Citations

Background: Particulated juvenile articular cartilage (PJAC) implantation has demonstrated promising early results in the treatment of symptomatic articular cartilage defects of the patella. However, some uncertainty exists regarding the stability of this cell-based technique in lesions that are not well contained or shouldered. Purpose: To compare clinical and magnetic resonance imaging (MRI) outcomes of PJAC treatment in shouldered versus unshouldered full-thickness cartilage defects of the patella. Study Design: Cohort study; Level of evidence, 3. Methods: A retrospective review of prospectively collected data from an institutional knee registry was conducted to identify patients treated with PJAC for full-thickness symptomatic patellar cartilage lesion between January 2009 and August 2017. Cartilage defects were graded arthroscopically according to the Outerbridge classification and characterized as shouldered or unshouldered. For the primary outcome, postoperative MRI studies were read by a musculoskeletal fellowship-trained radiologist who characterized the percentage of fill based on both coronal and sagittal images. Patient-reported outcome measures (PROMs) were obtained at baseline and 2-year follow-up. MRI and PROM results were compared between the shouldered and unshouldered cohorts. Results: A total of 64 knees in 60 patients (mean age, 26.3 ± 7.6 years) were identified, of which 62 (97%) knees underwent a concomitant patellar stabilization or offloading procedure. There were 32 (50%) shouldered and 32 (50%) unshouldered defects. On postoperative MRI, 68.8% of shouldered lesions demonstrated 67% to 100% fill, compared with 59.4% of unshouldered lesions; 12.5% of shouldered lesions and 15.6% of unshouldered lesions demonstrated 34% to 66% fill; and 18.8% of shouldered lesions and 25.0% of unshouldered lesions demonstrated 0% to 33% fill ( P = .604). At 2 years, significant improvements were observed in Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life (QOL) (23.1 to 65.3; P < .001), International Knee Documentation Committee (IKDC) (41.2 to 76.0; P < .001), KOOS Physical Function Shortform (PS) (34.8 to 13.9; P < .001), Kujala (52.0 to 87.5; P < .001), 12-Item Short Form Survey (SF-12) Physical Health (39.5 to 51.5; P < .001), Veterans Rand 12 Item Health Survey (VR-12) Physical Health (40.7 to 52.8; P < .001), Veterans Rand 6D (0.66 to 0.79; P < .001), SF-12 Mental Health (48.9 to 53.4; P = .015), and VR-12 Mental Health (49.4 to 54.0; P = .019) values. No significant change was observed in Pediatric Functional Activity Brief Scale score (9.1 to 10.3; P = .384). All PROMs were equivalent between shouldered and unshouldered defects at the 2-year follow-up ( P = .318-.980). There was a greater improvement in both KOOS PS (27.2 vs 10.7; P = .015) and Kujala (44.3 vs 26.9; P = .039) values from baseline to the 2-year follow-up in the shouldered group. Conclusion: PJAC implantation with concomitant patellar stabilization led to significantly improved PROMs for both shouldered and unshouldered patellar cartilage lesions over time. Additionally, minimal differences were observed between shouldered and unshouldered defects treated with PJAC at the 2-year follow-up.

ABSTRACTClinicians often repeat aquaporin‐4‐immunoglobulin G (AQP4‐IgG) testing in case of possible seroconversion. Compared with older, less sensitive immunofluorescence assays (IFA), cell‐based assays (CBA) offer higher sensitivity. This study assessed the frequency of seroconversion in a retrospective Cleveland Clinic cohort (2006–2024) of 451 patients with an initial negative AQP4‐IgG result who underwent serial testing. Seroconversion occurred in 4.3% (7/170) of patients initially tested by tissue IFA, but in none (0/263) of those tested by CBA. The lack of AQP4‐IgG seroconversion after a negative CBA, with only rare cases after a negative IFA, suggests that repeat AQP4‐IgG testing is low yield unless prior testing used older methods such as IFA.

Abstract INTRODUCTION Risk factors associated with sporadic non‐amnestic and early‐onset Alzheimer's disease (AD) remain underexamined. METHODS We investigated a large, clinically heterogeneous, AD cohort for frequencies of established risk factors (hypertension, hypercholesterolemia, diabetes mellitus) alongside novel factors (non–right‐handedness, learning disability, seizures, autoimmune disease). RESULTS Early‐onset AD possessed lower frequencies of established risk factors (hypertension, hypercholesterolemia, diabetes mellitus, all p < 0.001) and higher frequencies of novel factors (non–right‐handedness, learning disability, active seizure, all p < 0.001; remote seizure, p = 0.002; and autoimmune disease, p = 0.007). An age at onset < 70 maximally distinguished novel from typical factors. Principal component analysis loaded novel factors into two components, non–right‐handedness and learning disability versus seizure and autoimmune disease, which combined, resulted in an exponential decrease in age at onset from one factor alone. DISCUSSION Identifying novel factors enriched in early‐onset and non‐amnestic AD introduces new theories of AD susceptibility and phenotypic heterogeneity, with significant implications for disease prediction and treatment. Highlights We identified a suite of novel factors overrepresented in early‐onset and non‐amnestic AD. These factors can be broadly conceptualized as neurodevelopmental (non–right‐handedness and learning disability) and neural environmental (seizure and autoimmunity). The combination of these factors produced exponential decreases in AD age at onset, compared to each alone, supporting a new theoretical framework for understanding AD risk with implications for disease prediction, prevention, and therapeutic intervention.

Abstract Objectives Neurofilament light (NfL) is an established biofluid marker of neuroaxonal injury for neurological diseases. Several high-throughput and sensitive immunoassays have been developed to quantify NfL in blood and cerebrospinal fluid (CSF), facilitating the use of NfL as a biomarker in research and clinical practice. However, because of the lack of rigorous comparisons of assays, it has been difficult to determine whether data are comparable and whether assay performance differs. Here, we compared the performance of five NfL immunoassays. Methods To assess the five NfL immunoassays (Fujirebio, ProteinSimple, Quanterix, Roche and Siemens), we used pooled plasma or pooled CSF, as well as unique samples from 20 healthy controls and 20 individuals with El Escorial defined probable or definite amyotrophic lateral sclerosis (ALS), to evaluate precision, parallelism and/or bias. We also examined correlations between plasma and CSF NfL concentrations within and across assays and evaluated their ability to differentiate healthy controls from individuals with ALS. Results Four of the five assays demonstrated exemplary performance based on our analyses of precision and parallelism. Across the five assays, NfL concentrations were lower in plasma than in CSF, although they displayed a high degree of correlation. We noted bias across assays; plasma NfL concentrations were lowest for the Roche assay and highest for the ProteinSimple assay. In addition, all assays reliably distinguished healthy controls from individuals with ALS using plasma or CSF NfL. Conclusions Four NfL assays demonstrated similar analytic performance. Alongside performance, other factors such as costs, accessibility, usability, footprint, and intended use, should be considered.

Abstract Radiological protocol selection is a critical but time-consuming step in clinical workflow, requiring radiologists to match patient indications with the appropriate MRI or CT protocol. Manual selection can be prone to delays or potential errors, and automated approaches must contend with substantial data imbalance, site-specific variation, and evolving nomenclature. We investigated whether a large language model (LLM) can support reliable protocol selection at scale and whether retrieval-augmented generation (RAG) offers operational advantages over direct fine-tuning. Using 498,228 patient reports collected across three Mayo Clinic sites (Arizona, Florida, and Rochester) spanning six radiological divisions, we trained site-specific Llama 3.2 3B models for use with and without retrieval augmentation. Division-scoped Facebook AI Similarity Search (FAISS) indexes constructed from procedure and diagnosis text were used to supply contextual evidence in the RAG framework. Both fine-tuned and RAG-augmented models achieved strong performance across sites, with F1 scores of 0.88–0.90. RAG matched or modestly trailed direct fine-tuning overall but delivered consistent gains in specific divisions (e.g., musculoskeletal imaging). Importantly, the RAG model introduced abstention behavior (Not Enough Information), which concentrated in linguistically diverse divisions and provided an interpretable signal of uncertainty. These findings suggest that RAG-based models are viable for division-scoped protocol selection and offer practical advantages. Retrieval indexes can be refreshed far more easily and with fewer resources than retraining LLMs, enabling continual adaptation to evolving clinical workflows. Future prospective deployment will evaluate real-time accuracy, agreement with practitioners, and the role of abstention as a safety mechanism in clinical decision support.

ABSTRACT Objective Autosomal dominant progranulin ( GRN ) pathogenic variants are a genetic cause of frontotemporal lobar degeneration. Though clinical trials for GRN ‐related therapies are underway, there is an unmet need for biomarkers that can predict symptom onset and track disease progression. We previously showed that asymptomatic GRN variant carriers exhibit thalamocortical hyperconnectivity that increases with age, presumably as they are approaching symptom onset. Whether hyperconnectivity arises concomitantly with markers of neurodegeneration remains unclear. Methods Utilizing T1 and task‐free functional magnetic resonance imaging from 49 asymptomatic and 26 symptomatic GRN variant carriers, we determined the relationships between functional connectivity, as measured by voxelwise whole‐brain degree, and GRN ‐relevant markers of disease progression, including plasma neurofilament light chain concentrations, cerebrospinal fluid complement C1q and C3b protein levels, obsessive‐compulsive disorder symptom severity, and gray matter volume. Results Neurofilament light chain concentrations were associated with frontotemporoparietal and thalamic hyperconnectivity in asymptomatic GRN variant carriers and extensive regions of atrophy in symptomatic carriers. Complement levels were associated with regions of hyperconnectivity, but not gray matter volume, in symptomatic carriers. Obsessive‐compulsive disorder symptom severity was associated with hypoconnectivity across all GRN carriers. Asymptomatic carriers with thalamic hyperconnectivity tended to have lower gray matter volume in the bilateral insula and left lateral parietal cortex, early regions of atrophy in GRN ‐frontotemporal dementia. Interpretation In asymptomatic carriers, the co‐occurrence of hyperconnectivity, high neurofilament light chain, and low gray matter volume suggests that functional hyperconnectivity may portend the onset of clinical decline. These findings point toward hyperconnectivity as an indicator of approaching symptomatic onset.

Abstract Ovarian cancer is the deadliest gynecologic malignancy worldwide, with a 5-year overall survival rate of approximately 49%. Although complete resection is associated with the most favorable prognosis following primary debulking surgery, accurately assessing tumor resectability at diagnosis remains a major clinical challenge. We propose a decision support system (DSS) designed to predict residual tumor after primary debulking surgery, based on clinical and imaging data available at diagnosis. The system was developed and validated using a retrospective cohort of 465 patients with high-grade serous ovarian cancer, collected at the European Institute of Oncology in Milan, Italy. We developed a deep learning (DL) model that combines pre-trained Vision Transformer encoders with an attention mechanism and a classification head. When evaluated on an independent test cohort of 75 cases, our best-performing model achieved an area under the curve (AUC) of 0.80 (95% CI: 0.68–0.89) and a recall of 0.86 (95% CI: 0.71–0.97), demonstrating discriminatory ability with a particularly low rate of false negatives. Clinically, the model correctly identified 24 out of 28 patients (86%) with non-resectable disease, who would not have benefited from primary debulking surgery. Notably, within this subgroup, the model accurately predicted 93% (13 out of 14) of cases in which surgery was aborted intraoperatively due to unforeseen unresectable disease. These findings suggest that the model could have potential in preventing unnecessary and inappropriate surgical interventions. The proposed DSS is a fully automated, DL–based system for predicting tumor resectability at diagnosis, without the need for manual segmentation or expert evaluation of radiological features and structured clinical parameters. This approach could facilitate and accelerate personalized treatment planning in ovarian cancer. The work is part of the project Under-XAI: understanding ovarian cancer initiation and progression through explainable AI. Project code: PNRR-MAD-2022-12376574.

Technological innovations have improved many barriers in lung transplantation, but high rates of acute and chronic rejection still limit lung allograft survival. This may be explained by the unique environment of the lung. As a mucosal barrier organ, the lung is constantly exposed to the external environment, leading to unique immunological features that are not seen in other transplantable solid organ allografts such as hearts, kidneys, and livers. Thus, the higher rates of rejection and poor long-term survival of lung transplant recipients may stem from the global immunosuppression strategies that are indiscriminately used for all solid organ grafts. Data from our laboratory, and others, have demonstrated that the unique immunoregulatory pathways of the lung may require different strategies for long-term graft survival. For example, depletion of CD8 + T cells typically contributes to the acceptance of transplanted organs. However, these cells facilitate lung allograft acceptance through interferon gamma mediated nitric oxide production. Interestingly CD8 + T cells modify and polarize eosinophils to produce nitric oxide as a means of tolerance induction. Such eosinophils also function to maintain long-term lung allograft acceptance by interfering with humoral alloimmunity. For most other organs eosinophils are suspected to contribute to graft rejection. In this review, we aim to describe the cytokine pathways involved in lung allograft rejection or tolerance, contrast such pathways to those evident in other solid organs, and discuss the need for further studies that can be used to design rational methods for altering the cytokine environment to improve lung allograft survival.