(Ericaceae), a new species from Yunnan, China.

, a new species of Ericaceae from Yunnan, China, is described and illustrated. This new species resembles and , but differs from the former by its linear or narrowly oblong and bullate leaf blade with a strongly recurved leaf margin and obvious reticulate veinlets adaxially, and larger flowers with yellow green and glabrous corollas and longer stamens, and can be distinguished from the latter by having glabrous twigs, linear or narrowly oblong leaf blades, yellow green corollas and exerted style.

A study on the production of extracellular vesicles derived from novel immortalized human placental mesenchymal stromal cells.

Extracellular vesicles (EVs) are not only involved in cell-to-cell communications but have other functions as "garbage bags", as bringing nutrients to cells, and as inducing mineral during bone formation and ectopic calcification. These minuscule entities significantly contribute to the regulation of bodily functions. However, the clinical application of EVs faces challenges due to limited production yield and targeting efficiency.

Development and validation of the pain management competency scale for nurses and a nationwide survey of Chinese nurses.

Background: Nurses' competency in pain management is essential for effectively alleviating patients' acute pain, controlling chronic pain, and promoting patient recovery. However, reliable tools for evaluating these competencies across different clinical specialties and healthcare settings are lacking. This study aimed to develop and validate a Pain Management Competency Scale for Nurses (PMCSN) and to assess the pain management competencies of nurses in China through a nationwide survey.

Exploring the role of multimodal [F]F-PSMA-1007 PET/CT and multiparametric MRI data in predicting ISUP grading of primary prostate cancer.

Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.

Prediction of Radiation Therapy Induced Cardiovascular Toxicity from Pretreatment CT Images in Patients with Thoracic Malignancy via an Optimal Biomarker Approach.

Rationale And Objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.

Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT.