Dose of Product or Product Concentration: A Comparison of Change in Heart Rate by THC Concentration for Participants Using Cannabis Daily and Occasionally.

Introduction: Studies show that acute cannabis use significantly increases heart rate (HR) and mildly raises blood pressure in the minutes following smoked or inhaled use of cannabis. However, less is known about how the THC concentration of the product or an individual's frequency of use (i.e., tolerance) may affect the magnitude of the change in HR. It is also relatively unexamined how the physical effects of increased HR after acute cannabis use relate to self-reported drug effects or blood THC levels. Aims: To describe the relationship between THC concentration of product used, self-reported subjective intoxication, THC blood levels, and frequency of cannabis use with the change in HR after acute cannabis use. Materials and Methods: Participants (n = 140) were given 15 min to smoke self-supplied cannabis ad libitum, HR was measured at baseline and an average of 2 min post-cannabis smoking. The ARCI-Marijuana scale and Visual Analog Scales (VAS) were administered, and blood samples were taken at both time points. Participants were asked about their frequency of use. Information about the product used was recorded from the package. Linear regression was used to analyze the relationship between changes in HR (post-pre cannabis use) and post-cannabis use HR, blood THC concentration, THC product concentration, frequency of use, and self-reported drug effect. Results: There was a significantly higher HR among those who smoked cannabis compared to the controls (p < 0.001), which did not significantly differ by frequency of use (p = 0.18). Higher concentration THC (extract) products did not produce a significantly different HR than lower concentration (flower) products (p = 0.096). VAS score was associated with an increase in HR (p < 0.05). Overall, blood THC levels were not significantly related to the change in HR (p = 0.69); however, when probed, there was a slight positive association among the occasional use group only. Discussion: Cardiovascular effects of cannabis consumption may not be as subject to tolerance with daily cannabis use and do not directly increase with THC concentration of the product. This is a departure from other effects (i.e., cognitive, subjective drug effects) where tolerance is well established. These findings also suggest that, at least among those with daily use, higher concentration THC products (>60%) do not necessarily produce cardiovascular physiological effects that are significantly more robust than lower concentration (<20%) products.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.

MOTIVATION: Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. RESULTS: To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

A Study of Pupil Response to Light as a Digital Biomarker of Recent Cannabis Use.

Introduction: Given the traffic safety and occupational injury prevention implications associated with cannabis impairment, there is a need for objective and validated measures of recent cannabis use. Pupillary light response may offer an approach for detection. Method: Eighty-four participants (mean age: 32, 42% female) with daily, occasional, and no-use cannabis use histories participated in pupillary light response tests before and after smoking cannabis ad libitum or relaxing for 15 min (no use). The impact of recent cannabis consumption on trajectories of the pupillary light response was modeled using functional data analysis tools. Logistic regression models for detecting recent cannabis use were compared, and average pupil trajectories across cannabis use groups and times since light test administration were estimated. Results: Models revealed small, significant differences in pupil response to light after cannabis use comparing the occasional use group to the no-use control group, and similar statistically significant differences in pupil response patterns comparing the daily use group to the no-use comparison group. Trajectories of pupillary light response estimated using functional data analysis found that acute cannabis smoking was associated with less initial and sustained pupil constriction compared to no cannabis smoking. Conclusion: These analyses show the promise of pairing pupillary light response and functional data analysis methods to assess recent cannabis use.

Effects of acute cannabis inhalation on reaction time, decision-making, and memory using a tablet-based application.

BACKGROUND: Acute cannabis use has been demonstrated to slow reaction time and affect decision-making and short-term memory. These effects may have utility in identifying impairment associated with recent use. However, these effects have not been widely investigated among individuals with a pattern of daily use, who may have acquired tolerance. The purpose of this study was to examine the impact of tolerance to cannabis on the acute effects as measured by reaction time, decision-making (gap acceptance), and short-term memory. METHODS: Participants (ages 25-45) completed a tablet-based (iPad) test battery before and approximately 60 min after smoking cannabis flower. The change in performance from before to after cannabis use was compared across three groups of cannabis users: (1) occasional use (n = 23); (2) daily use (n = 31); or (3) no current use (n = 32). Participants in the occasional and daily use group self-administered ad libitum, by smoking or vaping, self-supplied cannabis flower with a high concentration of total THC (15-30%). RESULTS: The occasional use group exhibited decrements in reaction time (slowed) and short-term memory (replicated fewer shapes) from before to after cannabis use, as compared to the no-use group. In the gap acceptance task, daily use participants took more time to complete the task post-smoking cannabis as compared to those with no use or occasional use; however, the level of accuracy did not significantly change. CONCLUSIONS: The findings are consistent with acquired tolerance to certain acute psychomotor effects with daily cannabis use. The finding from the gap acceptance task which showed a decline in speed but not accuracy may indicate a prioritization of accuracy over response time. Cognitive and psychomotor assessments may have utility for identifying impairment associated with recent cannabis use.

Accuracy and replicability of identifying eyelid tremor as an indicator of recent cannabis smoking.

INTRODUCTION: Cannabis intoxication may increase the risk of motor vehicle crashes. However, reliable methods of assessing cannabis intoxication are limited. The presence of eyelid tremors is among the signs of cannabis use identified under the Drug Evaluation and Classification Program of the International Association of Chiefs of Police. Our objectives were to assess the accuracy and replicability of identifying eyelid tremor as an indicator of recent cannabis smoking using a blinded, controlled study design. METHODS: Adult subjects (N = 103) were recruited into three groups based on their cannabis use history: daily, occasional, and no current cannabis use. Participants' closed eyelids were video recorded for 30 seconds by infrared videography goggles before and at a mean ± standard deviation time of 71.4 ± 4.6 minutes after the onset of a 15-minute interval of ad libitum cannabis flower smoking or vaping. Three observers with expertise in neuro-ophthalmology and medical toxicology were trained on exemplar videos of eyelids to reach a consensus on how to grade eyelid tremor. Without knowledge of subjects' cannabis use history or time point (pre- or post-smoking), observers reviewed each video for eyelid tremor graded as absent, slight, moderate, or severe. During subsequent data analysis, this score was further dichotomized as a consensus score of absent (absent/slight) or present (moderate/severe). RESULTS: Kappa and intraclass correlation coefficient statistics demonstrated moderate agreement among the coders, which ranged from 0.44-0.45 and 0.58-0.61, respectively. There was no significant association between recent cannabis use and the observers' consensus assessment that eyelid tremor was present, and cannabis users were less likely to have tremors (odds ratio: 0.75; 95 percent confidence interval: 0.25, 2.40). The assessment of eyelid tremor as an indicator of recent cannabis smoking had a sensitivity of 0.86, specificity of 0.18, and accuracy of 0.64. DISCUSSION: Eyelid tremor has fair sensitivity but poor specificity and accuracy for identification of recent cannabis use. Inter-rater reliability for assessment of eyelid tremor was moderate for the presence and degree of tremor. The weak association between recent cannabis use and eyelid tremor does not support its utility in identifying recent cannabis use. LIMITATIONS: Videos were recorded at only one time point after cannabis use. Adherence to abstinence could not be strictly supervised. Due to regulatory restrictions, we were unable to control the cannabis product used or administer a fixed Δ9-tetrahydrocannabinol dose. Participants were predominately non-Hispanic and White. CONCLUSIONS: In a cohort of participants with a range of cannabis use histories, acute cannabis smoking was not associated with the presence of eyelid tremor, regardless of cannabis use history, at 70 minutes post-smoking. Additional research is needed to identify the presence of eyelid tremor accurately, determine the relationship between cannabis dose and timeline in relation to last cannabis use to eyelid tremor, and determine how it should be, if at all, utilized for cannabis Drug Recognition Evaluator examinations.

Statistical analysis of single-cell protein data.

Immune modulation is considered a hallmark of cancer initiation and progression, with immune cell density being consistently associated with clinical outcomes of individuals with cancer. Multiplex immunofluorescence (mIF) microscopy combined with automated image analysis is a novel and increasingly used technique that allows for the assessment and visualization of the tumor microenvironment (TME). Recently, application of this new technology to tissue microarrays (TMAs) or whole tissue sections from large cancer studies has been used to characterize different cell populations in the TME with enhanced reproducibility and accuracy. Generally, mIF data has been used to examine the presence and abundance of immune cells in the tumor and stroma compartments; however, this aggregate measure assumes uniform patterns of immune cells throughout the TME and overlooks spatial heterogeneity. Recently, the spatial contexture of the TME has been explored with a variety of statistical methods. In this PSB workshop, speakers will present some of the state-of-the-art statistical methods for assessing the TIME from mIF data.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.

Motivation: Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. Results: To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and Implementation: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

A Latent Variable Analysis of Psychomotor and Neurocognitive Performance After Acute Cannabis Smoking.

Objective: This paper evaluated a novel, tablet-based neurocognitive and psychomotor test battery for detecting impairment from acute cannabis smoking using advanced quantitative methods. The study was conducted in a state with legal, recreational cannabis use and included participants who use cannabis occasionally or daily, and a no use comparison group. Methods: Participants completed a tablet-based test assessing reaction time, decision making, working memory and spatial-motor performance. The test was completed before and after participants smoked cannabis (or after a rest period in the case of controls). An Exploratory Factor Analysis approach was implemented to reduce dimensionality and evaluate correlations across the four assessed domains. Linear regression models were utilized to quantify associations between factor scores and cannabis use groups (daily vs. occasional vs. no use). Results: Seven factors were identified explaining 56.7% of the variance among the 18 measures. Regression models of the change in factors after cannabis smoking indicated those who use cannabis daily demonstrated poorer performance on a latent factor termed Displaced and Delayed (standardized coefficient 0.567, 95% CI: 0.178, 0.955; P = 0.005) compared to those with no use. Those who use cannabis occasionally exhibited a decline in performance on a latent factor termed Recall and Reaction (standardized coefficient 0.714, 95% CI: 0.092, 1.336; P = 0.025) compared to no use. Conclusions: This analysis demonstrates an innovative, quantitative approach to study how cannabis consumption affects neurocognitive and psychomotor performance. Results demonstrated that acute cannabis use is associated with changes in neurocognitive and psychomotor performance, with differences based on the pattern of occasional or daily use.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

A Video Segmentation Pipeline for Assessing changes in Pupil Response to Light After Cannabis Consumption.

Due to long-standing federal restrictions on cannabis-related research, the implications of cannabis legalization on traffic and occupational safety are understudied. Accordingly, there is a need for objective and validated measures of acute cannabis impairment that may be applied in public safety and occupational settings. Pupillary response to light may offer an avenue for detection that outperforms typical sobriety tests and THC concentrations. We developed a video processing and analysis pipeline that extracts pupil sizes during a light stimulus test administered with goggles utilizing infrared videography. The analysis compared pupil size trajectories in response to a light for those with occasional, daily, and no cannabis use before and after smoking. Pupils were segmented using a combination of image pre-processing techniques and segmentation algorithms which were validated using manually segmented data and found to achieve 99% precision and 94% F-score. Features extracted from the pupil size trajectories captured pupil constriction and rebound dilation and were analyzed using generalized estimating equations. We find that acute cannabis use results in less pupil constriction and slower pupil rebound dilation in the light stimulus test.

mxnorm: An R Package to Normalize Multiplexed Imaging Data.

Multiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells (Angelo et al., 2014; Gerdes et al., 2013; Goltsev et al., 2018). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like spatialTime, imcRtools, MCMICR0, and Squidpy (Creed et al., 2021; Palla et al., 2021; Schapiro et al., 2021; Windhager et al., 2021). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source - intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust - optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data. mxnorm is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022), we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository.

Influence of cannabis use history on the impact of acute cannabis smoking on simulated driving performance during a distraction task.

Objective: This research explores the driving performance of people who use cannabis daily or occasionally during distraction tasks performed following acute cannabis use.Methods: Healthy adults aged 25 to 45 years with different cannabis usage histories were recruited to participate in a within-subjects controlled experiment using a car-based driving simulator. Participants were classified as having daily use (n = 31), occasional use (1 or 2 times per week; n = 24), or no-use (n = 30). Participants completed a practice drive followed by four 5-10 minute driving scenarios during the baseline period. Participants then smoked self-procured cannabis flower ad libitum for up to 15 minutes. Thirty minutes later, they completed four additional 5-10 minute scenarios. Scenarios were paired according to difficulty and randomized across the baseline and post-use periods. Each scenario contained between 0 and 3 repetitions of a distraction task where the participant was prompted by an audio message to select an app from a 4 × 5 grid displayed on a mounted tablet, a step that would require briefly looking away from the roadway. Measures of driving performance (lane departures, standard deviation of lateral position) were assessed during the five-second period following the audio trigger and analyzed using generalized linear mixed models.Results: Those with a pattern of occasional use were significantly more likely to experience a lane departure during distraction periods after acute cannabis use relative to baseline (OR = 3.71, p = 0.04, CI = 1.04, 13.17), while those with daily use did not exhibit a similar increase (OR = 1.56, p = 0.43, CI = 0.52, 4.64). Changes in departure risk were significantly greater for the occasional use group compared to no-use (p = 0.02), but not for the daily use group compared to no-use (p = 0.18). However, following acute use, those who use daily exhibited decreases in speed relative to baseline in comparison to the changes observed in the no-use group (p = 0.02), while differences between occasional and no-use did not reach statistical significance (p = 0.052). Differences in standard deviation of lateral position were not statistically significant, likely due to the short duration of tasks.Conclusions: These results find the largest potential safety concerns associated with a pattern of occasional use, who displayed an increase in lane departures after acute cannabis smoking. Those in the daily use group decreased their speed, which may be interpreted as compensation for drug effects. Further research is needed to understand the effects during longer and more complex secondary tasks.

On clustering for cell-phenotyping in multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) data.

OBJECTIVE: Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) images are usually phenotyped using a manual thresholding process. The thresholding is prone to biases, especially when examining multiple images with high cellularity. RESULTS: Unsupervised cell-phenotyping methods including PhenoGraph, flowMeans, and SamSPECTRAL, primarily used in flow cytometry data, often perform poorly or need elaborate tuning to perform well in the context of mIHC and MIBI data. We show that, instead, semi-supervised cell clustering using Random Forests, linear and quadratic discriminant analysis are superior. We test the performance of the methods on two mIHC datasets from the University of Colorado School of Medicine and a publicly available MIBI dataset. Each dataset contains a bunch of highly complex images.

SPF: A spatial and functional data analytic approach to cell imaging data.

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

DenVar: density-based variation analysis of multiplex imaging data.

Summary: Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies. Availability and implementation: The associated R package can be found here, https://github.com/sealx017/DenVar. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

The Spatial Context of Tumor-Infiltrating Immune Cells Associates with Improved Ovarian Cancer Survival.

Ovarian cancer is the deadliest gynecologic malignancy. Multi-omics techniques have provided a platform for improved predictive modeling of therapy response and patient outcomes. While high-grade serous carcinoma (HGSOC) tumors are immunogenic and numerous studies have defined positive correlation to immune cell infiltration, immunotherapies in clinical trials have exhibited low efficacy rates. There is a significant need to better comprehend the role and composition of immune cells in mediating ovarian cancer therapeutic response and progression. We performed multiplex IHC with an HGSOC tissue microarray (n = 127) to characterize the immune cell composition within tumors. After analyzing the composition and spatial context of T cells (CD4/CD8), macrophages (CD68), and B cells (CD19) within the tumor, we found that increased B-cell and CD4 T-cell presence correlated with overall survival. More importantly, we observed that the proximity between tumor-associated macrophages and B cells or CD4 T cells significantly correlated with overall survival. IMPLICATIONS: The results highlight the antitumor role of B cells and CD4 T cells, and that the spatial interactions between immune cell types are a novel predictor of therapeutic response and patient outcomes.

Simulated driving performance among daily and occasional cannabis users.

OBJECTIVE: Daily cannabis users develop tolerance to some drug effects, but the extent to which this diminishes driving impairment is uncertain. This study compared the impact of acute cannabis use on driving performance in occasional and daily cannabis users using a driving simulator. METHODS: We used a within-subjects design to observe driving performance in adults age 25 to 45 years with different cannabis use histories. Eighty-five participants (43 males, 42 females) were included in the final analysis: 24 occasional users (1 to 2 times per week), 31 daily users and 30 non-users. A car-based driving simulator (MiniSim™, National Advanced Driving Simulator) was used to obtain two measures of driving performance, standard deviation of lateral placement (SDLP) and speed relative to posted speed limit, in simulated urban driving scenarios at baseline and 30 min after a 15 min ad libitum cannabis smoking period. Participants smoked self-supplied cannabis flower product (15% to 30% tetrahydrocannabinol (THC). Blood samples were collected before and after smoking (30 min after the start of smoking). Non-users performed the same driving scenarios before and after an equivalent rest interval. Changes in driving performance were analyzed by repeated measures general linear models. RESULTS: Mean whole blood THC cannabinoids concentrations post smoking were use THC = 6.4 ± 5.6 ng/ml, THC-COOH = 10.9 ± 8.79 ng/mL for occasional users and THC = 36.4 ± 37.4 ng/mL, THC-COOH = 98.1 ± 90.6 ng/mL for daily users. On a scale of 0 to 100, the mean post-use score of subjective high was similar in occasional users and daily users (52.4 and 47.2, respectively). In covariate-adjusted analysis, occasional users had a significant increase in SDLP in the straight road segment from pre to post compared to non-users; non-users decreased by a mean of 1.1 cm (25.5 cm to 24.4 cm) while occasional users increased by a mean of 1.9 cm (21.7 cm to 23.6 cm; p = 0.02). Daily users also increased adjusted SDLP in straight road segments from baseline to post-use (23.2 cm to 25.0 cm), but the change relative to non-users was not statistically significant (p = 0.08). The standardized mean difference in unadjusted SDLP from baseline to post-use in the straight road segments comparing occasional users to non-users was 0.64 (95% CI 0.09 - 1.19), a statistically significant moderate increase. When occasional users were contrasted with daily users, the baseline to post changes in SDLP were not statistically significant. Daily users exhibited a mean decrease in baseline to post-use adjusted speed in straight road segments of 1.16 mph; a significant change compared to slight speed increases in the non-users and occasional users (p = 0.02 and p = 0.01, respectively). CONCLUSION: We observed a decrement in driving performance assessed by SDLP after acute cannabis smoking that was statistically significant only in the occasional users in comparison to the nonusers. Direct contrasts between the occasional users and daily users in SDLP were not statistically significant. Daily users drove slower after cannabis use as compared to the occasional use group and non-users. The study results do not conclusively establish that occasional users exhibit more driving impairment than daily users when both smoke cannabis ad libitum.

Cancer Cell-Specific Major Histocompatibility Complex II Expression as a Determinant of the Immune Infiltrate Organization and Function in the NSCLC Tumor Microenvironment.

INTRODUCTION: In patients with NSCLC, the prognostic significance of the tumor microenvironment (TME) immune composition has been revealed using single- or dual-marker staining on sequential tissue sections. Although these studies reveal that relative abundance and localization of immune cells are important parameters, deeper analyses of the NSCLC TME are necessary to refine the potential application of these findings to clinical care. Currently, the complex spatial relationships between cells of the NSCLC TME and potential drivers contributing to its immunologic composition remain unknown. METHODS: We used multispectral quantitative imaging on the lung adenocarcinoma TME in 153 patients with resected tumors. On a single slide per patient, we evaluated the TME with markers for CD3, CD8, CD14, CD19, major histocompatibility complex II (MHCII), cytokeratin, and 4',6-diamidino-2-phenylindole (DAPI). Image analysis, including tissue segmentation, phenotyping, and spatial localization, was performed. RESULTS: Specimens wherein greater than or equal to 5% of lung cancer cells expressed MHCII (MHCIIhi TME) had increased levels of CD4+ and CD8+ T cells and CD14+ cell infiltration. In the MHCIIhi TME, the immune infiltrate was closer to cancer cells and expressed an activated phenotype. Morphologic image analysis revealed cancer cells in the MHCIIhi TME more frequently interfaced with CD4+ and CD8+ T cells. Patients with an MHCIIhi TME experienced improved overall survival (p = 0.046). CONCLUSIONS: Lung cancer cell-specific expression of MHCII associates with levels of immune cell infiltration, spatial localization, and activation status within the TME. This suggests that cancer cell-specific expression of MHCII may represent a biomarker for the immune system's recognition and activation against the tumor.

Interferon-γ mediates anemia but is dispensable for fulminant toll-like receptor 9-induced macrophage activation syndrome and hemophagocytosis in mice.

OBJECTIVE: Macrophage activation syndrome (MAS) is a devastating cytokine storm syndrome complicating many inflammatory diseases and characterized by fever, pancytopenia, and systemic inflammation. It is clinically similar to hemophagocytic lymphohistiocytosis (HLH), which is caused by viral infection of a host with impaired cellular cytotoxicity. Murine models of MAS and HLH illustrate that interferon-γ (IFNγ) is the driving stimulus for hemophagocytosis and immunopathology. This study was undertaken to investigate the inflammatory contributors to a murine model of Toll-like receptor 9 (TLR-9)-induced fulminant MAS. METHODS: Wild-type, transgenic, and cytokine-inhibited mice were treated with an IL-10 receptor blocking antibody and a TLR-9 agonist, and parameters of MAS were evaluated. RESULTS: Fulminant MAS was characterized by dramatic elevations in IFNγ, IL-12, and IL-6 levels. Increased serum IFNγ levels were associated with enhanced IFNγ production within some hepatic cell populations but also with decreased numbers of IFNγ-positive cells. Surprisingly, IFNγ-knockout mice developed immunopathology and hemophagocytosis comparable to that seen in wild-type mice. However, IFNγ-knockout mice did not become anemic and had greater numbers of splenic erythroid precursors. IL-12 neutralization phenocopied disease in IFNγ-knockout mice. Interestingly, type I IFNs contributed to the severity of hypercytokinemia and weight loss, but their absence did not otherwise affect MAS manifestations. CONCLUSION: These data demonstrate that both fulminant MAS and hemophagocytosis can arise independently of IFNγ, IL-12, or type I IFNs. They also suggest that IFNγ-mediated dyserythropoiesis, not hemophagocytosis, is the dominant cause of anemia in fulminant TLR-9-induced MAS. Thus, our data establish a novel mechanism for the acute anemia of inflammation, but suggest that a variety of triggers can result in hemophagocytic disease.