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Objectives Artificial intelligence (AI) may be practical for image classification of small bowel capsule endoscopy (CE). However, creating a functional AI model is challenging. We attempted to create a dataset and an object detection CE AI model to explore modeling problems to assist in reading small bowel CE. Methods We extracted 18,481 images from 523 small bowel CE procedures performed at Kyushu University Hospital from September 2014 to June 2021. We annotated 12,320 images with 23,033 disease lesions, combined them with 6161 normal images as the dataset, and examined the characteristics. Based on the dataset, we created an object detection AI model using YOLO v5 and we tested validation. Results We annotated the dataset with 12 types of annotations, and multiple annotation types were observed in the same image. We test validated our AI model with 1396 images, and sensitivity for all 12 types of annotations was about 91%, with 1375 true positives, 659 false positives, and 120 false negatives detected. The highest sensitivity for individual annotations was 97%, and the highest area under the receiver operating characteristic curve was 0.98, but the quality of detection varied depending on the specific annotation. Conclusions Object detection AI model in small bowel CE using YOLO v5 may provide effective and easy‐to‐understand reading assistance. In this SEE‐AI project, we open our dataset, the weights of the AI model, and a demonstration to experience our AI. We look forward to further improving the AI model in the future.

We herein describe a 49‐year‐old man with severe heart failure due to fulminant myocarditis who underwent left ventricular assist device implantation and received clopidogrel and warfarin as antithrombotic agents. The patient developed anemia secondary to chronic bleeding gastric hyperplastic polyps, necessitating endoscopic mucosal resection. Despite attempts to manage post‐endoscopic mucosal resection bleeding from a gastric ulcer by endoscopic hemostasis using hemostatic forceps, local hemostatic agents, and polyglycolic acid sheets, the bleeding persisted. Hemostasis of the refractory bleeding was finally achieved by endoscopic hand‐suturing of the ulcer. One month later, the ulcer was almost completely scarred. This case has important clinical value in that it demonstrates the efficacy of endoscopic hand‐suturing even in challenging cases such as refractory bleeding gastric ulcers in patients with left ventricular assist devices.

A sustainable society requires high-energy storage devices characterized by lightness, compactness, a long life and superior safety, surpassing current battery and supercapacitor technologies. Single-walled carbon nanotubes (SWCNTs), which typically exhibit great toughness, have emerged as promising candidates for innovative energy storage solutions. Here we produced SWCNT ropes wrapped in thermoplastic polyurethane elastomers, and demonstrated experimentally that a twisted rope composed of these SWCNTs possesses the remarkable ability to reversibly store nanomechanical energy. Notably, the gravimetric energy density of these twisted ropes reaches up to 2.1 MJ kg −1 , exceeding the energy storage capacity of mechanical steel springs by over four orders of magnitude and surpassing advanced lithium-ion batteries by a factor of three. In contrast to chemical and electrochemical energy carriers, the nanomechanical energy stored in a twisted SWCNT rope is safe even in hostile environments. This energy does not deplete over time and is accessible at temperatures ranging from −60 to +100 °C. A single-walled carbon nanotube spring stores three times more mechanical energy than a lithium-ion battery, while offering wide temperature stability and posing no explosion risk.

Abstract BACKGROUND AND AIM Serum Leucine-rich alpha-2 glycoprotein (LRG) was identified as a novel biomarker for rheumatoid arthritis and IBD by using a proteomics approach. Several studies have suggested that serum LRG levels and fecal calprotectin (fCal) were correlated with clinical activities in patients with ulcerative colitis (UC). We compared utility of these biomarkers for monitoring disease activity in UC. METHODS A single-center observational study was conducted at Kyushu University Hospital. A total of 101 patients who underwent colonoscopy during the period from February to December 2021 were enrolled. The associations between endoscopic and histological activity and biomarkers were investigated. Endoscopic activity was defined as Mayo endoscopic score (MES) of 1 or less for mucosal healing and 0 for complete mucosal healing. For histological activity (n=90), Geboes score of 2 or less was defined as histological remission. RESULTS Subjects ranged in age from 16 to 80 years (35 males and 66 females). There were 63 cases of total colitistype, 32 cases of left-sided colitis type, and 6 cases of proctitis type. The MES at enrollment were as follows: MES 0 in 41 cases, MES 1 in 33 cases, MES 2 in 19 cases, and MES 3 in 8 cases. In comparison between the mucosal healing and non-mucosal healing groups, LRG was significantly lower in the mucosal healing group (p<0.0001). The correlation coefficient of LRG with MES was r=0.614 (p<0.0001), which was higher than that of fCal, r=0.414 (p<0.0001). In ROC analysis with mucosal healing as the outcome, the AUC and cut-off value of LRG were 0.781 and 17.9 μg/ml, respectively, and for fCal were 0.850 and 396 μg/g, respectively. In the ROC analysis with complete mucosal healing as the outcome, the AUC for LRG was 0.690, which was lower than the AUC of 0.846 for fCal . When compared by histological remission, LRG was lower in the histological remission group (p<0.001). In the ROC analysis with histological remission as the outcome, the AUC for LRG was 0.722 (cutoff value 14.2 μg/ml) and for fCal was 0.837 (cutoff value 119 μg/g). CONCLUSION Both LRG and fCal are valuable biomarkers reflecting disease activity in UC.

Small bowel capsule endoscopy (CE) produces lengthy videos that are time-consuming to review and susceptible to missed lesions. We evaluated whether an open-source, pretrained artificial intelligence (AI) model (SEE-AI) could improve diagnostic performance and interpretation efficiency compared with conventional reading. We retrospectively analyzed 249 PillCam SB3 examinations performed between 2007 and 2022 at six hospitals, using a two-reader crossover design. SEE-AI (confidence threshold 0.1) generated annotated videos with bounding boxes for eight lesion categories. The primary endpoints were sensitivity for lesion detection on a per-lesion and per-patient basis. Secondary endpoints included specificity, predictive values, overall accuracy, and reading time. A prespecified subgroup analysis evaluated cases of suspected small-bowel bleeding (SSBB), focusing on Saurin P1+P2 hemorrhagic lesions. Across 1550 adjudicated lesions, AI-assisted reading demonstrated higher sensitivity than conventional reading (per-lesion: 98.8% [1532/1550] vs. 86.4% [1339/1550]; per-patient: 99.1% [464/468] vs. 80.3% [376/468]; both < 0.0001). The mean reading time decreased from 17.9 to 13.7 min ( < 0.0001). In SSBB cases ( = 131), sensitivity for P1+P2 lesions improved on both a per-lesion basis (98.2% [439/447] vs. 82.8% [370/447]) and per-patient basis (98.6% [145/147] vs. 73.5% [108/147]), with a shorter reading time (14.1 vs. 18.0 min; all < 0.0001). In this multicenter evaluation, SEE-AI significantly improved lesion detection and reduced reading time for CE interpretation, including SSBB cases, while maintaining openness and reproducibility. AI-assisted reading may reduce clinicians' workload and support the adoption of SEE-AI as a practical tool - and a potential future standard of care - for small bowel CE. N/A.

Theoretical studies have focused on the environmental temperature of the universal common ancestor of life with conflicting conclusions. Here we provide experimental support for the existence of a thermophilic universal common ancestor. We present the thermal stabilities and catalytic efficiencies of nucleoside diphosphate kinases (NDK), designed using the information contained in predictive phylogenetic trees, that seem to represent the last common ancestors of Archaea and of Bacteria. These enzymes display extreme thermal stabilities, suggesting thermophilic ancestries for Archaea and Bacteria. The results are robust to the uncertainties associated with the sequence predictions and to the tree topologies used to infer the ancestral sequences. Moreover, mutagenesis experiments suggest that the universal ancestor also possessed a very thermostable NDK. Because, as we show, the stability of an NDK is directly related to the environmental temperature of its host organism, our results indicate that the last common ancestor of extant life was a thermophile that flourished at a very high temperature.

The blood and lymphatic vasculature networks are not yet fully understood even in mouse because of the inherent limitations of imaging systems and quantification methods. This study aims to evaluate the usefulness of the tissue-clearing technology for visualizing blood and lymphatic vessels in adult mouse. Clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC) enables us to capture the high-resolution 3D images of organ- or area-specific vascular structures. To evaluate these 3D structural images, signals are first classified from the original captured images by machine learning at pixel base. Then, these classified target signals are subjected to topological data analysis and non-homogeneous Poisson process model to extract geometric features. Consequently, the structural difference of vasculatures is successfully evaluated in mouse disease models. In conclusion, this study demonstrates the utility of CUBIC for analysis of vascular structures and presents its feasibility as an analysis modality in combination with 3D images and mathematical frameworks. Understanding blood and lymphatic vasculature networks is currently limited by existing imaging systems and quantification methods. Here the authors use the tissue clearing method CUBIC to generate 3D images, machine learning to capture the signals, and extract geometric features by topological data analysis.

Long noncoding RNAs are involved in a variety of cellular functions. In particular, an increasing number of studies have revealed the functions of long noncoding RNA in various cancers; however, their precise roles and mechanisms of action remain to be elucidated. NORAD, a cytoplasmic long noncoding RNA, is upregulated by irradiation and functions as a potential oncogenic factor by binding and inhibiting Pumilio proteins (PUM1/PUM2). Here, we show that NORAD upregulates transforming growth factor‐β (TGF‐β) signaling and regulates TGF‐β‐induced epithelial‐to‐mesenchymal transition (EMT)‐like phenotype, which is a critical step in the progression of lung adenocarcinoma, A549 cells. However, PUM1 does not appear to be involved in this process. We thus focused on importin β1 as a binding partner of NORAD and found that knockdown of NORAD partially inhibits the physical interaction of importin β1 with Smad3, inhibiting the nuclear accumulation of Smad complexes in response to TGF‐β. Our findings may provide a new mechanism underlying the function of NORAD in cancer cells. We here show that lncRNA NORAD upregulates transforming growth factor‐β signaling and regulates EMT‐like phenotype in A549 lung adenocarcinoma cells. The identified function of NORAD is mediated by enhancement of nuclear translocation of Smad3 through binding to importin β 1, independent of the known molecular mechanism involving Pulmilio proteins.

Zinc finger E‐box binding protein 1 (ZEB1) and ZEB2 induce epithelial‐mesenchymal transition (EMT) and enhance cancer progression. However, the global view of transcriptional regulation by ZEB1 and ZEB2 is yet to be elucidated. Here, we identified a ZEB1‐regulated inflammatory phenotype in breast cancer cells using chromatin immunoprecipitation sequencing and RNA sequencing, followed by gene set enrichment analysis (GSEA) of ZEB1‐bound genes. Knockdown of ZEB1 and/or ZEB2 resulted in the downregulation of genes encoding inflammatory cytokines related to poor prognosis in patients with cancer, including IL6 and IL8, therefore suggesting that ZEB1 and ZEB2 have similar functions in terms of the regulation of production of inflammatory cytokines. Antibody array and ELISA experiments confirmed that ZEB1 controlled the production of the IL‐6 and IL‐8 proteins. The secretory proteins regulated by ZEB1 enhanced breast cancer cell proliferation and tumor growth. ZEB1 expression in breast cancer cells also affected the growth of fibroblasts in cell culture, and the accumulation of myeloid‐derived suppressor cells in tumors in vivo. These findings provide insight into the role of ZEB1 in the progression of cancer, mediated by inflammatory cytokines, along with the initiation of EMT. ZEB1 and ZEB2 regulate the production of inflammatory cytokines, such as IL‐6 or IL‐8, in breast cancer cells. The secretory proteins regulated by ZEB1 enhance cancer cell proliferation in an autocrine manner. ZEB1 also regulates the proliferation of fibroblasts and the accumulation of myeloid‐derived suppressor cells (MDSCs) through the secretory proteins in a paracrine manner.