ObjectiveTo investigate the effects of Xixintang （XXT）-medicated serum on the amyloid β-protein （Aβ）_25-35-induced polarization of BV-2 microglial cells by a cell experiment and uncover the potential mechanisms of this formula in the prevention and treatment of Alzheimer's disease （AD）， thus providing scientific evidence for the clinical application of XXT. MethodsBV-2 microglial cells were subcultured. The optimal concentrations of XXT-medicated serum and Aβ_25-35 were determined via the cell-counting kit-8 （CCK-8） assay. The cell experiment was carried out with the following groups： blank control， model （Aβ_25-35 at 40 μmol·L^-1）， XXT-medicated serum （Aβ_25-35 at 40 μmol·L^-1 + 10% XXT-medicated serum）， and blank serum （Aβ_25-35 at 40 μmol·L^-1 + 10% blank serum）. After 24 hours of cell incubation， immunofluorescence was used to detect the expression of ionized calcium-binding adaptor molecule 1 （IBA1）， CD16/32， and CD206. Real-time PCR was performed to measure the mRNA levels of CD206， CD163， inducible nitric oxide synthase （iNOS）， and arginase 1 （Arg-1）. Enzyme-linked immunosorbent assay （ELISA） was employed to quantify the levels of nerve growth factor （NGF）， iNOS， and Arg-1. The nitric oxide （NO） concentration was determined via the nitrate reductase method. ResultsCompared with the blank control group， the model group showed increased expression of IBA1 and CD16/32 （P<0.01）， decreased expression of CD206 （P<0.05）， upregulation in the mRNA level （P<0.01） and content （P<0.05） of iNOS， downregulation in the mRNA levels of CD206， CD163， and Arg-1 （P<0.05， P<0.01）， lowered levels of Arg-1 and NGF （P<0.05）， and an elevation in the NO level （P<0.05）. Compared with the model group， the XXT-medicated serum group exhibited reduced expression of IBA1 and CD16/32 （P<0.05， P<0.01） and increased expression of CD206 （P<0.01）. Both the content and mRNA level of iNOS were downregulated （P<0.05， P<0.01）， while the mRNA levels of CD206， CD163， and Arg-1 were upregulated （P<0.01） in the XXT-medicated serum group. In addition， the XXT-medicated serum group showed elevated levels of Arg-1 and NGF （P<0.05） and a lowered level of NO （P<0.05）. The blank serum group showed no statistically significant differences in the measured parameters compared with the model group. ConclusionThe XXT-medicated serum can inhibit the polarization toward the M1 phenotype and promote the polarization toward the M2 phenotype， exerting anti-inflammatory and neurotrophic effects.

ObjectiveTo explore the potential mechanism of Xixin Decoction （洗心汤， XD） in treating Alzheimer's disease （AD）. MethodsXD-containing serum was prepared， and the BV-2 microglial cell viability was assessed using the CCK8 assay to determine the optimal intervention concentrations of XD-containing serum and amyloid-beta _25-35 （Aβ_25-35） for subsequent experiments. BV-2 cells were divided into four groups， control group， model group （Aβ_25-35）， XD-containing serum group （Aβ_25-35+ XD-containing serum）， and blank serum group （Aβ_25-35 + blank serum）. After 24 hours of culture， the levels of interleukin-1β（IL-1β）， cyclooxygenase-2 （COX-2）， and arginase-2 （Arg-2） in the supernatent were detected by ELISA. Immunofluorescence staining was performed to detect the protein levels of ionized calcium-binding adaptor molecule 1 （IBA1）， CD86， and CD206. RT-PCR was used to analyze the mRNA expression of IL-1β， interleukin-6 （IL-6）， and interleukin-10 （IL-10）. ResultsThe concentrations of 10% XD-containing serum and 40 μmol·L^-¹ Aβ_25-35 were selected for subsequent experiments. Compared to the control group， the model group showed significantly increased levels of IL-1β and COX-2 in the supernatant， as well as elevated protein expression of IBA1 and CD86 and increased mRNA expression of IL-1β and IL-6， while exhibiting significantly reduced levels of Arg-2 in the supernatant， CD206 protein expression， and IL-10 mRNA expression （P＜0.05 or P＜0.01）. Compared to the model group， the XD-containing serum group showed significant improvement in all these indicators （P＜0.01）， whereas no statistically significant differences were observed in the blank serum group （P＞0.05）. ConclusionXD may regulate microglial activation， inhibit pro-inflammatory factors， and enhance anti-inflammatory factor release， thereby improving neuroimmune inflammation and inhibiting the progression of Alzheimer's disease.

With the rapid development of sequencing technologies, the detection of alternative polyadenylation (APA) in mammals has become more precise. APA precisely regulates gene expression by altering the length and position of the poly(A) tail, and is involved in various biological processes such as disease occurrence and embryonic development. The research on APA in mammals mainly focuses on the following aspects:(1) identifying APA based on transcriptome data and elucidating their characteristics; (2) investigating the relationship between APA and gene expression regulation to reveal its important role in life regulation;(3) exploring the intrinsic connections between APA and disease occurrence, embryonic development, differentiation, and other life processes to provide new perspectives and methods for disease diagnosis and treatment, as well as uncovering embryonic development regulatory mechanisms. In this review, the classification, mechanisms and functions of APA were elaborated in detail and the methods for APA identifying and APA data resources based on various transcriptome data were systematically summarized. Moreover, we epitomized and provided an outlook on research on APA, emphasizing the role of sequencing technologies in driving studies on APA in mammals. In the future, with the further development of sequencing technology, the regulatory mechanisms of APA in mammals will become clearer.

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.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption. Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells. Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erkuclear factor-kappa B (NF-κB) pathway. Conclusion Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

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.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption. Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells. Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erkuclear factor-kappa B (NF-κB) pathway. Conclusion Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

Background: Both sodium-glucose cotransporters (SGLTs) and Na+/H+ exchangers (NHEs) rely on a favorable Na-electrochemical gradient. Gastrin, through the cholecystokinin B receptor (CCKBR), can induce natriuresis and diuresis by inhibiting renal NHEs activity. The present study aims to unveil the role of renal CCKBR in diabetes through SGLT2-mediated glucose reabsorption. Methods: Renal tubule-specific Cckbr-knockout (CckbrCKO) mice and wild-type (WT) mice were utilized to investigate the effect of renal CCKBR on SGLT2 and systemic glucose homeostasis under normal diet, high-fat diet (HFD), and HFD with a subsequent injection of a low dose of streptozotocin. The regulation of SGLT2 expression by gastrin/CCKBR and the underlying mechanism was explored using human kidney (HK)-2 cells. Results: CCKBR was downregulated in kidneys of diabetic mice. Compared with WT mice, CckbrCKO mice exhibited a greater susceptibility to obesity and diabetes when subjected to HFD. In vitro experiments using HK-2 cells revealed an upregulation of glucose transporters after incubation with high glucose, a response that was significantly attenuated following gastrin intervention. The glucose uptake from the culture medium of cells was altered accordingly. Moreover, gastrin administration effectively mitigated hyperglycemia in WT diabetic mice by inhibition of SGLT2 mediated glucose reabsorption, but this effect was compromised in the absence of CCKBR, as seen in CckbrCKO mice. Mechanistically, gastrin/CCKBR substantially reduced SGLT2 expression in HK-2 cells exposed to high glucose, via modulating Erkuclear factor-kappa B (NF-κB) pathway. Conclusion Our study underscores the crucial role of renal gastrin/CCKBR in SGLT2 regulation and glucose reabsorption, and renal gastrin/CCKBR can be a promising therapeutic target for diabetes.

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.