Ripening agents can accelerate the ripening of fruits and maintain a similar appearance to naturally ripe fruits, but the fruit flavor and quality will be changed compared to naturally ripe fruits. To find an efficient detection method to distinguish whether crab apples were artificial ripened, the naturally ripe and artificially ripe fruits were detected and analyzed using the electronic nose (e-nose) technique in this study. The fruit quality indexes of samples were determined by the traditional method as a reference. Significant differences were found between naturally ripe and artificially ripe fruits based on the analysis of soluble sugar content, titratable acidity content, sugar–acid ratio, soluble protein content, and soluble solids content. In addition, principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) analyses were performed on the electrical signals generated by the electronic nose sensor, respectively. The results showed that the RF is the best recognition algorithm for distinguishing which crab apples were naturally ripe or artificially ripe; the average recognition accuracy is 98.3%. On the other hand, the prediction models between the e-nose response data and fruit quality indexes were constructed by partial least squares regression (PLSR), which showed that the feature value of e-nose response curves extracted by wavelet transform was highly correlated with the quality indexes of fruits, the determination coefficients (R²) of regression models were higher than 0.91. The results demonstrated that the detection technology with an electronic nose could be used to test whether the fruit of the crab apple was artificially ripe, which is an economical and efficient method.

The Latin name of Wolfiporia cocos is Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb, it a medicinal and edible mushroom belonging to the family Polyporaceae. Traditional Chinese medicine believes that it can strengthen the spleen, diuretic, tranquillise the mind and dispel dampness. So far, the chemical and active metabolites isolated and extracted from Wolfiporia cocos are mainly polysaccharides, triterpenoids, and sterols. Modern pharmacology has found that these chemical and active metabolites have a wide range of pharmacological effects, including antitumour, antioxidation, anti-inflammatory, immunomodulation, regulation of intestinal flora, regulation of glycolipid metabolism, and improvement of organ function. By applying Poria cocos, Poria, Wolfiporia cocos, Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb as search terms, we searched all the relevant studies on Poria cocos from Web of Science and PubMed databases and classified these categories of chemical and active metabolites according to the main research content of each literature and summarized its mechanism of action, updated its latest research results, and discussed the direction of further research in the future to provide a better reference for future clinical applications with better therapeutic effects and potential medicinal value.

To predict the development of health resource allocation in Jilin Province during the 14th 5-Year Plan period, and to provide a scientific basis for promoting the improvement of its service capacity. The data of the health resource from 2015 to 2022 were obtained from the Jilin Statistical Yearbook, and the number of medical institutions, medical beds, health technicians, licensed (assistant) physicians, registered nurses and pharmacists were selected as evaluation indicators, and the grey prediction model constructed by Python was used to predict the development from 2023 to 2025. In the 14th 5-Year Plan period, the health resource in Jilin Province showed an increasing trend, and it is predicted that in 2025, the number of medical institutions, medical beds, health technicians, licensed (assistant) physicians, registered nurses, and pharmacists in Jilin Province will reach 28,999, 196,328, 262,219, 101,273, 129,586, and 9469, respectively. Except that the pharmacist team failed to meet the planning objectives of the 14th 5-Year Plan, the remaining health resources could meet the planning requirements. The allocation level of health resources in Jilin Province has been continuously improved, but it still faces the problems that the allocation of medical beds needs to be optimized, the doctor-nurse ratio needs to be improved, the reserve of registered nurses is insufficient, there is a gap in the pharmacist team, and the development of pharmacy services is slow.

Detection and classification of pesticide residues in dandelion (Taraxacum officinale L.) by electronic nose combined with chemometric approaches

Background: This study aims to explore the use of readily available complete blood count (CBC) indicators, combined with machine learning algorithms, to build a predictive model for mental disorders. Methods: This study recruited 1,379 university volunteers in September 2024, collecting data on age, gender, and 22 CBC variables. The dependent variable was a binary outcome assessed by the university’s mental health evaluation system based on the SCL-90 scale, consisting of a positive group with mental disorders and a negative group without mental disorders. SMOTETomek hybrid sampling was applied to resolve data imbalance. Random Forest (RF) was used for feature selection. This study then constructed and compared four machine learning models: eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). Model performance was evaluated using AUC, F1-score, accuracy, sensitivity, and specificity. The Shapley Additive exPlanations (SHAP) method was employed to interpret the optimal model. Furthermore, a logistic regression (LR) algorithm was used to build a nomogram. Results: Among the 1,379 volunteers, 1,023 tested negative and 356 tested positive. Fifteen volunteers had missing data for four indicators. Feature selection based on the random forest method identified 14 optimal variables for model construction. Among the six machine learning algorithms tested, XGBoost demonstrated the best performance with the highest AUC, reaching 0.860 on the training set and 0.827 on the testing set. A SHAP analysis of the XGBoost model and the nomogram results both confirmed that the top three contributing features were Basophil Percentage (BASO%), Basophil Count (BASO#), and Mean Corpuscular Hemoglobin (MCH). Conclusion: This study successfully developed a mental disorders prediction model based on the XGBoost algorithm and complete blood count data, providing clinicians with objective risk assessment indicators to assist in diagnosis and improve both efficiency and accuracy.