Disease X Testing: The results of an international external quality assessment exercise

The United Nations Secretary-General Mechanism (UNSGM) for investigation of the alleged use of chemical and biological weapons is the only established international mechanism of this type under the UN. The UNGSM may launch an international investigation, relying on a roster of expert consultants, qualified experts, and analytical laboratories nominated by the member states. Under the framework of the UNSGM, we organized an external quality assurance exercise for nominated laboratories, named the Disease X Test, to improve the ability to discover and identify new pathogens that may cause possible epidemics and to determine their animal origin. The "what-if" scenario was to identify the etiological agent responsible for an outbreak that has tested negative for many known pathogens, including viruses and bacteria. Three microbes were added to the samples, Dabie bandavirus, Mammarenavirus, and Gemella spp., of which the last two have not been taxonomically named or published. The animal samples were from Rattus norvegicus, Marmota himalayana, New Zealand white rabbit, and the tick Haemaphysalis longicornis. Of the 11 international laboratories that participated in this activity, six accurately identified pathogen X as a new Mammarenavirus, and five correctly identified the animal origin as R. norvegicus. These results showed that many laboratories under the UNSGM have the capacity and ability to identify a new virus during a possible international investigation of a suspected biological event. The technical details are discussed in this report.Copyright © 2022

Pharm Aco Kin Etics of Remdesivir and Its Metabolites in Participants with Moderate and Severe Hepatic Impairment

BACKGROUND: Remdesivir (RDV) is an RNA polymerase inhibitor approved for treatment of COVID-19 (200 mg loading dose, 100 mg qd thereafter) in adult and pediatric patients, primarily metabolized by the high-capacity carboxylesterase 1 pathway (80% of metabolism), and by cathepsin A and CYP3A (10% each). The extensive hepatic contribution to RDV elimination and the prevalence of liver comorbidities in COVID-19 patients warranted a study in participants with hepatic impairment (HI). METHOD(S): This is a phase 1, open-label study of RDV consisting of moderate and severe HI participants and healthy matched controls (HMC) based on age (+/- 10 years), sex, and BMI (+/- 20%). Participants received a single 100 mg IV dose of RDV and remained in the clinic for 8 days. The primary endpoint was pharmacokinetic (PK) parameters of RDV and metabolites. RESULT(S): Preliminary PK and safety data from 10 moderate and 6 severe HI participants and their HMC are available. The average PK fold-change for all analytes and matrices assessed in the study are presented (Table). No serious treatment-related adverse events and no clinically significant changes in participant lab values were reported. CONCLUSION(S): The 1.52 RDV AUCinf fold increase is within expected ranges and justifies no dose adjustment in COVID-19 patients with impaired hepatic function. (Table Presented).

Discovery of Travelling Companions from Trajectories with Different Sampling Rates

Discovery of travelling companions from trajectories can provide empirical support for various applications, such as COVID-19 contact tracing, suspects tracking and detection, tourist behavior analysis, etc. One challenge is trajectories of travelling companions are from different data sets with different sampling rates and granularities. Most current researches for discovery of travelling companions focus on using snapshot-based clustering methods to identify travelling groups, or using trajectory similarity algorithms to mine companion relationships. However, the constantly changing sampling rate limits the application of clustering methods in the companion relationship mining. Although some similarity algorithms can mitigate this negative impact, they usually focus on the spatial distribution of trajectories and the time complexity is very high. In this paper, we designed a Spatio-Temporal Trajectory Companion DEtection Framework (STCDEF) to detect travelling companions from trajectories with different sampling rates, which can effectively reduce the time consumption caused by the matching mechanism. Within the STCDEF, an approximate trajectory similarity algorithm, Fast Spatio-Temporal Similarity (FSTS) measure, is presented. Moreover, the concept of Mutual Following Degree (MFD) is introduced into STCDEF to detect travelling companions with FSTS, so as to further improve the efficiency when dealing with trajectories of varying sampling rates. © 2020 ACM.