Cavernous transformation of the portal vein （CTPV） is a significant challenge in clinical practice. In recent years， transjugular intrahepatic portosystemic shunt has gradually been applied in the treatment of CTPV patients， but it had a technical success rate and a relatively high surgical risk. The scholars have adopted various modified techniques of portosystemic shunt to improve success rate and safety. However， for CTPV patients with extensive occlusion of the main portal vein， this surgery remains difficult， and individualized strategies for portosystemic shunt should be formulated. This article reports a case of CTPV treated by transjugular extrahepatic portosystemic shunt assisted by endoscopic ultrasound-guided portal vein localization， in order to explore a new pathway for the clinical management of patients with CTPV.

Objective:To analyze the key β-amyloid protein (Aβ) deposition in brain regions affecting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).Methods:Based on the positron emission tomography data of Aβ in the Alzheimer's disease neuroimaging initiative database, the penalized generalized estimating equation (PGEE) and the mixed effects regression forest algorithm (MERF) were used to conduct dimensionality reduction analysis on 164 brain regions with Aβ deposition. Additionally, a multivariate longitudinal data joint model was used to screen the key Aβ deposition brain regions that influence the progression from MCI to AD.Results:Five key brain regions were commonly screened out by the PGEE and MERF models, they were the right prefrontal orbital cortex, the left superior temporal sulcus shore cortex, the right medial orbitofrontal cortex, the left putamen, and the right transverse temporal cortex, respectively. The results of the multivariate longitudinal data joint model based on these 5 Aβ deposition brain regions showed that, except the left superior temporal sulcus shore cortex, the longitudinal change trajectories of the other 4 Aβ deposition brain regions all affected the progression from MCI to AD ( P<0.05). Conclusion:The Aβ deposition in the right prefrontal orbital cortex, right medial orbitofrontal cortex, left putamen and right transverse temporal cortex affect the progression from MCI to AD.

Objective To differentiate disguised visual acuity,(VA)by analyzing discrepancies across diversified visual acuity tests.Methods Volunteers were recruited,and VA(V,and V2)was measured before and after the experiment.Volunteers independently selected an experimental VA(Vt)and were assigned to either the disguised group or the control group.A specially designed heterogeneous optotype chart and a single-optotype chart with variable test distances were used to measure VA(t1,t2,t3),while a standard logarithmic VA chart was used to measure VA(t4).Based on the maximum VA discrepancy(△t),t4 was classified as"real"(≤ 1 line),"disguised"(≥ 2 lines),or"suspicious"(＞1 and＜2 lines with anomaly).Results A total of 126 valid cases were collected,including 30 in the control group and 96 in the disguised group,with VA ranging from 0.2～1.5.All 88 opinions classified as"disguised"were from the disguised group,and all 29 classified as"real"were from the control group.Of the 7 cases deemed"suspicious,"6 were from the disguised group.The sensitivity,specificity,and overall diagnostic accuracy were 91.7％,96.7％(P＜0.0001),and 92.9％(P＜0.0001),respectively.In the disguised group,38 cases exhibited unexplained identifying anomalies.Conclusion A VA discrepancy of ≥ 2 lines is a reliable indicator for detecting disguised visual acuity.Identifying anomalies may serve as a novel and sensitive marker for recognizing visual disguise.

Background Prenatal chronic psychological stress may increase the risk of allergic diseases in children, and eczema is the most common allergic disease in children, the pathogenesis of which is not yet fully understood. Objective To preliminarily clarify the changes in offspring intestinal flora after chronic stress exposure during pregnancy in rats that increases offspring immune imbalance and eczema susceptibility. Methods Thirty SPF-grade adult female SD rats were selected and randomly divided into a model group and a control group (n=15). Sixteen male rats were randomly divided into a model mating group and a control mating group (n=8). A 28-day chronic unpredictable mild stress (CUMS) model during pregnancy was established. On the 7th day of stress, male and female rats were caged in a ratio of 3:1. Blood samples were collected from female rats in each group via angular vein on the 1st day before stress, and on the 7th, 14th, 21st, and 28th days after stress. The content of plasma corticosterone during pregnancy was determined by enzyme-linked immunosorbent assay (ELISA). For the offspring rats, an eczema model was constructed using 2,4-dinitrochlorobenzene (DNCB). The number of scratching times of the offspring rats within 5 min was recorded. The offspring rats were divided into 4 groups: DNCB-CUMS group (MM), DNCB-control group (MC), solvent control-CUMS group (CM), and blank control group (CC), with 8 rats in each group. The eczema was induced once every 3 days, and the induction period was 12 d. The expression level of immunoglobulin E (IgE) in the serum of offspring rats after the eczema induction experiment were determined by ELISA. The concentrations of tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), interleukin-2 (IL-2), interleukin-4 (IL-4), and interleukin-13 (IL-13) in the serum were quantified by multi-parameter flow cytometry. The composition and abundance of intestinal microbiota in the feces of offspring rats were detected by 16S rRNA high-throughput sequencing technology. Results The plasma corticosterone concentrations in the model group were higher than those in the control group on the 7th and 21st days of stress (P<0.05). On the 14th and 21st days of stress, the 1% sucrose preference percentages of female rats in the model group were lower than that in the control group. On the 7th, 14th, and 21st days of stress, the horizontal movement scores of female rats in the model group and the vertical movement scores on the 7th and 14th days were lower than those in the control group (P<0.05). After 6, 9, and 12 d of model building, the scratching frequencies in the MC group and MM group were significantly higher than those in the CC group and CM group (P<0.05). Moreover, there were differences in the contents of cytokines including IFN-γ, IL-2, TNF-α, IL-4, IL-13, and IgE among the offspring rat groups (P<0.05). The CM group and MM group led to an increase in the contents of TNF-α, IL-4, IL-13, and IgE cytokines (P<0.05), while the MM group caused a decrease in the contents of IFN-γ and IL-2 (P<0.05). After the eczema induction experiment, the α-diversity analysis showed that the Simpson index and Shannon index in the CM were higher than those in the CC (P<0.05), indicating that CUMS during the pregnancy of female rats could increase the species abundance of their offspring. The abundances of Prevotella and Lactobacillus in the CM group decreased (P<0.05). Conclusion Intestinal dysbiosis in offspring due to chronic prenatal psychological stress, which may be one of the mechanisms linking maternal stress to immune imbalance and increased susceptibility to eczema in offspring.

Objective:To analyze the key β-amyloid protein (Aβ) deposition in brain regions affecting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).Methods:Based on the positron emission tomography data of Aβ in the Alzheimer's disease neuroimaging initiative database, the penalized generalized estimating equation (PGEE) and the mixed effects regression forest algorithm (MERF) were used to conduct dimensionality reduction analysis on 164 brain regions with Aβ deposition. Additionally, a multivariate longitudinal data joint model was used to screen the key Aβ deposition brain regions that influence the progression from MCI to AD.Results:Five key brain regions were commonly screened out by the PGEE and MERF models, they were the right prefrontal orbital cortex, the left superior temporal sulcus shore cortex, the right medial orbitofrontal cortex, the left putamen, and the right transverse temporal cortex, respectively. The results of the multivariate longitudinal data joint model based on these 5 Aβ deposition brain regions showed that, except the left superior temporal sulcus shore cortex, the longitudinal change trajectories of the other 4 Aβ deposition brain regions all affected the progression from MCI to AD ( P<0.05). Conclusion:The Aβ deposition in the right prefrontal orbital cortex, right medial orbitofrontal cortex, left putamen and right transverse temporal cortex affect the progression from MCI to AD.

Objective To explore the mechanism of action of Neiyi Soft Extract in the treatment of endometriosis(EMS)based on network pharmacology and transcriptomics.Methods A model of SD rat with EMS was replicated by autotransplantation method.After successful modeling,the rats were randomly divided into model group,Neiyi Soft Extract low-,medium-and high-dose groups and dienogest group,with 10 rats in each group.Another sham-operated group(10 rats)was set up.After four consecutive weeks of intervention,the volume size of the endometriotic lesions was measured,and its pathological changes were detected by hematoxylin-eosin(HE)staining.RNA was extracted from the rat lesions for transcriptomic sequencing,and Gene Ontology(GO)enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)functional enrichment analysis were performed on the differential genes among various groups.The main active ingredients and their targets of Neiyi Soft Extract were collected and screened in databases such as TCMSP,the disease targets of EMS were collected through databases such as OMIM,the intersection targets between the drug ingredient targets and the diseases were obtained by using Venn diagrams,GO enrichment analysis and KEGG pathway enrichment analysis of genes in the intersection targets of the drug ingredients and the diseases were performed by using gene enrichment analysis online tool(Metascape).The network pharmacology enrichment pathway and transcriptomics enrichment pathway were taken for intersection,the mRNA and protein expression levels of the key targets on the intersection pathway were correspondingly detected by real-time quantitative polymerase chain reaction(qPCR)and Western Blot,respectively.Results The volume of lesions in the model group was significantly increased compared with that of the sham-operated group(P<0.01);the volume of lesions in rats in the drug-administered groups was significantly reduced compared with that of the model group(P<0.05).A total of 341 active ingredients were obtained from Neiyi Soft Extract,and there were 2 178 disease-related targets of EMS,and 278 intersections between drug targets and disease targets;189 differential genes were screened in the sham-operated group and the model group;255 differential genes were screened in the model group and Neiyi Soft Extract high-dose group;and 740 differential genes were screened in the sham-operated group and the Neiyi Soft Extract high-dose group,including 390 up-regulated genes and 350 down-regulated genes.The result of intersection of KEGG enrichment pathways between the network pharmacology and transcriptomics showed that the distriution mainly included P53 signaling pathway,FOXO signaling pathway,cell cycle,etc.The qPCR and Western Blot validation results showed that Neiyi Soft Extract could inhibit the proliferation of endometrial stromal cells and up-regulated the mRNA and protein expression levels of BAX,Caspase3 in EMS rats(P<0.05 or P<0.01),and down-regulated BCL-2 mRNA and protein expression levels(P<0.05 or P<0.01).Conclusion Neiyi Soft Extract may play a therapeutic role in the treatment of EMS by regulating the P53 signaling pathway.

Objective:To analyze the prenatal ultrasonographic features and prognosis of fetal perirenal urinoma.Methods:This retrospective study included eight fetuses with perirenal urinoma diagnosed by prenatal ultrasound in the Women and Children's Hospital of Chongqing Medical University from January 2017 to August 2023. Descriptive analysis was performed on their prenatal ultrasonographic features, intrauterine intervention measures, postnatal treatment, and prognosis.Results:Among the eight cases, the prenatal ultrasonographic diagnosis was consistent with the postnatal clinical diagnosis in seven cases, with one misdiagnosis (postnatally confirmed as giant renal cyst with multicystic dysplastic kidney). Prenatal ultrasonographic features of fetal perirenal urinoma included cystic mass adjacent to the renal capsule; obvious compression of the surrounding organs and the affected kidney; increased craniocaudal diameter of the affected kidney with thin parenchyma, increased echogenicity, indistinct corticomedullary differentiation, and pyeletasis; poorly visualized partial renal arteries; normal or mildly enlarged contralateral kidney; adequate bladder filling; normal amniotic fluid volume. Two cases underwent intrauterine cyst aspiration with subsequent cyst size reduction (slight re-enlargement during follow-up). Among the remaining six untreated cases, one case showed stable cyst size, while five cases exhibited initial significant cyst enlargement followed by stabilization or regression trend. All eight cases were born at full term (three delivered vaginally and five by cesarean section). The affected kidney was significantly atrophied within one week after birth by ultrasound observation. Impaired function of the affected kidney was shown in four patients by follow-up after discharge (including two cases of intrauterine treatment),one case abandoned treatment due to other illnesses, two cases were lost to follow-up after discharge.Conclusions:Prenatal ultrasound images of perirenal urinary cysts infetuses exhibit typical features. Intrauterine treatment can reduce the compression of large cysts and hydronephrosis on fetal organs, but there is no significant improvement in the recovery of renal function.

Objective:To explore the causal association between insulin-like growth factor-1(IGF-1)and colorectal cancer(CRC)based on two sample Mendelian randomization(MR)analysis.Methods:A bidirectional two sample MR analysis was conducted based on publicly aggregated data from the IEU OpenGWAS project.The inverse variance weighted(IVW)method was used as the main analysis model to assess the causal relationship between IGF-1 and CRC.Additional analyses were performed using weighted median(WM),MR-Egger regression,weighted mode estimator(WME),and simple mode(SM)methods.Sensitivity analysis was performed to assess the robustness of the results.Results:A total of 386 single nucleotide polymorphisms(SNPs)were selected as instrumental variables(IVs)with IGF-1 as the exposure factor.The MR analysis results revealed a positive causal association between IGF-1 and the risk of CRC[odds ratio(OR)=1.178,95％confidence interval(CI):1.092-1.272)](P＜0.001),and the association remained significant after adjusting for height[OR(95％CI)=1.214(1.111,1.327)](P＜0.001).Cochran's Q-test showed heterogeneity among the IVs(P＜0.05),while the horizontal pleiotropy of IV was not detected by the MR-Egger regression(P＞0.05).The leave-one-out analysis showed that the MR results were robust.Reverse MR analysis indicated no reverse causal relationship between IGF-1 and CRC[OR(95％CI):1.017(0.997,1.037)](P=0.103).Conclusion:There is a causal relationship between IGF-1 level and CRC,and elevated IGF-1 level could be a risk factor for CRC.

Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.