Citations

ABSTRACT Background Long‐term health effects of thymectomy in adults remain uncertain. Previous reports suggested associations with increased mortality, cancer incidence, and autoimmune disease, although these findings may reflect differences in patient selection, surgical indication, or follow‐up duration. We aimed to examine long‐term outcomes following adult thymectomy. Methods We performed a propensity score‐matched retrospective study of patients who underwent thymectomy or comparator cardiothoracic procedures, with follow‐up extending to 10 years. Primary outcomes were all‐cause mortality, new‐onset malignancy, and autoimmune disease. Findings were compared with those reported in a recent study that has raised caution regarding thymic excision. Results We identified 425 patients who underwent thymectomy and 5401 controls who underwent coronary artery bypass or valve surgery. After matching, 398 patients remained in each group. Thymectomy was not associated with increased mortality at 5 years ( HR = 0.63; 95% CI , 0.38–1.04) or 10 years ( HR = 0.78; 95% CI , 0.54–1.13). Excluding reclassified thymic cancers, 5‐year extrathymic malignancy incidence was 2.3% versus 1.8% ( p = 0.54); no significant differences were observed in immune‐related disease. Mortality was higher among patients with thymic malignancy. Among MG patients, only one event occurred, precluding reliable estimation. Conclusions Thymectomy was not associated with excess mortality or malignancy at 10 years compared with matched cardiothoracic controls. Indication‐stratified analyses within the thymectomy cohort showed higher mortality for thymic malignancy, with insufficient events to assess outcomes in MG patients. These findings do not support a generalized survival penalty from thymectomy, though caution is warranted for incidental thymic removal.

Abstract Epigenetic inheritance relies on the precise recycling of parental histones during DNA replication. The N-terminal region of Pol1, the catalytic subunit of DNA polymerase α, has previously been implicated in chaperoning parental histone recycling. In this study, we identify a single amino acid mutation in the C-terminal region of Saccharomyces cerevisiae Pol1 (pol1-F1396S) that destabilizes the protein and disrupts the transfer of parental histones to the lagging strand during DNA replication. As a primary consequence, pol1-F1396S cells exhibit decreased chromatin stability. Furthermore, we demonstrate that the cellular abundance—or dosage—of all three DNA polymerases (Pol α, Pol δ, and Pol ε) influences the distribution of parental histones between the leading and lagging strands, revealing a dosage-sensitive mechanism of epigenetic regulation during DNA replication.

Abstract Background There is limited data on immune checkpoint inhibitor (ICI) use in patients with non-small cell lung cancer (NSCLC) and known pericardial effusions. Methods This international multi-center observational retrospective study included patients with advanced NSCLC with or without pericardial effusions receiving ICIs. Immune-related adverse events (irAEs) were graded per CTCAE v5.0. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier method. Cox proportional hazards multivariable models assessed the association between survival outcomes and clinicopathologic variables, and an inverse probability of treatment weighting (IPTW) model compared outcomes between the two groups. Results A total of 1248 patients with advanced NSCLC treated with ICIs across 13 institutions were included. Of these, 85 patients (6.8%) had pre-existing pericardial effusions. Compared to controls, patients with pericardial effusions were younger (median age 63 vs 67 years, P = .007), diagnosed more frequently with PD-L1 positive tumors (96% vs 76%, P < .001), and had a higher median number of baseline metastatic sites (3 vs 1, P < .001). IrAEs were similar between both groups (42% vs 38%), and no cardiac irAEs occurred in patients with pericardial effusions. PFS (median 5.6 vs 4.6 months, P = .3) and OS (median 10.2 vs 14.7 months, P = .50) were not statistically different between patients with pericardial effusions and the controls, respectively. In both multivariable and IPTW models, pericardial effusions were not associated with PFS or OS. Conclusions Among patients with NSCLC and pre-existing pericardial effusions, ICIs demonstrate meaningful clinical benefit similar to those without effusions, with no new cardiac or severe toxicities.

Automated diagnostic report generation lies at the core of clinical diagnosis and can alleviate clinician shortages. Existing diagnostic report generation methods have two major limitations: they rely on unimodal inputs (e.g., images), ignoring textual biomarkers like medical history, and lack proactive dialogue capabilities to elicit personalized clinical information. To address those issues, we propose a ProActive Multimodal Agentic (PAMA) system, which performs comprehensive disease analysis by examining biomarkers in diverse sources, including multi-view medical images, medical histories, and diagnostic conversations. Built upon a knowledge graph and recommendation-based dialogue architecture, PAMA actively initiates adaptive, multi-turn conversations with patients, which is integrated with visual data for robust and reliable report generation. Specifically, PAMA actively generates adaptive multi-turn questions to collect clinically relevant background information, and then fuses the resulting dialogue context with visual representations for robust diagnostic report generation. We validate our approach on two real-world benchmark datasets, MIMIC-CXR and IU-Xray, through extensive quantitative evaluations and comparisons with state-of-the-art baselines. Furthermore, we conduct user studies to assess the realism and clinical quality of the generated reports. Finally, we present real-world case studies to examine the performance of our system across diverse scenarios, demonstrating its robustness on scalability, complexity, and data variability.

Abstract Deep mutational scanning (DMS) has proven effective for mapping protein–protein interactions (PPIs), but it cannot provide complete coverage of the mutation landscape, particularly for multi‐mutant variants. To address this limitation, we trained machine‐learning (ML) models on previously generated DMS data for a stabilized amyloid precursor protein inhibitor (APPI) binding to either of two serine proteases, mesotrypsin and kallikrein‐6 (KLK6), which are implicated in various human disorders. We combined the models to accurately predict the binding selectivity of APPI variants, including double‐mutant variants, for the two serine proteases. We achieved a Pearson correlation of 0.937 between predicted log 2 selectivity enrichment ratios and DMS‐derived values. We further validated the predictions of our combined model by yeast‐surface‐display measurements and inhibition assays of purified APPI variants and revealed epistatic interactions that shape protease selectivity. Guided by binding selectivity predictions, we identified highly selective APPI variants, including the most selective mesotrypsin inhibitor reported to date. Together, these findings support the use of DMS and ML as a framework for predicting PPI selectivity and prioritizing selective therapeutic protein variants.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Background Lysosomal dysfunction plays a crucial role in the pathogenesis of Parkinson’s disease (PD), particularly among GBA1 mutation carriers. Beyond GBA1 , genes such as TMEM175 , SCARB2 , and CTSB identified in genome-wide association studies (GWAS) are also implicated in lysosomal pathways contributing to PD risk, although their functional effects in patients remain unclear. Proteins encoded by these lysosome-related genes have been explored as potential therapeutic targets in experimental models. Biomarker profiles, including clinical measures, α-syn seeding activity, lysosomal proteins, and sphingolipids, may facilitate patient stratification and support therapeutic monitoring in future clinical trials. Aim The aim of this study is to investigate the impact of genetic variants of three lysosomal-related genes ( TMEM175 , SCARB2 , and CTSB ) on biomarker profiles in PD with and without GBA1 variants. Cross-sectional data from two German cohorts: the Tuebingen Parkinson Cohort (TUEPAC) and the DESCRIBE PD cohort of the German Center for Neurodegenerative Diseases were used as explorative cohorts, and data from Accelerating Medicines Partnership® Parkinson’s Disease (AMP-PD) were used as a validation cohort. The ultimate goal is to provide new data for patient stratification based on genetics, which might serve as a readout for target engagement and treatment efficiency assessment. Methods Three cohorts were analyzed: TUEPAC, DESCRIBE PD, and AMP-PD. TUEPAC and DESCRIBE PD were combined into a single German discovery cohort (TUEPAC-DESCRIBE-PD), while AMP-PD served as an independent validation cohort. Within each cohort, for subgroup analyses, PD patients were classified as the overall PD cohort (PD all ), and further stratified by GBA1 mutation status into PD patients without GBA1 mutations (PD GBA1 _wildtype ), and PD patients carrying GBA1 mutations (PD GBA1 ). We evaluated cognitive and motor function, as well as depression using the Montreal Cognitive Assessment (MoCA), Unified Parkinson Disease Rating Scale-part I II (UPDRS III), and Beck Depression Inventory-II(BDI-II) scales. Analyzed biomarkers included CSF α-syn seeding activity using seed amplification assay (SAA), CSF lysosomal protein levels of lysosomal integral membrane protein 2 (LIMP2), also known as SCARB2, cathepsin B (CTSB) and lysosome-associated membrane protein 2 (LAMP2), blood-based enzyme activity of the lysosomal glucocerebrosidase (GCase), and CSF sphingolipid profiles. PD patients carrying risk alleles in TMEM175 , SCARB2 , and CTSB were compared to non-carriers. Results Genotype-phenotype correlation analysis in TUEPAC-DESCRIBE-PD and AMP-PD revealed: (1) In PD all , the TMEM175 p.M393T risk variant was nominally associated with decreased cognitive function when adjusted for GBA1 mutation status in TUEPAC-DESCRIBE-PD; this association could not be replicated, although a similar trend was observed in the slightly smaller, but multicentric AMP-PD cohort; TMEM175 p.M393T was not significantly associated with BDI-I I or UPDRS-III scores in either cohort. (2) In PD GBA1 _wildtype , GCase activity was significantly lower in PD patients with SCARB2 rs6812193 risk allele in TUEPAC-DESCRIBE-PD, while a similar but non-significant trend was observed in AMP-PD; (3) In PD all , CSF levels of CTSB were nominally lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; in PD GBA1 _wildtype , LAMP2 was significantly lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; (4) In PD all , TMEM175 p.M393T risk allele was nominally associated with altered sphingolipid profiles across both TUEPAC-DESCRIBE-PD and AMP-PD cohorts. Conclusion These findings demonstrate that genetic variants in lysosomal-related genes ( TMEM175 , SCARB2 , and CTSB ) have a functional impact on biomarker profiles in PD patients. Integrating genetic characterization with biochemical profiling provides a framework for patient stratification and may serve as a translational strategy to monitor target engagement and evaluate treatment efficacy in future clinical trials.

Abstract Background An accurate discrete spot scanning (DSS) beam delivery time (BDT) model is essential, particularly as 4D dynamic dose accumulation calculations rely on machine‐specific time models for reliable interplay evaluation. Purpose To derive a BDT model for a synchrotron‐based Hitachi Probeat PBS proton system from oscilloscope measurements of key BDT structures, including multi‐energy extraction (MEE) characteristics. Methods Key BDT structures were characterized using oscilloscope measurements, supplemented by delivery log files where appropriate. Spot‐to‐spot scanning trajectories were visualized using a CROSSmini 2D strip ionization chamber (Liverage Biomedical Inc, Taiwan) via the dose‐driven continuous scanning (DDCS) mode. The MEE design and MEE charge recapture efficiencies were also characterized. In addition, the impact of the number of delivered spots on both the extractable fraction of charge and MEE recapture efficiency was investigated. Results An improved synchrotron‐based BDT model was derived that incorporates dead times between radiofrequency knockout (RFK) and high‐speed steering magnet (HSST) signals. Spot‐to‐spot movement follows a “hockey‐stick”‐like behavior where the effective spot‐to‐spot move time is governed by the slower axis. An energy‐dependent variation in the number of MEE layers deliverable per spill was also observed, with fewer MEE layers per spill (1–3) was observed in low and high energy ranges compared to the mid‐energy range (4–5), deviating from the nominal vendor value of five MEE layers per spill per block. A reduction in the extractable fraction of charge was observed with an increasing number of spots per spill, while increasing the number of delivered spots in the preceding layer significantly reduced the MEE recapture efficiency. Conclusion MEE characteristics should be incorporated into synchrotron‐based time models for accurate beam delivery modeling. The methodology presented here also provides a framework for centers seeking to derive machine‐specific delivery time models for their proton systems.