A Literature Review on Oral Lumenoscopy: A Non-invasive Method for Diagnosing Oral Potentially Malignant Disorders.

Dentistry still faces difficulties in diagnosing oral precancer and cancer, especially when it comes to early phase changes or disease detection, evaluation, and treatment. In essence, oral lumenography is the process of identifying oral lesions using a chemiluminescent light source and a toluidine blue labeling system. Since neoplastic epithelial cells have a changed nuclear-cytoplasmic ratio, acetic acid dehydration brings out this nuclear density and gives the tissue an "acetowhite" look.

Bibliometric Analysis of the Role of Artificial Intelligence in Detecting Maxillofacial Fractures.

Facial bone fractures are a common occurrence in trauma cases, particularly in India where road traffic accidents contribute significantly. Over the past few years, artificial intelligence (AI) has become a potent instrument to help medical professionals diagnose and treat facial fractures. This study aims to perform a bibliometric analysis, that is, a quantitative and qualitative analysis, of publications focusing on the role of AI in detecting facial bone fractures.

The future of lactoferrin: A closer look at LipoDuo technology.

Background: Lactoferrin (Lf), a multifunctional glycoprotein known for its roles in immune modulation, iron metabolism, and antimicrobial activity, has limited therapeutic efficacy due to poor bioavailability. Liposomal encapsulation of lactoferrin (LLf) offers a potential solution by improving its stability, absorption, and sustained release, making it a promising candidate for various clinical applications. This study aims to compare the effectiveness of LLf and plain Lf in cellular uptake, proliferation, and wound healing using HEK-293T and Caco-2 cell lines.

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC samples relative to adjacent healthy tissues using an explainable artificial intelligence (XAI) framework.