Recent legalization of cannabis in various states has sparked research on the impact of cannabis use on postnatal outcomes. While previous research has yielded contradictory findings, some studies suggest increased cannabis use is directly associated with increased depression and psychological distress.We assert that prenatal mental health should be an essential consideration in the discourse surrounding prenatal cannabis use, as issues such as untreated maternal depression are risk factors for adverse postnatal outcomes like low birth weight and preterm delivery.However, few studies have considered the relationship between prenatal maternal mental health and cannabis use. Here we question: what is the association between cannabis use and psychological distress in pregnant individuals? For our sample population, we recruited pregnant individuals in the greater Seattle area. 12 individuals reported using cannabis (CB) at least 3 to 5 times a week throughout their first trimester of pregnancy, and 22 were non-cannabis users (n-CB). In early pregnancy, we administered the Brief Symptom Inventory (BSI), a self-reported measure, to evaluate psychological distress levels. We tracked cannabis use with weekly surveys from time of enrollment to birth, evaluating reasons for use and amount consumed, among other variables. We hypothesize that prenatal cannabis use will be associated with elevated BSI T-scores compared to the control group; within the prenatal cannabis use population, we expect cannabis use for mental health reasons to be associated with elevated BSI T-scores. More research is needed on possible causal versus correlational associations between cannabis use and psychological distress, and the role of psychosocial distress as a possible confounder in previous prenatal cannabis use and infant development studies.

The pupillary light reflex (PLR) curve is an important point-of-care biomarker for the diagnosis of traumatic brain injury (TBI). Using PLR, first responders can determine the severity of TBI in the field and direct patients to a trauma center where staff can continually assess PLR to monitor TBI severity. Manual pupillometry, the most commonly available method for first responders and most clinicians wishing to assess PLR, is qualitative and often inaccurate. The current gold-standard device for PLR measurement is digital infrared pupillometry, but such devices are fragile and expensive. Our research team has developed a smartphone-based pupillometer (PupilScreen) with the ability to assess PLR using a standard iPhone, assisted by a cloud-based neural network. To demonstrate the feasibility of using PupilScreen in a realistic clinical setting and compare the accuracy of the device to the current clinical gold-standard, we have built an annotated dataset of the PLR in n=120 patients with TBI who are hospitalized in a neurological intensive care unit. Pupillometry is performed using the mobile device and the gold-standard digital infrared pupillometer. Pupil videos are manually annotated and used in the further training of our machine learning algorithm that generates a PLR curve for each patient. We anticipate that our technology will demonstrate accuracy in assessing the PLR that exceeds that of manual pupillometry and is at least equivalent to the gold-standard digital pupillometer. This technology has the potential to alleviate the current undertreatment of many TBI patients in the United States and abroad that results from a lack of accurate and cost-effective pupillometry equipment.