This article reported a case of autosomal dominant intellectual developmental disorder type 22 caused by a heterozygous mutation in the ZBTB18 gene. At 24 +4 weeks of gestation, prenatal ultrasound indicated a short outer diameter of the fetal corpus callosum and bilateral ventricular dilatation. Whole-genome copy number variation analysis of the fetus showed no abnormalities. Whole exome sequencing (WES) and Sanger sequencing validation of the family revealed the fetus carried a c.1374_1383del(p.S459*) heterozygous mutation in the ZBTB18 gene (NM_205768.3), which was neither phenotypically present nor genotypically detected in the parents, suggesting a de novo mutation. Based on the clinical manifestations, the fetus was diagnosed with autosomal dominant intellectual developmental disorder type 22. After genetic counseling, the pregnant woman opted for termination of the pregnancy. This case highlights the correlation between prenatal ultrasonic detection of callosal dysgenesis and lateral ventricular enlargement and intellectual developmental disorders caused by gene mutations. Furthermore, it expands the mutation spectrum of the ZBTB18 gene, thereby facilitating prenatal diagnosis and genetic counseling.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To explore the genetic and clinical characteristics of Guizhou patients with enlarged vestibular aqueduct（EVA） syndrome through combined SLC26A4 variant analysis and clinical phenotype analysis. Methods:Seventy-two EVA patients underwent comprehensive genetic testing using a multiplex PCR-based deafness gene panel and next-generation sequencing（NGS）. The audiological and temporal bone imaging characteristics were compared across mutation subtypes. Results:A total of 27 pathogenic loci of SLC26A4 were detected in 72 patients, including c.919-2A>G in 79.2%（57/72）. A novel deletion（c.1703_1707+6del） was discovered. Among 65 cases, truncated mutations were 89.2%（58/65）, 52.3%（34/65）, 28（43.1%） and 7（10.8%）. No significant differences were observed in the midpoint diameter of the vestibular aqueduct and the incidence of incomplete partitioning typeⅡ（IP-Ⅱ） of the cochlea among the three groups of patients. Moreover, there was no difference in the midpoint diameter of different vestibular pipes or the combination with IP-Ⅱ. Conclusion:The most common mutation site of SLC26A4 in EVA patients in Guizhou is c.919-2A>G, though genotype-phenotype correlations remain elusive. The detection of 27 mutation sites and the discovery of new mutation sites suggested the precise diagnostic significance of NGS technology in EVA patients in Guizhou.
Humans ; Sulfate Transporters ; Vestibular Aqueduct/abnormalities* ; Mutation ; Membrane Transport Proteins/genetics* ; Hearing Loss, Sensorineural/genetics* ; Male ; Female ; Child ; Adolescent ; Child, Preschool ; Adult ; Young Adult ; Phenotype ; High-Throughput Nucleotide Sequencing

Background: Epimedii Folium, first recorded in the Shennong’s Classic of Materia Medica (Shen Nong Ben Cao Jing), is a traditional Chinese medicine (TCM) known for its effects of “benefiting Qi and strengthening the heart.” Icariside II (ICS II) is one of the main active components of Epimedii Folium, possessing cardiovascular protective and anti-inflammatory properties. However, the potential mechanisms of ICS II on myocardial ischemia (MI) remain unclear. Objective: The aim of the study was to investigate the effects and preliminary molecular mechanisms of ICS II in treating isoproterenolinduced MI in rats. Methods: A rat model of MI was established by subcutaneous injection of isoproterenol. Electrocardiography, echocardiography, myocardial enzymes analysis, heart weight index, triphenyltetrazolium chloride staining, histopathology, TUNEL staining, RT-qPCR, and Western blot were employed to evaluate the effects and preliminary molecular mechanisms of ICS II on MI rats. Results: Pharmacodynamic studies suggested that ICS II inhibited ST-segment elevation in electrocardiograms, improved cardiac function, reduced heart weight index and myocardial enzyme levels, decreased myocardial infarct size, alleviated cardiac histological damage, and inhibited apoptosis, thereby exerting cardioprotective effects in MI rats. Further studies revealed that ICS II may partially inhibit the expression of NLRP3/Caspase-1 axis-related targets at both protein and mRNA levels. Conclusions: Our findings indicate that ICS II exerts anti-MI effects, and its preliminary molecular mechanisms may be related to inhibiting the activation of the NLRP3/Caspase-1 axis to alleviate inflammatory responses.

Background: Epimedii Folium, first recorded in the Shennong’s Classic of Materia Medica (Shen Nong Ben Cao Jing), is a traditional Chinese medicine (TCM) known for its effects of “benefiting Qi and strengthening the heart.” Icariside II (ICS II) is one of the main active components of Epimedii Folium, possessing cardiovascular protective and anti-inflammatory properties. However, the potential mechanisms of ICS II on myocardial ischemia (MI) remain unclear. Objective: The aim of the study was to investigate the effects and preliminary molecular mechanisms of ICS II in treating isoproterenolinduced MI in rats. Methods: A rat model of MI was established by subcutaneous injection of isoproterenol. Electrocardiography, echocardiography, myocardial enzymes analysis, heart weight index, triphenyltetrazolium chloride staining, histopathology, TUNEL staining, RT-qPCR, and Western blot were employed to evaluate the effects and preliminary molecular mechanisms of ICS II on MI rats. Results: Pharmacodynamic studies suggested that ICS II inhibited ST-segment elevation in electrocardiograms, improved cardiac function, reduced heart weight index and myocardial enzyme levels, decreased myocardial infarct size, alleviated cardiac histological damage, and inhibited apoptosis, thereby exerting cardioprotective effects in MI rats. Further studies revealed that ICS II may partially inhibit the expression of NLRP3/Caspase-1 axis-related targets at both protein and mRNA levels. Conclusions: Our findings indicate that ICS II exerts anti-MI effects, and its preliminary molecular mechanisms may be related to inhibiting the activation of the NLRP3/Caspase-1 axis to alleviate inflammatory responses.

Background: Epimedii Folium, first recorded in the Shennong’s Classic of Materia Medica (Shen Nong Ben Cao Jing), is a traditional Chinese medicine (TCM) known for its effects of “benefiting Qi and strengthening the heart.” Icariside II (ICS II) is one of the main active components of Epimedii Folium, possessing cardiovascular protective and anti-inflammatory properties. However, the potential mechanisms of ICS II on myocardial ischemia (MI) remain unclear. Objective: The aim of the study was to investigate the effects and preliminary molecular mechanisms of ICS II in treating isoproterenolinduced MI in rats. Methods: A rat model of MI was established by subcutaneous injection of isoproterenol. Electrocardiography, echocardiography, myocardial enzymes analysis, heart weight index, triphenyltetrazolium chloride staining, histopathology, TUNEL staining, RT-qPCR, and Western blot were employed to evaluate the effects and preliminary molecular mechanisms of ICS II on MI rats. Results: Pharmacodynamic studies suggested that ICS II inhibited ST-segment elevation in electrocardiograms, improved cardiac function, reduced heart weight index and myocardial enzyme levels, decreased myocardial infarct size, alleviated cardiac histological damage, and inhibited apoptosis, thereby exerting cardioprotective effects in MI rats. Further studies revealed that ICS II may partially inhibit the expression of NLRP3/Caspase-1 axis-related targets at both protein and mRNA levels. Conclusions: Our findings indicate that ICS II exerts anti-MI effects, and its preliminary molecular mechanisms may be related to inhibiting the activation of the NLRP3/Caspase-1 axis to alleviate inflammatory responses.

Depression is a common mental disorder with adverse effects on human health and quality of life.Although its pathogenesis remains unclear,numerous recent studies have indicated a close association between depression and mitochondrial dysfunction.This review explores the critical role of mitochondrial dysfunction in the pathogenesis of depression from the aspects of energy metabolism disorders,oxidative stress and inflammatory responses,mitochondrial DNA damage and mutations,and the imbalance of the mitochondrial quality control systems.We also summarize recent advances in the use of traditional Chinese medicine(TCM)to treat depression by regulating mitochondrial function.The findings show the potential of TCM in enhancing mitochondrial energy metabolism,reducing oxidative stress and inflammatory responses,improving mitochondrial DNA,and modulating the mitochondrial quality control systems,including for the in-depth study of the pathogenesis of depression and its treatment with TCM.

Objective To construct a risk prediction model for atherosclerosis(AS)in patients with rheumatoid arthritis(RA)based on Lasso-Logistic regression analysis and provide a scientific basis for individualized clinical intervention.Methods The retrospective clinical data were collected from 344 RA patients,including 86 patients with AS(RA+AS group)and 258 patients with without AS(RA group).The clinical characteristics and initial laboratory test results were compared between the two groups.Lasso regression was used to screen the key predictive variables,and Logistic regression was combined to construct the prediction mode.The discrimination of the model was evaluated through the receiver operating characteristic(ROC)curve and the area under the curve(AUC).The Hosmer-Lemeshow test was used to assess the calibration,and decision curve analysis was used to verify the clinical applicability of the model.Results Seven predictive variables were identified including RA disease duration,DAS28 score,C-reactive protein(CRP),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),fasting blood glucose(FBG)and hypertension.The risk prediction model for AS in RA patients was:Logit(P)=-2.674+0.605×RA disease duration+0.393×DAS28 score+0.310×CRP+1.346×TG-2.289×HDL-C+0.679×FBG+0.711×hypertension.The AUC of the model was 0.965(95%CI:0.943-0.987),and the Hosmer-Lemeshow test showed χ2=0.547,P=1.000,indicating good discrimination and calibration.Clinical decision curve analysis showed that the probability threshold ranged from 7%to 92%,demonstrating high clinical applicability.Conclusion The AS risk prediction model constructed in this study for RA patients can effectively identify high-risk individuals,supporting the development of personalized prevention and treatment strategies.

This article reported a case of autosomal dominant intellectual developmental disorder type 22 caused by a heterozygous mutation in the ZBTB18 gene. At 24 +4 weeks of gestation, prenatal ultrasound indicated a short outer diameter of the fetal corpus callosum and bilateral ventricular dilatation. Whole-genome copy number variation analysis of the fetus showed no abnormalities. Whole exome sequencing (WES) and Sanger sequencing validation of the family revealed the fetus carried a c.1374_1383del(p.S459*) heterozygous mutation in the ZBTB18 gene (NM_205768.3), which was neither phenotypically present nor genotypically detected in the parents, suggesting a de novo mutation. Based on the clinical manifestations, the fetus was diagnosed with autosomal dominant intellectual developmental disorder type 22. After genetic counseling, the pregnant woman opted for termination of the pregnancy. This case highlights the correlation between prenatal ultrasonic detection of callosal dysgenesis and lateral ventricular enlargement and intellectual developmental disorders caused by gene mutations. Furthermore, it expands the mutation spectrum of the ZBTB18 gene, thereby facilitating prenatal diagnosis and genetic counseling.

Objective To construct a risk prediction model for atherosclerosis(AS)in patients with rheumatoid arthritis(RA)based on Lasso-Logistic regression analysis and provide a scientific basis for individualized clinical intervention.Methods The retrospective clinical data were collected from 344 RA patients,including 86 patients with AS(RA+AS group)and 258 patients with without AS(RA group).The clinical characteristics and initial laboratory test results were compared between the two groups.Lasso regression was used to screen the key predictive variables,and Logistic regression was combined to construct the prediction mode.The discrimination of the model was evaluated through the receiver operating characteristic(ROC)curve and the area under the curve(AUC).The Hosmer-Lemeshow test was used to assess the calibration,and decision curve analysis was used to verify the clinical applicability of the model.Results Seven predictive variables were identified including RA disease duration,DAS28 score,C-reactive protein(CRP),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),fasting blood glucose(FBG)and hypertension.The risk prediction model for AS in RA patients was:Logit(P)=-2.674+0.605×RA disease duration+0.393×DAS28 score+0.310×CRP+1.346×TG-2.289×HDL-C+0.679×FBG+0.711×hypertension.The AUC of the model was 0.965(95%CI:0.943-0.987),and the Hosmer-Lemeshow test showed χ2=0.547,P=1.000,indicating good discrimination and calibration.Clinical decision curve analysis showed that the probability threshold ranged from 7%to 92%,demonstrating high clinical applicability.Conclusion The AS risk prediction model constructed in this study for RA patients can effectively identify high-risk individuals,supporting the development of personalized prevention and treatment strategies.