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Among the brain tumors, glioma is the most common. In general, different biochemical mechanisms, involving nicotinic acetylcholine receptors (nAChRs) and the arachidonic acid cascade are involved in oncogenesis. Although the engagement of the latter in survival and proliferation of rat C6 glioma has been shown, there are practically no data about the presence and the role of nAChRs in C6 cells. In this work we studied the effects of nAChR antagonists, marine snail α-conotoxins and snake α-cobratoxin, on the survival and proliferation of C6 glioma cells. The effects of the lipoxygenase and cyclooxygenase inhibitors either alone or together with α-conotoxins and α-cobratoxin were studied in parallel. It was found that α-conotoxins and α-cobratoxin promoted the proliferation of C6 glioma cells, while nicotine had practically no effect at concentrations below 1 µL/mL. Nordihydroguaiaretic acid, a nonspecific lipoxygenase inhibitor, and baicalein, a 12-lipoxygenase inhibitor, exerted antiproliferative and cytotoxic effects on C6 cells. nAChR inhibitors weaken this effect after 24 h cultivation but produced no effects at longer times. Quantitative real-time polymerase chain reaction showed that mRNA for α4, α7, β2 and β4 subunits of nAChR were expressed in C6 glioma cells. This is the first indication for involvement of nAChRs in mechanisms of glioma cell proliferation.Background Strongyloidiasis is a neglected tropical disease caused by the intestinal nematode Strongyloides stercoralis and characterized by gastrointestinal and pulmonary involvement. We report a pediatric case of strongyloidiasis to underline the response of the host microbiota to the perturbation induced by the nematode. Methods We performed a 16S rRNA-metagenomic analysis of the gut microbiota of a 7-year-old female during and after S. stercolaris infection, investigating three time-point of stool samples' ecology T0- during parasite infection, T1- a month after parasite infection, and T2- two months after parasite infection. Targeted-metagenomics were used to investigate ecology and to predict the functional pathways of the gut microbiota. Results an increase in the alpha-diversity indices in T0-T1 samples was observed compared to T2 and healthy controls (CTRLs). Beta-diversity analysis showed a shift in the relative abundance of specific gut bacterial species from T0 to T2 samples. Moreover, the functional prediction of the targeted-metagenomics profiles suggested an enrichment of microbial glycan and carbohydrate metabolisms in the T0 sample compared with CTRLs. Conclusions The herein report reinforces the literature suggestion of a putative direct or immune-mediated ability of S. stercolaris to promote the increase in bacterial diversity.Information regarding urban-rural differences in health indicators are scarce in Brazil. This study sought to identify rural-urban differences in cardiorespiratory fitness (CRF) and cardiometabolic risk (CMR) in Brazilian children and adolescents whilst controlling for the important confounding variables including social economic status (SES). This is a cross-sectional study developed with children and adolescents (n = 2250, age 11.54 ± 2.76) selected from a city in the south of Brazil. CRF was estimated using a 6-minute run/walk test. CMR scores were calculated by summing different cardiometabolic risk indicators. CRF was analysed assuming a multiplicative model with allometric body-size components. CMR differences in residential locations was assessed using Analysis of caovariance (ANCOVA) adopting SES, Body Mass Index (BMI), waist circumference (WC), age and fitness as covariates. Results indicated a main effect of location (p less then 0.001) with children living a rural environment having the highest CRF, and children living in the periphery of towns having the lowest. Analysis also revealed significant main effects of location (p less then 0.001) with children living a rural environment having the lowest CMR and children living in the centre of towns having the highest. Therefore, Brazilian children living in a rural environment appear to have superior health benefits.<br /><br /> The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians' diagnostic accuracy was shown. However, considering the negative effects of AI-driven differential-diagnosis lists such as omission (physicians reject a correct diagnosis suggested by AI) and commission (physicians accept an incorrect diagnosis suggested by AI) errors, the efficacy of AI-driven automated medical-history-taking systems without AI-driven differential-diagnosis lists on physicians' diagnostic accuracy should be evaluated.<br /><br /> The present study was conducted to evaluate the efficacy of AI-driven automated medical-history-taking systems with or without AI-driven differential-diagnosis lists on physicians' diagnostic accuracy.<br /><br /> This randomized controlled study was conducted in January 2021 and included 22 physicians working at a university hospital. Participants were required to read 16 clinical vignettes in which the AI-driven medical history of real patients generated up to three differential diagnoses per case. Participants were divided into two groups with and without an AI-driven differential-diagnosis list.<br /><br /> There was no significant difference in diagnostic accuracy between the two groups (57.4% vs. 56.3%, respectively; <br /><br /> = 0.91). Vignettes that included a correct diagnosis in the AI-generated list showed the greatest positive effect on physicians' diagnostic accuracy (adjusted odds ratio 7.68; 95% CI 4.68-12.58; <br /><br /> &lt; 0.001). [https://www.selleckchem.com/products/kpt-185.html find more] In the group with AI-driven differential-diagnosis lists, 15.9% of diagnoses were omission errors and 14.8% were commission errors.<br /><br /> Physicians' diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.<br /><br />Physicians' diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.