Publications by authors named "L J Zong"
Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine.
World J Gastrointest Oncol
December 2024
Article Synopsis
- Survival rates after surgery for gastric neuroendocrine neoplasms (g-NENs) are low, and traditional prognostic models like the CoxPH show limited ability to predict patient outcomes post-surgery.
- Machine learning techniques, particularly the random survival forest (RSF) model, can analyze complex data to improve predictions of survival outcomes.
- The study highlights that the RSF model, which uses the lymph node ratio (LNR), is more effective than CoxPH in predicting disease-specific survival in g-NEN patients and could lead to better personalized treatment strategies.
With the rapid development of science and technology, high-temperature-resistant resin systems are facing more severe challenges in extreme applications. To further improve the comprehensive thermal properties of phthalonitrile resins, an in situ generation of a high-temperature-resistant phthalonitrile resin achieving an organic-inorganic hybridization network is reported. A 3-aminophenol phthalonitrile containing -NH is used as a material to hybridize with prepared calcium phosphate nano-oligomers (CPOs), and the hybrid precursor is named as CAPN.
View Article and Find Full Text PDFFactors Influencing Perioperative Occult Blood Loss in Elderly Hip Fracture Patients: A Retrospective Analysis.
J Musculoskelet Neuronal Interact
December 2024
Article Synopsis
- The study examines the occurrence and factors influencing hidden blood loss during surgery in elderly patients with hip fractures.
- Out of 145 patients analyzed, 42 experienced occult blood loss, resulting in an incidence rate of about 29%.
- Key factors linked to this blood loss include the type of surgical procedure, anesthesia used, drainage post-surgery, and autologous blood transfusion practices, indicating the need for preventive strategies in clinical settings.
The aim of this study was to evaluate which frozen embryo transfer (FET) strategy benefits the recurrent implantation failure (RIF) population. A retrospective study of 336 women with RIF was performed from July 2020 to October 2023. The patients were divided into three groups according to the FET protocol.
View Article and Find Full Text PDFArticle Synopsis
- A recent study by Hong developed an AI-driven prediction system to assess complications for patients undergoing laparoscopic radical gastrectomy for gastric cancer.
- This new system uses random forest models and incorporates data from various medical centers to improve prediction accuracy and patient management.
- The research emphasizes AI's role in clinical decision support and suggests potential for future studies to enhance AI applications in diagnosing and treating gastric cancer.