A meta-analysis published in BMCPsychiatry finds that depression in children and adolescents is significantly associated with being bullied, bullying, and bullying-bullied behavior. The authors suggest further studies to confirm these findings.
]. It can be seen that bullying, being bullied and bullying-being bullied should all receive higher social attention, and the physical and mental development of children and adolescents should not be ignored.
In recent years, studies in China and other countries have found that bullying and being bullied predict the occurrence of depression in children and adolescents []. In general, in the past meta-analysis, bullying and bullying—being bullied were not included in the study, and the relationship between the above three factors and depression was unclear. Therefore, the aim of the present study was to summarize the relevant research and explore associations through a meta-analysis.
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Automatic disease prediction from human gut metagenomic data using boosting GraphSAGE - BMC BioinformaticsBackground The human microbiome plays a critical role in maintaining human health. Due to the recent advances in high-throughput sequencing technologies, the microbiome profiles present in the human body have become publicly available. Hence, many works have been done to analyze human microbiome profiles. These works have identified that different microbiome profiles are present in healthy and sick individuals for different diseases. Recently, several computational methods have utilized the microbiome profiles to automatically diagnose and classify the host phenotype. Results In this work, a novel deep learning framework based on boosting GraphSAGE is proposed for automatic prediction of diseases from metagenomic data. The proposed framework has two main components, (a). Metagenomic Disease graph (MD-graph) construction module, (b). Disease prediction Network (DP-Net) module. The graph construction module constructs a graph by considering each metagenomic sample as a node in the graph. The graph captures the relationship between the samples using a proximity measure. The DP-Net consists of a boosting GraphSAGE model which predicts the status of a sample as sick or healthy. The effectiveness of the proposed method is verified using real and synthetic datasets corresponding to diseases like inflammatory bowel disease and colorectal cancer. The proposed model achieved a highest AUC of 93%, Accuracy of 95%, F1-score of 95%, AUPRC of 95% for the real inflammatory bowel disease dataset and a best AUC of 90%, Accuracy of 91%, F1-score of 87% and AUPRC of 93% for the real colorectal cancer dataset. Conclusion The proposed framework outperforms other machine learning and deep learning models in terms of classification accuracy, AUC, F1-score and AUPRC for both synthetic and real metagenomic data.
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Rapid antigen-based and rapid molecular tests for the detection of SARS-CoV-2: a rapid review with network meta-analysis of diagnostic test accuracy studies - BMC MedicineBackground The global spread of COVID-19 created an explosion in rapid tests with results in | 1 hour, but their relative performance characteristics are not fully understood yet. Our aim was to determine the most sensitive and specific rapid test for the diagnosis of SARS-CoV-2. Methods Design: Rapid review and diagnostic test accuracy network meta-analysis (DTA-NMA). Eligibility criteria: Randomized controlled trials (RCTs) and observational studies assessing rapid antigen and/or rapid molecular test(s) to detect SARS-CoV-2 in participants of any age, suspected or not with SARS-CoV-2 infection. Information sources: Embase, MEDLINE, and Cochrane Central Register of Controlled Trials, up to September 12, 2021. Outcome measures: Sensitivity and specificity of rapid antigen and molecular tests suitable for detecting SARS-CoV-2. Data extraction and risk of bias assessment: Screening of literature search results was conducted by one reviewer; data abstraction was completed by one reviewer and independently verified by a second reviewer. Risk of bias was not assessed in the included studies. Data synthesis: Random-effects meta-analysis and DTA-NMA. Results We included 93 studies (reported in 88 articles) relating to 36 rapid antigen tests in 104,961 participants and 23 rapid molecular tests in 10,449 participants. Overall, rapid antigen tests had a sensitivity of 0.75 (95% confidence interval 0.70–0.79) and specificity of 0.99 (0.98–0.99). Rapid antigen test sensitivity was higher when nasal or combined samples (e.g., combinations of nose, throat, mouth, or saliva samples) were used, but lower when nasopharyngeal samples were used, and in those classified as asymptomatic at the time of testing. Rapid molecular tests may result in fewer false negatives than rapid antigen tests (sensitivity: 0.93, 0.88–0.96; specificity: 0.98, 0.97–0.99). The tests with the highest sensitivity and specificity estimates were the Xpert Xpress rapid molecular test by Cepheid (sensitivity: 0.9
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Acclaimed island mystery Call of the Sea comes to Meta Quest 2 next weekCall of the Sea VR, the virtual reality adaptation of developer Out of the Blue's acclaimed 2020 tropical island myster…
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Ultra-processed food and cancer: is there a link?Ultra-processed food and cancer: is there a link? Cancer Food NOVA UPF die tnutrition Ultraprocessedfood UiB haukeland_no
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Wavy changes in the whiskers of domestic cats are correlated with feline leukemia virus infection - BMC Veterinary ResearchBackground Feline leukemia virus (FeLV) is a retrovirus with global impact on the health of domestic cats and is usually examined by serology. In our daily clinical practice, we noticed that cats infected with FeLV often possess wavy whiskers (sinus hairs on the face). To investigate the relationship between wavy whiskers (WW) and FeLV infection, the association between the presence or absence of wavy changes in whiskers and serological FeLV infection was examined in a total of 358 cats including 56 cats possessing WW, using the chi-square test. The results of blood tests from 223 cases were subjected to multivariate analysis (logistic analysis). Isolated whiskers were observed under light microscopy, and upper lip tissues (proboscis) were subjected to histopathological and immunohistochemical analyses. Results The prevalence of WW was significantly correlated with FeLV antigen positivity in the blood. Of 56 cases with WW, 50 (89.3%) were serologically positive for FeLV. The significant association between WW and serological FeLV positivity was also confirmed by multivariate analysis. In WW, narrowing, degeneration, and tearing of the hair medulla were observed. Mild infiltration of mononuclear cells in the tissues, but no degeneration or necrosis, was found. By immunohistochemistry, FeLV antigens (p27, gp70 and p15E) were observed in various epithelial cells including the sinus hair follicular epithelium of the whisker. Conclusions The data suggest that the wavy changes in whiskers, a unique and distinctive external sign on a cat’s face, were associated with FeLV infection.
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Air pollution may increase risk for dementiaExposure to fine particulate air pollutants (PM2.5) may increase the risk of developing dementia, according to a new meta-analysis from Harvard T.H. Chan School of Public Health.
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