Citations

Objective Lewy body disease (LBD) is a complex neurodegenerative disorder characterized by the accumulation of misfolded α‐synuclein in the brain. Neuroinflammation has long been implicated in LBD pathogenesis, and recent genetic studies in Parkinson's disease (a clinical manifestation of LBD) have shown consistent association with the human leukocyte antigen (HLA) gene complex. Here, we assessed whether variation in HLA alleles influences neuropathological burden in a neuropathologically‐defined series of LBD cases. Methods We conducted a comprehensive analysis of HLA allelic variants in a cohort of 539 LBD cases of European descent from the Mayo Clinic brain bank. High‐resolution whole‐genome sequencing was used, and the HLA alleles of each sample were called using the HLA*LA tool and assessed for association with neuropathological outcomes. Results Our analysis identified 1 significant ( P < 3.43 × 10 −5 ) and 4 suggestive ( P < 0.001) associations between certain HLA alleles and specific neuropathological outcomes in LBD, suggesting a potential role for HLA‐mediated immune mechanisms in disease progression and subtype differentiation. Specifically, HLA‐DPB1*06:01 was significantly associated with lower Lewy body counts in the parahippocampal gyrus ( P = 3.30 × 10 −5 ), with weaker and suggestive associations observed in the middle frontal ( P = 1.80 × 10 −4 ) and inferior parietal gyrus ( P = 6.33 × 10 −4 ). Additionally, although only suggestive, HLA‐DRB1*11:01 correlated with a lower Thal amyloid phase ( P = 1.56 x 10 −4 ), and HLA‐B*15:01 correlated with an increased risk of diffuse LBD ( P = 7.58 x 10 −4 ). Interpretation This study provides a detailed evaluation of the relationship between HLA alleles and LBD pathology, highlighting the importance of immune‐related genetic factors in the etiology of LBD. ANN NEUROL 2026;99:492–501

Abstract Alzheimer’s Disease (AD) 1 is a progressive neurodegenerative disorder with limited therapeutic options, driving interest in drug repurposing to accelerate treatment discovery. Drug repurposing has emerged as a promising strategy to accelerate therapeutic discovery by repositioning existing drugs for new clinical indications. Recent computational repurposing approaches, including knowledge graph reasoning, transcriptomic signature analysis, and integrative literature mining, have demonstrated strong predictive capabilities 2 . However, these methods often yield divergent drug rankings, which makes it difficult to decide which candidates to advance for experimental follow-up and results in substantial gaps between computational predictions and feasible in vivo validation 2 .To bridge this computational-experimental gap, we proposed an advanced prioritization framework leveraging large language models (LLMs). Our method systematically evaluated three state-of-the-art (SOTA) and representative computational methods (TxGNN 3 , Composition-based Graph Convolutional Network (CompGCN) 4 , and a regularized logistic regression (RLR) 5 , to analyze both their predictive performance and pharmaceutical class distributions. By integrating the strengths and divergences of these models, we generated a unified, streamlined list of 90 candidate drugs for further prioritization. We then utilized an LLM-based agent to perform evidence synthesis from biomedical literature abstracts for each candidate. This process mimics expert manual curation but significantly reduces human effort and time by efficiently distilling vast textual data into actionable insights. Applying consistent and transparent selection criteria, we obtained a refined and prioritized list of drug candidates suitable for subsequent in vivo experimental validation. The robustness and clinical relevance of our framework were validated using real-world data from Alzheimer’s patient cohorts, clinical trial registries, and expert pharmacological reviews. This comprehensive validation confirmed that our LLM-driven approach enhances efficiency, consistency, scalability, and generalizability. By integrating computational predictions with scalable evidence synthesis and multifaceted validation, our framework facilitated rapid and informed prioritization of repurposed drugs. Our framework can potentially accelerate the translational pathway toward viable AD therapeutics. Moreover, the versatility of our framework can also be applied to drug repurposing efforts for other diseases beyond AD.

Objective: An endoscopically placed duodenal-jejunal bypass liner (DJBL) may provide a safe adjunctive therapy for those with poorly controlled type 2 diabetes mellitus (T2DM) and obesity. Background: Although some endoscopic therapies have been shown to improve glycemic indices secondary to weight loss, small bowel interventions may have direct metabolic effects. A meta-analysis of observational studies demonstrated reduction in HbA1c by 1.3% at 1 year after DJBL in patients with T2DM and obesity. Methods: This was a multicenter, double-blind, randomized, sham-controlled trial comparing DJBL to sham procedure with medical management and lifestyle modification. Primary endpoints included mean difference in changes in HbA1c at 12 months between arms, and device-related serious adverse events (SAEs). Secondary endpoints included percent total weight loss (%TWL) and subjects achieving HbA1c≤7% and TWL≥5% at 12 months. Results: Three hundred twenty subjects were randomized to DJBL (n=212) and sham (n=108). Baseline HbA1c and BMI were 8.79±0.92% and 38.45±5.75 kg/m 2 . On modified intent-to-treat analysis, change in HbA1c at 12 months was −1.10±1.45% and −0.28±1.54% for DJBL and sham groups, respectively ( P =0.0004). Rate of device-related SAEs was 9.4% including intolerance (3.7%), hemorrhage (2.8%), and hepatic abscess (2.3% stopping study early). At 12 months, DJBL group experienced greater weight loss compared with sham (7.7±9.6% TWL and 2.1±5.4% TWL, respectively; P <0.0001), with significantly more patients achieving HbA1c ≤ 7% (28.3% vs. 9.4%; P <0.0003) and TWL ≥ 5% (60.4% vs. 21.3%; P <0.0001). Conclusions: DJBL met primary glycemic control efficacy and primary safety endpoints, while providing clinically significant weight loss, and comorbidity improvement.

Background: Acute postoperative major salivary gland adenitis is a rare complication characterized by swelling of the salivary glands, most commonly associated with skull base neurosurgical procedures. Its occurrence following supratentorial non-skull base craniotomies is uncommon and may be overlooked. Case Description: We report a 45-year-old man who developed ipsilateral parotid gland inflammation within 24 h after undergoing a frontal interhemispheric craniotomy for resection of a craniopharyngioma. The procedure was performed in the supine position with 90° head rotation and 45° neck flexion. Postoperative imaging confirmed acute parotitis without abscess or hematoma. The patient was treated conservatively with hydration, warm compresses, and empiric antibiotics, leading to complete resolution of swelling without airway compromise. Conclusion: Acute postoperative parotitis can occur after supratentorial non-skull base procedures, particularly in cases involving extreme and prolonged head positioning. Although we presented a rather mild case of postoperative parotitis following a non-skull base craniotomy, acute major salivary gland adenitis after craniotomy may cause significant morbidity and early recognition and conservative management are essential to prevent airway compromise and other complications. Awareness of this rare entity may help neurosurgeons implement preventive measures during surgery.

Objective: Vestibular schwannomas (VSs) treated with stereotactic radiosurgery (SRS) often show transient loss of central tumor enhancement. We hypothesized that this loss of enhancement may correlate with better tumor control on follow-up imaging. Methods: We evaluated 198 consecutive patients from a single center who met eligibility criteria. Tumor volumes on the pre-SRS and post-SRS scans were quantified along with regions of loss of enhancement on the first post-SRS study. The latter was correlated with tumor volumes at follow-up, radiation isocenter density, and loss of serviceable hearing. Results: For the entire cohort, the median loss of enhancement on the first post-SRS scan was 14% (IQR: 6 to 25) and median rate of change in tumor volume was −4.5% per year (IQR: −10.5 to 1.5). Percent loss of central enhancement showed significant negative correlation with tumor growth not only for the entire cohort (correlation coefficient: −0.18, P =0.01), but also for the subset of patients who had available imaging until at least 2 years (n=185) and 4 years (n=179) post-SRS. Loss of enhancement also strongly correlated positively with tumor volumes and number of radiation isocenters and negatively with isocenter density. Finally, patients with greater percent loss of enhancement had an increased likelihood of progression to non-serviceable hearing, although this association was not statistically significant. Conclusions: These findings suggest that loss of central tumor enhancement in VSs post-SRS may be associated with improved tumor control.

Purpose of review The diuretic agents are commonly used in ICUs for various purposes. Despite the widespread utilization of diuretics, there remains substantial ambiguity regarding their indications, ICU-related changes in their pharmacodynamics and pharmacokinetics, dosing, form, and timing of administration, the differences in their utilities based on each organ failure, and their monitoring and safety issues. Recent findings In the recent past, there have been several clinical trials and large registries or systematic reviews with a focus on the use of diuretic agents in ICUs. Summary In this review article, we outline the essential changes in drug behavior during critical illnesses and organ failures, describe the indications of their use in ICUs (management of fluid overload, electrolyte imbalances, or diagnostic or prognostic tests), assess the impact of different organ failures on the utility and effectiveness of diuretics, review major recent clinical trials related to diuretic comparison, use of multiple classes of diuretics, and monitoring and safety of their use. We also provide some information regarding the safety and adverse effects of diuretic use in the ICU and outline the importance of individualizing their use during critical illnesses.

Abstract Background Clinical nutrition (CN) is becoming increasingly complex because of the rising prevalence of chronic illness, cancer, and malnutrition‐related conditions such as short bowel syndrome and refeeding syndrome. Despite its clinical significance, nutrition education among US physicians remains limited. Simultaneously, large language model (LLM)–based artificial intelligence assistants (AIAs) have emerged as tools to support complex clinical decision‐making but remain largely untested in CN. Methods This retrospective study evaluated four LLM‐based AIAs—ChatGPT (OpenAI), OpenEvidence (OpenEvidence Inc), Gemini (Google, Google DeepMind), and Copilot (Microsoft Corporation)—using five complex CN cases from our nutrition support service. Each AIA was queried with patient‐specific CN questions. Responses were blinded and reviewed by five physician CN experts using an eight‐item assessment tool evaluating clarity, relevance, evidence, and clinical utility. Results All AIAs produced clinically appropriate responses, with Gemini scoring highest in relevance (4.04) and clarity (4.16). Overall satisfaction scores ranged from 3.08 (Copilot) to 3.84 (Gemini). Citation quality and originality of insights varied and were generally limited, and no consistent differences in performance were observed across the five cases among the four AIAs. Conclusion LLM‐based AIAs can reliably replicate expert reasoning in CN. Although not yet a source of novel clinical insights, the true potential of this approach may lie in its application among physicians without specialized expertise in CN, helping to bridge existing knowledge gaps in nutrition care. Presenting full clinical cases, as shown in this study, could support AIA‐enabled e‐consultation in the future, thereby addressing gaps in CN education.