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Objective To investigate the role of autologous platelet‐rich plasma (PRP) on the repair of meniscal white‐white zone injury through promoting the proliferation of canine bone marrow‐derived mesenchymal stem cells (BMSCs). Methods A total of 24 beagle dogs were selected to construct meniscal white‐white zone injury models in both lateral knee joints. All subjects were divided into four groups: control, BMSCs, PRP, and PRP + BMSCs. Immunohistochemistry was applied in the expression detection of type I and type II collagens. HE staining and methylene blue staining were performed to observe the injury of cartilage of lateral femoral condyle in each group. ELISA was used to detect the osteopontin (OPN) content in cartilage of lateral femoral condyle. HE staining and magnetic resonance imaging (MRI) were used to observe the healing of meniscus in each group. Outcome measures include the expression of OPN in the synovial fluid of knee joint, the expression of type I collagen and type II collagen, the healing of meniscus injury, and the damage degree of lateral femoral condyle cartilage. Results Compared with the control group, the expressions of type I and type II collagens were enhanced in the PRP group and the PRP + BMSCs group. Compared with 1 week before modeling, the expression of OPN was elevated in the control group and the BMSCs group at 3 weeks after modeling. There were no significant differences in the above indicators between the PRP group and the PRP + BMSCs group. According to MRI and pathological section after HE staining, meniscal healing in the PRP group and the PRP + BMSCs group was significantly improved as compared to that of the control group and the BMSCs group (all P 0.05). All subjects were divided into the non‐healing group and the healing group in accordance with the HE staining results in previous experiment. The injury of cartilage of lateral femoral condyle was significantly heavier in the non‐healing group than that in the healing group. Conclusion The application of PRP alone or in combination with BMSCs could promote the clinical healing rate of meniscal white‐white zone injury.

Background: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models. Methods: Using 38 214 patients presenting with acute abdominal pain at Zhongnan Hospital of Wuhan University between March 1, 2014, and March 1, 2022, we identified all adult patients (aged ≥18 years). We utilized routinely available triage data in electronic medical records as predictors, including structured data (eg, triage vital signs, gender, and age) and unstructured data (chief complaints and physical examinations in free-text format). The primary outcome measure was whether emergency surgery was performed. The dataset was randomly sampled, with 80% assigned to the training set and 20% to the test set. We developed 5 machine learning models: Light Gradient Boosting Machine (Light GBM), eXtreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Random Forest (RF). Logistic regression (LR) served as the reference model. Model performance was calculated for each model, including the area under the receiver-work characteristic curve (AUC) and net benefit (decision curve), as well as the confusion matrix. Results: Of all the 38 214 acute abdominal pain patients, 4208 underwent emergency abdominal surgery while 34 006 received non-surgical treatment. In the surgery outcome prediction, all 4 machine learning models outperformed the reference model (eg, AUC, 0.899 [95%CI 0.891-0.903] in the Light GBM vs. 0.885 [95%CI 0.876-0.891] in the reference model), Similarly, most machine learning models exhibited significant improvements in net reclassification compared to the reference model (eg, NRIs of 0.0812[95%CI, 0.055-0.1105] in the XGBoost), with the exception of the RF model. Decision curve analysis shows that across the entire range of thresholds, the net benefits of the XGBoost and the Light GBM models were higher than the reference model. In particular, the Light GBM model performed well in predicting the need for emergency abdominal surgery with higher sensitivity, specificity, and accuracy. Conclusions: Machine learning models have demonstrated superior performance in predicting emergency abdominal pain surgery compared to traditional models. Modern machine learning improves clinical triage decisions and ensures that critically needy patients receive priority for emergency resources and timely, effective treatment. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB), which is primarily caused by Fusarium graminearum, is a widespread and devastating disease of wheat. In the absence of resistant varieties, the control of FHB relies heavily on the application of fungicides, and the new generation SDHI fungicide, pydiflumetofen, has recently been registered in China for the control of FHB in wheat. The current study explored three genetically stable, highly resistant laboratory mutants (S2-4-2R, S27-3R, and S28-2R, with EC50 values of 25.10, 28.57, and 19.22 μg/mL, respectively) to investigate the potential risks associated with pydiflumetofen resistance. Although the mycelial growth of the mutants differed little compared to their parental isolates, the study found that the resistant mutants exhibited significantly reduced (p < 0.05) levels of sporulation and pathogenicity, which suggests a significant fitness cost associated with pydiflumetofen resistance in F. graminearum. Sequence analysis of the Sdh target protein identified numerous amino acid substitutions in the predicted sequences of the four subunits: FgSdhA, FgSdhB, FgSdhC, and FgSdhD. Indeed, the mutants were found to have a series of substitution in multiple subunits such that all three exhibited five identical changes, including Y182F in the FgSdhA subunit; H53Q, C90S, and A94V in FgSdhB; and S31F in FgSdhC. In addition, gene expression analysis revealed that all of the FgSdh genes had significantly altered expression (p < 0.05), particularly FgSdhA and FgdhC, which exhibited remarkably low levels of expression. However, the study found no evidence of cross-resistance between pydiflumetofen and tebuconazole, fludioxonil, prochloraz, fluazinam, carbendazim, pyraclostrobin, or difenoconazole, which indicates that these fungicides, either in rotation or combination with pydiflumetofen, could mitigate the risk of resistance emerging and provide ongoing control of FHB to ensure high and stable wheat yields. [ABSTRACT FROM AUTHOR]