This approach can be taken as a basis for the future development of neuromodulation and neurorehabilitation therapy for patients with spinal cord injury.Among the different interacting molecules implicated in bone metastases, connexin43 (Cx43) may increase sensitivity of prostate cancer (PCa) cells to bone microenvironment, as suggested by our in silico and human tissue samples analyses that revealed increased level of Cx43 expression with PCa progression and a Cx43 specific expression in bone secondary sites. The goal of the present study was to understand how Cx43 influences PCa cells sensitivity and aggressiveness to bone microenvironment. By means of Cx43-overexpressing PCa cell lines, we revealed a Cx43-dependent promigratory effect of osteoblastic conditioned media (ObCM). This effect on directional migration relied on the presence of Cx43 at the plasma membrane and not on gap junctional intercellular communication and hemichannel functions. ObCM stimulation induced Rac1 activation and Cx43 interaction with cortactin in protrusions of migrating PCa cells. Finally, by transfecting two different truncated forms of Cx43 in LNCaP cells, we determined that the carboxy terminal (CT) part of Cx43 is crucial for the responsiveness of PCa cells to ObCM. Our study demonstrates that Cx43 level and its membrane localization modulate the phenotypic response of PCa cells to osteoblastic microenvironment and that its CT domain plays a pivotal role.This study examines how experience of severe acute respiratory syndrome (SARS) influences the impact of coronavirus disease (COVID-19) on international tourism demand for four Asia-Pacific Economic Cooperation (APEC) economies, Taiwan, Hong Kong, Thailand, and New Zealand, over the 1 January-30 April 2020 period. To proceed, panel regression models are first applied with a time-lag effect to estimate the general effects of COVID-19 on daily tourist arrivals. In turn, the data set is decomposed into two nation groups and fixed effects models are employed for addressing the comparison of the pandemic-tourism relationship between economies with and without experiences of the SARS epidemic. Specifically, Taiwan and Hong Kong are grouped as economies with SARS experiences, while Thailand and New Zealand are grouped as countries without experiences of SARS. The estimation result indicates that the number of confirmed COVID-19 cases has a significant negative impact on tourism demand, in which a 1% COVID-19 case incliterature focusing on the knowledge regarding the nexus between tourism and COVID-19, the paper’s findings also highlight the importance of risk perception and the need of transmission prevention and control of the epidemic for the tourism sector.Lung cancer (LC) screening often focuses heavy smokers as a target for screening group. Heavy smoking can thus be regarded as an LC pre-screening test with sensitivities and specificities being different in various populations due to the differences in smoking histories. We derive here expected sensitivities and specificities of various criteria to preselect individuals for LC screening in 27 European countries with diverse smoking prevalences. Sensitivities of various heavy-smoking-based pre-screening criteria were estimated by combining sex-specific proportions of people meeting these criteria in the target population for screening with associations of heavy smoking with LC risk. Expected specificities were approximated by the proportion of individuals not meeting the heavy smoking definition. Estimated sensitivities and specificities varied widely across countries, with sensitivities being generally higher among men (range 33-80%) than among women (range 9-79%), and specificities being generally lower among men (range 48-90%) than among women (range 70-99%). read more Major variation in sensitivities and specificities was also seen across different pre-selection criteria for LC screening within individual countries. Our results may inform the design of LC screening programs in European countries and serve as benchmarks for novel alternative or complementary tests for selecting people at high risk for CT-based LC screening.The intestinal microbiota consists of numerous microbial species that collectively interact with the host, playing a crucial role in health and disease. Colorectal cancer is well-known to be related to dysbiotic alterations in intestinal microbiota. It is evident that the microbiota is significantly affected by colorectal surgery in combination with the various perioperative interventions, mainly mechanical bowel preparation and antibiotic prophylaxis. The altered postoperative composition of intestinal microbiota could lead to an enhanced virulence, proliferation of pathogens, and diminishment of beneficial microorganisms resulting in severe complications including anastomotic leakage and surgical site infections. Moreover, the intestinal microbiota could be utilized as a possible biomarker in predicting long-term outcomes after surgical CRC treatment. Understanding the underlying mechanisms of these interactions will further support the establishment of genomic mapping of intestinal microbiota in the management of patients undergoing CRC surgery.This study evaluates the diagnostic performance of radiomic features and machine learning algorithms for renal tumor subtype assessment in venous computed tomography (CT) studies from clinical routine. Patients undergoing surgical resection and histopathological assessment of renal tumors at a tertiary referral center between 2012 and 2019 were included. Preoperative venous-phase CTs from multiple referring imaging centers were segmented, and standardized radiomic features extracted. After preprocessing, class imbalance handling, and feature selection, machine learning algorithms were used to predict renal tumor subtypes using 10-fold cross validation, assessed as multiclass area under the curve (AUC). In total, n = 201 patients were included (73.7% male; mean age 66 ± 11 years), with n = 131 clear cell renal cell carcinomas (ccRCC), n = 29 papillary RCC, n = 11 chromophobe RCC, n = 16 oncocytomas, and n = 14 angiomyolipomas (AML). An extreme gradient boosting algorithm demonstrated the highest accuracy (multiclass area under the curve (AUC) = 0.