Citations

Background. The VPS35 p.D620N variant causes autosomal dominant Parkinson's disease (PD) and has been shown to activate the LRRK2 kinase pathway, resulting in increased Rab substrate phosphorylation in peripheral immune cells and elevated urinary bis(monoacylglycero)phosphate (BMP) levels. Recently, a VPS35 variant of unknown significance (c.959C>T; p.A320V) was described in two late-onset sporadic PD patients. Methods. We ascertained a family from the Canary Islands in which six siblings were chronically exposed to high doses of pesticides. Three siblings developed levodopa-responsive, akinetic-rigid PD, while the other three remained unaffected. Whole-exome sequencing was performed in the three affected siblings. The frequency of the resulting candidate variant was assessed in 23,327 PD patients and 9,235 controls from four independent cohorts. Members of this pedigree and unrelated controls were assessed for LRRK2 kinase activity in monocytes and neutrophils and BMP levels in urine. Results. The three affected siblings were all heterozygous for p.A320V, whereas the three unaffected siblings did not carry the variant. In the combined PD case-control cohort, p.A320V was identified in six patients and one control. However, unlike p.D620N, heterozygous carrier status for p.A320V was not associated with increased LRRK2 kinase activity or elevated urine BMP levels. Conclusions. While VPS35 p.A320V co-segregated with PD in this family, it did not exhibit the characteristic LRRK2-associated biomarker signature observed in VPS35 p.D620N carriers. It is possible that p.A320V exerts a subtle effect on VPS35 function that was not captured by the assays performed and that chronic pesticide exposure contributed to disease penetrance in this pedigree.

Background Pericarditis may be associated with significant morbidity and mortality and can be a clinically challenging entity to diagnose. There is a clinical need for automated artificial intelligence (AI) algorithms to act as a screening aid to assist with the early and accurate detection of pericarditis. Methods We developed p-Cal, a browser-based pericarditis risk calculator that fuses the patient clinical data and 12-lead ECG image to estimate risk for pericarditis. The ECG encoder was designed based on a pretrained MedCLIP Vision Transformer and random forest classifier was used for the clinical tabular data. These two modalities were fused at the decision-level using a meta classifier. Model reasoning for both modalities was assessed using advanced explainable AI techniques—attention maps from the transformer model were used to interpret ECG images, while SHapley Additive exPlanations (SHAP) force plots were applied to analyse the tabular data. Results A total of 6508 patients (mean age 54.8±16.1 years, 50.9% female) were used for training and internal validation of the AI model, of whom 902 patients had a confirmed diagnosis of pericarditis (13.8%) and the remainder were normal controls. On 1302 hold-out test patients, the Fusion Model demonstrated the best discriminatory performance, achieving an area under the curve (AUC) of 0.89, which surpassed the ECG (AUC = 0.85) and the clinical data only models (AUC = 0.74) performance. On external validation in the Medical Information Mart for Intensive Care (MIMIC) Database, the fusion model achieved an AUC of 0.81. Conclusion A late fusion AI model integrating tabular clinical electronic medical record data and ECG signal has robust predictive capability for pericarditis as a screening tool.

Mesothelioma is a rare cancer that originates from the mesothelial surfaces of certain sites within the body. Pleural mesothelioma is the most common type and represents approximately 85% of mesotheliomas. The NCCN Guidelines for Mesothelioma: Pleural provide recommendations for evaluation and treatment in patients with pleural mesothelioma. The NCCN Guidelines will continue to be updated annually based on available clinical evidence and panel consensus.

BACKGROUND: Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy management, yet Doppler-based measurements are technically demanding and require expertise. The objective of this work was to develop a multi-view deep learning model capable of classifying LVOT obstruction (>20 mm Hg) using routine 2-dimensional echocardiographic windows without reliance on Doppler imaging. METHODS: We trained and externally validated a cross-attention-based video-to-video fusion framework that integrated EchoPrime-derived video representations from 3 standard transthoracic echocardiographic views to classify LVOT gradients. RESULTS: Training was performed on a derivation cohort (N=1833) from a tertiary care system in the United States, with model performance evaluated on an internally held-out test set (N=275) and a Korean external validation cohort (N=46). Single-view baselines showed limited discrimination (external area under the receiver operating curves, 0.47–0.70). Conversely, the domain-specific foundational model (EchoPrime) achieved superior single-view performance (area under the receiver operating curves, 0.75–0.80 internal; 0.79–0.83 external), highlighting the importance of echo-specific pretraining and temporal modeling. The proposed multi-view fusion further enhanced predictive performance, with the late fusion model reaching an area under the receiver operating curve of 0.84 on the external cohort with significant population-shift. CONCLUSIONS: These results suggest LVOT physiology is encoded in routine 2-dimensional imaging and can be leveraged for clinically relevant gradient classification without Doppler input. The proposed artificial intelligence-guided strategy demonstrates substantial cost savings compared with the screen-all approach. By integrating complementary spatial-temporal information across multiple views, our approach generalizes robustly across populations and may enable real-time decision support, extend LVOT assessment to portable or resource-limited settings, and complement Doppler-based evaluation for longitudinal hypertrophic cardiomyopathy management.

Background: Pulsatile tinnitus (PT) is frequently caused by venous sinus stenosis and may be associated with idiopathic intracranial hypertension (IIH). Venous sinus stenting (VSS) directly addresses venous outflow obstruction and has emerged as a therapeutic option; however, outcome data remain heterogeneous. We performed an updated systematic review and meta-analysis to evaluate the efficacy and durability of VSS for PT and to compare outcomes between IIH-associated and isolated PT. Methods: MEDLINE, Scopus, and Web of Science were searched from inception through December 2025 following PRISMA guidelines. Clinical studies reporting PT outcomes after VSS were included. Random-effects meta-analyses of proportions were conducted to estimate pooled rates of overall improvement, complete resolution, and recurrence. Prespecified subgroup analyses compared IIH versus isolated PT. Results: Thirty-two studies comprising 850 patients were included (641 with IIH; 209 with isolated PT). Venous sinus stenosis accounted for 90.3% of treated etiologies. The pooled rate of overall PT improvement was 89.8% (95% CI, 85.5-92.9). Complete resolution occurred in 87.1% (95% CI, 82.5-90.6). Recurrence at last follow-up was 10.9% (95% CI, 8.3-14.2). Improvement and complete resolution rates did not differ significantly between IIH and isolated PT. Recurrence was higher in IIH (12.0%) than in isolated PT (5.7%), without reaching statistical significance. Conclusions: VSS is associated with high rates of PT improvement and complete resolution with low recurrence. Recurrence trends appear higher in IIH, suggesting underlying pathophysiology may influence durability.

Glioblastoma is the most common form of brain cancer in adults and remains one of the most lethal forms of cancer. Treating glioblastoma is uniquely challenging due to its aggressive nature and its critical location, the brain, which must remain largely undamaged. Due to these difficulties, there is a lack of effective treatment options. Computer models simulating tumors and their response to therapy, known as digital twins, provide a promising new strategy. These model systems are mathematical equations that capture the biological knowledge of how cells interact during tumor growth and in response to treatment. By solving these equations, we can create virtual patients that provide accurate predictions of real patients’ tumor progression. This approach has the potential to improve outcomes by designing therapies for the specific needs of each patient, moving beyond the limitations of the “one-size-fits-all” approach that is currently used.

Background Lewy body disease (LBD) is a progressive neurodegenerative a-synucleinopathy, whereas isolated REM sleep behavior disorder (iRBD) is recognized as a prodromal stage of LBD. Although growing evidence implicates the gut–brain axis in neurodegeneration, the taxonomic and functional roles of the gut microbiome across the prodromal-to-symptomatic LBD continuum remain poorly defined. Methods Here, we performed shotgun metagenomic sequencing on stool samples from 25 patients with LBD (10 mild cognitive impairment due to LBD [MCI-LB] and 15 dementia with Lewy bodies [DLB]), 10 individuals with iRBD, and their household matched cohabitant controls to characterize disease-associated microbial alterations while minimizing environmental confounding. Results Despite no significant differences in global microbial diversity, we identified convergent shifts in microbial taxa, metabolic pathways, and gene families across disease stages. Both LBD and iRBD showed increased abundance of microbial taxa potentially associated with gut barrier disruption, as well as higher abundance of functional pathways related to lipopolysaccharide biosynthesis. LBD showed lower abundance of pathways related to complex carbohydrate fermentation, and both groups showed lower abundance of pathways associated with neurotransmitter-related metabolism. In particular, pathways and gene families associated with starch degradation were reduced in LBD, and those associated with histidine-to-glutamate/ GABA metabolism were reduced in both groups. Discussion These exploratory findings represent the first high-resolution, shotgun metagenomic characterization of gut microbiome alterations across the LBD continuum, highlighting functional patterns that may serve as candidate markers of disease progression in future longitudinal and mechanistic studies.

Abstract Genetic studies have significantly advanced our understanding of tauopathies, yet the genetic aetiology of Pick’s disease, a rare 3-Repeat tauopathy, remains unclear. The MAPT p.A152T variant has been identified as a risk factor for Alzheimer’s disease and progressive supranuclear palsy, but its role in Pick’s disease is unknown. In this study, we examined the prevalence of MAPT p.A152T in the largest series of neuropathologically confirmed Pick’s disease cases to date (n = 401). Through genotyping, we identified a single mutation carrier in the Pick’s disease cohort (minor allele frequency = 0.12%). We previously reported MAPT p.A152T at a 0.20% frequency in healthy controls (n = 2456), suggesting that it does not associate with 3-Repeat tauopathy risk. To further investigate the effect of the variant on MAPT transcript expression, we used bulk RNA sequencing in Alzheimer’s disease and progressive supranuclear palsy A152T mutation carriers. We did not detect significant differences in 4-Repeat tau levels, though preliminary trends may indicate more nuanced effects that need to be examined with long-read sequencing in a larger series. Overall, our study suggests that MAPT p.A152T does not increase Pick’s disease risk and may instead be linked to 4-Repeat or mixed tau pathologies, warranting further functional investigation.