ABSTRACT
Widely available low-cost electronics encourage the development of open-source tools for neuroscientific research. In recent years, many neuroscientists recognized the open science movement for its potential to stimulate and encourage science that is less focused on money, and more on robustness, validity, questioning and understanding. Here, we wanted to contribute to this global community by creating a research platform based on a common digital kitchen scale. This everyday ordinary kitchen tool is sometimes used in neuroscience research in various ways; however, its use is limited by sampling rate and inability to store and analyze data. To tackle this problem we developed a Platform for Acoustic STArtle or PASTA. This robust and simple platform enables users to obtain data from kitchen scale load cells at a high sampling rate, store it and analyze it. Here, we used it to analyze acoustic startle and prepulse inhibition sensorimotor gating in rats treated intracerebroventricularly with streptozotocin, but the system can be easily modified and upgraded for other purposes. In accordance with open science principles, we shared complete hardware design with instructions. Furthermore, we also disclose our software codes written for PASTA data acquisition (C++, Arduino) and acoustic startle experimental protocol (Python) and analysis (ratPASTA R package-R-based Awesome Toolbox for PASTA, and pastaWRAP-Python wrapper package for ratPASTA). To further encourage the development of our PASTA platform we demonstrate its sensitivity by using PASTA-gathered data to extract breathing patterns during rat freezing behavior in our experimental protocol.
ABSTRACT
The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.
ABSTRACT
While the coronavirus disease 2019 (COVID-19) pandemic advances, the scientific community continues to struggle in the search for treatments. Several improvements have been made, including discovery of the clinical efficacy of chloroquine (CQ) in patients with COVID-19, but effective treatment protocols remain elusive. In the search for novel treatment options, many scientists have used the in-silico approach to identify compounds that could interfere with the key molecules involved in entrance, replication or dissemination of severe acute respiratory syndrome coronavirus-2. However, most of the identified molecules are not available as pharmacological agents at present, and assessment of their safety and efficacy could take many months. This review took a different approach based on the proposed pharmacodynamic model of CQ in COVID-19. The main mechanism of action responsible for the favourable outcome of patients with COVID-19 treated with CQ seems to be related to a pH-modulation-mediated effect on endolysosomal trafficking, a characteristic of chemical compounds often called 'lysosomotropic agents' because of the physico-chemical properties that enable them to diffuse passively through the endosomal membrane and undergo protonation-based trapping in the lumen of the acidic vesicles. This review discusses lysosomotropic and lysosome targeting drugs that are already in clinical use and are characterized by good safety profiles, low cost and wide availability. Some of these drugs -particularly azithromycin and other macrolides, indomethacin and some other non-steroidal anti-inflammatory drugs, proton pump inhibitors and fluoxetine - could provide additional therapeutic benefits in addition to the potential antiviral effect that is still to be confirmed by well-controlled clinical trials. As some of these drugs have probably been used empirically in the treatment of COVID-19, it is hoped that colleagues worldwide will publish patient data to enable evaluation of the potential efficacy of these agents in the clinical context, and rapid implementation in therapeutic protocols if they are shown to have a beneficial effect on clinical outcome.
