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Klippel-Trénaunay syndrome (KTS) is a rare congenital vascular-overgrowth disorder within the broader spectrum of PIK3CA -related overgrowth syndromes. Although clinical, radiologic, and orthopedic manifestations are well described, the histologic spectrum of KTS-associated pathology remains under-characterized. Clinicopathologic findings from 21 patients with clinically established KTS were reviewed, including 18 with longitudinal clinical follow-up and 3 consultation cases of rare malignant mesenchymal neoplasms. Cases were identified from institutional pathology and clinical databases, and available hematoxylin and eosin-stained sections and ancillary studies were reviewed. The cohort included 11 males and 10 females. Among the 18 clinically followed patients, 12 had limb length discrepancy or asymmetric limb overgrowth, 9 had documented deep vascular malformations, 4 had intra-articular/synovial malformations, and 10 underwent initial orthopedic evaluation or intervention before age 18. Orthopedic morbidity clustered into mechanical overgrowth-related, vascular malformation-related, and soft tissue/wound-related complications. Histologic findings included superficial and/or deep slow-flow vascular malformations, ulceration and granulation tissue, hemosiderin-laden reactive synovium, and destructive hemorrhagic arthropathy. Intra-articular disease recapitulated a hemophilic arthropathy-like pattern, with hemosiderotic synovitis, papillary synovial hyperplasia, and progressive joint destruction with loss of articular cartilage, exposed subchondral bone destruction and remodeling, all attributable to long-standing intra-articular hemorrhage. Three consultation cases showed malignant mesenchymal neoplasia, including 2 angiosarcomas. Recognition of these patterns is important for pathologists evaluating specimens from patients with known or suspected KTS, particularly when findings mimic hemophilic arthropathy, diffuse-type tenosynovial giant cell tumor, nonsyndromic vascular malformation, and/or end-stage destructive arthropathy.

Introduction Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase involved in immune signaling and has been implicated in tumor-intrinsic resistance to DNA-damaging therapies. Although SYK activity has been associated with poor outcomes in triple-negative breast cancer (TNBC), its role in resistance to combined radiotherapy and immune checkpoint blockade remains unclear. Methods SYK expression and clinical relevance were assessed across multiple breast cancer cohorts, including radiotherapy-treated datasets. Integrated transcriptomic analyses were used to characterize SYK-associated immune states. The functional effects of pharmacologic SYK inhibition were evaluated using in vitro assays and an immunocompetent bilateral 4T1 tumor model in female BALB/c mice treated with radiotherapy and PD-1 blockade. Treatment-associated immune remodeling was assessed by flow cytometry and transcriptomic profiling. Results Across multiple cohorts, elevated SYK expression, particularly in TNBC, was associated with inferior clinical outcomes and immune dysregulation. Pharmacologic SYK inhibition enhanced tumor responses to radiotherapy and PD-1 blockade at both irradiated and distant tumor sites. These effects were accompanied by coordinated remodeling of the tumor immune microenvironment, including suppression of neutrophil-associated signatures and reductions in immunosuppressive myeloid populations, including M2-like macrophages and myeloid-derived suppressor cells. Discussion Pharmacologic SYK inhibition enhances responses to radiotherapy and PD-1 blockade in preclinical TNBC models and is accompanied by remodeling of the immunosuppressive myeloid compartment. These findings support further investigation of SYK-targeted strategies to improve responses to combined radiotherapy and immune checkpoint blockade.

ABSTRACT Chronic liver disease (CLD) is a major cause of global morbidity and mortality, with outcomes closely linked to the severity of liver fibrosis. While liver biopsy has historically been the reference standard for fibrosis staging, its invasiveness, sampling variability and limited suitability for longitudinal monitoring have driven the widespread adoption of non‐invasive tests (NITs). Imaging‐based biomarkers, particularly elastography, now play a central role in the clinical assessment and care pathway design. This review focuses on imaging techniques for non‐invasive assessment of liver fibrosis, with emphasis on elastography techniques: vibration‐controlled transient elastography (VCTE), ultrasound‐based shear wave elastography (SWE) and magnetic resonance elastography (MRE). Technical principles, advantages, limitations and implementation considerations are discussed to support informed selection across clinical contexts. VCTE offers rapid point‐of‐care assessment with extensive validation for identifying advanced fibrosis but is limited by operator dependence, reduced repeatability and lack of anatomical information. SWE provides real‐time, radiologist‐controlled assessment with improved applicability in obesity and ascites, though intervendor variability and operator dependence remain challenges. MRE demonstrates the highest diagnostic accuracy, reproducibility and technical success, with assessment of larger liver volume and strong performance across etiologies, albeit with limited access due to the requirement of an MRI system. Adjunct MRI biomarkers, including proton‐density fat fraction and iron‐corrected T1 mapping, further enhance liver phenotyping by characterising steatosis and fibro‐inflammatory activity. Understanding the strengths and limitations of each technique is essential for radiologists supporting multidisciplinary CLD management and service implementation, particularly in evolving healthcare environments such as Australia.

Background. Rare diseases collectively affect an estimated 300 million people worldwide, yet patients wait an average of 4-8 years for a correct diagnosis after visiting multiple physicians, accumulating unexplained findings, and suffering preventable disease progression before a unifying diagnosis is reached. Electronic health records (EHRs) capture this pre-diagnostic trajectory in rich detail, but conventional methods typically collapse years of longitudinal phenotypic signals into a single static snapshot; our approach instead models this temporal structure directly. Methods. We conducted a retrospective cohort study using longitudinal EHR data from approximately 3 million patients at Mayo Clinic Platform_Accelerate. Cases were patients with confirmed diagnoses of 10 rare diseases; controls (~5,200 per disease) had no record of any rare disease. HPO phenotype terms were extracted from clinical notes and laboratory results using a medspaCy NLP pipeline; ICD codes were grouped at three-character level. We developed and trained two temporal models, GRU-Attn and Conformer, that encode patient histories as quarterly time-binned sequences spanning up to 30 years, and compared these against three static baselines (CatBoost, XGBoost, logistic regression). Models were evaluated systematically at prediction horizons h = 1-10 years before diagnosis. Results. In the full held-out cohort benchmark, temporal models outperformed static baselines across the large majority of diseases and prediction horizons, with the advantage widening substantially as the horizon lengthened. At 1-2 years prior to diagnosis, mean AUROC was 0.928 (GRU-Attn) versus 0.871 (CatBoost); at 6-10 years prior to diagnosis, 0.726 versus 0.693, with static models degrading to near-chance for some diseases. Combining HPO and ICD features yielded the strongest performance, with a mean gain of +0.063 AUROC over the best single source at 1-2 years prior to diagnosis. Separately, in a manually curated cohort of 143 confirmed-undiagnosed cases with extended EHR histories, GRU-Attn identified 138 cases before first disease mention and all 143 cases before formal diagnosis at the prespecified 99th-percentile specificity threshold, with median lead times of 1.1-8.3 years and 1.9-21.7 years, respectively. Conclusions. By learning from the temporal trajectory of phenotypic signals rather than their static aggregate, temporal EHR models improved retrospective early detection performance across several rare diseases, most powerfully for conditions where pre-diagnostic findings accrue gradually and heterogeneously over years. These results show that longitudinal EHR trajectories carry rare-disease signal beyond static phenotypic burden and support prospective evaluation of temporal EHR modeling for rare-disease risk stratification.

Abstract Purpose Traditional adverse event (AE) reporting in oncology trials focuses on the single worst CTCAE grade, which may underrepresent chronic or recurrent toxicity burden. We applied a longitudinal toxicity framework to compare toxicity profiles of chemotherapy with or without cetuximab in metastatic colorectal cancer. Patients and Methods We pooled two randomized first-line trials in the ARCAD database. AEs examined were diarrhea, rash, hand-foot syndrome (HFS), fatigue, anorexia, and mucositis. Outcomes included grade ≥3 AE, time to first occurrence of maximum grade, early onset (≤6 weeks), and AEL, a normalized measure of cumulative toxicity burden over treatment. Analyses were adjusted for chemotherapy backbone, ECOG performance status, sex, primary tumor location, dose reduction, and treatment duration. Results Compared with Chemotherapy alone (n = 564), Chemotherapy plus cetuximab (n = 738) was associated with higher grade ≥3 rash (21% vs 0.5%; adjusted odds ratio [OR] 49.89, 95% CI 15.8–157.4), higher rash AEL (0.257 vs 0.069; adjusted mean difference 0.22, 95% CI 0.21–0.23), and more frequent early-onset maximum rash (67% vs 34%; adjusted OR 4.28, 95% CI 2.72–6.74). Rash AEL remained higher with cetuximab within maximum-grade strata. HFS showed higher grade ≥3 risk and overall AEL with cetuximab, but no within-grade AEL difference, indicating higher overall HFS burden reflected grade distribution rather than within-grade chronicity. No consistent differences were observed for other AEs. Conclusions Incorporating onset timing and AEL distinguished persistent toxicity burden from isolated peak events. This framework may support comparative tolerability assessment when chronic toxicity burden is central to decision-making.

Background: Accurate identification of patients with metabolic dysfunction–associated steatohepatitis (MASH) at risk of disease progression (at-risk MASH) is critical for guiding resmetirom and semaglutide therapy. Inconsistent definitions of at-risk MASH complicate clinical risk classification and comparisons across studies. This study aimed to standardize the definition of at-risk MASH and propose optimized MRE-based liver stiffness (LS-MRE) cutoffs to improve noninvasive risk stratification and better guide pharmacotherapy. Methods: In this prospective study, 413 patients with suspected or diagnosed MASLD from two medical centers underwent liver biopsy and MRE. Results: A simplified definition of at-risk MASH (MASH with fibrosis stage≥2) identified a broader group of patients requiring clinical attention than a stricter definition requiring NAFLD Activity Score≥4 with ≥1 point in steatosis, inflammation, and ballooning (n=122 vs. 77). LS-MRE alone showed the highest diagnostic accuracy for identifying at-risk MASH using the simplified definition (AUC 0.91 [0.87, 0.95]) and cirrhosis (AUC 0.93 [0.88, 0.99]), compared with FIB-4 and individual laboratory parameters (AST, ALT, and platelet count) ( p <0.01 for all, paired DeLong tests). Optimized LS-MRE cutoffs (2.8–6.4 kPa) captured 25 additional biopsy-proven pharmacotherapy candidates (28% of all eligible patients) compared to AASLD guidelines–recommended cutoffs (3.1–4.4 kPa), improving sensitivity (80% vs. 50%) with acceptable specificity (73% vs. 88%). Conclusions: A simplified definition of at-risk MASH enables broader identification of treatment candidates. LS-MRE demonstrates excellent diagnostic performance and improves noninvasive risk stratification when paired with optimized cutoffs. These findings support the use of MRE to guide resmetirom and semaglutide eligibility and improve access to emerging therapies.

Abstract Artificial intelligence is spreading through public health agencies and healthcare systems faster than workforces are trained to govern it. We describe RHAMI (Responsible Health AI Readiness and Maturity Index), the first human-centered automated composite index for health organizations to score AI readiness and maturity as one measurable system of science, economics and ethics, and the physician certification-to-Master’s pathway built to teach what it measures—both designed jointly by frontline public health professionals, clinicians, ethicists, engineers, leaders, and policy-makers in low- and middle-income countries and high-income countries.

Background: Societal guidance recommends sequential noninvasive testing for metabolic dysfunction–associated steatotic liver disease (MASLD), using Fibrosis-4 (FIB-4) ≥1.3 as a gatekeeper before second-line assessment with the Enhanced Liver Fibrosis (ELF) score (<7.7 as a rule-out). The impact of this pathway design on the detection of fibrotic MASH remains uncertain. We aimed to evaluate the performance of guideline-endorsed sequential testing and to determine optimized ELF thresholds within alternative strategies. Methods: In this prospective study, patients with or at risk for MASLD underwent FIB-4, ELF testing, magnetic resonance elastography (MRE), and liver biopsy within 1 month. Fibrotic MASLD was defined as fibrosis stage ≥2 on histology or liver stiffness ≥3.1 kPa on MRE. Diagnostic performance of ELF was assessed, and sequential (ELF applied after FIB-4 ≥1.3) and concurrent (either test positive) testing strategies were compared. Results: Among 186 participants (median age 51 years; 69% women), fibrotic MASLD was present in 71 (38%). ELF demonstrated AUROC 0.77 (95% CI 0.70–0.84); the cohort-derived cutoff was 9.4 (80% sensitivity, 65% specificity). Lower cutoffs (8.7 and 7.7) increased sensitivity (≥90% and 100%) but low specificity (34% and 4%). FIB-4 ≥1.3 showed limited sensitivity (57%), resulting in 43% of fibrotic MASH cases being excluded from second-line testing. Sequential application of ELF, therefore, failed to overcome this sensitivity ceiling. A concurrent strategy using FIB-4 ≥1.3 or ELF ≥9.4 improved sensitivity to 87% with 64% specificity. Conclusions: Sequential FIB-4–based testing substantially limits the detection of fibrotic MASLD due to gatekeeping sensitivity constraints. Concurrent first-line testing with FIB-4 and ELF improves case identification, underscoring the importance of pathway design in optimizing noninvasive fibrosis assessment.

Deregulated inflammatory signaling via STAT family transcription factors, particularly STAT1, underlies a variety of immune-related diseases, including inflammatory bowel disease. Whereas activation of STATs by JAKs via canonical receptor-driven JAK–STAT signaling is well understood, little is known about JAK-independent mechanisms of STAT activation. Here, we identify the understudied nonreceptor tyrosine kinase TNK1 as a therapeutically targetable, JAK-independent activator of STAT signaling. Using a multiomics approach, we mapped a network of TNK1 substrates associated with protein condensates and proinflammatory signaling, including STAT1. We found that TNK1, but not its sister kinase ACK1, directly phosphorylates STATs at well described STAT-activating JAK sites. In cells, TNK1-mediated STAT1 phosphorylation and activation occurs independently of JAKs. Imaging and interactomics data suggest that TNK1 interacts with STAT1 in cytosolic condensates, which likely compartmentalize TNK1-substrate interactions. We show that an intrinsically disordered proline-rich region in TNK1, which includes a 14-3-3 docking phosphorylation site, is required for the formation of kinase-active TNK1 condensates and STAT1 phosphorylation. Mutations within the proline-rich region that eliminate 14-3-3 binding increase formation of TNK1 condensates, suggesting a model in which 14-3-3 acts as a clamp that constrains the flexible PRR to inhibit condensate formation and STAT1 activation. Finally, we show that TNK1 is a targetable driver of STAT1-mediated inflammation in the gut as inhibition of TNK1 reduces active STAT1 in the colon and ameliorates colitis symptoms in mice.