Objective To analyze personal external exposure dose monitoring results of pet hospital radiation workers in Beijing from 2022 to 2024, and to provide scientific evidence for developing effective monitoring protocols, standardizing radiological practices in pet hospitals, and protecting the health of both workers and the public. Methods Personal dose equivalents, H_p(10), were measured using GR-200A thermoluminescent dosimeters. Monitoring included radiation workers at 286 pet hospitals across all 16 districts of Beijing from 2022 to 2024. Each monitoring cycle lasted no more than 90 days. Annual effective doses were calculated by summing the results of four consecutive quarterly cycles. Data were processed and analyzed using Access and TLPS 6.41 software. All evaluations complied with national laws, regulations, and technical standards. Results A total of 1,579 person-time measurements were recorded. Only 51.87% of workers completed the full four-cycle annual monitoring schedule. Over the study period, the mean annual effective dose was 0.29 mSv, and the median dose was 0.14 mSv. The collective effective dose was 238.34 man-mSv. Notably, 94.26% of workers had annual effective doses below 1.0 mSv, and only 0.12% had doses at or above 5 mSv. Dose levels showed a statistically significant downward trend (P<0.05). Both independent and chain-branded clinics had a median annual effective dose of 0.14 mSv. In independent clinics, the proportion of workers with doses below 1.0 mSv increased from 80.77% in 2022 to 98.82% in 2024. In chain-branded clinics, this proportion rose from 96.88% to 98.11% over the same period. Conclusion Radiation exposure among pet hospital workers in Beijing remains at low levels, well below regulatory limits, and shows an overall declining trend. The disparity in personal doses between independent and chain-branded pet hospitals has diminished over time. Nevertheless, the industry still faces challenges such as high staff turnover and low monitoring completion rates. Expanding monitoring coverage and harmonizing protection standards would provide a scientific basis for precise, nationwide regulation.

OBJECTIVE: To examine the effectiveness of Chinese medicine (CM) Lianhua Qingwen Granule (LHQW) and Jingyin Gubiao Prescription (JYGB) in asymptomatic or mild patients with Omicron infection in the shelter hospital. METHODS: This single-center retrospective cohort study was conducted in the largest shelter hospital in Shanghai, China, from April 10, 2022 to May 30, 2022. A total of 56,244 asymptomatic and mild Omicron cases were included and divided into 4 groups, i.e., non-administration group (23,702 cases), LHQW group (11,576 cases), JYGB group (12,112 cases), and dual combination of LHQW and JYGB group (8,854 cases). The length of stay (LOS) in the hospital was used to assess the effectiveness of LHQW and JYGB treatment on Omicron infection. RESULTS: Patients aged 41-60 years, with nadir threshold cycle (C_T) value of N gene <25, or those fully vaccinated preferred to receive CM therapy. Before or after propensity score matching (PSM), the multiple linear regression showed that LHQW and JYGB treatment were independent influence factors of LOS (both P<0.001). After PSM, there were significant differences in LOS between the LHQW/JYGB combination and the other groups (P<0.01). The results of factorial design ANOVA proved that the LHQW/JYGB combination therapy synergistically shortened LOS (P=0.032). CONCLUSIONS Patients with a nadir C_T value <25 were more likely to accept CM. The LHQW/JYGB combination therapy could shorten the LOS of Omicron-infected individuals in an isolated environment.
Humans ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Female ; Middle Aged ; Adult ; China/epidemiology* ; Hospitalization ; COVID-19 Drug Treatment ; COVID-19/epidemiology* ; SARS-CoV-2 ; Retrospective Studies ; Treatment Outcome ; Length of Stay ; Young Adult ; Aged

This study aims to explore the pharmacodynamic material basis of Jinbei Oral Liquid(JBOL) against idiopathic pulmonary fibrosis(IPF) based on serum pharmacochemistry and network pharmacology. The ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technology was employed to analyze and identify the components absorbed into rat blood after oral administration of JBOL. Combined with network pharmacology, the study explored the pharmacodynamic material basis and potential mechanism of JBOL against IPF through protein-protein interaction(PPI) network construction, "component-target-pathway" analysis, Gene Ontology(GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. First, a total of 114 compounds were rapidly identified in JBOL extract according to the exact relative molecular mass, fragment ions, and other information of the compounds with the use of reference substances and a self-built compound database. Second, on this basis, 70 prototype components in blood were recognized by comparing blank serum with drug-containing serum samples, including 28 flavonoids, 25 organic acids, 4 saponins, 4 alkaloids, and 9 others. Finally, using these components absorbed into blood as candidates, the study obtained 212 potential targets of JBOL against IPF. The anti-IPF mechanism might involve the action of active ingredients such as glycyrrhetinic acid, cryptotanshinone, salvianolic acid B, and forsythoside A on core targets like AKT1, TNF, and ALB and thereby the regulation of multiple signaling pathways including PI3K/AKT, HIF-1, and TNF. In conclusion, JBOL exerts the anti-IPF effect through multiple components, targets, and pathways. The results would provide a reference for further study on pharmacodynamic material basis and pharmacological mechanism of JBOL.
Drugs, Chinese Herbal/pharmacokinetics* ; Animals ; Tandem Mass Spectrometry ; Network Pharmacology ; Rats ; Chromatography, High Pressure Liquid ; Rats, Sprague-Dawley ; Male ; Idiopathic Pulmonary Fibrosis/metabolism* ; Humans ; Administration, Oral ; Protein Interaction Maps/drug effects* ; Signal Transduction/drug effects*

This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
Humans ; Computational Biology ; Diabetic Nephropathies/physiopathology* ; Protein Interaction Maps ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Phagocytosis/genetics* ; Efferocytosis

Aim To study the difference in hepatotox-icity of psoralen on normal rats and Yin-deficiency rats from the perspective of lipid metabolism,so as to help explain the mechanism of psoralen cautiously used in patients with Yin deficiency recorded in ancient books.Methods SD rats were randomly divided into the nor-mal control group(carboxymethyl cellulose-Na,CMC-Na),normal administration group(CMC-Na+psor-alen),Yin-deficiency control group(CMC-Na+thy-roxine)and Yin-deficiency administration group(CMC-Na+thyroxine+psoralen).The model of Yin-deficiency was established by thyroxine(1 mg·kg-1)for ten days,and then psoralen(200 mg·kg-1)was given for three days.The serum indexes related to liver injury were detected by automatic biochemical analy-zer,the morphological changes of liver tissue were ob-served using HE and oil red O staining,and the relative transcription levels of lipid metabolism related enzymes and mRNA of transporter and endoplasmic reticulum stress related factors were detected using Real-time PCR.Results After intragastric administration of psoralen for three days,compared with the normal group,the levels of serum alanine aminotransferase(ALT),aspartate transaminase(AST),total bile acid(TBA)and triglyeride(TG)in Yin deficiency group increased more significantly,while TC,ALB and TP de-creased more significantly,and liver HE and oil red O staining showed more obvious lipid degeneration.TG synthesis factors adrenocortical carcinoma(ACC),fatty acid synthase(FASN)and sterolregulatory element binding protein-1(SREBP-1)were down-regulated more significantly,TG transport factors mili-total pro-tein(MTP)and lipoprotein pipase(LPL)were down-regulated more evidently,fatty acid β-oxidation related factors carnitine palmitoyltransferase 1A(CPT1A),carnitine/organic cation transporter 2(OCTN2)and peroxisome proliferators-activated receptors-alpha(PPARα)were down-regulated more apparently,TC transporter adenosine triphosphate binding cassette transporter G8(ABCG8)and bile acid receptor farne-soid X receptor(FXR)were down-regulated more ob-viously,and endoplasmic reticulum stress factor activa-ting transcription factor 4(ATF4)was up-regulated more significantly.Conclusions Psoralen can cause more severe hepatotoxicity in Yin deficiency rats than that in normal administration group,and its mechanism may be related to the disorder of hepatic lipid metabo-lism,aggravation of hepatic cholestasis and steatosis,and activation of endoplasmic reticulum stress re-sponse.

Objective To evaluate the effects of transcatheter aortic valve replacement(TAVR)on left ventricular reverse remodeling(LVRR)and outcomes in patients with mixed aortic valve disease(MAVD)and predominant aortic stenosis(AS).Methods Patients undergoing TAVR at our center between January 2020 and December 2022 were enrolled consecutively.Propensity score matching(PSM)(1∶1 ratio)was used to reduce selection bias.Transthoracic echocardiography(TTE)was used to monitor left ventricular ejection fraction(LVEF)and other structural parameters over time.The study outcome was a composite of cardiovascular death and rehospitalization due to cardiovascular causes.Linear mixed-effects models and logistic regression were utilized for comparing echocardiographic changes across groups and identifying independent risk factors for no-LVRR,respectively.Results After PSM,126 patients were included.MAVD group exhibited larger structural parameters(left ventricular end-systolic/end-diastolic diameter and volume,left ventricular mass index)and a lower left ventricular ejection fraction(LVEF)(all P＜0.05).However,more pronounced improvements in left ventricular structure and hemodynamics were observed during follow-up.Multivariate logistic regression analysis indicated that the left ventricular mass index(LVMI)was an independent predictor of left ventricular reverse remodeling(LVRR)after TAVR,whereas persistent moderate or greater mitral regurgitation(MR)and paravalvular leak(PVL)significantly reduced the incidence of LVRR.During a median follow-up period of 23 months,a total of 31 endpoint events occurred,and there was no statistically significant difference in long-term prognosis between the two groups(Log-rank P=0.330).Conclusions Compared to patients in the AS group,those in the MAVD group exhibited more severe left ventricular remodeling before TAVR.However,more significant LVRR was observed during postoperative follow-up.Additionally,the long-term prognosis was comparable between the two groups.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the distribution patterns of traditional Chinese medicine(TCM)syndromes in patients with metabolic syndrome caused by antipsychotic drugs.Methods A standardized TCM syndrome survey was performed to collect diagnostic information from 160 patients diagnosed with metabolic syndrome due to antipsychotic drug use.Subsequent frequency analysis,cluster analysis,and Bayesian network analysis were carried out.The syndrome pattern distribution was ultimately determined through relevant literatures and expert opinions.Results Five TCM syndromes were identified through frequency,cluster,and Bayesian network analyses.The most common syndrome was qi deficiency with phlegm-dampness(30％),followed by spleen deficiency with phlegm-Heat(23.75％),qi and yin deficiency Pattern(21.88％),yin deficiency with damp-heat(17.50％),and stomach fire hyperactivity pattern(6.88％).Conclusion The pathogenesis of antipsychotics-induced metabolic syndrome involves a complex interplay of deficiency and excess factors.The primary disease is mainly located at the spleen and stomach,with involvement of the liver,kidney,and heart.Pathogenic factors include qi deficiency,yin deficiency,dampness,heat,pathogenic fire,and phlegm.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.