AIM:To investigate the effects of the Qingfei-Jiedu-Huatan formula(QJHF)on damage to the lung vascular endothelial barrier induced by Klebsiella pneumoniae in mice with severe pneumonia,as well as to elucidate its underlying mechanisms.METHODS:Fifty-one C57BL/6J mice were randomly divided into control group(n=6),model group(n=15),QJHF group(n=15),and ceftriaxone sodium(CRO)group(n=15).Severe pneumonia was in-duced in the mice by a single tracheal intubation with 50 μL of 1×1011 CFU/mL Klebsiella pneumoniae on day 0.Six hours after modeling,the mice in QJHF and CRO groups received their respective treatments,while those in control and model groups were administered an equal volume of saline.All mice were sacrificed on day 3 after the end of gavage.Lung histo-pathological changes were assessed using hematoxylin-eosin(HE)staining.Levels of tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1β),and IL-6 in lung tissues were measured by enzyme-linked immunosorbent assay(ELISA).Flow cytometry was used to detect CD11b+Ly6g+cells in bronchoalveolar lavage fluid(BALF).Proteomics and network pharma-cology analyses were conducted to elucidate the mechanisms of drug action.Western blot was conducted to assess the ex-pression levels of vascular endothelial cadherin(VE-cadherin),zonula occludens-1(ZO-1),occludin,vascular endothe-lial growth factor(VEGF),P38 mitogen-activated protein kinase(P38),and phosphorylated P38(p-P38)in lung tis-sues.RESULTS:Treatment with QJHF significantly attenuated the symptoms such as mental status and respiratory dis-tress,reduced mortality,mitigated lung tissue lesions,and decreased levels of IL-6,TNF-α,IL-1β,as well as BALF to-tal protein concentration,total cell count and neutrophil content in a mouse model of severe pneumonia(P＜0.05 or P＜0.01).Additionally,QJHF increased the expression of VE-cadherin,ZO-1 and occludin proteins in lung tissues.Pro-teomic analysis demonstrated that QJHF modulated the expression of 129 proteins in the lung tissues of mice suffering from severe pneumonia.Network pharmacology identified 328 potential targets associated with 14 major bioactive components of QJHF and 1 665 genes related to severe pneumonia,with 125 overlapping genes between the two datasets.The construc-tion of a protein-protein interaction(PPI)network,along with Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses of the regulated proteins and overlapping genes,indicated that QJHF primarily in-fluenced the PI3K-Akt,MAPK and Rap1 signaling pathways,as well as VEGFR.Western blot analysis showed that QJHF significantly inhibited the expression of VEGF and P38 in lung tissues(P＜0.05 or P＜0.01).CONCLUSION:Treatment with QJHF attenuates severe pneumonia in mice,potentially by inhibiting VEGF/P38 signaling to protect the vascular endothelial barrier.

Objective:This study aims to explore the job preferences of county-level Centers for Disease Control and Prevention(CDC)personnel and to provide a basis for the development of effective incentive mechanisms.Methods:This study used a combination of stratified sampling and latent class sampling to investigate 1 809 respondents from 56 county-level CDCs in Shandong Province,Hubei Province,and Guizhou Province.Data were analysed using a mixed logit model and a latent class model,and willingness to pay was calculated.Results:The results of the mixed logit model showed that,all attributes and their levels had a significant influence(P<0.05),with establishment being the most important motivating factor(β=2.249).In the latent class model,respondents were divided into three categories.The main differences between the three classes were the choice of exit options and differences in preferences for job attributes.Conclusion:County-level CDC personnel preferred jobs with higher incomes,very good benefit levels,establishment,low workload,better recognition and respect from the public,more opportunities for career advancement,and abundant training opportunities.It is recommended that the total number of establishment should be rationally controlled and dynamically adjusted to balance the differences between working conditions within and outside the establishment;that a comprehensive approach should be adopted to improve both hygiene and motivation factors;and that different incentives should be adopted for different categories of CDC staff. Those who are willing to make a change should be provided with more opportunities for training and career advancement.

Objective:This study aims to explore the job preferences of county-level Centers for Disease Control and Prevention(CDC)personnel and to provide a basis for the development of effective incentive mechanisms.Methods:This study used a combination of stratified sampling and latent class sampling to investigate 1 809 respondents from 56 county-level CDCs in Shandong Province,Hubei Province,and Guizhou Province.Data were analysed using a mixed logit model and a latent class model,and willingness to pay was calculated.Results:The results of the mixed logit model showed that,all attributes and their levels had a significant influence(P<0.05),with establishment being the most important motivating factor(β=2.249).In the latent class model,respondents were divided into three categories.The main differences between the three classes were the choice of exit options and differences in preferences for job attributes.Conclusion:County-level CDC personnel preferred jobs with higher incomes,very good benefit levels,establishment,low workload,better recognition and respect from the public,more opportunities for career advancement,and abundant training opportunities.It is recommended that the total number of establishment should be rationally controlled and dynamically adjusted to balance the differences between working conditions within and outside the establishment;that a comprehensive approach should be adopted to improve both hygiene and motivation factors;and that different incentives should be adopted for different categories of CDC staff. Those who are willing to make a change should be provided with more opportunities for training and career advancement.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.

Aim To perform high-throughput screen-ing of immunomodulatory traditional Chinese medicine(TCMs)based on an in vitro B cell differentiation-antibody secretion model,identifying active herbal candidates with immune-enhancing properties to pro-vide novel therapeutic options and theoretical support for influenza virus treatment in immunocompromised in-dividuals.Methods B cells were stimulated with dif-ferent concentrations of cytosine-phosphate-guanine oli-godeoxynucleotide 2006(CpG)and nterleukin-2(IL-2)to promote proliferation,differentiation,and anti-body secretion,and the effects of varying concentra-tions of the solvent DMSO were also evaluated.The op-timal conditions for the B cell differentiation-anti-body secretion model were determined based on the se-cretion levels of three antibody isotypes.The feasibility of the model was further validated using rapamycin,a known B cell function inhibitor.On this basis,a high-throughput screening platform for immunomodulatory a-gents was optimized and established.Subsequently,the immune-enhancing activity of 465 polarity extract from TCMs was evaluated.Results The optimal con-ditions for the model were determined as 2 mg·L-1 CpG,1.67 × 106 nkat·L-1 IL-2,and DMSO with a volume fraction of 0.1％.Rapamycin effectively inhib-ited B cell differentiation into plasmablast and signifi-cantly reduced antibody production,indicating the reli-ability of the model.Multiple rounds of screening re-vealed that the dichloromethane extract of licorice,the dichloromethane extract of Vinegar-processed Curcumae Rhizoma,the cyclohexane extract of Honey-prepared Radix Asteris,and the aqueous extract of Siphonostegia chinensis Benth were identified to significantly promote both B cell proliferation and differentiation and anti-body secretion at a concentration of 600 μg·L-1.Conclusion This study successfully optimizes an in vitro B cell differentiation-antibody secretion model and identifies several TCM extracts,including licorice,with potential immune-enhancing activity.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.

Aim To perform high-throughput screen-ing of immunomodulatory traditional Chinese medicine(TCMs)based on an in vitro B cell differentiation-antibody secretion model,identifying active herbal candidates with immune-enhancing properties to pro-vide novel therapeutic options and theoretical support for influenza virus treatment in immunocompromised in-dividuals.Methods B cells were stimulated with dif-ferent concentrations of cytosine-phosphate-guanine oli-godeoxynucleotide 2006(CpG)and nterleukin-2(IL-2)to promote proliferation,differentiation,and anti-body secretion,and the effects of varying concentra-tions of the solvent DMSO were also evaluated.The op-timal conditions for the B cell differentiation-anti-body secretion model were determined based on the se-cretion levels of three antibody isotypes.The feasibility of the model was further validated using rapamycin,a known B cell function inhibitor.On this basis,a high-throughput screening platform for immunomodulatory a-gents was optimized and established.Subsequently,the immune-enhancing activity of 465 polarity extract from TCMs was evaluated.Results The optimal con-ditions for the model were determined as 2 mg·L-1 CpG,1.67 × 106 nkat·L-1 IL-2,and DMSO with a volume fraction of 0.1％.Rapamycin effectively inhib-ited B cell differentiation into plasmablast and signifi-cantly reduced antibody production,indicating the reli-ability of the model.Multiple rounds of screening re-vealed that the dichloromethane extract of licorice,the dichloromethane extract of Vinegar-processed Curcumae Rhizoma,the cyclohexane extract of Honey-prepared Radix Asteris,and the aqueous extract of Siphonostegia chinensis Benth were identified to significantly promote both B cell proliferation and differentiation and anti-body secretion at a concentration of 600 μg·L-1.Conclusion This study successfully optimizes an in vitro B cell differentiation-antibody secretion model and identifies several TCM extracts,including licorice,with potential immune-enhancing activity.

Objective:To develop the Proactive Health Behavior Scale for Population at High Risk of Type 2 Diabetes Mellitus to provide community healthcare workers with an assessment tool to measure proactive health behaviors in a multidimensional manner.Methods:Guided by the information-motivation-behavioral skills theory, a test version of the Proactive Health Behavior Scale for Population at High Risk of Type 2 Diabetes Mellitus was developed through literature research, semi-structured interviews, Delphi expert consultation, and pre-survey. Convenience sampling was used to select 696 cases of type 2 diabetes mellitus high-risk people in the community of Lubei District, Tangshan City, from February to June 2024 for the item analysis, reliability and validity test. A total of 696 questionnaires were distributed and 664 valid questionnaires were recovered, with a valid recovery rate of 95.40%.Results:The Proactive Health Behavior Scale for Population at High Risk of Type 2 Diabetes Mellitus included four dimensions, including prevention knowledge, prevention motivation, prevention skills, and prevention behaviors, with a total of 38 items. Exploratory factor analysis extracted four common factors, with a cumulative variance contribution rate of 64.203%. Confirmatory factor analysis showed good model fit. The total Cronbach's α coefficient for the scale was 0.940, the half fold reliability coefficient was 0.964, and the test-retest reliability coefficient was 0.859.Conclusions:The Proactive Health Behavior Scale for Population at High Risk of Type 2 Diabetes Mellitus has good reliability and validity, which can be used to evaluate the proactive health behaviors in population at high-risk of type 2 diabetes mellitus.

Neurosteroids play an important role as endogenous neuromodulators that are locally produced in the central nervous system and rapidly change the excitability of neurons and the activation of microglial cells and astrocytes. Here we review the mechanisms of synthesis, metabolism, and actions of neurosteroids in the central nervous system. Neurosteroids are able to play a variety of roles in the central nervous system under physiological conditions by binding to membrane ion channels and receptors such as gamma-aminobutyric acid type A receptors, Nmethyl-D-aspartate receptors, L- and T-type calcium channels, and sigma-1 receptors. In addition, numerous neurological disorders, including persistent neuropathic pain, multiple sclerosis, and seizures, have altered the levels of neurosteroids in the central nervous system. Thus, we review how local synthesis and metabolism of neurosteroids are modulated in the central nervous system and describe the role of neurosteroids under pathological conditions. Furthermore, we discuss whether neurosteroids may play a role as a new therapeutic for the treatment of neurological disorders.

Neurosteroids play an important role as endogenous neuromodulators that are locally produced in the central nervous system and rapidly change the excitability of neurons and the activation of microglial cells and astrocytes. Here we review the mechanisms of synthesis, metabolism, and actions of neurosteroids in the central nervous system. Neurosteroids are able to play a variety of roles in the central nervous system under physiological conditions by binding to membrane ion channels and receptors such as gamma-aminobutyric acid type A receptors, Nmethyl-D-aspartate receptors, L- and T-type calcium channels, and sigma-1 receptors. In addition, numerous neurological disorders, including persistent neuropathic pain, multiple sclerosis, and seizures, have altered the levels of neurosteroids in the central nervous system. Thus, we review how local synthesis and metabolism of neurosteroids are modulated in the central nervous system and describe the role of neurosteroids under pathological conditions. Furthermore, we discuss whether neurosteroids may play a role as a new therapeutic for the treatment of neurological disorders.