AIMC Topic: Tryptophan
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Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Feb 6, 2025
Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatocellular carcinoma (HCC). In this study, attempts were made to investigate the excited-state kinetics of intrinsic fluorophores, tryptophan and nicotin...
Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.
Journal of translational medicine
Dec 20, 2024
BACKGROUND: Ulcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic disruptions. Tryptophan metabolism has been believed as a significant factor in UC pathogenesis, with s...
Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools.
International journal of antimicrobial agents
Dec 5, 2024
BACKGROUND: Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble. Machine learning tools allow the straightforward i...
Cuprospinel decorated biopolymer functionalized CNFs based electrocatalytic platform integrated with grid search optimized neural network for detection of vital amino acid in beverages.
Food chemistry
Oct 5, 2024
An electrocatalytic platform based on a novel nanocomposite integrated with a grid search-optimized neural network (GSNN) was proposed for intelligent sensing of tryptophan. The cuprospinel-decorated chitosan-functionalized carbon nanofibers (CuFeO/C...
Machine learning-powered wearable interface for distinguishable and predictable sweat sensing.
Biosensors & bioelectronics
Aug 28, 2024
The constrained resources on wearable devices pose a challenge in meeting the demands for comprehensive sensing information, and current wearable non-enzymatic sensors face difficulties in achieving specific detection in biofluids. To address this is...
Immune-based Machine learning Prediction of Diagnosis and Illness State in Schizophrenia and Bipolar Disorder.
Brain, behavior, and immunity
Aug 14, 2024
BACKGROUND: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine...
Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.
Journal of fluorescence
Aug 3, 2023
The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an ex...
Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods.
Scientific reports
Jan 26, 2021
Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the qua...
Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome.
Nature communications
Dec 11, 2020
The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcrip...
Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.
Nature communications
Sep 25, 2020
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...
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