. Across age groups, recent hRV illness was associated with lower risk of hCOV, PIV, and RSV illness (aHRs 0.07-0.27). SARS-CoV-2 illness was associated with a higher short-term risk of adenovirus and PIV illness (aHRs 3.20-3.33). In children aged 6 months-4 years, recent influenza illness was associated with higher short-term risk of SARS-CoV-2 illness. Including asymptomatic infections generally attenuated these associations. Interpretation: Some viral illnesses may alter short-term susceptibility to subsequent viral illnesses. Interactions may differ by age and symptom status. Funding: US Centers for Disease Control and Prevention; Washington State Department of Health.

BACKGROUND: The chromosome 9p21.3 locus is among the strongest genetic risk loci for cardiovascular disease (CVD), yet the regulatory mechanisms underlying this association remain poorly understood. Previous vascular disease studies focused predominantly on the role of this locus in smooth muscle cell (SMC) dysfunction, whereas its potential role in endothelial cells (ECs) remains less well characterized. Here, we sought to identify candidate regulatory variants at 9p21.3, their distal target genes, and effector transcripts that contribute to CVD-related phenotypes in ECs. METHODS: Promoter capture Hi-C was used to map chromatin interactions at 9p21.3 in primary human ascending aortic SMCs (n=6), supplementing the previously obtained ECs (n=16) dataset. To support the regulatory relevance of these interactions, expression quantitative trait locus analysis was performed in 136 vascular biopsies. Variant effects were prioritized in silico using AlphaGenome. The regulatory activity of the prioritized region was validated using CRISPR activation. RESULTS: Chromatin interaction mapping identified a complex regulatory architecture at the 9p21.3 locus involving 19 putative regulatory elements and 15 genes across ECs and SMCs. MIR31HG showed the most recurrent distal promoter connectivity with the 9p21.3 region. Consistent with the chromatin interaction map, expression of MIR31HG transcripts was associated with genetic variation at the 9p21.3 locus. In ECs, the lncRNA MIR31HG, but not miR-31, was responsive to hemodynamic flow. Depletion of MIR31HG in ECs was associated with endothelial activation. Epigenetic tracks and deep learning predictions highlighted an active enhancer element at 9p21.3, which was subsequently validated by CRISPR activation and increased MIR31HG expression, confirming its capacity to regulate MIR31HG over a large genomic distance of approximately 538 kb. CONCLUSIONS: Altogether, these data implicate lncRNA MIR31HG as a likely effector of the 9p21.3 CVD locus, linking the cis-regulatory element and heritable genetic variation in the respective region to endothelial activation through long-range enhancer regulation.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Background. In people with type 2 diabetes (T2D), postprandial glucose weighs on glycaemic control, and carbohydrate counting is difficult. Patients can describe their meal but rarely quantify it. Objective. To assess (1) whether a small open large language model (LLM), fine-tuned on public data, estimates the carbohydrate content of a meal described in words, and (2) what this carbohydrate adds to the prediction of the postprandial glucose rise in two public cohorts. Methods. Step 1: the Qwen3-4B LLM was fine-tuned with QLoRA on Nutrition5k dishes and compared with an automatically matched USDA food composition table and with Qwen3-4B and Qwen3-8B used without training, on test dishes (internal control) and on 100 real meals of ShanghaiT2DM patients, whose reference carbohydrate was computed from the recorded quantities. Step 2: the maximum glucose rise between 0 and 120 minutes ({Delta}Gmax) was predicted by personalised gradient boosting (M2), compared with a spline linear regression and with the Chronos-2 foundation model, using only information available at meal start and person-level cross-validation, in ShanghaiT2DM (people with T2D recruited in two Shanghai hospitals; carbohydrate estimated by the LLM) and CGMacros (free-living adults in Santa Barbara, California; carbohydrate entered in the dataset, no text). Step 3: the model was transferred between datasets. 95% confidence intervals (CI) were obtained by cluster BCa bootstrap (2,000 resamples). Results. On the 100 real meals, the carbohydrate mean absolute error (MAE) of the fine-tuned model was 12.2 [9.9, 15.2] g, almost half that of the table (22.6 g; paired difference -10.5 [-16.8, -6.0] g) and nearly a third of that of the same model without fine-tuning (34.3 g), with a bias of -10.3 g; on test dishes it was 2.2 [2.0, 2.5] g. Personalised gradient boosting (M2) reached a correlation between predicted and observed rises of r = 0.63 [0.55, 0.69] in CGMacros (1,286 meals, 45 adults) and 0.49 [0.42, 0.54] in ShanghaiT2DM (2,339 meals, 99 people with T2D), about twice the linear reference (r = 0.32 and 0.23); for a rise above 50 mg/dL, its area under the ROC curve (AUROC) was 0.80 and 0.74, versus 0.64 and 0.68 with pre-meal glucose alone. Adding carbohydrate to pre-meal glucose changed r by 0.095 [0.068, 0.128] in CGMacros and by 0.004 [-0.010, 0.017] in ShanghaiT2DM; difference between datasets: 0.091 [0.060, 0.126]. Applied from one dataset to the other, the model lost correlation ({Delta}r = -0.11 [-0.17, -0.05] towards ShanghaiT2DM and -0.12 [-0.19, -0.04] towards CGMacros). The full "text [-&gt;] LLM [-&gt;] glucose" chain was evaluated in ShanghaiT2DM only. Conclusion. An open 4-billion-parameter LLM, fine-tuned with QLoRA on public data and usable offline, almost halves the error of carbohydrate counting for real meals described in words compared with an automatically matched table, with an underestimation of large meals. Personalised gradient boosting, based on pre-meal glucose and the person's usual response, doubles the correlation of a linear reference for predicting the glucose rise in both cohorts. Carbohydrate improved this prediction in CGMacros but not in ShanghaiT2DM; the two datasets differ in many respects, and the cause of this gap is not established.

Biomedical artificial intelligence (AI) increasingly relies on heterogeneous data sources, yet differences in data structure, terminology, evidence quality, and provenance complicate their integration into reliable knowledge resources. Here, we develop an evidence-aware framework for processing and integrating heterogeneous pharmacovigilance data from FDA Adverse Event Reporting System (FAERS) reports, biomedical literature, clinical trials, and electronic health records in Mass General Brigham. The framework combines large language model (LLM)-based information extraction, ontology-driven semantic normalization, cross-source evidence assessment, statistical and machine learning-based prioritization, and ingredient-based adverse drug event (ADE) relationship inference to transform heterogeneous evidence into a structured and evidence-based knowledge resource. The constructed knowledge system contains 11,588 drug nodes, 44,843 ingredient nodes, 3,275 ADE nodes, 993 indication nodes, and more than 2.47 million relationships, with each drug-ADE relationship assigned a confidence score. We further developed a drug-level web interface for accessing the knowledge resource and validated its utility in downstream AI tasks. Specifically, incorporating the developed knowledge resource improved the comprehensiveness, relevance, and overall quality of LLM-based drug-specific ADE synthesis and improved the precision and F1 score of clinical ADE relation extraction. These results demonstrate that AI-assisted processing combined with evidence-aware curation can transform heterogeneous biomedical evidence into reusable, AI-ready knowledge with explicit provenance and evidence strength information. The framework provides a general strategy for integrating heterogeneous biomedical data to support reproducible knowledge construction and downstream biomedical AI applications.

Schizophrenia spectrum disorders (SSD) have been linked to immune dysregulation and altered intestinal barrier function, although findings across studies remain inconsistent. The present study investigated circulating markers of intestinal permeability and immune activation in individuals with early-stage SSD and healthy controls and examined their associations with clinical outcomes. Serum concentrations of intestinal fatty acid-binding protein (iFABP), zonulin, soluble CD14 (sCD14), bactericidal/permeability-increasing protein (BPI), and lipopolysaccharide-binding protein (LBP) were measured in 36 patients with SSD at baseline and one-year follow-up and in 36 matched healthy controls. Linear mixed-effects models adjusted for age, sex, and body mass index (BMI) were used to evaluate group differences and longitudinal changes. No biomarker differences between patients and controls, and no longitudinal changes within patients, remained statistically significant after correction for multiple testing. Similarly, biomarker concentrations were not associated with symptom severity, global functioning, or neutrophil-to-lymphocyte ratio (NLR). Exploratory analyses identified a robust positive association between BMI and zonulin concentrations and additional associations among iFABP, LBP, and sCD14. Exploratory mixed-effects models identified a negative non-significant association between iFABP and sCD14 after adjustment for age, sex, and BMI. Overall, the findings do not support a generalized alteration of gut permeability or immune activation markers in SSD. Instead, they suggest that variation in gut-related biomarkers may be more strongly influenced by metabolic and individual factors than by diagnosis or current symptom severity.

Exposure to armed conflict can leave persistent effects on mental health, yet its neurophysiological correlates remain poorly characterized, particularly in populations exposed to prolonged violence outside narrowly defined post traumatic stress disorder samples. Here we examined resting state electroencephalographic activity in 122 Colombian adults, including 74 participants exposed to armed conflict and 48 controls. Eyes closed EEG was analysed for 240 seconds per participant. We quantified absolute and relative spectral power across delta, theta, alpha and beta bands and regional functional connectivity using weighted phase lag index in theta, alpha and beta. Group comparisons were adjusted for age, education, sex and assessment year, with correction across 78 EEG contrasts. Median occipital and global alpha power was lower in exposed participants, although adjusted estimates were close to the null and confidence intervals were broad. Temporal theta relative power was lower in exposed participants at nominal significance, but this association did not survive Holm correction. No spectral power or regional connectivity contrast remained significant after multiple testing correction. Exploratory associations between EEG measures and MoCA performance were modest in participants with confirmed scores. These findings provide a systematic characterization of resting EEG after exposure to armed conflict in a population shaped by prolonged violence and displacement. They also indicate that large or widespread alterations in resting spectral power and connectivity were not detectable with the current sample and analytical framework, while defining candidate signals and methodological priorities for larger studies of conflict related brain health.

Neuropsychiatric symptoms (NPS) are highly prevalent in Mild Cognitive Impairment (MCI), conceptualized as an intermediate clinical state between normal aging and dementia. Although neuroimaging studies have reported associations between NPS and brain alterations in MCI, longitudinal evidence on NPS and their relationship with multimodal MRI measures remains limited. We aimed to investigate (1) longitudinal changes in NPS, volumetric assessment and measurement of regional cerebral blood flow (CBF) within the medial temporal lobe (MTL); (2) associations between longitudinal changes in MRI measures and NPS; (3) whether baseline MRI measures predicted longitudinal changes in NPS. Thirty individuals with MCI aged over 65 years were recruited, of whom eighteen followed up for a mean period of 29 {+/-} 8.9 months. There were significant reductions in total grey matter and white matter volumes, together with volume loss in the bilateral hippocampus, bilateral parahippocampal gyrus and right entorhinal cortex (pFDR[&le;]0.05) over the follow up period. Volume loss in the right parahippocampal gyrus was associated with increase in aggression (pFDR<0.01), and CBF increase in the right hippocampus and parahippocampal gyrus was associated with worsening in agitation (pFDR<0.01). Furthermore, lower baseline volume of the right entorhinal cortex was associated with increase in aggression, and lower baseline CBF in the right hippocampus and parahippocampal gyrus was associated with worsening of agitation (pFDR<0.05). Our findings highlight the importance of identifying NPS in individuals with MCI and the role of structural and perfusion MRI measures as promising biomarkers for monitoring disease progression and improving risk prediction of conversion to dementia.

Fetal development is highly sensitive to environmental influences, including maternal smoking during pregnancy (MSDP) and air pollution. The placenta may play a central role in mediating these effects through epigenetic mechanisms, including small non-coding RNAs (sncRNAs). While most studies have focused on microRNAs (miRNAs), other sncRNA classes remain poorly characterized. Here, we characterized the placental sncRNA landscape and investigated its associations with prenatal exposure to MSDP and air pollution. The sncRNA expression profile of 390 chorionic villi samples from the Barcelona Life Study Cohort (BiSC) was measured using small RNA sequencing and the seqBuster/seqCluster pipeline. MSDP was assessed via questionnaire, and prenatal exposure to black carbon (BC), particulate matter [&le;]2.5 m (PM2.5), and nitrogen dioxide (NO2) was estimated using hybrid dispersion-land-use regression (LUR) spatiotemporal models. Associations were evaluated using linear regressions adjusted for relevant covariates. We identified 979 placental sncRNA clusters, assigned primarily to miRNAs (33.2%), piRNAs (26.4%), snoRNAs (12.2%), tRFs (7.8%), and others (20.4%). MSDP was associated with the downregulation of miR-421 and the upregulation of multiple snoRNAs from the imprinted DLK1-DIO3 locus (SNORD113-4, SNORD114-2, SNORD114-3, and SNORD114-17), as well as the piR-26394. No sncRNAs remained significantly associated with air pollution after multiple-testing correction, although several miRNAs showed suggestive associations. Overall, this study provides a comprehensive characterization of placental sncRNAs and identifies previously unreported smoking-associated sncRNAs within the imprinted DLK1-DIO3 locus, expanding our understanding of placental epigenetic responses to prenatal environmental exposures.

Estimating the correlation between subject-specific rates of change is a common objective in longitudinal studies. A natural approach is to fit ordinary least squares regressions per subject and correlate the resulting slope estimates directly. Under complete data, this slopes-first estimator is algebraically equivalent to a contrast-based plug-in estimator derived from the joint variance-covariance matrix of the two signals across time. Under independent dropout, however, the slopes-first approach implicitly applies heterogeneous weights across missing-data patterns, introducing bias whenever the cross-covariance between the two signals is not constant over time. We formalise this result and propose an unbiased alternative that estimates the joint covariance matrix before applying the contrast. We further extend our results to include covariates as well. Simulations confirm that the slopes-first estimator is biased under non-constant cross-covariance while the proposed alternative remains unbiased. Further, we show in a real-world example that the two estimators can lead to different scientific conclusions and recommend that researchers working with longitudinal data inspect the missing data pattern to make an informed decision about the estimator of choice.