Automated artificial intelligence-based phase-recognition system for esophageal endoscopic submucosal dissection (with video).

BACKGROUND AND AIMS: Endoscopic submucosal dissection (ESD) for superficial esophageal cancer is a multistep treatment involving several endoscopic processes. Although analyzing each phase separately is worthwhile, it is not realistic in practice owing to the need for considerable manpower. To solve this problem, we aimed to establish a state-of-the-art artificial intelligence (AI)-based system, specifically, an automated phase-recognition system that can automatically identify each endoscopic phase based on video images. METHODS: Ninety-four videos of ESD procedures for superficial esophageal cancer were evaluated in this single-center study. A deep neural network-based phase-recognition system was developed in an automated manner to recognize each of the endoscopic phases. The system was trained with the use of videos that were annotated and verified by 2 GI endoscopists. RESULTS: The overall accuracy of the AI model for automated phase recognition was 90%, and the average precision, recall, and F value rates were 91%, 90%, and 90%, respectively. Two representative ESD videos predicted by the model indicated the usability of AI in clinical practice. CONCLUSIONS: We demonstrated that an AI-based automated phase-recognition system for esophageal ESD can be established with high accuracy. To the best of our knowledge, this is the first report on automated recognition of ESD treatment phases. Because this system enabled a detailed analysis of phases, collecting large volumes of data in the future may help to identify quality indicators for treatment techniques and uncover unmet medical needs that necessitate the creation of new treatment methods and devices.

Improvement in Epigenetic Aging Clock Induced by BioBran Containing Rice Kefiran in Relation to Various Biomarkers: A Pilot Study.

Many lifestyle-related diseases such as cancer, dementia, myocardial infarction, and stroke are known to be caused by aging, and the WHO's ICD-11 (International Classification of Diseases, 11th edition) created the code "aging-related" in 2022. In other words, aging is irreversible but aging-related diseases are reversible, so taking measures to treat them is important for health longevity and preventing other diseases. Therefore, in this study, we used BioBran containing rice kefiran as an approach to improve aging. Rice kefiran has been reported to improve the intestinal microflora, regulate the intestines, and have anti-aging effects. BioBran has also been reported to have antioxidant effects and improve liver function, and human studies have shown that it affects the diversity of the intestinal microbiota. Quantitative measures of aging that correlate with disease risk are now available through the epigenetic clock test, which examines the entire gene sequence and determines biological age based on the methylation level. Horvath's Clock is the best known of many epigenetic clock tests and was published by Steve Horvath in 2013. In this study, we examine the effect of using Horvath's Clock to improve aging and report on the results, which show a certain effect.

Golgi pH homeostasis stabilizes the lysosomal membrane through N-glycosylation of membrane proteins.

Protein glycosylation plays a vital role in various cellular functions, many of which occur within the Golgi apparatus. The Golgi pH regulator (GPHR) is essential for the proper functioning of the Golgi apparatus. The lysosomal membrane contains highly glycosylated membrane proteins in abundance. This study investigated the role of the Golgi luminal pH in N-glycosylation of lysosomal membrane proteins and the effect of this protein modification on membrane stability using Gphr-deficient MEFs. We showed that Gphr deficiency causes an imbalance in the Golgi luminal pH, resulting in abnormal protein N-glycosylation, indicated by a reduction in sialylated glycans and markedly reduced molecular weight of glycoproteins. Further experiments using FRAP and PLA revealed that Gphr deficiency prevented the trafficking dynamics and proximity condition of glycosyltransferases in the Golgi apparatus. In addition, incomplete N-glycosylation of lysosomal membrane proteins affected lysosomal membrane stability, as demonstrated by the increased susceptibility to lysosomal damage. Thus, this study highlights the critical role of Golgi pH regulation in controlling protein glycosylation and the impact of Golgi dysfunction on lysosomal membrane stability.

Evaluating surgical expertise with AI-based automated instrument recognition for robotic distal gastrectomy.

Introduction: Complexities of robotic distal gastrectomy (RDG) give reason to assess physician's surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments. Methods: Fifty-five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi-stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non-experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed. Results: We annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument's usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non-experienced group. Conclusions: This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill.

Vero cell-adapted SARS-CoV-2 strain shows increased viral growth through furin-mediated efficient spike cleavage.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes several host proteases to cleave the spike (S) protein to enter host cells. SARS-CoV-2 S protein is cleaved into S1 and S2 subunits by furin, which is closely involved in the pathogenicity of SARS-CoV-2. However, the effects of the modulated protease cleavage activity due to S protein mutations on viral replication and pathogenesis remain unclear. Herein, we serially passaged two SARS-CoV-2 strains in Vero cells and characterized the cell-adapted SARS-CoV-2 strains in vitro and in vivo. The adapted strains showed high viral growth, effective S1/S2 cleavage of the S protein, and low pathogenicity compared with the wild-type strain. Furthermore, the viral growth and S1/S2 cleavage were enhanced by the combination of the Δ68-76 and H655Y mutations using recombinant SARS-CoV-2 strains generated by the circular polymerase extension reaction. The recombinant SARS-CoV-2 strain, which contained the mutation of the adapted strain, showed increased susceptibility to the furin inhibitor, suggesting that the adapted SARS-CoV-2 strain utilized furin more effectively than the wild-type strain. Pathogenicity was attenuated by infection with effectively cleaved recombinant SARS-CoV-2 strains, suggesting that the excessive cleavage of the S proteins decreases virulence. Finally, the high-growth-adapted SARS-CoV-2 strain could be used as the seed for a low-cost inactivated vaccine; immunization with this vaccine can effectively protect the host from SARS-CoV-2 variants. Our findings provide novel insights into the growth and pathogenicity of SARS-CoV-2 in the evolution of cell-cell transmission. IMPORTANCE: The efficacy of the S protein cleavage generally differs among the SARS-CoV-2 variants, resulting in distinct viral characteristics. The relationship between a mutation and the entry of SARS-CoV-2 into host cells remains unclear. In this study, we analyzed the sequence of high-growth Vero cell-adapted SARS-CoV-2 and factors determining the enhancement of the growth of the adapted virus and confirmed the characteristics of the adapted strain by analyzing the recombinant SARS-CoV-2 strain. We successfully identified mutations Δ68-76 and H655Y, which enhance viral growth and the S protein cleavage by furin. Using recombinant viruses enabled us to conduct a virus challenge experiment in vivo. The pathogenicity of SARS-CoV-2 introduced with the mutations Δ68-76, H655Y, P812L, and Q853L was attenuated in hamsters, indicating the possibility of the attenuation of excessive cleaved SARS-CoV-2. These findings provide novel insights into the infectivity and pathogenesis of SARS-CoV-2 strains, thereby significantly contributing to the field of virology.

Unveiling the physics underlying symmetry breaking of the actin cytoskeleton: An artificial cell-based approach.

Single-cell behaviors cover many biological functions, such as cell division during morphogenesis and tissue metastasis, and cell migration during cancer cell invasion and immune cell responses. Symmetry breaking of the positioning of organelles and the cell shape are often associated with these biological functions. One of the main players in symmetry breaking at the cellular scale is the actin cytoskeleton, comprising actin filaments and myosin motors that generate contractile forces. However, because the self-organization of the actomyosin network is regulated by the biochemical signaling in cells, how the mechanical contraction of the actin cytoskeleton induces diverse self-organized behaviors and drives the cell-scale symmetry breaking remains unclear. In recent times, to understand the physical underpinnings of the symmetry breaking exhibited in the actin cytoskeleton, artificial cell models encapsulating the cytoplasmic actomyosin networks covered with lipid monolayers have been developed. By decoupling the actomyosin mechanics from the complex biochemical signaling within living cells, this system allows one to study the self-organization of actomyosin networks confined in cell-sized spaces. We review the recent developments in the physics of confined actomyosin networks and provide future perspectives on the artificial cell-based approach. This review article is an extended version of the Japanese article, The Physical Principle of Cell Migration Under Confinement: Artificial Cell-based Bottom-up Approach, published in SEIBUTSU BUTSURI Vol. 63, p. 163-164 (2023).