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Background: The abuse of ketamine and cannabis has generated serious health concerns worldwide. Studies have shown that ketamine abuse is associated with the syndrome of cystitis. The nature and mechanism of immunomodulation induced by ketamine and cannabis abuse, however, remains obscure, in particular, in aggravating Staphylococcus aureus (S. aureus, MRSA) associated pneumonia infection. Thus, there is a need to explore the underlying mechanisms by which ketamine and cannabis mediate immunomodulation in relation to susceptibility to respiratory infection in humans.

Aim: To determine the effects of ketamine and cannabinoid on increasing Staphylococcus aureus lung infection in vitro and in vivo and investigate the molecular mechanisms by which ketamine and cannabinoid increase susceptibility to S. aureus colonization in the lungs.

Method: This experiment first explored and verified the effects of ketamine and cannabinoid abuse on S. aureus lung infection through in vitro cell experiments and in vivo mouse pneumonia models, and then uncovered the underlying molecular mechanism.

Results: Ketamine and cannabinoid (AEA and CP 55, 940) treatment significantly increased S. aureus adhesion and invasion ability in human lung epithelial cell lines in vitro. Importantly, ketamine injection significantly deteriorated S. aureus colonization and infection in the intranasal pneumonia model via upregulating fibronectin 1 (FN1) and integrin αV gene (ITGAV) expression in the lung in vivo and in vitro.

Conclusion: Ketamine high doses usage increased the S. aureus pulmonary infection levels (both adhesion and invasion) in vivo and in vitro, and ketamine increased S. aureus adhesion infection in the lungs by upregulating FN1 and ITGAV expression. Cannabinoids increased the S. aureus invasion infection in human epithelial cells. By clarifying the relationship between ketamine and cannabinoid abuse and pulmonary infection and the possible mechanism, we will promote the general awareness of different sectors of the community about the previously unknown harms of drugs with a view to encouraging and enabling them to play a more active role in drug prevention, early identification and intervention.

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Background: Automatic Retinal Image Analysis (ARIA) is an advance non-invasive technology that can screen for different diseases by analysing a retinal image of an individual at a relatively low cost.

Aim: To develop a set of objective measures for detecting psychotropic substances abuse using the ARIA technology.

Method: This was a matched case-control study conducted in Hong Kong with 100 cases and 200 controls. Case subjects were defined as adults who have been or were now abusing psychoactive substances and have registered with local drug treatment and rehabilitation centres. Control subjects were general adults without drug-taking history. Retinal characteristics were obtained via ARIA with deep learning algorithm. Data analyses were further conducted to identify independent retinal characteristics as well as a multivariate model building.

Results: Retinal characteristics such as exudates, bifurcation coefficient of venule, bifurcation angle of arteriole and venule, and fractal dimension were significantly different between the controls and the psychoactive substance abusers (p<0.05). The complex retinal characteristic components obtained by deep learning algorithms were also significantly associated with the drug abuse (p<0.001). The performance of the prediction models achieved a high accuracy of 95.0% and an area under the receiver operating characteristic curve (AUC) of 0.987 in discriminating the psychoactive substance abusers and the controls.

Conclusion: With the promising exploratory findings, ARIA is expected to apply to screening applications, including an effectiveness monitoring of different anti-addiction treatments or rehabilitation programs for rehabilitee, thus to help clinicians and social workers planning suitable programs.

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Background: Pupillary light reflex (PLR) is known to be affected by the use of psychoactive substances (PAS) through their effects on the autonomic nervous system. Stimulants such as crystal methamphetamine (Ice), ketamine, and cocaine may cause pupil dilatation (mydriasis) while narcotics including heroin may cause pupil constriction (miosis). The impairment of PLR by PAS can be quantified by infrared pupillometry (iP) as the temporal sequence of changes in the pupil size upon varying light stimulus.

Aim: To build a next-generation non-intrusive device to identify subjects under influence of psychoactive drug.

Method: The study team modified a commercially available Virtual Reality (VR) headset and developed a software to conduct iP using an eye-tracking add-on camera, which samples both eyes at 120Hz. Initial pupil size, response time, 2/3 constriction time, 1/3 re-dilatation time and slow-phase dilatation velocity using light of low (PLR 64) (RGB 64, 64, 64), medium (PLR 128) (RGB 128, 128, 128) and high intensity (PLR 64) (RGB 255, 255,255) that based on initial pupil diameter (PLR+) were recorded. Eye tracking were then performed while showing paired images with or without drug-related clues.

Results: Significant differences exist in iP and eye-tracking (ET) data between healthy and Psychoactive Substance Abusers (PSA) group, especially for the subjects with history of taking cannabis. Pupillary unrest (PU), unstable PLR responses, were highly prevalent in PSA (95.2% and 93.3% in first and follow-up visits) using this infrared pupillometry and eye-tracking (iP&ET) device. Importantly iP&ET test has much longer detection window compared to the current gold standard of urine test by mass spectrometry in which only 30% of the PSA were tested positive.

Conclusion: The VR iP&ET setup were shown to be sensitive, cost-efficient and portable and ready to be deployed for frontline use. The study team received lots of positive feedbacks from the users including the general public, PSA, and social workers working at the rehabilitation centers. Centers also show interests in future cooperation. Our device has good potential as first-line screening in schools and community centers. Further hardware and software upgrade may further enhance the testing time and target specific drug e.g. cannabis as the emerging PSA locally.

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