AIMC Topic: Coloring Agents

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Stain-free detection of embryo polarization using deep learning.

Scientific reports Feb 14, 2022
Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, im...
Humans Fertilization in Vitro Coloring Agents Embryo, Mammalian Animals Embryonic Development Deep Learning Cell Polarity Mice Staining and Labeling
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Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.

Scientific reports Aug 16, 2021
Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires ...
Middle Aged Deep Learning ROC Curve Lung Neoplasms Aged Aged, 80 and over Adult Male Mutation Coloring Agents Sex Factors Datasets as Topic Smoking Staining and Labeling Carcinoma, Non-Small-Cell Lung Age Factors Area Under Curve Humans Female Adenocarcinoma of Lung Eosine Yellowish-(YS) Hematoxylin
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Deep learning-based transformation of H&E stained tissues into special stains.

Nature communications Aug 12, 2021
Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstra...
Deep Learning Algorithms Humans Diagnosis, Differential Staining and Labeling Reproducibility of Results Coloring Agents Kidney Sensitivity and Specificity Kidney Diseases Diagnosis, Computer-Assisted Reference Standards Biopsy, Large-Core Needle Pathology, Clinical
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Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross-institutional study.

Histopathology Aug 3, 2021
AIMS: Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on sli...
Biopsy Kidney Glomerulus Coloring Agents Pathology, Clinical Humans Neural Networks, Computer Image Processing, Computer-Assisted Staining and Labeling
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Learning the Exciton Properties of Azo-dyes.

The journal of physical chemistry letters Jun 22, 2021
The determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine lear...
Machine Learning Models, Molecular Quantum Theory Molecular Conformation Coloring Agents Azo Compounds
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A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores.

Scientific reports May 10, 2021
We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists' evaluations. Average precision, precision, and recall were asses...
Spermatogonia Eosine Yellowish-(YS) Hematoxylin Seminiferous Tubules Humans Azoospermia Spermatids Spermatocytes Adult Coloring Agents Male Machine Learning Histocytochemistry Infertility, Male Automation, Laboratory
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An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Journal of neuroscience methods Feb 17, 2021
BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise an...
Image Processing, Computer-Assisted Humans Staining and Labeling Algorithms Coloring Agents Reproducibility of Results Deep Learning
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Hg(II) sensing, catalytic, antioxidant, antimicrobial, and anticancer potential of Garcinia mangostana and α-mangostin mediated silver nanoparticles.

Chemosphere Jan 28, 2021
This study reports synthesis of Garcinia mangostana fruit pericarp (unwanted waste material) and α-mangostin mediated silver nanoparticles (AgNPs). These AgNPs were efficiently produced using 1:10 (extract and salt) ratio under stirring and heating, ...
Garcinia mangostana Mercury Anti-Bacterial Agents Plant Extracts Xanthones Anti-Infective Agents Metal Nanoparticles Molecular Docking Simulation Silver Antioxidants Coloring Agents
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A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Dec 9, 2020
Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tis...
Staining and Labeling Liver Cirrhosis Liver Predictive Value of Tests Coloring Agents Microscopy Azo Compounds Biopsy Non-alcoholic Fatty Liver Disease Middle Aged Severity of Illness Index Adolescent Child Aged, 80 and over Retrospective Studies Humans Young Adult Image Interpretation, Computer-Assisted Clinical Decision-Making Female Neural Networks, Computer Adult Male Reproducibility of Results Eosine Yellowish-(YS) Software Aged Hematoxylin Decision Support Techniques Methyl Green
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A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Nature communications Nov 26, 2020
Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide trea...
Microscopy Deep Learning Humans Image Interpretation, Computer-Assisted Staining and Labeling Lymphoma, Large B-Cell, Diffuse Diagnosis, Differential Eosine Yellowish-(YS) Lymph Nodes Biopsy Hospitals Hematoxylin Feasibility Studies Coloring Agents
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