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.

Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

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.

Lactylation (Kla), a protein post-translational modification characterized by the covalent conjugation of lactyl groups to lysine residues in proteins, is widely present in living organisms. Since its discovery in 2019, it has attracted much attention for its role in regulating major pathological processes such as tumorigenesis, neurodegenerative diseases, and cardiovascular diseases. By mediating core biological processes such as signal transduction, epigenetic regulation, and metabolic homeostasis, lactylation contributes to disease progression. However, the lactylation donor lactyl-CoA has a low intracellular concentration, and the specific enzyme catalyzing lactylation is not yet clear, which has become an urgent issue in lactate research. A groundbreaking study in 2024 found that alanyl-transfer t-RNA synthetase 1/2 (AARS1/2), members of the aminoacyl-tRNA synthetase (aaRS) family, can act as protein lysine lactate transferases, modifying histones and metabolic enzymes directly with lactate as a substrate, without relying on the classical substrate lactyl-CoA, promoting a new stage in lactate research. Although exercise significantly increases lactate levels in the body and can induce changes in lactylation in multiple tissues and cells, the regulation of lactylation by exercise is not entirely consistent with lactate levels. Research has found that high-intensity exercise can induce upregulation of lactate at 37 lysine sites in 25 proteins of adipose tissue, while leading to downregulation of lactate at 27 lysine sites in 22 proteins. The level of lactate is not the only factor regulating lactylation through exercise. We speculate that the lactate transferase AARS1/2 play an important role in the process of lactylation regulated by exercise, and AARS1/2 should also be regulated by exercise. This review introduces the molecular biology characteristics, subcellular localization, and multifaceted biological functions of AARS, including its canonical roles in alanylation and editing, as well as its newly identified lactate transferase activity. We detail the discovery of AARS1/2 as lactylation catalysts and the specific process of them as lactate transferases catalyzing protein lactylation. Furthermore, we discuss the pathophysiological significance of AARS in tumorigenesis, immune dysregulation, and neuropathy, with a focus on exploring the expression regulation and possible mechanisms of AARS through exercise. The expression of AARS in skeletal muscle regulated by exercise is related to exercise time and muscle fiber type; the skeletal muscle AARS2 upregulated by long-term and high-intensity exercise catalyzes the lactylation of key metabolic enzymes such as pyruvate dehydrogenase E1 alpha subunit (PDHA1) and carnitine palmitoyltransferase 2 (CPT2), reducing exercise capacity and providing exercise protection; physiological hypoxia caused by exercise significantly reduces the ubiquitination degradation of AARS2 by inhibiting its hydroxylation, thereby maintaining high levels of AARS2 protein and exerting lactate transferase function; exercise induced lactate production can promote the translocation of AARS1 cytoplasm to the nucleus, exert lactate transferase function upon nuclear entry, regulate histone lactylation, and participate in gene expression regulation; exercise induced lactate production promotes direct interactions between AARS and star molecules such as p53 and cGAS, and is widely involved in the occurrence and development of tumors and immune diseases. Elucidating the regulatory mechanism of exercise on AARS can provide new ideas for improving metabolic diseases and promote health through exercise.

OBJECTIVE: To examine the therapeutic effect of Fangji Fuling Decoction (FFD) on sepsis through network pharmacological analysis combined with in vitro and in vivo experiments. METHODS: A sepsis mouse model was constructed through intraperitoneal injection of 20 mg/kg lipopolysaccharide (LPS). RAW264.7 cells were stimulated by 250 ng/mL LPS to establish an in vitro cell model. Network pharmacology analysis identified the key molecular pathway associated with FFD in sepsis. Through ectopic expression and depletion experiments, the effect of FFD on multiple organ damage in septic mice, as well as on cell proliferation and apoptosis in relation to the mitogen-activated protein kinase 14/Forkhead Box O 3A (MAPK14/FOXO3A) signaling pathway, was analyzed. RESULTS: FFD reduced organ damage and inflammation in LPS-induced septic mice and suppressed LPS-induced macrophage apoptosis and inflammation in vitro (P<0.05). Network pharmacology analysis showed that FFD could regulate the MAPK14/FOXO signaling pathway during sepsis. As confirmed by in vitro cell experiments, FFD inhibited the MAPK14 signaling pathway or FOXO3A expression to relieve LPS-induced macrophage apoptosis and inflammation (P<0.05). Furthermore, FFD inhibited the MAPK14/FOXO3A signaling pathway to inhibit LPS-induced macrophage apoptosis in the lung tissue of septic mice (P<0.05). CONCLUSION FFD could ameliorate the LPS-induced inflammatory response in septic mice by inhibiting the MAPK14/FOXO3A signaling pathway.
Mice ; Animals ; Mitogen-Activated Protein Kinase 14/metabolism* ; Wolfiporia ; Lipopolysaccharides/pharmacology* ; Sepsis/complications* ; Signal Transduction ; Inflammation/drug therapy* ; Oxygen Radioisotopes

Aim To investigate the role of metabolites of eicosapentaenoic acid (EPA) in promoting the transdifferentiation of pancreatic α cells to β cells. Methods Male C57BL/6J mice were injected intraperitoneally with 60 mg/kg streptozocin (STZ) for five consecutive days to establish a type 1 diabetes (T1DM) mouse model. After two weeks, they were randomly divided into model groups and 97% EPA diet intervention group, 75% fish oil (50% EPA +25% DHA) diet intervention group, and random blood glucose was detected every week; after the model expired, the regeneration of pancreatic β cells in mouse pancreas was observed by immunofluorescence staining. The islets of mice (obtained by crossing GCG