Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning.

Objectives: Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general purpose deep learning-based cytology models.

Methods: Using 11 types of human cancer cell lines, we prepared Papanicolaou- and May-Grünwald-Giemsa (MGG)-stained specimens.

Effect of liquid-based cytology fixing solution on immunocytochemistry: Efficacy of antigen retrieval in cytologic specimens.

Background: Immunocytochemistry (ICC) is an indispensable technique to improve diagnostic accuracy. ICC using liquid-based cytology (LBC)-fixed specimens has been reported. However, problems may arise if the samples are not fixed appropriately.

Relationship between a deep learning model and liquid-based cytological processing techniques.

Objective: Artificial intelligence (AI)-based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid-based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies according to the LBC processing technique used. In this study, we elucidated the relationship between two LBC techniques and cell detection and classification using a deep learning model.

Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence.

Objectives: Cytomorphology is known to differ depending on the processing technique, and these differences pose a problem for automated diagnosis using deep learning. We examined the as-yet unclarified relationship between cell detection or classification using artificial intelligence (AI) and the AutoSmear (Sakura Finetek Japan) and liquid-based cytology (LBC) processing techniques.

Methods: The "You Only Look Once" (YOLO), version 5x, algorithm was trained on the AutoSmear and LBC preparations of 4 cell lines: lung cancer (LC), cervical cancer (CC), malignant pleural mesothelioma (MM), and esophageal cancer (EC).

Immunocytochemical Reactivity Comparison between Formalin-Fixed and Liquid-Based Cytology-Fixed Specimens.

Introduction: Liquid-based cytology (LBC)-fixed samples can be used for preparing multiple specimens of the same quality and for immunocytochemistry (ICC); however, LBC fixing solutions affect immunoreactivity. Therefore, in this study, we examined the effect of LBC fixing solutions on immunoreactivity.

Methods: Samples were cell lines, and specimens were prepared from cell blocks of 10% neutral buffered formalin (NBF)-fixed samples and the four types of LBC-fixed samples: PreservCyt®, CytoRich™ Red, CytoRich™ Blue, and TACAS™ Ruby, which were post-fixed with NBF.

Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network.

Introduction: Deep learning is a subset of machine learning that has contributed to significant changes in feature extraction and image classification and is being actively researched and developed in the field of cytopathology. Liquid-based cytology (LBC) enables standardized cytological preparation and is also applied to artificial intelligence (AI) research, but cytological features differ depending on the LBC preservative solution types. In this study, the relationship between cell detection by AI and the type of preservative solution used was examined.

Effects of application method on skin penetration of carboxyfluorescein incorporated in liposomes.

We investigated the skin penetration of liposomes under two different application conditions; occluded and large application amount (1 ml/cm(2)), and open and small application amount (10 mul/cm(2)). Liposomes containing fluorescence-labeled phospholipids or carboxyfluorescein (CF) were used. In application under occluded conditions, phospholipids showed no penetration, even in the stratum corneum (SC).