<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.

<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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 (<i>p</i> &#x3c; 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 (<i>p</i> &#x3c; 0.01). The NBF-fixed and LBC-fixed samples showed no significant differences in the percentage of positive cells of CA125 and EMA. <b><i>Discussion/Conclusion:</i></b> 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.

<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> The specimens were prepared from five preservative solutions of LBC and stained using the Papanicolaou method. The YOLOv5 deep convolutional neural network algorithm was used to create a deep learning model for each specimen, and a BRCPT model from five specimens was also created. Each model was compared to the specimen types used for detection. <b><i>Results:</i></b> Among the six models, a difference in the detection rate of approximately 25% was observed depending on the detected specimen, and within specimens, a difference in the detection rate of approximately 20% was observed depending on the model. The BRCPT model had little variation in the detection rate depending on the type of the detected specimen. <b><i>Conclusions:</i></b> The same cells were treated with different preservative solutions, the cytologic features were different, and AI clarified the difference in cytologic features depending on the type of solution. The type of preservative solution used for training and detection had an extreme influence on cell detection using AI. Although the accuracy of the deep learning model is important, it is necessary to understand that cell morphology differs depending on the type of preservative solution, which is a factor affecting the detection rate of AI.

Phosphatidylinositol 3‐kinase (PI3K) signaling promotes the differentiation and proliferation of regulatory B (Breg) cells, and the lipid phosphatase phosphatase and tensin homolog deleted on chromosome 10 (PTEN) antagonizes the PI3K–Akt signaling pathway. We previously demonstrated that cardiac Akt activity is increased and that restraint stress exacerbates hypertension and both heart and adipose tissue (AT) inflammation in DS/obese rats, an animal model of metabolic syndrome (MetS). We here examined the effects of restraint stress and pharmacological inhibition of PTEN on heart and AT pathology in such rats. Nine‐week‐old animals were treated with the PTEN inhibitor bisperoxovanadium‐pic [bpV(pic)] or vehicle in the absence or presence of restraint stress for 4 weeks. BpV(pic) treatment had no effect on body weight or fat mass but attenuated hypertension in DS/obese rats subjected to restraint stress. BpV(pic) ameliorated left ventricular (LV) inflammation, fibrosis, and diastolic dysfunction as well as AT inflammation in the stressed rats. Restraint stress reduced myocardial capillary density, and this effect was prevented by bpV(pic). In addition, bpV(pic) increased the proportions of Breg and B‐1 cells as well as reduced those of CD8 + T and B‐2 cells in AT of stressed rats. Our results indicate that inhibition of PTEN by bpV(pic) alleviated heart and AT inflammation in stressed rats with MetS. These positive effects of bpV(pic) are likely due, at least in part, to a reduction in blood pressure, an increase in myocardial capillary formation, and an altered distribution of immune cells in fat tissue that result from the activation of PI3K–Akt signaling.

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.