Effects of tumor, operative stress and tumor removal, and postoperative TPN of varying amino acid compositions on brain levels of tryptophan or tyrosine as predicted by their brain influx rates were studied in normals and in malnourished cancer patients. Concentrations of the large neutral amino acids (LNAA) were determined in patients before and after tumor removal, and in postoperative patients before and after receiving either a standard TPN solution (STD-TPN), or a branched-chain amino acid solution (BCAA-TPN). The LNAA were altered in all groups versus normals. Brain influx rates showed the following: in preoperative patients, predicted brain tryptophan levels were below normal (P less than 0.001), whereas tyrosine levels were within or above normal; no significant differences between pre- and postoperative tryptophan or tyrosine levels; postoperative STD-TPN did not change predicted brain tryptophan concentration from preinfusion values, but BCAA-TPN decreased it (P less than 0.001), underscoring the common transport carrier; and preinfusion predicted brain tyrosine levels were decreased (P less than 0.001) by both types of TPN solutions. These results imply low substrate levels for brain serotonin and catecholamine synthesis, possibly affecting functions dependent on their control.
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http://dx.doi.org/10.1002/1097-0142(19870315)59:6<1192::aid-cncr2820590627>3.0.co;2-j | DOI Listing |
BMC Med Inform Decis Mak
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Introduction: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Design: We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.
BMC Psychiatry
January 2025
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFNeurocrit Care
January 2025
Faculty of Psychology, Chulalongkorn University, Bangkok, Thailand.
Background: Super-refractory status epilepticus (SRSE) is an extremely serious neurological emergency. Risk factors and mechanisms involved in transition from refractory status epilepticus (RSE) to SRSE are insufficiently studied.
Methods: This was a multicenter retrospective cohort study of consecutive patients diagnosed and treated for RSE at two reference hospital over 5 years in Ecuador.
Sci Rep
January 2025
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China.
Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV.
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