DCLK1 in gastrointestinal cancer: A driver of tumor progression and a promising therapeutic target.

Cancers of the gastrointestinal (GI) tract, including colorectal, pancreatic, and hepatocellular carcinomas, represent a significant global health burden due to their high incidence and mortality rates. Doublecortin-like kinase 1 (DCLK1), initially identified for its role in neurogenesis, has emerged as a crucial player in GI cancer progression. This review comprehensively examines the multifaceted roles of DCLK1 in GI cancers, focusing on its structural isoforms, functions in normal and inflammatory states, and contributions to cancer progression and metastasis.

Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants.

High-grade glioma (HGG) is an aggressive brain tumor with poor survival rates. Predicting survival outcomes is critical for personalized treatment planning. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has emerged as a promising approach for enhancing prognostic accuracy in HGG but this study especially focused on the potential of AI in the recurrence of HGG.

Identifying abdominal aortic aneurysm size and presence using Natural Language Processing of radiology reports: a systematic review and meta-analysis.

Background And Aim: Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.

Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants.

It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines.

Evaluation and Comparison of Serum Vitamin D Levels between Injection Drug Users with and without Chronic Hepatitis C Infection in Hamadan-Iran.

Hepatitis C virus (HCV) infection causes disruption in the metabolism of biochemicals, such as vitamin D, and subsequently alters their serum level via the involvement of the liver. The current study aimed at determining the serum level of vitamin D in injection drug users with chronic HCV infection. The current case-control study was conducted on 46 injection drug users referred to Imam Khomeini Specialty Clinic in Hamadan City, Iran, between 2017 and 2019, when their HCV infection was diagnosed with HCV PCR test, and 46 healthy injection drug users as the control group in order to compare serum vitamin D levels and liver enzymes.