Ability to form cellular aggregations such as tumorspheres and spheroids have been used as a morphological marker of malignant cancer cells and in particular cancer stem cells (CSC). However, the common definition of the types of cellular aggregation formed by cancer cells has not been available. We examined morphologies of 67 cell lines cultured on three dimensional morphology enhancing NanoCulture Plates (NCP) and classified the types of cellular aggregates that form. Among the 67 cell lines, 49 cell lines formed spheres or spheroids, 8 cell lines formed grape-like aggregation (GLA), 8 cell lines formed other types of aggregation, and 3 cell lines formed monolayer sheets. Seven GLA-forming cell lines were derived from adenocarcinoma among the 8 lines. A neuroendocrine adenocarcinoma cell line PC-3 formed asymmetric GLA with ductal structures on the NCPs and rapidly growing asymmetric tumors that metastasized to lymph nodes in immunocompromised mice. In contrast, another adenocarcinoma cell line DU-145 formed spheroids in vitro and spheroid-like tumors in vivo that did not metastasize to lymph nodes until day 50 after transplantation. Culture in the 3D nanoenvironment and in a defined stem cell medium enabled the neuroendocrine adenocarcinoma cells to form slowly growing large organoids that expressed multiple stem cell markers, neuroendocrine markers, intercellular adhesion molecules, and oncogenes in vitro. In contrast, the more commonly used 2D serum-contained environment reduced intercellular adhesion and induced mesenchymal transition and promoted rapid growth of the cells. In addition, the 3D stemness nanoenvironment promoted secretion of HSP90 and EpCAM-exosomes, a marker of CSC phenotype, from the neuroendocrine organoids. These findings indicate that the NCP-based 3D environment enables cells to form stem cell tumoroids with multipotency and model more accurately the in vivo tumor status at the levels of morphology and gene expression.

In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

Members of matrix metalloproteinase (MMP) family promote cancer cell migration, invasion, and metastasis through alteration of the tumor milieu, intracellular signaling pathways, and transcription. We examined gene expression signatures of colon adenocarcinoma cell lines with different metastatic potentials and found that rapidly metastatic cells powerfully expressed genes encoding MMP3 and MMP9. The non-proteolytic PEX isoform and proteolytic isoforms of MMPs were significantly expressed in the metastatic cells in vitro. Knockdown of MMP3 attenuated cancer cell migration and invasion in vitro and lung metastasis in vivo. Profound nuclear localization of MMP3/PEX was found in tumor-stroma marginal area. In contrast, MMP9 was localized in central area of subcutaneous tumors. Overexpression of the PEX isoform of MMP3 promoted proliferation and migration of the rapidly metastatic cells in vitro. Taken together, the non-proteolytic PEX isoform of MMPs locating in cell nuclei involves proliferation, migration, and subsequent metastasis of aggressive adenocarcinoma cells.

Differential expression of Wnt ligands in different ameloblastoma subtypes suggests that the canonical and non-canonical Wnt pathways are selectively activated or repressed depending on the tumor cell differentiation status. Canonical Wnt pathway is most likely the main transduction pathway while Wnt-1 might be the key signaling molecule involved in ameloblastoma tumorigenesis.