Isolated prehospital hypotension correlates with injury severity and outcomes in patients with trauma

Objective Patients normotensive in the trauma bay despite documented prehospital hypotension may not be recognized as significantly injured. The purpose of this study was to determine whether isolated prehospital hypotension portends poor outcomes and correlates with injury severity. Methods Prospective cohort study conducted at a level 1 university trauma center. The lowest recorded prehospital systolic blood pressure (SBP) and the first recorded SBP on hospital arrival were used to divide patients into either the normotensive (NP) or hypotensive (HP) group. Patients who failed to achieve normotension on hospital arrival were excluded. Hypotension was defined as SBP≤110 mmHg. Results Compared to NP (n=206), HP (n=81) had lower Glasgow Coma Scores both prehospital (12.81±0.44 vs 14.38±0.13) and at hospital admission (12.78±0.47 vs 14.37±0.14). Injury Severity Score positively correlated with prehospital hypotension (HP 12.27±1.12 vs NP 9.22±0.49). Prehospital hypotension positively correlated with intensive care unit (ICU) admission (HP 56.79% vs NP 22.82%), ICU length of stay (LOS) (HP 3.23±0.71 vs NP 0.71±0.17), hospital LOS (HP 8.58±1.39 vs NP 4.86±0.33), ventilator days (HP 3.38±1.20 vs NP 0.27±0.08 days), and repeat hypotensive episodes during their hospital stay (HP 81.71% vs NP 38.16%). HP also required more packed red blood cells in the first 24 hours after admission (22% vs 6%). Significance was set at p\u3c0.05. Conclusions Isolated prehospital hypotension in patients in the trauma and emergency department correlates with increased injury severity and portends worse outcomes despite a normal blood pressure reading at admission. Prehospital hypotension must be given heavy consideration in triage, as these patients may be transiently hypotensive and appear less critical than their true status. Level of Evidence Level II, Prognostic study

Cell death mechanism in an isolated wood smoke inhalation induced-ARDS large animal model

Acute respiratory distress syndrome (ARDS) is a lethal disease condition in critically ill patients with a reported mortality rate reaching 45%. The current treatment modalities available for severe ARDS are invasive and carry significant risk for patients. Most published studies involving smoke inhalation utilize another simultaneous injury (such as cutaneous burn) to increase pathology burden of their animal models. This introduces confounding variables to investigations which aim to concentrate on inhalation injury. In this study, we evaluated the potential molecular targets associated with isolated smoke inhalation-induced ARDS. We observed an increase in lung injury score and wet/dry ratio 48h post smoke inhalation together with upregulation of inflammatory markers, IL-1βand IL-6 levels. Furthermore, there was a decrease in phosphorylation of cell survival marker Akt and an increase in pro-apoptotic protein BAX at 48h post smoke inhalation. These results indicate that smoke inhalation induced inflammatory processes resulting in increased apoptosis and decreased cell survival in lung parenchymal cells. Use of this unique model may be of benefit in studying the pathophysiology of inhalation injury and for the development of novel therapeutic strategies.https://digitalcommons.unmc.edu/surp2021/1045/thumbnail.jp

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variational optimisation of the posterior distribution on coordinates and locally linear maps from the latent space to the observation space given the data. Thus, the LL-LVM encapsulates the local-geometry preserving intuitions that underlie non-probabilistic methods such as locally linear embedding (LLE). Its probabilistic semantics make it easy to evaluate the quality of hypothesised neighbourhood relationships, select the intrinsic dimensionality of the manifold, construct out-of-sample extensions and to combine the manifold model with additional probabilistic models that capture the structure of coordinates within the manifold

Testing oxygenated microbubbles via intraperitoneal and intrathoracic routes on a large pig model of LPS-induced acute respiratory distress syndrome

With a mortality rate of 46% before the onset of COVID-19, acute respiratory distress syndrome (ARDS) affected 200,000 people in the US, causing 75,000 deaths. Mortality rates in COVID-19 ARDS patients are currently at 39%. Extrapulmonary support for ARDS aims to supplement mechanical ventilation by providing life-sustaining oxygen to the patient. A new rapid-onset, human-sized pig ARDS model in a porcine intensive care unit (ICU) was developed. The pigs were nebulized intratracheally with a high dose (4 mg/kg) of the endotoxin lipopolysaccharide (LPS) over a 2 h duration to induce rapid-onset moderate-to- severe ARDS. They were then catheterized to monitor vitals and to evaluate the therapeutic effect of oxygenated microbubble (OMB) therapy delivered by intrathoracic (IT) or intraperitoneal (IP) administration. Post-LPS administration, the PaO2 value dropped below 70 mmHg, the PaO2/FiO2 ratio dropped below 200 mmHg, and the heart rate increased, indicating rapidly developing (within 4 h) moderate-to- severe ARDS with tachycardia. The SpO2 and PaO2 of these LPS-injured pigs did not show significant improvement after OMB administration, as they did in our previous studies of the therapy on small animal models of ARDS injury. Furthermore, pigs receiving OMB or saline infusions had slightly lower survival than their ARDS counterparts. The OMB administration did not induce a statistically significant or clinically relevant therapeutic effect in this model; instead, both saline and OMB infusion appeared to lower survival rates slightly. This result is significant because it contradicts positive results from our previous small animal studies and places a limit on the efficacy of such treatments for larger animals under more severe respiratory distress. While OMB did not prove efficacious in this rapid-onset ARDS pig model, it may retain potential as a novel therapy for the usual presentation of ARDS in humans, which develops and progresses over days to weeks

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Democratic population decisions result in robust policy-gradient learning: A parametric study with GPU simulations

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. © 2011 Richmond et al