Short-term and long-term fluoride stress induce differential molecular and transcriptional regulation and variable ranges of fluoride tolerance in two indica rice () varieties.

The aim of this study was to decipher the reprogramming of protective machineries and sulfur metabolism, as responses to time-dependent effect of fluoride stress for 10 and 20days in two indica rice (Oryza sativa ) varieties. Unregulated accumulation of fluoride via chloride channels (CLC1 and CLC2) in 10-day-old (cv. Khitish) and 20-day-old (cv.

Role of artificial intelligence in treatment planning and outcome prediction of jaw corrective surgeries by using 3-D imaging-a systematic review.

Objective: Artificial intelligence (AI) has been increasingly utilized in diagnosis of skeletal deformities, while its role in treatment planning and outcome prediction of jaw corrective surgeries with 3-dimensional (3D) imaging remains underexplored.

Methods: The comprehensive search was done in PubMed, Google scholar, Semantic scholar and Cochrane Library between January 2000 and May 2024. Inclusion criteria encompassed studies on AI applications in treatment planning and outcome prediction for jaw corrective surgeries using 3D imaging.

Cancer Stem Cells in Oral Squamous Cell Carcinoma: A Narrative Review on Experimental Characteristics and Methodological Challenges.

Cancer stem cells (CSCs) represent a subpopulation of cancer cells that are believed to initiate and drive cancer progression. In animal models, xenotransplanted CSCs have demonstrated the ability to produce tumors. Since their initial isolation in blood cancers, CSCs have been identified in various solid human cancers, including oral squamous cell carcinoma (OSCC).

Aspiring to clinical significance: Insights from developing and evaluating a machine learning model to predict emergency department return visit admissions.

Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs.