Incorporating replication in higher education: Supervisors' perspectives and institutional pressures.

Background: Even though replication research has gained traction within academia over the recent years, it is not often well-received as a stand-alone thesis topic by supervisors and university administrators.Methods: In this qualitative investigation, we delve into the perspectives of academic supervisors on the feasibility of replication as a thesis topic within the field of applied linguistics (AL). Drawing on Institutional Theory, administrative pressures facing supervisors on what to be considered permissible for a thesis were also explored. By conducting semi-structured e-mail interviews with a global cohort of AL supervisors and a thematic analysis of their responses, a nuanced landscape was brought to light.Results: Supervisors outlined numerous benefits associated with replication including fostering academic advancement as well as providing opportunities for reevaluating prior research. Nonetheless, they also pointed to several obstacles along the way, such as concerns over originality, constraints on time and resources, and the necessity for mentorship. Moreover, supervisors emphasized their pivotal role as decision-makers in accepting or rejecting replication for a thesis project, while acknowledging the partial influence of institutional pressures.Conclusions: Lastly, some implications and recommendations on allocating more resources to replication research are provided.

Comparing the Predictive Value of SOFA and SIRS for Mortality in the Early Hours of Hospitalization of Sepsis Patients: A Systematic Review and Meta-analysis.

BACKGROUND: Sepsis, a deadly infection causing organ failure and Systemic Inflammatory Response Syndrome (SIRS), is detected early in hospitalization using the SIRS criteria, while sequential organ failure (SOFA) assesses organ failure severity. A systematic review and meta-analysis was evaluated to investigate the predictive value of the SIRS criteria and the SOFA system for mortality in early hospitalization of sepsis patients. METHODS: Inclusion criteria were full reports in peer-reviewed journals with data on sepsis assessment using SOFA and SIRS, and their relationship with outcomes. For quality assessment, we considered study population, sepsis diagnosis criteria, and outcomes. The area under the curve (AUC) of these criteria was extracted for separate meta-analysis and forest plots. RESULTS: Twelve studies met the inclusion criteria. The studies included an average of 56.1% males and a mean age of 61.9 (±6.1) among 32,979 patients. The pooled AUC was 0.67 (95% CI: 0.60-0.73) for SIRS and 0.79 (95% CI: 0.73-0.84) for SOFA. Significant heterogeneity between studies was indicated by an I2 above 50%, leading to a meta-regression analysis. This analysis, with age and patient number as moderators, revealed age as the major cause of heterogeneity in comparing the predictive value of the SOFA score with SIRS regarding the in-hospital mortality of sepsis patients (P<0.05). CONCLUSION: The SOFA score outperformed the SIRS criteria in predicting mortality, emphasizing the need for a holistic approach that combines clinical judgment and other diagnostic tools for better patient management and outcomes.

Identifying factors related to mortality of hospitalized COVID-19 patients using machine learning methods.

Background: The COVID-19 pandemic has had a profound impact globally, presenting significant social and economic challenges. This study aims to explore the factors affecting mortality among hospitalized COVID-19 patients and construct a machine learning-based model to predict the risk of mortality. Methods: The study examined COVID-19 patients admitted to Imam Reza Hospital in Tabriz, Iran, between March 2020 and November 2021. The Elastic Net method was employed to identify and rank features associated with mortality risk. Subsequently, an artificial neural network (ANN) model was developed based on these features to predict mortality risk. The performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: The study included 706 patients with 96 features, out of them 26 features were identified as crucial predictors of mortality. The ANN model, utilizing 20 of these features, achieved an area under the ROC curve (AUC) of 98.8 %, effectively stratifying patients by mortality risk. Conclusion: The developed model offers accurate and precipitous mortality risk predictions for COVID-19 patients, enhancing the responsiveness of healthcare systems to high-risk individuals.

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.

Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

Psychological pathways between type D personality and COVID-19.

BACKGROUND: Patients with a type D personality have worse social functioning and mental health and more affective constraints than non-type D personalities. They have a negative outlook on life and health-related issues. The aim of this study was to examine the mediating role of stress and anxiety in the relationship between type D personality and COVID-19 by adjustment of the effect of demographic characteristics and perceived symptoms as confounder variables. PARTICIPANTS AND PROCEDURE: A total of 196 patients out of those suspected of having COVID-19 and visiting the reference hospitals were selected. They had completed the type D personality and the anxiety and stress scales along with their hospital admission form before undergoing COVID-19 testing. After their COVID-19 test, the participants were divided into two groups based on their disease, an infected group (n = 90) and a non-infected group (n = 106). RESULTS: Type D personality has no significant direct effect on infection with the disease, but taking into account the mediating variable of stress, the odds of an event in those with type D personality is 2.21 times higher than those without this personality (p = .027) and, taking into account the mediating variable of anxiety, having a type D personality increases the odds of an event by 2.62 times (p = .011), holding demographic characteristics and perceived symptoms constant. CONCLUSIONS: Given the indirect relationship between COVID-19 and type D personality, the mediating variables of stress and anxiety can be considered full mediating variables.

Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence.

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18-25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.