Development of a plasma maltose assay method as a screening test for upper gastrointestinal disorders.

BACKGROUND AND OBJECTIVE: The disaccharide loading test is a method to assess gastric mucosal damage. Since Trelan-G75, which is used for the sugar tolerance test, contains disaccharide maltose, if maltose is detected at a high sensitivity in the sample blood used in the sugar tolerance test, screening for upper gastrointestinal mucosal damage can be made simultaneously with the sugar tolerance test for the diagnosis of diabetes. METHODS: Glucose-6-phosphate is generated by treating maltose with maltose phosphorylase, ß-phosphoglucomutase, and glucose-1,6-bisphosphate. Then, change in the absorbance at 405 nm is measured by the enzymatic cycling method using Thio-NADP, ß-NADPH, and Glucose-6-phosphate dehydrogenase. After evaluating the optimal condition for this method, it is mounted on an automatic biochemical analyzer, and samples after the sugar tolerance test were assayed. RESULTS: Regarding the performance of this method, the repeatability was 10-50 µmol/L with a CV of ≤1.1%. Concerning the assay range, a curve passing the origin with a range of linearity up to 120 µmol/L was obtained. No effect of dyes or sugars in the blood was noted. As a result of application to patients with gastric mucosal disorders (those who had a health checkup), significant differences were observed depending on the stage of atrophic gastritis. DISCUSSION: This method has a high sensitivity and a high precision and can be used for high-speed analysis on an automatic analyzer. It has the potential to be used as a screening test for gastric mucosal damage.

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. METHODS: Cytological specimens were prepared using the ThinPrep and SurePath methods. The accuracy of cell detection and cell classification was examined using the one- and five-cell models, which were trained with one and five cell types, respectively. RESULTS: When the same LBC processing techniques were used for the training and detection preparations, the cell detection and classification rates were high. The model trained on ThinPrep preparations was more accurate than that trained on SurePath. When the preparation types used for training and detection were different, the accuracy of cell detection and classification was significantly reduced (P < 0.01). The model trained on both ThinPrep and SurePath preparations exhibited slightly reduced cell detection and classification rates but was highly accurate. CONCLUSIONS: For the two LBC processing techniques, cytomorphology varied according to cell type; this difference affects the accuracy of cell detection and classification by deep learning. Therefore, for highly accurate cell detection and classification using AI, the same processing technique must be used for both training and detection. Our assessment also suggests that a deep learning model should be constructed using specimens prepared via a variety of processing techniques to construct a globally applicable AI model.

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. ICC was performed using 24 different antibodies, and immunocytochemically stained specimens were analyzed for the percentage of positive cells. RESULTS: Immunoreactivity differed according to the type of antigen detected. For nuclear antigens, the highest percentage of positive cells of Ki-67, WT-1, ER, and p63 was observed in the NBF-fixed samples, and the highest percentage of positive cells of p53, TTF-1, and PgR was observed in the TACAS™ Ruby samples. For cytoplasmic antigens, the percentage of positive cells of CK5/6, Vimentin, and IMP3 in LBC-fixed samples was higher than or similar to that in NBF-fixed samples. The percentage of positive cells of CEA was significantly lower in CytoRich™ Red and CytoRich™ Blue samples than in the NBF-fixed sample (p < 0.01). Among the cell membrane antigens, the percentage of positive cells of Ber-EP4, CD10, and D2-40 was the highest in NBF-fixed samples and significantly lower in CytoRich™ Red and CytoRich™ Blue samples than that in NBF-fixed samples (p < 0.01). The NBF-fixed and LBC-fixed samples showed no significant differences in the percentage of positive cells of CA125 and EMA. DISCUSSION/CONCLUSION: ICC using LBC-fixed samples showed the same immunoreactivity as NBF-fixed samples when performed on cell block specimens post-fixed with NBF. The percentage of positive cells increased or decreased based on the type of fixing solution depending on the amount of antigen in the cells. Further, the detection rate of ICC with LBC-fixed samples varied according to the type of antibody and the amount of antigen in the cells. Therefore, we propose that ICC using LBC-fixed samples, including detection methods, should be carefully performed.

Characterizing the Effect of Processing Technique and Solution Type on Cytomorphology Using Liquid-Based Cytology.

INTRODUCTION: Liquid-based cytology (LBC) is increasingly used for nongynecologic applications. However, the cytological preparation of LBC specimens is influenced by the processing technique and the preservative used. In this study, the influence of the processing techniques and preservatives on cell morphology was examined mathematically and statistically. METHODS: Cytological specimens were prepared using the ThinPrep (TP), SurePath (SP), and AutoSmear methods, with 5 different preservative solutions. The cytoplasmic and nuclear areas of Papanicolaou-stained specimens were measured for all samples. RESULTS: The cytoplasmic and nuclear areas were smaller in cells prepared using the 2 LBC methods, compared to that prepared using the AutoSmear method, irrespective of the preservative used. The cytoplasmic and nuclear areas of cells prepared using the SP method were smaller than those of cells prepared using the TP method, irrespective of the preservative used. There were fewer differences among the cytoplasmic areas of cells prepared with different preservative solutions using the TP method; however, the cytoplasmic areas of cells prepared using the SP method changed with the preservative solution used. CONCLUSIONS: The most significant difference affecting the cytoplasmic and nuclear areas was the processing technique. The TP method increased the cytoplasmic and nuclear areas, while the methanol-based PreservCyt solution enabled the highest enlargement of the cell. LBC is a superior preparation technique for standardization of the specimens. Our results offer a better understanding of methods suitable for specimen preparation for developing precision AI-based diagnosis in cytology.