Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2214/ajr.182.1.1820180 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Berberine inhibits the glycolysis and proliferation of hepatocellular carcinoma cells by down-regulating HIF-1α.
Pak J Pharm Sci
January 2025
Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China.
Berberine (BBR), an isoquinoline alkaloid abundant in Coptis chinensis, exhibits anti-tumor and hypoglycemic properties. The regulation of tumor cell homeostasis and metabolism is greatly influenced by Hypoxia-inducible factor-1α (HIF-1α). This research aims to elucidate whether BBR inhibits the progression of hepatocellular carcinoma (HCC) by modulating HIF-1α expression.
View Article and Find Full Text PDFMulticlass Steroid Profiling in Short-Finned Pilot Whale Blubber Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS).
Rapid Commun Mass Spectrom
March 2025
Chemical Sciences Division, National Institute of Standards and Technology, Charleston, South Carolina, USA.
Rationale: Wildlife scientists are quantifying steroid hormones in a growing number of tissues and employing novel methods that must undergo validation before application. This study tested the accuracy and precision of liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods for use on blubber samples from short-finned pilot whales (Globicephala macrorhynchus). We expanded upon a method for corticosteroid quantification by adding analytes and optimizing internal standard (IS) application.
View Article and Find Full Text PDFBayesian network for predicting mandibular third molar extraction difficulty.
BMC Oral Health
January 2025
Sub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.
Background: This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.
Methods: Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network.
A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.
BMC Geriatr
January 2025
Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.
Objective: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
Methods: A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients.
Background: Outpatient training for resident physicians has been attracting attention in recent years. However, to our knowledge, there have only been a few surveys on outpatient training, particularly in Japan. This study evaluates outpatient care among Japanese resident physicians by determining how the volume of outpatient encounters and length of outpatient training correlate with residents' clinical competence.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!