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 With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Selective inhibitors are essential for targeted therapeutics and for probing enzyme functions in various biological systems. The two main challenges in identifying such protein‐based inhibitors lie in the extensive experimental effort required, including the generation of large libraries, and in tailoring the selectivity of inhibitors to enzymes with homologous structures. To address these challenges, machine learning (ML) is being used to improve protein design by training on targeted libraries and identifying key interface mutations that enhance affinity and specificity. However, such ML‐based methods are limited by inaccurate energy calculations and difficulties in predicting the structural impacts of multiple mutations. Here, we present an ML‐based method that leverages HTS data to streamline the design of selective protease inhibitors. To demonstrate its utility, we applied our new method to find inhibitors of matrix metalloproteinases (MMPs), a family of homologous proteases involved in both physiological and pathological processes. By training ML models on binding data for three MMPs (MMP‐1, MMP‐3, and MMP‐9), we successfully designed a novel N‐TIMP2 variant with a differential specificity profile, namely, high affinity for MMP‐9, moderate affinity for MMP‐3, and low affinity for MMP‐1. Our experimental validation showed that this novel variant exhibited a significant specificity shift and enhanced selectivity compared with wild‐type N‐TIMP2. Through molecular modeling and energy minimization, we obtained structural insights into the variant's enhanced selectivity. Our findings highlight the power of ML‐based methods to reduce experimental workloads, facilitate the rational design of selective inhibitors, and advance the understanding of specific inhibitor–enzyme interactions in homologous enzyme systems.

Abstract Aims Mitral and tricuspid regurgitation (MR and TR) are common in older adults and associated with substantial morbidity and mortality. While transthoracic echocardiography (TTE) is the diagnostic gold standard, access remains limited in many care settings. Artificial intelligence (AI)–based echocardiographic analysis may help address this diagnostic gap. Methods and results We externally validated a deep learning algorithm developed by Aisap.ai using TTE studies from the Mayo Clinic Health System (2013–23). The model analyses echocardiographic images to classify atrioventricular regurgitation severity and was evaluated against cardiologist interpretations. Performance was assessed using binary (normal–mild vs. moderate–severe) and ordinal (normal, mild, moderate, severe) classification schemes. Among 1541 eligible TTEs, the model returned predictions for 578 studies (38%). Performance analysis was limited to these cases. The MR cohort included 280 studies and the TR cohort 298. For MR, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 [95% confidence interval (CI): 0.97–0.99], with 91% accuracy, 95% sensitivity, and 89% specificity. For TR, the AUC was 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion In cases where a prediction was generated, the model demonstrated high diagnostic performance in identifying clinically significant atrioventricular regurgitation. These findings support the feasibility of AI-assisted echocardiography in diverse populations, while underscoring the need for technical alignment between model requirements and local acquisition practices to ensure real-world applicability.

ABSTRACTIntroductionThe diagnosis of antibody‐mediated rejection (AMR) requires input from several transplant professionals. Bringing clinical and laboratory experts together may help standardize care. Yet, little is known about current global practices of multidisciplinary meetings for AMR management.MethodsThe Banff Antibody‐Mediated Injury Working Group approached professional societies worldwide to distribute a survey on the availability, content, participants, perceived value, and barriers to the implementation of multidisciplinary meetings.ResultsFour hundred two transplant professionals from six continents caring for kidney (90.55%), liver (21.14%), pancreas (20.65%), heart (15.17%), and lung (14.18%) transplant recipients participated in the survey, and 302 (75.12%) reported attending multidisciplinary meetings. Multidisciplinary meetings were more prevalent in academic centers, in high‐ versus low‐to‐middle‐income regions (81.03% and 65.99%, respectively; p < 0.001), and in mid‐to‐large size transplant programs compared to smaller programs. Perceived value included continued professional development (97.68%) and trainee education (95.70%). AMR was reported to be discussed at these meetings by 217 respondents with case presentations reviewing patient characteristics, histology, and HLA antibody data. A third of the respondents reviewed non‐HLA/pathogenic autoantibodies and/or molecular diagnostics, with the latter being more frequently applied in high‐ versus low‐to‐middle‐income regions (46.71% and 12.31%, respectively; p < 0.001). AMR case presentations allowed diagnosis revision, actionable management plans and were perceived as improving care. The primary barrier to the implementation of multidisciplinary meetings (63.27%) was the unavailability of transplant professionals (e.g., transplant immunologists).ConclusionFacilitating multidisciplinary meetings through the remote participation of pertinent experts and incentivizing participation through remuneration, protected time, or continued medical education may help standardize AMR diagnosis and harmonize its management.

Background Data on the safety profiles and clinical outcomes of patients with solid tumors and cardiac metastasis treated with immune checkpoint inhibitors (ICIs) are limited. Methods This is an international multicenter retrospective study of patients with cancer and cardiac metastasis at baseline. Patients who had received ≥1 dose of ICI were included. Treatment-related adverse events (trAEs) were graded per Common Terminology Criteria for Adverse Event V.5.0. Objective response rates (ORR) were evaluated by Response Evaluation Criteria in Solid Tumors V.1.1 when available. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier method. Results Among 110 pts, median age at ICI initiation was 65 (IQR: 59–75). Median follow-up time since ICI initiation was 36 (95% CI: 26 to 51) months. Melanoma (38%, n=42) and non-small cell lung cancer (24%, n=26) were the most common. 68 (62%) patients received ICIs as first-line, and 29 (26%) patients were treated with combination anti-programmed death-1 and anti-cytotoxic T-lymphocyte antigen 4. The most common location of cardiac metastasis was in the atria (37%, n=41) and ventricles (35%, n=39). 15 patients (13.6%) had bilateral cardiac/pericardial metastasis, 44 (40%) had left-sided, and 43 (39.8%) had right-sided. At ICI initiation, 21% (n=23) had a cardiac thrombus. Cardiology referrals and cardiac MRIs at the time of cancer diagnosis were completed on 58 (53%) and 52 (47%) patients, respectively. Cardiac events occurred in 40 (36%) patients, including arrhythmias (n=14, 13%), arterial/venous emboli (n=4, 3.6%), and cardiac tamponade (n=3, 2.7%). 53 (47%) patients developed trAEs; most common were colitis/diarrhea (n=16, 15%), dermatitis (n=13, 12%), and hepatitis (n=9, 8.2%). ICI-related major cardiac trAEs occurred in 2 (1.8%) patients. 22 patients (20%) developed grade ≥3 trAE. Patients with multiple cardiac metastases had significantly lower responses to ICI-based regimens compared with patients with single cardiac metastasis (11% vs 63%, p=0.02). For melanoma, ORR, median PFS, and median OS were 38%, 9.0 months, and 28.9 months, respectively. 83% of patients with melanoma had concordant responses in overall disease burden and cardiac disease. 91 patients discontinued ICIs, and the main reason was progression or death in 55 (49%) patients. Conclusions Among patients with pre-existing cardiac metastasis, ICIs demonstrated meaningful clinical efficacy with no increase in safety signals. Most patients had concordant responses in the overall disease burden and cardiac mass. Multidisciplinary teams are crucial for the appropriate management of patients with cardiac metastasis.

Abstract Rationale Optogenetics offers unmatched cellular specificity and control over cellular activity. Various opsins have been tested in animal models of epilepsy, each contributing to our understanding of seizure circuit dynamics. However, inhibitory optogenetic tools based on microbial rhodopsins have low light sensitivity and, thus, are less suitable for applications involving larger brains. We evaluated eOPN3, a red-shifted, highly sensitive inhibitory G-protein coupled receptor opsin in a porcine seizure model using integrated electro-optical sensing and modulation. The results demonstrated the feasibility of eOPN3 circuit modulation in a large animal epilepsy model. Methods MRI-guided stereotactic surgery was used to deliver 20–60 µL of AAV-eOPN3 (AAV5-/AAV9-CaMKII-eOPN3-mScarlet) into the hippocampus (HPC) of three Göttingen minipigs. Each hemisphere received either an active or a control viral vector (AAV5/9-CaMKII-mScarlet) with gadolinium to visualize the injection sites and diffusion volume via post-operative MRI. Two to three months post-injection, bilateral deep brain stimulation electrodes integrated with optic fibers were stereotactically implanted into the anterior nucleus of the thalamus (ANT) and HPC to assess: 1) opsin expression using fiber photometry, 2) optogenetic modulation of stimulation evoked response potentials (SERPs), 3) induction and propagation of seizure-like activity via intrahippocampal kainic acid (KA) injection, and 4) optogenetic modulation of KA-induced seizure activity. After the electrophysiology recording, brains were harvested for histological analysis to evaluate injection target precision, eOPN3 expression, and estimate eOPN3-modulated volume. Results eOPN3 expression was confirmed during surgery via fiber photometry. ANT electrical stimulation elicited robust SERPs in the HPCs, which were attenuated by HPC light illumination. HPC stimulation similarly induced SERPs in the ipsilateral ANT and the contralateral HPC. The HPC stimulation-induced SERPs were significantly reduced by illuminating the site of the recording areas, the ipsilateral ANT and the contralateral HPC, demonstrating the optogenetic inhibition of the synaptic release from the HPC. KA injection into the HPC induced 20-30 Hz seizure-like activity. The ANT and HPC light illumination suppressed the localized KA-induced seizure activity in the early stage. However, after the generalization of KA-induced seizures, the ANT-HPC illumination lost efficacy for the control of seizures. Histological analysis confirmed eOPN3 expression in the HPC, ANT and other Papez circuit nodes. Conclusion Our pilot study highlights that eOPN3-mediated inhibition alters SERP and the latency and spread of KA-induced seizure-like activity. We developed a platform incorporating pre- and postoperative MRI for precise viral vector delivery, real-time fiber photometry for quantifying opsin expression, and integrated electro-optical sensing and stimulation to assess optogenetic efficacy in a large animal model. The large animal model provides a solid foundation for future translational research to develop electro-optical devices and cellular therapies for human epilepsy.

Introduction: Progression-free survival (PFS), a common endpoint used in multiple myeloma (MM) trials, is defined as the time to the earliest occurrence of disease progression or death. Disease progression in MM is determined by the International Myeloma Working Group (IMWG) Uniform Response Criteria based on imaging and biomarkers from blood, urine, and bone marrow biopsy testing. In clinical trials, IMWG-recommended assessments are protocol-based. However, in routine care, they are often performed and recorded differently based on a multitude of factors. Algorithms to ascertain progression events using routinely collected biomarker data are important to classify disease progression and align with trial-defined endpoints. The aim of this study was to construct and evaluate an optimized real-world Progression (rwP) algorithm using real-world data (RWD) and demonstrate how biomarker data are captured and used to derive endpoints in routine practice. Method: A cohort of 270 transplant ineligible newly diagnosed MM (NDMM) patients starting on lenalidomide and dexamethasone (Rd) and meeting select MAIA (NCT02252172) trial eligibility criteria were included from the nationwide Flatiron Health electronic health record-derived deidentified database from 2015 to 2023. A rwP algorithm was developed using key IMWG-recommended biomarkers, including serum protein electrophoresis (SPEP), urine protein electrophoresis (UPEP), and serum free light chains (FLCs). The initial baseline value for each biomarker was identified, if available, during either the pre-treatment period (45 days prior through 7 days after first line treatment (1L) start) or the on-treatment period (8 days after 1L start through 1L end). Available biomarkers were followed for disease assessment. If any subsequent lab value during 1L treatment was less than the initial baseline value, the subsequent “on-treatment baseline” value was adopted as the new lowest value; otherwise, the initial value was used as the “on-treatment baseline” value for assessing progression events. Three hierarchies, which describe the order of utilizing key IMWG-recommended biomarkers to define rwP events, were explored, including: i) strict hierarchy (SH) [SPEP &gt; UPEP &gt; FLCs], ii) partial hierarchy (PH) [SPEP = UPEP &gt; FLCs], and iii) no hierarchy (NH) [SPEP = UPEP = FLCs]. The PH option closely resembles the IMWG criteria, with FLCs only used for rwP assessments when no measurable SPEP/UPEP are identified. The NH approach considers all available biomarkers equally and resembles how they are utilized in real-world clinical practice. The feasibility of requiring confirmatory events following the first suggestion of progression was also explored. Assays following the progression event up to the end of 1L were considered as confirmatory progression events if they met IMWG criteria. Results: We applied the rwP algorithm with each hierarchy to all eligible NDMM patients (N = 270). The SH, PH, and NH approaches respectively identified 106 (39.3%), 106 (39.3%), and 123 (45.6%) patients with progression events. Results from the SH and PH approach are consistent likely due to the rarity of UPEP usage in clinical practice. The NH approach captured more progression events because FLCs are used in the presence of measurable SPEP or UPEP, resulting in 73 progression events captured by FLCs in comparison to 32 from other approaches, potentially increasing false-positive capture due to the volatility of FLC results. Among the 106 PH-identified patients who progressed, 84 (79.2%) had IMWG-recommended tests following their progression event, and 74 (69.8%) had their progression confirmed before the end of 1L, with 35 (33.0%) receiving their confirmatory reading within 2 months after progression. These results suggest that requiring confirmatory testing as part of the rwP is feasible, though it may introduce false negatives for patients receiving clinical progression confirmation instead of getting confirmatory labs in routine clinical practice. Conclusion: We present a comprehensive assessment of important design choices for an rwP algorithm for NDMM, including a hierarchy of key assays and the inclusion of confirmatory testing. Further validation of the algorithm with health care provider assessments and the inclusion of bone marrow biopsy and imaging data is important for evaluating the performance of the algorithm in RWD.

The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government through its health research funding agency, the Canadian Institutes for Health Research (CIHR), in 2014, as a response to the G7 initiative to fight dementia. Two five-year funding cycles (2014–2019; 2019–2024) have occurred following peer review, and a third cycle (Phase 3) has just begun. A unique construct was mandated, consisting of 20 national teams in Phase I and 19 teams in Phase II (with research topics spanning from basic to clinical science to health resource systems) along with cross-cutting programs to support them. Responding to the needs of researchers within the CCNA teams, a unique sample of 1173 deeply phenotyped patients with various forms of dementia was accrued and studied over eight years (COMPASS-ND). In the second phase of funding (2019–2024), a national dementia prevention program (CAN-THUMBS UP) was set up. In a short time, this prevention program became a member of the World Wide FINGERS prevention consortium. In this article, the challenges, successes, and impacts of CCNA in Canada and internationally are discussed. Short-term deliverables have occurred, along with considerable promise of impacts in the longer term. The creation of synergy, networking, capacity building, engagement of people with lived experience, and economies of scale have contributed to the considerable success of CCNA by all measures. CCNA is evidence that an organized “centrally-organized” approach to dementia research can catalyze important progress nationally and yield significant and measurable results.