Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

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.

italic>Salmonella has emerged as a promising tumor-targeting strategy in recent years due to its good tumor targeting ability and certain safety. In order to further optimize its therapeutic effect, scientists have tried to modify Salmonella, including its attenuation and drug loading. This paper summarizes the mechanism and research progress of Salmonella-mediated targeted tumor therapy, and introduces the strategies and related progress of its modification and optimization. At the same time, the advantages, current challenges and future development directions of Salmonella-mediated tumor therapy are summarized.

Objective To evaluate the effects of high-fat diet on the pharmacokinetics of metronidazole in Chinese healthy adult subjects.Methods This program is designed according to a single-center,randomized,open,single-dose trial.Forty-seven healthy subjects were assigned to receive single dose of metronidazole tablets 200 mg in either fasting and high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of metronidazole in plasma were determined by high performance liquid chromatography-mass spectromentry.Results The main pharmacokinetic parameters of metronidazole in fasting state and high-fat diet state were as follows:Cmax were(4 799.13±1 195.32)and(4 044.17±773.98)ng·mL-1;tmax were 1.00 and 2.25 h;t1/2 were(9.11±1.73)and(9.37±1.79)h;AUC0_t were(5.59±1.19)x 104 and(5.51±1.18)x 104 ng·mL-1·h;AUC0_∞ were(5.79±1.33)x 104 and(5.74±1.32)× 104 ng·mL-1·h.Compared to the fasting state,the tmaxof the drug taken after a high fat diet was delayed by 1.25 h(P＜0.01),Cmax,AUC0_t,AUC0-∞ were less or decreased in different degrees,but the effects were small(all P＞0.05).Conclusion High-fat diet has little effects on the pharmacokinetic parameters of metronidazole,which does not significantly change the degree of drug absorption,but can significantly delay the time to peak.

Objective: To understand trends and related factors influencing overweight and obesity among primary and secondary school students in Tianjin, so as to provide a basis for formulating overweight and obesity prevention and control strategies. Methods: In September of each year from 2019 to 2023, a survey was conducted among 197 707 primary and secondary school students in 16 districts of Tianjin through a stratified random cluster sampling method. Physical examination was carried out in accordance with the Technical Standard for Physical examination for Student, and overweight and obesity survey was carried out. Basic information, smoking, drinking, diet, physical exercise, and sleep status were collected through questionnaire surveys. Results: The detection rates of overweight and obesity among primary and secondary school students in Tianjin from 2019 to 2023 were 39.07%, 43.33%, 41.54%, 43.92%, and 40.24%, respectively，showing an increasing trend(χ2trend=7.96,P<0.01). The detection rates of overweight increased in both vocational high schools and suburban counties (χ2trends=9.08, 47.18, P<0.01). The detection rates of obesity increased among both male and female students, in primary and vocational high schools and suburban counties (χ2trends=108.34, 15.99, 7.32, 10.95, 14.75, P<0.01). Multivariate Logistic regression analysis showed that smoking, drinking, unhealthful diet, and lack of proper physical exercise had a higher risk of obesity among primary and secondary school students (OR=1.26, 1.13, 1.08, 1.21, P<0.05). Stratified analysis showed that the risk of obesity was higher among boys with unhealthful and moderate lifestyle habits, as well as primary school students with unhealthful lifestyle habits (OR=1.15, 1.11, 1.27, P<0.05). Boys, girls and primary school students with unhealthful lifestyle habits, girls and ordinary high school students with moderate lifestyle habits had higher risk of being overweight (OR=1.14, 1.32, 1.21, 1.18, 1.40, P<0.05). Conclusions The detection rates of overweight and obesity among primary and secondary school students in Tianjin shows an increasing trend. Comprehensive lifestyle should be implemented to better prevent and control the risk of overweight and obesity.

Objective:To explore the prognostic value of MUC13 expression in gastric cancer(GC)patients and its impact on the biological behavior of GC cells.Methods:Comprehensive anal-ysis of the expression pattern of MUC genes in GC tissues based on the TCGA database to screen for differentially expressed genes.Spearman correlation analysis determined the correlation of ex-pression between MUC genes in GC tissues.Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway(KEGG)enrichment analysis were used to explore the potential biological functions of MUC genes.Univariate COX regression analysis was performed to explore the relationship between all differentially expressed MUC genes and the prog-nosis of GC patients to screen out MUC genes that were significantly related to the prognosis of GC.Clinical GC tissue samples were used to further verify the expression of MUC13 through im-munofluorescence,and its relationship with the clinicopathological characteristics and prognosis of GC was analyzed.siRNA was used to silence the expression of MUC13 in GC cells,and the effect of MUC13 on cell proliferation,migration and invasion was analyzed through CCK-8,colony forma-tion and Transwell experiments.Results:Among all MUC members,the expression levels of MUC1,MUC2,MUC3A,MUC4,MCU5B,MUC12,and MUC13 were significantly upregulated in GC tissues(P＜0.05).There are certain interactions between these MUC genes,and they are mainly en-riched in pathways related to digestive system processes,epithelial structure maintenance,apical plasma membrane,saliva secretion,etc.Importantly,upregulation of MUC13 in GC tissues indicates poor patient prognosis(Log-rank P＜0.05).In addition,MUC13 expression was significantly correlat-ed with the age(P＜0.001)of GC patients and tumor size(P=0.035).Further cell function experiments showed that after silencing MUC13,the proliferation ability of GC cells was significantly reduced(P＜0.05),while their migration and invasion abilities were not significantly affected(P＞0.05).Con-clusions:Highly expressed MUC13 is closely related to the poor prognosis of gastric cancer,par-ticipates in the regulation of tumor progression and is a potential therapeutic target and prognostic marker for gastric cancer.