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*

OBJECTIVES: To investigate the inhibitory effect of multimerin-2 (MMRN2) overexpression on growth and metastasis of cutaneous melanoma cells. METHODS: Clinical data of patients with cutaneous melanoma were obtained from the GEO database to compare MMRN2 expressions between normal and tumor tissues. A protein-protein interaction network was constructed using the STRING database, and the intersecting genes from GEPIA2.0 were subjected to GO and KEGG enrichment analysis. The prognostic relevance of MMRN2 expression level was assessed using Cox regression and "timeROC". The correlations of MMRN2 expression level with immune infiltration and angiogenesis-related genes were analyzed using GSCA database and the ssGSEA algorithm. Colony-forming assay, Transwell assay, and wound healing assay were used to examine the changes in proliferation and migration of cultured cutaneous melanoma cells following MMRN2 knockdown. In a mouse model bearing cutaneous melanoma xenograft, the effect of MMRN2 knockdown on vital organ pathologies, survival of the mice and GM-CSF, CXCL9, and TGF‑β1 protein expressions were analyzed. RESULTS: MMRN2 was significantly upregulated in metastatic cutaneous melanoma (P<0.001). Protein interaction network analysis identified 15 intersecting genes, which were enriched in endothelium development and cell-cell junctions. In patients with cutaneous melanoma, a high MMRN2 expression was correlated with a poor prognosis, an advanced T stage, a greater Breslow depth, and ulceration (P<0.05). MMRN2 expression level was strongly correlated with 24 immune cell types (P<0.001), fibroblasts, endothelial cells, and expressions of the pro-angiogenic genes (KCNJ8, SLCO2A1, NRP1, and COL3A1; P<0.001). In cultured B16F10 cells, MMRN2 knockdown significantly suppressed cell proliferation, migration and invasion and caused remo-deling of the immunosuppressive microenvironment. CONCLUSIONS MMRN2 overexpression drives progression of cutaneous melanoma by enhancing tumor metastasis, angiogenesis and immune evasion, highlighting its potential as a therapeutic target for melanomas.
Humans ; Melanoma/metabolism* ; Animals ; Cell Movement ; Prognosis ; Skin Neoplasms/metabolism* ; Mice ; Cell Proliferation ; Neoplasm Invasiveness ; Cell Line, Tumor ; Protein Interaction Maps

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To explore the diagnostic value of electrocardiogram parameters combined with serum microRNA(miR)-322 and miR-568 levels in patients with chronic heart failure(CHF)complicated with ventricular arrhythmias(VA).Methods A total of 230 CHF patients admitted to Zhangjiakou First Hospital from April 2020 to April 2023 were selected as the study objects,including 120 VA patients(VA group)and 110 non VA patients(non VA group),and 110 patients who underwent health examinations in Zhangjiakou First Hospital during the same period as the control group.Compared three sets of general information,electrocardiogram parameters,left ventricular ejection fraction(LVEF),cardiac function grade and serum miR-322 and miR-568 levels.Logistic regression analyzed the influencing factors of VA in CHF patients.Receiver operating characteristic curve(ROC)analyzed electrocardiogram parameters combined with serum miR-322 and miR-568 for the diagnostic value of VA in CHF patients.Results The three groups showed statistically obvious differences in classification of nyha heart function(NYHA)and left ventricular ejection fraction(LVEF)(F=6.033,691.853,all P<0.05).The electrocardiogram parameters of CHF patients,including QT interval dispersion(QTD),QRS wave duration and corrected QT systolic time(QTc)were obviously higher than those in the control group(t=16.539,17.709,14.414),and the VA group were obviously higher than the non VA group(q=10.984,7.794,10.174),and the differences were statistically significan(all P<0.05).The serum levels of miR-322 and miR-568 were obviously lower than those in the control group(t=23.719,17.359).and the VA group were obviously lower than the non VA group(q=10.345,9.941),the differences were statistically significant(all P<0.05).Logistic regression analysis showed that NYHA grading,LVEF,QTD,QRS,wave duration,QTc,serum miR-322 and miR-568 levels were all influencing factors in the complication of VA in patients with CHF(Wald χ2=4.267～9.839,all P<0.05).The combination of electrocardiogram parameters and serum miR-322,miR-568 was better diagnosing CHF patients with concurrent VA that QTD,QRS duration,QTc and serum miR-332,miR-568 were measured separately.Conclusion Serum levels of miR-322 and miR-568 are obviously reduced,and the combination of electrocardiogram parameters with serum miR-322 and miR-568 has high diagnostic value for CHF patients with VA.

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.

Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.

Objective To explore the correlation between gallbladder stones and small intestinal bacterial overgrowth(SIBO).Methods A retrospective analysis was conducted on the clinical data of 393 patients who attended the Department of Gastroenterology of the Sixth Medical Center of Chinese PLA General Hospital from January 2021 to September 2023.They were divided into gallbladder stones group(n=190)and control group(n=203)based on the presence of gallbladder stones.Their general clinical data,laboratory test results,and abdominal symptoms were compared.Multivariate logistic regression was used to analyze the risk factors for gallbladder stones.Additionally,the total population was divided into SIBO-positive group(n=239)and SIBO-negative group(n=154),and their clinical characteristics were analyzed by logistic regression to explore the risk factors for SIBO.Results Univariate analysis revealed that gallbladder stones group had a higher rate of age,body mass index(BMI),fasting plasma glucose(FPG),glutaminase levels,prevalence of hypertension,diabetes,coronary heart disease,non-alcoholic fatty liver disease,gallbladder polyps,and SIBO,as well as a higher prevalence of CH4-positive and H2-positive in SIBO group than control group(P＜0.05).In terms of abdominal symptoms,the incidence of bad breath(48.4%vs.35.5%),dyspepsia(38.4%vs.28.6%),abdominal pain(30.5%vs.14.8%),bloating(42.1%vs.28.6%),diarrhea(20.5%vs.7.4%),and more exhaustion(46.8%vs.34.5%)were significantly higher in gallbladder stones group than those in control group(P＜0.05).Multivariate logistic regression analysis showed that independent positive determinants for incident gallbladder stones were age,BMI,FPG,total bilirubin(TBIL),coronary heart disease,gallbladder polyps,and SIBO.Univariate analysis revealed that age,prevalence of gallbladder stones,proportion of single stones,triglycerides(TG),total cholesterol(TC),and low-density lipoprotein cholesterol(LDL-C)were significantly higher in SIBO-positive group than those in SIBO-negative group(P＜0.05).Multivariate logistic regression analysis showed that the risk factors for SIBO were age,coronary heart disease,and gallbladder stones,while the protective factor for SIBO was high-density lipoprotein cholesterol(HDL-C).Conclusion There is a significant correlation between gallbladder stones and small SIBO;interventions on related factors of gallbladder stones and small SIBO may help reduce their incidence.

OBJECTIVE: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently develop central nervous system damage, yet the mechanisms driving this pathology remain unclear. This study investigated the primary pathways and key factors underlying brain tissue damage induced by the SARS-CoV-2 beta variant (lineage B.1.351). METHODS: K18-hACE2 and C57BL/6 mice were intranasally infected with the SARS-CoV-2 beta variant. Viral replication, pathological phenotypes, and brain transcriptomes were analyzed. Gene Ontology (GO) analysis was performed to identify altered pathways. Expression changes of host genes were verified using reverse transcription-quantitative polymerase chain reaction and Western blot. RESULTS: Pathological alterations were observed in the lungs of both mouse strains. However, only K18-hACE2 mice exhibited elevated viral RNA loads and infectious titers in the brain at 3 days post-infection, accompanied by neuropathological injury and weight loss. GO analysis of infected K18-hACE2 brain tissue revealed significant dysregulation of genes associated with innate immunity and antiviral defense responses, including type I interferons, pro-inflammatory cytokines, Toll-like receptor signaling components, and interferon-stimulated genes. Neuroinflammation was evident, alongside activation of apoptotic and pyroptotic pathways. Furthermore, altered neural cell marker expression suggested viral-induced neuroglial activation, resulting in caspase 4 and lipocalin 2 release and disruption of neuronal molecular networks. CONCLUSION These findings elucidate mechanisms of neuropathogenicity associated with the SARS-CoV-2 beta variant and highlight therapeutic targets to mitigate COVID-19-related neurological dysfunction.
Animals ; COVID-19/genetics* ; Mice ; Brain/metabolism* ; Apoptosis ; Mice, Inbred C57BL ; SARS-CoV-2/physiology* ; Pyroptosis ; Gene Expression Profiling ; Transcriptome ; Male ; Female

Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

Objective To predict the risk of coronary atherosclerosis using cerebral small vessel disease and cerebral artery stenosis in order to assist early identification of coronary artery disease(CHD).Methods A retrospective analysis was conducted on 130 patients(aged≥65 years)who underwent cerebral MRI,magnetic resonance angiography(MRA),and coronary computed tomo-graphy angiography in the Chinese PLA General Hospital between January 2019 and December 2024.Based on coronary computed tomography angiography results,the patients were categorized into an asymptomatic CHD group(56 cases)and a non-CHD group(74 cases).Cerebral MRI and MRA were used to assess white matter hyperintensity,enlarged perivascular spaces,lacunar infarcts,cerebral microbleeds(CMB)scores,total score of cerebral small vessel disease and degree of cerebral artery stenosis.Multivariate logistic regression analysis was employed to identify the clinical and radiological indicators related to CHD.Then a predictive model was constructed,and then its performance in early predicting CHD was evaluated.Results Blood glucose,low-density lipoprotein cholesterol,CMB score≥ 1,and≥50％stenosis of the posterior cerebral artery were independent risk factors for CHD(P＜0.05).The combined predictive model integrating clinical indicators,cerebral small vessel disease,and cerebral artery stenosis demonstrated the best performance,with an AUC value of 0.867(95％CI:0.807-0.927),outperforming the clinical model and the clinical-cerebral small vessel disease model.Conclusion cerebral small vessel dis-ease and cerebral artery stenosis are closely related to coronary atherosclerosis.The predictive model integrating clinical features and cerebrovascular disease imaging markers provides impor-tant reference for early screening of CHD.