Prediction of myofascial pelvic pain syndrome based on random forest model.

Objective: The objective is to construct a random forest model for predicting the occurrence of Myofascial pelvic pain syndrome (MPPS) and compare its performance with a logistic regression model to demonstrate the superiority of the random forest model. Methods: We retrospectively analyze the clinical data of female patients who underwent pelvic floor screening due to chronic pelvic pain at the Pelvic Floor Rehabilitation Center of the Third Affiliated Hospital of Zhengzhou University from January 2021 to December 2023. A total of 543 female patients meeting the study's inclusion and exclusion criteria are randomly selected from this dataset and allocated to the MPPS group. Furthermore, 702 healthy female patients who underwent pelvic floor screening during routine physical examinations within the same timeframe are randomly selected and assigned to the non-MPPS group. Chi-square test and rank-sum test are used to select demographic variables, pelvic floor pressure assessment data variables, and modified Oxford muscle strength grading data for logistic univariate analysis. The selected variables are further subjected to multivariate logistic regression analysis, and a random forest model is also established. The predictive performance of the two models is evaluated by comparing their accuracy, sensitivity, specificity, precision, receiver operating characteristic (ROC) curve, and area under the curve (AUC) area. Results: Based on a dataset of 1245 cases, we implement the random forest algorithm for the first time in the screening of MPPS. In this investigation, the Logistic regression model forecasts the accuracy, sensitivity, specificity, and precision of MPPS at 69.96 %, 57.46 %, 79.63 %, and 68.57 % respectively, with an AUC of the ROC curve at 0.755. Conversely, the random forest prediction model exhibits accuracy, sensitivity, specificity, and precision rates of 87.11 %, 90.66 %, 90.91 %, and 83.51 % respectively, with an AUC of the ROC curve at 0.942. The random forest model showcases exceptional predictive performance during the initial screening of MPPS. Conclusion: The random forest model has exhibited exceptional predictive performance in the initial screening evaluation of MPPS disease. The development of this predictive framework holds significant importance in refining the precision of MPPS prediction within clinical environments and elevating treatment outcomes. This research carries profound global implications, given the potentially elevated misdiagnosis rates and delayed diagnosis proportions of MPPS on a worldwide scale, coupled with a potential scarcity of seasoned healthcare providers. Moving forward, continual refinement and validation of the model will be imperative to further augment the precision of MPPS risk assessment, thereby furnishing clinicians with more dependable decision-making support in clinical practice.

De Novo Assembly, Characterization and Comparative Transcriptome Analysis of the Gonads of Jade Perch (Scortum barcoo).

Due to the high meat yield and rich nutritional content, jade perch (Scortum barcoo) has become an important commercial aquaculture species in China. Jade perch has a slow growth rate, taking 3-4 years to reach sexual maturity, and has almost no difference in body size between males and females. However, the study of its gonad development and reproduction regulation is still blank, which limited the yield increase. Herein, the gonad transcriptomes of juvenile males and females of S. barcoo were identified for the first time. A total of 107,060 unigenes were successfully annotated. By comparing male and female gonad transcriptomes, a total of 23,849 differentially expressed genes (DEGs) were identified, of which 9517 were downregulated, and 14,332 were upregulated in the testis. In addition, a large number of DEGs involved in sex differentiation, gonadal development and differentiation and gametogenesis were identified, and the differential expression patterns of some genes were further verified using real-time fluorescence quantitative PCR. The results of this study will provide a valuable resource for further studies on sex determination and gonadal development of S. barcoo.

Effects of salinity on growth, physiology, biochemistry and gut microbiota of juvenile grass carp (Ctenopharyngodon idella).

Grass carp (Ctenopharyngodon idella) is among the most important freshwater fish species in China. However, it remained unclear how salinity could affect grass carp. Two experiments were performed. The first experiment was a 4-day acute salt tolerance experiment with six salinities (0, 4, 8, 12, 16, and 20 ppt). The second experiment was an 8-week chronic salt stress experiment with three salinities (0, 2 and 6 ppt). To investigate the intestinal bacterial community of grass carp from three salinities (0, 2, and 6 ppt), the 16S rDNA sequencing was performed. The results showed that grass carp exhibited great adaptability to low salinity (2 ppt), with no significant difference in growth and maintained stable physiological and immune status. However, exposed to high salinity (6 ppt) caused significant deleterious effects on grass carp, including growth inhibition as well as physiological and immune-related changes. The gut microbiota in grass carp changed with salinity. With the increase of salinity, the proportion of beneficial bacteria in the gut of grass carp gradually decreased, while some harmful bacteria gradually occupied the dominant position. Changes in gut microbial composition ultimately affected the growth of grass carp. This study helps further clarify the effects of salinity on grass carp.