Citations

Background Frontotemporal dementia is an umbrella term that encompasses several clinical syndromes with impaired behavioral, language, and motor functions. These syndromes show considerable overlap in clinical features and imaging patterns. Therefore, there is a need to investigate the syndromic heterogeneity in FTD using unbiased data-driven approaches. Methods We used data-driven clustering analysis of structural magnetic resonance imaging (MRI) data on 400 patients with clinical FTD diagnoses [behavioral variant of frontotemporal dementia (bvFTD), semantic variant of primary progressive aphasia (svPPA), right temporal variant of frontotemporal dementia (rtvFTD), apraxia of speech with agrammatic aphasia (AOS-PAA), primary progressive apraxia of speech (PPAOS), progressive supranuclear palsy (PSP), corticobasal syndrome (CBS) and primary progressive aphasia who did not fit into the other diagnostic categories (PPA-other)]. MR images were w-scored relative to cognitively unimpaired individuals, and principal component analysis was performed. A clustering ensemble approach, including hierarchical algorithms, was applied to the MR-based principal components, and imaging and clinical characteristics of the clusters were investigated. Various numbers of clusters ( K = 2, 3, or 4) were evaluated. Results The K = 3 solution offered the most clinically meaningful separation of FTD syndromes. The first cluster captured mostly frontal MRI abnormalities related to the speech, language and behavioral clinical dimensions, including patients with AOS-PAA, PPAOS, PPA-other, and bvFTD. The second cluster captured mostly temporal abnormalities and included mainly patients with svPPA and rtvFTD, but also bvFTD, AOS-PAA, and PPA-other. The third cluster captured cortical and subcortical atrophy, particularly in the midbrain, and included atypical Parkinsonian syndromes, with all PSP and CBS patients captured in this cluster, as well as most PPAOS patients. Considerable overlap of clinical syndromes was noted across these clusters, whereby patients with AOS-PAA, svPPA, PPA-other, and bvFTD were captured in more than one cluster. Discussion Our findings highlight heterogeneity in FTD, which mainly exists along three axes: speech, language and behavioral deficits reflecting frontal atrophy, language deficits reflecting temporal atrophy, and motor and motor speech deficits reflecting mostly midbrain and subcortical atrophy, with cortical involvement.

Abstract INTRODUCTION : Despite expanding use of plasma biomarkers for Alzheimer's disease (AD), minimal examination has been undertaken in early‐onset AD (EOAD). Prior analyses assessed criterion and convergent validity of common plasma biomarkers in a well‐characterized sample with sporadic EOAD. METHODS : Plasma amyloid beta (Aβ) 42/40, tau phosphorylated at threonine 231 (p‐tau231), glial fibrillary acidic protein (GFAP), and neurofilament light change (NfL) levels were obtained for 189 EOAD, 52 early‐onset non‐AD (EOnonAD), and 83 cognitively normal (CN) participants. Diagnostic group differences, convergence with imaging biomarkers, and the capacity to predict specific cognitive domains were investigated. RESULTS : After controlling for cognitive status, EOAD participants exhibited more pathologic levels of Aβ42/40, p‐tau231, and GFAP relative to EOnonAD participants. However, NfL displayed the greatest sensitivity to non‐amnestic cognitive impairment across the sample. DISCUSSION : These results establish criterion and convergent validity of plasma biomarkers in sporadic EOAD and highlight complementary roles that AD‐specific and non‐specific plasma markers may play across the course of AD care.

Abstract Background Coccidioidomycosis is the most prevalent endemic mycosis in the United States. Diagnosis of coccidioidomycosis can be challenging due to limitations of conventional diagnostics. Detection of fungal plasma cell-free DNA (cfDNA) is a novel noninvasive testing modality for the diagnosis of invasive fungal disease. In this study, we characterized the performance of Coccidioides plasma cfDNA polymerase chain reaction (PCR). Methods A retrospective study was conducted in patients with suspected coccidioidomycosis who underwent plasma cfDNA PCR testing and conventional fungal testing for Coccidioides immitis and Coccidioides posadasii. Patients were categorized per the EORTC/MSGERC case definitions as proven or probable for sensitivity analyses and no coccidioidomycosis for specificity analyses. Results Overall, 362 plasma samples from unique patients were included in this study. The overall sensitivity, specificity, and positive- and negative-predictive values of Coccidioides plasma cfDNA PCR at 0.04% prevalence were 57.5% (27/47; 95% CI, 42.2%–71.7%), 100% (315/315; 95% CI, 98.8%–100%), 100% (95% CI, 87.2%–100%), and 100% (95% CI, 100%–100%), respectively. The sensitivity was 33.3% (2/6; 95% CI, 4.3%–77.7%) in patients with pulmonary focal disease, 66.7% (14/21; 95% CI, 43.0%–85.4%; P≥.05) in patients with pulmonary multifocal disease, and 52.6% (10/19; 95% CI, 39.6%–77.9%; P≥.05) in patients with disseminated coccidioidomycosis. In patients with concurrent antigen results, the sensitivity of cfDNA PCR was 58.3% (14/24; 95% CI, 39.6%–77.9%) compared with 37.5% (9/24; 95% CI, 18.8%–59.4%; P≥.05) for antigen. Conclusions Coccidioides plasma cfDNA PCR is modestly sensitive but highly specific, which can be useful for rapid and noninvasive diagnosis of coccidioidomycosis.

Abstract Background Osteoarthritis is a leading cause of disability worldwide, and cell‐based treatments including adipose‐derived and bone marrow aspirate have been sought by the lay and medical community as treatment options. Objective To perform a scoping review of published literature on cell‐based injections allowed by the U.S. Food and Drug Administration for the treatment of osteoarthritis to synthesize existing evidence and identify research gaps for future evaluation. Methods A comprehensive search of five databases was executed from inception through January 2, 2025. Studies that met the inclusion criteria were original research studies written in English on Food and Drug Administration‐allowed cell‐based treatments in adults with osteoarthritis of any joint. Results The database search yielded 4257 unique records. After screening, 84 studies met the inclusion criteria, encompassing 9996 patients and a total of 10,508 procedures. The primary research study designs were cohort studies ( n = 62), focused on treatment of knee osteoarthritis ( n = 63), and described bone marrow aspirate ( n = 42) and adipose‐derived ( n = 42) treatments. Postprocedure monitoring ranged from 6 weeks to 5 years, with most studies ≤1 year ( n = 59). Patient‐reported outcomes were reported in 83 of 84 studies; few provided imaging outcomes including magnetic resonance imaging ( n = 9) or radiographs ( n = 2). Conclusion This review identified limited randomized controlled trials, limited studies outside the knee, limited description of cell‐based treatments, and treatment protocols, along with inconsistent patient‐reported outcomes limited to 1 year in most studies. We propose establishing reporting guidelines in research on cell‐based therapies.

PURPOSE Most oncology trials define superiority according to dichotomized P value thresholds, which are frequently misinterpreted. Posterior probability, however, directly estimates the probability of the hypothesis at hand. Here, we reanalyze a large collection of modern phase III trials and benchmark posterior probability versus the standard trial interpretation based on statistical significance. METHODS Outcomes from 194,129 patients were manually reconstructed from the primary end points of 230 phase III, superiority-design oncology trials. Posterior probabilities of treatment effect were then calculated across multiple priors and several effect sizes of clinical relevance, including minimum clinically important difference (MCID) defined as hazard ratio (HR) < 0.8 per ASCO criteria or HR < 0.64 per European Society of Medical Oncology (ESMO) criteria. RESULTS All trials interpreted as superior using P value thresholds had probabilities >90% for achieving at least marginal benefits (HR < 1). However, only 62% of positive trials (74/120) had >90% probabilities of achieving the ASCO MCID (HR < 0.8), even under an enthusiastic prior, including 70% of trials (57/82) leading to regulatory approval. Only 30% of positive trials (36/120) had >90% probability of achieving the ESMO MCID (HR < 0.64). Conversely, 24% of trials (26/110) interpreted as not superior had >90% probability of achieving marginal benefits (HR < 1), even under a skeptical prior. CONCLUSION Bayesian models, although often in agreement with statistical significance thresholds, add considerable unique interpretative value for a subset of phase III oncology trials. Posterior probability may provide a solution for overcoming the discrepancies between refuting the null hypothesis and detecting clinically relevant effects.

ABSTRACT Bispecific T cell engagers (BTEs) induce MHC‐independent cytotoxicity by bridging T cells to tumor cells via binding a T cell–activating receptor and a tumor‐associated antigen. BTEs have proven effective in hematologic malignancies and some solid tumors, yet their potential in glioblastoma (GBM) is largely unexplored. We developed a fully humanized BTE (hBTE) targeting interleukin‐13 receptor alpha 2 (IL13RA2), a tumor‐associated antigen widely expressed in GBM and associated with poor prognosis. In vitro, hBTE activated T cells and induced antigen‐dependent cytokine release and cytotoxicity against IL13RA2‐positive GBM cells. In vivo, hBTE showed robust target‐specific activity and markedly prolonged survival in primary and recurrent GBM xenograft models, without detectable off‐target local or systemic toxicity. Beyond GBM, hBTE also exhibited antitumor activity in IL13RA2‐expressing solid tumors, demonstrating selective tumor accumulation and therapeutic efficacy in models of breast cancer brain metastases and extracranial lung cancer. This work highlights the therapeutic potential of BTEs in IL13RA2‐expressing tumors and establishes a strong preclinical rationale for advancing hBTE therapy toward clinical translation in GBM and other tumors.

Background and Objectives: Roux-en-Y gastric bypass (RYGB) is one of the most commonly performed metabolic and bariatric surgeries worldwide. Marginal ulcers (MU) are a common complication following RYGB, yet their pathophysiology and the contributing risk factors to their development are not fully understood. Materials and Methods: This retrospective cohort study examined patients who underwent RYGB between January 2008 and December 2023, with 1 to 5 years of follow-up. Data collected included patient- and procedure-related risk factors, as well as postoperative MU events. Statistical analysis methods included the independent samples t-tests, multivariate regression, and Cox regression analyses. Results: Our final cohort included 2106 patients and was predominantly female (80.5%), with a mean age of 47.8 ± 12.1 years and body mass index (BMI) of 45.5 ± 7.5 kg/m2. MU occurred in 241 (11.4%) patients, with a mean time to occurrence of 4.5 ± 0.02 years. History of smoking (HR = 1.87, p < 0.001) and gastroesophageal reflux disease (HR = 2.36, p < 0.001) significantly increased hazard for MU, while proton pump inhibitor use (HR = 0.18, p < 0.001) was associated with reduced hazard. Aspirin exposure, regardless of dose and chronicity, did not impact MU. Conclusions: Our findings highlight the importance of preoperative assessment and counseling in patients planning to undergo RYGB. Patient-related factors should guide postoperative monitoring and prophylaxis of MU, as this remains a debated topic amongst experts.

Study objectives To evaluate and compare the performance of two proprietary frontier large language models (LLMs), ChatGPT-5 and Grok-4, on diagnostic reasoning and foundational knowledge tasks within the specialty domain of sleep medicine. Methods The models were evaluated on two tasks: case-based reasoning using 79 clinical vignettes from the AASM Case Book of Sleep Medicine and knowledge assessment using 897 multiple-choice questions (MCQs) from board review materials. For vignettes, final diagnosis was scored by concept-level exact match, and differential diagnosis (DDx) was scored on a fixed top-5 output using concept-level matching with synonym normalization to compute precision, recall, and F 1-score. MCQ performance was the proportion correct. Inter-model performance was compared using the Mann–Whitney U test. Results Both models achieved high accuracy for final diagnosis (92.4% for both; 95% CI 86.4, 98.4) and MCQs (ChatGPT-5: 93.0%; Grok-4: 92.8%). However, performance on generating a comprehensive differential diagnosis was suboptimal, with modest F 1-scores for both ChatGPT-5 (0.55 ± 0.20) and Grok-4 (0.59 ± 0.20). There were no statistically significant differences in performance between the two models across any metric ( p &gt; 0.05). Conclusions Frontier LLMs demonstrated high accuracy in sleep medicine tasks requiring knowledge recall and direct pattern recognition but showed more limited performance in complex clinical reasoning tasks such as generating a comprehensive differential diagnosis. These findings suggest that current general-purpose models may be more reliable for focused knowledge support than for broad hypothesis generation. Future studies should evaluate whether domain-adapted models or clinician-in-the-loop workflows can improve real-world diagnostic usefulness and safety.

Retrieval-augmented generation (RAG) is rapidly emerging as a transformative paradigm for large language models (LLMs), especially in high-stakes domains like oncology that demand precision, factual grounding, and up-to-date knowledge. By pairing LLMs with external knowledge repositories, RAG systems explicitly ground model outputs in relevant retrieved documents, helping to reduce hallucinations and ensure responses reflect current evidence. In oncology, where clinical knowledge evolves continually with new research and drug approvals, RAG offers a way to integrate the latest data (e.g., trial results, guidelines, genomic databases) into decision-making. This review synthesizes the technical foundations of RAG, including its architecture and key components, and examines current applications in oncology such as clinical decision support, patient education, radiology reporting, pathology analysis, and genomics-driven precision medicine. We highlight recent studies that demonstrate RAG’s potential—for instance, improving treatment recommendations by incorporating genetic profiles and literature, and enhancing diagnostic accuracy by integrating guidelines. We also discuss emerging developments like multimodal RAG (combining text with imaging or other data), ensemble model approaches, and new explainability tools that trace model outputs to sources. Finally, we critically analyze the limitations and challenges of deploying RAG in healthcare, including computational costs, retrieval errors, noise or conflicts in retrieved information, and ethical and regulatory considerations. While RAG-based systems show promise in augmenting oncologists’ expertise with timely knowledge, careful implementation, high-quality curation of knowledge bases, and human oversight will be crucial for safe and effective adoption in clinical practice.