Genetic aspects of immunoglobulins and cyclophilin A in milk as potential indicators of mastitis resistance in Holstein cows.

Mastitis is one of the most frequent and costly diseases affecting dairy cattle. Natural antibodies (immunoglobulins) and cyclophilin A (CyPA), the most abundant member of the family of peptidyl prolyl cis/trans isomerases, in milk may serve as indicators of mastitis resistance in dairy cattle. However, genetic information for CyPA is not available, and knowledge on the genetic and nongenetic relationships between these immune-related traits and somatic cell score (SCS) and milk yield in dairy cattle is sparse. Therefore, we aimed to comprehensively evaluate whether immune-related traits consisting of 5 Ig classes (IgG, IgG1, IgG2, IgA, and IgM) and CyPA in the test-day milk of Holstein cows can be used as genetic indicators of mastitis resistance by evaluating the genetic and nongenetic relationships with SCS in milk. The nongenetic factors affecting immune-related traits and the effects of these traits on SCS were evaluated. Furthermore, the genetic parameters of immune-related traits according to health status and genetic relationships under different SCS environments were estimated. All immune-related traits were significantly associated with SCS and directly proportional. Additionally, evaluation using a classification tree revealed that IgA, IgG2, and IgG were associated with SCS levels. Genetic factor analyses indicated that heritability estimates were low for CyPA (0.08) but moderate for IgG (0.37), IgA (0.44), and IgM (0.44), with positive genetic correlations among Ig (0.25-0.96). We also evaluated the differences in milk yield and SCS of cows between the low and high groups according to their sires' estimated breeding value for immune-related traits. In the high group, IgA had a significantly lower SCS in milk at 7 to 30 d compared with that in the low group. Furthermore, the Ig in milk had high positive genetic correlations between healthy and infected conditions (0.82-0.99), suggesting that Ig in milk under healthy conditions could interact with those under infected conditions, owing to the genetic ability based on the level of Ig in milk. Thus, Ig in milk are potential indicators for the genetic selection of mastitis resistance. However, because only the relationship between immune-related traits and SCS was investigated in this study, further study on the relationship between clinical mastitis and Ig in milk is needed before Ig can be used as an indicator of mastitis resistance.

Prevention of mastitis in multiparous dairy cows with a previous history of mastitis by oral feeding with probiotic Bacillus subtilis.

Mastitis is a very common inflammatory disease of the mammary gland of dairy cows, resulting in a reduction of milk production and quality. Probiotics may serve as an alternative to antibiotics to prevent mastitis, and the use of probiotics in this way may lessen the risk of antibiotic resistant bacteria developing. We investigated the effect of oral feeding of probiotic Bacillus subtilis (BS) C-3102 strain on the onset of mastitis in dairy cows with a previous history of mastitis. BS feeding significantly decreased the incidence of mastitis, the average number of medication days and the average number of days when milk was discarded, and maintained the mean SCC in milk at a level substantially lower than the control group. BS feeding was associated with lower levels of cortisol and TBARS and increased the proportion of CD4+ T cells and CD11c+ CD172ahigh dendritic cells in the blood by flow cytometry analysis. Parturition increased the migrating frequency of granulocytes toward a milk chemoattractant cyclophilin A in the control cows, however, this was reduced by BS feeding, possibly indicating a decreased sensitivity of peripheral granulocytes to cyclophilin A. These results reveal that B. subtilis C-3102 has potential as a probiotic and has preventative capacity against mastitis in dairy cows.

Automatic anatomical classification of colonoscopic images using deep convolutional neural networks.

BACKGROUND: A colonoscopy can detect colorectal diseases, including cancers, polyps, and inflammatory bowel diseases. A computer-aided diagnosis (CAD) system using deep convolutional neural networks (CNNs) that can recognize anatomical locations during a colonoscopy could efficiently assist practitioners. We aimed to construct a CAD system using a CNN to distinguish colorectal images from parts of the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. METHOD: We constructed a CNN by training of 9,995 colonoscopy images and tested its performance by 5,121 independent colonoscopy images that were categorized according to seven anatomical locations: the terminal ileum, the cecum, ascending colon to transverse colon, descending colon to sigmoid colon, the rectum, the anus, and indistinguishable parts. We examined images taken during total colonoscopy performed between January 2017 and November 2017 at a single center. We evaluated the concordance between the diagnosis by endoscopists and those by the CNN. The main outcomes of the study were the sensitivity and specificity of the CNN for the anatomical categorization of colonoscopy images. RESULTS: The constructed CNN recognized anatomical locations of colonoscopy images with the following areas under the curves: 0.979 for the terminal ileum; 0.940 for the cecum; 0.875 for ascending colon to transverse colon; 0.846 for descending colon to sigmoid colon; 0.835 for the rectum; and 0.992 for the anus. During the test process, the CNN system correctly recognized 66.6% of images. CONCLUSION: We constructed the new CNN system with clinically relevant performance for recognizing anatomical locations of colonoscopy images, which is the first step in constructing a CAD system that will support us during colonoscopy and provide an assurance of the quality of the colonoscopy procedure.

Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images.

BACKGROUND AND AIM: We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basis of endoscopic images. However, that study included only H. pylori-positive and -negative patients, excluding patients after H. pylori-eradication. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to ascertain all H. pylori infection statuses. METHODS: A deep CNN was pre-trained and fine-tuned on a dataset of 98,564 endoscopic images from 5236 patients (742 H. pylori-positive, 3649 -negative, and 845 -eradicated). A separate test data set (23,699 images from 847 patients; 70 positive, 493 negative, and 284 eradicated) was evaluated by the CNN. RESULTS: The trained CNN outputs a continuous number between 0 and 1 as the probability index for H. pylori infection status per image (Pp, H. pylori-positive; Pn, negative; Pe, eradicated). The most probable (largest number) of the three infectious statuses was selected as the 'CNN diagnosis'. Among 23,699 images, the CNN diagnosed 418 images as positive, 23,034 as negative, and 247 as eradicated. Because of the large number of H. pylori negative findings, the probability of H. pylori-negative was artificially re-defined as Pn -0.9, after which 80% (465/582) of negative diagnoses were accurate, 84% (147/174) eradicated, and 48% (44/91) positive. The time needed to diagnose 23,699 images was 261 seconds. CONCLUSION: We used a novel algorithm to construct a CNN for diagnosing H. pylori infection status on the basis of endoscopic images very quickly. ABBREVIATIONS: H. pylori: Helicobacter pylori; CNN: convolutional neural network; AI: artificial intelligence; EGD: esophagogastroduodenoscopies.

Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus.

BACKGROUND AND AIMS: The endocytoscopic system (ECS) helps in virtual realization of histology and can aid in confirming histological diagnosis in vivo. We propose replacing biopsy-based histology for esophageal squamous cell carcinoma (ESCC) by using the ECS. We applied deep-learning artificial intelligence (AI) to analyse ECS images of the esophagus to determine whether AI can support endoscopists for the replacement of biopsy-based histology. METHODS: A convolutional neural network-based AI was constructed based on GoogLeNet and trained using 4715 ECS images of the esophagus (1141 malignant and 3574 non-malignant images). To evaluate the diagnostic accuracy of the AI, an independent test set of 1520 ECS images, collected from 55 consecutive patients (27 ESCCs and 28 benign esophageal lesions) were examined. RESULTS: On the basis of the receiver-operating characteristic curve analysis, the areas under the curve of the total images, higher magnification pictures, and lower magnification pictures were 0.85, 0.90, and 0.72, respectively. The AI correctly diagnosed 25 of the 27 ESCC cases, with an overall sensitivity of 92.6%. Twenty-five of the 28 non-cancerous lesions were diagnosed as non-malignant, with a specificity of 89.3% and an overall accuracy of 90.9%. Two cases of malignant lesions, misdiagnosed as non-malignant by the AI, were correctly diagnosed as malignant by the endoscopist. Among the 3 cases of non-cancerous lesions diagnosed as malignant by the AI, 2 were of radiation-related esophagitis and one was of gastroesophageal reflux disease. CONCLUSION: AI is expected to support endoscopists in diagnosing ESCC based on ECS images without biopsy-based histological reference.

Revolving Vernier Mechanism Controls Size of Linear Homomultimer.

A new kind of the Vernier mechanism that is able to control the size of linear assembly of DNA origami nanostructures is proposed. The mechanism is realized by mechanical design of DNA origami, which consists of a hollow cylinder and a rotatable shaft in it connected through the same scaffold. This nanostructure stacks with each other by the shape complementarity at its top and bottom surfaces of the cylinder, while the number of stacking is limited by twisting angle of the shaft. Experiments have shown that the size distribution of multimeric assembly of the origami depends on the twisting angle of the shaft; the average lengths of the multimer are decamer, hexamer, and tetramer for 0°, 10°, and 20° twist, respectively. In summary, it is possible to affect the number of polymerization by adjusting the precise shape and movability of a molecular structure.