Citations

Breathing is a physiological process that extends beyond gas exchange, linking neural, cardiovascular, glymphatic, and autonomic systems. This narrative review summarizes how breathing-based practices modulate physiological processes in the body and brain, emphasizing their applicability in clinical care. Regulating the pace and depth of breathing, particularly with relaxed exhalation, may engage vagal pathways that lower heart rate and blood pressure and promote a shift toward increased parasympathetic tone relative to sympathetic activity. Neuroimaging studies show that controlled breathing reduces activity in stress-related brain regions while enhancing networks supporting calm attention and emotional steadiness. Changes in breathing depth also influence cerebrospinal fluid movement, though direct evidence of waste clearance in humans is lacking. This review identifies populations requiring caution, including individuals with panic symptoms, significant cardiopulmonary disease, or pregnancy. Clinicians should distinguish between breathing-only and multimodal protocols when interpreting research findings, as combined interventions preclude attribution of benefit to breathing alone. Study design quality, particularly the use of active rather than waitlist controls, should inform how effect sizes are interpreted. Breathing practices are not a replacement for medical treatment but may represent accessible, low-cost adjuncts for stress reduction and cardiovascular regulation.

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.

Abstract Aims Transcatheter mitral valve replacement (TMVR) with the Tendyne Mitral Valve System is a treatment option for patients with severe symptomatic mitral regurgitation (MR) unsuitable for conventional mitral valve surgery or transcatheter edge-to-edge repair (TEER). This study sought to evaluate the safety and effectiveness of TMVR through 5-year follow-up. Methods and Results The Tendyne Expanded Clinical Study is a prospective, single-arm, multicentre study that enrolled patients between November 2014 and June 2020. The study enrolled 191 patients (mean age 74.1 ± 8.0 years, 62.8% male, 70.2% New York Heart Association Class (NYHA) class III/IV, 88.5% secondary MR), of whom 186 (97.4%) underwent TMVR with Tendyne. MR grade decreased from ≥3+ in 99.5% patients at baseline to grade ≤1+ in 95.3% at 5 years. In those patients surviving to 5 years (n = 49), durable symptomatic improvement was evidenced by 73.5% patients being in NYHA class I/II at 5 years. The Kansas City Cardiomyopathy Questionnaire overall summary score increased from 48.5 ± 22.5 points at baseline to 67.6 ± 21.5 points at 5 years. Serious adverse events that occurred through 5 years included life-threatening bleeding (11.5%), fatal bleeding (2.6%), renal insufficiency/failure (28.8%), endocarditis (7.3%), device thrombosis (5.8%), and new onset atrial fibrillation (13.1%). No structural device degeneration or device embolism occurred through 5 years. Conclusions The Tendyne TMVR was effective at achieving immediate and sustainable elimination of MR, which was associated with symptomatic improvement and absence of structural valve degeneration in a high-risk cohort over 5 years. These findings support TMVR with the Tendyne System as an alternative for patients with appropriate mitral valve anatomy and symptomatic secondary MR unsuitable for mitral surgery or TEER.

Importance Clinical trials in cardiovascular medicine aim to deliver high-quality evidence with greater efficiency, including smaller sample sizes and shorter timelines. The selection of sensitive and reliable end points is central to this goal. Right ventricular (RV) structure and function play a critical role in many cardiovascular conditions; however, RV-specific parameters remain underused and inconsistently applied in interventional clinical trials. In this article, the current and emerging role of RV-focused imaging and hemodynamic parameters as end points in clinical trials are explored and a comprehensive framework for their development, validation, and broader implementation is proposed. Observations Across multiple disease domains, RV-focused parameters derived from echocardiography, cardiac magnetic resonance imaging, computed tomography, and right-sided heart catheterization demonstrate potential value in assessing the therapeutic effects of new medicines or devices across a wide range of conditions including heart failure, cardiomyopathies, valve diseases, pulmonary hypertension, and congenital heart conditions. However, their use is inconsistent, and feasibility challenges are common, particularly in multicenter clinical trial settings. Key limitations include variability in imaging acquisition, incomplete data capture, lack of standardized protocols, and insufficient validation linking short-term RV changes to long-term clinical outcomes. Emerging tools such as artificial intelligence may improve reproducibility and efficiency, while collaborative, multidisciplinary efforts are essential for advancing end-point development. Conclusions and Relevance RV-specific end points hold substantial promise for enhancing the sensitivity and mechanistic insight of clinical trials but remain inadequately integrated into current practice. Optimal end-point selection requires balancing clinical relevance, analytical validity, and operational feasibility, with consideration of disease-specific pathophysiology and trial design. Standardization of acquisition, validation of clinically meaningful changes, and centralized data analysis are important next steps. Broader adoption of robust RV metrics supported by technological innovation and cross-sector collaboration has the potential to improve trial efficiency, accelerate therapeutic development, and ultimately enhance patient outcomes.

Background: A diagnosis of cancer creates a stressful situation owing to the uncertainties related to the thought of possible suffering from complications associated with treatment toxicities, the fear of not responding to the prescribed treatment, or death. The prevalence of mental health disorders is relatively higher in cancer patients vis-à-vis the general population. Methods: We performed a retrospective cohort study to evaluate the impact of mental health disorders on outcomes of relapsed and/or refractory diffuse large B-cell lymphoma (DLBCL) treated with chimeric antigen receptor T-cell (CAR T-cell) therapy using data from the TriNetX research network. Results: A total of 1587 patients met the inclusion criteria (858 with a mental health diagnosis and 729 without). After propensity score matching for age and gender, 1320 patients were included in the analysis (660 in each group). The 1-year OS was higher in patients without a mental health disorder (76.0% versus 69.4%, P = 0.004). Patients with mental health disorders had a higher incidence of cytokine release syndrome (all grades) compared with those without (35.5% versus 27.6%, P < 0.001) and immune effector cell-associated neurotoxicity syndrome (all grades) (16.8% versus 10.1%, P < 0.001). Conclusions: These findings emphasize the importance of thorough psychiatric evaluation and management in patients receiving CAR T-cell therapy. Patients with mental health disorders may need significant assistance with coordinating medical services to better adhere to required care management. Future studies are needed to help determine whether preventive and real-time mental health interventions can favorably impact outcomes of these patients.

Abstract Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, arises from various pathogenic mechanisms including gene translocations and fusions. Detection of gene rearrangements using fluorescence in situ hybridization (FISH) is a standard practice for DLBCL diagnosis and guides treatment decisions. High-throughput chromosome conformation capture (Hi-C) DNA sequencing is a next-generation sequencing-based technology to identify genome-wide rearrangements using a single assay. In this study, Hi-C sequencing was performed using FFPE tissues from 159 patients with DLBCL, and identified 746 cancer genes at or proximal to the rearrangement breakpoints with a total of 1903 occurrences. Focusing on clinically important rearrangements, Hi-C detected 102 rearrangements of MYC, BCL2, and/or BCL6 in 92 patients and revealed the fusion partners, including 25 rearrangements missed by FISH. Causes of FISH negative results included FISH-cryptic breakpoints, complex or faint FISH signals, low percentage of FISH positivity, and issues related to tissue fixation. Moreover, in 20 patients (12.6%), Hi-C sequencing detected 22 rearrangements characteristic of other lymphoma types, including CCND1 rearrangements that could lead to reclassification as mantle cell lymphoma. Survival analysis for genome-wide rearrangements using machine learning models identified MYC, PD-L1, CCND1, BCL2, NTRK1/PRCC, RRAS, and FANCE rearrangements with significant prognostic effects in the DLBCL cohort. In conclusion, Hi-C sequencing detects gene rearrangements crucial for diagnosis in an unbiased and molecular manner and showed high sensitivity and specificity in our study. These advantages of Hi-C sequencing offer help to improve the workflow of clinical pathology laboratories, diagnostic precision, and treatment of large B-cell lymphoma.

Abstract Genome sequencing of the heterogeneous primary mitochondrial disorders (PMD) frequently reveals variants of uncertain significance that require functional tests for diagnosis, and does not identify variants in all patients. We analyzed mitochondrial enzyme assays, blue native polyacrylamide gel electrophoresis (BN-PAGE) with in-gel activity staining, complex I assembly blot, and select protein abundances in fibroblasts of a case series of 204 PMD patients divided into functional classes, in comparison to 51 controls and 53 differential diagnostic conditions. Overall, sensitivity and specificity for respiratory chain enzyme assays were 46% and 93% respectively, for BN-PAGE 40% and 98%, for complex I assembly assay 49% and 99%. The overall sensitivity of all tests was 76%, specificity 93%, with positive predictive value 96% and negative predictive value 67%. Categories with high sensitivity were isolated complex deficiencies, nuclear DNA-encoded mitochondrial protein synthesis defects, co-factor defects, and mitochondrial amino-acyl-tRNA synthetase conditions when aided by protein abundance. Mitochondrial DNA mutations and maintenance disorders showed poor sensitivities. Secondary dysfunctions were rare. A complete battery of functional tests showed strong diagnostic clinical utility in fibroblasts. One sentence summary A combination of four mitochondrial functional tests to identify or confirm suspected primary mitochondrial disease in fibroblasts had good sensitivity and excellent specificity, well beyond what was perceived using enzyme assays only.

ABSTRACT Aging reshapes the architecture of human skeletal muscle, yet objective tissue‐level markers that capture this process remain limited. We combined large‐scale histology with deep learning to identify reproducible features of muscle aging and to test their biological relevance. We analyzed 974 hematoxylin–eosin whole‐slide images from a population resource using a dual‐attention convolutional neural network and an independent Mask R‐CNN model to quantify nuclear size and density, verified by manual review. The classifier distinguished young from aged muscle with high accuracy (AUC 0.91; accuracy 86.2%), and attention maps consistently highlighted nuclear enlargement and spatial disorganization as salient features. Nuclear diameter increased with age (Spearman's ρ = 0.71, p < 0.0001) across automated and manual measurements. Transcriptomes matched to the same donors showed that samples with larger nuclei were enriched for pathways related to chromatin remodeling, proteostasis, cellular senescence, mitochondrial activity, and telomere regulation, whereas smaller nuclei aligned with anti‐inflammatory and DNA repair programs. External pediatric inflammatory myopathies exhibited nuclear enlargement comparable to aged muscle, suggesting inflammation‐related premature histologic aging. These findings identify nuclear enlargement as a robust, quantifiable feature that integrates structural and molecular signatures of muscle aging. The proposed deep learning–based nuclear morphometry provides a scalable framework for tissue‐level aging biomarkers and suggests a potential “muscle aging clock” applicable to both physiological aging and disease states.