Samples are drawn from hemophiliacs to retrieve plasma and serum for testing and storage. The samples for testing are tested and the results compiled using a database computer program. The samples for storage are coded and stored at -70 degrees C under controlled conditions. The location of the samples is then entered into the database computer program. The results of the tests, or the stored sample location, can be easily retrieved using the computer.
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Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.
Clin Exp Dent Res
February 2025
Department of Dental Research Cell, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pune, India.
Objectives: Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Consequently, innovative diagnostic tools are required to increase TMD diagnosis efficiency and precision. Therefore, the purpose of this umbrella review was to examine the existing evidence about the usefulness of artificial intelligence (AI) in TMD diagnosis.
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Front Immunol
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Department of Orthopedics, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
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Iran J Pharm Res
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College of Traditional Chinese Medicine, Hebei North University, Zhangjiakou City, 075000, China.
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Network pharmacological approach combined with weighted gene co-expression network analysis identifies CDKN2A as the keg target of Changweiqing against colorectal cancer.
Hereditas
March 2025
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J Transl Med
March 2025
Unit for Data Science and Computing, North-West University, 11 Hofman Street, Potchefstroom, 2520, South Africa.
Article Synopsis
- Advancements in AI and ML are transforming the medical field, enhancing patient care and disease modeling, but challenges like data variability and class imbalance hinder optimal predictive performance.
- A new AI framework combining Gradient Boosting Machines and Deep Neural Networks was tested on two datasets, showing better results in accuracy metrics compared to traditional models, including achieving an AUROC of 0.96 on the UK Biobank dataset.
- The framework not only demonstrated superior accuracy but also trained quickly, making it well-suited for real-time clinical applications, with future enhancements aimed at improving scalability and interpretability for broader use.
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