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Purpose of Review: Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients., Recent Findings: Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications., Summary: Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Dysglycemia is common among hospitalized patients. Accurate point-of-care (POC) glucose monitoring is necessary for the safe administration of insulin. Unfortunately, POC glucose meters are not all created equal. Interfering factors such as abnormal hematocrit, abnormal oxygen tension, and oxidizing/reducing substances can lead to inaccurate glucose measurements and result in inappropriate insulin dosing. The introduction of autocorrecting glucose meters has changed the POC testing landscape. Autocorrecting glucose meters provide more accurate measurements and have been associated with improved glycemic control in hospitalized patients. Continuous glucose monitoring has also created interest in using these platforms in at-risk inpatient populations. Future glucose monitoring technologies such as artificial intelligence/machine learning, wearable smart devices, and closed-loop insulin management systems are poised to transform glycemic management. The goal of this review is to provide an overview of glucose monitoring technology, summarize the clinical impact of glucose monitoring accuracy, and highlight emerging and future POC glucose monitoring technologies.

Cocaine use disorder (CUD) is a significant problem worldwide, with no FDA-approved treatments. Epidemiological data indicate that only about 17 % of people that use cocaine will meet DSM criteria for CUD. Thus, the identification of biomarkers predictive of eventual cocaine use may be of great value. Two potentially useful predictors of CUD are social hierarchies in nonhuman primates and delay discounting. Both social rank and preference for a smaller, immediate reinforcer relative to a larger, delayed reinforcer have been predictive of CUD. Therefore, we wanted to determine if there was also a relationship between these two predictors of CUD. In the present study, monkeys cocaine-naive responded under a concurrent schedule of 1- vs. 3-food pellets and delivery of the 3-pellet option was delayed. The primary dependent variable was the indifference point (IP), which is the delay that results in 50 % choice for both options. In the initial determination of IP, there were no differences based on sex or social rank of the monkeys. When the delays were redetermined after ~25 baseline sessions (range 5-128 sessions), dominant females and subordinate males showed the largest increases in IP scores from the first determination to the second. Because 13 of these monkeys had prior PET scans of the kappa opioid receptor (KOR), we examined the relationship between KOR availability and IP values and found that the change in IP scores from the first to the second determination significantly negatively predicted average KOR availability in most brain regions. Future studies will examine acquisition to cocaine self-administration in these same monkeys, to determine if IP values are predictive of vulnerability to cocaine reinforcement., (Copyright © 2023. Published by Elsevier Inc.)