Optimizing colorectal cancer segmentation with MobileViT-UNet and multi-criteria decision analysis.

Colorectal cancer represents a significant health challenge as one of the deadliest forms of malignancy. Manual examination methods are subjective, leading to inconsistent interpretations among different examiners and compromising reliability. Additionally, process is time-consuming and labor-intensive, necessitating the development of computer-aided diagnostic systems.

Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.

Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC grading. However, current methods for TILs quantification are based on subjective visual assessments, leading to inter-observer variability and inconsistent diagnostic reproducibility.

Assessment of different U-Net backbones in segmenting colorectal adenocarcinoma from H&E histopathology.

Adenocarcinoma, the most prevalent type of colorectal cancer, makes up roughly 95 % of all cases and is associated with a notably high mortality rate. Owing to the various risk factors which might include personal choices and habits or genetic factors, the risk of developing the cancer for every individual might vary. However, given the statistics, the rate of acquiring the disease is pretty high.

Genetic variants and type 2 diabetes in India: a systematic review and meta-analysis of associated polymorphisms in case-control studies.

Background: India, with the largest population and second-highest type 2 diabetes mellitus (T2DM) prevalence, presents a unique genetic landscape. This study explores the genetic profiling of T2DM, aiming to bridge gaps in existing research and provide insights for further explorations.

Methods: We conducted a systematic review and meta-analysis of literature published up to September 2024 using databases like PubMed, Web of Science, Scopus, and Google Scholar to identify SNPs associated with T2DM in case-control studies within the Indian population.

Iron oxide nanoparticles: a versatile nanoplatform for the treatment and diagnosis of ovarian cancer.

Ovarian cancer remains one of the main causes of human mortality, accounting for millions of deaths every year. Despite of several clinical options such as chemotherapy, photodynamic therapy (PDT), hormonal treatment, radiation therapy, and surgery to manage this disease, the mortality rate is still very high. This alarming statistic highlights the urgent need for innovative approaches to improve both diagnosis and treatment.