Clonal hematopoiesis of indeterminate potential and the risk of pulmonary embolism: an observational study.

Background: Pulmonary embolism causes a substantial burden of morbidity and mortality. Although there are several well-established risk factors for pulmonary embolism, a substantial proportion of cases cannot be attributed to provoked or known risk factors. Accumulating evidence has suggested an association of clonal hematopoiesis of indeterminate potential (CHIP) with the risk of arterial thromboembolism.

Impact of cardiac factors on central airway anatomical parameters in patients undergoing lung mass surgery.

Background: The correlation between central airway anatomical parameters and demographic factors, such as sex, age, weight, height, body mass index (BMI), and cardiac factors, remains unclear. This study examined the correlation between these factors and central airway anatomical parameters in adult patients.

Methods: All consecutive patients who underwent lung mass surgery at our hospital between December 2020 and December 2023 were included in this study.

[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].

To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Specimens were collected from 209 breast cancer patients who received NAT at Renmin Hospital of Wuhan University from October 2019 to June 2023. TSR levels in pre-NAT biopsy specimens were automatically computed using a deep learning algorithm and categorized into low stroma (TSR≤30%), intermediate stroma (TSR 30% to ≤60%), and high stroma (TSR>60%) groups.

The network structural entropy for single-cell RNA sequencing data during skin aging.

Aging is a complex and heterogeneous biological process at cellular, tissue, and individual levels. Despite extensive effort in scientific research, a comprehensive understanding of aging mechanisms remains lacking. This study analyzed aging-related gene networks, using single-cell RNA sequencing data from >15 000 cells.