ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

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.

ObjectiveTo study the correlations between traditional Chinese medicine （TCM） syndromes and lipid metabolism in children with Wilson disease （WD）. MethodsClinical data and lipid metabolism indicators ［total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， apolipoprotein A1 （ApoA1）， apolipoprotein B （ApoB）， and lipoprotein a （Lpa）］ were retrospectively collected from 341 children with WD. The clinical data were compared among WD children with different syndromes， and the correlations between TCM syndromes and lipid metabolism in children with WD were analyzed. Least absolute shrinkage and selection operator （LASSO） regression was used for variable screening， and unordered multinomial Logistic regression was employed to analyze the effects of lipid metabolism indicators on TCM syndromes. ResultsThe 341 children with WD included 121 （35.5%） children with the dampness-heat accumulation syndrome， 103 （30.2%） children with the liver-kidney Yin deficiency syndrome， 68 children with the combined phlegm and stasis syndrome， 29 children with the spleen-kidney Yang deficiency syndrome， and 20 children with the liver qi stagnation syndrome. The liver-kidney Yin deficiency syndrome， combined phlegm and stasis syndrome， and spleen-kidney Yang deficiency syndrome had correlations with the levels of lipid metabolism indicators （P<0.05）. Lipid metabolism abnormalities occurred in 232 （68.0%） children， including hypertriglyceridemia （108）， hypercholesterolemia （23）， mixed hyperlipidemia （67）， lipoprotein a-hyperlipoproteinemia （12）， and hypo-HDL-cholesterolemia （22）. The percentages of hypertriglyceridemia and hypo-HDL-cholesterolemia varied among children with different TCM syndromes （P<0.05）. Correlations existed for the liver-kidney Yin deficiency syndrome with TG， TC， and HDL-C， the combined phlegm and stasis syndrome with TG， the spleen-kidney Yang deficiency syndrome with TG， TC， and LDL-C， and the liver Qi stagnation syndrome with TC and LDL-C （P<0.05， P<0.01）. ConclusionThe TCM syndromes of children with WD are dominated by the dampness-heat accumulation syndrome and the liver-kidney Yin deficiency syndrome， and dyslipidemia in the children with WD is dominated by hypertriglyceridemia and mixed hyperlipidemia. There are different correlations between TCM syndromes and lipid metabolism indicators， among which TG， TC， LDL-C， and HDL-C could assist in identifying TCM syndromes in children with WD.

Abstract In recent years, digital exercise games based on motion capture and body sensing interaction technologies have gradually emerged and become a new trend in sports. Due to the ingenious integration of physical activity and electronic games, digital exercise games have demonstrated significant advantages in improving the health of children and adolescents. The paper aims to systematically analyze the intervention effects of digital exercise games on children and adolescents body composition, motor ability, muscle strength and bone density, cardiovascular health, executive function as well asemotions, aiming to explore new approaches and methods to improve the health status of children and adolescents in China.

On August 18,2023,the U.S.Food and Drug Administration(FDA)officially approved Regeneron's Pozelimab-bbfg(Veopoz?)for the treatment of complement factor H-related protein 5 deficiency type protein-losing enteropathy(CHAPLE)disease in children aged 1 and above,as well as adult patients.CHAPLE disease is a rare condition.Pazelimab monoclonal antibody is a recombinant monoclonal antibody(immunoglobulin G4 isotype).This antibody works by binding to the terminal complement protein C5,thereby preventing the cleavage of C5 into C5a(an anaphylatoxin)and C5b,and inhibit the activation of the terminal complement pathway.This article provides an overview of Pozelimab-bbfg,including its pharmacological research,pharmacokinetics,clinical studies,safety profile,and more,to introduce the current state of research and the achievements made with this drug.