To investigate the effect of Ad-ING4 on proliferation and migration of glioma cells and explore its probable mechanism.U251 were infected with Ad-ING4. ING4 gene expression was evaluated by RT-PCR. MTT assay was adopted to evaluate the effect of ING4 on proliferation of U251; Boyden chamber assay was used to check the effect of ING4 on the migration of U251. In ING4 transfected U251, Western blot was used for detecting NGF and TrkA expression; Pull-down assay was used for detecting active RhoA expression.ING4 was overexpressed in Ad-ING4 transfected U251 cells. ING4 inhibited proliferation and migration of U251 significantly. Moreover, overexpression of ING4 result in depression of NGF, TrkA and active RhoA.ING4 mediated inhibition of the proliferation and migration of human glioma cells by down regulating NGF, TrkA and active RhoA expression.
Background and Purpose: Whether imaging parameters would independently predict stroke recurrence in low-risk minor ischemic stroke (MIS) or transient ischemic attack (TIA) according to traditional score system (such as ABCD 2 score, which was termed on the basis of the initials of the five factors: age, blood pressure, clinical features, duration, diabetes) remains unclear. We sought to evaluate the association between imaging parameters and 1-year stroke recurrence in patients with TIA or MIS in different risk stratum stratified by ABCD 2 score. Methods: We included patients with TIA and MIS (National Institutes of Health Stroke Scale score ≤3) with complete baseline vessel and brain imaging data from the Third China National Stroke Registry III. Patients were categorized into different risk groups based on ABCD 2 score (low risk, 0–3; moderate risk, 4–5; and high risk, 6–7). The primary outcome was stroke recurrence within 1 year. Multivariable Cox proportional-hazards regression models were used to assess whether imaging parameters (large artery stenosis, infarction number) were independently associated with stroke recurrence. Results: Of the 7140 patients included, 584 patients experienced stroke recurrence within 1 year. According to the ABCD 2 score, large artery stenosis was associated with higher stroke recurrence in both low-risk (adjusted hazard ratio, 1.746 [95% CI, 1.200–2.540]) and moderate-risk group (adjusted hazard ratio, 1.326 [95% CI, 1.042–1.687]) but not in the high-risk group ( P >0.05). Patients with multiple acute infarctions or single acute infarction had a higher risk of recurrent stroke than those with no infarction in both low- and moderate-risk groups, but not in the high-risk group. Conclusions: Large artery stenosis and infarction number were independent predictors of 1-year stroke recurrence in low-moderate risk but not in high-risk patients with TIA or MIS stratified by ABCD 2 score. This finding emphasizes the importance of early brain and vascular imaging evaluation for risk stratification in patients with TIA or MIS.
Abstract The clinical and mycobacterial features of tuberculous meningitis (TBM) cases in China are not well described; especially in western provinces with poor tuberculosis control. We prospectively enrolled patients in whom TBM was considered in Shaanxi Province, northwestern China, over a 2-year period (September 2010 to December 2012). Cerebrospinal fluid specimens were cultured for Mycobacterium tuberculosis ; with phenotypic and genotypic drug susceptibility testing (DST), as well as genotyping of all positive cultures. Among 350 patients included in the study, 27 (7.7%) had culture-confirmed TBM; 84 (24.0%) had probable and 239 (68.3%) had possible TBM. DST was performed on 25/27 (92.3%) culture positive specimens; 12/25 (48.0%) had “any resistance” detected and 3 (12.0%) were multi-drug resistant (MDR). Demographic and clinical features of drug resistant and drug susceptible TBM cases were similar. Beijing was the most common genotype (20/25; 80.0%) with 9/20 (45%) of the Beijing strains exhibiting drug resistance; including all 3 MDR strains. All (4/4) isoniazid resistant strains had mutations in the katG gene; 75% (3/4) of strains with phenotypic rifampicin resistance had mutations in the rpoB gene detected by Xpert MTB/RIF®. High rates of drug resistance were found among culture-confirmed TBM cases; most were Beijing strains.
Lampreys are early jawless vertebrates that are the key to understanding the evolution of vertebrates. However, the lack of cytomic studies on multiple lamprey organs has hindered progress in this field. Therefore, the present study constructed a comprehensive cell atlas comprising 604,460 cells/nuclei and 70 cell types from 14 lamprey tissue samples. Comparison of cellular evolution across species revealed that most lamprey cell types are homologous to those in jawed vertebrates. We discovered acinar- and islet-like cell populations despite the lack of parenchymal organs in lampreys, providing evidence of pancreatic function in vertebrates. Furthermore, we investigated the heterogeneity of lamprey immune cell populations. Natterin was highly expressed in granulocytes, and NATTERIN was localized to the lipid droplets. Moreover, we developed a transgenic mouse model expressing Natterin to elucidate the role of NATTERIN in lipid metabolism, whereas the browning of white adipose tissue was induced. These findings elucidate vertebrate cellular evolution and advance our understanding of adipose tissue plasticity and metabolic regulation in lampreys.
Background Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making. Objective This study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. Methods A multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables. Results The RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups. Conclusions The ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies.
Background: Microbiological confirmation of tuberculous meningitis (TBM) remains problematic. We assessed the diagnostic performance of a modified Ziehl-Neelsen (MZN) staining method that showed promise in earlier studies. Methods: Patients evaluated for TBM in Shaanxi province, China, were prospectively enrolled from May, 2011 to April, 2013. Cerebrospinal fluid (CSF) specimens were evaluated using the Xpert MTB/RIF® assay, MZN staining, and standard biochemical and microbiological tests, together with detailed clinical and radiological assessment. Results: Among 316 patients included in the study, 38 had definite TBM, 66 probable TBM, 163 possible TBM and 49 "no TBM," using consensus uniform research case definition criteria. Comparing "definite or probable TBM" to "no TBM" MZN staining had higher sensitivity than Xpert MTB/RIF® (88.5 vs. 36.5%), but greatly reduced specificity (71.4 vs. 100.0%); 14/49 (28.6%) cases with "no TBM" tested positive on MZN. Mycobacterium tuberculosis culture was performed in 104/179 (58.1%) of MZN positive samples; 12.5% (13/104) were positive. Using Xpert MTB/RIF® as the reference standard, MZN had a sensitivity of 92.1% (95% CI 79.2-97.3) and specificity of 71.4% (95% CI 57.6-82.2). Conclusion: Xpert MTB/RIF® offered a rapid and specific TBM diagnosis, but sensitivity was poor. MZN was mainly hampered by false positives. Strategies to enhance the sensitivity of Xpert MTB/RIF® or improve the diagnostic accuracy of MZN should be explored.
Along with the rapid development of information technology, big data is playing an important role of support technology in the field of modern modeling. Especially, since the current social actual application of all sectors of big data has become more and more frequent, the rise of the actual application of big data leads people to research big data from all walks of life, and also lead to an increase in application research. As an important part of social development, archives management can protect the basic rights and interests of the people to a certain extent. As the center of archives management, archives management personnel need to take archives management as the central point, and pay attention to improving the overall quality of archives management, to provide support for archives management work.
Accurate quantification of the root surface area (RSA) plays a decisive role in the advancement of periodontal, orthodontic, and restorative treatment modalities. In this study, we aimed to develop a dynamic threshold-based computer-aided system for segmentation and calculation of the RSA of isolated teeth on cone-beam computed tomography (CBCT) and to assess the accuracy of the measured data.We selected 24 teeth to be extracted, including single-rooted and multi-rooted teeth, from 22 patients who required tooth extraction. In the experimental group, we scanned 24 isolated teeth using CBCT with a voxel size of 0.3 mm. We designed a computer-aided system based on a personalized dynamic threshold algorithm to automatically segment the roots of 24 isolated teeth in CBCT images and calculate the RSA. In the control group, we employed digital intraoral scanner devices to perform optical scanning on 24 isolated teeth and subsequently manually segmented the roots using 3-matic software to calculate the RSA. We used the paired t-test (P < 0.05) and Bland-Altman plots to analyze the consistency of the two measurement methods.The results of the paired t-test showed that there was no significant difference in the RSAs obtained using the dynamic threshold method and the optical scanning image reconstruction (t = 1.005, P = 0.325 > 0.05). As per the Bland-Altman plot, the results were evenly distributed within the region of ± 1.96 standard deviations of the mean, with no increasing or decreasing trends and good consistency.In this study, we designed a computer-aided root segmentation system based on a personalized dynamic threshold algorithm to automatically segment the roots of isolated teeth in CBCT images with a voxel size of 0.3 mm. We found that the RSA calculated using this approach was highly accurate, and a voxel of 0.3 mm in size could accurately display the surface area data in CBCT images. Overall, our findings in this study provide a foundation for future work on accurate automatic segmentation of tooth roots in full-mouth CBCT images and the computation of RSA.
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