Illicit drugs in the emergency department: Can we determine on clinical grounds if patients are intoxicated? Results from the Western Australian Illicit Substance Evaluation (WISE) study.

INTRODUCTION: Presentations related to illicit drugs are a feature of emergency department practice. Clinicians may form a belief that a patient is intoxicated with illicit drugs based on patient self-report, clinical features on presentation and the local prevalence of illicit drug use. But evidence of the accuracy of this assessment is lacking. The Western Australian Illicit Substance Evaluation (WISE) study enrolled patients believed by their treating clinician to be intoxicated with illicit drugs, and this analysis aims to evaluate the validity of this belief. METHODS: A blood sample was taken on patient arrival and details of patient history, examination and interventions were collected by clinical and research staff. Toxicological examination of biological samples used liquid chromatography-mass spectrometry techniques including Quadrupole Time of Flight screening and Triple Quadrupole targeted analyses. RESULTS: Of 632 study presentations, 518 had illicit drugs detected representing a positive predictive value of 0.82 (95% confidence interval 78.7, 84.9). Those with illicit drugs detected were significantly less likely to arrive by police transport (p = 0.010) or to have used alcohol (p < 0.001). They were significantly more likely to report illicit drug use (p < 0.001) and a much smaller proportion were admitted to a psychiatric ward (3.5% vs. 19.3%, p < 0.0001). Heart rate and systolic blood pressure were significantly higher in the illicit drug group (p = 0.004 and p = 0.003). DISCUSSION AND CONCLUSIONS: In this study, the positive predictive value of clinicians determining if their patient had taken illicit drugs was 0.82. Contemporaneous biochemical analysis in the clinical setting would increase this accuracy and inform patient care.

Deaths from undetected COVID-19 infections as a fraction of COVID-19 deaths can be used for early detection of an upcoming epidemic wave.

With countries across the world facing repeated epidemic waves, it becomes critical to monitor, mitigate and prevent subsequent waves. Common indicators like active case numbers may not be sensitive enough in the presence of systemic inefficiencies like insufficient testing or contact tracing. Test positivity rates are sensitive to testing strategies and cannot estimate the extent of undetected cases. Reproductive numbers estimated from logarithms of new incidences are inaccurate in dynamic scenarios and not sensitive enough to capture changes in efficiencies. Systemic fatigue results in lower testing, inefficient tracing and quarantining thereby precipitating the onset of the epidemic wave. We propose a novel indicator for detecting the slippage of test-trace efficiency based on the number of deaths/hospitalizations resulting from known and hitherto unknown infections. This can also be used to forecast an epidemic wave that is advanced or exacerbated due to a drop in efficiency in situations where the testing has come down drastically and contact tracing is virtually nil as is prevalent currently. Using a modified SEIRD epidemic simulator we show that (i) Ratio of deaths/hospitalizations from an undetected infection to total deaths converges to a measure of systemic test-trace inefficiency. (ii) This index forecasts the slippage in efficiency earlier than other known metrics. (iii) Mitigation triggered by this index helps reduce peak active caseload and eventual deaths. Deaths/hospitalizations accurately track the systemic inefficiencies and detect latent cases. Based on these results we make a strong case that administrations use this metric in the ensemble of indicators. Further, hospitals may need to be mandated to distinctly register deaths/hospitalizations due to previously undetected infections. Thus the proposed metric is an ideal indicator of an epidemic wave that poses the least socio-economic cost while keeping the surveillance robust during periods of pandemic fatigue.

In-silico neuro musculoskeletal model reproduces the movement types obtained by spinal micro stimulation.

BACKGROUND AND OBJECTIVES: Virtual patients and physiologies allow experimentation, design, and early-stage clinical trials in-silico. Virtual patient technology for human movement systems that encompasses musculoskeleton and its neural control are few and far in between. Our major goal is to create a neuro- musculoskeletal upper limb in-silico model, which is modular in architecture and generates movement as an emergent phenomenon out of a multiscale co-simulation of spinal cord neural control and musculoskeletal dynamics. METHODS: The model is developed on the NEUROiD movement simulation platform that enables a co-simulation of popular neural simulator NEURON and the musculoskeletal simulator OpenSim. We further characterized and demonstrated the use of this model in generating a range of commonly observed upper limb movements by means of a spatio-temporal stimulation pattern delivered to the cervical spinal cord. RESULTS: We were able to characterize the model based on proprioception (Ia, Ib and II fibers), afferent conduction delay and inital postures of the musculoskeletal system. A smooth movement was achieved in all the considered experiments. The generated movements in all degrees of freedom were reproduced in accordance with the previous experimental studies. CONCLUSION: In this work, design and development of the upper limb model was described in a modular fashion, while reusing existing models and modules. We believe this work enables a first and small step towards an in-silico paradigms for understanding upper limb movement, disease pathology, medication, and rehabilitation.

Using epidemic simulators for monitoring an ongoing epidemic.

Prediction of infection trends, estimating the efficacy of contact tracing, testing or impact of influx of infected are of vital importance for administration during an ongoing epidemic. Most effective methods currently are empirical in nature and their relation to parameters of interest to administrators are not evident. We thus propose a modified SEIRD model that is capable of modeling effect of interventions and inward migrations on the progress of an epidemic. The tunable parameters of this model bear relevance to monitoring of an epidemic. This model was used to show that some of the commonly seen features of cumulative infections in real data can be explained by piecewise constant changes in interventions and population influx. We also show that the data of cumulative infections from twelve Indian states between mid March and mid April 2020 can be generated from the model by applying interventions according to a set of heuristic rules. Prediction for the next ten days based on this model, reproduced real data very well. In addition, our model also reproduced the time series of recoveries and deaths. Our work constitutes an important first step towards an effective dashboard for the monitoring of epidemic by the administration, especially in an Indian context.

Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform.

Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.

An early warning system for emerging drugs of concern in the emergency department: Protocol for the Western Australian Illicit Substance Evaluation (WISE) study.

OBJECTIVE: An ever-increasing number of novel psychoactive substances are being detected worldwide. These emerging drugs have been demonstrated to cause toxicity in clusters, and deaths have been reported. We urgently need to learn more about their effects. We report the protocol for the Western Australian Illicit Substance Evaluation (WISE) study, a research project investigating illicit drug use in the ED. METHODS: Patients can be enrolled if the treating clinician strongly suspects they are currently intoxicated with a stimulant, hallucinogenic or cannabinoid drug; and an i.v. cannula or blood tests are required for routine clinical care. Patients are enrolled under a waiver of consent. A single additional blood tube is collected, de-identified and frozen on site. A temporary link between patient identification number and study identification number is retained for up to 10 business days post-hospital discharge to allow for clinical data collection, before this is destroyed and the patients become permanently de-identified. Samples are transported for external liquid chromatography-mass spectrometry analysis in batches once de-identified. RESULTS: The key outcome will be identification of any psychoactive drugs present in the blood sample, together with their respective concentration. This will be linked to the clinical effects, as well as being compared with the substance the patient believed they had taken. CONCLUSION: We consider the novel approach outlined forms a template for an early warning system for emerging drugs of concern, while also providing vital and comprehensive information on current drugs of abuse, their clinical effects and their impact on the health system.

Reach task-associated excitatory overdrive of motor cortical neurons following infusion with ALS-CSF.

Converging evidence from transgenic animal models of amyotrophic lateral sclerosis (ALS) and human studies suggest alterations in excitability of the motor neurons in ALS. Specifically, in studies on human subjects with ALS the motor cortex was reported to be hyperexcitable. The present study was designed to test the hypothesis that infusion of cerebrospinal fluid from patients with sporadic ALS (ALS-CSF) into the rat brain ventricle can induce hyperexcitability and structural changes in the motor cortex leading to motor dysfunction. A robust model of sporadic ALS was developed experimentally by infusing ALS-CSF into the rat ventricle. The effects of ALS-CSF at the single neuron level were examined by recording extracellular single unit activity from the motor cortex while rats were performing a reach to grasp task. We observed an increase in the firing rate of the neurons of the motor cortex in rats infused with ALS-CSF compared to control groups. This was associated with impairment in a specific component of reach with alterations in the morphological characteristics of the motor cortex. It is likely that the increased cortical excitability observed in the present study could be the result of changes in the intrinsic properties of motor cortical neurons, a dysfunctional inhibitory mechanism and/or an underlying structural change culminating in a behavioral deficit.

Synconset waves and chains: spiking onsets in synchronous populations predict and are predicted by network structure.

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define 'synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in 'synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

A study of epileptogenic network structures in rat hippocampal cultures using first spike latencies during synchronization events.

Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not.