AIMC Topic: Lymphocytes
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Cell dynamic morphology classification using deep convolutional neural networks.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
May 15, 2018
Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here ...
Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.
Journal of computational biology : a journal of computational molecular cell biology
Apr 25, 2018
Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream co...
Investigation of genotoxic effects of paraben in cultured human lymphocytes.
Drug and chemical toxicology
Dec 27, 2017
Paraben is a phenolic derivative of benzoic acid extensively used as preservatives in food, pharmaceutical, and cosmetic industries due to its antimicrobial characteristics. The objective of this study was to evaluate the in vitro genotoxic effects o...
White blood cells identification system based on convolutional deep neural learning networks.
Computer methods and programs in biomedicine
Nov 16, 2017
BACKGROUND AND OBJECTIVES: White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear im...
Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.
Scientific reports
Jul 27, 2017
Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescenc...
Metastasis detection from whole slide images using local features and random forests.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Apr 20, 2017
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...
Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.
Translational psychiatry
Mar 21, 2017
To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the comp...
Lymphocytes
Heart Rate
Adrenocorticotropic Hormone
Linear Models
Urine
Wounds and Injuries
Psychological Trauma
Receptors, Glucocorticoid
Humans
Emergency Service, Hospital
Adult
Support Vector Machine
Norepinephrine
Machine Learning
Male
Longitudinal Studies
Hydrocortisone
Algorithms
Risk Assessment
Blood Pressure
Saliva
Female
Stress Disorders, Post-Traumatic
Area Under Curve
Young Adult
ROC Curve
PARP activity in peripheral blood lymphocytes as a predictive biomarker for PARP inhibition in tumor tissues - A population pharmacokinetic/pharmacodynamic analysis of rucaparib.
Clinical pharmacology in drug development
Jan 26, 2015
PURPOSE: Rucaparib is a potent Poly (ADP-ribose) Polymerase (PARP) inhibitor currently under clinical development. The objectives of this analysis were to establish population PK and PK/PD models for rucaparib, and to evaluate the predictability of P...
Robotic radiation shielding system reduces radiation-induced DNA damage in operators performing electrophysiological procedures.
Scientific reports
May 28, 2025
Fluoroscopically guided electrophysiology (EP) procedures expose operators to low doses of ionizing radiation, which can induce DNA double-strand breaks (DSBs) and raises increasing concerns regarding potential health risks. A novel robotic radiation...
Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.
The clinical respiratory journal
Dec 1, 2024
BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict...
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