ObjectiveBased on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， to identify the in vivo and in vitro chemical components of total phenolic acids in Gei Herba（TPAGH）， and to clarify the pharmacological effects and potential mechanisms of the effective part in promoting acute wound healing and inhibiting scar formation. MethodsUPLC-Q-Orbitrap-MS was used to identify the chemical components of TPAGH and ingredients absorbed in vivo after topical administration. A total of 120 ICR mice were randomly divided into the model group， recombinant human epidermal growth factor（rhEGF） group（4 mg·kg^-1）， and low， medium， and high dose groups of TPAGH（3.5， 7， 14 mg·kg^-1）， with 24 mice in each group. A full-thickness skin excision model was constructed， and each administration group was coated with the drug at the wound site， and the model group was treated with an equal volume of normal saline， the treatment was continued for 30 days， during which 8 mice from each group were sacrificed on days 6， 12， and 30. The healing of the wounds in the mice was observed， and histopathological changes in the skin tissues were dynamically observed by hematoxylin-eosin（HE）， Masson， and Sirius red staining， and enzyme-linked immunosorbent assay（ELISA） was used to dynamically measure the contents of interleukin-6（IL-6）， tumor necrosis factor-α（TNF-α）， vascular endothelial growth factor A（VEGFA）， matrix metalloproteinase（MMP）-3 and MMP-9 in skin tissues. Network pharmacology was used to predict the targets related to the promotion of acute wound healing and the inhibition of scar formation by TPAGH， and molecular docking of key components and targets was performed. Gene Ontology（GO） biological process analysis and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis were carried out for the related targets， so as to construct a network diagram of herbal material-compound-target-pathway-pharmacological effect-disease for further exploring its potential mechanisms. ResultsA total of 146 compounds were identified in TPAGH， including 28 phenylpropanoids， 31 tannins， 23 triterpenes， 49 flavonoids， and 15 others， and 16 prototype components were found in the serum of mice. Pharmacodynamic results showed that， compared with the model group， the TPAGH groups showed a significant increase in relative wound healing rate and relative scar inhibition rate（P<0.05）， and the number of new capillaries， number of fibroblasts， number of new skin appendages， epidermal regeneration rate， collagen deposition ratio， and Ⅲ/Ⅰ collagen ratio in the tissue were significantly improved（P<0.05， 0.01）， the levels of IL-6， TNF-α， MMP-3 and MMP-9 in the skin tissues were reduced to different degrees， while the level of VEGFA was increased. Network pharmacology analysis screened 10 core targets， including tumor protein 53（TP53）， sarcoma receptor coactivator（SRC）， protein kinase B（Akt）1， signal transducer and activator of transcription 3（STAT3）， epidermal growth factor receptor（EGFR） and so on， participating in 75 signaling pathways such as advanced glycation end-products（AGE）-receptor for AGE（AGE/RAGE） signaling pathway， phosphatidylinositol 3-kinase（PI3K）/Akt signaling pathway， mitogen-activated protein kinase（MAPK） signaling pathway. Molecular docking confirmed that the key components genistein， geraniin， and casuariin had good binding ability to TP53， SRC， Akt1， STAT3 and EGFR. ConclusionThis study comprehensively reflects the chemical composition of TPAGH and the absorbed components after topical administration through UPLC-Q-Orbitrap-MS. TPAGH significantly regulates key indicators of skin healing and tissue reconstruction， thereby clarifying its role in promoting acute wound healing and inhibiting scar formation. By combining in vitro and in vivo component identification with network pharmacology， the study explores how key components may bind to targets such as TP53， Akt1 and EGFR， exerting therapeutic effects through related pathways such as immune inflammation and vascular regeneration.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

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.

Polyunsaturated fatty acids (PUFAs) have diverse health-promoting effects, such as potentially protecting in immune, nervous, and cardiovascular systems by targeting a variety of sites, including most ion channels. Voltage-gated potassium channels of the K_V7 family and large-conductance Ca²⁺- and voltage-activated K⁺ (BK_Ca) channels are expressed in many tissues, therefore, their physiological importance is evident from the various disorders linked to dysfunctional K_V7 channels and BK_Ca channels. Thus, it is extremely important to learn how potassium channels are regulated by PUFAs. The aim of this review is to provide an overview of the effects of PUFAs on K_V7 channels and BK_Ca channels functions, as well as the mechanisms underlying these effects. In summarizing reported effects of PUFAs on K_V7 and BK_Ca channels mediated currents, we generally conclude that PUFAs increase the current amplitude, meanwhile, differential molecular and biophysical mechanisms are associated with the current increase. In K_V7 channels the currents increasement are associated with a shift in the voltage dependence of channel opening and increased maximum conductance in K_V7 channels, while in BK_Ca channels, they are associated with destabilization the pore domain closed conformation. Furthermore, PUFA effects are influenced by auxiliary subunits of K_V7 and BK_Ca channels, associate with channels in certain tissues. although findings are conflicting. A better understanding of how PUFAs regulate K_V7 and BK_Ca channels may offer insight into their physiological regulation and may lead to new therapeutic strategies and approaches.

Alzheimer's disease (AD) is a leading cause of dementia in the elderly. Mitogen-activated protein kinase phosphatase 1 (MKP-1) plays a neuroprotective role in AD. However, the molecular mechanisms underlying the effects of MKP-1 on AD have not been extensively studied. MicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level, thereby repressing mRNA translation. Here, we reported that the microRNA-429-3p (miR-429-3p) was significantly increased in the brain of APP23/PS45 AD model mice and N2A^APP AD model cells. We further found that miR-429-3p could downregulate MKP-1 expression by directly binding to its 3'-untranslated region (3' UTR). Inhibition of miR-429-3p by its antagomir (A-miR-429) restored the expression of MKP-1 to a control level and consequently reduced the amyloidogenic processing of APP and Aβ accumulation. More importantly, intranasal administration of A-miR-429 successfully ameliorated the deficits of hippocampal CA1 long-term potentiation and spatial learning and memory in AD model mice by suppressing extracellular signal-regulated kinase (ERK1/2)-mediated GluA1 hyperphosphorylation at Ser831 site, thereby increasing the surface expression of GluA1-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs). Together, these results demonstrate that inhibiting miR-429-3p to upregulate MKP-1 effectively improves cognitive and synaptic functions in AD model mice, suggesting that miR-429/MKP-1 pathway may be a novel therapeutic target for AD treatment.

G protein-coupled receptor (GPR) 40, as one of GPRs family, plays a potential role in regulating glucose and lipid metabolism. To study the effect of GPR40 novel agonist SZZ15-11 on hyperglycemia and hyperlipidemia and its potential mechanism, spontaneous type 2 diabetic KKA^y mice, human hepatocellular carcinoma HepG2 cells and murine mature adipocyte 3T3-L1 cells were used. KKA^y mice were divided into four groups, vehicle group, TAK group, SZZ (50 mg·kg^-1) group and SZZ (100 mg·kg^-1) group, with oral gavage of 0.5% sodium carboxymethylcellulose (CMC), 50 mg·kg^-1 TAK875, 50 and 100 mg·kg^-1 SZZ15-11 respectively for 45 days. Fasting blood glucose, blood triglyceride (TG) and total cholesterol (TC), non-fasting blood glucose were tested. Oral glucose tolerance test and insulin tolerance test were executed. Blood insulin and glucagon were measured via enzyme-linked immunosorbent assay (ELISA). After mice′s execution, liver tissue was harvested to test TG and TC content. Then pathological morphology of liver was observed through hematoxylin-eosin (HE) staining, and the lipid metabolism relative signal pathway was analyzed by Western blot and RT-PCR. The experiments were approved by the Institutional Animal Care and Use Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College. At the same time, Akt phosphorylation level in HepG2 cells and adiponectin in 3T3-L1 cells treated with TNFα were measured with Western blot. The results show that SZZ15-11 not only decreased blood glucose and lipid, improved insulin sensitivity, but also increased fasting blood glucagon and promoted insulin secretion after glucose loading in KKA^y mice. Additionally, SZZ15-11 alleviated hepatic steatosis and liver dysfunction in KKA^y mice. In liver tissue, SZZ15-11 increased AMPKα phosphorylation level and cholesterol metabolism relative gene Abcg8 transcription. In HepG2 cells, SZZ15-11 increased Akt phosphorylation level. In adipocyte 3T3-L1, SZZ15-11 recovered the decreased adiponectin expression by TNFα. This study proved that GPR40 agonist SZZ15-11 could be a candidate compound for regulating glucolipid metabolic disorder.