The manifestation of Specific Learning Disorder (SLD) during adulthood is one of the least examined research areas among the relevant literature. Therefore, the adult population with SLD is considered a "rare" and "unique" population of major scientific interest. The aim of the current study was to investigate, describe, and analyze the clinical, academic, and socio-demographic characteristics, and other everyday functioning life-skills of adults with SLD, in an attempt to shed more light on this limited field of research. The overall sample consisted of 318 adults, who were assessed for possible SLD. The diagnostic procedure included self-report records (clinical interview), psychometric/cognitive, and learning assessments. The main finding of the study was that SLD, even during adulthood, continues to affect the individuals' well-being and functionality in all of their life domains. There is an ongoing struggle of this population to obtain academic qualifications in order to gain vocational rehabilitation, as well as a difficulty to create a family, possibly resulting from their unstable occupational status, their financial insecurity, and the emotional/self-esteem issues they usually encounter, due to their ongoing learning problems. Moreover, the various interpersonal characteristics, the comorbidity issues, and the different developmental backgrounds observed in the clinical, academic, personal, social, and occupational profiles of the participants, highlight the enormous heterogeneity and the continuum that characterizes SLD during adulthood. We conclude that there is an imperative need for further research and the construction of more sufficient tools for the assessment and diagnosis of SLD during adulthood, which will take into account the developmental challenges and milestones in a series of domains, in order to assist this "vulnerable" population with their life struggles.The elderly and patients with several comorbidities experience more severe cases of coronavirus disease 2019 (COVID-19) than healthy patients without underlying medical conditions. However, it is unclear why these people are prone to developing alveolar pneumonia, rapid exacerbations, and death. Therefore, we hypothesized that people with comorbidities may have a genetic predisposition that makes them more vulnerable to various factors; for example, they are likely to become more severely ill when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To test this hypothesis, we searched the literature extensively. Polymorphisms of genes, such as those that encode angiotensin-converting enzyme 1 (ACE1), have been associated with numerous comorbidities, such as cardiovascular disease, hypertension, diabetes, chronic kidney disease, and obesity, and there are potential mechanisms to explain these associations (e.g., DD-type carriers have greater ACE1 activity, and patients with a genetic alpha-1 anti-trypsin (AAT) deficiency lack control over inflammatory mediators). Since comorbidities are associated with chronic inflammation and are closely related to the renin-angiotensin-aldosterone system (RAAS), these individuals may already have a mild ACE1/ACE2 imbalance before viral infection, which increases their risk for developing severe cases of COVID-19. However, there is still much debate about the association between ACE1 D/I polymorphism and comorbidities. The best explanation for this discrepancy could be that the D allele and DD subtypes are associated with comorbidities, but the DD genotype alone does not have an exceptionally large effect. This is also expected since the ACE1 D/I polymorphism is only an intron marker. We also discuss how polymorphisms of AAT and other genes are involved in comorbidities and the severity of SARS-CoV-2 infection. Presumably, a combination of multiple genes and non-genetic factors is involved in the establishment of comorbidities and aggravation of COVID-19.2'-hydroxy-chalcones are naturally occurring compounds with a wide array of bioactivity. In an effort to delineate the structural features that favor antioxidant and lipoxygenase (LOX) inhibitory activity, the design, synthesis, and bioactivity profile of a series of 2'-hydroxy-chalcones bearing diverse substituents on rings A and B, are presented. Among all the synthesized derivatives, chalcone 4b, bearing two hydroxyl substituents on ring B, was found to possess the best combined activity (82.4% DPPH radical scavenging ability, 82.3% inhibition of lipid peroxidation, and satisfactory LOX inhibition value (IC50 = 70 μM). Chalcone 3c, possessing a methoxymethylene substituent on ring A, and three methoxy groups on ring B, exhibited the most promising LOX inhibitory activity (IC50 = 45 μM). A combination of in silico techniques were utilized in an effort to explore the crucial binding characteristics of the most active compound 3c and its analogue 3b, to LOX. iCARM1 A common H-bond interaction pattern, orienting the hydroxyl and carbonyl groups of the aromatic ring A towards Asp768 and Asn128, respectively, was observed. Regarding the analogue 3c, the bulky (-OMOM) group does not seem to participate in a direct binding, but it induces an orientation capable to form H-bonds between the methoxy groups of the aromatic ring B with Trp130 and Gly247.To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.