AIMC Topic: Humans

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Global genomic survey of Kentucky: discovery of a chromosomeborne and the emergence of ST314, an MDR clone mediated by the IncR plasmid.

Emerging microbes & infections
Antimicrobial resistance (AMR) in enterica serotype Kentucky ( Kentucky) is a global challenge, with increasing resistance to cephalosporins, ciprofloxacin, and carbapenems significantly limiting treatment strategies, yet its worldwide dissemination...

Using a coloring activity to identify children's development of visual-motor integration: an application of artificial intelligence.

Annals of medicine
AIM: Visual-motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children's VMI developmental status.

Synergistic Integration of Frequency-Dependent Impedance and Machine Learning in Semiconductor Metal Oxide-Based Breath Sensors for High-Performance Gas Discrimination.

ACS sensors
Frequency-dependent impedance spectroscopy in combination with machine learning offers a powerful strategy for discriminating among gas species using mutually interacting semiconductor metal oxide (SMO) gas sensors. In this study, 0.3 at% platinum-lo...

Entropy-Driven Nucleic Acid Amplifier Based on Spatial Confinement as a "Booster" for Detection of Extracellular Vesicle MicroRNAs to Diagnose Gastric Cancer and Monitor Therapeutic Response.

Analytical chemistry
Gastric cancer (GC) continues to pose a significant global health burden with persistent diagnostic challenges, especially in the detection of early-stage GC. Herein, a strand displacement reaction-mediated nucleic acid amplifier based on the spatial...

Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW), defined as a newborn weighing less than 2500 g, is an increasingly significant public health concern. Exploring the risk and protective factors for LBW is getting more and more important. This study aimed to utiliz...

A predictive model for evaluating the risk of latent tuberculosis relapse via machine learning.

BMC infectious diseases
BACKGROUND: Reactivation of latent tuberculosis infection (LTBI) is a major obstacle to tuberculosis eradication. Predicting LTBI relapse is crucial for effective disease management but remains underexplored.

Evaluating the performance of five large language models in answering Delphi consensus questions relating to patellar instability and medial patellofemoral ligament reconstruction.

BMC musculoskeletal disorders
PURPOSE: Artificial intelligence (AI) has become incredibly popular over the past several years, with large language models (LLMs) offering the possibility of revolutionizing the way healthcare information is shared with patients. However, to prevent...

Performance of artificial intelligence-assisted ultrasound elastography in classifying benign and malignant breast tumors: a systematic review and meta-analysis.

BMC medical imaging
BACKGROUND: Precise benign and malignant breast tumors classification is essential for effective treatment planning and outcome prognostication. Medical imaging's capability to classify breast tumors has been greatly improved by the accelerated advan...

Harris Hawks optimization based deep learning models for heart disease diagnosis.

Scientific reports
The medical community demands accurate predictive models for early heart disease diagnosis because heart disease remains a significant worldwide health concern. Deep learning research presents a predictive system for heart disease that uses K-mode cl...

Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach.

Scientific reports
Bone deterioration from osteoporosis creates fractures that primarily affect females who have reached menopause and older adults. Early detection of osteoporosis requires affordable methods because current diagnostic systems are both expensive and ch...