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Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Gibberellin (GA) is a phytohormone that regulates various developmental processes during the plant life cycle. In this study, we identify a new GA agonist, diphegaractin, using a wheat cell-free based drug screening system with grape GA receptor. A GA-dependent interaction assay system using GA receptors and DELLA proteins from Vitis vinifera was constructed using AlphaScreen technology and cell-free produced proteins. From the chemical compound library, diphegaractin was found to enhance the interactions between GA receptors and DELLA proteins from grape in vitro. In grapes, we found that diphegaractin induces elongation of the bunch and increases the sugar concentration of grape berries. Furthermore, diphegaractin shows GA-like activity, including promotion of root elongation in lettuce and Arabidopsis, as well as reducing peel pigmentation and suppressing peel puffing in citrus fruit. To the best of our knowledge, this study is the first to successfully identify a GA receptor agonist showing GA-like activity in agricultural plants using an in vitro molecular-targeted drug screening system., (© 2023. The Author(s).)

Objectives: Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases., Methods: One hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies., Results: The tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types., Conclusions: The proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.

Purpose: The purpose of our study was to analyze dental panoramic radiographs and contribute to dentists' diagnosis by automatically extracting the information necessary for reading them. As the initial step, we detected teeth and classified their tooth types in this study. Approach: We propose single-shot multibox detector (SSD) networks with a side branch for 1-class detection without distinguishing the tooth type and for 16-class detection (i.e., the central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, and third molar, distinguished by the upper and lower jaws). In addition, post-processing was conducted to integrate the results of the two networks and categorize them into 32 classes, differentiating between the left and right teeth. The proposed method was applied to 950 dental panoramic radiographs obtained at multiple facilities, including a university hospital and dental clinics. Results: The recognition performance of the SSD with a side branch was better than that of the original SSD. In addition, the detection rate was improved by the integration process. As a result, the detection rate was 99.03%, the number of false detections was 0.29 per image, and the classification rate was 96.79% for 32 tooth types. Conclusions: We propose a method for tooth recognition using object detection and post-processing. The results show the effectiveness of network branching on the recognition performance and the usefulness of post-processing for neural network output., (© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Polymerase chain reaction (PCR) combined with restriction fragment length polymorphism (RFLP) is commonly used for genotyping bovine leukemia virus (BLV) in slaughterhouses. However, unclassified BLV genotypes have been sporadically reported. To assess the current status of BLV genetic characterization in cattle, PCR-RFLP was performed on blood samples of 170 cattle (84 Japanese Black, 60 Japanese Black x Holstein, and 26 Holstein) from 17 farms (5 prefectures) at a slaughterhouse in Aichi Prefecture in 2019. A total of 65 samples (38.2%) were BLV positive, and genotype 1 was the most predominant (56/65 samples), followed by genotypes 3 (6 samples) and 5 (1 sample), and two unclassified samples. No relationship between the genotypes and breeds was observed. Sequence and phylogenetic analyses demonstrated that unclassified BLV genotypes clustered with genotype 1 sequences were, therefore, not new genotypes.

Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. The pretrained model was constructed from an artificial dataset generated using a generative adversarial network (GAN). Materials and Methods: Three public datasets containing images of lung nodules/lung cancers were used: LUNA16 dataset, Decathlon lung dataset, and NSCLC radiogenomics. The LUNA16 dataset was used to generate an artificial dataset for lung cancer segmentation with the help of the GAN and 3D graph cut. Pretrained models were then constructed from the artificial dataset. Subsequently, the main segmentation model was constructed from the pretrained models and the Decathlon lung dataset. Finally, the NSCLC radiogenomics dataset was used to evaluate the main segmentation model. The Dice similarity coefficient (DSC) was used as a metric to evaluate the segmentation performance. Results: The mean DSC for the NSCLC radiogenomics dataset improved overall when using the pretrained models. At maximum, the mean DSC was 0.09 higher with the pretrained model than that without it. Conclusion: The proposed method comprising an artificial dataset and a pretrained model can improve lung cancer segmentation as confirmed in terms of the DSC metric. Moreover, the construction of the artificial dataset for the segmentation using the GAN and 3D graph cut was found to be feasible., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nishio, Fujimoto, Matsuo, Muramatsu, Sakamoto and Fujita.)

Objective: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses., Methods: The imaging data for healthy maxillary sinuses (587 sinuses, Class 0), inflamed maxillary sinuses (416 sinuses, Class 1), cysts of maxillary sinus regions (171 sinuses, Class 2) were assigned to training, testing 1, and testing 2 data sets. A learning process of 1000 epochs with the training images and labels was performed using DetectNet, and a learning model was created. The testing 1 and testing 2 images were applied to the model, and the detection sensitivities and the false-positive rates per image were calculated. The accuracies, sensitivities and specificities were determined for distinguishing the inflammation group (Class 1) and cyst group (Class 2) with respect to the healthy group (Class 0)., Results: Detection sensitivities of healthy (Class 0) and inflamed (Class 1) maxillary sinuses were 100% for both testing 1 and testing 2 data sets, whereas they were 98 and 89% for cysts of the maxillary sinus regions (Class 2). False-positive rates per image were nearly 0.00. Accuracies, sensitivities and specificities for diagnosis maxillary sinusitis were 90-91%, 88-85%, and 91-96%, respectively; for cysts of the maxillary sinus regions, these values were 97-100%, 80-100%, and 100-100%, respectively., Conclusion: Deep learning could reliably detect the maxillary sinuses and identify maxillary sinusitis and cysts of the maxillary sinus regions., Advances in Knowledge: This study using a deep leaning object detection technique indicated that the detection sensitivities of maxillary sinuses were high and the performance of maxillary sinus lesion identification was ≧80%. In particular, performance of sinusitis identification was ≧90%.

Purpose: To develop and evaluate a three-dimensional (3D) generative model of computed tomography (CT) images of lung nodules using a generative adversarial network (GAN). To guide the GAN, lung nodule size was used., Materials and Methods: A public CT dataset of lung nodules was used, from where 1182 lung nodules were obtained. Our proposed GAN model used masked 3D CT images and nodule size information to generate images. To evaluate the generated CT images, two radiologists visually evaluated whether the CT images with lung nodule were true or generated, and the diagnostic ability was evaluated using receiver-operating characteristic analysis and area under the curves (AUC). Then, two models for classifying nodule size into five categories were trained, one using the true and the other using the generated CT images of lung nodules. Using true CT images, the classification accuracy of the sizes of the true lung nodules was calculated for the two classification models., Results: The sensitivity, specificity, and AUC of the two radiologists were respectively as follows: radiologist 1: 81.3%, 37.7%, and 0.592; radiologist 2: 77.1%, 30.2%, and 0.597. For categorization of nodule size, the mean accuracy of the classification model constructed with true CT images was 85% (range 83.2-86.1%), and that with generated CT images was 85% (range 82.2-88.1%)., Conclusions: Our results show that it was possible to generate 3D CT images of lung nodules that could be used to construct a classification model of lung nodule size without true CT images., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: The aim of this study was to compare time and storage space requirements, diagnostic performance, and consistency among 3 image recognition convolutional neural networks (CNNs) in the evaluation of the relationships between the mandibular third molar and the mandibular canal on panoramic radiographs., Study Design: Of 600 panoramic radiographs, 300 each were assigned to noncontact and contact groups based on the relationship between the mandibular third molar and the mandibular canal. The CNNs were trained twice by using cropped image patches with sizes of 70 × 70 pixels and 140 × 140 pixels. Time and storage space were measured for each system. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were determined. Intra-CNN and inter-CNN consistency values were calculated., Results: Time and storage space requirements depended on the depth of CNN layers and number of learned parameters, respectively. The highest AUC values ranged from 0.88 to 0.93 in the CNNs created by 70 × 70 pixel patches, but there were no significant differences in diagnostic performance among any of the models with smaller patches. Intra-CNN and inter-CNN consistency values were good or very good for all CNNs., Conclusions: The size of the image patches should be carefully determined to ensure acquisition of high diagnostic performance and consistency., (Copyright © 2020. Published by Elsevier Inc.)

Training of a convolutional neural network (CNN) generally requires a large dataset. However, it is not easy to collect a large medical image dataset. The purpose of this study is to investigate the utility of synthetic images in training CNNs and to demonstrate the applicability of unrelated images by domain transformation. Mammograms showing 202 benign and 212 malignant masses were used for evaluation. To create synthetic data, a cycle generative adversarial network was trained with 599 lung nodules in computed tomography (CT) and 1430 breast masses on digitized mammograms (DDSM). A CNN was trained for classification between benign and malignant masses. The classification performance was compared between the networks trained with the original data, augmented data, synthetic data, DDSM images, and natural images (ImageNet dataset). The results were evaluated in terms of the classification accuracy and the area under the receiver operating characteristic curves (AUC). The classification accuracy improved from 65.7% to 67.1% with data augmentation. The use of an ImageNet pretrained model was useful (79.2%). Performance was slightly improved when synthetic images or the DDSM images only were used for pretraining (67.6 and 72.5%, respectively). When the ImageNet pretrained model was trained with the synthetic images, the classification performance slightly improved (81.4%), although the difference in AUCs was not statistically significant. The use of the synthetic images had an effect similar to the DDSM images. The results of the proposed study indicated that the synthetic data generated from unrelated lesions by domain transformation could be used to increase the training samples., Competing Interests: Declaration of competing interest None declared., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Streptococcus suis strains isolated from porcine endocarditis and tonsils in the Tokai area of Japan during 2004-2007 and 2014-2016 (n=114) were tested for antimicrobial susceptibility and distribution of selected resistance genes. No strains showed resistance to penicillin, ampicillin, cefotaxime, meropenem, vancomycin, and levofloxacin. High resistance to tetracycline (80.7%), clindamycin (65.8%), erythromycin (56.1%), and clarithromycin (56.1%) was observed. In chloramphenicol and sulfamethoxazole-trimethoprim, there was a trend towards increased resistance between the first (2004-2007) and second (2014-2016) periods. tet(O) and erm(B) genes were the most frequently detected, and tet(M) and mef(A/E) genes were only detected in strains isolated during 2014-2016. These results indicate that chloramphenicol and sulfamethoxazole-trimethoprim resistance, and tet(M) and mef(A/E) genes emerged in S. suis of this area after 2014.

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. Retinal fundus photography is frequently obtained for various eye disease diagnosis and record and is a suitable screening exam for its simplicity and low cost. However, the number of ophthalmologists who are specialized in glaucoma diagnosis is limited. We have been studying automated schemes for detection of nerve fiber layer defects and analysis of optic disc deformation, two major signs of glaucoma, in assisting ophthalmologists' accurate and efficient diagnosis. In this chapter, our recent progress in computerized methods is discussed.

Objective: The aim of this study was to investigate whether a deep learning object detection technique can automatically detect and classify radiolucent lesions in the mandible on panoramic radiographs., Study Design: Panoramic radiographs of patients with mandibular radiolucent lesions of 10 mm or greater, including ameloblastomas, odontogenic keratocysts, dentigerous cysts, radicular cysts, and simple bone cysts, were included. Lesion labels, including region of interest coordinates, were created in text format. In total, 210 training images and labels were imported into the deep learning GPU training system (DIGITS). A learning model was created using the deep neural network DetectNet. Two testing data sets (testing 1 and 2) were applied to the learning model. Similarities and differences between the prediction and ground-truth images were evaluated using Intersection over Union (IoU). Sensitivity and false-positive rate per image were calculated using an IoU threshold of 0.6. The detection performance for each disease was assessed using multiclass learning., Results: Sensitivity was 0.88 for both testing 1 and 2. The false-positive rate per image was 0.00 for testing 1 and 0.04 for testing 2. The best combination of detection and classification sensitivity occurred with dentigerous cysts., Conclusions: Radiolucent lesions of the mandible can be detected with high sensitivity using deep learning., (Copyright © 2019. Published by Elsevier Inc.)

Computer-aided diagnosis systems for assisting the classification of various diseases have the potential to improve radiologists' diagnostic accuracy and efficiency, as reported in several studies. Conventional systems generally provide the probabilities of disease types in terms of numerical values, a method that may not be efficient for radiologists who are trained by reading a large number of images. Presentation of reference images similar to those of a new case being diagnosed can supplement the probability outputs based on computerized analysis as an intuitive guide, and it can assist radiologists in their diagnosis, reporting, and treatment planning. Many studies on content-based medical image retrievals have been reported on. For retrieval of perceptually similar and diagnostically relevant images, incorporation of perceptual similarity data by radiologists has been suggested. In this paper, studies on image retrieval methods are reviewed with a special focus on quantification, utilization, and the evaluation of subjective similarities between pairs of images.

Rationale: Multilineage-differentiating stress enduring (Muse) cells, pluripotent marker stage-specific embryonic antigen-3 + cells, are nontumorigenic endogenous pluripotent-like stem cells obtainable from various tissues including the bone marrow. Their therapeutic efficiency has not been validated in acute myocardial infarction., Objective: The main objective of this study is to clarify the efficiency of intravenously infused rabbit autograft, allograft, and xenograft (human) bone marrow-Muse cells in a rabbit acute myocardial infarction model and their mechanisms of tissue repair., Methods and Results: In vivo dynamics of Nano-lantern-labeled Muse cells showed preferential homing of the cells to the postinfarct heart at 3 days and 2 weeks, with ≈14.5% of injected GFP (green fluorescent protein)-Muse cells estimated to be engrafted into the heart at 3 days. The migration and homing of the Muse cells was confirmed pharmacologically (S1PR2 [sphingosine monophosphate receptor 2]-specific antagonist JTE-013 coinjection) and genetically (S1PR2-siRNA [small interfering ribonucleic acid]-introduced Muse cells) to be mediated through the S1P (sphingosine monophosphate)-S1PR2 axis. They spontaneously differentiated into cells positive for cardiac markers, such as cardiac troponin-I, sarcomeric α-actinin, and connexin-43, and vascular markers. GCaMP3 (GFP-based Ca calmodulin probe)-labeled Muse cells that engrafted into the ischemic region exhibited increased GCaMP3 fluorescence during systole and decreased fluorescence during diastole. Infarct size was reduced by ≈52%, and the ejection fraction was increased by ≈38% compared with vehicle injection at 2 months, ≈2.5 and ≈2.1 times higher, respectively, than that induced by mesenchymal stem cells. These effects were partially attenuated by the administration of GATA4 -gene-silenced Muse cells. Muse cell allografts and xenografts efficiently engrafted and recovered functions, and allografts remained in the tissue and sustained functional recovery for up to 6 months without immunosuppression., Conclusions: Muse cells may provide reparative effects and robust functional recovery and may, thus, provide a novel strategy for the treatment of acute myocardial infarction., (© 2018 American Heart Association, Inc.)