Citations

Importance Amblyopia is the leading cause of monocular decreased best-corrected vision in children. Early detection and intervention are key for optimal treatment outcomes; however, limited access to pediatric eye specialists may hinder timely care and affect visual outcomes. Given the shortage of specialty-trained eye care professionals in the US, there is need to support comprehensive eye care professionals who are willing to examine and treat children with amblyopia. Objective A panel from the Pediatric Eye Disease Investigator Group synthesized relevant literature and incorporated consensus opinion where direct evidence was lacking to create the Amblyopia Navigator Decision-Support Instrument, a free, web-based amblyopia decision support tool for eye care professionals. Evidence Review The decisions related to optimal diagnostic measures, pass/refer criteria, and management recommendations were informed by 147 publications. In areas where direct evidence was lacking, guidance was supplemented with clinical trial protocols or existing professional guidelines. When data were conflicting or unavailable, recommendations were established through committee consensus. A single reviewer independently performed standardized (though not systematic) literature reviews to avoid missing pertinent recent publications. Findings Presented are the process used, supporting evidence, and recommendations for testing methods, refractive correction, amblyopia treatment options, treatment adjustments, and monitoring schedules. Conclusions and Relevance Because amblyopia is common and typically responds well to treatment, decision support tools that optimize its management could increase access to pediatric eye care by facilitating earlier detection, enabling broader participation of primary eye care professionals, and ultimately improving visual outcomes. Through this work and the development of the forthcoming free, web-based application version, Amblyopia Navigator Decision-Support Instrument, the Pediatric Eye Disease Investigator Group has created decision support tools designed to assist optometrists and ophthalmologists in the care of children aged 3 to 17 years with amblyopia.

Objective Positive α‐synuclein seed amplification assay (SAA) is a biomarker found in most people with Parkinson's disease (PD). We explored if free‐water (FW) imaging detects microstructural differences in the brains of patients with early PD with SAA+ or SAA– status. Methods We studied patients with PD with baseline diffusion imaging and α‐synuclein SAA data from the Parkinson's Progression Markers Initiative (PPMI). We compared FW, FW corrected fractional anisotropy (FA T ), and clinical characteristics between SAA+ and SAA– groups. We also applied the Automated Imaging Differentiation for Parkinsonism (AIDP) at baseline to classify PD versus atypical parkinsonism, stratified by SAA status. Results Among 462 participants (41 SAA– and 421 SAA+), individuals with SAA+ had hyposmia and shorter motor symptom duration before baseline magnetic resonance imaging (MRI). AIDP identified PD in 92.4% (n = 427, 91.6% had SAA+) and classified 7.6% as atypical parkinsonism (n = 35, 85.7% had SAA+). At baseline, SAA+ individuals had lower FW in the superior cerebellar peduncle, compared to SAA– (pFDR < 0.05). No significant differences in FA T were found between groups. Interpretation Positive α‐synuclein SAA was associated with focal microstructural differences but did not distinguish broader diffusion MRI (dMRI) changes across FW and FA T metrics. These findings indicate that molecular confirmation of synuclein aggregation (via SAA) provides limited stratification of neurodegeneration detected by FW imaging in early PD. ANN NEUROL 2026;100:295–304

Abstract The genetic contributions of the X chromosome to Alzheimer’s disease (AD) remain poorly understood yet are expected to importantly shape sex differences in AD. We therefore performed large-scale X-chromosome-wide association studies (N=1,240,451), evaluating differential risk due to sex, APOE *4, and escape from X-chromosome inactivation, finding most X-linked loci appear relevant to female-biased AD etiology. In evaluating genetic pleiotropy with hormonal, lipid, and brain imaging traits, we discovered X-linked AD loci converged on white matter traits, particularly in the anterior corona radiata and splenium of the corpus callosum. Through brain-centric functional genomics analyses, we then nominated candidate causal genes, including 5 that appeared highly robust. Notably, we found the escape gene RBBP7 decreases AD risk in APOE *4 carriers likely through higher expression in excitatory neurons to counter tau-related neurodegeneration. Altogether, we provide an atlas of sex and APOE *4-informed candidate X-linked AD risk loci, genes, and mechanisms that will guide future studies.

Background: Considering the pathophysiological importance of the intestine-passing microbiota, we hypothesized that the mutational and microbial profiles of colorectal cancer might gradually change along detailed anatomical locations and that these trends might differ by the age of diagnosis and the microsatellite instability (MSI) status. Methods: We assessed somatic mutations and selected bacterial species in colorectal carcinomas along the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum, utilizing targeted sequencing data ( N = 5685) from the Genetics and Epidemiology of Colorectal Cancer Consortium and tissue microbial data ( N = 1389) from the Health Professionals Follow-up Study and Nurses’ Health Study. Results: There were decreasing trends from the cecum to rectum in the prevalence of gene mutations in the IGF/PI3K, receptor tyrosine kinase (RTK)/RAS, TGFB, and WNT signaling pathways (all P trend < 0.0001), as well as high levels of Bacteroides fragilis ( P trend < 0.0001) and high levels of Fusobacterium nucleatum ( P trend = 0.0029) (but not enterotoxigenic Bacteroides fragilis or pks+ Escherichia coli ). Conversely, TP53 pathway mutations showed an increasing cecum-to-rectum trend ( P trend < 0.0001). Similar to later-onset cases diagnosed at ages 50 years and older, in early-onset cases diagnosed under the age of 50 years, we found generally decreasing cecum-to-rectum trends of IGF/PI3K, RTK/RAS, TGFB, and WNT pathway mutations and an increasing cecum-to-rectum trend in TP53 pathway mutations. By contrast, the cecum-to-rectum prevalence-changing trends in IGF/PI3K, RTK/RAS, TGFB, and TP53 pathway mutations were present in non–MSI-high tumors but not in MSI-high tumors (all P interaction < 0.0052). Conclusion: Non–MSI-high colorectal cancers exhibited decreasing cecum-to-rectum trends of IGF/PI3K, RTK/RAS, and TGFB pathway mutations (and an increasing trend of TP53 pathway mutations), in contrast to MSI-high tumors that demonstrated no such trends. Our study highlights the importance of detailed colorectal subsite information combined with tumor molecular and microbial characteristics in colorectal cancer research.

Abstract Expression quantitative trait locus (eQTL) mapping is used to identify the functional link between a genomic variant and a gene’s expression. A significant eQTL association does not mean a causal relationship or mechanism, and further investigation is needed to understand how a single-nucleotide polymorphism (SNP) impacts gene expression. One of the most plausible explanations for eQTL is that a genomic variant affects transcription factor (TF) binding and thus impacts its regulation on target genes (TGs). However, the current eQTL model does not provide information on the TF and how its regulation is mediated by the SNP’s genotypes. Here, we propose a new method called differential co-expression QTL (ceQTL) among different alleles using Chow statistics to specifically detect eQTLs that are bound by a particular TF. We start with building a trio of TF, its TG, and related SNP, and then test the significant coefficient difference among different genotypes of the SNP. We applied this ceQTL model to simulated data and the lung tissue datasets from the genotype-tissue expression project. The simulated data results showed that the model was robust to detect true ceQTLs at variable sample sizes and different minor allele frequencies as measured by area under the curve. Our tool also performed a TF binding affinity analysis to add another layer of evidence for functional interpretation. In summary, ceQTL analysis provides a more interpretable and biological insight into the mechanism of eQTL and transcriptomic regulation, which would help us better understand how genomic variants affect phenotypes and diseases.

Background Recent studies have disputed the association between bisphenol A (BPA) and endometriosis. This updated meta-analysis was conducted to investigate the impact of BPA exposure on endometriosis. Methods This study evaluated BPA exposure and endometriosis risk, analyzing studies from Web of Science, PubMed, Embase, Cochrane Library, and Chinese databases (CNKI, CBM, Wanfang, VIP) through August 2025. Methodological quality was assessed via the Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality Tool (AHRQ). Heterogeneity was quantified using I 2 statistics. Pooled odds ratios (ORs) and 95% CIs were calculated using fixed- or random-effects models according to heterogeneity. Subgroup analysis, sensitivity analysis, and publication bias assessment were supplemented to validate the overall finding. Results Seven studies comprising 2,488 participants were included in quantitative synthesis. The meta-analyses demonstrated that BPA exposure was associated with a statistically significant increase in endometriosis risk (OR = 1.36, 95% CI = 1.13–1.63, p = 0.008). Additionally, subgroup analysis indicated that BPA exposure may further elevate endometriosis risk in cases with different regions, populations, and measurement methods. Sensitivity analysis confirmed that the results of these meta-analyses were relatively robust. No publication bias was detected. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251019552 , identifier PROSPERO (CRD420251019552).

ABSTRACT Background Platelet factor 4‐polyanion enzyme‐linked immunosorbent assays (ELISAs) are considered highly sensitive for diagnosing heparin‐induced thrombocytopenia (HIT), such that current practice guidelines recommend use of ELISA‐negative results to exclude HIT. Once HIT is ruled out, alternative, non‐heparin‐based anticoagulant treatments are ceased, and heparin reintroduction frequently occurs. Methods Antigen‐based and PF4‐dependent functional testing were used to study PF4/polyvinylsulfonate ELISA‐negative platelet‐activating antibodies in HIT‐suspected patients. Results Three patients with clinical presentations consistent with HIT tested negative in an ELISA using PF4‐polyvinylsulfonate (PF4/PVS), an antigenic target very commonly used for HIT antibody screening. All three patients demonstrated PF4‐dependent platelet activation in functional testing that was sensitive to blockade of platelet FcγRIIa receptors and inhibited by high concentrations of heparin, consistent with pathogenic HIT antibodies. Functional testing‐based screening of 500 ELISA‐negative patients identified three additional patients whose sera activated platelets in a PF4‐ and FcγRIIa‐dependent manner and had clinical histories consistent with HIT. Five of the six ELISA‐negative HIT patients were re‐exposed to heparin, which precipitated a decrease in platelet counts in all re‐exposed patients, and one patient developed a new thrombus. Conclusions Recognition of ELISA‐negative HIT is critical to avoid harm due to the cessation of alternative anticoagulation therapy and re‐exposure of these patients to heparin.

Objective To examine the potential errors of a general large language model (LLM) (ie, Claude 3.5 Sonnet) on data extraction from randomised controlled trials (RCTs). Design and setting An empirical study comparing Claude 3.5 Sonnet extractions against a human-performed verification dataset. The extraction tasks for Claude 3.5 Sonnet were based solely on original RCT portable document format (PDF) files. For PDFs that could not be directly extracted by Claude 3.5 Sonnet, optical character recognition was employed to convert them into text format before extraction. Participants A random sample of 664 trials was selected from a well-established trial bank and a final data pool was established based on rigorous manual cross-checking as a reference standard. Data sources PubMed, EMBASE, Scopus, Web of Science (all databases) and the Cochrane Central Register of Controlled Trials (CENTRAL) up to February 2023. Eligibility criteria for selecting studies RCTs on children involving medication and adverse events. Main outcome measures Claude 3.5 Sonnet was applied to extract the basic information (eg, trial design, population information and source of funding) and adverse outcomes (ie, name of adverse events, number of events). Claude 3.5 Sonnet outputs were compared against the final data pool and all errors were recorded. Results are presented as error rates and with 95% CI, estimated using a generalised linear mixed model. Results For the 664 trials, a total of 23 069 data cells were extracted via Claude 3.5 Sonnet, with 10 624 for basic information and 12 445 for adverse outcomes. The overall error rate for data extraction was 6.6% (95% CI 5.4% to 8.2%), with 5.7% (95% CI 5.2% to 6.1%) in basic information and 7.6% (95% CI 4.9% to 11.8%) in adverse outcomes. When stratified the 1542 total errors by error types, misallocation (assigning data to incorrect fields; 57.1%, 881/1542) and missed or omitted data (incomplete extraction of available data; 23.2%, 357/1542) accounted for the two most frequent errors, with misallocation occurring more in basic information (53.3%, 470/881), while missed or omitted data occurred more in adverse outcomes (96.1%, 343/357). Post hoc analysis examining the association between trial reporting quality (assessed using Consolidated Standards of Reporting Trials (CONSORT) 2025 and LLM data extraction error rates indicated that higher CONSORT adherence was associated with lower extraction error rates. Conclusions The data extraction error of Claude was relatively low, but it alerts LLM applications in evidence synthesis. Detailed checking for LLM outputs should be the primary consideration for evidence synthesisers.

Abstract Purpose We evaluated healthcare resource utilization (HCRU) and costs in patients with eosinophilic granulomatosis with polyangiitis (EGPA). Methods Patients with newly diagnosed EGPA (2017–2021), ≥12 months’ pre-diagnosis health plan enrollment, and ≥1 inpatient or ≥2 outpatient claims with an EGPA diagnosis were included. Follow-up was from EGPA diagnosis until disenrollment or database end. HCRU and health insurer payment costs during follow-up were compared with those for matched cohorts of general insured patients without EGPA (comparison A) and without EGPA but with severe uncontrolled asthma (SUA; comparison B). Results In comparison A, all-cause HCRU was higher in the EGPA cohort (n = 213) versus matched patients (n = 779) for all clinical encounters/pharmacy claim types; annualized, mean total all-cause costs were 16-fold higher ($117,563/patient) versus matched patients ($7,520/patient). In comparison B, all-cause HCRU was higher for the EGPA cohort (n = 182) versus the matched SUA cohort (n = 640) for all clinical encounters/pharmacy claim types, with 5-fold higher mean total all-cause costs ($118,127/patient vs $22,286/patient). In both EGPA cohorts, HCRU and associated costs increased between the baseline and follow-up periods. Conclusions These findings highlight the need for more effective treatments to reduce the clinical and economic burden of EGPA.

Background/Objectives: Characterizing spinal cord multiple sclerosis (MS) lesions in MRI is critical for diagnosis, monitoring, and treatment evaluation. However, current automated approaches for lesion detection and segmentation are typically designed for specific MRI contrasts or acquisition sites, limiting their generalizability in real-world clinical settings where imaging protocols vary widely. This work proposes a robust multi-site, multi-contrast segmentation framework for spinal cord lesions. Methods: The segmentation model was trained and evaluated on a large-scale dataset comprising 4428 annotated images from 1849 persons with MS across 23 imaging centers, encompassing six MRI contrasts (T1w, T2w, T2*w, PSIR, STIR, and UNIT1) acquired at 1.5 tesla (T), 3 T, and 7 T. Results: Likert-type assessment performed by neuroradiologist ratings demonstrated superior generalization of the model compared to existing contrast-specific pipelines ( p < 0.01). Additional experiments evaluated robustness across spinal levels, acquisition resolutions, binarization thresholds, and quantitative evaluation on external labeled datasets. Conclusions: The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barrier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher. Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast