Recent studies from Western countries indicate significant levels of questionable research practices, but similar data from low and middle-income countries are limited. Our aims were to assess the prevalence of and attitudes regarding research misconduct among researchers in several universities in the Middle East and to identify factors that might account for our findings. We distributed an anonymous questionnaire to a convenience sample of investigators at several universities in Egypt, Lebanon, and Bahrain. Participants were asked to a) self-report their extent (...) of research misconducts, as well as their knowledge of colleagues engaging in similar research misconducts and b) provide their extent of agreement with certain attitudes about research misconduct. We used descriptive, bivariate, and multivariate logistic regression statistics to analyze the data. Data from 278 participants showed a high prevalence of misconduct, as 59.4% of our respondents self-reported to committing at least one misbehaviors and 74.5% reported having knowledge of any misbehaviors among any of their colleagues. The most common type of self-report misconduct was “circumventing research ethics regulations” followed by “fabrication and falsification”. A significant predictor of misconduct included a lack of “prior ethics training”. Scientific misconduct represents a significant issue in several universities in the Middle East. The demonstration that a lack of “prior ethics training” was a significant predictor of misconduct should lead to educational initiatives in research integrity. Further studies are needed to confirm whether our results can be generalized to other universities in the Middle East. (shrink)

Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis. Artificial intelligence has played an essential role in the medical field in general and in analyzing medical images and diagnosing diseases in particular, as it can reduce human errors that can occur with the medical expert when analyzing medical image. In this research study, we have done a systematic survey of the research published during the last 5 years in the diagnosis of lung cancer classification of (...) lung nodules in 4 reliable databases, and we selected 50 research paper using systematic literature review. The goal of this review work is to provide a concise overview of recent advancements in lung cancer diagnosis issues by machine learning and deep learning algorithms. This article summarizes the present state of knowledge on the subject. Addressing the findings offered in recent research publications gives the researchers a better grasp of the topic. We checked all the characteristics, such as challenges, recommendations for future work were analyzed in detail, and the published datasets and their source were presented to facilitate the researchers’ access to them and use it to develop the results achieved previously. (shrink)