AIMC Topic: Stem Cells
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Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.
Journal of chemical information and modeling
Feb 5, 2025
Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches i...
Effects of aqueous and ethanolic extracts of Chinese propolis on dental pulp stem cell viability, migration and cytokine expression.
PeerJ
Dec 18, 2024
BACKGROUND: Propolis is a natural substance produced by honeybees that has various biological properties including, anti-inflammatory, antioxidant and antimicrobial properties. Although previous studies have evaluated the antimicrobial effects of pro...
Effect of Boswellic Acid on Viability of Dental Pulp Stem Cells Compared to the Commonly Used Intracanal Medications: An Study.
The journal of contemporary dental practice
Dec 1, 2023
AIM: This study was aimed at evaluating the effect of acetyl-11-keto-β-boswellic acid (AKBA) on dental pulp stem cells (DPSCs) viability and proliferation to be used as a potential root canal medicament.
Acute psychological stress-induced progenitor cell mobilization and cardiovascular events.
Journal of psychosomatic research
Jun 14, 2023
OBJECTIVE: Certain brain activation responses to psychological stress are associated with worse outcomes in CVD patients. We hypothesized that elevated acute psychological stress, manifesting as greater activity within neural centers for emotional re...
Triplet-Net Classification of Contiguous Stem Cell Microscopy Images.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 5, 2023
Cellular microscopy imaging is a common form of data acquisition for biological experimentation. Observation of gray-level morphological features allows for the inference of useful biological information such as cellular health and growth status. Cel...
Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning.
Journal of assisted reproduction and genetics
Mar 30, 2023
PURPOSE: Rapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is crucial for clinicians dealing with male infertility caused by prepubertal testicular damage. Deep learning (DL) methods may offer visual tools for tracking SSPCs on...
Robot-assisted in situ bioprinting of gelatin methacrylate hydrogels with stem cells induces hair follicle-inclusive skin regeneration.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Dec 17, 2022
Large skin defects caused by accidents or disease can cause fluid loss, water and electrolyte disorders, hypoproteinemia and serious infection and remain a difficult problem in clinical practice. In situ bioprinting is a promising, recently developed...
Predicting multipotency of human adult stem cells derived from various donors through deep learning.
Scientific reports
Dec 14, 2022
Adult stem cell-based therapeutic approaches have great potential in regenerative medicine because of their immunoregulatory properties and multidifferentiation capacity. Nevertheless, the outcomes of stem cell‑based therapies to date have shown inco...
Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning.
Computational intelligence and neuroscience
Apr 5, 2022
The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segm...
Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation.
Sensors (Basel, Switzerland)
Jan 27, 2022
U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabi...
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