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Background Knowledge sharing of family doctor teams under the county medical community is an important way of interaction between the county and rural institutions, which plays an important role in enhancing the quality of regional medical and health services and improving the health of residents. Objective To investigate the knowledge sharing among family doctor teams under the county medical community, to explore its influencing factors, and to provide a scientific basis for promoting knowledge management and capacity enhancement within family doctor teams. Methods From October to December 2022, multistage sampling was used for selecting 381 family doctor teams under two county medical communities were selected as study subjects in Hubei Province using the convenience sampling method, and a self-administered team knowledge-sharing questionnaire was used to investigate the knowledge-sharing level of the included subjects, and multivariate linear stepwise regression analyses were used to explore the influencing factors of the knowledge-sharing of family doctor teams, including two dimensions of explicit knowledge sharing and implicit knowledge sharing. Results The total knowledge sharing score of family doctor teams under county medical communities was (27.84±3.84), and the mean item level scores of the two dimensions of explicit and implicit knowledge sharing were (5.51±0.79) and (5.61±0.77), respectively; the results of the multivariate linear stepwise regression analysis showed that the heterogeneity of academic qualifications, intensity of team communication, intensity of team activities, motivation of members to learn, the use of platform carriers, and the team leader's role were the influencing factors of knowledge sharing of family doctor teams under the county medical community (P

Background The effectiveness of family physician teams under the county medical community is low, and the current status of team management is not yet clear, the perceived status of team management for members with different characteristics is not yet clear. Objective To understand the current situation of family doctor team management under the county medical community, and to provide a basis for further refinement and improvement of family doctor team management. Methods From October to December 2022, a total of 1 724 key members of 429 family doctor teams under the county medical communities in Hubei Province were surveyed using a self-developed questionnaire, which included the management of team configurations, the management of team interactions and the results of team management. Differences in perceptions of team management between family doctor team members with different characteristics were analysed using chi-squared test and ANOVA, and Pearson correlation was used to analysis the correlation between team interaction management and team management outcome factors. Results Among 1 724 survey respondents, 62.9% (1 084/1 724) perceived that team assessment and incentive measures were effective, and 88.7% (1 530/1 724) perceived that they had a reasonable allocation of family doctor team personnel. The respondents had a score of (22.3±5.3) for the intensity of internal team interactions and a score of (22.0±5.3) for the perceived intensity of the team's interaction and liaison with the outside. Regarding family doctor team members' perception of team management results, team members' perceived team goal achievement, i.e., team task performance, scored (33.1±7.4), perceived satisfaction with the team scored (22.3±4.9), and perceived team's ability and potential for future development scored (27.9±6.3), while the total scores of the three dimensions were 0-36, 0-24, 0-30. Comparison of team configuration management, team interaction management, and perceptions of team management outcomes between family doctor team members of different positions, titles, and ages showed statistically significant differences (P

Background: diabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development. Objective: The aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques. Methods: The input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent dataset comprising 248 cases was employed for external validation. This validation process evaluated the model's applicability and reliability across diverse populations and clinical settings. Importantly, the external dataset required no additional tuning or adjustment of parameters, enabling an unbiased assessment of the model's generalizability and its capacity to predict the risk of DFU. Results: The ensemble learning method outperformed individual classifiers in various performance evaluation metrics. Based on the ROC analysis, the AUC of the AutoML model for assessing diabetic foot risk was 88.48 % (74.44–97.83 %). Other results were found to be as follows: 87.23 % (63.33 %–100.00 %) for sensitivity, 87.43 % (70.00 %–100.00 %) for specificity, 87.33 % (76.66 %–95.00 %) for accuracy, 87.69 % (75.00 %–100.00 %) for positive predictive value, and 87.70 % (71.79 %–100.00 %) for negative predictive value. In addition to traditional DFU risk factors such as cardiovascular disorders, peripheral artery disease, and neurological damage, we identified new risk factors such as lower limb varicose veins, history of cerebral infarction, blood urea nitrogen, GFR (Glomerular Filtration Rate), and type of diabetes that may be related to the development of DFU. In the external validation set of 158 samples, originating from an initial 248 with exclusions due to missing labels or features, the model still exhibited strong predictive accuracy. The AUC score of 0.762 indicated a strong discriminatory capability of the model. Furthermore, the Sensitivity and Specificity values provided insights into the model's ability to correctly identify both DFU cases and non-cases, respectively. Conclusion: The predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.

The purpose of our study was to explore and compare the tumor burden of different bone marrow infiltration patterns and evaluate the feasibility of apparent diffusion coefficient (ADC) value to identify patterns in multiple myeloma (MM).

Based on sensory descriptors developed by a sensory evaluation panel for 48 commercial Chinese brewed soy sauce samples, a flavor wheel of Chinese brewed soy sauce with 55 descriptors in the dimensions of aroma and flavor was developed for the first time. Meanwhile, a lexicon of sensory descriptors for brewed soy sauce containing reference samples with different intensities was established, and 20 brewed soy sauce samples of different grades and from different geographical origins were subjected to sensory evaluation by quantitative descriptive analysis (QDA) using this lexicon. The results showed that the characteristic flavor attributes of soy sauce were soy paste-like, salty, umami, Chinese herbal, and cooked soybean-like aromas. Through analysis of variance (ANOVA), principal component analysis (PCA) and discriminant analysis (DA), it was found that there were significant differences in sensory properties among soy sauce of different grades and from different regions. This study can provide a methodological reference for sensory quality analysis of Chinese brewed soy sauce.

In the 21st century, oral communication skills are increasingly important for business graduates who will start their careers. Therefore, the purpose of this research is to discover the best method to help business students enhance their oral communication skills during their college years. This research also helps professors to make their business course more effective. A thorough review of the literature reveals this method can promote the effectiveness of communication class and oral communication ability for students. This review of the literature examines the impact of discussion based pedagogy in business communication courses, comparing this new method with the traditional lecture method. Findings reveal that discussion based pedagogy is an available and valuable method can improve students' communication skills during the course.