Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.

Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing primary there is from SCC presenting as lung nodules on Computed Tomography (CT) scans. We studied individuals with histopathologically proven adenocarcinoma or SCC type NSCLC tumours, detected as lung nodules on Chest CT.

1,5- diaryl pyrazole-loaded chitosan nanoparticles as COX-2 inhibitors, mitigate neoplastic growth by regulating NF-κB pathway in-vivo zebrafish model.

Non-steroidal anti-inflammatory drugs (NSAIDs) have been researched for their capacity to reduce cancer incidence, primarily due to their COX-2 inhibition properties. However, concerns have arisen regarding the precision of their targeting abilities. Nanoparticle approaches are revolutionizing cancer treatment by enabling targeted drug delivery, which enhances the efficacy and reduces the toxicity of chemotherapy.

Breast self-examination practices among young rural women and its associated knowledge and attitudes in Tirunelveli District, Tamil Nadu.

Context: Breast self-examination (BSE) is a simple and cost-effective screening procedure in downstaging breast tumors.

Aim: To assess the BSE practices and its associated knowledge and attitudes of rural women from Tirunelveli District, Tamil Nadu during the COVID-19 pandemic.

Settings And Design: A descriptive cross-sectional survey design was employed, and snowball sampling was used to recruit the sample of rural women from Tirunelveli.

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation.