No abstract

No abstract

Psoriasis is a chronic T-cell-mediated autoimmune disease, and FMS-like tyrosine kinase 3 (FLT3) has been considered as a potential molecular target for the treatment of psoriasis. In this investigation, structural optimization was performed on a lead compound, 1-(4-(1H-pyrazolo[3,4-d]pyrimidin-4-yloxy)phenyl)-3-(4-chloro-3-(trifluoromethyl)phenyl)urea (1), which showed a moderate inhibitory activity againt FLT3. A series of pyrazolo[3,4-d]pyrimidine derivatives were synthesized, and structure-activity relationship analysis led to the discovery of a number of potent FLT3 inhibitors. One of the most active compounds, 1-(4-(1H-pyrazolo[3,4-d]pyrimidin-4-yloxy)-3-fluorophenyl)-3-(5-tert-butylisoxazol-3-yl)urea (18b), was then chosen for in-depth antipsoriasis studies because this compound displayed the highest potency in a preliminary antipsoriasis test. Compound 18b exhibited significant antipsoriatic effects in the K14-VEGF transgenic mouse model of psoriasis, and no recurrence was found 15 days later after the last administration. Detailed mechanisms of action of compound 18b were also investigated. Collectively, compound 18b could be a potential drug candidate for psoriasis treatment.

Purpose A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems. Design/methodology/approach The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system. Findings The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations. Originality/value The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

No abstract