Citations

Abstract Background/Purpose Diagnosing wrist ligament injuries is challenging; early detection and treatment are important to prevent osteoarthritis progression. Interosseous proximity maps, a proxy measure for joint space, can be generated from volumetric imaging data and may provide important information about wrist health. Artificial intelligence (AI) could enhance accuracy of noninvasive diagnosis based on imaging-derived metrics. This work demonstrates feasibility of AI training using synthetic proximity map data generated from finite element models (FEMs). Methods Personalized wrist FEMs for two asymptomatic participants were created from four-dimensional computed tomography-derived anatomic and kinematic data. Monte Carlo sampling varied 22 ligament material properties and simulated 7,500 unique injury scenarios generating 9,000,000 labeled red, green, and blue (RGB) images of interosseous proximity vector fields from FEM-derived motions. Images were associated with 17 descriptive metrics, including gross wrist angles and bone surface pairs, and used to develop mixed-input convolutional neural networks (CNNs). Model performance was evaluated for identifying specific ligament injuries. Results Average area under receiver operating characteristic curve (AUROC) for CNNs was 0.757 across all injury types and kinematics. In a subset with clinically-relevant functional angles, the average AUROC was 0.824. Best-performing individual ligament AUROCs ranged from 0.807 to 0.999. Sensitivities and specificities exceeded 0.99 for some ligament injury simulations under specific wrist angles and bone surface pairs. Conclusion This study demonstrates the feasibility of using synthetic data from FEMs to train AI models for classifying wrist ligament injuries. Proximity-based RGB images may be a relevant biomarker of ligamentous injury.

53BP1 nuclear bodies are dynamic structures with properties resembling biomolecular condensates, but the molecular determinants that govern 53BP1 higher-order assembly at DNA double-strand breaks (DSBs) remain to be established. Here, we show that 53BP1 condensation is stimulated by phosphatidylinositol 3-phosphate (PI(3)P) in vitro through a highly specific interaction with its C-terminal tandem BRCT domain (tBRCT). Consequently, mutational inactivation of the 53BP1 tBRCT domain compromised PI(3)P binding and suppressed 53BP1 optodroplet formation in vivo. We further show that rapid 53BP1 clustering following DNA damage precedes its stable assembly on DSB-flanking chromatin, requires its tBRCT, and is suppressed by sequestration of nuclear PI(3)P. Taken together, our findings identify PI(3)P binding as a mechanism that promotes the formation and maturation of 53BP1 condensate-like assemblies on damaged chromatin.

Abstract Skin biopsies analyzed with α-synuclein seed amplification assays (SAAs) are a simple way to clinically interrogate the presence of α-synuclein aggregates. We determined the accuracy of skin biopsy SAA in predicting the clinical diagnoses of Parkinson disease (PD), PD with dementia (PDD) and dementia with Lewy bodies (DLB). Blinded SAAs were performed in 3 independent laboratories. Subjects diagnosed with PD, PDD and DLB were analyzed together as group 1, clinically unaffected subjects as group 2 and those with risk factors for LBD as group 3. Punch biopsies were taken from the posterior neck and analyzed by the 3 labs in 6 separate SAAs. Pairwise agreement between labs and assays ranged from excellent (kappa 0.82) to moderate (kappa 0.40-0.68). Sensitivity across assays ranged between 50.0% and 61.3%; specificity ranged between 68.6% and 100%. Comparisons of group 1 vs group 2 produced the greatest specificities, between 77.3% and 100%. In 17 cases that subsequently came to autopsy, 93% of SAAs were positive in those at the neocortical LBD stage but in only <10% of those at lower stages. Skin biopsy α-synuclein SAA may be useful as a diagnostic and progression biomarker in Lewy body dementia clinical trials.

Abstract Lichen Planopilaris (LPP) is a lymphocyte-mediated scarring alopecia characterized by progressive follicular destruction and fibrosis. In this clinical trial, patients with biopsy proven LPP were treated with deucravacitinib (an oral inhibitor of tyrosine kinase 2 (TYK2)) 6 mg BID for 24 weeks (NCT-06091956). Bulk and single-cell RNA sequencing was performed on paired pre- and post-treatment scalp biopsies from baseline and week 4. Patients (N=10) demonstrated improvements in PGA (88.9%, p=0.008), LPPAI (−2.3 points, SD 1.1, p=0.002) and Skindex-16 (−21.0 points, SD 22.1, p=0.014) scores at week 24. Bulk transcriptomic analysis of untreated LPP revealed upregulation of type I Interferon (IFN)-stimulated genes and pathways related to inflammation, immune activation, keratinization, and extracellular matrix remodelling, with downregulation of immune and inflammatory pathways following treatment. Single-cell RNA-seq of LPP was characterized by enrichment of CD8+GZMK+ T cells which showed downregulation of T-cell receptor signaling as well as antiviral pathways with treatment. Basal keratinocytes exhibited reduced cytokine and interferon signaling and decreased communication with NK cells following treatment. CCL19+ fibroblasts were prominent in untreated disease was attenuated after treatment, with downregulation of type I IFN signaling. Selective TYK2 inhibition with deucravacitinib effectively suppresses these inflammatory circuits in LPP and represents a promising therapeutic strategy.

In this 5-year follow-up from the CheckMate 743 study in patients with unresectable pleural mesothelioma (PM), we evaluated updated efficacy and safety outcomes and biomarkers and performed treatment-switching analyses with first-line nivolumab plus ipilimumab versus chemotherapy. With a median follow-up of 66.8 months, nivolumab plus ipilimumab demonstrated continued overall survival (OS) benefit versus chemotherapy in all randomly assigned patients (5-year OS rates, 14% v 6%; hazard ratio [HR], 0.74 [95% CI, 0.62 to 0.88]); similar benefit was observed regardless of tumor histology. Of biomarker-evaluable patients treated with nivolumab plus ipilimumab (n = 242), high baseline monocytic myeloid-derived suppressor cell (M-MDSC) levels correlated with worse OS versus low M-MDSC levels (HR, 1.25 [95% CI, 1.09 to 1.43]). After adjusting for 24% of patients in the chemotherapy arm who received subsequent immunotherapy, nivolumab plus ipilimumab demonstrated continued OS benefit versus chemotherapy (HR, 0.64 [95% CI, 0.53 to 0.78]). No new safety signals were observed. These results demonstrate long-term, durable clinical benefit with nivolumab plus ipilimumab versus chemotherapy, which continued to be preserved even after treatment-switching adjustment in the chemotherapy arm. Exploratory analyses suggested greater benefit with nivolumab plus ipilimumab in the low M-MDSC subgroup. These results further support first-line nivolumab plus ipilimumab as standard of care for unresectable PM.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.