AIMC Topic: Phospholipids
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Automated spectral decomposition and reconstruction of optical properties using a mixed autoencoder approach.
Journal of biomedical optics
Apr 9, 2025
SIGNIFICANCE: Investigating optical properties (OPs) is crucial in the field of biophotonics, as it has a broad impact on understanding light-tissue interactions. However, current techniques, such as inverse Monte Carlo simulations (IMCS), have limit...
Multiple Instance Learning-Based Prediction of Blood-Brain Barrier Opening Outcomes Induced by Focused Ultrasound.
IEEE transactions on bio-medical engineering
Mar 21, 2025
OBJECTIVE: Targeted blood-brain barrier (BBB) opening using focused ultrasound (FUS) and micro/nanobubbles is a promising method for brain drug delivery. This study aims to explore the feasibility of multiple instance learning (MIL) in accurate and f...
Machine learning approach in canine mammary tumour classification using rapid evaporative ionization mass spectrometry.
Analytical and bioanalytical chemistry
Nov 19, 2024
Rapid evaporative ionization mass spectrometry (REIMS) coupled with a monopolar handpiece used for surgical resection and combined with chemometrics has been previously explored by our research group (Mangraviti et al. in Int J Mol Sci 23(18):10562, ...
Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Jun 13, 2024
The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would ...
-derived postbiotics inhibited digestion of triglycerides, glycerol phospholipids and sterol lipids allosteric regulation of BSSL, PTL and PLA2 to prevent obesity: perspectives on deep learning integrated multi-omics.
Food & function
Aug 14, 2023
The anti-obesity potential of probiotics has been widely reported, however their utilization in high-risk patients and potential adverse reactions have led researchers to focus their attention on postbiotics. Herein, pseudo-targeted lipidomics linked...
Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck.
Journal of chemical theory and computation
Apr 6, 2022
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. In this work, we focus on the recently develop...
Cohort profile: Japanese human milk study, a prospective birth cohort: baseline data for lactating women, infants and human milk macronutrients.
BMJ open
Dec 30, 2021
PURPOSE: The Japanese Human Milk Study, a longitudinal prospective cohort study, was set up to clarify how maternal health, nutritional status, lifestyle and sociodemographic and economic factors affect breastfeeding practices and human milk composit...
Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence.
Journal of healthcare engineering
Dec 16, 2021
This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnosis of nipple discharge. In this paper, a new model (ODNS) is proposed based on the unsupervised lear...
Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter.
Scientific reports
Sep 13, 2019
Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of m...
Novel machine learning application for prediction of membrane insertion potential of cell-penetrating peptides.
International journal of pharmaceutics
Jun 21, 2019
Cell-penetrating peptides (CPPs) are often used as transporter systems to deliver various therapeutic agents into the cell. We developed a novel machine learning application which can quantitatively screen the insertion/interaction potential of vario...
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