Association Between Marijuana Use and Clinical Outcomes After Coronary Artery Bypass Grafting.

INTRODUCTION: Though marijuana use has been linked to an increase in heart failure admissions, no prior study has explored the association between its use and outcomes after coronary artery bypass grafting (CABG). This study examines the relationship between marijuana use and postoperative outcomes in CABG patients. METHODS: We utilized data from the National Inpatient Sample database from 2008 to 2018 for CABG patients ≥18 y old. Patients were divided into two groups based on marijuana use (abuse/dependency versus nonuse). Primary outcomes include in-hospital mortality, favorable discharge, and length of stay (LOS). Secondary outcomes include acute kidney injury (AKI), acute myocardial infarction (AMI), and transient ischemic attack (TIA)/stroke. A multivariable model, adjusted for confounding variables, was utilized for each outcome. RESULTS: A total of 343,796 patients met inclusion criteria for the study, 590 of which were marijuana users. In both marijuana user and nonuser groups, most patients were male and White with an average age of 56.0 and 66.3 y, respectively. There was a nonsignificant decreased odds of in-hospital mortality among marijuana users (odds ratio [OR] = 0.41, [0.141-1.124]). Marijuana users exhibited significantly decreased odds of home discharge (OR = 1.50, [1.24-1.81]), and increased odds of longer LOS (mean 10.4 d versus 9.8 d; OR = 1.14, [1.09-1.20]), AKI (OR = 1.40, [1.11-1.78]), AMI (OR = 1.56, [1.32-1.84]), and TIA/stroke (OR = 1.64, [1.21-2.22]). CONCLUSIONS: Marijuana use and dependency are associated with increased nonhome discharge, AKI, AMI, TIA/stroke, and longer LOS. Further studies are needed to delineate the pathophysiologic derangements that contribute to these unfavorable post-CABG outcomes.

Pulse Rate Guided Oxygen Saturation Monitoring Using a Wearable Armband Sensor.

Continuous clinical grade measurement of SpO2 in out-of-hospital settings remains a challenge despite the widespread use of photoplethysmography (PPG) based wearable devices for health and wellness applications. This article presents two SpO2 algorithms: PRR (pulse rate derived ratio-of-ratios) and GPDR (green-assisted peak detection ratio-of-ratios), that utilize unique pulse rate frequency estimations to isolate the pulsatile (AC) component of red and infrared PPG signals and derive SpO2 measurements. The performance of the proposed SpO2 algorithms are evaluated using an upper-arm wearable device derived green, red, and infrared PPG signals, recorded in both controlled laboratory settings involving healthy subjects (n=36) and an uncontrolled clinic application involving COVID-19 patients (n=52). GPDR exhibits the lowest root mean square error (RMSE) of 1.6±0.6% for a respiratory exercise test, 3.6 ±1.0% for a standard hypoxia test, and 2.2±1.3% for an uncontrolled clinic use-case. In contrast, PRR provides relatively higher error but with greater coverage overall. Mean error across all combined datasets were 0.2±2.8% and 0.3±2.4% for PRR and GPDR respectively. Both SpO2 algorithms achieve great performance of low error with high coverage on both uncontrolled clinic and controlled laboratory conditions.

Novel Continuous Respiratory Rate Monitoring Using an Armband Wearable Sensor.

Photoplethysmography (PPG) and accelerometer (ACC) are commonly integrated into wearable devices for continuous unobtrusive pulse rate and activity monitoring of individuals during daily life. However, obtaining continuous and clinically accurate respiratory rate measurements using such wearable sensors remains a challenge. This article presents a novel algorithm for estimation of respiration rate (RR) using an upper-arm worn wearable device by deriving multiple respiratory surrogate signals from PPG and ACC sensing. This RR algorithm is retrospectively evaluated on a controlled respiratory clinical testing dataset from 38 subjects with simultaneously recorded wearable sensor data and a standard capnography monitor as an RR reference. The proposed RR method shows great performance and robustness in determining RR measurements over a wide range of 4-59 brpm with an overall bias of -1.3 brpm, mean absolute error (MAE) of 2.7±1.6 brpm, and a meager outage of 0.3±1.2%, while a standard PPG Smart Fusion method produces a bias of -3.6 brpm, an MAE of 5.5±3.1 brpm, and an outage of 0.7±2.5% for direct comparison. In addition, the proposed algorithm showed no significant differences (p=0.63) in accurately determining RR values in subjects with darker skin tones, while the RR performance of the PPG Smart Fusion method is significantly (P<0.001) affected by the darker skin pigmentation. This study demonstrates a highly accurate RR algorithm for unobtrusive continuous RR monitoring using an armband wearable device.

Novel Human Insulin Isoforms and Cα-Peptide Product in Islets of Langerhans and Choroid Plexus.

Human insulin (INS) gene diverged from the ancestral genes of invertebrate and mammalian species millions of years ago. We previously found that mouse insulin gene (Ins2) isoforms are expressed in brain choroid plexus (ChP) epithelium cells, where insulin secretion is regulated by serotonin and not by glucose. We further compared human INS isoform expression in postmortem ChP and islets of Langerhans. We uncovered novel INS upstream open reading frame isoforms and their protein products. In addition, we found a novel alternatively spliced isoform that translates to a 74-amino acid (AA) proinsulin containing a shorter 19-AA C-peptide sequence, herein designated Cα-peptide. The middle portion of the conventional C-peptide contains ß-sheet (GQVEL) and hairpin (GGGPG) motifs that are not present in Cα-peptide. Islet amyloid polypeptide (IAPP) is not expressed in ChP, and its amyloid formation was inhibited in vitro more efficiently by Cα-peptide than by C-peptide. Of clinical relevance, the ratio of the 74-AA proinsulin to proconvertase-processed Cα-peptide was significantly increased in islets from type 2 diabetes mellitus autopsy donors. Intriguingly, 100 years after the discovery of insulin, we found that INS isoforms are present in ChP from insulin-deficient autopsy donors.

Auditory salience using natural scenes: An online study.

Salience is the quality of a sensory signal that attracts involuntary attention in humans. While it primarily reflects conspicuous physical attributes of a scene, our understanding of processes underlying what makes a certain object or event salient remains limited. In the vision literature, experimental results, theoretical accounts, and large amounts of eye-tracking data using rich stimuli have shed light on some of the underpinnings of visual salience in the brain. In contrast, studies of auditory salience have lagged behind due to limitations in both experimental designs and stimulus datasets used to probe the question of salience in complex everyday soundscapes. In this work, we deploy an online platform to study salience using a dichotic listening paradigm with natural auditory stimuli. The study validates crowd-sourcing as a reliable platform to collect behavioral responses to auditory salience by comparing experimental outcomes to findings acquired in a controlled laboratory setting. A model-based analysis demonstrates the benefits of extending behavioral measures of salience to broader selection of auditory scenes and larger pools of subjects. Overall, this effort extends our current knowledge of auditory salience in everyday soundscapes and highlights the limitations of low-level acoustic attributes in capturing the richness of natural soundscapes.

Robust PPG-based Ambulatory Heart Rate Tracking Algorithm.

Recent advances in wearable devices with optical Photoplethysmography (PPG) and actigraphy have enabled inexpensive, accessible, and convenient Heart Rate (HR) monitoring. Nevertheless, PPG's susceptibility to motion presents challenges in obtaining reliable and accurate HR estimates during ambulatory and intense activity conditions. This study proposes a lightweight HR algorithm, TAPIR: a Time-domain based method involving Adaptive filtering, Peak detection, Interval tracking, and Refinement, using simultaneously acquired PPG and accelerometer signals. The proposed method is applied to four unique, wrist-wearable based, publicly available databases that capture a variety of controlled and uncontrolled daily life activities, stress, and emotion. The results suggest that the current HR prediction is significantly (P<0.01) more accurate during intense activity conditions than the contemporary algorithms involving Wiener filtering, time-frequency analysis, and deep learning. The current HR tracking algorithm is validated to be of clinical-grade and suitable for low-power embedded wearable systems as a powerful tool for continuous HR monitoring in real-world ambulatory conditions.

Push-pull competition between bottom-up and top-down auditory attention to natural soundscapes.

In everyday social environments, demands on attentional resources dynamically shift to balance our attention to targets of interest while alerting us to important objects in our surrounds. The current study uses electroencephalography to explore how the push-pull interaction between top-down and bottom-up attention manifests itself in dynamic auditory scenes. Using natural soundscapes as distractors while subjects attend to a controlled rhythmic sound sequence, we find that salient events in background scenes significantly suppress phase-locking and gamma responses to the attended sequence, countering enhancement effects observed for attended targets. In line with a hypothesis of limited attentional resources, the modulation of neural activity by bottom-up attention is graded by degree of salience of ambient events. The study also provides insights into the interplay between endogenous and exogenous attention during natural soundscapes, with both forms of attention engaging a common fronto-parietal network at different time lags.

When walking into a busy restaurant or café, our sense of hearing is bombarded with different sounds that our brain has to sort through to make sense of our surroundings. Our brain has to balance the desire to focus our attention on sounds we choose to listen to (such as the friend we are having a conversation with) and sounds that attract our attention (such as the sound of someone else's phone ringing). Without the ability to be distracted, we might miss a noise that may or may not be crucial to our survival, like the engine roar of an approaching vehicle or a ping notifying us of an incoming email. However, it remains unclear what happens in our brains that enables us to shift our attention to background sounds. To investigate this further, Huang and Elhilali asked 81 participants to focus their attention on a repeating sound while being exposed to background noises from everyday life, such as sounds from a busy café. The experiment showed that when a more noticeable sound happened in the background, such as a loud voice, the participants were more likely to lose attention on their task and miss changes in the tone of the repeating sound. Huang and Elhilali then measured the brain activity of 12 participants as they counted the number of altered tones in a sequence of sounds, again with noise in the background. This revealed that brain waves synchronized with tones that the participants were concentrating on. However, once there was a noticeable event in the background, this tone synchronization was reduced and the brain waves aligned with the background noise. Huang and Elhilali found that distracting noises in the background activate the same region of the brain as sounds we choose to listen to. This demonstrates how background sounds are able to re-direct our attention. These results are consistent with the idea that we have a limited capacity for attention, and that new sensory information can divert brain activity. Having a better understanding of how these processes work could help develop better communication aids for people with impaired hearing, and improve software for interpreting sounds with a noisy background.

Release of insulin produced by the choroid plexis is regulated by serotonergic signaling.

The choroid plexus (ChP) is a highly vascularized tissue found in the brain ventricles, with an apical epithelial cell layer surrounding fenestrated capillaries. It is responsible for the production of most of the cerebrospinal fluid (CSF) in the ventricular system, subarachnoid space, and central canal of the spinal cord, while also constituting the blood-CSF barrier (BCSFB). In addition, epithelial cells of the ChP (EChP) synthesize neurotrophic factors and other signaling molecules that are released into the CSF. Here, we show that insulin is produced in EChP of mice and humans, and its expression and release are regulated by serotonin. Insulin mRNA and immune-reactive protein, including C-peptide, are present in EChP, as detected by several experimental approaches, and appear in much higher levels than any other brain region. Moreover, insulin is produced in primary cultured mouse EChP, and its release, albeit Ca2+ sensitive, is not regulated by glucose. Instead, activation of the 5HT2C receptor by serotonin treatment led to activation of IP3-sensitive channels and Ca2+ mobilization from intracellular storage, leading to insulin secretion. In vivo depletion of brain serotonin in the dorsal raphe nucleus negatively affected insulin expression in the ChP, suggesting an endogenous modulation of ChP insulin by serotonin. Here, we show for the first time to our knowledge that insulin is produced by EChP in the brain, and its release is modulated at least by serotonin but not glucose.

Identification of novel mouse and rat CB1R isoforms and in silico modeling of human CB1R for peripheral cannabinoid therapeutics.

Targeting peripheral CB1R is desirable for the treatment of metabolic syndromes without adverse neuropsychiatric effects. We previously reported a human hCB1b isoform that is selectively enriched in pancreatic beta-cells and hepatocytes, providing a potential peripheral therapeutic hCB1R target. It is unknown whether there are peripherally enriched mouse and rat CB1R (mCB1 and rCB1, respectively) isoforms. In this study, we found no evidence of peripherally enriched rodent CB1 isoforms; however, some mCB1R isoforms are absent in peripheral tissues. We show that the mouse Cnr1 gene contains six exons that are transcribed from a single promoter. We found that mCB1A is a spliced variant of extended exon 1 and protein-coding exon 6; mCB1B is a novel spliced variant containing unspliced exon 1, intron 1, and exon 2, which is then spliced to exon 6; and mCB1C is a spliced variant including all 6 exons. Using RNAscope in situ hybridization, we show that the isoforms mCB1A and mCB1B are expressed at a cellular level and colocalized in GABAergic neurons in the hippocampus and cortex. RT-qPCR reveals that mCB1A and mCB1B are enriched in the brain, while mCB1B is not expressed in the pancreas or the liver. Rat rCB1R isoforms are differentially expressed in primary cultured neurons, astrocytes, and microglia. We also investigated modulation of Cnr1 expression by insulin in vivo and carried out in silico modeling of CB1R with JD5037, a peripherally restricted CB1R inverse agonist, using the published crystal structure of hCB1R. The results provide models for future CB1R peripheral targeting.

Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals.

Deep neural networks have been recently shown to capture intricate information transformation of signals from the sensory profiles to semantic representations that facilitate recognition or discrimination of complex stimuli. In this vein, convolutional neural networks (CNNs) have been used very successfully in image and audio classification. Designed to imitate the hierarchical structure of the nervous system, CNNs reflect activation with increasing degrees of complexity that transform the incoming signal onto object-level representations. In this work, we employ a CNN trained for large-scale audio object classification to gain insights about the contribution of various audio representations that guide sound perception. The analysis contrasts activation of different layers of a CNN with acoustic features extracted directly from the scenes, perceptual salience obtained from behavioral responses of human listeners, as well as neural oscillations recorded by electroencephalography (EEG) in response to the same natural scenes. All three measures are tightly linked quantities believed to guide percepts of salience and object formation when listening to complex scenes. The results paint a picture of the intricate interplay between low-level and object-level representations in guiding auditory salience that is very much dependent on context and sound category.

Cell Type-Specific Expression of Corticotropin-Releasing Hormone-Binding Protein in GABAergic Interneurons in the Prefrontal Cortex.

Corticotropin-releasing hormone-binding protein (CRH-BP) is a secreted glycoprotein that binds CRH with very high affinity to modulate CRH receptor activity. CRH-BP is widely expressed throughout the brain, with particularly high expression in regions such as the amygdala, hippocampus, ventral tegmental area and prefrontal cortex (PFC). Recent studies suggest a role for CRH-BP in stress-related psychiatric disorders and addiction, with the PFC being a potential site of interest. However, the molecular phenotype of CRH-BP-expressing cells in this region has not been well-characterized. In the current study, we sought to determine the cell type-specific expression of CRH-BP in the PFC to begin to define the neural circuits in which this key regulator is acting. To characterize the expression of CRH-BP in excitatory and/or inhibitory neurons, we utilized dual in situ hybridization to examine the cellular colocalization of CRH-BP mRNA with vesicular glutamate transporter (VGLUT) or glutamic acid decarboxylase (GAD) mRNA in different subregions of the PFC. We show that CRH-BP is expressed predominantly in GABAergic interneurons of the PFC, as revealed by the high degree of colocalization (>85%) between CRH-BP and GAD. To further characterize the expression of CRH-BP in this heterogenous group of inhibitory neurons, we examined the colocalization of CRH-BP with various molecular markers of GABAergic interneurons, including parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP) and cholecystokinin (CCK). We demonstrate that CRH-BP is colocalized predominantly with SST in the PFC, with lower levels of colocalization in PV- and CCK-expressing neurons. Our results provide a more comprehensive characterization of the cell type-specific expression of CRH-BP and begin to define its potential role within circuits of the PFC. These results will serve as the basis for future in vivo studies to manipulate CRH-BP in a cell type-specific manner to better understand its role in stress-related psychiatric disorders, including anxiety, depression and addiction.

Auditory salience using natural soundscapes.

Salience describes the phenomenon by which an object stands out from a scene. While its underlying processes are extensively studied in vision, mechanisms of auditory salience remain largely unknown. Previous studies have used well-controlled auditory scenes to shed light on some of the acoustic attributes that drive the salience of sound events. Unfortunately, the use of constrained stimuli in addition to a lack of well-established benchmarks of salience judgments hampers the development of comprehensive theories of sensory-driven auditory attention. The present study explores auditory salience in a set of dynamic natural scenes. A behavioral measure of salience is collected by having human volunteers listen to two concurrent scenes and indicate continuously which one attracts their attention. By using natural scenes, the study takes a data-driven rather than experimenter-driven approach to exploring the parameters of auditory salience. The findings indicate that the space of auditory salience is multidimensional (spanning loudness, pitch, spectral shape, as well as other acoustic attributes), nonlinear and highly context-dependent. Importantly, the results indicate that contextual information about the entire scene over both short and long scales needs to be considered in order to properly account for perceptual judgments of salience.

A PCR-based approach to assess genomic DNA contamination in RNA: Application to rat RNA samples.

Genomic DNA (gDNA) contamination of RNA samples can lead to inaccurate measurement of gene expression by reverse transcription quantitative real-time PCR (RT-qPCR). We describe an easily adoptable PCR-based method where gDNA contamination in RNA samples is assessed by comparing the amplification of intronic and exonic sequences from a housekeeping gene. Although this alternative assay was developed for rat RNA samples, it could be easily adapted to other species. As a proof of concept, we assessed the effects of detectable gDNA contamination levels on the expression of a few genes that illustrate the importance of RNA quality in acquiring reliable data.

Suboptimal use of neural information in a mammalian auditory system.

Establishing neural determinants of psychophysical performance requires both behavioral and neurophysiological metrics amenable to correlative analyses. It is often assumed that organisms use neural information optimally, such that any information available in a neural code that could improve behavioral performance is used. Studies have shown that detection of amplitude-modulated (AM) auditory tones by humans is correlated to neural synchrony thresholds, as recorded in rabbit at the level of the inferior colliculus, the first level of the ascending auditory pathway where neurons are tuned to AM stimuli. Behavioral thresholds in rabbit, however, are ∼10 dB higher (i.e., 3 times less sensitive) than in humans, and are better correlated to rate-based than temporal coding schemes in the auditory midbrain. The behavioral and physiological results shown here illustrate an unexpected, suboptimal utilization of available neural information that could provide new insights into the mechanisms that link neuronal function to behavior.

Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric.

Sorting action potentials (spikes) from tetrode recordings can be time consuming, labor intensive, and inconsistent, depending on the methods used and the experience of the operator. The techniques presented here were designed to address these issues. A feature related to the slope of the spike during repolarization is computed. A small subsample of the features obtained from the tetrode (ca. 10,000-20,000 events) is clustered using a modified version of k-means that uses Mahalanobis distance and a scaling factor related to the cluster size. The cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size. The full data set is then classified from the statistics of the clusters. The technique yields consistent results for a chosen number of clusters. A MATLAB implementation is able to classify more than 5000 spikes per second on a modern workstation.