Citations

Background: Acute myeloid leukemia (AML) with CBFB::MYH11 fusion and myeloid/lymphoid neoplasms with eosinophilia and tyrosine kinase gene fusions (MLN-TK) are genetically defined and typically mutually exclusive entities. Case Presentation: We report a unique case of de novo AML harboring two clonal, transcriptionally active class-defining fusions: CBFB::MYH11 and GOLGA4::PDGFRB. A 61-year-old woman presented with leukocytosis with neutrophilia, eosinophilia, and monocytosis; circulating blasts; and a markedly hypercellular marrow. Cytogenetic analysis revealed inv(16)(p13.1q22) and t(3;5)(p21;q32) in all 20 metaphases, and RNA sequencing confirmed expression of both CBFB::MYH11 and GOLGA4::PDGFRB fusions. In addition, an oncogenic WT1 frameshift variant was identified. Hematopathologic findings were largely consistent with AML with CBFB::MYH11 fusion but exhibited features reminiscent of PDGFRB-rearranged MLN-TK. The patient achieved complete remission following the standard 7 + 3 induction chemotherapy regimen for AML with gemtuzumab ozogamicin. Conclusions: This case illustrates the diagnostic challenges posed by concomitant class-defining alterations in hematologic neoplasms and underscores the importance of integrated genomic assessment.

ABSTRACT Background A rare autosomal recessive disorder known as hyperphosphatasia with impaired intellectual development syndrome (HPMRS), also referred to as Mabry syndrome, is caused by a deficiency in glycosylphosphatidylinositol (GPI). Elevated blood alkaline phosphatase (ALP) levels, cognitive impairment, and epileptic seizures are among its key features. These pathways are involved in the synthesis of GPI and the transfer of GPI anchor to the proteins, fatty acid remodeling, and transport of GPI‐anchored proteins (GPI‐APs). Methods Using exome sequencing (ES), the cause of hyperphosphatasia and ID of an 11‐year‐old girl from non‐consanguineous parents was solved, and the results confirmed by direct Sanger sequencing method. Results ES identified a novel homozygous pathogenic variant, PGAP3 (NM_033419.5: c.202dupT, p.Cys68fs*2) that segregated within the family members. To the best of our knowledge at the time of writing this manuscript, this variant has not been reported in HPMRS in the literature. Conclusion Our findings indicate that the p.Cys68fs*2 variant may disrupt normal protein function and likely disrupt its interaction with its associated partner proteins, potentially leading to disruption of GPI biosynthesis. We present a novel pathogenic variant thus expanding the phenotypic and mutational spectrum of this extremely rare disorder. Elevated ALP assays and ES are valuable diagnostic tools for HPMRS. Additionally, a comprehensive literature review was conducted to expand the phenotypic and genotypic spectrum within the PGAP3 gene responsible for HPMRS.

Abstract Models that estimate the probability of an adverse clinical outcome require an operational cutoff to translate continuous estimated probabilities into discrete labels that can trigger clinical action. Although statistical methods identify ‘optimal’ cut-offs, threshold selection ultimately reflects value judgments regarding harm tolerance, resource allocation, and workflow feasibility. We describe a governance-informed approach to selecting a deployment threshold for an asthma exacerbation (AE) prediction model integrated into clinical workflows. Using prevalence-adjusted performance metrics and real-world provider capacity modeling, we evaluated multiple candidate thresholds and quantified downstream workload and missed-event trade-offs. We demonstrate that statistically optimal thresholds may produce operationally infeasible alert volumes or unacceptable miss rates. We propose a structured threshold governance framework integrating statistical performance, clinical utility, stakeholder input, and human oversight safeguards. This case illustrates how threshold decisions should be treated as organizational governance processes rather than purely technical optimizations.

Abstract Lichen Planopilaris (LPP) is a lymphocyte-mediated scarring alopecia characterized by progressive follicular destruction and fibrosis. In this clinical trial, patients with biopsy proven LPP were treated with deucravacitinib (an oral inhibitor of tyrosine kinase 2 (TYK2)) 6 mg BID for 24 weeks (NCT-06091956). Bulk and single-cell RNA sequencing was performed on paired pre- and post-treatment scalp biopsies from baseline and week 4. Patients (N=10) demonstrated improvements in PGA (88.9%, p=0.008), LPPAI (−2.3 points, SD 1.1, p=0.002) and Skindex-16 (−21.0 points, SD 22.1, p=0.014) scores at week 24. Bulk transcriptomic analysis of untreated LPP revealed upregulation of type I Interferon (IFN)-stimulated genes and pathways related to inflammation, immune activation, keratinization, and extracellular matrix remodelling, with downregulation of immune and inflammatory pathways following treatment. Single-cell RNA-seq of LPP was characterized by enrichment of CD8+GZMK+ T cells which showed downregulation of T-cell receptor signaling as well as antiviral pathways with treatment. Basal keratinocytes exhibited reduced cytokine and interferon signaling and decreased communication with NK cells following treatment. CCL19+ fibroblasts were prominent in untreated disease was attenuated after treatment, with downregulation of type I IFN signaling. Selective TYK2 inhibition with deucravacitinib effectively suppresses these inflammatory circuits in LPP and represents a promising therapeutic strategy.

Abstract Despite growing interest in AI for pancreatic cyst management, no prior study has systematically investigated how lesion characteristics influence model behavior or provided a comprehensive explainability analysis in IPMN malignancy risk stratification. We present the first multi-center study integrating explainable AI with uncertainty quantification to evaluate how cyst type, size, and location affect deep learning performance in IPMN malignancy prediction. Our retrospective study analyzed 170 IPMNs from seven centers using a radiomics-deep learning fusion model. Cases were stratified by dysplasia grade, IPMN type, size, and location; with model interpretability assessed using SHAP, LIME, Grad-CAM, and uncertainty quantification. Overall accuracy was 67.1%, with model uncertainty lower for correct versus incorrect predictions (0.72 vs 0.78, p < 0.001). On subgroup analysis: Low-Risk lesion accuracy exceeded High-Risk (81.3% vs. 42.9%, p < 0.001), BD accuracy was inversely correlated with cyst size while MD remained stable, and body and whole-pancreas cysts stratified more accurately than tail cysts (77.8%, 76.6% vs. 47.8%). AI models face significant, previously uncharacterized performance drops when evaluating complex high-risk IPMNs. Our study shows that integrating uncertainty quantification can successfully flag unreliable predictions, enabling a selective-prediction framework where clinicians can confidently rely on AI for low-risk, low-uncertainty lesions while deferring high-uncertainty cases to expert review.

Abstract Reactive arthritis is an immune-mediated arthropathy caused by a preceding remote infection. Typically, reactive arthritis affects the joints of the lower extremities but can occur in the upper extremity. There are a few case reports of reactive arthritis primarily involving the joints of the hand and wrist, necessitating surgical intervention. We describe the case of a 37-year-old, right-hand dominant female who presented with severe left wrist pain. After obtaining radiographs, she was diagnosed with severe erosive changes in the radiolunate joint and distal radioulnar joint (DRUJ) and secondary reactive gonococcal arthritis of the DRUJ. The patient failed conservative measures and ultimately underwent radioscapholunate (RSL) arthrodesis, distal ulna resection, and wrist denervation. Postoperatively, she had resolution of her pain and an excellent functional outcome. Refractory reactive arthritis of the DRUJ can cause concomitant adjacent joint erosive changes. Performing RSL arthrodesis and distal ulna resection can provide an excellent functional outcome and still allow motion to be preserved through the midcarpal joint. IV: Case report level IV or higher evidence.

ABSTRACT Background The optimal timing for basal insulin initiation in diabetic ketoacidosis (DKA) remains unclear. British guidelines endorse early basal insulin (EBI), while the American Diabetes Association emphasises overlap duration with intravenous insulin, without mention of timing. This meta‐analysis evaluates whether EBI administration of basal insulin improves clinical outcomes in adults with DKA. Methods A systematic review and meta‐analysis were performed according to the PRISMA guidelines. Databases searched included MEDLINE, Embase, Scopus, Web of Science and the Cochrane Central Register of Controlled Trials through December 11, 2024. Inclusion criteria included articles in the English‐language randomised controlled trials (RCTs) or observational studies evaluating EBI in adult DKA patients. Non‐human studies, conference abstracts, and case reports were excluded. The primary outcome for this study was hospital length of stay (LOS). Additional outcomes included intensive care unit (ICU) LOS, time to DKA resolution, hypoglycemia, and rebound hyperglycemia between EBI and usual care. Risk of bias was assessed using the Cochrane and Newcastle‐Ottawa tools. Grading of Recommendations Assessment, Development, and Evaluation was performed to evaluate the quality of evidence. Results From 1214 identified studies, eight (4 RCTs, 4 observational) met inclusion criteria for a total of 247 patients in the EBI group and 552 patients in the control group. No significant difference in hospital LOS was found (mean difference −11.17 h; 95% CI: −29.91 to 7.56). ICU LOS, time to DKA resolution, incidence of hypoglycemia, and rebound hyperglycemia also did not demonstrate any significant differences between groups. Significant heterogeneity existed across studies for most outcomes. All studies had a high risk of bias, and the quality of evidence was very low. Conclusion EBI did not result in significant differences in hospital LOS, ICU LOS, or time to DKA resolution; however, there were no increased adverse events with EBI. Current studies of early EBI have significant limitations. Future research should focus on developing high‐quality RCTs.

ABSTRACT Background Postoperative venous thromboembolism (VTE) is a major source of morbidity and mortality. Real‐time risk stratification for VTE is important to guide measures for thromboprophylaxis. We sought to compare the discrimination of three VTE predictive systems in classifying 90‐day postoperative VTE among cancer patients undergoing pancreas and liver surgery using administrative data. Methods We conducted a retrospective analysis of the 2001–2015 SEER‐Medicare data for patients with newly diagnosed primary pancreatic or liver malignancies who underwent surgery. The primary outcome was 90‐day postoperative VTE (including deep venous thrombosis and pulmonary embolism [PE]). We used the 2001–2010 dataset to create a new VTE predictive model using multivariable logistic regression. We then compared the discrimination of the Caprini score (adapted for claims data), the Elixhauser comorbidity score, and the new predictive model using the 2011–2015 dataset. Results Our sample included 15 479 patients (12 005 pancreas and 3474 liver surgery). Median age was 74 years (interquartile range: 69–78) and 7619 were female (49%). The overall incidence of 90‐day VTE was 10.0% (90‐day PE: 4.5%). The new VTE model included age, race, history of VTE, cancer stage, preoperative chemotherapy, and Elixhauser score. The discrimination of all predictive models was modest for both VTE ( c ‐statistic 63.0%, 59.4%, and 67.8%) and PE (61.5%, 59.7%, 65.6% for Caprini, Elixhauser, and new score respectively). Conclusions The existing tools for predicting postoperative VTE show poor performance using claims data. Future research should supplement administrative data with additional data sources to better predict patients at high risk for postoperative VTE.