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Background: Direct surgery is an important option to treat vertebral artery (VA) stenosis. Method: A patient with symptomatic stenosis at the origin of the right VA underwent transposition of the right VA to the common carotid artery (CCA). Using the sternocleidomastoid sparing approach, the VA was anastomosed to the posterior wall of the CCA by twisting the CCA to expose its posterior wall to face the operative field. Conclusion: This approach, consisting of securing the proximal VA and then following it to its distal end, not only preserves the sternocleidomastoid muscle but also protects the sympathetic chains and thoracic duct. [ABSTRACT FROM AUTHOR]

Background: The worldwide vaccination response to COVID-19 has been associated with rare thrombotic complications, including the case of postvaccination splanchnic venous thrombosis we report here. Case presentation: An 80-year-old Japanese male with abdominal pain presented to our hospital six days after receiving a dose of the COVID-19 messenger ribonucleic acid vaccine. Abdominal computed tomography showed localized edema of the small intestine, increased density of the surrounding adipose tissue, and a thrombus in the superior mesenteric vein. Conservative inpatient treatment with unfractionated heparin relieved the thrombosis, and the patient is currently receiving oral apixaban as an outpatient. Conclusion: Reported cases of thrombosis after COVID-19 vaccination typically have been associated with viral vector vaccines, with few reports of thrombosis induced by mRNA vaccines. The potential for venous thrombosis should be explored when patients present with abdominal pain soon after COVID-19 vaccination. [ABSTRACT FROM AUTHOR]

For a 2-connected graph G , the relative length of G , denoted by diff (G) , is the difference between the orders of a longest path and a longest cycle in G. This parameter is used as a measure to estimate how close a given graph is to a hamiltonian graph. Let σ k (G) be the least value of the sums of degrees of vertices in independent sets of cardinality k. In 2008, Paulusma and Yoshimoto proved that a 2-connected triangle-free graph G of order n with σ 4 (G) ≥ n + 2 satisfies diff (G) ≤ 1 unless G is isomorphic to one exceptional graph G 0. In this paper, we extend their result and prove that for an integer s with 2 3 (n + 4) < s ≤ n + 2 , a 2-connected triangle-free graph of order n with σ 4 (G) ≥ s satisfies diff (G) ≤ n + 3 − s unless s = σ 4 (G) = n + 2 and G = G 0. [ABSTRACT FROM AUTHOR]

Epithelial tissues cover the surfaces and lumens of the internal organs of multicellular animals and crucially contribute to internal environment homeostasis by delineating distinct compartments within the body. This vital role is known as epithelial barrier function. Epithelial cells are arranged like cobblestones and intricately bind together to form an epithelial sheet that upholds this barrier function. Central to the restriction of solute and fluid diffusion through intercellular spaces are occluding junctions, tight junctions in vertebrates and septate junctions in invertebrates. As part of epithelial tissues, cells undergo constant renewal, with older cells being replaced by new ones. Simultaneously, the epithelial tissue undergoes relative rearrangement, elongating, and shifting directionally as a whole. The movement or shape changes within the epithelial sheet necessitate significant deformation and reconnection of occluding junctions. Recent advancements have shed light on the intricate mechanisms through which epithelial cells sustain their barrier function in dynamic environments. This review aims to introduce these noteworthy findings and discuss some of the questions that remain unanswered. • Epithelial barrier function is maintained even in highly dynamic environments. • Barrier maintenance is achieved through the rearrangement of occluding junctions. • The rearrangement process and its associated molecular mechanisms are discussed. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.Methods: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.Results: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.Conclusion: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.Graphical abstract: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC. [ABSTRACT FROM AUTHOR]

Introduction: The traditional Japanese herbal medicine hochuekkito (TJ-41) has been reported to ameliorate systemic inflammation and malnutrition in patients with chronic obstructive pulmonary disease (COPD). TJ-41 has also been known to have preventive effects against influenza virus infection. However, its role in the acute exacerbation of COPD (AECOPD) remains to be elucidated. Our previous study established a murine model of viral infection-associated AECOPD that was induced by intratracheal administration of porcine pancreatic elastase (PPE) and polyinosinic-polycytidylic acid [poly(I:C)]. Here, we used this model and investigated the effects of TJ-41 in AECOPD. Methods: Specific pathogen-free C57BL/6J mice were used. A COPD model was induced by treating mice intratracheally with PPE on day 0. To generate the murine model of AECOPD, poly(I:C) was administered intratracheally following PPE treatment on days 22–24. Mice were sacrificed and analyzed on day 25. Mice were fed a diet containing 2% TJ-41 or a control diet. Results: Daily oral intake of TJ-41 significantly decreased the numbers of neutrophils and lymphocytes in the bronchoalveolar lavage fluid (BALF), which was accompanied by decreased transcripts of CXC chemokines involved in neutrophil migration, viz., Cxcl1 and Cxcl2, in whole lung homogenates and reduced Cxcl2 concentration in BALF. Conclusion: This study demonstrates the anti-inflammatory effects of TJ-41 in a mouse model of AECOPD, suggesting the effectiveness of TJ-41 for the management of COPD. Clinical investigations evaluating the therapeutic efficacy of TJ-41 in AECOPD would be meaningful. [ABSTRACT FROM AUTHOR]

The spleen is a key source of circulating and tumor-infiltrating immune cells. However, the effect of splenectomy on tumor growth remains unclear. At 3 weeks after splenectomy, we subcutaneously injected LuM1 cells into BALB/c mice and evaluated the growth of primary tumors and lung metastases at 4 weeks after tumor inoculation. In addition, we examined the phenotypes of immune cells in peripheral blood by using flow cytometry and in tumor tissue by using multiplex immunohistochemistry. The growth of primary tumors was reduced in splenectomized mice compared with the sham-operated group. Conversely, splenectomized mice had more lung metastases. Splenectomized mice had fewer CD11b+cells, especially monocytic MDSCs (CD11b+Gr-1neg-lowLy6chigh), and NK cells (CD49b+CD335+). The proportion of NK cells was inversely correlated with the number of lung metastases. In splenectomized mice, the density of CD3+ and granzyme B+ CD8+ T cells was increased, with fewer M2-type macrophages in primary tumors, but NK cells were decreased markedly in lung. Splenectomy concurrently enhances T cell-mediated acquired immunity by reducing the number of monocytic MDSCs and suppresses innate immunity by decreasing the number of NK cells. Splenectomy has opposite effects on primary and metastatic lesions through differential regulation on these two immune systems. [ABSTRACT FROM AUTHOR]

Background: Recently, intestinal bacteria have attracted attention as factors affecting the prognosis of patients with cancer. However, the intestinal microbiome is composed of several hundred types of bacteria, necessitating the development of an analytical method that can allow the use of this information as a highly accurate biomarker. In this study, we investigated whether the preoperative intestinal bacterial profile in patients with esophageal cancer who underwent surgery after preoperative chemotherapy could be used as a biomarker of postoperative recurrence of esophageal cancer. Methods: We determined the gut microbiome of the patients using 16S rRNA metagenome sequencing, followed by statistical analysis. Simultaneously, we performed a machine learning analysis using a random forest model with hyperparameter tuning and compared the data obtained. Results: Statistical and machine learning analyses revealed two common bacterial genera, Butyricimonas and Actinomyces, which were abundant in cases with recurrent esophageal cancer. Butyricimonas primarily produces butyrate, whereas Actinomyces are oral bacteria whose function in the gut is unknown. Conclusion: Our results indicate that Butyricimonas spp. may be a biomarker of postoperative recurrence of esophageal cancer. Although the extent of the involvement of these bacteria in immune regulation remains unknown, future research should investigate their presence in other pathological conditions. Such research could potentially lead to a better understanding of the immunological impact of these bacteria on patients with cancer and their application as biomarkers. [ABSTRACT FROM AUTHOR]