Citations

Importance Classifying disease based on underlying pathobiology rather than clinical phenotype has implications for the development of biomarkers and therapy development. Observations Transactive response DNA-binding protein 43 kDa (TDP-43) pathology is observed across a range of clinically defined neurodegenerative disorders including limbic predominant age-related encephalopathy (LATE), most cases of amyotrophic lateral sclerosis (ALS), inclusion body myositis, multisystem proteinopathy, and approximately half the cases of frontotemporal dementia (FTD). Despite this shared biology, the current nosology for these neurodegenerative disorders is based on their distinct clinical phenotypes. An alternative approach recognizes the central role of TDP-43 pathology in disease pathogenesis, reserving the use of clinical terms like ALS, FTD, or LATE to describe phenotypic manifestations of underlying pathobiology. This approach also recognizes the converging biomarker and neuropathological data indicating that pathology begins presymptomatically, before the overt clinical manifestations of disease appear. Conclusions and Relevance In proposing a pathobiological definition of disease, the goal is to provide a road map for developing biomarkers that accurately reflect the underlying pathobiology of disease and for advancing therapeutic candidates that effectively target fundamental disease mechanisms.

Background Systemic corticosteroids (SCS) remain the cornerstone of eosinophilic granulomatosis with polyangiitis (EGPA) and hypereosinophilic syndrome (HES) therapy but are associated with substantial toxicity. Mepolizumab, an anti-interleukin-5 biologic, has demonstrated steroid-sparing effects in addition to EGPA and HES disease control, yet evidence of reductions in SCS-related complications is limited. Methods This retrospective cohort study used US claims data from the Komodo Research Database to quantify SCS-related complications, healthcare resource utilization, and costs in patients with EGPA or HES treated with mepolizumab vs. chronic SCS. Patients were stratified as mepolizumab (300 mg) or chronic SCS users (≥6 months continuous SCS use with >7.5 mg/day for EGPA and >10 mg/day for HES) based on treatment received during a 6-month landmark period post-index (first claim of mepolizumab or SCS). An inverse probability of treatment weighting approach was applied to the cohorts to minimize confounding. Outcomes were assessed up to 12 months post-landmark. Results 578 patients with EGPA (305 mepolizumab users vs. 273 chronic SCS users) and 272 patients with HES (161 vs. 111) were included. In patients with EGPA, rates of any SCS-related complications were 39% lower with mepolizumab compared with chronic SCS (rate ratio [95% confidence interval]: 0.61 [0.49, 0.76], p < 0.001). Mepolizumab users had 57% lower rate of all-cause hospitalizations (0.43 [0.27, 0.69], p < 0.001), and lower outpatient and emergency department (ED) visits vs. chronic SCS. Patients with HES had significantly lower rates of any SCS-related complications with mepolizumab vs. chronic SCS (0.59 [0.42, 0.82], p = 0.002). Mepolizumab users had 66% lower rates of all-cause hospitalizations (0.34 [0.17, 0.65], p = 0.001), and lower outpatient visits relative to chronic SCS users. In EGPA and HES SCS-complication-related hospitalizations and ED visits were lower with mepolizumab compared with chronic SCS. SCS-complication-related medical costs per patient per year were $16,194 ( p = 0.008) and $3,976 ( p = 0.676) lower, in the mepolizumab vs. chronic SCS cohort of patients with EGPA and HES, respectively. Conclusion This first of its kind study suggests that real-world mepolizumab use is associated with reduced SCS-related complications in EGPA and HES, reinforcing its clinical and economic steroid-sparing benefits in reducing healthcare burden among EGPA and HES populations.

Abstract Introduction: The intricate cellular structure is a constantly evolving environment where cells interact, communicate, and adapt to their surroundings. The fate, role, and actions of each cell are shaped by its specific location within the tissue. Therefore, studying the spatial proteome is crucial for gaining a deeper understanding of physiological or pathological processes. With significant advancements in microscopy and mass spectrometry, it has become feasible to explore the cellular proteome at low or even single cell numbers. However, spatial proteomics is an evolving field with scope for newer sample preparation and data analysis techniques. Our objective was to develop a sample preparation workflow for analyzing proteome from limited cells extracted from formalin fixed paraffin embedded (FFPE) tissue using laser capture microdissection (LCM). Methods: One hundred cells from the FFPE normal colon tissue were extracted by LCM and collected into an AFA-compatible 96-well plate. Decrosslinking was performed at 900C for 50 minutes followed by lysis in a buffer composed of 100 mM triethylammonium bicarbonate and 0.1% n-dodecyl-β-D-maltoside using adaptive focused acoustic (AFA) energetics in scanning mode for 5 minutes. After lysis, the proteins were digested using Trypsin/Lys-C mix using AFA energetics for 1 hour. The resulting peptides were acidified with trifluoroacetic acid and analyzed on a timsTOF Ultra 2 mass spectrometer coupled to a nanoElute 2 liquid chromatography system using a 15 cm (75 μm) IonOpticks column, in DDA-PASEF mode for a total run time of 34 minutes. The data were searched against the UniProt Human Reviewed protein database using MSFragger. Results: We utilized AFA energetics to extract and digest proteins from a limited number of cells isolated from FFPE tissue using LCM. This experiment was conducted with technical replicates to ensure accuracy and reliability. A total of 3,723 proteins were identified across the three replicates, with replicate 1 identifying 3,895 proteins, replicate 2 identifying 3,754 proteins, and replicate 3 identifying 3,519 proteins. In total, we identified 21,005 peptides, with replicate 1 identifying 22,896 peptides, replicate 2 identifying 22,377 peptides, and replicate 3 identifying 17,741 peptides from 100 colon cells. Notably, we observed a 70% overlap in the proteins identified across the three replicates, which highlights the reproducibility of the approach. Conclusion: The entire sample preparation process, after cell collection, was completed within a concise four-hour timeframe, demonstrating the efficiency of this methodology for analyzing protein profiles from small cell populations. Further, this approach is highly adaptable to automation and high-throughput applications. Its adaptability and scalability make it a promising strategy for spatial proteomics applications. Citation Format: Dong-Gi Mun, Kiran Mangalaparthi, Daigo Gunji, Amy J. French, Raghavendra Pasupuleti, Cristine Charlesworth, Sameer Vasantgadkar, DEB BHATTACHARYYA, Akhilesh Pandey. An AFA assisted workflow for mass-spectrometry based proteomics of FFPE samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7657.

Abstract Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework’s utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.

This study proposes a novel two-dimensional Empirical Mode Decomposition (2D EMD)-based deep learning framework to enhance model performance in multi-class image classification tasks and potential early detection of diseases in healthcare using medical imaging. To validate this approach, we apply it to gastrointestinal (GI) endoscopic image classification using the publicly available Kvasir dataset, which contains eight GI image classes with 1000 images each. The proposed 2D EMD-based design procedure decomposes images into a full set of intrinsic mode functions (IMFs) to enhance image features beneficial for AI model development. Integrating 2D EMD into a deep learning pipeline, we evaluate its impact on four popular models (ResNet152, VGG19bn, MobileNetV3L, and SwinTransformerV2S). The results demonstrate that subtracting IMFs from the original image consistently improves accuracy, F1-score, and AUC for all models. The study reveals a notable enhancement in model performance, with an approximately 9% increase in accuracy compared to counterparts without EMD integration for ResNet152. Similarly, there is an increase of around 18% for VGG19L, 3% for MobileNetV3L, and 8% for SwinTransformerV2. Additionally, explainable AI (XAI) techniques, such as Grad-CAM, illustrate that the model focuses on GI regions for predictions. This study highlights the efficacy of 2D EMD in enhancing deep learning model performance for GI image classification, with potential applications in other domains.

Accurate teeth segmentation and orientation are fundamental in modern oral healthcare, enabling precise diagnosis, treatment planning, and dental implant design. In this study, we present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep-learning techniques. We built an end-to-end instance segmentation network that uses an encoder–decoder architecture reinforced with grid-aware attention gates along the skip connections. We introduce oriented bounding box (OBB) generation through principal component analysis (PCA) for precise tooth orientation estimation. Evaluating our approach on the publicly available DNS dataset, comprising 543 panoramic X-ray images, we achieve the highest Intersection-over-Union (IoU) score of 82.43% and a Dice Similarity Coefficient (DSC) score of 90.37% among compared models in teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU) score of 82.82%. We also conduct detailed analyses of individual tooth labels and categorical performance, shedding light on strengths and weaknesses. The proposed model’s accuracy and versatility offer promising prospects for improving dental diagnoses, treatment planning, and personalized healthcare in the oral domain.

Accurate analysis of medical videos remains a major challenge in deep learning (DL) due to the need for effective spatiotemporal feature mapping that captures both spatial detail and temporal dynamics. Despite advances in DL, most existing models in medical AI focus on static images, overlooking critical temporal cues present in video data. To bridge this gap, a novel DL-based framework is proposed for spatiotemporal feature extraction from medical video sequences. As a feasibility use case, this study focuses on gastrointestinal (GI) endoscopic video classification. A 3D convolutional neural network (CNN) is developed to classify upper and lower GI endoscopic videos using the hyperKvasir dataset, which contains 314 lower and 60 upper GI videos. To address data imbalance, 60 matched pairs of videos are randomly selected across 20 experimental runs. Videos are resized to 224 × 224, and the 3D CNN captures spatiotemporal information. A 3D version of the parallel spatial and channel squeeze-and-excitation (P-scSE) is implemented, and a new block called the residual with parallel attention (RPA) block is proposed by combining P-scSE3D with a residual block. To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. The model achieves an average accuracy of 0.933, precision of 0.932, recall of 0.944, F1-score of 0.935, and AUC of 0.933. It is also observed that the integration of P-scSE3D increased the F1-score by 7%. This preliminary work opens avenues for exploring various GI endoscopic video-based prospective studies.

Importance SARS-CoV-2 infection has been linked to neurotoxic effects and cognitive deficits. Objective To determine whether decreases in cognitive function were accelerated after SARS-CoV-2 infection compared with individuals not infected. Design, Setting, and Participants Multicenter, prospective cohort study from 2016 to 2022 among 3525 participants alive on March 1, 2020, and enrolled in The Atherosclerosis Risk in Communities (ARIC) study and the Collaborative Cohort of Cohorts for COVID-19 Research study who completed a prepandemic cognitive assessment and a pandemic-era assessment of SARS-CoV-2 infection. Final analyses performed in November 2024. Exposure SARS-CoV-2 infection determined via self-report of a positive SARS-CoV-2 test or health care professional diagnosis of COVID-19, a positive SARS-CoV-2 antinucleocapsid antibody response, or presence of an administrative code for COVID-19 on medical records. Main outcomes and measures A neuropsychological battery assessed multiple cognitive domains, and a cocalibrated confirmatory factor analysis generated factor scores for global cognitive function. The primary outcome was the rate of excess change in cognitive function. Results The 3525 eligible participants had a mean (SD) age of 80.8 (4.7) years, 2085 (59.1%) were female, 752 (21.4%) were Black, and 2773 (78.6%) were White. SARS-CoV-2 infection was detected among 307 participants (8.7%), 103 of whom (33.6%) were hospitalized. Among uninfected participants, the mean annualized change in cognitive function was −0.09 (95% CI, −0.13 to −0.04). Compared with this rate, change was faster (β = −0.06; 95% CI, −0.09 to −0.02) among participants hospitalized for infection, but not different from participants who were infected but not hospitalized (β = 0.00; 95% CI, −0.02 to 0.03). The association among participants hospitalized for infection was evident in the cognitive domains of memory and executive function, but not language. Conclusions and relevance This cohort study of older participants found accelerated decreases in cognition among individuals hospitalized for SARS-CoV-2 infection, but not nonhospitalized infection, in comparison with individuals not yet infected.

OBJECTIVE It is not clear whether smoking impacts patient-reported outcomes (PROs) in patients with cervical spondylotic myelopathy (CSM). The aim of this study was to explore the impact of smoking status on baseline symptom severity and the rate of achieving satisfaction and the minimal clinically important differences (MCIDs) for PROs in patients with CSM. METHODS This study was an analysis of the prospective Quality Outcomes Database CSM module. Patients aged ≥ 18 years diagnosed with primary CSM who underwent elective surgery were included. PROs (visual analog scale [VAS] neck and arm pain, Neck Disability Index [NDI], modified Japanese Orthopaedic Association [mJOA], and EQ-5D scores and North American Spine Society [NASS] patient satisfaction) were collected at baseline and 24 months of follow-up. RESULTS Of the 1141 patients with CSM, 202 (17.7%) were smokers and 939 (82.3%) were nonsmokers. Compared with the nonsmokers, smokers were younger (56.3 ± 11.3 years vs 61.5 ± 11.7 years, p < 0.01) and had a lower BMI (29.3 ± 6.8 vs 30.3 ± 6.3, p = 0.04). A higher proportion of smokers had depression, anxiety, and chronic obstructive pulmonary disease (all p < 0.01). At baseline, smokers had worse pain (VAS neck pain score: 6.0 ± 3.2 vs 5.1 ± 3.3; VAS arm pain score: 5.7 ± 3.2 vs 4.7 ± 3.5), disability (NDI score: 45.2 ± 20.0 vs 37.1 ± 20.6), myelopathy (mJOA score: 11.5 ± 2.9 vs 12.2 ± 2.8), and quality of life (EQ-5D score: 0.51 ± 0.23 vs 0.57 ± 0.22) (all p < 0.01). At the 24-month follow-up, a higher proportion of smokers achieved the MCID in mJOA (69.4% vs 56.6%, p < 0.01) compared with nonsmokers. However, after accounting for significant covariates, there was no significant difference in the baseline disease severity. There was also no difference between the PROs at the 24-month follow-up. The two groups also achieved similar MCIDs for most PROs and were similarly satisfied (NASS score of 1 or 2: smoker 81.2% vs nonsmoker 84.6%, p = 0.29) 24 months after surgery. CONCLUSIONS Smokers and nonsmokers with CSM had similar baseline disease severity and 24-month PROs. They also achieved similar rates of MCIDs for PROs and satisfaction after surgery. Numerous previous studies have documented the association between tobacco usage and inferior clinical outcomes after spine surgery. However, in the context of severe and debilitating spinal disorders such as cervical myelopathy, meaningful and impactful improvements are still seen in properly selected patients.