Immune system-related plasma extracellular vesicles in healthy aging.

Objectives: To identify age-related plasma extracellular vehicle (EVs) phenotypes in healthy adults. Methods: EV proteomics by high-resolution mass spectrometry to evaluate EV protein stability and discover age-associated EV proteins (n=4 with 4 serial freeze-thaws each); validation by high-resolution flow cytometry and EV cytokine quantification by multiplex ELISA (n=28 healthy donors, aged 18-83 years); quantification of WI-38 fibroblast cell proliferation response to co-culture with PKH67-labeled young and old plasma EVs. The EV samples from these plasma specimens were previously characterized for bilayer structure, intra-vesicle mitochondria and cytokines, and hematopoietic cell-related surface markers. Results: Compared with matched exo-EVs (EV-depleted supernatants), endo-EVs (EV-associated) had higher mean TNF-α and IL-27, lower mean IL-6, IL-11, IFN-γ, and IL-17A/F, and similar mean IL-1ß, IL-21, and IL-22 concentrations. Some endo-EV and exo-EV cytokine concentrations were correlated, including TNF-α, IL-27, IL-6, IL-1ß, and IFN-γ, but not IL-11, IL-17A/F, IL-21 or IL-22. Endo-EV IFN-γ and exo-EV IL-17A/F and IL-21 declined with age. By proteomics and confirmed by flow cytometry, we identified age-associated decline of fibrinogen (FGA, FGB and FGG) in EVs. Age-related EV proteins indicated predominant origins in the liver and innate immune system. WI-38 cells (>95%) internalized similar amounts of young and old plasma EVs, but cells that internalized PKH67-EVs, particularly young EVs, underwent significantly greater cell proliferation. Conclusion: Endo-EV and exo-EV cytokines function as different biomarkers. The observed healthy aging EV phenotype reflected a downregulation of EV fibrinogen subpopulations consistent with the absence of a pro-coagulant and pro-inflammatory condition common with age-related disease.

Comparing Causal Bayesian Networks Estimated from Data.

The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering common molecular mechanisms among different diseases can lead to drug repurposing. The problem of comparing causal mechanisms among multiple systems is non-trivial, since the causal mechanisms are usually unknown and need to be estimated from data. If we estimate the causal mechanisms from data generated from different systems and directly compare them (the naive method), the result can be sub-optimal. This is especially true if the data generated by the different systems differ substantially with respect to their sample sizes. In this case, the quality of the estimated causal mechanisms for the different systems will differ, which can in turn affect the accuracy of the estimated similarities and differences among the systems via the naive method. To mitigate this problem, we introduced the bootstrap estimation and the equal sample size resampling estimation method for estimating the difference between causal networks. Both of these methods use resampling to assess the confidence of the estimation. We compared these methods with the naive method in a set of systematically simulated experimental conditions with a variety of network structures and sample sizes, and using different performance metrics. We also evaluated these methods on various real-world biomedical datasets covering a wide range of data designs.

Trajectories and predictors of response to social cognition training in people with schizophrenia: A proof-of-concept machine learning study.

BACKGROUND: Social cognition training (SCT) can improve social cognition deficits in schizophrenia. However, little is known about patterns of response to SCT or individual characteristics that predict response. METHODS: 76 adults with schizophrenia randomized to receive 8-12 weeks of remotely-delivered SCT were included in this analysis. Social cognition was measured with a composite of six assessments. Latent class growth analyses identified trajectories of social cognitive response to SCT. Random forest and logistic regression models were trained to predict membership in the trajectory group that showed improvement from baseline measures including symptoms, functioning, motivation, and cognition. RESULTS: Five trajectory groups were identified: Group 1 (29 %) began with slightly above average social cognition, and this ability significantly improved with SCT. Group 2 (9 %) had baseline social cognition approximately one standard deviation above the sample mean and did not improve with training. Groups 3 (18 %) and 4 (36 %) began with average to slightly below-average social cognition and showed non-significant trends toward improvement. Group 5 (8 %) began with social cognition approximately one standard deviation below the sample mean, and experienced significant deterioration in social cognition. The random forest model had the best performance, predicting Group 1 membership with an area under the curve of 0.73 (SD 0.24; 95 % CI [0.51-0.87]). CONCLUSIONS: Findings suggest that there are distinct patterns of response to SCT in schizophrenia and that those with slightly above average social cognition at baseline may be most likely to experience gains. Results may inform future research seeking to individualize SCT treatment for schizophrenia.

AAV-mediated hepatic LPL expression ameliorates severe hypertriglyceridemia and acute pancreatitis in Gpihbp1 deficient mice and rats.

GPIHBP1 plays an important role in the hydrolysis of triglyceride (TG) lipoproteins by lipoprotein lipases (LPLs). However, Gpihbp1 knockout mice did not develop hypertriglyceridemia (HTG) during the suckling period but developed severe HTG after weaning on a chow diet. It has been postulated that LPL expression in the liver of suckling mice may be involved. To determine whether hepatic LPL expression could correct severe HTG in Gpihbp1 deficiency, liver-targeted LPL expression was achieved via intravenous administration of the adeno-associated virus (AAV)-human LPL gene, and the effects of AAV-LPL on HTG and HTG-related acute pancreatitis (HTG-AP) were observed. Suckling Gpihbp1-/- mice with high hepatic LPL expression did not develop HTG, whereas Gpihbp1-/- rat pups without hepatic LPL expression developed severe HTG. AAV-mediated liver-targeted LPL expression dose-dependently decreased plasma TG levels in Gpihbp1-/- mice and rats, increased post-heparin plasma LPL mass and activity, decreased mortality in Gpihbp1-/- rat pups, and reduced the susceptibility and severity of both Gpihbp1-/- animals to HTG-AP. However, the muscle expression of AAV-LPL had no significant effect on HTG. Targeted expression of LPL in the liver showed no obvious adverse reactions. Thus, liver-targeted LPL expression may be a new therapeutic approach for HTG-AP caused by GPIHBP1 deficiency.

Comprehensive characterization of pathogenic synovial fluid extracellular vesicles from knee osteoarthritis.

Synovial fluid (SF) extracellular vesicles (EVs) play a pathogenic role in osteoarthritis (OA). However, the surface markers, cell and tissue origins, and effectors of these EVs are largely unknown. We found that SF EVs contained 692 peptides that were positively associated with knee radiographic OA severity; 57.4% of these pathogenic peptides were from 46 proteins of the immune system, predominantly the innate immune system. CSPG4, BGN, NRP1, and CD109 are the major surface markers of pathogenic SF EVs. Genes encoding surface marker CSPG4 and CD109 were highly expressed by chondrocytes from damaged cartilage, while VISG4, MARCO, CD163 and NRP1 were enriched in the synovial immune cells. The frequency of CSPG4+ and VSIG4+ EV subpopulations in OA SF was high. We conclude that pathogenic SF EVs carry knee OA severity-associated proteins and specific surface markers, which could be developed as a new source of diagnostic biomarkers or therapeutic targets in OA.

Copper induced intestinal inflammation response through oxidative stress induced endoplasmic reticulum stress in Takifugu fasciatus.

Copper (Cu) pollution in aquaculture water has seriously threatened the healthy and sustainable development of the aquaculture industry. Recently, many researchers have studied the toxic effects of Cu exposure on fish. However, the relationship between endoplasmic reticulum stress (ERS) and the inflammatory response, as well as its possible mechanisms, remain unclear. Particularly, information related to fish intestines must be expanded. Our study initially investigated the mechanisms underlying intestinal toxicity and inflammation resulting from Cu-induced ERS in vivo and in vitro in Takifugu fasciatus. In vivo study, T. fasciatus were treated with different concentrations (control, 20, and 100 µg/L) of Cu exposure for 28 days, causing intestinal oxidative stress, ERS, inflammatory responses, and histopathological and ultrastructural damage. Transcriptomic data further showed that Cu exposure caused ERS, as well as inflammatory responses, in the intestinal tracts of T. fasciatus. In vitro experiments on the intestinal cells of T. fasciatus showed that Cu exposure treatment (7.5 µg/mL) for 24 h induced ERS and increased mitochondrial numbers and inflammatory responses. In contrast, the addition of 4-phenylbutyric acid (4-PBA) alleviated ERS and inflammatory response in the Cu-exposed group. Furthermore, the reactive oxygen species (ROS) inhibitor, N-Acetyl-l-cysteine (NAC), effectively alleviated Cu-induced ERS. In conclusion, our in vivo and in vitro studies have confirmed that oxidative stress triggers the ERS pathway, which is involved in the intestinal inflammatory response. Our study provides new insights into the relationship among Cu-induced oxidative stress, ERS, and inflammatory responses in fish, as well as for the healthy culture of fish in aqueous environments.

Causal pathways to social and occupational functioning in the first episode of schizophrenia: uncovering unmet treatment needs.

BACKGROUND: We aimed to identify unmet treatment needs for improving social and occupational functioning in early schizophrenia using a data-driven causal discovery analysis. METHODS: Demographic, clinical, and psychosocial measures were obtained for 276 participants from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) trial at baseline and 6-months, along with measures of social and occupational functioning from the Quality of Life Scale. The Greedy Fast Causal Inference algorithm was used to learn a partial ancestral graph modeling causal relationships across baseline variables and 6-month functioning. Effect sizes were estimated using a structural equation model. Results were validated in an independent dataset (N = 187). RESULTS: In the data-generated model, greater baseline socio-affective capacity was a cause of greater baseline motivation [Effect size (ES) = 0.77], and motivation was a cause of greater baseline social and occupational functioning (ES = 1.5 and 0.96, respectively), which in turn were causes of their own 6-month outcomes. Six-month motivation was also identified as a cause of occupational functioning (ES = 0.92). Cognitive impairment and duration of untreated psychosis were not direct causes of functioning at either timepoint. The graph for the validation dataset was less determinate, but otherwise supported the findings. CONCLUSIONS: In our data-generated model, baseline socio-affective capacity and motivation are the most direct causes of occupational and social functioning 6 months after entering treatment in early schizophrenia. These findings indicate that socio-affective abilities and motivation are specific high-impact treatment needs that must be addressed in order to promote optimal social and occupational recovery.

Development and Validation of an Electronic Health Record-Based Risk Assessment Tool for Hypoglycemia in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: The recent availability of high-quality data from clinical trials, together with machine learning (ML) techniques, presents exciting opportunities for developing prediction models for clinical outcomes. METHODS: As a proof-of-concept, we translated a hypoglycemia risk model derived from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study into the HypoHazardScore, a risk assessment tool applicable to electronic health record (EHR) data. To assess its performance, we conducted a 16-week clinical study at the University of Minnesota where participants (N = 40) with type 2 diabetes mellitus (T2DM) had hypoglycemia assessed prospectively by continuous glucose monitoring (CGM). RESULTS: The HypoHazardScore combines 16 risk factors commonly found within the EHR. The HypoHazardScore successfully predicted (area under the curve [AUC] = 0.723) whether participants experienced least one CGM-assessed hypoglycemic event (glucose <54 mg/dL for ≥15 minutes from two CGMs) while significantly correlating with frequency of CGM-assessed hypoglycemic events (r = 0.38) and percent time experiencing CGM-assessed hypoglycemia (r = 0.39). Compared to participants with a low HypoHazardScore (N = 19, score <4, median score of 4), participants with a high HypoHazardScore (N = 21, score ≥4) had more frequent CGM-assessed hypoglycemic events (high: 1.6 ± 2.2 events/week; low: 0.3 ± 0.5 events/week) and experienced more CGM-assessed hypoglycemia (high: 1.4% ± 2.0%; low: 0.2% ± 0.4% time) during the 16-week follow-up. CONCLUSIONS: We demonstrated that a hypoglycemia risk model can be successfully adapted from the ACCORD data to the EHR, with validation by a prospective study using CGM-assessed hypoglycemia. The HypoHazardScore represents a significant advancement toward implementing an EHR-based decision support system that can help reduce hypoglycemia in patients with T2DM.

Z-Scheme Modulated Charge Transfer on InVO4 @ZnIn2 S4 for Durable Overall Water Splitting.

The charge transfer within heterojunction is crucial for the efficiency and stability of photocatalyst for overall water splitting (OWS). Herein, InVO4 nanosheets have been employed as a support for the lateral epitaxial growth of ZnIn2 S4 nanosheets to produce hierarchical InVO4 @ZnIn2 S4 (InVZ) heterojunctions. The distinct branching heterostructure facilitates active site exposure and mass transfer, further boosting the participation of ZnIn2 S4 and InVO4 for proton reduction and water oxidation, respectively. The unique Z-scheme modulated charge transfer, visualized by simulation and in situ analysis, has been proved to promote the spatial separation of photoexcited charges and strengthen the anti-photocorrosion capability of InVZ. The optimized InVZ heterojunction presents improved OWS (153.3 µmol h-1  g-1 for H2 and 76.9 µmol h-1  g-1 for O2 ) and competitive H2 production (21090 µmol h-1  g-1 ). Even after 20 times (100 h) of cycle experiment, it still holds more than 88% OWS activity and a complete structure.

Psychosocial problems and high amount of opioid administration are associated with opioid dependence and abuse after first exposure for chronic pain patients.

Controversy surrounding the use of opioids for the treatment and the unique characteristics of chronic pain heighten the risks for abuse and dependence; however, it's unclear if higher doses of opioids and first exposure are associated with dependence and abuse. This study aimed to identify patients who developed dependence or opioid abuse after exposed to opioids for the first time and what were the risks factors associated with the outcome. A retrospective observational cohort study analyzed 2,411 patients between 2011 and 2017 who had a diagnosis of chronic pain and received opioids for the first time. A logistic regression model was used to estimate the likelihood of opioid dependence/abuse after the first exposure based on their mental health conditions, prior substance abuse disorders, demographics, and the amount of MME per day patients received. From 2,411 patients, 5.5 % of the patients had a diagnosis of dependence or abuse after the first exposure. Patients who were depressed (OR = 2.09), previous non-opioid substance dependence or abuse (OR = 1.59) or received greater than 50 MME per day (OR = 1.03) showed statistically significant relationship with developing opioid dependence or abuse, while age (OR = -1.03) showed to be a protective factor. Further studies should stratify chronic pain patients into groups who is in higher risk in developing opioid dependence or abuse and develop alternative strategies for pain management and treatments beyond opioids. This study reinforces the psychosocial problems as determinants of opioid dependence or abuse and risk factors, and the need for safer opioid prescribing practices.

Biomimetic mineralization of nacre-inspired multiple crosslinked PVA/CaAlg/SiO2 membrane with simultaneously enhanced mechanical and separation properties.

Inspired by the natural nacre structure, we propose a new strategy to fabricate mineralized, multiple crosslinked hydrogel membranes with the "rigid silica in soft polymer" nacre-like structure. In-situ SiO2 nanoparticles (NPs) and polyvinyl alcohol/sodium alginate (PVA/NaAlg) are used to simulate the rigid "bricks" and soft "mortar" compositions of nacre, respectively. The nacre-like mineralized (PVA/CaAlg/SiO2) membrane showed a higher tensile strength of 4.1 ± 0.08 MPa, excellent pure water flux of 170 ± 3 L/m2h, and an oil/water rejection rate of 99 %. The interwoven hierarchal structure, similar to nacre, was determined by SEM analysis. In addition, incorporating SiO2 NPs increases the anti-swelling, roughness, and hydrophilicity of the membranes. PVA/CaAlg/SiO2 membrane exhibited excellent superhydrophilicity (WCA value was 0°) and superoleophobicity underwater (OCA value was 162°). PVA/CaAlg/SiO2 membrane also showed excellent separation performance for water-soluble organic pollutants and can be used for dye separation with rejection efficiencies of 99.5 %, 99.1 %, and 98.3 % for Congo red (CR), Alizarin red (AR), and Sunset yellow (SY), respectively. Moreover, PVA/CaAlg/SiO2 membrane had outstanding long-term filtration and antifouling performance. The biomineralization-inspired structure provides a promising technique that can be used to prepare high-performance organic-inorganic membranes with great promise for wastewater separation application.

Integrating Causal Discovery and Clinically-Relevant Insights to Explore Directional Relationships between Autistic Features, Sex at Birth, and Cognitive Abilities.

Prevalence in autism spectrum disorder (ASD) diagnosis has long been strongly male-biased. Yet, consensus has not been reached on mechanisms and clinical features that underlie sex-based discrepancies. Whereas females may be under-diagnosed because of inconsistencies in diagnostic/ascertainment procedures (sex-biased criteria, social camouflaging), diagnosed males may have exhibited more overt behaviors (e.g., hyperactivity, aggression) that prompted clinical evaluation. Applying a novel network-theory-based approach, we extracted data-driven, clinically-relevant insights from a large, well-characterized sample (Simons Simplex Collection) of 2175 autistic males (Ages = 8.9±3.5 years) and 334 autistic females (Ages = 9.2±3.7 years). Exploratory factor analysis (EFA) and expert clinical review reduced data dimensionality to 15 factors of interest. To offset inherent confounds of an imbalanced sample, we identified a subset of males (N=331) matched to females on key variables (Age, IQ) and applied data-driven CDA using Greedy Fast Causal Inference (GFCI) for three groups (All Females, All Males, and Matched Males). Structural equation modeling (SEM) extracted measures of model fit and effect sizes for causal relationships between sex, age, and, IQ on EFA-selected factors capturing phenotypic representations of autism across sensory, social, and restricted and repetitive behavior domains. Our methodology unveiled sex-specific directional relationships to inform developmental outcomes and targeted interventions.

Mental health progress requires causal diagnostic nosology and scalable causal discovery.

Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults.

BACKGROUND: The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover direct causes of longevity from medically accessible data. METHODS: Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the "Markov boundaries" of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study. FINDINGS: As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers of small high-density lipoprotein particles, younger age, and fewer pack years of cigarette smoking were the strongest determinants of longevity at 2-, 5- and 10-years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity. INTERPRETATION: The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan. FUNDING: NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.

Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder.

Cannabis Use Disorder (CUD) has been linked to a complex set of neuro-behavioral risk factors. While many studies have revealed sex and gender differences, the relative importance of these risk factors by sex and gender has not been described. We used an "explainable" machine learning approach that combined decision trees [gradient tree boosting, XGBoost] with factor ranking tools [SHapley's Additive exPlanations (SHAP)] to investigate sex and gender differences in CUD. We confirmed that previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification of cannabis use levels and diagnostic status. Risk factors with larger effect sizes in men included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, risk factors with larger effect sizes in women included environmental (low education level, low instrumental support) factors. In summary, environmental factors contributed more strongly to CUD in women, whereas individual factors had a larger importance in men.

Robust Methods for Quantifying the Effect of a Continuous Exposure From Observational Data.

A cornerstone of clinical medicine is intervening on a continuous exposure, such as titrating the dosage of a pharmaceutical or controlling a laboratory result. In clinical trials, continuous exposures are dichotomized into narrow ranges, excluding large portions of the realistic treatment scenarios. The existing computational methods for estimating the effect of continuous exposure rely on a set of strict assumptions. We introduce new methods that are more robust towards violations of these assumptions. Our methods are based on the key observation that changes of exposure in the clinical setting are often achieved gradually, so effect estimates must be "locally" robust in narrower exposure ranges. We compared our methods with several existing methods on three simulated studies with increasing complexity. We also applied the methods to data from 14 k sepsis patients at M Health Fairview to estimate the effect of antibiotic administration latency on prolonged hospital stay. The proposed methods achieve good performance in all simulation studies. When the assumptions were violated, the proposed methods had estimation errors of one half to one fifth of the state-of-the-art methods. Applying our methods to the sepsis cohort resulted in effect estimates consistent with clinical knowledge.

Clinical Data Cohort Quality Improvement: The Case of the Medication Data in The University of Minnesota's Clinical Data Repository.

Clinical and translational research centers (CTRCs) have emerged as key centers for electronic medical record related research through integrated data repositories (IDRs) and the 'secondary use' of clinical data. Researchers accessing and pre-processing ever increasing amounts of electronic medical records for data mining tasks have a growing need for best practice approaches for clinical data quality assessment and improvement. This project focused on a large data extract for 7 statin medication prescriptions for patients with cardiovascular disease. After the initial data extraction, we proceeded to analyze the data for completeness, correctness, currency, and percentage populated using established data quality frameworks. Assessment of the said data was performed through medication possession ratios, medication discontinuation reasons, and drug dosages. When we compared distributions of data elements such as drug dosage before and after changes were introduced by our pre-processing protocols, only a minimal noticeable difference was found as the clinical data cohort quality assessment and pre-processing were completed without substantially altering the original data structure. Our study demonstrated practical steps for clinical data cohort quality improvement using medication data and illustrates a best practice approach in clinical data cohort quality improvement for any data mining tasks.

Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities.

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30-90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error due to Changes in Sensor Location.

BACKGROUND: Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN. METHODS: In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location. RESULTS: We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro-Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes (P < .05). CONCLUSION: We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

Reward versus motoric activations in nucleus accumbens core of rats during Pavlovian conditioning.

The nucleus accumbens (NAc) core plays an important role in processing of events related to food reward, such as conditioned cues, approach or consumption. Nonetheless, there is lack of clarity regarding whether NAc core processes these separable events differently. We used the high temporal and spatial resolution of single unit recording with trial-by-trial video analysis to examine firing during three distinct categories termed cue, approach and consumption in a Pavlovian task. We had three goals. First, we sought to precisely define task-related behaviour in terms of distinct phases, in order to compare neural activity between motorically matched behaviours. We found that cue-evoked firing did not distinguish between trials on which animals initiated an approach versus ones on which they did not. Firing associated with consumption was greater than firing associated with motorically similar uncued head entry, indicating that previously reported decreases in NAc core firing during consumption relative to approach or baseline may reflect differences in motor behaviour. Secondly, we assessed changes in firing over the course of training. We found that NAc core neurons acquired a response to the tone cue within three sessions but did not change further across 10 total sessions. Thirdly, we correlated individual neuron firing during a given event with its firing during the same event on subsequent sessions. We found substantial variation in processing of cue and approach but not consumption, indicating that a given neuron may process certain events differently from session to session, while maintaining more stable processing of appetitive reward.