Bednarski, M. | Search bioRxiv

bioRxiv2021

Are Machines-learning Methods More Efficient than Humans in Triaging Literature for Systematic Reviews?

Systematic literature reviews provide rigorous assessments of clinical, cost-effectiveness, and humanistic data. Accordingly, there is a growing trend worldwide among healthcare agencies and decision-makers to require them in order to make informed decisions. Because these reviews are labor-intensive and time consuming, we applied advanced analytic methods (AAM) to determine if machine learning methods could classify abstracts as well as humans. Literature searches were run for metastatic non-small cell lung cancer treatments (mNSCLC) and metastatic castration-resistant prostate cancer (mCRPC). Records were reviewed by humans and two AAMs. AAM-1 involved a pre-trained data-mining model specialized in biomedical literature, and AAM-2 was based on support vector machine algorithms. The AAMs assigned an accept/reject status, with reasons for exclusion. Automatic results were compared to those of humans. For mNSCLC, 5820 records were processed by humans and 440 (8%) records were accepted and the remaining items rejected. AAM-1 correctly accepted 6% of records and correctly excluded 79%. AAM-2 correctly accepted 6% of records and correctly excluded 82%. The review was completed by AAM-1 or AAM-2 in 52 hours, compared to 196 hours for humans. Work saved was estimated to be 76% and 79% by AAM-1 and AAM-2, respectively. For mCRPC, 2434 records were processed by humans and 26% of these were accepted and 74% rejected. AAM-1 correctly accepted 23% of records and rejected 62%. AAM-2 correctly accepted 20% of records and rejected 66%. The review was completed by AAM-1, AAM-2, and humans in 25, 25 and 85 hours, respectively. Work saved was estimated to be 61% and 68% by AAM-1 and AAM-2, respectively. AAMs can markedly reduce the time required for searching and triaging records during a systematic review. Methods similar to AAMs should be assessed in future research for how consistent their performances are in SLRs of economic, epidemiological and humanistic evidence.

bioinformatics↗

bioRxiv2020

KD-64 - a new selective A2A adenosine receptor antagonist has anti-inflammatory activity but contrary to the non-selective antagonist - caffeine does not reduce diet-induced obesity in mice

The A2 adenosine receptors play an important role, among others, in the regulation of inflammatory process and glucose homeostasis in diabetes and obesity. Thus, the presented project evaluated of influence of the selective antagonist of A2A adenosine receptor - KD-64 as compared to the known non-selective antagonist - caffeine on these two particular processes. Two different inflammation models were induced namely local and systemic inflammation. Obesity was induced in mice by high-fat diet and the tested compounds (KD-64 and caffeine) were administrated for 21 days. KD-64 showed anti-inflammatory effect in both tested inflammation models and administered at the same dose as ketoprofen exerted stronger effect than this reference compound. Elevated levels of IL-6 and TNF- observed in obese control mice were significantly lowered by the administration of KD-64 and were similar to the values observed in control non-obese mice. Interestingly, caffeine increased the levels of these parameters. In contrast to caffeine which had no influence on AlaT activity, KD-64 administration significantly lowered AlaT activity in the obese mice. Although, contrary to caffeine, KD-64 did not reduce diet-induced obesity in mice, it improved glucose tolerance. Thus, the activity of the selective adenosine A2A receptor antagonist was quite different from that of the non-selective.

pharmacology and toxicology↗