The Remarkable Roles of the Receptor for Advanced Glycation End Products (RAGE) and Its Soluble Isoforms in COVID-19: The Importance of RAGE Pathway in the Lung Injuries

The respiratory symptoms of acute respiratory distress syndrome (ARDS) in the coronavirus disease 2019 (COVID-19) patients is associated with accumulation of pre-inflammatory molecules such as advanced glycation end-products (AGES), calprotectin, high mobility group box family-1 (HMGB1), cytokines, angiotensin converting enzyme 2 (ACE2), and other molecules in the alveolar space of lungs and plasma. The receptor for advanced glycation end products (RAGEs), which is mediated by the mitogen-activated protein kinase (MAPK), plays a critical role in the severity of chronic inflammatory diseases such as diabetes mellitus (DM) and ARDS. The RAGE gene is most expressed in the alveolar epithelial cells (AECs) of the pulmonary system. Several clinical trials are now being conducted to determine the possible association between the levels of soluble isoforms of RAGE (sRAGE and esRAGE) and the severity of the disease in patients with ARDS and acute lung injury (ALI). In the current article, we reviewed the most recent studies on the RAGE/ligands axis and sRAGE/esRAGE levels in acute respiratory illness, with a focus on COVID-19–associated ARDS (CARDS) patients. According to the research conducted so far, sRAGE/esRAGE measurements in patients with CARDS can be used as a powerful chemical indicator among other biomarkers for assessment of early pulmonary involvement. Furthermore, inhibiting RAGE/MAPK and Angiotensin II receptor type 1 (ATR1) in CARDS patients can be a powerful strategy for diminishing cytokine storm and severe respiratory symptoms.     

Cultural acceptability and personal willingness of Iranian students toward cadaveric donation

Cadaver dissection stands as a crucial component in medical curricula around the world, although computer‐based multimedia programs have been introduced in order to replace the need for cadaver donations. Due to a decrease in the number of unclaimed bodies and rather few donations, there is an insufficient number of cadavers for anatomical studies in Iran. This study was carried out to evaluate medical students' awareness and willingness regarding body donation in Kashan University of Medical Sciences, Iran. In this study, a questionnaire was designed to focus on the cultural acceptability and personal willingness to donate one's body after death. Students from the university's anatomy classes (n = 331) participated in this study. Seventy‐seven percent of the students expressed their agreement toward the idea of utilizing body donation services, though only 25.4% of participants were willing to donate their own bodies. None of the demographic factors were associated with cultural acceptability or personal willingness towards body donation. These findings indicated that besides “payment”, other factors were associated with students' willingness to become donors. All factors of awareness except “previous awareness of organization” were associated with cultural acceptability. In this study, students suggested that encouraging people to register for body donation using mass media (25.6%) and teaching students to respect cadavers in the dissection environment (24.8%) were the best solutions for addressing the lack of cadavers. These findings indicated that a lack of awareness about body donation might be the main factor responsible for unwillingness towards body donation; therefore, improving the public's awareness and addressing the willingness of students regarding body donation may help overcome the current lack of donated cadavers. Anat Sci Educ 10: 120–126. © 2016 American Association of Anatomists.     

Enantioselective assembly of multi-layer 3D chirality

The first enantioselective assembly of sandwich-shaped organo molecules has been achieved by conducting dual asymmetric Suzuki-Miyaura couplings and nine other reactions. This work also presents the first fully C-C anchored multi-layer 3D chirality with optically pure enantiomers. As confirmed by X-ray diffraction analysis that this chiral framework is featured by a unique C₂-symmetry in which a nearly parallel fashion consisting of three layers: top, middle and bottom aromatic rings. Unlike the documented planar or axial chirality, the present chirality shows its top and bottom layers restrict each other from free rotation, i.e., this multi-layer 3D chirality would not exist if either top or bottom layer is removed. Nearly all multi-layered compounds showed strong luminescence of different colors under UV irradiation, and several randomly selected samples displayed aggregation-induced emission (AIE) properties. This work is believed to have broad impacts on chemical, medicinal and material sciences including optoelectronic materials in future.     

Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Q_u), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (L_w/B). Apart from Q_u, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when L_w/B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.     

Prevalence of reading failure in boys compared with girls

The hypothesis that proportionately more boys than girls experience reading failure was tested on a sample of 708 children using both test-identified and teacher-identified criteria. Test-identified reading failure was defined by low scores on standardized reading achievement tests given at the end of first and third grade. For Severely Reading Disabled (total reading score at the 10th percentile or lower), the ratio of boys to girls was 1.4:1 at first grade and 1.3:1 at third. At both grades equal proportions of boys and girls were represented in the Reading Disabled category (total reading score between the 11th and 30th percentile). Teacher-identified reading failure criteria consisted of enrollment in LD and Chapter One (remedial reading) programs at first and third grades. Teacher-identified ratios of boys to girls in LD were 2:1 at both first and third grade, exceeding the test-identified ratios, while identification for Chapter One services did not show a gender difference.     

Leveraging textual properties of bug reports to localize relevant source files

Bug reports are an essential part of a software project's life cycle since resolving them improves the project's quality. When a new bug report is received, developers usually need to reproduce the bug and perform code review to locate the bug and assign it to be fixed. However, the huge number of bug reports and the increasing size of software projects make this process tedious and time-consuming. To solve this issue, bug localization techniques try to rank all the source files of a project with respect to how likely they are to contain a bug. This process reduces the search space of source files and helps developers to find relevant source files quicker. In this paper, we propose a multi-component bug localization approach that leverages different textual properties of bug reports and source files as well as the relations between previously fixed bug reports and a newly received one. Our approach uses information retrieval, textual matching, stack trace analysis, and multi-label classification to improve the performance of bug localization. We evaluate the performance of the proposed approach on three open source software projects (i.e., AspectJ, SWT, and ZXing) and the results show that it can rank appropriate source files for more than 52% of bugs by recommending only one source file and 78% by recommending ten files. It also improves the MRR and MAP values compared to several existing state-of-the-art bug localization approaches.