Food-derived bioactive peptides (FBPs), particularly those with ten or fewer amino acid residues and a molecular weight below 1300 Da, have gained increasing attention for their safe, diverse structures and specific biological activities. The development of FBP-based functional foods and potential medications depends on understanding their structure‒activity relationships (SARs), stability, and bioavailability properties. In this review, we provide an in-depth overview of the roles of FBPs in treating various diseases, including Alzheimer's disease, hypertension, type 2 diabetes mellitus, liver diseases, and inflammatory bowel diseases, based on the literature from July 2017 to Mar. 2023. Subsequently, attention is directed toward elucidating the associations between the bioactivities and structural characteristics (e.g., molecular weight and the presence of specific amino acids within sequences and compositions) of FBPs. We also discuss in silico approaches for FBP screening and their limitations. Finally, we summarize recent advancements in formulation techniques to improve the bioavailability of FBPs in the food industry, thereby contributing to healthcare applications.
Humans ; Peptides/therapeutic use* ; Structure-Activity Relationship ; Functional Food ; Diabetes Mellitus, Type 2/drug therapy* ; Biological Availability ; Alzheimer Disease/drug therapy* ; Inflammatory Bowel Diseases/drug therapy* ; Hypertension/drug therapy* ; Liver Diseases/drug therapy* ; Bioactive Peptides, Dietary

Objective To analyze dynamic contrast-enhanced MRI (DCE-MRI) radiomic features using machine learning algorithms, and to develop and validate a predictive model for HER-2 status in breast cancer. Methods The DCE-MRI images of 272 treatment-naive female patients with breast cancer between 2020 and 2022 were included in this study. Regions of interest (ROIs) were manually segmented using 3d-Slicer software, and radiomic features were extracted. All patients were randomly divided into training sets or validation sets at a ratio of 4∶1. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature screening on the training set, followed by the development of predictive models using six machine learning algorithms. Internal cross-validation was performed to compare the performance differences between the models. The best-performing model was selected, trained on the training set, and evaluated on the validation set. Evaluation metrics included area under the curve (AUC), sensitivity, specificity, precision, and recall rate. Results The clinical data of patients in the training set and validation set showed no significant differences. Five features were identified by the LASSO algorithm. With these features, six machine learning models were developed on the training set, and their predictive performance was internally cross-validated using the bagging method. XGBoost model had the highest mean AUC (0.696), followed by RF model (0.690); XGBoost model had the highest mean precision (0.756), followed by LR and RF models. Therefore, XGBoost was the optimal model. An HER-2 predictive model was built using the XGBoost algorithm on the training set and applied to the validation set. The AUC, precision, sensitivity, and specificity of the predictive model on the validation set were calculated, and ROC curves, precision-recall curves, calibration curves, and decision-making curves were plotted. Conclusion This study constructed and evaluated different DCE-MRI radiomics-based machine learning models for predicting HER-2 status in breast cancer. Among them, XGBoost algorithm performed the best and has the potential to become a new non-invasive method for preoperative prediction of HER-2 status, providing reliable evidence for personalized clinical diagnosis and treatment.

This tutorial addresses the current lack of standardized protocols for closed reduction and cast immobilization for distal radius fractures in China, along with a high incidence of the complications of these fractures. Based on the 2024 Evidence-Based Guidelines for Diagnosis and Treatment of Adult Distal Radius Fractures, it establishes a standardized operational procedure. Using the classic Colles fracture as an example, it provides a comprehensive and step-by-step explanations of the closed reduction and cast immobilization techniques, including detailed descriptions and schematic illustrations covering patient positioning, measurement, reduction maneuvers, cast fabrication, cast application, molding, and assessment.

Objective:To report construction of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) using the Delphi method.Methods:Literature related to the study of adult distal radius fractures was fully searched for and evaluated. An expert group was established from representative experts from all over the nation. The related clinical issues were established by consulting the experts in the form of electronic questionnaires, strictly following the Delphi research method. After the first draft of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) was written, an expert consultation questionnaire was designed for the recommendation opinions to determine the recommendation strength.Results:The clinical issues were determined by 2 rounds of correspondence based on the Delphi method. For the both rounds of correspondence, the questionnaire recovery rates were respectively 88.68% (47/53) and 98.11% (52/53), and the expert authority coefficients >0.7. According to the screening criteria based on the importance of clinical issues (mean importance score <3.5 points or a coefficient of variation ≥0.25 points and a full score ratio <30%) and expert opinions, a total of 40 clinical issues were deleted in the first round of determination of clinical issues, and a total of 5 clinical issues deleted in the second round of determination of clinical issues. The reliability analysis of the results of the 2 rounds of questionnaires showed that the Cronbach α coefficient was >0.9. In the questionnaire to determine the recommendation strength, according to the screening criteria for the consistency of recommendation strength (consistency ≥ 70%) and expert opinions, a total of 26 recommendations were screened in the first round. In the second round when the remaining 4 recommendations were investigated, one recommendation reached the consistency of recommendation strength ≥ 70%. Eventually, 27 recommendations were formed.Conclusion:The Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) constructed using the Delphi method shows good scientific validity, authority, and reliability, providing methodological references for guideline development and research.

This tutorial addresses the current lack of standardized protocols for closed reduction and cast immobilization for distal radius fractures in China, along with a high incidence of the complications of these fractures. Based on the 2024 Evidence-Based Guidelines for Diagnosis and Treatment of Adult Distal Radius Fractures, it establishes a standardized operational procedure. Using the classic Colles fracture as an example, it provides a comprehensive and step-by-step explanations of the closed reduction and cast immobilization techniques, including detailed descriptions and schematic illustrations covering patient positioning, measurement, reduction maneuvers, cast fabrication, cast application, molding, and assessment.

Objective:To report construction of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) using the Delphi method.Methods:Literature related to the study of adult distal radius fractures was fully searched for and evaluated. An expert group was established from representative experts from all over the nation. The related clinical issues were established by consulting the experts in the form of electronic questionnaires, strictly following the Delphi research method. After the first draft of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) was written, an expert consultation questionnaire was designed for the recommendation opinions to determine the recommendation strength.Results:The clinical issues were determined by 2 rounds of correspondence based on the Delphi method. For the both rounds of correspondence, the questionnaire recovery rates were respectively 88.68% (47/53) and 98.11% (52/53), and the expert authority coefficients >0.7. According to the screening criteria based on the importance of clinical issues (mean importance score <3.5 points or a coefficient of variation ≥0.25 points and a full score ratio <30%) and expert opinions, a total of 40 clinical issues were deleted in the first round of determination of clinical issues, and a total of 5 clinical issues deleted in the second round of determination of clinical issues. The reliability analysis of the results of the 2 rounds of questionnaires showed that the Cronbach α coefficient was >0.9. In the questionnaire to determine the recommendation strength, according to the screening criteria for the consistency of recommendation strength (consistency ≥ 70%) and expert opinions, a total of 26 recommendations were screened in the first round. In the second round when the remaining 4 recommendations were investigated, one recommendation reached the consistency of recommendation strength ≥ 70%. Eventually, 27 recommendations were formed.Conclusion:The Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) constructed using the Delphi method shows good scientific validity, authority, and reliability, providing methodological references for guideline development and research.

Objective:To design an electronic nose that can detect and identify urinary volatile organic compounds (VOCs) as marker gases for bladder cancer.Methods:Isopropyl alcohol, ethylbenzene, acetic acid, and ammonia were selected as target gases, and 8 metal oxide gas sensors were used to construct sensor arrays for testing and collecting experimental data, and different characteristics were normalized. Recursive feature elimination (RFE) was used to select the best feature subset, and principal component analysis (PCA) and linear discriminant analysis (LDA) were further introduced to reduce the data dimension and facilitate visual analysis. In addition, three machine learning algorithms, including support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), were combined to train and verify the model.Results:When the feature number was 12, the accuracy of the model classification had the best performance. The feature subset consisted of 5 differences, 5 sensitivities, and 2 integrals, and the data was reduced to 12 dimensions. Only PCA couldn’t distinguish the four gases. The LDA classification performance was significantly better than that of PCA, except that isopropyl alcohol and acetic acid had a small overlap area. LDA could distinguish ethylbenzene and ammonia from isopropyl alcohol and acetic acid; the sample points were gathered, which means the clustering performance was also better. The prediction accuracy of SVM, RF, and KNN was 0.85, 0.56, and 0.79, respectively. After model verification, the classification accuracy of PCA+SVM, LDA+RF, and LDA+KNN was 0.97, 0.94, and 0.97, respectively.Conclusions:An electronic nose was designed to detect and identify urinary VOCs marker gases for bladder cancer.

Objective To explore the effect of mindfulness training on mental health of elite athletes and its possible mechanism.Methods Totally 462 Chinese elite athletes(Mage=18.16,SD=3.14,Range=12～28,44.8%female)were conducted a cross-sectional questionnaire survey.SPSS23 was employed for data statistical analysis,and the mediation model was tested through the Bootstrap program of the extension program PROCESS.Results Mindfulness significantly and negatively predicted anxiety(β=-0.386,P<0.001),depression(β=-0.342,P<0.001),and poor sleep quality(β=-0.324,P<0.001),but significantly and positively predicted training&competition satisfaction(β=0.432,P<0.001)and psychological well-being(β=0.399,P<0.001).Moreover,mindfulness showed significant effects on anxi-ety,poor sleep quality,and satisfaction to training and competition through experiential acceptance,cognitive defusion,and decentering.However,it performed significant impacts on their depression and psychological well-being only through experiential acceptance and cognitive defusion.Conclusion Mind-fulness directly predicts negative reactions such as anxiety,depression and poor sleep quality,as well as positive ones including training and competition satisfaction and psychological well-being in elite ath-letes.Moreover,it has indirect effects on anxiety,poor sleep and training and competition satisfaction through experiential acceptance,cognitive defusion and decentering,together with on depression and psychological well-being through the former two factors.

Objective To investigate the effects of volumetric modulated arc therapy (VMAT) with 6 MV and 10 MV X-ray photon energies in patients with cervical cancer. Methods From March 2019 to May 2020, 24 patients with cervical cancer who underwent radiation therapy in the Oncology Radiotherapy Department of our hospital were selected. VMAT plans with 6 MV and 10 MV photon energies were re-designed for each patient. The target parameters (D_98%, D_2%, D_mean), conformal index, and homogeneity index of the two groups were compared. The radiation doses received by the bladder, rectum, small intestine, left femoral head, right femoral head, and normal tissue other than planning target volume (Body-PTV), as well as monitor units and estimated total delivery time, were also compared. Results D_2%, D_mean, homogeneity index, and monitor units were significantly lower in the 10 MV group than in the 6 MV group (50.78 ± 0.33 Gy vs. 50.35 ± 0.29 Gy; 49.05 ± 0.2 Gy vs. 48.93 ± 0.17 Gy; 0.08 ± 0.01 vs. 0.07 ± 0.01; 1179.26 ± 110.55 vs. 1099.3 ± 80.9). Comparison of organs at risk showed that compared with the 10 MV group, the 6 MV group demonstrated significantly lower V_40Gy of the bladder, D_5% of the right femoral head, and D_2% and V_5Gy of Body-PTV (60.37 ± 13.66 vs. 60.84 ± 13.88; 37.09 ± 5.43 Gy vs. 38.03 ± 5.68 Gy; 41.71 ± 1.39 Gy vs. 42.16 ± 1.31 Gy; 47.88 ± 9.75 vs. 48.23 ± 9.74). There were no significant differences in other parameters between the two groups. Conclusion The VMAT plans with 6 MV and 10 MV X-ray photon energies both meet the clinical needs of cervical cancer radiotherapy. The cervical cancer VMAT plan with 6 MV photon energy is recommended due to better protection of the organs at risk.

Objective To investigate the effects of volumetric modulated arc therapy (VMAT) with 6 MV and 10 MV X-ray photon energies in patients with cervical cancer. Methods From March 2019 to May 2020, 24 patients with cervical cancer who underwent radiation therapy in the Oncology Radiotherapy Department of our hospital were selected. VMAT plans with 6 MV and 10 MV photon energies were re-designed for each patient. The target parameters (D_98%, D_2%, D_mean), conformal index, and homogeneity index of the two groups were compared. The radiation doses received by the bladder, rectum, small intestine, left femoral head, right femoral head, and normal tissue other than planning target volume (Body-PTV), as well as monitor units and estimated total delivery time, were also compared. Results D_2%, D_mean, homogeneity index, and monitor units were significantly lower in the 10 MV group than in the 6 MV group (50.78 ± 0.33 Gy vs. 50.35 ± 0.29 Gy; 49.05 ± 0.2 Gy vs. 48.93 ± 0.17 Gy; 0.08 ± 0.01 vs. 0.07 ± 0.01; 1179.26 ± 110.55 vs. 1099.3 ± 80.9). Comparison of organs at risk showed that compared with the 10 MV group, the 6 MV group demonstrated significantly lower V_40Gy of the bladder, D_5% of the right femoral head, and D_2% and V_5Gy of Body-PTV (60.37 ± 13.66 vs. 60.84 ± 13.88; 37.09 ± 5.43 Gy vs. 38.03 ± 5.68 Gy; 41.71 ± 1.39 Gy vs. 42.16 ± 1.31 Gy; 47.88 ± 9.75 vs. 48.23 ± 9.74). There were no significant differences in other parameters between the two groups. Conclusion The VMAT plans with 6 MV and 10 MV X-ray photon energies both meet the clinical needs of cervical cancer radiotherapy. The cervical cancer VMAT plan with 6 MV photon energy is recommended due to better protection of the organs at risk.