using AI

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data...

Previous research has demonstrated that Dexmedetomidine (DEX), an α2 adrenergic agonist commonly used for its sedative and analgesic properties, can attenuate lipopolysaccharide (LPS)-induced acute kidney injury (AKI). This study explores the possibility that DEX's protective effects in LPS-induced AKI are mediated through the inhibition of ferroptosis, a form of regulated cell death characterized by iron-dependent lipid peroxidation, and the activation of the antioxidant response through the Keap1/Nrf2/HO-1 signaling pathway...

BACKGROUND: Artificial intelligence (AI) such as ChatGPT by OpenAI holds great promise to improve the quality of life of patients with dementia and their caregivers by providing high-quality responses to their questions about typical dementia behaviors. So far, however, evidence on the quality of such ChatGPT responses is limited. A few recent publications have investigated the quality of ChatGPT responses in other health conditions. Our study is the first to assess ChatGPT using real-world questions asked by dementia caregivers themselves...

OBJECTIVES: This study evaluated the expression and significance of SNHG3 in Rheumatoid arthritis (RA) aiming to explore a biomarker and regulator for RA. METHODS: The expression of SNHG3 in serum and synovial tissue was compared between RA patients and healthy individuals using PCR. The RA animal models were induced by the porcine type II collagen with Wistar rats and validated by the foot volume and AI score. The human fibroblast-like synoviocytes (H-FLS) were treated with LPS to mimic the injury during RA onset and the cell growth was assessed by CCK8 assay...

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes...

INTRODUCTION: Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events...

INTRODUCTION: In developing Computer-Aided Diagnosis (CAD), a Convolutional Neural Network (CNN) has been commonly used as a Deep Learning (DL) model. Although it is still early, DL has excellent potential in implementing computers in medical diagnosis. METHODS: This study reviews the use of DL for Anterior Cruciate Ligament (ACL) tear diagnosis. A comprehensive search was performed in PubMed, Embase, and Web of Science databases from 2018 to 2024. The included study criteria used MRI images to evaluate ACL tears, and the diagnosis of ACL tears was performed using the DL model...

Translational control is important in all life, but it remains a challenge to accurately quantify. When ribosomes translate messenger (m)RNA into proteins, they attach to the mRNA in series, forming poly(ribo)somes, and can co-localize. Here, we computationally model new types of co-localized ribosomal complexes on mRNA and identify them using enhanced translation complex profile sequencing (eTCP-seq) based on rapid in vivo crosslinking. We detect long disome footprints outside regions of non-random elongation stalls and show these are linked to translation initiation and protein biosynthesis rates...

This study evaluates the clinical accuracy of OpenAI's ChatGPT in pediatric dermatology by comparing its responses on multiple-choice and case-based questions to those of pediatric dermatologists. ChatGPT's versions 3.5 and 4.0 were tested against questions from the American Board of Dermatology and the "Photoquiz" section of Pediatric Dermatology. Results show that human pediatric dermatology clinicians generally outperformed both ChatGPT iterations, though ChatGPT-4.0 demonstrated comparable performance in some areas...

BACKGROUND: The pioneering work by Dr. Payne et al. in time-lapse cinematography for observation of the morphokinetic features of human embryos inspired us to develop a new in vitro culture system with high-resolution time-lapse cinematography (hR-TLC) back in 2001. METHODS: This in vitro culture system was capable of maintaining stable culture and was constructed on an inverted microscope stage. Embryos were observed and photographed noninvasively for an extended period, up to 7 days...

AIM: To quantify the performance of artificial intelligence (AI) in detecting glaucoma with spectral-domain optical coherence tomography (SD-OCT) images. METHODS: Electronic databases including PubMed, Embase, Scopus, ScienceDirect, ProQuest and Cochrane Library were searched before May 31, 2023 which adopted AI for glaucoma detection with SD-OCT images. All pieces of the literature were screened and extracted by two investigators. Meta-analysis, Meta-regression, subgroup, and publication of bias were conducted by Stata16...

Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students...

As the area of robotics achieves promising results, there is an increasing need to scale robotic software architectures towards real-world domains. Traditionally, robotic architectures are integrated using common frameworks, such as ROS. Therefore, systems with a uniform structure are produced, making it difficult to integrate third party contributions. Virtualisation technologies can simplify the problem, but their use is uncommon in robotics and general integration procedures are still missing. This paper proposes and evaluates a containerised approach for designing and integrating multiform robotic architectures...

Purpose  Tinnitus is a phantom perception of sound in the absence of acoustic source. Previous evidence has indicated that miR-375-3p is downregulated in rats with tinnitus in comparison to the controls. Nevertheless, its molecular mechanism underlying tinnitus pathogenesis is unclarified. Methods  SH-SY5Y cells were differentiated into neuronlike cells and stimulated with salicylate to mimic tinnitus in vitro. Immunofluorescence staining was utilized for measuring expression of NR2B (glutamate ionotropic receptor NMDA type subunit 2B)...

INTRODUCTION: Uncontrolled blood sugar levels may result in complications, namely diabetic neuropathy. Diabetic neuropathy is a nerve disorder that causes symptoms of numbness, foot deformity, dry skin, and thickening of the feet. The severity of diabetic neuropathy carries the risk of developing diabetic ulcers and amputation. Early detection of diabetic neuropathy can prevent the risk of diabetic ulcers. The purpose: to identify early detection of diabetic neuropathy based on the health belief model...

Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction...

BACKGROUND: The prevalence of Type 2 Diabetes Mellitus (T2D) and its significant role in increasing Coronary Heart Disease (CHD) risk highlights the urgent need for effective CHD screening within this population. Despite current advancements in T2D management, the complexity of cardiovascular complications persists. Our study aims to develop a comprehensive CHD screening model for T2D patients, employing multimodal data to improve early detection and management, addressing a critical gap in clinical practice...

BACKGROUND: Accurate classification of breast nodules into benign and malignant types is critical for the successful treatment of breast cancer. Traditional methods rely on subjective interpretation, which can potentially lead to diagnostic errors. Artificial intelligence (AI)-based methods using the quantitative morphological analysis of ultrasound images have been explored for the automated and reliable classification of breast cancer. This study aimed to investigate the effectiveness of AI-based approaches for improving diagnostic accuracy and patient outcomes...

BACKGROUND: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. METHODS: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS)...

BACKGROUND: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. METHODS: This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers...