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.

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:Spleen has the functions of blood storage,hematopoiesis,and immunity.With the increase of age,the structural degeneration and functional decline of spleen lead to the impairment of immune system function,thus accelerating the aging process of the body.The treatment of spleen aging in tree shrews with highly active umbilical cord mesenchymal stem cells has not been reported. OBJECTIVE:To explore the intervention effect and mechanism of highly active umbilical cord mesenchymal stem cells on spleen aging in tree shrews. METHODS:Highly active umbilical cord mesenchymal stem cells were isolated,cultured,and obtained from the umbilical cord tissue of newborn tree shrews by caesarean section.The differentiation abilities of adipogenesis,osteogenesis,and chondrogenesis were detected by three-line differentiation kit.Cell cycle and surface markers were detected by flow cytometry.The second generation of highly active umbilical cord mesenchymal stem cells were transfected with Genechem Green Fluorescent Protein with infection complex values of 100,120,140,160,180,and 200,respectively,to screen the best transfection conditions.After transfection,the fourth generation of highly active umbilical cord mesenchymal stem cells was injected into the tail vein of tree shrews in the elderly treatment group.The young control group and the aged model group were not given special treatment.After 4 months of treatment,the spleen tissue was taken and the structure of the spleen was observed by hematoxylin-eosin staining.β-Galactosidase staining was used to detect the activity of aging-related galactosidase.Immunohistochemical staining was used to detect the expression levels of p21 and p53 proteins.Ki67 and PCNA immunofluorescence staining was used to detect cell proliferation activity.Immunofluorescence staining was used to detect the expression levels of spleen autophagy protein molecules Beclin 1 and APG5L/ATG5.Reactive oxygen species fluorescence staining was used to detect the content of reactive oxygen species in spleen tissue.CD3 immunofluorescence staining was used to detect the change of the proportion of total T lymphocytes.The secretion levels of interleukin 1β and transforming growth factor β1 in spleen were detected by enzyme linked immunosorbent assay.The distribution of highly active umbilical cord mesenchymal stem cells labeled with green fluorescent protein in spleen tissue was observed by DAPI double staining of nucleus. RESULTS AND CONCLUSION:(1)Highly active umbilical cord mesenchymal stem cells grew in a short spindle shape with fish-like growth,with a large proportion of G0/G1 phase,and had the potential to differentiate into adipogenesis,osteogenesis,and chondrogenesis.(2)Multiplicity of infection=140 and transfection for 72 hours were the best conditions for labeling tree shrews highly active umbilical cord mesenchymal stem cells with Genechem Green Fluorescent Protein.(3)Compared with the aged model group,in the aged treatment group,the spleen tissue cells of tree shrews were arranged closely,and the area of white pulp was increased(P<0.01);the boundary between red pulp and white pulp was clear;the proportion of germinal centers did not show statistically significant difference(P>0.05).The activity level of galactosidase related to spleen tissue aging was decreased(P<0.001),and the expression levels of aging protein molecules p21 and p53 were down-regulated(P<0.001).The expression levels of proliferation-related molecules Ki67 and PCNA were up-regulated(P<0.001,P<0.05);expression levels of autophagy-related molecules Beclin 1 and APG5L/ATG5 were up-regulated(P<0.001),and the content of reactive oxygen species decreased(P<0.001),and the proportion of CD3+T cells increased(P<0.05).The secretion level of interleukin 1β in the aging-related secretion phenotype decreased(P<0.001);no significant difference was found in transforming growth factor β1 level(P>0.05).Compared with the young control group,the above indexes were significantly different in the elderly treatment group(P<0.05).(4)Green fluorescent cells labeled with green fluorescent protein were observed in spleen tissue of tree shrews the elderly treatment group by frozen tissue section observation.The results show that intravenous infusion of highly active umbilical cord mesenchymal stem cells can migrate to spleen tissue,inhibit the production of reactive oxygen species,down-regulate the expression of aging-related proteins,induce autophagy,promote cell proliferation,reduce chronic inflammation,and then improve the structure and function of spleen tissue.

ObjectiveNeuroinflammation plays a crucial role in both the onset and progression of ischemic stroke, exerting a significant impact on the recovery of the central nervous system. Excessive neuroinflammation can lead to secondary neuronal damage, further exacerbating brain injury and impairing functional recovery. As a result, effectively modulating and reducing neuroinflammation in the brain has become a key therapeutic strategy for improving outcomes in ischemic stroke patients. Among various approaches, targeting immune regulation to control inflammation has gained increasing attention. This study aims to investigate the role of in vitro induced regulatory T cells (Treg cells) in suppressing neuroinflammation after ischemic stroke, as well as their potential therapeutic effects. By exploring the mechanisms through which Tregs exert their immunomodulatory functions, this research is expected to provide new insights into stroke treatment strategies. MethodsNaive CD4⁺ T cells were isolated from mouse spleens using a negative selection method to ensure high purity, and then they were induced in vitro to differentiate into Treg cells by adding specific cytokines. The anti-inflammatory effects and therapeutic potential of Treg cells transplantation in a mouse model of ischemic stroke was evaluated. In the middle cerebral artery occlusion (MCAO) model, after Treg cells transplantation, their ability to successfully migrate to the infarcted brain region and their impact on neuroinflammation levels were examined. To further investigate the role of Treg cells in stroke recovery, the changes in cytokine expression and their effects on immune cell interactions was analyzed. Additionally, infarct size and behavioral scores were measured to assess the neuroprotective effects of Treg cells. By integrating multiple indicators, the comprehensive evaluation of potential benefits of Treg cells in the treatment of ischemic stroke was performed. ResultsTreg cells significantly regulated the expression levels of both pro-inflammatory and anti-inflammatory cytokines in vitro and in vivo, effectively balancing the immune response and suppressing excessive inflammation. Additionally, Treg cells inhibited the activation and activity of inflammatory cells, thereby reducing neuroinflammation. In the MCAO mouse model, Treg cells were observed to accumulate in the infarcted brain region, where they significantly reduced the infarct size, demonstrating their neuroprotective effects. Furthermore, Treg cell therapy notably improved behavioral scores, suggesting its role in promoting functional recovery, and increased the survival rate of ischemic stroke mice, highlighting its potential as a promising therapeutic strategy for stroke treatment. ConclusionIn vitro induced Treg cells can effectively suppress neuroinflammation caused by ischemic stroke, demonstrating promising clinical application potential. By regulating the balance between pro-inflammatory and anti-inflammatory cytokines, Treg cells can inhibit immune responses in the nervous system, thereby reducing neuronal damage. Additionally, they can modulate the immune microenvironment, suppress the activation of inflammatory cells, and promote tissue repair. The therapeutic effects of Treg cells also include enhancing post-stroke recovery, improving behavioral outcomes, and increasing the survival rate of ischemic stroke mice. With their ability to suppress neuroinflammation, Treg cell therapy provides a novel and effective strategy for the treatment of ischemic stroke, offering broad application prospects in clinical immunotherapy and regenerative medicine.

Metastasis is a key cause of death in tumor patients, and a number of tumor patients have comorbid psychosomatic abnormalities and are in a state of chronic stress. Chronic stress affects the release of many kinds of hormones and neurotransmitters, such as epinephrine, norepinephrine, dopamine, glucocorticoids, cortisol, sex hormones, etc., through the hypothalamus–pituitary–adrenal axis and sympathetic nervous system. These substances can act on the β-adrenergic receptor, glucocorticoid receptor, etc., on tumor cells, immune cells, and other cells in the tumor microenvironment and promote the tumor progression and metastasis by directly enhancing the invasive and metastatic ability of tumor cells, inducing the formation of the immunosuppressive microenvironment and promoting tumor angiogenesis and other pathways. Antipsychotic drugs, β-blockers, and glucocorticoid receptor antagonists have inhibitory effects on chronic stress-mediated tumor metastasis and have achieved certain clinical efficacy. Relevant studies have been carried out on traditional Chinese medicine decoctions and monomers, which can inhibit tumor metastasis by modulating the immune microenvironment and reversing chronic stress-mediated hormonal changes. The psychological problems of tumor patients have gradually received attention, and the development of new anti-metastatic drugs based on the mechanism of action of chronic stress in promoting tumor progression and metastasis provides new ideas for the improvement of the overall efficiency of tumor prevention and treatment.

OBJECTIVE To establish the fingerprint of Gerbera delavayi and the methods for the content determination of 11 components in G. delavayi. METHODS High-performance liquid chromatography（HPLC）was adopted to establish the fingerprints of 13 batches of G. delavayi（No. S1-S13）， and the similarities were evaluated according to Similarity Evaluation System of Chromatographic Fingerprint of TCM （2012 edition）， while the common peaks were identified. Hierarchical clustering analysis （HCA）， principal component analysis （PCA） and orthogonal partial least square-discriminant analysis （OPLS-DA） were carried out by using SPSS 25.0 software and SIMCA 14.1 software. The contents of neochlorogenic acid， chlorogenic acid， cryptochlorogenic acid， 3，8-dihydroxy-4-methoxy-2-oxo-2H-1-benzopyran-5-carboxylic acid， caffeic acid， 3-hydroxy-4-methoxy-2- oxo-2H-1-benzopyran- 5-carboxylic acid， luteolin-7-O-β-D-glucoside， isochlorogenic acid A， apigenin-7-O-β-D-glucoside， isochlorogenic acid C and xanthotoxin were determined by HPLC. RESULTS The similarities in HPLC fingerprint of 13 batches of G. delavayi were 0.801-0.994； a total of 38 common peaks were identified and 13 common peaks were identified. The results of HCA showed that S1-S5 and S7 were clustered into one group， S6 into one category， S8 into one category， S9 and S11 into one category， S10， S12 and S13 into one category， and the results of PCA were consistent with them. The results of OPLS-DA showed that variable importance values for the projection of peak 7 （chlorogenic acid）， peak 21 （isochlorogenic acid A）， peak 26 （xanthotoxin）， peak 19 （isochlorogenic acid B）， peak 33， peak 13， peak 23 （isochlorogenic acid C）， peak 2 （new chlorogenic acid）， peak 17 （luteolin-7-O-β-D- glucoside） were greater than 1. The above 11 components had good linearity in their respective detection concentration ranges （r was greater than 0.999）. RSDs of precision， repeatability， and stability tests were not more than 2% （n＝6）. The average recovery rates were 92.54%-105.55%， and the RSDs were 0.83%-1.93% （n＝6）. The average contents of 11 components were 0.744， 5.014， 0.646， 0.431， 0.069， 0.582， 0.979， 2.754， 0.157， 1.284 and 2.943 mg/g， respectively. CONCLUSIONS The constructed HPLC fingerprint and content determination methods are simple， accurate and stable， which can provide reference for quality control of G. delavayi. Xanthotoxin， chlorogenic acid， isochlorogenic acid A， luteolin-7-O- β -D-glucoside， isochlorogenic acid C and new chlorogenic acid can be used as markers for G. delavayi.

Objective: To analyze the demographic characteristics of patients with red blood cell transfusion reactions, the usage of red blood cell preparations, and the differences in the composition ratio of adverse reactions based on multi-center data from the Haemovigilance Network, in order to reveal the clinical characteristics of red blood cell transfusion and its underlying issues. Methods: Clinical data of patients who experienced transfusion reactions after red blood cell transfusion in the Haemovigilance Network from 2018 to 2023 were collected. The demographic characteristics of patients who experienced transfusion reactions with different types of red blood cell preparations, the utilization of these preparations, and the differences of the composition ratios of transfusion reactions were analyzed. Count data were expressed as numbers (n) or percentages (%), and comparisons between groups were performed using the Chi-square test. Results: Red blood cell transfusion reactions were more common in females (53.56%), with the majority of patients aged 50-69 years (35.54%). The Han polulation accounted for the vast majority of patients (92.77%), and patients in the hematology and obstetrics/gynecology departments had a relatively high proportion of transfusion reactions (13.26% and 14.26%, respectively). Leukocyte-reduced red blood cells and suspended red blood cells were the most common types of transfusion reactions reported among red blood cell preparations. Allergic reactions and non-hemolytic febrile reactions were the most common transfusion reactions, and there were significant differences in the composition ratios of allergic reactions (χ =869.89, P<0.05) and non-hemolytic febrile reactions (χ =812.75, P<0.05) across various types of red blood cell preparations. Conclusion: There are differences in the demographic characteristics and composition ratio of transfusion reactions among different red blood cell preparations. The management of red blood cell transfusion reactions should be tailored to patient characteristics and conditions, and the selection and use of blood products should be optimized to reduce or avoid the occurrence of transfusion reactions, such as considering the use of washed red blood cells for patients with a history of transfusion allergies or those prone to allergies.

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.