Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: Extraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC). MATERIALS AND METHODS: A retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors. RESULTS: The GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843), and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful. CONCLUSION: The GPTV3 radiomics model was superior to the GTV, GPTV6, and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Triglyceride glucose index is associated with functional coronary artery stenosis in hypertensive patients.

Background: The triglyceride glucose (TyG) index is an effective method for determining insulin resistance (IR). Limited research has explored the connection between the TyG index and functionally significant stenosis in hypertensive patients. Furthermore, the connections between the TyG index, fat attenuation index (FAI) and atherosclerotic plaque characteristics are also worth exploring. Methods: The study screened 1622 hypertensive participants without coronary artery disease history who underwent coronary computed tomography angiography. The TyG index was calculated as ln (fasting glucose [mg/dL] * fasting TG [mg/dL]/2). Adverse plaque characteristics (HRPCs), high-risk plaques (HRPs), FAI, and CT-derived fractional flow reserve (FFRCT) were analyzed and measured for all patients. Functionally significant stenosis causing ischemia is defined as FFRCT ≤ 0.80. Two patient groups were created based on the FFRCT: the FFRCT < 0.80 group and the FFRCT > 0.80 group. In hypertensive patients, the association between the TyG index and FFRCT was examined applying a logistic regression model. Results: The TyG index was higher for people with FFRCT ≤ 0.80 contrast to those with FFRCT > 0.80. After controlling for additional confounding factors, the logistic regression model revealed a clear connection between the TyG index and FFRCT ≤ 0.80 (OR = 1.718, 95% CI 1.097-2.690, p = 0.018). The restricted cubic spline analysis displayed a nonlinear connection between the TyG index and FFRCT ≤ 0.80 (p for nonlinear = 0.001). The TyG index increased the fraction of individuals with HRPs and HRPCs, FAI raised, and FFRCT decreased (p < 0.05). The multivariate linear regression analysis illustrated a powerfulcorrelation between high TyG index levels and FAI, FFRCT, positive remodeling (PR), and low-attenuation plaque (LAPs) (standardized regression coefficients: 0.029 [p = 0.007], -0.051 [p < 0.001], 0.029 [p = 0.027], and 0.026 [p = 0.046], separately). Conclusion: In hypertensive patients, the TyG index showed an excellent association with a risk of FFRCT ≤ 0.80. Additionally, the TyG index was also linked to FAI, FFRCT, PR, and LAPs.

Genome-Wide Association Study Reveals Novel QTLs and Candidate Genes for Grain Number in Rice.

Grain number per panicle (GNPP), determined mainly by panicle branching, is vital for rice yield. The dissection of the genetic basis underlying GNPP could help to improve rice yield. However, genetic resources, including quantitative trait loci (QTL) or genes for breeders to enhance rice GNPP, are still limited. Here, we conducted the genome-wide association study (GWAS) on the GNPP, primary branch number (PBN), and secondary branch number (SBN) of 468 rice accessions. We detected a total of 18 QTLs, including six for GNPP, six for PBN, and six for SBN, in the whole panel and the indica and japonica subpanels of 468 accessions. More importantly, qPSG1 was a common QTL for GNPP, PBN, and SBN and was demonstrated by chromosome segment substitution lines (CSSLs). Considering gene annotation, expression, and haplotype analysis, seven novel and strong GNPP-related candidate genes were mined from qPSG1. Our results provide clues to elucidate the molecular regulatory network of GNPP. The identified QTLs and candidate genes will contribute to the improvement of GNPP and rice yield via molecular marker-assisted selection (MAS) breeding and genetic engineering techniques.

Shaping the Trans-Scale Properties of Schizophrenia via Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions.

Schizophrenia is a complex mental illness with genetic heterogeneity, which is often accompanied by alterations in brain structure and function. The neurobiological mechanism of schizophrenia associated with heredity remains unknown. Recently, the development of trans-scale and multi-omics methods that integrate gene and imaging information sheds new light on the nature of schizophrenia. In this article, we summarized the results of brain structural and functional changes related to the specific single-nucleotide polymorphisms (SNPs) in the past decade, and the SNPs were divided into non-coding regions and coding regions, respectively. It is hoped that the relationship between SNPs and cerebral alterations can be displayed more clearly and intuitively, so as to provide fresh approaches for the discovery of potential biomarkers and the development of clinical accurate individualized treatment decision-making.