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Background And Aims: Hepatocellular carcinoma (HCC), which is prevalent worldwide and has a high mortality rate, needs to be effectively diagnosed. We aimed to evaluate the significance of plasma microRNA-15a/16-1 (miR-15a/16) as a biomarker of hepatitis B virus-related HCC (HBV-HCC) using the machine learning model. This study was the first large-scale investigation of these two miRNAs in HCC plasma samples.
Methods: Using quantitative polymerase chain reaction, we measured the plasma miR-15a/16 levels in a total of 766 participants, including 74 healthy controls, 335 with chronic hepatitis B (CHB), 47 with compensated liver cirrhosis, and 310 with HBV-HCC. The diagnostic performance of miR-15a/16 was examined using a machine learning model and compared with that of alpha-fetoprotein (AFP). Lastly, to validate the diagnostic efficiency of miR-15a/16, we performed pseudotemporal sorting of the samples to simulate progression from CHB to HCC.
Results: Plasma miR-15a/16 was significantly decreased in HCC than in all control groups ( < 0.05 for all). In the training cohort, the area under the receiver operating characteristic curve (AUC), sensitivity, and average precision (AP) for the detection of HCC were higher for miR-15a (AUC = 0.80, 67.3%, AP = 0.80) and miR-16 (AUC = 0.83, 79.0%, AP = 0.83) than for AFP (AUC = 0.74, 61.7%, AP = 0.72). Combining miR-15a/16 with AFP increased the AUC to 0.86 (sensitivity 85.9%) and the AP to 0.85 and was significantly superior to the other markers in this study ( < 0.05 for all), as further demonstrated by the detection error tradeoff curves. Moreover, miR-15a/16 impressively showed potent diagnostic power in early-stage, small-tumor, and AFP-negative HCC. A validation cohort confirmed these results. Lastly, the simulated follow-up of patients further validated the diagnostic efficiency of miR-15a/16.
Conclusions: We developed and validated a plasma miR-15a/16-based machine learning model, which exhibited better diagnostic performance for the early diagnosis of HCC compared to that of AFP.
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http://dx.doi.org/10.1016/j.livres.2024.05.003 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFNano Lett
September 2025
School of Materials and Chemistry, University of Shanghai for Science & Technology, Shanghai 200093, China.
Developing low-temperature gas sensors for parts per billion-level acetone detection in breath analysis remains challenging for non-invasive diabetes monitoring. We implement dual-defect engineering via one-pot synthesis of Al-doped WO nanorod arrays, establishing a W-O-Al catalytic mechanism. Al doping induces lattice strain to boost oxygen vacancy density by 31.
View Article and Find Full Text PDFAm J Reprod Immunol
September 2025
Department of Laboratory Animal Science, Kunming Medical University, Kunming, China.
Objective: To explore B cell infiltration-related genes in endometriosis (EM) and investigate their potential as diagnostic biomarkers.
Methods: Gene expression data from the GSE51981 dataset, containing 77 endometriosis and 34 control samples, were analyzed to detect differentially expressed genes (DEGs). The xCell algorithm was applied to estimate the infiltration levels of 64 immune and stromal cell types, focusing on B cells and naive B cells.