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Learning from Inborn Errors of Immunity: From mechanisms to translation.
Semin Immunol
January 2025
Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.
Orphanet J Rare Dis
January 2025
Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus, Groningen, 30001 - 9700 RB, the Netherlands.
Background: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on a combination of different biomarkers, mostly routine clinical chemical markers and subsequent genetic confirmation.
View Article and Find Full Text PDFAn fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.
NPJ Sci Learn
December 2024
Academy of Medical Engineering and Translational Medicine (AMT), Tianjin University, Tianjin, China.
Article Synopsis
- Generalization is key in motor learning, yet research on BCI-actuated supernumerary robotic fingers (BCI-SRF) and their long-term neuroplastic effects is limited.
- In a study with 20 right-handed participants, the BCI-SRF group showed a 350% improvement in finger opposition accuracy compared to the traditional finger group after 4 weeks of training.
- This improvement was linked to increased functional connectivity in brain regions involved in motor control, suggesting that BCI-SRF training may enhance motor learning by reorganizing the sensorimotor network.
Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC-MS urinary metabolomics for diseases screening.
Int J Med Inform
December 2024
Department of Genetics and Metabolism, the Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China. Electronic address:
Article Synopsis
- Gas chromatography-mass spectrometry (GC-MS) is an effective method for urine analysis, but its application for screening inborn errors of metabolism (IEM) is limited due to the rarity of IEM and the complexity of data interpretation.
- A machine learning model based on 355,197 GC-MS test cases from China was developed to better identify and classify rare IEMs, using techniques like undersampling and oversampling to handle imbalanced data.
- The proposed model demonstrates high sensitivity and accuracy in identifying specific IEMs, suggesting that machine learning can significantly enhance the interpretation and efficiency of GC-MS for IEM screening.
Neuropsychological profile of French adults with early-treated phenylketonuria: a multicenter study.
J Neurol
December 2024
Service de Médecine Interne, Centre de Référence Des Maladies Héréditaires du Métabolisme, UMR INSERM 1253 « iBraiN », Université de Tours, CHU de Tours, Tours, France.
Background And Objective: Adult patients with early-treated phenylketonuria (AwET-PKU) may present some subtle neurocognitive deficits. The aim of the study was to investigate 1) neurocognitive functions in a large group of AwET-PKU 2) the influence of plasma phenylalanine (Phe).
Methods: Participants: 187 AwET-PKU (classic PKU [cPKU] 81%, mild PKU [mPKU] 14%, and mild persistent hyperphenylalaninemia [MPH] 5%).
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