Rheumatology

Objective: To assess the accuracy of codes and algorithms used to identify selected connective tissue diseases (CTDs) in electronic health records (EHRs) and administrative databases. Methods: We searched MEDLINE, Embase, and CENTRAL databases for studies that validated case definitions in EHRs against a reference standard, including rheumatologist-confirmed diagnosis or clinical classification criteria. Title/abstract screening, full-text review, data extraction, and quality appraisal were independently performed in duplicate. Findings were synthesised narratively and through a bivariate random-effects meta-analysis of sensitivity and specificity. Results: A total of 38 studies were included. Systemic lupus erythematosus (SLE) was the most frequently studied disease, followed by systemic sclerosis (SSc). Across diseases, progressively restrictive algorithms generally improved positive predictive value (PPV), although the optimal strategy varied by disease. In SLE, multiple diagnostic codes provided the most favorable overall performance, whereas adding clinical data provided limited incremental benefit. In SSc, disease-specific clinical features, particularly Raynaud's phenomenon, improved case identification, while specialist or inpatient-based algorithms performed better for polymyositis/dermatomyositis, although evidence was limited. For SLE, meta-analysis yielded pooled sensitivity of 0.90 (95% Cis 0.70-0.97), specificity of 0.83 (95% CIs 0.54-0.95), and PPV of 0.75 (95% CIs 0.59-0.86). Evidence for Sjogren's Disease was limited to one study, and no studies evaluated MCTD/UCTD. Conclusions: Optimal EHR case-identification strategies vary by disease and should be tailored to disease-specific clinical and diagnostic characteristics rather than algorithm complexity alone. Future research should develop and externally validate robust, standardised algorithms in representative populations, using rigorous validation methods, and assess the added value of longitudinal EHR data, biomarkers, and computational phenotyping approaches.

Introduction: Across major phases of the COVID-19 pandemic (March 2020-April 2023), we compared the burden of severe COVID-19 among individuals with systemic autoimmune rheumatic diseases (SARDs) versus immunocompetent individuals with and without comorbidities. Methods: This population-based cohort study used administrative datasets to estimate crude and age- and sex-standardized SARS-CoV-2 and severe COVID-19 incidences among adults >18 years with SARDs, chronic comorbidities, or no comorbidities. Relative to the latter, we used Cox models to derive adjusted hazard ratios (aHRs) for infection, hospitalization, and intensive care unit admission or death (ICU/death) during the pre-vaccination (March-December 2020), vaccination pre-Omicron (December 2020-December 2021), and Omicron (December 2021-April 2023) periods. Results: We included 81,434 adults with SARDs, and 2,020,356 with comorbidities versus 3,144,700 without comorbidities. Despite similar adjusted infection rates, the SARDS cohort had higher risks of hospitalization and ICU/death during the pre-vaccination period (aHRs: 3.4, 95%CI:2.8-4.1; and 4.0, 95%CI:2.8-5.7, respectively), persisting through the Omicron period (aHRs: 4.5, 95%CI:4.0-4.9; and 4.4, 95%CI:3.6-5.4, respectively). Among SARDS, rheumatoid arthritis, psoriatic arthritis and systemic lupus erythematosus contributed highest burden, with comparable hospitalization and mortality rates among younger rheumatoid arthritis versus healthy older adults. Highly immunosuppressive rituximab- or mycophenolate-treated patients had highest hospitalization and ICU/death risks, particularly during the Omicron period (aHRs: 11.9, 95%CI:9.9-14.3; and 17.6, 95%CI:13.0-23.9, respectively). Conclusion: Individuals with SARDs, especially receiving highly immunosuppressive therapies, experienced elevated COVID-19 burden and severe outcome risks throughout the pandemic, warranting their prioritized consideration during future public health emergencies.

Systemic sclerosis (SSc) is a rare, heterogeneous autoimmune disease characterized by immune dysregulation, vasculopathy and fibrosis. While typically sporadic, SSc can in a minority of cases show familial clustering. Whole exome sequencing (WES), performed on six families with two affected members each, identified a total of 253 genes with rare, disease co-segregating variants predicted to affect protein function. Of these, we prioritized PRKD2, one of only three genes identified in two families. PRKD2 encodes Protein kinase D2: an intracellular serine-threonine kinase that amplifies T cell receptor (TCR) signaling. Both familial SSc-associated PRKD2 missense variants increased kinase activation, exerting overlapping, activating effects on TCR signaling in heterozygous knock-in Jurkat cells. A pan-PRKD inhibitor suppressed T cell activation in peripheral blood mononuclear cells from both familial SSc cases and controls. The enrichment of PRKD2 rare variants in a sporadic SSc cohort provides further support for its relevance to SSc pathogenesis. Together, these findings identify PRKD2 rare variants as potential genetic contributors to SSc, and PRKD2-mediated TCR signaling as a candidate disease-relevant and potentially targetable pathway.

Objective: The principal diagnostic test for rheumatoid arthritis (RA) relies on the detection of anti-cyclic citrullinated peptide (CCP) antibodies. The CCP test exhibits moderate sensitivity and specificity but does not adequately detect a high proportion of cases or effectively distinguish RA from related conditions such as systemic lupus erythematosus (SLE). To discover improved diagnostic biomarkers for RA, we performed a comprehensive proteomic analysis of antibody-bound antigens present in RA, SLE, and healthy control (HC) sera and identified proteins associated with transforming growth factor-beta-1 (TGF-beta-1) signaling and assessed the sensitivity and specificity of antibodies against these antigens in comparison to CCP. Methods: Total antibodies were immunoprecipitated from pooled sera of HC individuals (n=3) and patients with clinically diagnosed RA (n=3) or SLE (n=3). Antibody-bound antigens were identified using multidimensional protein identification technology (MudPIT) shotgun proteomics. Candidate antigens associated with TGF-beta-1 signaling were selected for evaluation, and corresponding serum autoantibodies were assessed for their ability to classify RA using a POC lateral-flow immunochromatographic format. Results: Proteins present in antibody immunoprecipitations included TGF-beta-1-induced protein (TGFBI), thrombospondin 1 (TSP1), serum amyloid A1 (SAA), and fibronectin (FN). POC assays using these candidates, as well as TGF-beta-1 itself, as capture antigens were used to detect corresponding serum autoantibodies in 107 subjects with clinically diagnosed RA, 42 subjects with other rheumatic diseases (non-RA), and 121 HC subjects. Receiver operating characteristic curves were constructed using RA and HC groups to assess diagnostic performance, and optimal cutpoints identified using the Youden Index (maximizing the sum of sensitivity and specificity). CCP test results were determined using previously established thresholds. Using an analysis et of 169 subjects in the HC and RA groups who had values for TGFBI, SAA, and CCP, TGFBI autoantibodies achieved a sensitivity of 82%, specificity of 98.8%, positive likelihood ratio (PLR) of 65.6, and NLR of 0.2. SAA autoantibodies achieved a sensitivity of 92.1%, specificity of 85%, PLR of 6.1, and NLR of 0.1. Both outperformed CCP (sensitivity of 68.5%, specificity of 95%, PLR of 13.7, and NLR of 0.3). Using an analysis set of 120 subjects in the RA and non-RA groups who had values for TGFBI, SAA, and CCP, TGFBI and SAA autoantibodies exhibited sensitivities and specificities of 82 and 92.1% and 74.2 and 45.2%, respectively, compared to 68.5 and 74.2% for CCP. Autoantibodies to TGF-beta-1, TSP1, and FN exhibited only modest diagnostic performance. Conclusion: RA sera contain autoantibodies targeting multiple proteins associated with the TGF-beta-1 signaling pathway that is implicated in inflammatory processes in RA. Among the candidates tested, TGFBI and SAA autoantibodies exhibited the highest diagnostic performance in a POC lateral-flow format, supporting their potential utility as biomarkers for RA diagnosis. These findings also support further investigation of the TGF-beta-1 signaling pathway as a potential therapeutic target in RA.

Objectives. Before-and-after transcriptomics in rheumatic disease often yields no reproducible treatment signal, though disease-state stratification is robust. We asked whether exposure and response-prediction labels fall below the detection floor of the designs defining them, while matched disease-state labels do not. Methods. Eight public datasets were analysed (paired juvenile dermatomyositis n = 10; RA before/after methotrexate, n = 28 pairs; lupus nephritis n = 10; controlled exposures; a single-cell dose series). Response labels in current use (one benchmark trajectory, five published signatures) were audited in every analysable source cohort, using effect sizes (dz) against each design's exact minimum detectable effect. Results. Disease-state labels were detectable: the interferon signature declined in 10/10 juvenile dermatomyositis patients (p = 0.002; 59.5% of score variance). No exposure label cleared its design floor in any clinical cohort; a drug-matched methotrexate prior was nominally significant in juvenile dermatomyositis yet did not clear its floor (dz = -0.80; CI spanning it). Exposure became detectable only where prior-context and intensity were jointly favourable (multi-day target tissue dz = 1.24, q = 0.018; well-level pseudobulk dz = +3.70, floor 1.80). Audited response labels failed in 14 of 15 cohorts (93%; descriptive binomial 95% CI 68-100): eleven below their floors, three sign-reversed; the largest training cohort pooled to zero (dz = +0.07; I2 = 69%). Effect size ordered reported AUCs monotonically. Conclusion. Under conventional treatment intensities, exposure and response-prediction labels are either undetectable below the design floor or detected with a sign that does not replicate; disease-state labels are detectable. Part of the negative literature reflects a design floor.

Objectives. Observational studies report large, conflicting associations between common drugs and hip or knee replacement. We tested them in the serious adverse event (SAE) tables of randomized trials on ClinicalTrials.gov. Methods. We included randomized drug trials with at least 500 participants. A language model classified arms from summaries without outcome counts (masked by instruction only). We pooled osteoarthritis-related SAEs by drug class as Mantel-Haenszel risk ratios (RRs). Because the plan followed a preliminary analysis of 72 trials, we report estimates rather than tests, checked by positive and negative controls and calibration. Results. The 881 placebo- or usual-care-controlled trials included 1.75 million participants and 9,408 osteoarthritis-related SAEs. The tables reproduced CANTOS's own canakinumab effect (RR 0.57, 95% CI 0.39 to 0.82), the anti-nerve growth factor excess (2.31, 1.82 to 2.92) and two of three modest effects specified before extraction: gallbladder disease with glucagon-like peptide-1 receptor agonists (GLP-1RAs; 1.36, 1.22 to 1.51) and ketoacidosis with sodium-glucose cotransporter-2 (SGLT2) inhibitors (1.96, 1.48 to 2.60). In the same trials, osteoarthritis-related SAEs did not differ detectably with GLP-1RAs (0.97, 0.86 to 1.10; calibrated 0.87, 0.54 to 1.41) or SGLT2 inhibitors (0.96, 0.82 to 1.12; calibrated 0.86, 0.53 to 1.40). Other proposed drugs gave imprecise estimates. Post hoc, 8 of 21 published observational estimates differed from randomized ones, in both directions (3 to 6 after calibration). Conclusions. Over about two years, GLP-1RAs and SGLT2 inhibitors had no detectable effect on osteoarthritis-related SAEs in trials that detected their known effects. Slow or modest structural effects remain possible.

BACKGROUND Primary sclerosing cholangitis (PSC) is characterized by multifocal biliary strictures on MRI/MRCP. However,we have observed that young PSC patients often displayed a marked intrahepatic bile duct visibility,sometimes with gallbladder enlargement, before developing biliary strictures. This study aimed to evaluatewhether this imaging pattern was associated with subsequent diagnosis of PSC. PATIENTS AND METHODS This retrospective two-center study included patients younger than 21 years who had a baseline liverMRI/MRCP, without biliary strictures. The endpoint was a definite diagnosis of large duct PSC during follow-up. The association of baseline imaging findings with the endpoint was evaluated by multivariable logisticregression, Cox models, and Kaplan-Meier analyses. RESULTS Ninety-four patients were included: 54% were women, the mean age was 16.7 years, and 23% had IBD. Overa mean follow-up of 7.75 years, 21 patients were subsequently diagnosed with large duct PSC after a mediantime of 5.60 years. Marked intrahepatic bile duct visibility and gallbladder enlargement displayed goodperformance to predict the subsequent diagnosis of PSC (AUC, 0.77 and 0.69, respectively). The presence ofboth signs displayed a specificity of 1.00. In multivariable analyses, marked intrahepatic bile duct visibility andgallbladder enlargement were independently associated with subsequent PSC diagnosis (OR 17.00 (95% CI,3.10-93.24; p = 0.001) and 16.08 (95% CI, 2.29-113.02; p = 0.005) respectively) and shorter time to diagnosis(HR 9.42 (95% CI, 2.50-35.59; p < 0.001) and 7.59 (95% CI, 2.03-28.41; p = 0.003) respectively). Survivalanalyses confirmed the association between these two imaging signs and the subsequent PSC diagnosis (HR7.78 (95% CI, 2.62-23.16; p < 0.001) and 3.85 (95% CI, 1.64-9.08; p < 0.001) respectively). CONCLUSIONS In young patients without biliary strictures, marked intrahepatic bile duct visibility and gallbladder enlargementon MRI/MRCP were strongly associated with subsequent diagnosis of large duct PSC.

Objective: Autoantibodies are central for pathology of rheumatoid arthritis (RA) and diabetes mellitus (DM) carrying high predictive and diagnostic value. Here, we explore the frequency and functional potential of DM-related antibodies (DMab) in RA. Methods: DMab targeting glutamic acid decarboxylase (GAD65ab), tyrosine phosphatase-like islet antigen 2 (IA2ab) and zinc transporter 8 (ZnT8ab) were measured in 326 DM-free RA patients (260 females, 66 males), using specific ELISAs. The DMab profile was analysed in relation to 30 clinical and serological variables. ZnT8+ cells were detailed by a public single-cell transcriptome in RA synovial tissue. Results: DMab production was more frequent in females, dominated by ZnT8ab (86%), followed by GAD65ab (26.4%) and IA2ab (7.0%). DMab-positive patients, and specifically ZnT8ab-positive women, had higher levels of insulin and HOMA-IR index and lower serum IL-6, IFN{gamma}, and IL-9. Multivariable logistic regression estimating ZnT8ab, IA2ab, GAD65ab production revealed that BDNF, insulin and DAS28 were positive predictors of ZnT8ab. Increasing ZnT8ab levels were inversely related to RAab in the mixed interaction model. CD14+ cell transcriptome identified association between an activated secretory cell type and increasing ZnT8ab levels. Additionally, ZnT8ab levels were in direct proportion with ZnT8 production in RA CD4+ T cells. Single-cell transcriptome of RA synovial tissue revealed ZnT8+ cells among the lubricin producing lining layer fibroblasts programmed for invasive behaviour. Conclusion: This study reveals frequent production of ZnT8ab in non-diabetic RA patients and identifies their antigenic target expressed in lining synovial fibroblasts of RA joints.

Objectives: Patients with pediatric acute-onset neuropsychiatric syndrome (PANS) frequently report pain and fatigue. While some patients experience resolution of pain and fatigue following improvement in psychiatric symptoms, others develop persistent symptoms resembling fibromyalgia. Understanding these trajectories is critical for early identification and intervention in patients at risk for chronic pain or fatigue. In this study we characterize fibromyalgia symptoms in PANS and identify predictors of symptom progression following a PANS flare. Design: Prospective observational cohort study. Setting: Stanford University Immune Behavioral Health (IBH) Clinic in Menlo Park, California, USA. Participants: 204 consecutive patients with PANS evaluated by the IBH Clinic. The average (SD) age at first neuropsychiatric deterioration was 8.2 (3.3) years. Main outcome measures: We prospectively collected data on pain, fatigue, and other fibromyalgia symptoms using the 2016 American College of Rheumatology (ACR) Fibromyalgia Survey Questionnaire (FSQ), which includes the Widespread Pain Index (WPI) and Symptom Severity Scale (SSS). For the reference standard, clinician-documented diagnoses of pain syndromes were ascertained through a retrospective review of participants' electronic medical records. We report the prevalence of these symptoms in PANS patients and controls and evaluate factors associated with time to meeting ACR fibromyalgia criteria threshold scores following clinic presentation using a Cox proportional hazards model. Results: Among 204 patients with PANS (110 boys [54%]), eighty-five (42%) met ACR fibromyalgia criteria threshold scores at least once during follow-up (95% CI at 10 years follow-up time: 60.5 [46.7-74.5]). Twenty-eight of these 85 patients (13.7% of total cohort) progressed to meet full ACR fibromyalgia criteria (95% CI at 10 years follow-up time: 24.1 [15.6-36.2]). Compared with controls, patients with PANS reported substantially elevated rates of pain, daytime fatigue, waking unrefreshed, and cognitive symptoms (brain fog). Higher scores on the WPI and SSS at presentation were associated with a higher hazard of subsequently meeting ACR fibromyalgia criteria threshold scores. Conclusions: Fibromyalgia symptoms were common in this PANS cohort, with a notable subset developing symptoms meeting full ACR fibromyalgia criteria. These findings suggest that pain and fatigue may represent post-inflammatory sequelae of PANS and may overlap mechanistically with other post-infectious conditions. Recognizing these symptom patterns may inform clinical management, development of outcome measures, and future clinical trials in these patients, as well as support investigation into shared neuroimmune mechanisms underlying post-infectious pain and fatigue syndromes.

Giant cell arteritis (GCA) is an immune-mediated vasculitis that can cause serious morbidity including sight loss, stroke and aortic aneurysm. There is an unmet need for blood-based biomarkers and novel therapeutics. We measured plasma protein levels using the SomaScan v4.1 platform, comprising 7,273 protein measurements (SOMAmers) reflecting 6,596 unique protein targets. We profiled the proteome in 113 patients who presented acutely with possible GCA. 56 had biopsy-proven GCA and 57 did not have GCA (non-GCA). We found 144 SOMAmers increased and 151 reduced in GCA. Dysregulated biological pathways included IL6-JAK-STAT3 signalling, chemokines for both myeloid (e.g. CXCL2, CXCL3, CCL18, CCL23) and lymphoid cells (e.g. CXCL9, CXCL13), and the complement system (e.g. C9, CFHR5, C5a, Factor B). Enzyme-Linked Immunosorbent Assays (ELISAs) confirmed elevated plasma levels of C5a, the terminal complement complex, Factor B and CFHR5 in GCA. We developed a 26-protein signature that distinguished GCA from non-GCA (area-under-the curve, AUC, 0.93) and validated the diagnostic signature in external datasets. Our data implicate terminal complement pathway activation in GCA, raising the possibility of complement-directed therapies. We show proof-of-principle that a multiplex protein biomarker could be used as a diagnostic adjunct in suspected GCA.

Objectives: To determine how strongly clinical referral text, rather than image content, drives the decisions of multimodal large language models (LLMs) differentiating glioma from brain abscess on MRI. Materials and methods: This retrospective single-center study included 60 contrast-enhanced T1-weighted brain MRI examinations (30 gliomas, 30 abscesses; reference-standard diagnosis). One axial slice per case was paired with five referral texts (explicitly inflammatory to explicitly tumor-suggestive) under three prompt instructions and submitted 20 times each to GPT-4o, Claude Sonnet 5, Gemini 3.5 Flash, and Qwen 3.6 (72,000 answers). The primary outcome was the text-driven span (difference in tumor-call frequency between explicit inflammatory and explicit tumor text). Two blinded radiologists classified the images without text; sensitivity, specificity, and accuracy were compared descriptively. Results: Patients (29 women) had a mean age of 55.8 +/- 16.4 years. With neutral text, models answered tumor in 45-67% of queries (specificity, 0.38-0.78), versus 52% and 60% for radiologists (specificity, 0.77 and 0.63). The text-driven span was 0.79-1.00 (p < 0.001 for all models), whereas confirmed gliomas and abscesses differed by only 0.01-0.10 in three models and 0.25 in Gemini 3.5 Flash. Subtle wording produced at least 83% of the span in three models. An instruction to ignore the text reduced the span to 0.48-0.70 in three models and to 0.04 in Gemini 3.5 Flash, partly through reversion to a tumor default. Model accuracy was 0.52-0.73 versus 0.78 and 0.73 for radiologists. Conclusion: Multimodal LLMs deferred to referral text over imaging evidence in differentiating glioma from brain abscess, and prompt instructions did not reliably correct this.

Rationale: Conventional CT-derived pulmonary vascular metrics quantify vascular pruning but do not assess the spatial heterogeneity of vascular architecture. In COPD, peripheral microvascular pruning coexists with compensatory central vessel dilation, paradoxically preserving total vessel volume while reducing effective vascular penetration into lung parenchyma. Objectives: To develop and evaluate Vascular Proximity Coverage (VPC), a distance-based CT biomarker quantifying the spatial efficiency of pulmonary vascular distribution. Methods: This retrospective analysis included 7,881 COPDGene participants [PHASE] with complete vascular segmentations. VPC was estimated by integrating the area under the cumulative lung tissue coverage curve at distances of 0-10 mm from segmented vessel voxels. Multivariable models assessed associations with lung function, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ) score, mMRC dyspnea score, and all-cause mortality over a median follow-up of [10.8] years, adjusting for demographics, smoking, emphysema, scanner type, and BV5. The cohort was stratified at median BV5 and VPC into concordant (high/high, low/low) and discordant (high-BV5/low-VPC, low-BV5/high-VPC) vascular phenotypes. Results: VPC declined across disease severity (5.82 {+/-} 0.50 [UNITS] in non-smokers to 5.21 {+/-} 0.64 in GOLD 4; p < 0.001). Among spirometrically normal smokers, current smokers had lower VPC than former smokers (5.31 vs. 5.68; p < 0.001). Each standard deviation decrease in VPC was independently associated with reduced 6MWD ({beta} = -16.8 m, 95% CI: -19.5 to -14.0), worse SGRQ ({beta} = 1.9, 95% CI: 1.4 to 2.4), and greater dyspnea (RR = 1.05, 95% CI: 1.03 to 1.08; all p < 0.001). The lowest VPC quartile was associated with 38% higher mortality than the reference quartile (HR = 1.38, 95% CI: 1.18 to 1.62; p < 0.001). The high-BV5/low-VPC phenotype showed 24% higher mortality than high-BV5/high-VPC (HR = 1.24, 95% CI: 1.08 to 1.42; p = 0.002) despite preserved vessel volume. Conclusions: VPC captures spatial heterogeneity of vascular architecture independent of vessel volume, is reduced in smokers without airflow obstruction, and identifies excess mortality risk among individuals whose total vessel volume appears preserved.

Objectives To evaluate discrepancies between radiologists and referring physicians in the interpretation of diagnostic probabilities conveyed by common Japanese expressions used in radiology reports. Materials and Methods Fifteen expressions commonly used to convey diagnostic probabilities in radiology reports were selected by five attending radiologists. An anonymous online survey was distributed to 26 radiologists and 33 referring physicians at a single institution. Participants assigned a diagnostic probability (from 0% to 100% in 10% increments) to each expression. Differences in median probabilities between radiologists and referring physicians were analyzed using the Mann-Whitney U test. Variability in interpretation was assessed using interquartile range (IQR), with IQR [&ge;] 30% defined as substantial variability. Results All radiologists (26/26) and 97% of referring physicians (32/33) completed the survey. Seven of the 15 expressions showed statistically significant differences in interpretation between radiologists and referring physicians: "c/w" (80% vs. 60%; p = 0.031), the Japanese equivalents of "present" (100% vs. 100%; p = 0.024), "suspect of" (80% vs. 70%; p = 0.007), "possibly" (50% vs. 70%; p < 0.001), "unlikely" (10% vs. 10%; p = 0.022), "cannot be ruled out" (25% vs. 40%; p < 0.001), and "not inconsistent with" (80% vs. 90%; p = 0.008). Substantial variability in interpretation (IQR [&ge;] 30%) was observed for four expressions: "cannot be ruled out" (IQR, 10%-40%), "s/o" (IQR, 60%-90%), "c/w" (IQR, 50%-90%), and "not inconsistent with" (IQR, 70%-100%). Conclusion This study revealed that nearly half of the Japanese expressions commonly used to convey diagnostic probabilities were interpreted differently by radiologists and referring physicians. These discrepancies highlight the need for a standardized lexicon to improve clarity and consistency in radiology reporting.

Objective: To determine outcomes by referral route (any Advice and Guidance [A&G] versus elective referrals only) for patients with inflammatory bowel disease (IBD), Parkinson's disease, and systemic lupus erythematosus (SLE). Design: Population-based retrospective cohort study. Setting: Linked Clinical Practice Research Datalink (CPRD) Aurum primary care records, 2015-2022. Participants: Patients with incident diagnosis of IBD, Parkinson's disease, or SLE. Main Outcome Measures: Time from the earliest recorded A&G or elective referral to diagnosis, first recorded definitive treatment and first attended outpatient appointment. Secondary outcomes included counts of primary care consultations, prescriptions, referrals, and outpatient appointments. Propensity score weighted flexible parametric survival analyses accounted for differences between exposure groups. Results: Among 71,145 eligible patients, A&G was recorded for 1,137 (3.0%) with IBD, 971 (3.4%) with Parkinson's, and 243 (4.9%) patients with SLE. Compared with elective referral only, A&G was associated with slower time to diagnosis for IBD (adjusted median difference 15.9 weeks; hazard ratio 0.58, 95% confidence interval 0.52 to 0.66), Parkinson's disease (11.4 weeks; 0.57, 0.51 to 0.64), and SLE (6.3 weeks; 0.82, 0.73 to 0.91). However, A&G was not associated with a longer time to treatment or first attended outpatient appointment. An increase in primary care consultations following A&G was observed in patients later diagnosed with IBD and SLE. Conclusion: A&G was associated with a longer diagnostic interval than elective referral only. These findings do not establish that A&G caused diagnostic delay. A&G might be used at an earlier stage of more complex diagnostic pathways.

The upper cervical spinal cord is essential for motor, sensory and autonomic function, yet its normative morphometric trajectories and disease-related deviations remain unclear. Using three-dimensional T1-weighted images from 10,159 healthy individuals aged 8-85 years, we established normative references for averaged and level-specific upper cervical spinal cord morphometry, revealing measure-specific trajectories with peak ages of 29.7-44.1 years. These references were applied to 2,394 patients with seven neurological disorders: mild cognitive impairment, Alzheimer' s disease, Parkinson' s disease, cerebral small vessel disease, multiple sclerosis, neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein antibody-associated disease to derive individualized deviation scores. These deviation scores captured disease-specific abnormalities, enabled disease classification, and were associated with cognition, physical disability and disease progression. Finally, a personalized upper cervical spinal cord report was developed to support individualized imaging interpretation. Together, these findings provide a normative framework for quantifying upper cervical spinal cord morphometry and advancing personalized assessment across neurological disorders.

Purpose: The aims of this study were to develop an externally validated deep learning vascular segmentation model for digital enhancement of vertebral artery dissection detection on CTA and to assess clinical utility of the model through a paired crossover reader study. Materials and Methods: This retrospective, IRB-approved study conducted from September 2024 to July 2026 included an internal training cohort of 84 manually segmented CTAs plus 17 CTAs from the RSNA Intracranial Aneurysm Challenge (101 CTAs total) and an external testing cohort of 40 CTAs (22 positive, 18 negative for vertebral artery dissection). A nnU-Net (version 2) model was trained using five-fold cross-validation with the entire training cohort. Technical performance was evaluated using the Dice similarity coefficient (DSC). A two-part crossover reader study included 10 readers who assessed diagnostic accuracy, confidence, and interpretation time with and without segmentation-based augmentation. Statistical analyses included McNemar's exact test (accuracy) and Wilcoxon signed-rank test (confidence, time), with P < .05 considered significant. Results: The model achieved a DSC of 0.96 (SD 0.01). In the reader study, diagnostic accuracy was lower with augmentation than without (0.72 vs 0.84, P < 0.001), while confidence (3.98 vs 3.96, P = .77) and interpretation time (137.3 vs 166.7 seconds, P = .54) did not differ significantly. Subjectively, 7 of 10 readers reported they would use the tool routinely in acute or trauma settings. Conclusion: Although the deep learning model demonstrated accurate segmentation of vascular structures, further validation is needed to establish its utility for enabling accurate diagnosis of VAD in fast-paced clinical settings.

Thrombotic thrombocytopenic purpura (TTP) is a rare, life-threatening thrombotic microangiopathy caused by a severe deficiency of the ADAMTS13 protease. We performed the first large-scale genome-wide association study (GWAS) of TTP in 1,344 individuals of Han Chinese ancestry, comprising a discovery cohort (n = 913) and a validation cohort (n = 431). We identified two independent susceptibility loci, including the known locus 6p21 and the novel locus 14q32. At the known locus 6p21, the lead signal rs2187668 is near HLA-DQA1 and strongly associated with TTP risk (P = 5.35e-10; Odds Ratio [OR] = 3.94). At the novel locus 14q32, the lead SNP rs1024350 (P = 1.01e-10; OR = 1.90) is located between LINC00221 and MIR5195. Fine-mapping prioritized rs2187668 as the leading signal at 6p21, whereas HLA imputation and stepwise conditional analysis further pinpointed amino acid position 74 of HLA-DRB1 as the primary driver of the broader MHC association (P = 2.28e-10), superseding classical allele-based models. Structural analysis revealed that position 74 lines pocket 4 of the HLA-DRB1 peptide-binding groove; the risk-conferring Leu/Arg residues at this position carried by HLA-DRB1*08:03 and DRB1*03 may favour presentation of ADAMTS13-derived autoantigenic peptides, whereas the common Ala residue is protective. In summary, we reconstructed the genetic architecture of TTP in East Asians and identified structural gatekeepers of autoimmunity, providing novel targets for precision medicine and ethnic-specific risk stratification.

Background: Cervical spinal cord (CSC) compression and myelopathy are frequent, severe complications in mucopolysaccharidosis (MPS) types I and VI. Early detection is critical to prevent irreversible neurological deficits and preserve quality of life. This prospective study aimed to identify quantitative neuroimaging biomarkers of structural CSC damage and myelopathy in MPS I and VI. Methods: Structural and diffusion magnetic resonance imaging (MRI) of the CSC was analyzed in 17 MPS I patients, 3 MPS VI patients (19{+/-}7 years), and 19 case-matched healthy controls (20{+/-}7 years). CSC macrostructure (cross-sectional area, diameters) and microstructure (fractional anisotropy [FA], radial [RD], mean, axial diffusivities) were evaluated within and beyond the maximal compression areas. Results: MPS patients demonstrated reduced CSC cross-sectional area and anterior-posterior diameter at upper cervical levels, alongside widespread microstructural alterations (lower FA, higher RD) throughout the CSC versus controls (pFDR<0.05). Restricted foramen magnum space was associated with microstructural damage at lower cervical levels without overt macrostructural compression. Morphological and microstructural metrics were sensitive to CSC compression and myelopathy severity (pFDR<0.05). Lower CSC integrity correlated with more advanced growth retardation (p<0.001). Among patients receiving hematopoietic stem cell transplantation, earlier treatment correlated with preserved upper CSC morphology (p<0.05). Distinct neuroimaging phenotypes were observed across MPS genotypes (pFDR<0.05). Conclusions: CSC MRI provides candidate neuroimaging biomarkers for early detection and characterization of myelopathy in MPS I and VI. Structural damage extends beyond primary maximal compression areas, highlighting these markers for clinical monitoring and future therapeutic trials.

Multiple sclerosis (MS) is approximately three times more common in females than in males, but sex-linked epigenetic mechanisms remain incompletely understood. We tested whether X-chromosome DNA methylation differs in females with MS and whether associated sites preferentially map to genes normally subject to X-chromosome inactivation (XCI). An X-chromosome-focused epigenome-wide association study was performed in whole blood from three datasets comprising 496 females with MS and 509 female controls, with independent evaluation in isolated CD4+ T cells (80 cases, 76 controls). At FDR < 0.01, 3 182 X-chromosome CpGs were hypomethylated in MS (mean {Delta}{beta} = -0.035). Hypomethylated CpGs were strongly enriched in non-escape genes normally subject to XCI relative to escape genes (OR 6.77; p = 1.55x10-57) and mapped to immune and chromatin-related processes. Exploratory cis-mQTL analysis identified strong local genetic effects at a small subset of loci, including WAS, IRAK1 and TLR7. In CD4+ T cells, 2 080 of the 3,182 whole-blood hypomethylated CpGs (65.4%) were also significantly hypomethylated. These findings identify a reproducible X-chromosome hypomethylation signature in female MS and support direct investigation of inactive-X regulation, XCI escape and X-linked immune gene dosage.

Background Accurate prediction of 90-day outcomes following ischemic stroke is crucial for optimizing clinical strategies. This study aimed to systematically review and meta-analyze studies using deep learning (DL) and hand-crafted radiomics (HCR) models for predicting 90-day modified Rankin Scale (mRS) outcomes after ischemic stroke. Methods We followed the PRISMA guidelines for systematic reviews. Four databases (PubMed, Embase, Web of Science, and Cochrane Library) were searched comprehensively for studies published up to September 2025. Inclusion criteria encompassed original research focusing on ischemic stroke patients with mRS at 90 days as the primary outcome and predictive models utilizing imaging biomarkers from CT or MRI. Quality assessment of the included studies was conducted using the PROBAST and and METhodological RadiomICs Score (METRICS) tools. Results Seventeen studies were included in the quantitative meta-analysis. Models demonstrated good prognostic performance for 90-day outcomes, with pooled AUCs of 0.85 (95% CI: 0.80-0.86) for internal and 0.74 (95% CI: 0.67-0.89) for external validation sets, with no significant difference (p=0.42). Deep learning and hand-crafted radiomics models showed comparable diagnostic accuracy in the primary analysis (p=0.667); a difference in sensitivity favouring hand-crafted radiomics emerged only in a post-hoc analysis after exclusion of one outlier study. Models incorporating clinical variables significantly outperformed imaging-only models (p<0.01). CT- and MRI-based models showed similar performance. Treatment-specific analyses revealed higher sensitivity for IVT-based models and higher specificity for combined MT+IVT models. Conclusions DL and HCR models demonstrate robust potential for predicting 90-day outcomes in ischemic stroke. Future research should focus on standardizing model evaluation metrics and incorporating explainable AI techniques to improve clinical applicability.