Deep learning-based diagnosis of lung cancer using a nationwide respiratory cytology image set: improving accuracy and inter-observer variability.

Deep learning (DL)-based image analysis has recently seen widespread application in digital pathology. Recent studies utilizing DL in cytopathology have shown promising results, however, the development of DL models for respiratory specimens is limited. In this study, we designed a DL model to improve lung cancer diagnosis accuracy using cytological images from the respiratory tract. This retrospective, multicenter study used digital cytology images of respiratory specimens from a quality-controlled national dataset collected from over 200 institutions. The image processing involves generating extended z-stack images to reduce the phase difference of cell clusters, color normalizing, and cropping image patches to 256 × 256 pixels. The accuracy of diagnosing lung cancer in humans from image patches before and after receiving AI assistance was compared. 30,590 image patches (1,273 whole slide images [WSIs]) were divided into 27,362 (1,146 WSIs) for training, 2,928 (126 WSIs) for validation, and 1,272 (1,272 WSIs) for testing. The Densenet121 model, which showed the best performance among six convolutional neural network models, was used for analysis. The results of sensitivity, specificity, and accuracy were 95.9%, 98.2%, and 96.9% respectively, outperforming the average of three experienced pathologists. The accuracy of pathologists after receiving AI assistance improved from 82.9% to 95.9%, and the inter-rater agreement of Fleiss' Kappa value was improved from 0.553 to 0.908. In conclusion, this study demonstrated that a DL model was effective in diagnosing lung cancer in respiratory cytology. By increasing diagnostic accuracy and reducing inter-observer variability, AI has the potential to enhance the diagnostic capabilities of pathologists.

Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology.

The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model's performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.

Gut Microbiome and Metabolome Profiles Associated with High-Fat Diet in Mice.

Obesity can be caused by microbes producing metabolites; it is thus important to determine the correlation between gut microbes and metabolites. This study aimed to identify gut microbiota-metabolomic signatures that change with a high-fat diet and understand the underlying mechanisms. To investigate the profiles of the gut microbiota and metabolites that changed after a 60% fat diet for 8 weeks, 16S rRNA gene amplicon sequencing and gas chromatography-mass spectrometry (GC-MS)-based metabolomic analyses were performed. Mice belonging to the HFD group showed a significant decrease in the relative abundance of Bacteroidetes but an increase in the relative abundance of Firmicutes compared to the control group. The relative abundance of Firmicutes, such as Lactococcus, Blautia, Lachnoclostridium, Oscillibacter, Ruminiclostridium, Harryflintia, Lactobacillus, Oscillospira, and Erysipelatoclostridium, was significantly higher in the HFD group than in the control group. The increased relative abundance of Firmicutes in the HFD group was positively correlated with fecal ribose, hypoxanthine, fructose, glycolic acid, ornithine, serum inositol, tyrosine, and glycine. Metabolic pathways affected by a high fat diet on serum were involved in aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism, cysteine and methionine metabolism, glyoxylate and dicarboxylate metabolism, and phenylalanine, tyrosine, and trypto-phan biosynthesis. This study provides insight into the dysbiosis of gut microbiota and metabolites altered by HFD and may help to understand the mechanisms underlying obesity mediated by gut microbiota.

A Case of Peritoneal Tuberculosis Developed after Infliximab Therapy for Refractory RA.

Recently, interferon gamma releasing assay has been recommended to compensate the tuberculin skin test (TST) for screening for latent tuberculosis infection (LTBI). Although it improved the detection of LTBI before treatment with tumor necrosis factor blocker, its application to immune suppressed patients is limited. We report a case of peritoneal tuberculosis (TB) developed in a patient who tested positive for TST and QuantiFERON-TB Gold (QFT-G) before infliximab therapy, to emphasize the importance of monitoring during treatment. A 52-year-old woman presented with abdominal distension. She had been diagnosed with seropositive rheumatoid arthritis six years ago. She had started taking infliximab six months ago. All screening tests for TB were performed and the results of all were negative. At admission, the results of repeated TST and QFT-G tests were positive. Histopathological examination confirmed peritoneal TB. The patient started anti-TB therapy and the symptoms were relieved.

Coronary artery intervention after cytostatics treatment in unstable angina patient with essential thrombocythemia. A case report and literature review.

Essential thrombocythemia (ET) is a clonal disorder of myeloid stem cells that causes thrombocytosis. As a result, ET can lead to vascular thrombosis and tissue ischemia; the association of coronary artery abnormalities such as myocardial infarction or unstable angina is rare. Here we describe a 45,-year-old male patient with essential thrombocythemia who presented with unstable angina. Elective coronary angiography showed total occlusion of mid right coronary artery and mid left anterior descending coronary artery. ET was confirmed by a bone marrow biopsy; treatment was started with antiplatelet therapy including aspirin and clopidogrel along with cytostatic therapy with hydroxyurea and anagrelide. After the initiation of the treatment, the platelet count decreased to 20 x 10(4)/microL. In addition, percutaneous coronary angioplasty was successfully performed with stent placement at the right coronary artery without hemorrhagic or thrombotic complications.