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Objective: This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms., Methods: In this retrospective observational study, data on nulliparous women without contraindications for vaginal delivery with a singleton pregnancy ≥37 weeks and before the onset of labor from September 2020 to September 2021 were divided into a training set and a temporal validation set. Transperineal ultrasound was performed to collect angle of progression, head-perineum distance, subpubic arch angle, and their levator hiatal dimensions. The cervical length was measured via transvaginal ultrasound. The delivery methods were later recorded. Through LASSO regression analysis, indicators that can affect SVD failure were selected. Seven common machine learning algorithms were selected for model training, and the optimal algorithm was selected based on the area under the curve (AUC) to evaluate the effectiveness of the validation model., Results: Four indicators related to SVD failure were identified through LASSO regression screening: angle of progression, cervical length, subpubic arch angle, and estimated fetal weight. The Gaussian NB algorithm was found to yield the highest AUC (0.82, 95% confidence interval [CI] 0.65-0.98) during model training, and hence it was chosen for verification with the temporal validation set, in which an AUC of 0.79 (95% CI 0.64-0.95) was obtained with accuracy, sensitivity, and specificity rates of 80.9%, 72.7%, and 75.0%, respectively., Conclusion: The Gaussian NB model showed good predictive effect, proving its potential as a clinical reference for predicting SVD failure of term nulliparous women before actual delivery., (© 2024 International Federation of Gynecology and Obstetrics.)

This study was undertaken to observe the biochemical changes in striatum of Parkinson's disease (PD) rat model by modified proton magnetic resonance spectroscopy. 12 SD rats were divided into model (n=7) and control (n=5) groups. At 3 weeks after the injection of 6-hydroxydopamine into right striatum, 1H-MRS on the striatum was taken by modified proton magnetic resonance spectroscopy, and then tyrosine hydroxylase (TH) immunostatining was used to visualize the changes of the neurons in substantia nigra and neurites in striatum. The results showed that TH positive neurons and neurites in the substantia nigra compacts (SNc) and striatum in the normal side of the rat model of PD were decreased (P < 0.05), which proved the successful establishment of PD models. The NAA/Cr ratio of the injected side striatum of model group was lower than that of the normal side (P < 0.05). The ratios of Cho/Cr showed no significant difference between the two sides (P > 0.05). These results indicated that the modified 1.5T 1H-MRS should be a noninvasive technique which could provide useful information about the biochemical metabolites in striatum for the study of PD in rat model.