Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.

The objective of this study was to observe the effect of Astragalus membranaceus on high sugar-induced Caenorhabditis elegans, and to explore its mechanism of action. UPLC-MS method was used to identify the components of Astragalus membranaceus. A high glucose model was established by using Caenorhabditis elegans as a model organism, and the effects of Astragalus membranaceus on body length, body bending, swallowing frequency, and reactive oxygen species (ROS) of the nematode were determined; the effects of Astragalus membranaceus on the expression of mRNA of genes related to the protein skinhead-1 (SKN-1) signaling pathway were examined by using the real-time fluorescence quantitative polymerase chain reaction (PCR). The results showed that compared with the normal group, the nematode body length, body bending, and swallowing frequency expression were significantly reduced and the ROS content in the body was significantly increased in the high glucose state; after the administration of Astragalus membranaceus, the body length, body bending, and swallowing frequency expression were significantly increased, and the ROS content was significantly reduced (P < 0.01). Compared with the normal group, SKN-1, superoxide dismutas-3 (SOD-3), glutathione S-transferase 4 (GST-4), and glutathione S-transferase 7 (GST-7) expression were significantly decreased in Caenorhabditis elegans in the high glucose condition; SKN-1, SOD-3, GST-4, and GST-7 expression were significantly increased after administration of Astragalus membranaceus (P < 0.01). In the present study, we demonstrated that Astragalus membranaceus has an effect on high glucose-induced Caenorhabditis elegans nematodes, and its mechanism of action may be through the modulation of the SKN-1 signaling pathwaym in order to ameliorate the oxidative stress response induced by high glucose.

Objective:To construct a representative index system for evaluating pediatric orthopedic nursing quality, providing a basis for hospital pediatric orthopedic nursing quality assessment and monitoring.Methods:From April to July 2023, using the "structure-process-outcome" three-dimensional quality structure model as the theoretical framework, a literature review was conducted, and an item pool was formulated. Through two rounds of Delphi method expert consultations, the hierarchical analysis method was finally employed to determine the indicators and their weights at each level.Results:The effective recovery rates of the questionnaire of the two rounds of expert consultations were 100% (20/20), the authority coefficients of experts were 0.87 and 0.88, the coefficients of variation were 0.00 to 0.27 and 0.00 to 0.24. The Kendell harmony coefficients of the second and third indicators in the two rounds of inquiry were 0.140, 0.166 and 0.192, 0.161(all P<0.05). The final pediatric orthopedic nursing quality evaluation index system included 3 primary indicators, 21 secondary indicators and 83 tertiary indicators. Among the primary indicators, the weight of process quality was the highest at 0.493 4, followed by outcome quality at 0.310 8, and the lowest was structural quality at 0.195 8. In the secondary indicators, "assessment criteria of limb blood circulation" had the highest weight at 0.099 8. Conclusions:The constructed pediatric orthopedic nursing quality evaluation index system covers key aspects and is more operationally feasible. It provides better guidance for nursing interventions and quality control.

Protein as the allergens could lead to allergy. In addition, a widespread class of allergens were known as glycans of N-glycoprotein. N-glycoprotein contained oligosaccharide linked by covalent bonds with protein. Recently,studies implicated that allergy was associated with glycans of heterologous N-glycoprotein found in food, inhalants, insect toxins, etc. The N-glycan structure of N-glycoprotein allergen has exerted an influence on the binding between allergens and IgE, while the recognition and presentation of allergens by antigen-presenting cells (APCs) were also affected. Some researches showed thatN-glycan structure of allergen was remodeled by N-glycosidase, such as cFase I, gpcXylase, as binding of allergen and IgE partly decreased. Thus, allergic problems caused by N-glycoproteins could potentially be solved by modifying or altering the structure ofN-glycoprotein allergens, addressing the root of the issue. Mechanism of N-glycans associated allergy could also be elaborated through glycosylation enzymes, alterations of host glycosylation. This article hopes to provide a separate insight for glycoimmunology perspective, and an alternative strategy for clinical prevention or therapy of allergic diseases.