Eosinophilic esophagitis (EoE) is an increasingly common food allergy disease of the esophagus that received its medical designation code in 2008. Despite this recency, great strides have been made in the understanding of EoE pathophysiology and type 2 immunity through basic and translational scientific investigations conducted at the bench. These advances have been critical to our understanding of disease mechanisms and generating new hypotheses, however, there currently is only one very recently approved FDA-approved therapy for EoE, leaving a great deal to be uncovered for patients with this disease. Here we review some of the innovative methods, models and tools that have contributed to the advances in EoE discovery and suggest future directions of investigation to expand upon this foundation.
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http://dx.doi.org/10.3389/fimmu.2022.943518 | DOI Listing |
Sci Rep
January 2025
Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
In applied research, fractional calculus plays an important role for comprehending a wide range of intricate physical phenomena. One of the Klein-Gordon model's peculiar case yields the Phi-four equation. Additionally, throughout the past few decades it has been utilized to explain the kink and anti-kink solitary waveform contacts that occur in biological systems and in the field of nuclear mechanics.
View Article and Find Full Text PDFDeep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) models in practical settings limits their clinical application. This study aims to review and analyze current SOTA deep learning models for lung cancer risk prediction (malignant-benign classification).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2019 and December 2023 to develop and validate a multi-classification machine learning (ML) model for assessing depression risk. Addressing the limitations of traditional assessment tools, six ML models were constructed using univariate analysis and the LASSO algorithm, with the categorical boosting (Catboost) model emerging as the best performer, demonstrating strong predictive ability across different depression severity levels (none_F1 = 0.879, mild_F1 = 0.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia. Electronic address:
The integration of artificial intelligence (AI) into dental imaging has led to significant advancements, particularly in the analysis of panoramic radiographs, also known as orthopantomograms (OPGs). One emerging application of AI is in determining gender from these radiographs, a task traditionally performed by forensic experts using manual methods. This systematic review and meta-analysis aim to evaluate the accuracy of AI algorithms in gender determination using OPGs, focusing on the reliability and potential clinical and forensic applications of these technologies.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Institute of Clinical Science, Zhongshan Hospital, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Intelligent Medicine Institute, School of Life Sciences, Fudan University, No. 180 Fenglin Road, Shanghai 200032, China.
Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of specific genes and are associated with multiple diseases. In silico methods for prescreening potential NR binders with predictive binding ability are highly desired for NR-related drug development but are rarely reported. Here, we present the PbsNRs (Predicting binders and scaffolds for Nuclear Receptors), a user-friendly web server designed to predict the potential NR binders and scaffolds through proteochemometric modeling.
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