Development and validation of recombinant A27L based indirect-ELISA for Sheeppox and Goatpox disease in India.

Sheeppox and Goatpox are highly contagious transboundary viral diseases of sheep and goats caused by Capripoxvirus, respectively. This study describes the development of indirect ELISA and its serodiagnostic potential as an alternative to the gold standard serum neutralization test (SNT). The homologue of vaccinia virus, ORF 117 (A27L) gene of the Romanian Fenner (RF) strain of Sheeppox virus (SPPV) was used for producing the full-length recombinant A27L (rA27L) protein (∼22 kDa) in a prokaryotic expression system.

Artificial Intelligence Unveils the Unseen: Mapping Novel Lung Patterns in Bronchiectasis via Texture Analysis.

Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques.

Sivan classification system for diagnosis of jaw lesions based on visual volumetric analysis of 3-dimensional cone-beam computed tomographic images.

A novel classification system, termed the Sivan classification, was developed to enhance the diagnosis of jaw lesions by utilizing visual volumetric analysis of three-dimensional Cone Beam Computed Tomography (CBCT) images. This classification groups lesions into ten categories, primarily divided into hypovolumetric, hypervolumetric, and normovolumetric groups. To validate this system, 10 raters-comprising 5 general dentists and 5 oral radiology specialists-assessed the CBCT images and diagnosed the lesions using the Sivan classification.

Targeting Yes-Associated Protein (YAP) in Breast Cancer: In Silico Molecular Dynamics, Luminescence-Based In Vitro, and In Vivo Validation of Rauvolfia tetraphylla-Derived Inhibitors.

The study aims to elucidate the pharmacological mechanism of Rauvolfia tetraphylla against breast cancer through a comprehensive, multi-faceted approach. This includes molecular docking, molecular dynamics, and experimental validation. Initial screening via ADME analysis and network pharmacology identified key compounds and potential targets.