Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.

OBJECTIVE: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC). MATERIALS AND METHODS: A retrospective analysis of preoperative DECT examination was conducted on 112 patients diagnosed with BUC. This cohort included 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. DECT can provide material decomposition images of venous phase Iodine maps and Water maps based on the differences in attenuation of substances, as well as VMIs at 40 to 140 keV (interval 10 keV). A total of 13 image sets were obtained, and radiomics features were extracted and analyzed from each set to achieve preoperative prediction of BUC. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. Receiver operating curves (ROC) were plotted to evaluate the performance of 13 models obtained from each image set. RESULTS: Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100 keV VMIs included the Dependence Variance of the GLDM feature set. There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models. In the testing set, the AUCs of the models established through 40 keV to 140 keV (interval of 10 keV) image sets were 0.895, 0.874, 0.855, 0.889, 0.841, 0.868, 0.852, 0.847, 0.889, 0.887 and 0.863 respectively. The AUCs for the models established using the Iodine maps and Water maps image sets were 0.873 and 0.852, respectively. CONCLUSION: Despite the differences in the selected radiomic features from DECT multi-parameter images, the performance of radiomics models in predicting the pathological grading of BUC was not affected by the variations in the types of images used for model training.

Multi-DECT image-based intratumoral and peritumoral radiomics for preoperative prediction of muscle invasion in bladder cancer.

OBJECTIVES: To assess the predictive value of intratumoral and peritumoral radiomics based on Dual-energy CT urography (DECTU) multi-images for preoperatively predicting the muscle invasion status of bladder cancer (BCa). MATERIAL AND METHODS: This retrospective analysis involved 202 BCa patients who underwent DECTU. DECTU-derived quantitative parameters were identified as risk factors through stepwise regression analysis to construct a DECT model. The radiomic features from the intratumoral and 3 mm outward peritumoral regions were extracted from the 120 kVp-like, 40 keV, 100 keV, and iodine-based material-decomposition (IMD) images in the venous-phase and were screened using Mann-Whitney U test, Spearman correlation analysis, and LASSO. Radiomics models were developed using the Multilayer Perceptron for the intratumoral, peritumoral and intra- and peritumoral (IntraPeri) regions. Subsequently, a nomogram was created by integrating the multi-image IntraPeri radiomics and DECT model. Model performance was evaluated using area-under-the-curve (AUC), accuracy, sensitivity, and specificity. RESULTS: Normalized iodine concentration (NIC) was identified as an independent predictor for the DECT model. The IntraPeri model demonstrated superior performance compared to the intratumoral and peritumoral models both in 40 keV (0.830 vs. 0.766 vs. 0.763) and IMD images (0.881 vs. 0.840 vs. 0.821) in the test cohort. In the test cohort, the nomogram exhibited the best predictability (AUC=0.886, accuracy=0.836, sensitivity=0.737, and specificity=0.881), outperformed the DECT model (AUC=0.763, accuracy=0.754, sensitivity=0.632, and specificity=0.810) in predicting muscle invasion status of BCa with a statistically significant difference (p < 0.05). CONCLUSION: The nomogram, incorporating IntraPeri radiomics and NIC, serves as a valuable and non-invasive tool for preoperatively assessing the muscle invasion status of BCa.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

OBJECTIVE: The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). MATERIALS AND METHODS: A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. RESULTS: In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. CONCLUSIONS: By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Comprehensive Evaluation and Selection of 192 Maize Accessions from Different Sources.

In the period 2022-2023, an analysis of fourteen phenotypic traits was conducted across 192 maize accessions in the Aral region of Xinjiang. The Shannon-Wiener diversity index was employed to quantify the phenotypic diversity among the accessions. Subsequently, a comprehensive evaluation of the index was performed utilizing correlation analysis, principal component analysis (PCA) and cluster analysis. The results highlighted significant findings: (1) A pronounced diversity was evident across the 192 maize accessions, accompanied by complex interrelationships among the traits. (2) The 14 phenotypic traits were transformed into 3 independent indicators through principal component analysis: spike factor, leaf width factor, and number of spikes per plant. (3) The 192 materials were divided into three groups using cluster analysis. The phenotypes in Group III exhibited the best performance, followed by those in Group I, and finally Group II. The selection of the three groups can vary depending on the breeding objectives. This study analysed the diversity of phenotypic traits in maize germplasm resources. Maize germplasm was categorised based on similar phenotypes. These findings provide theoretical insights for the study of maize accessions under analogous climatic conditions in Alar City, which lay the groundwork for the efficient utilization of existing germplasm as well as the development and selection of new varieties.

Identifying potential drug targets for varicose veins through integration of GWAS and eQTL summary data.

Background: Varicose veins (VV) are a common chronic venous disease that is influenced by multiple factors. It affects the quality of life of patients and imposes a huge economic burden on the healthcare system. This study aimed to use integrated analysis methods, including Mendelian randomization analysis, to identify potential pathogenic genes and drug targets for VV treatment. Methods: This study conducted Summary-data-based Mendelian Randomization (SMR) analysis and colocalization analysis on data collected from genome-wide association studies and cis-expression quantitative trait loci databases. Only genes with PP.H4 > 0.7 in colocalization were chosen from the significant SMR results. After the above analysis, we screened 12 genes and performed Mendelian Randomization (MR) analysis on them. After sensitivity analysis, we identified four genes with potential causal relationships with VV. Finally, we used transcriptome-wide association studies and The Drug-Gene Interaction Database data to identify and screen the remaining genes and identified four drug targets for the treatment of VV. Results: We identified four genes significantly associated with VV, namely, KRTAP5-AS1 [Odds ratio (OR) = 1.08, 95% Confidence interval (CI): 1.05-1.11, p = 1.42e-10] and PLEKHA5 (OR = 1.13, 95% CI: 1.06-1.20, p = 6.90e-5), CBWD1 (OR = 1.05, 95% CI: 1.01-1.11, p = 1.42e-2) and CRIM1 (OR = 0.87, 95% CI: 0.81-0.95, p = 3.67e-3). Increased expression of three genes, namely, KRTAP5-AS1, PLEKHA5, and CBWD1, was associated with increased risk of the disease, and increased expression of CRIM1 was associated with decreased risk of the disease. These four genes could be targeted for VV therapy. Conclusion: We identified four potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that KRTAP5-AS1, PLEKHA5, CBWD1, and CRIM1 might be potential drug targets for varicose veins.

The activation of HAMP promotes colorectal cancer cell proliferation through zinc-mediated upregulation of SMAD4 expression.

Background: Colorectal cancer (CRC) poses a significant challenge in digestive system diseases, and emerging evidence underscores the critical role of zinc metabolism in its progression. This study aimed to investigate the clinical implications of genes at the intersection of zinc metabolism and CRC. Methods: We downloaded CRC prognosis-related genes and zinc metabolism-related genes from public databases. Then, the overlapping genes were screened out, and bioinformatics analysis was performed to obtain the hub gene associated with CRC prognosis. Subsequently, in vitro assays were carried out to investigate the expression of this hub gene and its exact mechanism between zinc metabolism and CRC. Results: HAMP was identified as the hub CRC prognostic gene from overlapping zinc metabolism-related and CRC prognostic genes. In vitro analysis showed HAMP was over-expressed in CRC, and its knockdown inhibited RKO and HCT-116 cell invasion and migration significantly. ZnSO4 induced HAMP up-regulation to promote cell proliferation, while TPEN decreased HAMP expression to inhibit cell proliferation. Importantly, we further found that ZnSO4 enhanced SMAD4 expression to augment HAMP promoter activity and promote cell proliferation in CRC. Conclusions: HAMP stands out as an independent prognostic factor in CRC, representing a potential therapeutic target. Its intricate regulation by zinc, particularly through the modulation of SMAD4, unveils a novel avenue for understanding CRC biology. This study provides valuable insights into the interplay between zinc metabolism, HAMP, and CRC, offering promising clinical indicators for CRC patients. The findings present a compelling case for further exploration and development of targeted therapeutic strategies in CRC management.

Bacteremia caused by Nocardia farcinica: a case report and literature review.

BACKGROUND: Nocardia farcinica is one of the most common Nocardia species causing human infections. It is an opportunistic pathogen that often infects people with compromised immune systems. It could invade human body through respiratory tract or skin wounds, cause local infection, and affect other organs via hematogenous dissemination. However, N. farcinica-caused bacteremia is uncommon. In this study, we report a case of bacteremia caused by N. farcinica in China. CASE PRESENTATION: An 80-year-old woman was admitted to Peking Union Medical College Hospital with recurrent fever, right abdominal pain for one and a half month, and right adrenal gland occupation. N. farcinica was identified as the causative pathogen using blood culture and plasma metagenomics next-generation sequencing (mNGS). The clinical considerations included bacteremia and adrenal gland abscess caused by Nocardia infection. As the patient was allergic to sulfanilamide, imipenem/cilastatin and linezolid were empirically administered. Unfortunately, the patient eventually died less than a month after the initiation of anti-infection treatment. CONCLUSION: N. farcinica bacteremia is rare and its clinical manifestations are not specific. Its diagnosis depends on etiological examination, which can be confirmed using techniques such as Sanger sequencing and mNGS. In this report, we have reviewed cases of Nocardia bloodstream infection reported in the past decade, hoping to improve clinicians' understanding of Nocardia bloodstream infection and help in its early diagnosis and timely treatment.

Prevalence of diverse colorectal polyps and risk factors for colorectal carcinoma in situ and neoplastic polyps.

BACKGROUND: Most colorectal cancers originate from precancerous polyps. This study aimed to determine the prevalence of colorectal polyps with diverse pathological morphologies and to explore the risk factors for colorectal carcinoma in situ (CCS) and neoplastic polyps. METHODS: Inpatients admitted from January 2018 to May 2023 were screened through the hospital information system. Polyps were classified according to pathological morphology. The prevalence of polyps was described by frequency and 95% confidence interval. Univariate and multivariate logistic regression analyses were used to explore the risk factors for CCS and neoplastic polyps. RESULTS: In total, 2329 individuals with 3550 polyps were recruited. Among all patients, 76.99% had neoplastic polyps and 44.31% had advanced adenomas. Tubular adenoma had the highest prevalence at 60.15%, and the prevalence of CCS was 3.86%. Patients with a colorectal polyp diameter ≥ 1.0 cm or number ≥ 3 were 8.07 times or 1.98 times more likely to develop CCS than were those with a diameter < 1.0 cm or number < 3, respectively (OR 8.07, 95%CI 4.48-14.55, p < 0.0001; and OR 1.98, 95%CI 1.27-3.09, p = 0.002). The risk of CCS with schistosome egg deposition was also significantly increased (OR 2.70, 95%CI 1.05-6.98). The higher the levels of carbohydrate antigen (CA) 724 (OR 1.01, 95%CI 1.00-1.02) and CA211 (OR 1.16, 95%CI 1.03-1.32) in patients with colorectal polyps were, the greater the risk of CCS. When colorectal neoplastic polyps were analyzed, we discovered that for each 1-year increase in age, the risk of neoplastic polyps increased by 3% (OR 1.03, 95%CI 1.02-1.04), p < 0.0001. Patients with a polyp diameter ≥ 1.0 cm had a 2.11-fold greater risk of neoplastic polyps compared to diameter < 1.0 cm patients (OR 3.11, 95%CI 2.48-3.92), p < 0.0001. In addition, multiple polyps and CA199 levels are risk factors for neoplastic polyps. CONCLUSION: More than 3/4 of colorectal polyp patients have neoplastic polyps. Patients are more inclined to develop CCS and neoplastic polyps if they have large polyps (> 1.0 cm) or multifocal polyps. The levels of the tumor markers CA724 and CA211 show some potential usefulness for predicting CCS and may be exploited for early identification of high-risk populations.

Application of virtual diagnosis and treatment combined with medical record teaching method in standardized training of general practitioner.

The aim of this study was to explore the effect of virtual diagnosis and treatment combined with the medical record teaching method in standardized training of general practitioners. Eighty students who had standardized general practice training, from March 2020 to March 2022, in the grassroots practice base of general practitioner training in the affiliated Hospital of our Medical College were retrospectively analyzed and divided into 2 groups according to the teaching method that they received. The differences in assessment scores, critical thinking, clinical thinking ability, learning autonomy ability, and classroom teaching effectiveness were compared, and the students' satisfaction with teaching was investigated. The scores of theoretical knowledge, skill operation, medical history collection, and case analysis in the study group were notably higher (P < .05). In the study group, scores in truth-seeking, openness to knowledge, analytical ability, systematic ability, self-confidence, curiosity, and cognitive maturity were significantly higher (P < .05). A notable improvement was observed in the study group's scores on systematic thinking ability and evidence-based thinking ability, as well as the scores on critical thinking ability after teaching (P < .05). The scores of learning interest, self-management, plan implementation, and cooperation ability improved notably after teaching (P < .05). Learning target, learning processes, learning effects, classroom environment construction, teaching strategy, and technology application in the study group were significantly higher than those in the control group (P < .05). The satisfaction rate in the study group was significantly higher than that in the control group (P < .05). Virtual diagnosis and treatment combined with case-based learning teaching has a very good effect in the standardized training of general practitioners. Students are generally satisfied with their learning experience, which can improve their critical thinking ability and clinical thinking skills. This teaching method is worth further popularizing.

Screening of salt tolerance of maize (Zea mays L.) lines using membership function value and GGE biplot analysis.

Soil salinization is a widely recognized global environmental concern that has a significant impact on the sustainable development of agriculture at a global scale. Maize, a major crop that contributes to the global agricultural economy, is particularly vulnerable to the adverse effects of salt stress, which can hinder its growth and development from germination to the seedling stage. This study aimed to screen highly salt-tolerant maize varieties by using four NaCl concentrations of 0, 60, 120, and 180 mMol/L. Various agronomic traits and physiological and biochemical indices associated with salt tolerance were measured, and salt tolerance was evaluated using principal component analysis, membership function method, and GGE biplot analysis. A total of 41 local maize varieties were assessed based on their D values. The results show that stem thickness, germ length, radicle length, leaf area, germination rate, germination index, salt tolerance index, and seed vigor all decreased as salt concentration increased, while electrical conductivity and salt injury index increased with the concentration of saline solution. Under the stress of 120 mMol/L and 180 mMol/L NaCl, changes in antioxidant enzymes occurred, reflecting the physiological response mechanisms of maize under salt stress. Principal component analysis identified six major components including germination vigor, peroxidase (POD), plant height, embryo length, SPAD chlorophyll and proline (PRO) factors. After calculating the comprehensive index (D value) of each variety's performance in different environments using principal component analysis and the membership function method, a GGE biplot analysis was conducted to identify maize varieties with good salt tolerance stability: Qun Ce 888, You Qi 909, Ping An 1523, Xin Nong 008, Xinyu 66, and Hong Xin 990, as well as varieties with poor salt tolerance: Feng Tian 14, Xi Meng 668, Ji Xing 218, Gan Xin 2818, Hu Xin 712, and Heng Yu 369. Furthermore, it was determined that a 120 mMol/L NaCl concentration was suitable for screening maize varieties during germination and seedling stages. This study further confirmed the reliability of GGE biplot analysis in germplasm selection, expanded the genetic resources of salt-tolerant maize, and provided theoretical references and germplasm utilization for the introduction of maize in saline-alkali areas. These research findings contribute to a better understanding of maize salt tolerance and promote its cultivation in challenging environments.

IL1A regulates the inflammation in gout through the Toll-like receptors pathway.

Objective: Gout is a dangerous metabolic condition related to monosodium urate (MSU). Our aim is to study the molecular mechanisms underlying gout and to identify potential clinical biomarkers by bioinformatics analysis and experimental validation. Methods: In this study, we retrieved the overlapping genes between GSE199950-Differential Expressed Genes (DEGs) dataset and key module in Weighted Gene Co-Expression Network Analysis (WGCNA) on GSE199950. These genes were then analyzed by protein-protein interaction (PPI) network, expression and Gene Set Enrichment Analysis to identify the hub gene related to gout. Then, the gene was investigated by peripheral blood mononuclear cells (PBMCs), immunoassay and cell experiments like western blotting to uncover its underlying mechanism in gout cells. Results: From the turquoise module and 83 DEGs, we identified 62 overlapping genes, only 11 genes had mutual interactions in PPI network and these genes were highly expressed in MSU-treated samples. Then, it was found that the IL1A (interleukin 1 alpha) was the only one gene related to Toll-like receptor signaling pathway that was associated with the occurrence of gout. Thus, IL1A was determined as the hub gene in this study. In immunoassay, IL1A was significantly positively correlated with B cells and negatively correlated with macrophages. Moreover, IL1A is highly expressed in gout patients,it has a good clinical diagnostic value. Finally, the results of in vitro experiments showed that after knocking down IL1A, the expressions of pro-inflammatory cytokines and Toll-like receptor signaling pathway-related proteins (TLR2, TLR4, MyD88) were all reduced. Conclusion: It is confirmed that IL1A is a promoting gene in gout with a good diagnostic value, and specifically it affects the inflammation in gout through Toll-like receptor pathway. Our research offers fresh perspectives on the pathophysiology of gout and valuable directions for future diagnosis and treatment.

Estimation of renal function using iodine maps in dual-energy spectral computed tomography urography: a feasibility and accuracy study.

PURPOSE: To explore the feasibility of measuring glomerular filtration rate (GFR) using iodine maps in dual-energy spectral computed tomography urography (DEsCTU) and correlate them with the estimated GFR (eGFR) based on the equation of creatinine-cystatin C. MATERIALS AND METHODS: One hundred and twenty-eight patients referred for DEsCTU were retrospectively enrolled. The DEsCTU protocol included non-contrast, nephrographic, and excretory phase imaging. The CT-derived GFR was calculated using the above 3-phase iodine maps (CT-GFRiodine) and 120 kVp-like images (CT-GFR120kvp) separately. CT-GFRiodine and CT-GFR120kvp were compared with eGFR using paired t-test, correlation analysis, and Bland-Altman plots. The receiver operating characteristic curves were used to test the renal function diagnostic performance with CT-GFR120kvp and CT-GFRiodine. RESULTS: The difference between eGFR (89.91 ± 18.45 ml·min-1·1.73 m-2) as reference standard and CT-GFRiodine (90.06 ± 20.89 ml·min-1·1.73 m-2) was not statistically significant, showing excellent correlation (r = 0.88, P < 0.001) and agreement (± 19.75 ml·min-1·1.73 m-2, P = 0.866). The correlation between eGFR and CT-GFR120kvp (66.13 ± 19.18 ml·min-1·1.73 m-2) was poor (r = 0.36, P < 0.001), and the agreement was poor (± 40.65 ml·min-1·1.73 m-2, P < 0.001). There were 62 patients with normal renal function and 66 patients with decreased renal function based on eGFR. The CT-GFRiodine had the largest area under the curve (AUC) for distinguishing between normal and decreased renal function (AUC = 0.951). CONCLUSION: The GFR can be calculated accurately using iodine maps in DEsCTU. DEsCTU could be a non-invasive and reliable one-stop-shop imaging technique for evaluating both the urinary tract morphology and renal function.

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Mineral Density Assessment From Low-Dose Chest Computed Tomography.

RATIONALE AND OBJECTIVES: To develop an intelligent diagnostic model for osteoporosis screening based on low-dose chest computed tomography (LDCT). The model incorporates automatic deep-learning thoracic vertebrae of cancellous bone (TVCB) segmentation model and radiomics analysis. MATERIALS AND METHODS: A total of 442 participants who underwent both LDCT and quantitative computed tomography (QCT) examinations were enrolled and were randomly allocated to the training, internal testing, and external testing cohorts. The TVCB automatic segmentation model was trained using VB-Net. The accuracy of the segmentation was evaluated using the Dice coefficient. Predictive models for assessing bone mineral density (BMD) were constructed utilizing radiomics analysis based on automatic segmentation (ASeg model) and manual segmentation (MSeg model), respectively. The BMD predictive model based on ASeg and MSeg included the identification of normal and abnormal BMD (first-level model), and osteopenia and osteoporosis (second-level model). The diagnostic performance of the radiomics models were evaluated using the area under the curve (AUC), sensitivity and specificity. RESULTS: The Dice coefficients of the TVCB segmentation model in the internal and external testing cohorts were found to be 0.988 ± 0.014 and 0.939 ± 0.034, respectively. In the first-level model, the AUC of the ASeg model exhibited comparable performance to that of the MSeg model for both the internal (0.985 vs. 0.946, P = 0.080) and external (0.965 vs. 0.955, P = 0.724) testing cohorts. Similarly, in the second-level model, the AUC of the ASeg model was found to be comparable to that of the MSeg model for both the internal (0.933 vs. 0.920, P = 0.794) and external (0.907 vs. 0.892, P = 0.805) testing cohorts. CONCLUSION: A fully automated pipeline for TVCB segmentation and BMD assessment with radiomics analysis can be used for opportunistic BMD screening in chest LDCT.

Multicenter comparative genomic study of Klebsiella oxytoca complex reveals a highly antibiotic-resistant subspecies of Klebsiellamichiganensis.

BACKGROUND: The Klebsiella oxytoca complex is an opportunistic pathogen that has been recently identified as an actual complex. However, the characteristics of each species remain largely unknown. We aimed to study the clinical prevalence, antimicrobial profiles, genetic differences, and interaction with the host of each species of this complex. METHODS: One hundred and three clinical isolates of the K. oxytoca complex were collected from 33 hospitals belonging to 19 areas in China from 2020 to 2021. Species were identified using whole genome sequencing based on average nucleotide identity. Clinical infection characteristics of the species were analyzed. Comparative genomics and pan-genome analyses were performed on these isolates and an augmented dataset, including 622 assemblies from the National Center for Biotechnology Information. In vitro assays evaluating the adhesion ability of human respiratory epithelial cells and survivability against macrophages were performed on randomly selected isolates. RESULTS: Klebsiella michiganensis (46.6%, 48/103) and K. oxytoca (35.92%, 37/103) were the major species of the complex causing human infections. K. michiganensis had a higher genomic diversity and larger pan-genome size than did K. oxytoca. K. michiganensis isolates with blaoxy-5 had a higher resistance rate to various antibiotics, antimicrobial gene carriage rate, adhesion ability to human respiratory epithelial cells, and survival rate against macrophages than isolates of other species. CONCLUSION: Our study revealed the genetic diversity of K. michiganensis and firstly identified the highly antimicrobial-resistant profile of K. michiganensis carrying blaoxy-5.

Integrated Analysis of Chromatin and Transcriptomic Profiling Identifies PU.1 as a Core Regulatory Factor in Microglial Activation Induced by Chronic Cerebral Hypoperfusion.

In addition to causing white matter lesions, chronic cerebral hypoperfusion (CCH) can also cause damage to gray matter, but the underlying molecular mechanisms remain largely unknown. In order to obtain a better understanding of the relationship between gene expression and transcriptional regulation alterations, novel upstream regulators could be identified using integration analysis of the transcriptome and epigenetic approaches. Here, a bilateral common carotid artery stenosis (BCAS) model was established for inducing CCH in mice. The spatial cognitive function of mice was evaluated, and changes in cortical microglia morphology were observed. RNA-sequencing (RNA-seq) and the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) were performed on isolated mouse cortical brain tissue. Then, a systematic joint analysis of BCAS hypoperfusion-induced cortex-specific RNA-seq and ATAC-seq was conducted in order to assess the extent of the correlation between the two, and PU.1 was found to be greatly enriched through motif analysis and transcription factor annotation. Also, the core regulatory factor PU.1 induced by BCAS hypoperfusion was shown to be colocalized with microglia. Based on the above analysis, PU.1 plays a key regulatory role in microglial activation induced by CCH. And the transcriptome and epigenomic data presented in this study can help identify potential targets for future research exploring chronic hypoperfusion-induced brain injury.

Classification and Recognition of Building Appearance Based on Optimized Gradient-Boosted Decision Tree Algorithm.

There are high concentrations of urban spaces and increasingly complex land use types. Providing an efficient and scientific identification of building types has become a major challenge in urban architectural planning. This study used an optimized gradient-boosted decision tree algorithm to enhance a decision tree model for building classification. Through supervised classification learning, machine learning training was conducted using a business-type weighted database. We innovatively established a form database to store input items. During parameter optimization, parameters such as the number of nodes, maximum depth, and learning rate were gradually adjusted based on the performance of the verification set to achieve optimal performance on the verification set under the same conditions. Simultaneously, a k-fold cross-validation method was used to avoid overfitting. The model clusters trained in the machine learning training corresponded to various city sizes. By setting the parameters to determine the size of the area of land for a target city, the corresponding classification model could be invoked. The experimental results show that this algorithm has high accuracy in building recognition. Especially in R, S, and U-class buildings, the overall accuracy rate of recognition reaches over 94%.

Transcriptome Profiling of Hippocampus After Cerebral Hypoperfusion in Mice.

Chronic cerebral hypoperfusion (CCH) is considered to be one of the major mechanism in the pathogenesis of vascular cognitive impairment (VCI). Increased inflammatory cells, particularly microglia, often parallel hypoperfusion-induced gray matter damage such as hippocampal lesions, but the exact mechanism remains largely unknown. To understand the pathological mechanisms, we analyzed hippocampus-specific transcriptome profiles after cerebral hypoperfusion. The mouse hypoperfusion model was induced by employing the 0.16/0.18 mm bilateral common carotid artery stenosis (BCAS) procedure. Cerebral blood flow (CBF) was assessed after 3-week hypoperfusion. Pathological changes were evaluated via hematoxylin staining and immunofluorescence staining. RNA-sequencing (RNA-seq) was performed using RNA samples of sham- or BCAS-operated mice, followed by quantitative real-time PCR (qRT-PCR) validation. We found that the 0.16/0.18 mm BCAS induced decreased CBF, hippocampal neuronal loss, and microglial activation. Furthermore, GSEA between sham and BCAS mice showed activation of interferon-beta signaling along with inflammatory immune responses. In addition, integrative analysis with published single-cell RNA-seq revealed that up-regulated differentially expressed genes (DEGs) were enriched in a distinct cell type of "microglia," and down-regulated DEGs were enriched in "CA1 pyramidal," not in "interneurons" or "S1 pyramidal." This database of transcriptomic profiles of BCAS-hypoperfusion will be useful for future studies to explore potential targets for vascular cognitive dysfunction.

A novel trans-acting lncRNA of ACTG1 that induces the remodeling of ovarian follicles.

Previous studies have implicated the attractive role of long noncoding RNAs (lncRNAs) in the remodeling of mammalian tissues. The migration of granulosa cells (GCs), which are the main supporting cells in ovarian follicles, stimulates the follicular remodeling. Here, with the cultured GCs as the follicular model, the actin gamma 1 (ACTG1) was observed to significantly promote the migration and proliferation while inhibit the apoptosis of GCs, suggesting that ACTG1 was required for ovarian remodeling. Moreover, we identified the trans-regulatory lncRNA of ACTG1 (TRLA), which was epigenetically targeted by histone H3 lysine 4 acetylation (H3K4ac). Mechanistically, the 2-375 nt of TRLA bound to ACTG1's mRNA to increase the expression of ACTG1. Furthermore, TRLA facilitated the migration and proliferation while inhibited the apoptosis of GCs, thereby accelerating follicular remodeling. Besides, TRLA acted as a ceRNA for miR-26a to increase the expression of high-mobility group AT-hook 1 (HMGA1). Collectively, TRLA induces the remodeling of ovarian follicles via complementary to ACTG1's mRNA and regulating miR-26a/HMGA1 axis in GCs. These observations revealed a novel and promising trans-acting lncRNA mechanism mediated by H3K4ac, and TRLA might be a new target to restore follicular remodeling and development.

Bioinformatics analysis combined with molecular dynamics simulation validation to elucidate the potential molecular mechanisms of Jianshen Decoction for treatment of osteoporotic fracture.

Osteoporotic fracture (OPF) is a prevalent skeletal disease in the middle-aged and elderly. In clinical practice, Jianshen Decoction (JSD) has been used to treat OPFs. However, the specific effective components and mechanisms of JSD on OPF have not been explored. Therefore, this study used bioinformatics analysis combined with molecular dynamics simulation validation to explore the molecular mechanism of JSD treatment of OPF. Public databases (TCMSP, Batman TCM) were used to find the effective active components and corresponding target proteins of JSD (screening conditions: OB ≥ 30%, drug-likeness ≥ 0.18, half-life ≥ 4). Differentially expressed genes (DEGs) related to OPF lesions were obtained based on the gene expression omnibus database (screening conditions: adjust P value < .01, | log2 FC | ≥ 1.0). The BisoGenet plug-in and the CytoNCA plug-in of Cytoscape were used to derive the potential core target proteins of JSD in the treatment of OPF. The JSD active ingredient target interaction network and the JSD-OPF target protein core network were constructed using the Cytoscape software. In addition, the R language Bioconductor package and clusterProfiler package were used to perform gene ontology (GO)/Kyoto Encylopedia Of Genes And Genome (KEGG) enrichment analysis on core genes to explain the biological functions and signal pathways of core proteins. Finally, molecular docking and molecular dynamics simulations were carried out through PyMOL, AutoDockTools 1.5.6, Vina, LeDock, Discovery Studio (DS) 2019, and other software to verify the binding ability of drug active ingredients and core target proteins. A total of 245 targets and 70 active components were identified. Through protein-protein interaction (PPI) network construction, 39 core targets were selected for further research. GO/KEGG enrichment analysis showed that the DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, MAPK signaling pathway, and ErbB signaling pathway were mainly involved. The results of molecular docking and molecular dynamics simulations supported the good interaction between MYC protein and Quercetin/Stigmasterol. In this study, bioinformatics, molecular docking, and molecular dynamics simulations were used for the first time to clarify the active components, molecular targets, and key biological pathways of JSD in the treatment of OPF, providing a theoretical basis for further research.

Cortex-specific transcriptome profiling reveals upregulation of interferon-regulated genes after deeper cerebral hypoperfusion in mice.

Background: Chronic cerebral hypoperfusion (CCH) is commonly accompanied by brain injury and glial activation. In addition to white matter lesions, the intensity of CCH greatly affects the degree of gray matter damage. However, little is understood about the underlying molecular mechanisms related to cortical lesions and glial activation following hypoperfusion. Efforts to investigate the relationship between neuropathological alternations and gene expression changes support a role for identifying novel molecular pathways by transcriptomic mechanisms. Methods: Chronic cerebral ischemic injury model was induced by the bilateral carotid artery stenosis (BCAS) using 0.16/0.18 mm microcoils. Cerebral blood flow (CBF) was evaluated using laser speckle contrast imaging (LSCI) system. Spatial learning and memory were assessed by Morris water maze test. Histological changes were evaluated by Hematoxylin staining. Microglial activation and neuronal loss were further examined by immunofluorescence staining. Cortex-specific gene expression profiling analysis was performed in sham and BCAS mice, and then validated by quantitative RT-PCR and immunohistochemistry (IHC). Results: In our study, compared with the sham group, the right hemisphere CBF of BCAS mice decreased to 69% and the cognitive function became impaired at 4 weeks postoperation. Besides, the BCAS mice displayed profound gray matter damage, including atrophy and thinning of the cortex, accompanied by neuronal loss and increased activated microglia. Gene set enrichment analysis (GSEA) revealed that hypoperfusion-induced upregulated genes were significantly enriched in the pathways of interferon (IFN)-regulated signaling along with neuroinflammation signaling. Ingenuity pathway analysis (IPA) predicted the importance of type I IFN signaling in regulating the CCH gene network. The obtained RNA-seq data were validated by qRT-PCR in cerebral cortex, showing consistency with the RNA-seq results. Also, IHC staining revealed elevated expression of IFN-inducible protein in cerebral cortex following BCAS-hypoperfusion. Conclusion: Overall, the activation of IFN-mediated signaling enhanced our understanding of the neuroimmune responses induced by CCH. The upregulation of IFN-regulated genes (IRGs) might exert a critical impact on the progression of cerebral hypoperfusion. Our improved understanding of cortex-specific transcriptional profiles will be helpful to explore potential targets for CCH.