Elevating the rice blast disease immunity through CPKA protein targeting in () with natural compounds.

Rice blast disease, instigated by (), significantly impedes global rice production. Targeting the signaling protein, cAMP-Protein Kinase A (CPKA), which facilitates appressorium development and host penetration, this study explores the potential inhibitory effects of natural compounds. Virtual screening, molecular docking and text mining approaches were used to find the nimonol and curcumin that inhibit the CPKA protein.

A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins.

Transcatheter closure of a large aortopulmonary window using a Lifetech™ Konar-MF occluder device.

Aortopulmonary window is a rare CHD, which comprises a communication between the ascending aorta and the pulmonary artery. The standard treatment of aortopulmonary window is surgical; however, few cases are amenable to closure via percutaneous intervention. We present a case of aortopulmonary window closure using Lifetech™ Konar-MF occluder device (Lifetech Scientific Co.

RiceMetaSys: Drought-miR, a one-stop solution for drought responsive miRNAs-mRNA module in rice.

MicroRNAs are key players involved in stress responses in plants and reports are available on the role of miRNAs in drought stress response in rice. This work reports the development of a database, RiceMetaSys: Drought-miR, based on the meta-analysis of publicly available sRNA datasets. From 28 drought stress-specific sRNA datasets, we identified 216 drought-responsive miRNAs (DRMs).

Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis.

Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously.