AIMC Topic: Epithelial Cells

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Recognizing Epithelial Cells in Prostatic Glands Using Deep Learning.

Cells
Artificial intelligence (AI) is becoming an integral part of pathological assessment and diagnostic procedures in modern pathology. As most prostate cancers (PCa) arise from glandular epithelial tissue, an AI-based methodology has been developed to r...

[Application and evaluation of artificial intelligence TPS-assisted cytologic screening system in urine exfoliative cytology].

Zhonghua bing li xue za zhi = Chinese journal of pathology
To explore the application of manual screening collaborated with the Artificial Intelligence TPS-Assisted Cytologic Screening System in urinary exfoliative cytology and its clinical values. A total of 3 033 urine exfoliated cytology samples were co...

Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these method...

PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Cell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests th...

A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables.

International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists
Benign normal (NL), premalignant (endometrial intraepithelial neoplasia, EIN) and malignant (cancer, EMCA) endometria must be precisely distinguished for optimal management. EIN was objectively defined previously as a regression model incorporating m...

A reference library for assigning protein subcellular localizations by image-based machine learning.

The Journal of cell biology
Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morph...

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.

Journal of biomedical optics
SIGNIFICANCE: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biops...

[Toxicity of vehicle exhaust on BEAS-2B cells in vitro by air-liquid interface].

Wei sheng yan jiu = Journal of hygiene research
OBJECTIVE: To evaluate the toxic effect of vehicle exhaust( VE) on lung epithelial cells by air-liquid interface( ALI) method in vitro, and analyze the different toxicity of VE after being treated with 0. 2 μm filter.

Identification of human flap endonuclease 1 (FEN1) inhibitors using a machine learning based consensus virtual screening.

Molecular bioSystems
Human Flap endonuclease1 (FEN1) is an enzyme that is indispensable for DNA replication and repair processes and inhibition of its Flap cleavage activity results in increased cellular sensitivity to DNA damaging agents (cisplatin, temozolomide, MMS, e...