Informing the development of an outcome set and banks of items to measure mobility among individuals with acquired brain injury using natural language processing.

BACKGROUND: The sheer number of measures evaluating mobility and inconsistencies in terminology make it challenging to extract potential core domains and items. Automating a portion of the data synthesis would allow us to cover a much larger volume of studies and databases in a smaller fraction of the time compared to the usual process. Thus, the objective of this study was to identify a comprehensive outcome set and develop preliminary banks of items of mobility among individuals with acquired brain injury (ABI) using Natural Language Processing (NLP). METHODS: An umbrella review of 47 reviews evaluating the content of mobility measures among individuals with ABI was conducted. A search was performed on 5 databases between 2000 and 2020. Two independent reviewers retrieved copies of the measures and extracted mobility domains and items. A pre-trained BERT model (state-of-the-art model for NLP) provided vector representations for each sentence. Using the International Classification of Functioning, Disability, and Health Framework (ICF) ontology as a guide for clustering, a k-means algorithm was used to retrieve clusters of similar sentences from their embeddings. The resulting embedding clusters were evaluated using the Silhouette score and fine-tuned according to expert input. RESULTS: The study identified 246 mobility measures, including 474 domains and 2109 items. Encoding the clusters using the ICF ontology and expert knowledge helped in regrouping the items in a way that is more closely related to mobility terminology. Our best results identified banks of items that were used to create a 24 comprehensive outcome sets of mobility, including Upper Extremity Mobility, Emotional Function, Balance, Motor Control, Self-care, Social Life and Relationships, Cognition, Walking, Postural Transition, Recreation, and Leisure Activities, Activities of Daily Living, Physical Functioning, Communication, Work/Study, Climbing, Sensory Functions, General Health, Fatigue, Functional Independence, Pain, Alcohol and Drugs Use, Transportation, Sleeping, and Finances. CONCLUSION: The banks of items of mobility domains represent a first step toward establishing a comprehensive outcome set and a common language of mobility to develop the ontology. It enables researchers and healthcare professionals to begin exposing the content of mobility measures as a way to assess mobility comprehensively.

Natural Variation in Physicochemical Profiles and Bacterial Communities Associated with Aedes aegypti Breeding Sites and Larvae on Guadeloupe and French Guiana.

Aedes aegypti develop in aquatic habitats in which mosquito larvae are exposed to physicochemical elements and microorganisms that may influence their life cycle and their ability to transmit arboviruses. Little is known about the natural bacterial communities associated with A. aegypti or their relation to the biotic and abiotic characteristics of their aquatic habitats. We characterized the physicochemical properties and bacterial microbiota of A. aegypti breeding sites and larvae on Guadeloupe and in French Guiana. In addition, we explored whether geographic location, the type of breeding site and physicochemical parameters influenced the microbiota associated with this mosquito species. We used large-scale 16S rRNA gene sequencing of 160 breeding sites and 147 pools of A. aegypti larvae and recorded 12 physicochemical parameters at the sampled breeding sites. Ordination plots and multiple linear regression were used to assess the influence of environmental factors on the bacterial microbiota of water and larvae. We found territory-specific differences in physicochemical properties (dissolved oxygen, conductivity) and the composition of bacterial communities in A. aegypti breeding sites that influenced the relative abundance of several bacteria genera (e.g., Methylobacterium, Roseoccocus) on the corresponding larvae. A significant fraction of the bacterial communities identified on larvae, dominated by Herbiconiux and Microvirga genera, were consistently enriched in mosquitoes regardless the location. In conclusion, territory-specific differences observed in the biotic and abiotic properties of A. aegypti breeding sites raise concern about the impact of these changes on pathogen transmission by different A. aegypti populations.

Exploring polypharmacy with artificial intelligence: data analysis protocol.

BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada. METHODS: We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health, and Law&Ethics. The AI research axis will develop algorithms for finding frequent patterns of medication use that correlate with health events, considering data locality and temporality (explainable AI or XAI). The Health research axis will translate these patterns into polypharmacy indicators relevant to public health surveillance and clinicians. The Law&Ethics axis will assess the social acceptability of the algorithms developed using AI tools and the indicators developed by the Heath axis and will ensure that the developed indicators neither discriminate against any population group nor increase the disparities already present in the use of medications. DISCUSSION: The multi-disciplinary research team consists of specialists in AI, health data, statistics, pharmacy, public health, law, and ethics, which will allow investigation of polypharmacy from different points of view and will contribute to a deeper understanding of the clinical, social, and ethical issues surrounding polypharmacy and its surveillance, as well as the use of AI for health record data. The project results will be disseminated to the scientific community, healthcare professionals, and public health decision-makers in peer-reviewed publications, scientific meetings, and reports. The diffusion of the results will ensure the confidentiality of individual data.

Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study.

BACKGROUND: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS: A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS: Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.

Neuronal representation of individual heroin choices in the orbitofrontal cortex.

Drug addiction is a harmful preference for drug use over and at the expense of other non-drug-related activities. We previously identified in the rat orbitofrontal cortex (OFC) a mechanism that influences individual preferences between cocaine use and an alternative action rewarded by a non-drug reward (i.e. sweet water). Here, we sought to test the generality of this mechanism to a different addictive drug, heroin. OFC neuronal activity was recorded while rats responded for heroin or the alternative non-drug reward separately or while they chose between the two. First, we found that heroin-rewarded and sweet water-rewarded actions were encoded by two non-overlapping OFC neuronal populations and that the relative size of the heroin population represented individual drug choices. Second, OFC neurons encoding the preferred action-which was the non-drug action in the large majority of individuals-progressively fired more than non-preferred action-coding neurons 1 second after the onset of choice trials and around 1 second before the preferred action was actually chosen, suggesting a pre-choice neuronal competition for action selection. Together with a previous study on cocaine choice, the present study on heroin choice reveals important commonalities in how OFC neurons encode individual drug choices and preferences across different classes of drugs. It also reveals some drug-specific differences in OFC encoding activity. Notably, the proportion of neurons that non-selectively encode both the drug and the non-drug reward was higher when the drug was heroin (present study) than when it was cocaine (previous study). We will discuss the potential functional significance of these commonalities and differences in OFC neuronal activity across different drugs for understanding drug choice.

Knowledge by omission: the significance of omissions in the 5-choice serial reaction time task.

RATIONALE: The 5-choice serial reaction time task (5-CSRTT) is commonly used to assess attention in rodents. Manipulation of this task by decreasing the light stimulus duration is often used to probe attentional capacity and causes a decrease in accuracy and an increase in omissions. However, although a decrease in response accuracy is commonly interpreted as a decrease in attention, it is more difficult to interpret an increase in omissions in terms of attentional performance. METHODS: Here we present a series of experiments in rats that seeks to investigate the origins of these key behavioral measures of attention in the 5-CSRTT. After an initial training in the 5-CSRTT, rats were tested in a variable stimulus duration procedure to increase task difficulty and probe visual attentional capacity under several specific controlled conditions. CONCLUSIONS: We found that response accuracy reflects visuospatial sustained attentional processing, as commonly interpreted, while response omission reflects rats' ignorance about the stimulus location, presumably due to failure to pay attention to the curved wall during its presentation. Moreover, when rats lack of relevant information, they choose not to respond instead of responding randomly. Finally, pretreatment with nicotine selectively decreased response omissions, without affecting response accuracy, particularly when the attentional demand was high. Overall, our results indicate that response accuracy and response omission thus correspond to two distinct attentional states.

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED.

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

Chronic Stress Exposure Alters the Gut Barrier: Sex-Specific Effects on Microbiota and Jejunum Tight Junctions.

Background: Major depressive disorder (MDD) is the leading cause of disability worldwide. Of individuals with MDD, 30% to 50% are unresponsive to common antidepressants, highlighting untapped causal biological mechanisms. Dysfunction in the microbiota-gut-brain axis has been implicated in MDD pathogenesis. Exposure to chronic stress disrupts blood-brain barrier integrity; still, little is known about intestinal barrier function in these conditions, particularly for the small intestine, where absorption of most foods and drugs takes place. Methods: We investigated how chronic social or variable stress, two mouse models of depression, impact the jejunum intestinal barrier in males and females. Mice were subjected to stress paradigms followed by analysis of gene expression profiles of intestinal barrier-related targets, fecal microbial composition, and blood-based markers. Results: Altered microbial populations and changes in gene expression of jejunum tight junctions were observed depending on the type and duration of stress, with sex-specific effects. We used machine learning to characterize in detail morphological tight junction properties, identifying a cluster of ruffled junctions in stressed animals. Junctional ruffling is associated with inflammation, so we evaluated whether lipopolysaccharide injection recapitulates stress-induced changes in the jejunum and observed profound sex differences. Finally, lipopolysaccharide-binding protein, a marker of gut barrier leakiness, was associated with stress vulnerability in mice, and translational value was confirmed on blood samples from women with MDD. Conclusions: Our results provide evidence that chronic stress disrupts intestinal barrier homeostasis in conjunction with the manifestation of depressive-like behaviors in a sex-specific manner in mice and, possibly, in human depression.

Reducing touching eyes, nose and mouth ('T-zone') to reduce the spread of infectious disease: A prospective study of motivational, volitional and non-reflective predictors.

BACKGROUND: The route into the body for many pathogens is through the eyes, nose and mouth (i.e., the 'T-zone') via inhalation or fomite-based transfer during face touching. It is important to understand factors that are associated with touching the T-zone to inform preventive strategies. PURPOSE: To identify theory-informed predictors of intention to reduce facial 'T-zone' touching and self-reported 'T-zone' touching. METHODS: We conducted a nationally representative prospective questionnaire study of Canadians. Respondents were randomized to answer questions about touching their eyes, nose, or mouth with a questionnaire assessing 11 factors from an augmented Health Action Process Approach at baseline: intention, outcome expectancies, risk perception, individual severity, self-efficacy, action planning, coping planning, social support, automaticity, goal facilitation and stability of context. At 2-week follow-up, we assessed HAPA-based indicators of self-regulatory activities (awareness of standards, effort, self-monitoring) and self-reported behaviour (primary dependent variable). RESULTS: Of 656 Canadian adults recruited, 569 responded to follow-up (87% response rate). Across all areas of the 'T-zone', outcome expectancy was the strongest predictor of intention to reduce facial 'T-zone' touching, while self-efficacy was a significant predictor for only the eyes and mouth. Automaticity was the strongest predictor of behaviour at the 2-week follow-up. No sociodemographic or psychological factors predicted behaviour, with the exception of self-efficacy, which negatively predicted eye touching. CONCLUSION: Findings suggest that focusing on reflective processes may increase intention to reduce 'T-zone' touching, while reducing actual 'T-zone' touching may require strategies that address the automatic nature of this behaviour.

The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement.

BACKGROUND: Clinical guidelines for most adults with diabetes recommend maintaining hemoglobin A1c (HbA1c) levels ≤7% (≤53 mmol/mol) to avoid microvascular and macrovascular complications. People with diabetes of different ages, sexes, and socioeconomic statuses may differ in their ease of attaining this goal. OBJECTIVE: As a team of people with diabetes, researchers, and health professionals, we aimed to explore patterns in HbA1c results among people with type 1 or type 2 diabetes in Canada. Our research question was identified by people living with diabetes. METHODS: In this patient-led retrospective cross-sectional study with multiple time points of measurement, we used generalized estimating equations to analyze the associations of age, sex, and socioeconomic status with 947,543 HbA1c results collected from 2010 to 2019 among 90,770 people living with type 1 or type 2 diabetes in Canada and housed in the Canadian National Diabetes Repository. People living with diabetes reviewed and interpreted the results. RESULTS: HbA1c results ≤7.0% represented 30.5% (male people living with type 1 diabetes), 21% (female people living with type 1 diabetes), 55% (male people living with type 2 diabetes) and 59% (female people living with type 2 diabetes) of results in each subcategory. We observed higher HbA1c values during adolescence, and for people living with type 2 diabetes, among people living in lower income areas. Among those with type 1 diabetes, female people tended to have lower HbA1c levels than male people during childbearing years but higher HbA1c levels than male people during menopausal years. Team members living with diabetes confirmed that the patterns we observed reflected their own life courses and suggested that these results be communicated to health professionals and other stakeholders to improve the treatment for people living with diabetes. CONCLUSIONS: A substantial proportion of people with diabetes in Canada may need additional support to reach or maintain the guideline-recommended glycemic control goals. Blood sugar management goals may be particularly challenging for people going through adolescence or menopause or those living with fewer financial resources. Health professionals should be aware of the challenging nature of glycemic management, and policy makers in Canada should provide more support for people with diabetes to live healthy lives.

Increased responsiveness to punishment of cocaine self-administration after experience with high punishment.

One behavioral feature of drug addiction is continued drug use despite awareness that this causes negative consequences. Attempts to model this feature in animals typically involve punishing drug self-administration with electrical footshock to identify individuals whose drug use is differently suppressed by punishment. Here we sought to further study individual responsiveness of drug use to punishment in rats self-administering intravenous cocaine. Rats were first trained during several weeks to self-administer cocaine under a fixed-ratio 3 schedule of reinforcement. Then, their self-administration behavior was punished with increasing intensity of footshock (i.e., from 0.1 mA to 0.9 mA, every 30 min). With increasing intensity of punishment, rats first continued to self-administer cocaine before eventually stopping near completely. When retested, however, drug use became more responsive to punishment and was suppressed by a low and initially ineffective footshock intensity (i.e., 0.1 mA). This increase in responsiveness to punishment was seen in all individuals tested, albeit with varying degrees, and was acquired after one single experience with an intensity of punishment that near completely suppressed drug self-administration. Mere passive, non-contingent exposure to the same intensity, however, had no such effect. Once acquired, increased responsiveness to punishment persisted during at least one month when rats were tested every week, but not every day. Finally, increased responsiveness to punishment was not observed after exposure to a non-painful form of punishment (i.e., histamine). Overall, this study reveals that initial responsiveness of drug use to punishment can change rapidly and persistently with experience. We discuss several possible mechanisms that may account for this change in punishment responsiveness and also draw some of the implications and future perspectives for research on animal models of compulsion-like behavior.

The biological sink of atmospheric H2 is more sensitive to spatial variation of microbial diversity than N2O and CO2 emissions in a winter cover crop field trial.

The integration of winter cover crop (WCC) in culture rotations promotes multiple ecosystem services, but concomitant microbial diversity and functioning responses in soil have received less attention. A field trial was established to test the hypothesis that enhanced crop diversity with the integration of WCC in a conventional maize-soy rotation promotes microbial diversity and the biological sink of H2 in soil, while reducing N2O emissions to the atmosphere. Vicia villosa (hairy vetch), Avena sativa (oat), and Raphanus sativus (Daikon radish) were cultivated alone or in combinations and flux measurements were performed throughout two subsequent growing seasons. Soil acted as a net sink for H2 and as a net source for CO2 and N2O. CO2 flux was the most sensitive to WCC whereas a significant spatial variation was observed for H2 flux with soil uptake rates observed in the most productive area two-fold greater than the baseline level. Sequencing and quantification of taxonomic and functional genes were integrated to explain variation in trace gas fluxes with compositional changes in soil microbial communities. Fungal communities were the most sensitive to WCC, but neither community abundance nor beta diversity were found to be indicative of fluxes. The alpha diversity of taxonomic and functional genes, expressed as the number of effective species, was integrated into composite variables extracted from multivariate analyses. Only the composite variable computed with the inverse Simpson's index displayed a reproducible pattern throughout both growing seasons, with functional genes and bacterial 16S rRNA gene defining the two most contrasting gradients. The composite variable was decoupled from WCC treatment and explained 19-20% spatial variation of H2 fluxes. The coupling of composite alpha diversity metrics derived from multiple genes with soil processes warrants further investigations to implement novel indicators of soil health in response to changing management practices at the local scale.

Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features.

Purpose: The purpose of this study was to assess the feasibility of deep learning (DL) methods to enhance the prediction of visual acuity (VA) improvement after macular hole (MH) surgery from a combined model using DL on high-definition optical coherence tomography (HD-OCT) B-scans and clinical features. Methods: We trained a DL convolutional neural network (CNN) using pre-operative HD-OCT B-scans of the macula and combined with a logistic regression model of pre-operative clinical features to predict VA increase ≥15 Early Treatment Diabetic Retinopathy Study (ETDRS) letters at 6 months post-vitrectomy in closed MHs. A total of 121 MHs with 242 HD-OCT B-scans and 484 clinical data points were used to train, validate, and test the model. Prediction of VA increase was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 scores. We also extracted the weight of each input feature in the hybrid model. Results: All performances are reported on the held-out test set, matching results obtained with cross-validation. Using a regression on clinical features, the AUROC was 80.6, with an F1 score of 79.7. For the CNN, relying solely on the HD-OCT B-scans, the AUROC was 72.8 ± 14.6, with a F1 score of 61.5 ± 23.7. For our hybrid regression model using clinical features and CNN prediction, the AUROC was 81.9 ± 5.2, with an F1 score of 80.4 ± 7.7. In the hybrid model, the baseline VA was the most important feature (weight = 59.1 ± 6.9%), while the weight of HD-OCT prediction was 9.6 ± 4.2%. Conclusions: Both the clinical data and HD-OCT models can predict postoperative VA improvement in patients undergoing vitrectomy for a MH with good discriminative performances. Combining them into a hybrid model did not significantly improve performance. Translational Relevance: OCT-based DL models can predict postoperative VA improvement following vitrectomy for MH but fusing those models with clinical data might not provide improved predictive performance.

Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for Evaluation and Rehabilitation of Mobility Across Continuums of Care.

BACKGROUND: Rapid advances in technologies over the past 10 years have enabled large-scale biomedical and psychosocial rehabilitation research to improve the function and social integration of persons with physical impairments across the lifespan. The Biomedical Research and Informatics Living Laboratory for Innovative Advances of New Technologies (BRILLIANT) in community mobility rehabilitation aims to generate evidence-based research to improve rehabilitation for individuals with acquired brain injury (ABI). OBJECTIVE: This study aims to (1) identify the factors limiting or enhancing mobility in real-world community environments (public spaces, including the mall, home, and outdoors) and understand their complex interplay in individuals of all ages with ABI and (2) customize community environment mobility training by identifying, on a continuous basis, the specific rehabilitation strategies and interventions that patient subgroups benefit from most. Here, we present the research and technology plan for the BRILLIANT initiative. METHODS: A cohort of individuals, adults and children, with ABI (N=1500) will be recruited. Patients will be recruited from the acute care and rehabilitation partner centers within 4 health regions (living labs) and followed throughout the continuum of rehabilitation. Participants will also be recruited from the community. Biomedical, clinician-reported, patient-reported, and brain imaging data will be collected. Theme 1 will implement and evaluate the feasibility of collecting data across BRILLIANT living labs and conduct predictive analyses and artificial intelligence (AI) to identify mobility subgroups. Theme 2 will implement, evaluate, and identify community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. RESULTS: The biomedical infrastructure and equipment have been established across the living labs, and development of the clinician- and patient-reported outcome digital solutions is underway. Recruitment is expected to begin in May 2022. CONCLUSIONS: The program will develop and deploy a comprehensive clinical and community-based mobility-monitoring system to evaluate the factors that result in poor mobility, and develop personalized mobility interventions that are optimized for specific patient subgroups. Technology solutions will be designed to support clinicians and patients to deliver cost-effective care and the right intervention to the right person at the right time to optimize long-term functional potential and meaningful participation in the community. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/12506.

Cost-effectiveness and accuracy of prenatal Down syndrome screening strategies: should the combined test continue to be widely used?

OBJECTIVE: We analyzed the cost-effectiveness (CE) and performances of commonly used prenatal Down syndrome (DS) screening strategies. STUDY DESIGN: We performed computer simulations to compare 8 screening options by applying empirical data from Serum, Urine, and Ultrasound Screening Study trials on the population of 110,948 pregnancies. Screening strategies outcomes, CE ratios, and incremental CE ratios were measured. RESULTS: The most CE DS screening strategy was the contingent screening method (CE ratio of Can$26,833 per DS case). Its incremental CE ratio compared to the second-most CE strategy (serum integrated screening) was Can$3815 per DS birth detected. Among the procedures respecting guidelines, our results identified the combined test as the screening strategy with the highest CE ratio (Can$47,358) and the highest number of procedure-related euploid miscarriages (n = 71). CONCLUSION: In regard to CE, contingent screening is the best choice. The combined test, which is the most popular screening strategy, shows many limitations.

Pharmacokinetics trumps pharmacodynamics during cocaine choice: a reconciliation with the dopamine hypothesis of addiction.

Cocaine is known to increase brain dopamine at supranormal levels in comparison to alternative nondrug rewards. According to the dopamine hypothesis of addiction, this abnormally large dopamine response would explain why cocaine use is initially highly rewarding and addictive. Though resting on solid neuroscientific foundations, this hypothesis has nevertheless proven difficult to reconcile with research on cocaine choice in experimental animals. When facing a choice between an intravenous bolus of cocaine and a nondrug alternative (e.g., sweet water), both delivered immediately after choice, rats do not choose the drug, as would be predicted, but instead develop a strong preference for the nondrug alternative. Here we report evidence that reconciles this finding with the dopamine hypothesis of addiction. First, a systematic literature analysis revealed that the delays of effects of intravenous cocaine on nucleus accumbens dopamine are of the order of tens of seconds and are considerably longer than those of nondrug reward. Second, this was confirmed by measuring response times to cocaine omission during self-administration as a behavioral proxy of these delays. Finally, when the influence of the drug delays was reduced during choice by adding an increasing delay to both the drug and nondrug rewards, rats shifted their choice to cocaine. Overall, this study suggests that cocaine is indeed supranormal in reward magnitude, as postulated by the dopamine hypothesis of addiction, but is less preferred during choice because its pharmacokinetics makes it an inherently more delayed reward than the alternative. Reframing previous drug choice studies in rats as intertemporal choice studies reveals that the discounting effects of delays spare no rewards, including supranormal ones, and that during choice, pharmacokinetics trumps pharmacodynamics.

Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders.

OBJECTIVES: The study sought to assess the clinical performance of a machine learning model aiming to identify unusual medication orders. MATERIALS AND METHODS: This prospective study was conducted at CHU Sainte-Justine, Canada, from April to August 2020. An unsupervised machine learning model based on GANomaly and 2 baselines were trained to learn medication order patterns from 10 years of data. Clinical pharmacists dichotomously (typical or atypical) labeled orders and pharmacological profiles (patients' medication lists). Confusion matrices, areas under the precision-recall curve (AUPRs), and F1 scores were calculated. RESULTS: A total of 12 471 medication orders and 1356 profiles were labeled by 25 pharmacists. Medication order predictions showed a precision of 35%, recall (sensitivity) of 26%, and specificity of 97% as compared with pharmacist labels, with an AUPR of 0.25 and an F1 score of 0.30. Profile predictions showed a precision of 49%, recall of 75%, and specificity of 82%, with an AUPR of 0.60, and an F1 score of 0.59. The model performed better than the baselines. According to the pharmacists, the model was a useful screening tool, and 9 of 15 participants preferred predictions by medication, rather than by profile. DISCUSSION: Predictions for profiles had higher F1 scores and recall compared with medication order predictions. Although the performance was much better for profile predictions, pharmacists generally preferred medication order predictions. CONCLUSIONS: Based on the AUPR, this model showed better performance for the identification of atypical pharmacological profiles than for medication orders. Pharmacists considered the model a useful screening tool. Improving these predictions should be prioritized in future research to maximize clinical impact.

Relapse to cocaine use persists following extinction of drug-primed craving.

Craving often precedes relapse into cocaine addiction. This explains why considerable research effort is being expended to try to develop anti-craving strategies for relapse prevention. Recently, we discovered using the classic reinstatement model of cocaine craving that the reinstating or priming effect of cocaine can be extinguished with repeated priming in rats - a phenomenon dubbed extinction of cocaine priming because it is thought to involve extinction of the conditioned interoceptive cues of the drug. Here we measured the effect of this extinction strategy on subsequent relapse-like behavior in rats (i.e., return to the pre-extinction pattern of cocaine self-administration once the drug is made again available after extinction). We found that extensive extinction of the conditioned priming effects of cocaine had no major impact on relapse-like behavior. This lack of effect occurred despite evidence for post-extinction loss of neuronal responses to cocaine priming in brain regions causally involved in cocaine reinstatement (i.e., the dorsomedial prefrontal cortex and the core of the nucleus accumbens). These findings suggest that the conditioned priming effects of cocaine can be dissociated from and are thus not essential for relapse-like behavior, and that extinction of these effects is unlikely to represent a viable approach to relapse prevention. Overall, these findings are in general agreement with previous neurobiological dissociation studies and with research on extinction of exteroceptive drug cues.

Pre-trial cocaine biases choice toward cocaine through suppression of the nondrug option.

Being under the influence during choice between drug and nondrug options can have a dramatic effect on choice outcomes. When rats face a choice between cocaine and sweet water and are not under the influence, they prefer sweet water. In contrast, when they are under the influence of cocaine, this causes them to shift their choice to cocaine nearly exclusively. Here we sought to characterize the behavioral mechanisms underlying the influence of cocaine on choice. In theory, rats under the influence of cocaine should be in a mixed motivational state, at least temporarily, with both their motivation for cocaine and their motivation for the nondrug option suppressed by the drug satiating and anorexic effects of cocaine, respectively. For this mixed state to shift choice to cocaine, the satiated motivation for cocaine should recover before the suppressed motivation for the preferred nondrug option. The goal of the present study was to test this prediction in rats that expressed a preference for sweet water after extended access to cocaine self-administration. We measured their choice and response latencies to each option after pre-trial, passive administration of cocaine to estimate the duration of its drug satiating and anorexic effects. As expected, pre-trial cocaine caused most rats to shift their choice to cocaine. Though this shift was not simply due to a longer latency to respond for sweet water than for cocaine after pre-trial cocaine, it nevertheless occurred while rats' motivation for the nondrug option was still partially suppressed. Thus, cocaine seems to bias choice toward more cocaine mainly via suppression of the nondrug option.

A machine learning approach for online automated optimization of super-resolution optical microscopy.

Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.