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ABSTRACTBackgroundCortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls. MethodsWe recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback–Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level. ResultsWe included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status. LimitationsOur study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure. ConclusionThe genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.

A fresh approach to technical analysis utilizing a full view (multi-time frame) integrated analytical system. Has the bear market ended? Is the rebound lasting? Everybody wants an answer but nobody can provide one with a good degree of confidence. While fundamental analysis is notoriously weak when it comes to market timing decisions and price target forecasts, technical analysis is equally timid in providing any concrete answers to the above fundamentally important questions for market participants. No existing system has produced a firm answer with a respectable degree of conviction. This book will present a system to answer those questions with a high degree of confidence. Xin Xie is the Director for Institute of International Trade and Investment at the Upper Yangtze River Economic Research Center, Chongqing University of Business and Technology and PRC Ministry of Education. He has a PhD in Economics from Columbia University in New York and a Master of Arts Degree in Statistics at Zhongnan University of Finance in China. He has extensive experiences in banking and investment industries as Senior Economists and Strategists in Bank of America and UBS AG.

Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and treatment strategies for ccRCC. This study aims to create a pathomics model to predict OS in ccRCC patients. In this study, data from ccRCC patients in the TCGA database were used as a training set, with clinical data serving as a validation set. Pathological features were extracted from H&E-stained slides using PyRadiomics, and a pathomics model was constructed using the non-negative matrix factorization (NMF) algorithm. The model's predictive performance was assessed through Kaplan-Meier (KM) survival curves and Cox regression analysis. Additionally, differential gene expression, gene ontology (GO) enrichment analysis, immune infiltration, and mutational analysis were conducted to investigate the underlying biological mechanisms. A total of 368 pathomics features were extracted from H&E-stained slides of ccRCC patients, and a pathomics model comprising two subtypes (Cluster 1 and Cluster 2) was successfully constructed using the NMF algorithm. KM survival curves and Cox regression analysis revealed that Cluster 2 was associated with worse OS. A total of 76 differential genes were identified between the two subtypes, primarily involving extracellular matrix organization and structure. Immune-related genes, including CTLA4, CD80, and TIGIT, were highly expressed in Cluster 2, while the VHL and PBRM1 genes, along with mutations in the PI3K-Akt, HIF-1, and MAPK signaling pathways, exhibited mutation rates exceeding 40% in both subtypes. The machine learning-based pathomics model effectively predicts the OS of ccRCC patients and differentiates between subtypes. The critical roles of the immune-related gene CTLA4 and the PI3K-Akt, HIF-1, and MAPK signaling pathways offer new insights for further research on the molecular mechanisms, diagnosis, and treatment strategies for ccRCC.

Background: Siwu Decoction (SWD) is a well-known classical TCM formula that has been shown to be effective as a basis for preventing and reducing liver metastases (LM). However, the active ingredients and potential molecular mechanisms remain unclear. Objective: This study aimed to systematically analyze the active ingredients and potential molecular mechanisms of SWD on LM and validate mechanisms involved. Materials and methods: The active ingredients in SWD were extracted by UHPLC-MS/MS in a latest study. Protox II was retrieved to obtain toxicological parameters to detect safety. Swiss Target Prediction database was exploited to harvest SWD targets. Five databases, Gene Cards, DisGeNET, Drugbank, OMIM, and TTD, were employed to filter pathogenic targets of LM. STRING database was utilized to construct the protein–protein interaction network for therapeutic targets, followed by Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. GEPIA database and the Human Protein Atlas were taken to observe the expression of core genes and proteins. ImmuCellAI algorithm was applied to analyze the immune microenvironment and survival relevant to core genes. Molecular docking was performed to verify the affinity of SWD effective ingredients to core targets. In vivo experiments were carried out to validate the anti-LM efficacy of SWD and verify the pivotal mechanisms of action. Results: Eighteen main bioactive phytochemicals identified were all non-hepatotoxic. PPI network acquired 118 therapeutic targets, of which VEGFA, CASP3, STAT3, etc. were identified as core targets. KEGG analysis revealed that HIF-1 pathway and others were critical. After tandem targets and pathways, HIF-1/VEGF was regarded as the greatest potential pathway. VEGFA and HIF-1 were expressed differently in various stages of cancer and normal tissues. There was a negative regulation of immunoreactive cells by VEGFA, which was influential for prognosis. Molecular docking confirmed the tight binding to VEGFA. This study revealed the exact effect of SWD against LM, and identified significant inhibition the expression of HIF-1α, VEGF, and CD31 in the liver microenvironment. Conclusion: This study clarified the active ingredients of SWD, the therapeutic targets of LM and potential molecular mechanisms. SWD may protect against LM through suppressing HIF-1/VEGF pathway.

Objective To develop and validate a generalized prediction model that can classify epidermal growth factor receptor (EGFR) mutation status in non–small cell lung cancer patients. Methods A total of 346 patients (296 in the training cohort and 50 in the validation cohort) from four centers were included in this retrospective study. First, 1085 features were extracted using IBEX from the computed tomography images. The features were screened using the intraclass correlation coefficient, hypothesis tests and least absolute shrinkage and selection operator. Logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) were used to build a radiomics model for classification. The models were evaluated using the following metrics: area under the curve (AUC), calibration curve (CAL), decision curve analysis (DCA), concordance index (C-index), and Brier score. Results Sixteen features were selected, and models were built using LR, DT, RF, and SVM. In the training cohort, the AUCs was .723, .842, .995, and .883; In the validation cohort, the AUCs were .658, 0567, .88, and .765. RF model with the best AUC, its CAL, C-index (training cohort=.998; validation cohort=.883), and Brier score (training cohort=.007; validation cohort=0.137) showed a satisfactory predictive accuracy; DCA indicated that the RF model has better clinical application value. Conclusion Machine learning models based on computed tomography images can be used to evaluate EGFR status in patients with non–small cell lung cancer, and the RF model outperformed LR, DT, and SVM.

Objective: Colorectal cancer is one of the most important malignant cancer in the world with high incidence and mortality. Some studies have found that the expression of low serum L1 cell adhesion molecule is associated with poor prognosis in some malignancies. It is suggested that L1 cell adhesion molecule is a candidate serum marker for certain tumors. However, the relationship between serum L1 cell adhesion molecule and colorectal cancer, especially about the diagnostic value, is rarely reported. Therefore, this study aimed to evaluate the diagnostic potential of serum L1 cell adhesion molecule in patients with colorectal cancer. Methods: Enzyme-linked immunosorbent assay was carried out to detect L1 cell adhesion molecule level in sera of 229 patients with colorectal cancer and 145 normal controls. Receiver operating characteristic curves were employed to calculate the accuracy of diagnosis. Results: The levels of serum L1 cell adhesion molecule in the colorectal cancer group were significantly lower than that in normal controls (P < .05). In the normal group, the area under the receiver operating characteristic curve (area under the curve) of all colorectal cancer was 0.781 (95% confidence interval: 0.734-0.828) and early-stage colorectal cancer was 0.764 (95% confidence interval: 0.705-0.823). With optimized cutoff of 17.760 ng/mL, L1 cell adhesion molecule showed certain diagnostic value with specificity of 90.3% and sensitivities of 43.2% and 36.2% in colorectal cancer and early-stage colorectal cancer, respectively. Clinical data analysis showed that the levels of L1 cell adhesion molecule were significantly correlated with gender (P < .05) and early and late stages (P < .05). Furthermore, when compared with carcinoembryonic antigen, serum L1 cell adhesion molecule had significantly improved diagnostic accuracy for both colorectal cancer and early-stage colorectal cancer. Conclusions: Our study demonstrated that serum L1 cell adhesion molecule might be served as a potential biomarker for the diagnosis of colorectal cancer.

Introduction Breast cancer (BC) is a common cancer characterized by a high molecular heterogeneity. Therefore, understanding its biological properties and developing effective treatments for patients with different molecular features is imperative. Calcium-sensing receptor (CaSR) has been implicated in several regulatory functions in various types of human cancers. However, its underlying pathological mechanism in BC progression remains elusive. Methods We utilized The Cancer Genome Atlas and Gene Expression Omnibus databases to explore the function of CaSR in the metastasis of BC. Gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, and Gene Set Enrichment Analysis of biological processes and cell signaling pathways revealed that CaSR could be activated or inhibited. Importantly, quantitative reverse transcriptase-polymerase chain reaction and western blotting were used to verify the gene expression of the CaSR. Wound healing and transwell assays were conducted to assess the effect of CaSR on the migration of BC cells. Results We demonstrated that CaSR expression in metastatic BC was higher than that in non-metastatic BC. It is the first time that database information has been used to reveal the biological process and molecular mechanism of CaSR in BC. Moreover, the CaSR expression in normal breast epithelial cells was notably less compared to that in BC cells. The activation of CaSR by Cinacalcet (a CaSR agonist) significantly enhanced the migration of BC cells, whereas NPS-2143 (a CaSR antagonist) treatment dramatically inhibited these effects. Conclusion and future perspective Bioinformatics techniques and experiments demonstrated the involvement of CaSR in BC metastasis. Our findings shed new light on the receptor therapy and molecular pathogenesis of BC, and emphasize the crucial function of CaSR, facilitating the metastasis of BC.

Objective To investigate the relationship between the neutrophil-to-lymphocyte ratio (NLR) of patients with non-small cell lung cancer (NSCLC) and their risk of developing brain metastases after adjusting for confounding factors. Methods A retrospective observational study of the general data of patients with NSCLC diagnosed from January 2016 to December 2020. Multivariate logistic regression was used to calculate the dominance ratio (OR) with 95% confidence interval (CI) for NLR and NSCLC brain metastases with subgroup analysis. Generalized summation models and smoothed curve fitting were used to identify whether there was a nonlinear relationship between them. Results In all 3 models, NLR levels were positively correlated with NSCLC brain metastasis (model 1: OR: 1.12, 95% CI: 1.01-1.23, P = .025; model 2: OR: 1.16, 95% CI: 1.04-1.29, P = .007; model 3: OR: 1.20, 95% CI: 1.05-1.37, P = .006). Stratified analysis showed that this positive correlation was present in patients with adenocarcinoma (LUAD) and female patients (LUAD: OR: 1.30, 95% CI: 1.10-1.54, P = .002; female: OR: 1.52, 95% CI: 1.05-2.20, P = .026), while there was no significant correlation in patients with squamous carcinoma (LUSC) and male patients (LUSC: OR:0.76,95% CI:0.38- 1.53, P = .443; male: OR:1.13, 95% CI:0.95-1.33, P = .159). Conclusion This study showed that elevated levels of NLR were independently associated with an increased risk of developing brain metastases in patients with NSCLC, and that this correlation varied by TYPE and SEX, with a significant correlation in female patients and patients with LUAD.

Objective: Our study aimed to investigate the expression level and clinical significance of serum and exhaled breath condensate miR-186 and IL-1β in non-small cell lung cancer patients. Methods: The serum and exhaled breath condensate specimens of 62 non-small cell lung cancer patients and 60 healthy controls were collected to detect miR-186 expression levels by real-time fluorescent quantitative PCR. Enzyme linked immunosorbent assay was applied to examine IL-1β concentration. Statistical analyses were used to evaluate the correlation between miR-186 and IL-1β in serum and clinicopathological features, traditional serum tumor markers, and inflammatory markers. The diagnostic efficacy of miR-186 and IL-1β for non-small cell lung cancer was evaluated by receiver operating characteristic curve analysis. The correlation between miR-186 and IL-1β was determined. Results: ① The relative expression level of miR-186 was greatly reduced in the serum and EBC of patients with non-small cell lung cancer, and the miR-186 expression level was reduced in different TNM stages of non-small cell lung cancer, from the early to later stages. ② The IL-1β concentration in serum and exhaled breath condensate of patients with non-small cell lung cancer was increased. ③ Serum miR-186 and IL-1β levels were closely related to lymph node metastasis, and the low expression of serum miR-186 and the high concentration of IL-1β were associated with higher serum carcinoembryonic antigen, C-reactive protein, and erythrocyte sedimentation rate levels. ④ ROC curve analysis showed that exhaled breath condensate miR-186 had higher area under the curve than serum miR-186, and the combined detection showed higher diagnostic efficacy than the separate detection. In addition, the combined detection of IL-1β and miR-186 has a larger AUC than the separate detection of both. ⑤ The correlation between serum miR-186 and IL-1β was negative. Conclusion: miR-186 and IL-1β are expected to be potential diagnostic biomarkers for non-small cell lung cancer.