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This study aimed to assess the effect of distance learning on students' self-directed learning skills and motivation during the lockdown period of the COVID-19 pandemic. The study relies on a quantitative methodology. The data were collected using an administrative online survey distributed to 427 respondents with different majors (BBA) enrolled in the learning skills and scientific research skills course (obligatory course) in the second semester of the academic year 2020/2021 at The University of Jordan. Regression analysis was used to analyze the proposed hypotheses. The results showed that the independent variable (Distance Learning) positively influenced students' motivation and self-directed learning skills. The recommendations based on the outcomes of this research are useful for educational specialists to develop learning environments about the effects of distance learning on students' self-directed learning skills and motivation. In terms of limitations, the analysis was performed in one university only; therefore, attention must be paid when generalizing the results.
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Background: Multiple studies suggested that celiac disease (CD) may be associated with microscopic colitis (MC); however, most were limited by a small sample size or the main scope of interest. We aimed to analyze previously published literature on this association to determine its extent and significance. Methods: A systematic review was conducted in PubMed, Embase, PubMed Central, Cochrane, and ScienceDirect databases from inception through January 2022. The PRISMA guideline was followed for data extraction. Effect estimates were extracted and combined using random effect, the generic inverse variance method of DerSimonian and Laird and pooled odds ratio (OR), and event rates (ER) were calculated. The Newcastle-Ottawa scale was used to evaluate the risk of bias. Forest plots were generated and publication bias assessed via conventional techniques. Results: Twenty-six studies with a total of 22,802 patients with MC were included in this analysis. CD was significantly associated with MC (odds ratio [OR] 8.276, 95% confidence interval [CI] 5.888-11.632; P<0.001). The ER for MC in CD patients was 6.2% (95%CI 4.1-9.2%; P<0.001), while the ER for CD in MC patients was 6.1% (95%CI 3.9-9.5%; P<0.001). CD was prevalent in both types of MC: 5.2% (95%CI 2.2-12.1%; P<0.001) in collagenous colitis and 6.3% (95%CI 3.4-11.5%; P<0.001) in lymphocytic colitis. We found no publication bias, according to funnel plots and Egger's regression asymmetry testing. Conclusions: Our meta-analysis confirms a statistically significant association between CD and MC, with a high prevalence of CD in both types of MC. Gastroenterologists should be wary of this association when evaluating patients with either disease, particularly patients with a suboptimal response to first-line therapy.
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The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.
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Cancer treatments including chemotherapy and radiotherapy treat cancer by targeting rapidly dividing cells. Although these forms of treatment damage rapidly dividing cancer cells, they are also toxic to the cells of the gastrointestinal tract, leading to inflammation of the mucosal layer (mucositis) and causing nausea, vomiting, diarrhea, and abdominal pain. Improvement in symptoms may allow patients to have better performance status permitting ongoing treatment and possibly a better prognosis. This article describes the pathophysiology of chemotherapy-induced mucositis and includes 3 case reports of treatment of mucositis with serum bovine immunoglobulin.
