No abstract

Enterovirus 71 (EV71) and Coxsackievirus A16 (CVA16) are the two major causative agents of hand, foot and mouth disease (HFMD), for which there are currently no licenced treatments. Here, the acquisition of resistance towards two novel capsid-binding compounds, NLD and ALD, was studied and compared to the analogous compound GPP3. During serial passage, EV71 rapidly became resistant to each compound and mutations at residues I113 and V123 in VP1 were identified. A mutation at residue 113 was also identified in CVA16 after passage with GPP3. The mutations were associated with reduced thermostability and were rapidly lost in the absence of inhibitors. In silico modelling suggested that the mutations prevented the compounds from binding the VP1 pocket in the capsid. Although both viruses developed resistance to these potent pocket-binding compounds, the acquired mutations were associated with large fitness costs and reverted to WT phenotype and sequence rapidly in the absence of inhibitors. The most effective inhibitor, NLD, had a very large selectivity index, showing interesting pharmacological properties as a novel anti-EV71 agent.

Due to the COVID-19 pandemic, there is currently a need for accurate, rapid, and easy-to-administer diagnostic tools to help communities manage local outbreaks and assess the spread of disease. The use of Artificial Intelligence within the domain of breath analysis techniques has shown to have potential in diagnosing a variety of diseases such as cancer and lung disease by analyzing volatile organic compounds (VOCs) in exhaled breath. This combined with their rapid, easy-to-use, and non-invasive nature makes them a good candidate for use in diagnosing COVID-19 in large scale public health operations. However, there remains issues with their implementation when it comes to the infrastructure currently available to support their use on a broad scale. This includes issues of standardization, and whether or not a characteristic VOC pattern can be identified for COVID-19. Despite these difficulties, breathalysers offer potential to assist in pandemic responses and their use should be investigated.

No abstract

Drug recognition and examination programs are widely used to detect drug impairment in motor vehicle operators. Visual tests are a key assessment in the detection of cannabis-related impairment. Participants were recruited via social media from the medical cannabis community in Southwestern Ontario, Canada. Twenty-two participants completed the full observational trial design. The majority (n = 13 or 59.1%) were male, with a mean age of 36 years (SD = 9.4; range: 24–59). Participants underwent the following protocol: 1) First round of testing (vital signs, bio sample collection, visual tests, subjective data, neurocognitive testing) (Baseline phase); 2) Consumption of cannabis via inhalation; 3) Second round of testing 30 minutes following consumption (THC phase); 4) Additional rounds of testing at 90, 150, and 210 minutes following consumption (Recovery phase). Visual assessment data and vital signs did not follow typical patterns associated with acute cannabis intoxication. With blood THC levels more than double the Canadian legal limit (5 ng/mL), visual testing results were not diagnostic for cannabis impairment, as participants maintained normal pupil sizes and normal ocular convergence patterns. Visual testing is a key component in standardized examinations used for detecting cannabis-related impairment in Canadian drivers; however, our data indicate that visual testing may not be an effective diagnostic tool for the specific population of medical cannabis users.