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 investigate the learning curve of real-life vaginal delivery,including its difficult steps and influencing factors,to optimize the future training of vaginal delivery for residents in department of obstetrics and gynecology.Methods From 25 Sep 2020 to 12 Mar 2022,OBGYN residents without previous experiences in vaginal delivery were prospectively enrolled in Obstetrics and Gynecology Hospital,Fudan University.Residents performed normal vaginal delivery under the supervision of senior obstetricians and midwives.The performance score(PS)of vaginal delivery and its 9 steps were evaluated via a questionnaire fulfilled by the supervisor once each delivery was finished.Logistic regression models were performed for univariate and multivariate analyses to evaluate the factors that might be correlated with the PS.Results Eventually,233 deliveries performed by 60 residents were analyzed.Results showed that more than 10 deliveries were needed for 70%of residents to obtain minimal competence of vaginal delivery.Perineal protection,delivery of the fetal head,delivery of the fetal shoulders and repair of episiotomy or laceration were found to be the most difficult steps,which required more practices to achieve minimal competence level.Univariate analyses showed the delivery experience,the times of observation/simulation/training,and humanistic care skills might influence the total PS(P<0.05).However,only delivery experience(OR=1.43,95%CI:1.22-1.67)and the times of observation(OR=1.02,95%CI:1.00-1.04)were found to be independently correlated with the total PS in multivariate analyses.Conclusion More than 10 real-life practices were required to achieve the minimal competence of normal vaginal delivery.Enhancing the training on the four difficult steps of vaginal delivery might improve the learning efficiency when delivery opportunities are limited.

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 establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

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 investigate the clinical therapeutic effect of Rendu Tongtiao acupuncture (acupuncture for regulating and improving the circulation of the conception and governor vessels) on hyperandrogenism (HA) in polycystic ovary syndrome (PCOS) with kidney-yin deficiency induced fire hyperactivity. METHODS: A total of 80 PCOS-HA patients were selected and randomly divided into an observation group and a control group, 40 cases in each group. In the control group, ethinylestradiol and cyproterone acetate tablets were administered orally,2 mg each time, once daily and for 21 consecutive days as one menstrual cycle. In the observation group, Rendu Tongtiao acupuncture was delivered at Qihai (CV6), Zhongwan (CV12), Guanyuan (CV4), Zhongji (CV3), Mingmen (GV4), Yaoyangguan (GV3), etc. once daily till ovulation, which was taken as the treatment session of one menstrual cycle. The treatment was completed after 3 menstrual cycles in each group. Before and after treatment, the serum levels of testosterone (T), dihydrotestosterone (DHT), luteinizing hormone (LH), follicle-stimulating hormone (FSH), prolactin (PRL), sex hormone-binding globulin (SHBG), and the scores of acne and hirsutism were compared in the two groups; besides, menstrual recovery rate, ovulation recovery rate, basic body temperature (BBT) biphasic rate and clinical effect were compared between the two groups. RESULTS: Compared with those before treatment, the levels of T, DHT, LH and PRL, as well as the scores of acne and hirsutism were reduced in the two groups after treatment (P<0.05), and the levels of FSH and SHBG were increased (P<0.05). After treatment, the levels of T, DHT, LH and PRL, as well as the scores of acne and hirsutism in the observation group were lower than those in the control group (P<0.05); and FSH and SHBG were higher (P<0.05). After treatment, the menstrual recovery rate and ovulation recovery rate, as well as BBT biphasic rate in the observation group increased in comparison with the control group (P<0.05). The total effective rate was 97.5% (39/40) in the observation group, which was higher than 82.4% (33/40) of the control group (P<0.05). CONCLUSION Rendu Tongtiao acupuncture can effectively regulate the secretion of hormones, alleviate the clinical symptoms of HA, and accelerate the recovery of menstruation and natural ovulation in patients with PCOS-HA of kidney-yin deficiency induced fire hyperactivity .
Humans ; Female ; Polycystic Ovary Syndrome/complications* ; Acupuncture Therapy ; Adult ; Young Adult ; Hyperandrogenism/blood* ; Yin Deficiency/therapy* ; Kidney/physiopathology* ; Acupuncture Points ; Testosterone/blood* ; Luteinizing Hormone/blood* ; Follicle Stimulating Hormone/blood* ; Adolescent

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*

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 investigate the learning curve of real-life vaginal delivery,including its difficult steps and influencing factors,to optimize the future training of vaginal delivery for residents in department of obstetrics and gynecology.Methods From 25 Sep 2020 to 12 Mar 2022,OBGYN residents without previous experiences in vaginal delivery were prospectively enrolled in Obstetrics and Gynecology Hospital,Fudan University.Residents performed normal vaginal delivery under the supervision of senior obstetricians and midwives.The performance score(PS)of vaginal delivery and its 9 steps were evaluated via a questionnaire fulfilled by the supervisor once each delivery was finished.Logistic regression models were performed for univariate and multivariate analyses to evaluate the factors that might be correlated with the PS.Results Eventually,233 deliveries performed by 60 residents were analyzed.Results showed that more than 10 deliveries were needed for 70%of residents to obtain minimal competence of vaginal delivery.Perineal protection,delivery of the fetal head,delivery of the fetal shoulders and repair of episiotomy or laceration were found to be the most difficult steps,which required more practices to achieve minimal competence level.Univariate analyses showed the delivery experience,the times of observation/simulation/training,and humanistic care skills might influence the total PS(P<0.05).However,only delivery experience(OR=1.43,95%CI:1.22-1.67)and the times of observation(OR=1.02,95%CI:1.00-1.04)were found to be independently correlated with the total PS in multivariate analyses.Conclusion More than 10 real-life practices were required to achieve the minimal competence of normal vaginal delivery.Enhancing the training on the four difficult steps of vaginal delivery might improve the learning efficiency when delivery opportunities are limited.

Pregnancy is a special period for developing and treating oral diseases.Oral emergencies during pregnancy need to be handled appropriately.Changes in the physiological environments and personal habits of pregnant women in-crease susceptibility to some oral diseases.However,clinical treatment strategies are limited due to the need to ensure the safety of pregnant women and fetuses.Pregnant women should obtain oral health knowledge and enhance their aware-ness.Dentists should adhere to the principle of"prevention before pregnancy,controlling symptoms during pregnancy,and treating diseases after pregnancy"for different pregnancy periods.They should also formulate appropriate treatment plans to control emergencies,prevent disease progression,and avoid harmful effects on pregnant women by using the saf-est,simplest,and most effective strategies that avoid adverse effects on fetuses.Pregnant women and dentists should combine prevention and treatment while collaborating in maintaining oral health during pregnancy.This article focuses on the principles of treatment during pregnancy,and the treatment timing,clinical management,and treatment strategies of different diseases causing oral emergencies during pregnancy are reviewed.