Biological Journal of the Linnean Society

Forest trees pose numerous potential challenges to phylogenomic inference. Their large effective population sizes and relatively long generation times lead to deep allele coalescence and consequently incomplete lineage sorting (ILS), which biases inferences of divergence times toward older ages and introduces gene tree discordance. Deep phylogenetic divergences, reaching back into the Paleocene, introduce reference-mapping biases. Introgression--the movement of genes between lineages--may result in different phylogenies being inferred depending on which individuals are included in analysis, even if the plurality of the genome favors the divergence history unaffected by introgression. These factors influence phylogenetic inference across the Tree of Life but are particularly prevalent in forest trees. Oaks (Quercus) are notable for all three influences. In addition, our knowledge of the oak phylogeny is currently based strongly on restriction site associated DNA sequencing (RADseq) datasets published over the past decade, which may introduce additional sources of uncertainty. In this chapter, we analyze a 322-species RADseq dataset and genome resequencing data from across the genus to address sources of uncertainty in our understanding of the global oak phylogeny, which we hope will serve as a model for other research groups working on comparable woody plant groups.

Background Cardiometabolic-based chronic disease (CMBCD) at an individual level results from complex interactions among a multi-tiered network of sociodemographic, behavioral, and metabolic factors. Though a consensus set of risk factors drives CMBCD, population context influences risk factor effects and interactions. To better understand this phenomenon, we investigated the multi-tiered networking of cardiometabolic variables across diverse populations using a comparative modelling approach. Methods and Findings Utilizing nationally representative cross-sectional data from 48 countries participating in the World Health Organization "STEPwise approach to noncommunicable disease risk factor surveillance" survey, we learned country-specific Bayesian networks including sociodemographic, behavioral, and cardiometabolic variables (adiposity, diabetes, hypertension, hyperlipidemia, and cardiovascular disease). By computing the structural Hamming distance between pairs of networks, we compared differences in network structures across regions and country income levels. We then used the learned networks to assess individual risk factor influences and interactions on cardiometabolic outcomes. Country-specific Bayesian networks varied in terms of the risk factors directly and indirectly associated with the cardiometabolic outcomes. Network structures differed significantly across regions (p = 0.023) but not across income levels (p = 0.91). These results were robust to an alternative learning algorithm, network comparison metric, and data imputation approach. Older age (60+ vs. 30-44 years old) was associated with a greater increase in probability of obesity in Europe and Central Asia (+80%) compared to other regions. Higher education was associated with increased probability of obesity (+53%), diabetes (+18%), and hypertension (+2%) in South Asia but decreased probability of obesity (-10%), diabetes (-32%), hypertension (-16%), and hyperlipidemia (-25%) in Middle East and North Africa. The interaction between age and sex in predicting obesity was significant in the highest proportion of countries in Europe and Central Asia compared to other regions. While this dataset provided standardized data across multiple countries to define cardiometabolic risk factors and drivers, there was limited data on certain health outcomes and uneven availability of data across regions. Conclusions These results revealed specific regional patterns of multi-tiered cardiometabolic risk structures, emphasizing the need for regionally tailored public health strategies rather than applying generalized consensus evidence-based models. Future research should explore the structural drivers of regional differences in inter-relationships of cardiometabolic risk factors, drivers, and disease.

Neural oscillations provide temporal frameworks for coordinating communication within and across distributed brain networks. In essential tremor (ET), pathological synchronization within the cerebello-thalamo-cortical circuit produces rhythmic activity that manifests as an involuntary action tremor. Although deep brain stimulation can effectively suppress tremor, its invasiveness and cost highlight the need for non-invasive interventions capable of selectively modulating pathological oscillations. Transcranial magnetic stimulation (TMS) offers a non-invasive means of engaging cortical circuits, yet conventional stimulation protocols are delivered independently of the ongoing neural dynamics. Such open-loop approaches ignore the temporal structure of tremor-related activity, potentially stimulating during both amplifying and suppressing phases of the oscillation. To address this, we compared two phase-targeted TMS paradigms: first-pulse phase-locked TMS (First-pulse-TMS), in which only the initial pulse of a stimulation train is aligned to the tremor phase, and cycle-by-cycle phase-locked TMS (Continuous-TMS), in which each pulse is continuously triggered based on real-time tremor phase. Ten patients with ET underwent stimulation guided by peripheral tremor recordings using an accelerometer, with tremor phase estimated in real time via the Oscilltrack algorithm. Sixty-four trains of TMS pulses were delivered at nine discrete phase bins of the tremor cycle, such that each phase bin was repeated approximately seven times. Continuous-TMS maintained accurate phase-locking across consecutive cycles (mean phase-locking value ~0.9), whereas First-pulse-TMS exhibited progressive drift over time and low phase consistency (mean phase-locking value <0.2). The circular concentration of stimulation phase was significantly greater for Continuous-TMS than First-pulse-TMS (Mann-Whitney U-test, p < 0.001), indicating a significant difference in overall phase-locking accuracy between the two protocols. Critically, Continuous-TMS, unlike First-pulse-TMS, induced bidirectional, phase-dependent modulation of tremor amplitude. Circular-linear modelling revealed a sinusoidal relationship between stimulation phase and changes in tremor amplitude, with tremor amplification and suppression occurring at opposite phases of the cycle. Covariates including baseline tremor amplitude and trial number were accounted for. In some people, tremor suppression outlasted the stimulation period, suggesting phase-locked TMS may be a potentially useful therapeutic tool. By enabling reliable, phase-specific stimulation of the tremor cycle, Continuous-TMS allows identification of the individual phase that produces maximal tremor suppression, supporting the development of personalized, phase-specific neuromodulation strategies. This proof-of-principle study demonstrates that temporally precise, closed-loop TMS can interact with pathological oscillations in real time, providing a mechanistic framework for probing oscillatory contributions to motor symptoms and a scalable therapeutic approach for ET and other oscillopathies.

Abstract Objectives: Amyotrophic lateral sclerosis (ALS) is a clinically heterogeneous neurodegenerative disease requiring reliable biomarkers to improve patient stratification and trial design. While serum neurofilament light chain (sNfL) reflects neuroaxonal stress and disease aggressiveness, troponin T (TnT) may capture complementary aspects of neuromuscular involvement. We assessed the associations of TnT and sNfL with D50-derived measures of disease aggressiveness (D50) and disease accumulation (rD50) in ALS. Material and Methods: In this retrospective observation, TnT and sNfL levels from ALS patients in two independent German cohorts were analyzed using the D50 disease progression model; discovery cohort (Essen, n =433) and validation cohort (Bonn, n =185). Results: In both cohorts TnT demonstrated a robust correlation with rD50-defined phases across all aggressiveness subgroups (p<0.001). There was no consistent pattern regarding sNfL and the rD50 phases. sNfL concentrations demonstrated a significant and inverse correlation with D50 applied for all disease aggressiveness subgroups (p<0.001). Correlations of TnT levels with D50 disease aggressiveness groups were generally less strong and inconsistent between the two cohorts. In the discovery cohort only low aggressiveness subgroups correlated significantly (p<0.001), intermediate aggressiveness subgroups showed only a weak correlation (p<0.05) with TnT levels. High disease aggressiveness subgroups showed no significant correlation with TnT. Conclusion: In application of the D50 disease progression model, TnT was strongly associated with disease accumulation (rD50) across all disease phases, independent of disease aggressiveness (D50), whereas sNfL robustly reflected disease aggressiveness but not overall disease burden. These complementary biomarker profiles highlight the value of an integrated approach for refined disease stratification in ALS. Combining TnT and sNfL may enhance clinical decision-making, improve monitoring of disease progression and treatment response, and support optimized clinical trial design.

Environmental exposures are increasingly examined in relation to mental health, yet large-scale epidemiological analyses remain constrained by fragmented geospatial data, heterogeneous spatial and temporal resolutions, and privacy-preserving linkage requirements, limiting systematic investigation of multiple environmental domains at the population level. We present environMAP, a harmonised set of analysis-ready environmental exposure layers derived from open, global sources. environMAP spans the built environment, green and blue spaces, light exposure (solar radiation and night-time light), terrain, weather and extremes, and air pollution. We document data provenance, spatial buffers, preprocessing, projection alignment, and metadata, and provide a reproducible workflow for privacy-preserving linkage to cohort residential locations. To demonstrate utility, we linked environMAP to >200,000 adults in the German National Cohort (NAKO) and summarised self-reported lifetime doctor-diagnosed depression across exposure gradients using sex-stratified descriptive analyses. Gradients were interpretable and broadly consistent with prior evidence, supporting feasibility, scalability, and hypothesis generation. The framework is adaptable to other outcomes, cohorts, and regions.

Background. The European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets (ESCAT) ranks genomic alterations by the evidence supporting the predictive value of the molecular target for response to targeted therapies. No openly available, systematically curated set of standard care biomarkers mapped to the ESCAT framework exists to support clinical decision-making or harmonize biomarker interpretation. Methods. We mapped all OncoKBTM Level 1 biomarkers to ESCAT tiers using evidence cited by OncoKBTM, excluding abstract-only data. Eight board-certified oncologists and hematologists independently assigned ESCAT tiers, with discrepancies resolved through structured consensus meetings. Recurring evidence scenarios that did not correspond to any existing ESCAT tier informed a set of a priori defined modifications, which were subsequently applied to biomarkers that could not be classified using native ESCAT criteria. Results. Of 188 OncoKBTM Level 1 biomarkers, 16 were excluded due to abstract-only evidence. Using native ESCAT criteria, 51% of the remaining biomarkers were classified as Tier 1, 3% Tier 2, 18% Tier 3, 6% Tier X and 22% could not be assigned to any tier. Applying the modified ESCAT criteria resolved all previously unclassifiable biomarkers and increased Tier 1 assignments to 73%. Inter-rater reliability (Krippendorffs alpha) was moderate (0.586) and 62% of classifications required consensus discussions. Comparison with ESCAT tiers reported in ESMO Clinical Practice Guidelines showed improved concordance when using the modified criteria. Conclusions. The native ESCAT criteria are highly stringent, resulting in many FDA-recognized, clinically validated biomarkers that are currently assigned level 1 by OncoKBTM not mapping to any existing tier. Our predefined modifications improved alignment with OncoKBTM Level 1 designations and with published ESMO clinical practice guidelines. The mapped set of standard care biomarkers are provided on the OncoKBTM website, offering a practical resource that harmonizes ESCAT tiers of evidence with a widely adopted levels of evidence schema.

Foundation models (FMs) are powerful tools to allow the broad clinical application of artificial intelligence (AI) in healthcare systems, offering adaptability to different disease, modalities and clinical settings. However, FMs require large-scale datasets to train and fine-tune, while most real-world data are localized in siloed healthcare settings with strict data privacy protection, a restriction that poses a fundamental challenge in the cross-healthcare institution development of FMs. Here, we develop a fully homomorphic collaborative learning framework, named as FOCAL, that enables secure FM-driven diagnosis without exposing raw patient information. Different from traditional federated learning (FL) frameworks that aggregate locally trained models, FOCAL integrates fully homomorphic encryption (FHE) with split training to effectively execute collaborative learning completely over encrypted data. Specifically, we apply FOCAL on different types of retinal and pathology FMs to demonstrate its clinical performance. When facing gradient inversion attacks, FOCAL reduced the data leakage rate from 90.6% to 0% with comparable accuracy performance of the state-of-the-art FL paradigms, owing to the provable security provided by FHE. Moreover, under the same level of security, FOCAL can boost the macro-average AUROC by nearly 50% (from 0.5202 to 0.9831) when evaluated against fully encrypted FL models. In the multi-institution comparative experiments, FOCAL consistently outperforms all single-institution FMs, improving AUROCs by 9.62% and 14.46% on the ocular disease diagnosis and severity classification, respectively. Lastly, external validations on both retinal and pathology FMs further verified the accuracy and security advantages of FOCAL and highlighted its reliable interpretability and generalizability for cross-institution clinical development and implementation of FMs. FOCAL is a novel method to build a secure data-sharing AI community, facilitating healthcare institutions to benefit from and contribute to next-generation FMs development without compromising patient privacy and data security.

Clonal haematopoiesis (CH) becomes ubiquitous as humans age. The role of somatic driver mutations in its development has been studied widely, but little is known about CH without identified genetic drivers, also known as "CH with unknown drivers" (CH-UD). A fundamental unresolved question is whether CH-UD is driven by undiscovered somatic genetic drivers or by other cell-heritable traits. Here, to investigate this, we develop a new machine learning classifier to improve CH-UD detection from whole-genome sequencing data. After excluding 77,885 individuals with previously documented driver CH or mosaic chromosomal alterations (mCA), we applied our classifier to 407,512 UK Biobank participants and identified 26,963 (6.6%) with CH-UD. A genome-wide association study (GWAS) of common germline variants identified 31 polymorphic loci associated with predisposition to CH-UD. Of these, 25 were associated with other forms of CH at genome-wide significance. Linkage Disequilibrium Score Regression analyses revealed an unexpectedly high genetic correlation (rg=0.794) between CH-UD and non-DNMT3A driver CH, indicative of a remarkable overlap between the genetic aetiologies of the two phenomena. Analysis of 2,941 plasma protein measurements in 47,757 individuals revealed that TCL1A was the most significantly elevated plasma protein in CH-UD, mirroring the finding that the TCL1A locus was in the top two most significant associations of CH-UD GWAS and TET2-CH and ASXL1-CH GWAS, the two most common forms of non-DNMT3A-CH. Furthermore, TCL1A plasma levels rose steadily with age even in those without detectable CH, particularly among carriers of the common TCL1A risk variant (rs2887399-G), potentially via stochastic promoter demethylation as described in TET2-CH and ASXL1-CH. Phenome-wide association analysis of 13,225 binary and 1,682 quantitative traits revealed that, similarly to non-DNMT3A-CH, CH-UD was significantly associated with several malignant (haematological and solid organ) and non-malignant (including cardiovascular and renal) diseases. Our findings reveal striking genetic and phenotypic similarities between CH-UD and non-DNMT3A driver CH, including a strong dependence on TCL1A, a protein recently found to inhibit DNA methylation. Collectively, these observations propose that CH-UD develops through selection acting on ageing-associated epigenetic changes that mirror those of non-DNMT3A-CH, but without the need for somatic genetic drivers.

Background: Systematic literature reviews (SLRs) are essential in medical research, but are often time-consuming and costly, necessitating more efficient methods while maintaining accuracy. Objective: This study assessed the performance of a GPT-4o mini large language model (LLM) in automating the first phase of study selection based on titles and abstracts in systematic reviews. Specifically, we evaluated whether the model improved efficiency without compromising on quality. Methods: Structured prompts were created for a GPT-4o mini LLM to facilitate title and abstract screening. The model's performance was evaluated against expert human reviewers across five systematic reviews on inclusion rates, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results: The model screened a total of 15,605 records. It included a higher percentage of studies than human screeners, with 3.5% (n=549/15,605) true positives and 14.2% (n=2,218/15,605) false positives. The model achieved an overall accuracy of 85.1%, with a sensitivity of 83.2% and specificity of 85.2%. The positive predictive value was 19.8%, while the negative predictive value was 99.1%. The model was able to screen 1,000 titles and abstracts in 40 minutes, compared to 16 hours required by a human reviewer. Conclusion: This study demonstrated a strong performance and efficiency in the automation of title and abstract screening in SLRs using an advanced LLM. Further refinements could optimize the balance between sensitivity and specificity, supporting broader implementation in evidence synthesis. A hybrid AI-human approach is recommended to ensure accuracy, reduce reviewer burden, and maintain the methodological rigor required for high-quality SLRs.

Mental health challenges among university students have increased due to academic pressure, lifestyle changes, and continuous digital engagement. Existing approaches for mental health assessment often rely either on self-reported psychological scales or isolated behavioral indicators, limiting their ability to capture complex temporal and contextual patterns. This study proposes an interpretable multimodal framework for student mental health risk assessment using behavioral sensing, academic information, ecological momentary assessments (EMA), and psychometric survey data. A bidirectional Long Short-Term Memory autoencoder is employed to learn latent temporal representations from day-level behavioral sequences, while graph embeddings capture structural relationships among students using similarity-based neighborhood graphs. These representations are fused with academic and survey-derived features and reduced using Principal Component Analysis and Uniform Manifold Approximation and Projection. K-means clustering is then applied to identify behaviorally distinct student groups. Experimental analysis on the StudentLife dataset demonstrates meaningful clustering performance with a Silhouette Score of 0.4209 and Adjusted Rand Index stability of 0.6869. The identified clusters correspond to low-risk, moderate-risk, and high-risk behavioral profiles. To improve interpretability and practical usability, a fuzzy inference system is introduced to compute mental risk, academic risk, and wellbeing indices using psychometric indicators including PHQ-9, PSS, PANAS, VR-12, and Big Five personality traits. The results demonstrate the potential of combining multimodal behavioral modeling with interpretable fuzzy reasoning to support early mental health risk assessment in educational settings.

Peer reviewers provide a critical service to NIH by evaluating the scientific and technical merit of grant applications. While the tangible rewards for this service are limited, many reviewers feel review service makes them better applicants, improving their grant competitiveness. However, empirical evidence for this claim is limited. This study evaluates relationships between early career peer review service and subsequent application behavior and funding outcomes. Using NIH administrative data, applicants who served as Early Career Reviewers (ECRs) during the 2020 - 2021 council years were compared to a matched group of ECR-eligible applicants who had not served as reviewers (n=1,120 per group). To address non-random selection of ECRs, propensity score matching was used to balance groups on research field, demographics, productivity, career stage, and institutional resources. Outcomes, assessed over a three-year follow-up period, included submission volume, peer review scores, and funding outcomes for R01 and R01-equivalent applications. ECRs submitted more applications, were more likely to have their applications discussed, and were more likely to receive a high review score than matched controls. They were also more likely to receive R01 funding. While peer review scores do not solely determine award outcomes, these findings indicate that peer review service among ECRs is associated with improved grant application outcomes.

Background: The Colour Blind Quality of Life Scale (CBQoL) is a questionnaire developed to assess the quality of life of individuals with congenital colour vision deficiency (CVD). This study aimed to translate the English version of the CBQoL into Vietnamese and evaluate the validity and reliability of the Vietnamese version (CBQoL-VN). Methods: A forward-backward translation method was performed to produce the Vietnamese text. Content validity was assessed by six experts in vision care. Reliability testing involved 30 participants with congenital CVD, while discriminant validity was evaluated by comparing this group against 30 participants with normal colour vision. Results: Following expert consensus, two items were removed and one transportation-related item was added. The content validation index (CVI) values of 1.0 for relevance, clarity, and understandability indicated excellent content validity. Internal consistency was high, with a Cronbach's alpha of 0.95 for the full scale. Discriminant validity analysis showed that participants with congenital CVD scored significantly lower across all CBQoL-VN domains compared to those with normal colour vision. Conclusions: The modified CBQoL-VN is a valid and reliable instrument for assessing the quality of life of individuals with congenital CVD in the Vietnamese population.

Background: Malaria exposure has been hypothesized to alter immune responses to childhood vaccines, but evidence is inconsistent. We evaluated whether early-life malaria exposure and perennial malaria chemoprevention (PMC) modify antibody responses to the 10-valent pneumococcal conjugate vaccine (PCV-10) among infants in a high malaria transmission setting in eastern Uganda. Methods: This study was nested within the MIC-DroP trial (NCT04978272) whereby 202 infants were selected for inclusion. Serotype-specific IgG concentrations were measured using an in-house multiplex seroassay from samples obtained at 8 and 24 weeks of age. Immunogenicity was quantified as the log10 fold-change in IgG concentration between the 8 and 24-week timepoints, and seroconversion as [&ge;]0.35 g/mL at week 24 (i.e., seropositive). Generalized estimating equation models were used to assess associations of PCV-10 immunogenicity and seroconversion with malaria exposure, malaria chemoprevention and birth outcomes. Results: Among the 195 of 202 infants who completed the three-dose PCV-10 series, neither infant PMC nor malaria exposure from study enrollment to 14 weeks were associated with PCV-10 immunogenicity or seroconversion. In contrast, low birthweight (<2500g) was associated with lower immunogenicity (82% lower antibody fold-change, p=0.003) and reduced odds of seroconversion (OR=0.19, p=0.003); preterm birth (<37 weeks) showed similar associations (79% lower antibody fold-change, p=0.018; OR=0.181, p=0.009). Conclusion: In this malaria-endemic setting, early-life malaria exposure and chemoprevention did not measurably alter PCV-10 antibody responses. However, low birthweight and prematurity were associated with reduced vaccine immunogenicity.

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.

Rheumatic heart disease (RHD) is a leading preventable cause of cardiac death in children in low and middle-income countries. Nepals epidemiological data come mainly from auscultation surveys that miss subclinical disease, and no echocardiographic screening study had been conducted in Dhanusha district, a densely populated, low-income region in southern Nepal. We aimed to determine the prevalence of borderline and definite RHD among school children (6-16 years) in Dhanusha using the 2012 World Heart Federation (WHF) echocardiographic criteria, identify independent predictors, and quantify school-level clustering via the intraclass correlation coefficient (ICC). In a cross-sectional study (January 2023-December 2024), we screened 4,536 children from 8 public schools selected by four-stage cluster sampling. RHD was classified by WHF 2012 criteria; predictors were identified using random-effects logistic regression with school as random intercept. Ethical approval was from the Nepal Health Research Council (Protocol No. 155/2023). Overall prevalence of borderline or definite RHD was 18.7 per 1,000 (95% CI 15.1-23.0); definite RHD was 6.8 per 1,000 (95% CI 4.7-9.7) and borderline RHD 11.9 per 1,000 (95% CI 9.0-15.5). Prevalence was higher in girls (23.3 per 1,000) than boys (13.6 per 1,000; P=0.02), with the peak in girls aged 10-14 years (26.0 per 1,000). Subclinical disease accounted for 64.7% of cases; auscultation sensitivity was 35.3%. Mitral valve involvement predominated. Female sex was the sole independent predictor (OR 1.60, 95% CI 1.02-2.53; P=0.043). The school-level ICC was 0.19 (95% CI 0.07-0.44; P<0.001), giving a design effect of {approx}109. The echocardiographic RHD burden in Dhanusha (18.7 per 1,000) is the highest documented in Nepal. Two-thirds of cases are subclinical. Female sex and school attended explain a similar amount of variance in RHD risk, supporting school-targeted screening and informing sample size planning for future cluster-based surveillance.

Background: In severe aortic stenosis patients undergoing TAVR, whether coexisting coronary disease prompts revascularization and its optimal timing remain unclear. Aim: To evaluate the efficacy and safety of PCI before TAVR compared to deferred PCI in patients with severe aortic stenosis and concomitant coronary artery disease. Methods: We performed a meta-analysis of RCTs. PubMed, Embase, Scopus, CENTRAL, and Web of Science were searched for RCTs comparing PCI before TAVR versus no PCI. HRs with 95% CIs were pooled using random-effects models. Results: Three RCTs (ACTIVATION, NOTION 3, PRO-TAVI) enrolling 1,156 patients (579 PCI, 577 no PCI) were included. Routine PCI before TAVR did not reduce all-cause mortality (HR 0.88, 95% CI 0.67 to 1.17; p=0.38) or cardiovascular death (HR 0.77, 95% CI 0.49 to 1.19; p=0.23). PCI significantly reduced any revascularization (HR 0.24, 95% CI 0.06 to 0.86; p=0.029), and urgent revascularization (HR 0.33, 95% CI 0.12 to 0.87; p=0.025). MI was not significantly reduced with PCI (HR 0.84, 95% CI 0.44 to 1.59; p = 0.59). Stroke showed a borderline trend favoring PCI (HR 0.69, 95% CI 0.46 to 1.04; p=0.073). PCI significantly increased any bleeding (HR 1.96, 95% CI 1.28 to 3.0; p=0.002) and major bleeding (HR 1.88, 95% CI 1.07 to 3.31, p=0.027). Neither AKI nor rehospitalization differed significantly between groups. Leave-one-out sensitivity analyses confirmed the stability of mortality, stroke, and bleeding estimates. Conclusions: Routine PCI before TAVR does not reduce mortality. It lowers urgent revascularization and trends toward less stroke but nearly doubles bleeding. Findings support selective, individualized PCI rather than routine revascularization before TAVR.

Temperate regions of the world, such as southern Australia, often experience increased health burden from respiratory pathogens during winter. The ability to forecast short-term trends in cases of these pathogens is of significant interest to public health. Across the 2024 southern hemisphere winter period, the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) ran a pilot respiratory virus forecasting initiative in collaboration with the Victorian Department of Health. Each week from the 9th of May 2024 through to 12th September 2024, the consortium solicited 28-day forecasts of daily case incidence for influenza, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV) from multiple research groups. Four component model forecasts were contributed by three different research groups, with a fourth group utilising the component forecasts to generate ensemble forecasts (making a total of six models, four component models and two ensembles). Here we statistically evaluated the performance of each forecast and a baseline model against the observed case data. The two ensemble models were found to be frequently the top performing models. All models performed worse than the baseline model around the epidemic peaks for each pathogen.

Accurate risk stratification of pigmented skin lesions is critical for early melanoma detection and for reducing unnecessary excisions. Artificial intelligence (AI) is increasingly applied to dermoscopic image analysis, but its diagnostic performance relative to standard dermoscopy in real-world clinical settings remains uncertain. To address this gap, we conducted a systematic review and meta-analysis of prospective clinical studies directly comparing AI alone, dermoscopy, and AI-assisted clinicians for malignancy risk assessment of pigmented skin lesions. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library from inception to January 2026. Ten studies with 17 diagnostic arms (10 dermoscopy arms, 6 AI-alone arms, and 1 AI-assisted clinician arm) were included. Pooled sensitivity and specificity were 0.773 (95% CI, 0.648-0.863) and 0.793 (95% CI, 0.673-0.877) for dermoscopy, and 0.757 (95% CI, 0.428-0.928) and 0.859 (95% CI, 0.619-0.958) for standalone AI. Summary ROC curves showed overlapping performance, indicating that autonomous AI is broadly comparable to dermoscopy but does not demonstrate a consistent advantage. Heterogeneity in AI performance was driven almost entirely by threshold effects rather than by differences in inherent model capacity. AI-assisted clinicians showed promising results (sensitivity 1.000, specificity 0.837) in a single study, but more evidence is needed. Our findings suggest that, at present, AI should be viewed as a complementary decision-support tool rather than a replacement for dermoscopic evaluation. The study provides valuable evidence for clinicians, guideline developers, and researchers working on AI integration into melanoma diagnostic pathways.

Background: C-terminal binding protein 2 (CtBP2) has been implicated in metabolic regulation, but its association with specific measures of adiposity and lipid profiles in humans remains unclear. This study examined the relationship between circulating CtBP2 levels and key components of metabolic syndrome, focusing on body fat distribution and lipid markers. Methods: Data from 508 participants (259 men, 249 women) from a publicly available dataset were analyzed. Serum CtBP2 concentrations were measured using ELISA. Associations with obesity markers (BMI, waist circumference, waist-to-hip ratio) and lipid profiles (triglycerides, HDL cholesterol) were assessed using Spearman correlation and linear regression, adjusting for age and sex. Results: CtBP2 levels showed weak but statistically significant positive correlations with all measures of adiposity, with the strongest association observed for waist circumference ({rho} = 0.150, p < 0.001), followed by BMI ({rho} = 0.120, p = 0.007) and waist-to-hip ratio ({rho} = 0.098, p = 0.027). No significant correlations were found with triglycerides or HDL cholesterol. In the regression model predicting BMI, age, and sex were significant predictors, while CtBP2 demonstrated a trend toward association ({beta} = 0.080, p = 0.052). Conclusion: Circulating CtBP2 appears to be modestly associated with measures of adiposity, particularly abdominal fat, but not with lipid abnormalities. These findings suggest a potential role for CtBP2 in obesity-related metabolic dysregulation and underscore the need for further mechanistic studies to clarify its clinical relevance.

Objective: Lyme disease diagnosis in children is challenging due to atypical presentations and testing limitations. We sought to evaluate the association between Lyme disease and socio-geographic risk factors in children. Materials and methods: We enrolled children undergoing evaluation for acute Lyme disease at one of eight Pedi Lyme Net pediatric emergency departments located in high Lyme disease incidence states over a ten-year period (2015-2024). We defined a case of Lyme disease with an erythema migrans (EM) lesion or a positive two-tier serology result in a child with signs and/or symptoms of acute disease. We linked each childs primary residential county to the following factors: urban-rural residence, socioeconomic status, population-level disease incidence, wildland-urban interface, and "Lyme disease" Google searches. We performed a multi-level logistic regression analysis to evaluate associations between Lyme disease and county factors after adjusting for individual demographics. Results: Among 5,529 children enrolled, 1,396 (25.2%) had Lyme disease: 101 (7.2%) with early-localized disease, 584 (41.8%) with early-disseminated disease, and 711 (50.9%) with late-disseminated disease. Rural residence (aOR 1.9, 95% CI 1.3-2.9), higher socioeconomic advantage (aOR 1.3, 95% CI 1.1-1.4), more "Lyme disease" Google searches (aOR 1.1, 95% CI 1.0-1.2), and higher wildland urban interface (aOR 1.2, 95% CI: 1.0-1.4) were independently associated with Lyme disease. Conclusion: Incorporating socio-geographic factors alongside clinical data may augment diagnostic risk assessment in children with suspected Lyme disease. However, these factors should be incorporated carefully to ensure clinical assessments are not based on a childs geographic location alone.