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Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study.
Front Immunol
January 2025
Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Objective: Although pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.
Methods: Patients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital.
Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer.
Front Immunol
January 2025
School of Nursing, Zunyi Medical University, Zunyi, China.
Background: Most patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.
View Article and Find Full Text PDFDeveloping and validating a machine learning model to predict multidrug-resistant -related septic shock.
Front Immunol
January 2025
Department of Medical Laboratory, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Background: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.
Methods: A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024.
Prognostic value of TIMI risk score combined with systemic immune-inflammation index and lipoprotein(a) in patients with ST-Segment elevation myocardial infarction after percutaneous coronary intervention.
Int J Cardiol Heart Vasc
February 2025
Department of Cardiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225000, China.
Background: Thrombolysis in Myocardial Infarction (TIMI) risk score in patients with ST-segment elevation myocardial infarction (STEMI) is associated with major adverse cardiovascular events (MACE). This study aimed to develop a prediction model based on the TIMI risk score for MACE in STEMI patients after percutaneous coronary intervention (PCI).
Methods: We conducted a retrospective data analysis on 290 acute STEMI patients admitted to the Affiliated Hospital of Yangzhou University from January 2022 to June 2023 and met the inclusion criteria.
Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran.
Health Sci Rep
January 2025
Research Center for Environmental Determinants of Health (RCEDH), Health Institute Kermanshah University of Medical Sciences Kermanshah Iran.
Background And Aims: Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule-mining methods, to analyze diverse patient data and uncover relevant insights. This approach involves a thorough analysis of patients' clinical characteristics, dietary habits, and overall conditions to identify complex patterns and relationships that may contribute to infertility.
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