Influence of Action-Effect Associations Acquired by Ideomotor Learning on Imitation

According to the ideomotor theory, actions are represented in terms of their perceptual effects, offering a solution for the correspondence problem of imitation (how to translate the observed action into a corresponding motor output). This effect-based coding of action is assumed to be acquired through action-effect learning. Accordingly, performing an action leads to the integration of the perceptual codes of the action effects with the motor commands that brought them about. While ideomotor theory is invoked to account for imitation, the influence of action-effect learning on imitative behavior remains unexplored. In two experiments, imitative performance was measured in a reaction time task following a phase of action-effect acquisition. During action-effect acquisition, participants freely executed a finger movement (index or little finger lifting), and then observed a similar (compatible learning) or a different (incompatible learning) movement. In Experiment 1, finger movements of left and right hands were presented as action-effects during acquisition. In Experiment 2, only right-hand finger movements were presented during action-effect acquisition and in the imitation task the observed hands were oriented orthogonally to participants’ hands in order to avoid spatial congruency effects. Experiments 1 and 2 showed that imitative performance was improved after compatible learning, compared to incompatible learning. In Experiment 2, although action-effect learning involved perception of finger movements of right hand only, imitative capabilities of right- and left-hand finger movements were equally affected. These results indicate that an observed movement stimulus processed as the effect of an action can later prime execution of that action, confirming the ideomotor approach to imitation. We further discuss these findings in relation to previous studies of action-effect learning and in the framework of current ideomotor approaches to imitation.     

Gossip-Based Solutions for Discrete Rendezvous in Populations of Communicating Agents

The objective of the rendezvous problem is to construct a method that enables a population of agents to agree on a spatial (and possibly temporal) meeting location. We introduce the buffered gossip algorithm as a general solution to the rendezvous problem in a discrete domain with direct communication between decentralized agents. We compare the performance of the buffered gossip algorithm against the well known uniform gossip algorithm. We believe that a buffered solution is preferable to an unbuffered solution, such as the uniform gossip algorithm, because the use of a buffer allows an agent to use multiple information sources when determining its desired rendezvous point, and that access to multiple information sources may improve agent decision making by reinforcing or contradicting an initial choice. To show that the buffered gossip algorithm is an actual solution for the rendezvous problem, we construct a theoretical proof of convergence and derive the conditions under which the buffered gossip algorithm is guaranteed to produce a consensus on rendezvous location. We use these results to verify that the uniform gossip algorithm also solves the rendezvous problem. We then use a multi-agent simulation to conduct a series of simulation experiments to compare the performance between the buffered and uniform gossip algorithms. Our results suggest that the buffered gossip algorithm can solve the rendezvous problem faster than the uniform gossip algorithm; however, the relative performance between these two solutions depends on the specific constraints of the problem and the parameters of the buffered gossip algorithm.     

Numerical Relations and Skill Level Constrain Co-Adaptive Behaviors of Agents in Sports Teams

Similar to other complex systems in nature (e.g., a hunting pack, flocks of birds), sports teams have been modeled as social neurobiological systems in which interpersonal coordination tendencies of agents underpin team swarming behaviors. Swarming is seen as the result of agent co-adaptation to ecological constraints of performance environments by collectively perceiving specific possibilities for action (affordances for self and shared affordances). A major principle of invasion team sports assumed to promote effective performance is to outnumber the opposition (creation of numerical overloads) during different performance phases (attack and defense) in spatial regions adjacent to the ball. Such performance principles are assimilated by system agents through manipulation of numerical relations between teams during training in order to create artificially asymmetrical performance contexts to simulate overloaded and underloaded situations. Here we evaluated effects of different numerical relations differentiated by agent skill level, examining emergent inter-individual, intra- and inter-team coordination. Groups of association football players (national – NLP and regional-level – RLP) participated in small-sided and conditioned games in which numerical relations between system agents were manipulated (5v5, 5v4 and 5v3). Typical grouping tendencies in sports teams (major ranges, stretch indices, distances of team centers to goals and distances between the teams'' opposing line-forces in specific team sectors) were recorded by plotting positional coordinates of individual agents through continuous GPS tracking. Results showed that creation of numerical asymmetries during training constrained agents'' individual dominant regions, the underloaded teams'' compactness and each team''s relative position on-field, as well as distances between specific team sectors. We also observed how skill level impacted individual and team coordination tendencies. Data revealed emergence of co-adaptive behaviors between interacting neurobiological social system agents in the context of sport performance. Such observations have broader implications for training design involving manipulations of numerical relations between interacting members of social collectives.     

Toward Discovering New Anti-Cancer Agents Targeting Topoisomerase IIα: A Facile Screening Strategy Adaptable to High Throughput Platform

Topoisomerases are a family of vital enzymes capable of resolving topological problems in DNA during various genetic processes. Topoisomerase poisons, blocking reunion of cleaved DNA strands and stabilizing enzyme-mediated DNA cleavage complex, are clinically important antineoplastic and anti-microbial agents. However, the rapid rise of drug resistance that impedes the therapeutic efficacy of these life-saving drugs makes the discovering of new lead compounds ever more urgent. We report here a facile high throughput screening system for agents targeting human topoisomerase IIα (Top2α). The assay is based on the measurement of fluorescence anisotropy of a 29 bp fluorophore-labeled oligonucleotide duplex. Since drug-stabilized Top2α-bound DNA has a higher anisotropy compared with free DNA, this assay can work if one can use a dissociating agent to specifically disrupt the enzyme/DNA binary complexes but not the drug-stabilized ternary complexes. Here we demonstrate that NaClO₄, a chaotropic agent, serves a critical role in our screening method to differentiate the drug-stabilized enzyme/DNA complexes from those that are not. With this strategy we screened a chemical library of 100,000 compounds and obtained 54 positive hits. We characterized three of them on this list and demonstrated their effects on the Top2α–mediated reactions. Our results suggest that this new screening strategy can be useful in discovering additional candidates of anti-cancer agents.     

Learning and stabilization of altruistic behaviors in multi-agent systems by reciprocity

Optimization of performance in collective systems often requires altruism. The emergence and stabilization of altruistic behaviors are difficult to achieve because the agents incur a cost when behaving altruistically. In this paper, we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. This strategy in conjunction with learning capabilities make altruistic agents cooperate only between themselves, thus preventing their exploitation by selfish agents, if future benefits are greater than the current cost of altruistic acts. Our multi-agent system is made up of agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so achieving optimal (or near-to-optimal) performances in unknown and changing environments. Received: 1 August 1997 / Accepted in revised form: 28 November 1997     

Search Algorithms as a Framework for the Optimization of Drug Combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms—originally developed for digital communication—modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6–9 interventions in 80–90% of tests, compared with 15–30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.     

Biological trade and markets

Cooperation between organisms can often be understood, like trade between merchants, as a mutually beneficial exchange of services, resources or other ‘commodities’. Mutual benefits alone, however, are not sufficient to explain the evolution of trade-based cooperation. First, organisms may reject a particular trade if another partner offers a better deal. Second, while human trade often entails binding contracts, non-human trade requires unwritten ‘terms of contract’ that ‘self-stabilize’ trade and prevent cheating even if all traders strive to maximize fitness. Whenever trading partners can be chosen, market-like situations arise in nature that biologists studying cooperation need to account for. The mere possibility of exerting partner choice stabilizes many forms of otherwise cheatable trade, induces competition, facilitates the evolution of specialization and often leads to intricate forms of cooperation. We discuss selected examples to illustrate these general points and review basic conceptual approaches that are important in the theory of biological trade and markets. Comparing these approaches with theory in economics, it turns out that conventional models—often called ‘Walrasian’ markets—are of limited relevance to biology. In contrast, early approaches to trade and markets, as found in the works of Ricardo and Cournot, contain elements of thought that have inspired useful models in biology. For example, the concept of comparative advantage has biological applications in trade, signalling and ecological competition. We also see convergence between post-Walrasian economics and biological markets. For example, both economists and biologists are studying ‘principal–agent’ problems with principals offering jobs to agents without being sure that the agents will do a proper job. Finally, we show that mating markets have many peculiarities not shared with conventional economic markets. Ideas from economics are useful for biologists studying cooperation but need to be taken with caution.     

Probing Relevant Molecules in Modulating the Neurite Outgrowth of Hippocampal Neurons on Substrates of Different Stiffness

Hippocampal neurons play a critical role in learning and memory; however, the effects of environmental mechanical forces on neurite extension and associated underlying mechanisms are largely unexplored, possibly due to difficulties in maintaining central nervous system neurons. Neuron adhesion, neurite length, and mechanotransduction are mainly influenced by the extracellular matrix (ECM), which is often associated with structural scaffolding. In this study, we investigated the relationship between substrate stiffness and hippocampal neurite outgrowth by controlling the ratios of polydimethylsiloxane (PDMS) base to curing agent to create substrates of varying stiffness. Immunostaining results demonstrated that hippocampal neurons have longer neurite elongation in 35∶1 PDMS substrate compared those grown on 15∶1 PDMS, indicating that soft substrates provide a more optimal stiffness for hippocampal neurons. Additionally, we discovered that pPKCα expression was higher in the 15∶1 and 35∶1 PDMS groups than in the poly-l-lysine-coated glass group. However, when we used a fibronectin (FN) coating, we found that pFAKy397 and pFAKy925 expression were higher in glass group than in the 15∶1 or 35∶1 PDMS groups, but pPKCα and pERK1/2 expression were higher in the 35∶1 PDMS group than in the glass group. These results support the hypothesis that environmental stiffness influences hippocampal neurite outgrowth and underlying signaling pathways.     

A Tradeoff Between Performance and Accuracy in Bird Song Learning

Some of nature’s most complex behaviors, such as human speech and oscine bird song, are acquired through imitative learning. Accurate imitative learning tends to preserve patterns of behavior across generations, thus limiting the scope of cultural evolution. Less well studied are the routes by which cultural novelties arise during development, beyond simple copy error. In this study we assess, in a species of songbird, the relationship in song learning between two potentially conflicting learning goals: accuracy in copying and maximization of vocal performance. In our study species, the swamp sparrow (Melospiza georgiana), vocal performance can be defined for a given song type and frequency range by the rate of note repetition (‘trill rate’), with faster trills being more difficult to sing. We trained young swamp sparrows with song models with experimentally modified trill rates and characterized both the accuracy and performance levels of copies. Our main finding is that birds elevated the trill rates of low‐performance models, but at the expense of imitative accuracy. By contrast, birds reproduced normal and high‐performance models with typically high accuracy in structure and timing. Developmental mechanisms that enable songbirds to balance imitative accuracy and vocal performance are likely favored by sexual selection and may help explain some current patterns of variation in birdsong. Such mechanisms may also explain how behaviors that are learned by imitation can nevertheless respond to selection for high‐performance levels in their expression.     

Human Collective Intelligence under Dual Exploration-Exploitation Dilemmas

The exploration-exploitation dilemma is a recurrent adaptive problem for humans as well as non-human animals. Given a fixed time/energy budget, every individual faces a fundamental trade-off between exploring for better resources and exploiting known resources to optimize overall performance under uncertainty. Colonies of eusocial insects are known to solve this dilemma successfully via evolved coordination mechanisms that function at the collective level. For humans and other non-eusocial species, however, this dilemma operates within individuals as well as between individuals, because group members may be motivated to take excessive advantage of others'' exploratory findings through social learning. Thus, even though social learning can reduce collective exploration costs, the emergence of disproportionate “information scroungers” may severely undermine its potential benefits. We investigated experimentally whether social learning opportunities might improve the performance of human participants working on a “multi-armed bandit” problem in groups, where they could learn about each other''s past choice behaviors. Results showed that, even though information scroungers emerged frequently in groups, social learning opportunities reduced total group exploration time while increasing harvesting from better options, and consequentially improved collective performance. Surprisingly, enriching social information by allowing participants to observe others'' evaluations of chosen options (e.g., Amazon''s 5-star rating system) in addition to choice-frequency information had a detrimental impact on performance compared to the simpler situation with only the choice-frequency information. These results indicate that humans groups can handle the fundamental “dual exploration-exploitation dilemmas” successfully, and that social learning about simple choice-frequencies can help produce collective intelligence.     

Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.     

Improved nearest-neighbor parameters for the stability of RNA/DNA hybrids under a physiological condition

The stability of Watson–Crick paired RNA/DNA hybrids is important for designing optimal oligonucleotides for ASO (Antisense Oligonucleotide) and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)–Cas9 techniques. Previous nearest-neighbour (NN) parameters for predicting hybrid stability in a 1 M NaCl solution, however, may not be applicable for predicting stability at salt concentrations closer to physiological condition (e.g. ∼100 mM Na⁺ or K⁺ in the presence or absence of Mg²⁺). Herein, we report measured thermodynamic parameters of 38 RNA/DNA hybrids at 100 mM NaCl and derive new NN parameters to predict duplex stability. Predicted ΔG°₃₇ and T_m values based on the established NN parameters agreed well with the measured values with 2.9% and 1.1°C deviations, respectively. The new results can also be used to make precise predictions for duplexes formed in 100 mM KCl or 100 mM NaCl in the presence of 1 mM Mg²⁺, which can mimic an intracellular and extracellular salt condition, respectively. Comparisons of the predicted thermodynamic parameters with published data using ASO and CRISPR–Cas9 may allow designing shorter oligonucleotides for these techniques that will diminish the probability of non-specific binding and also improve the efficiency of target gene regulation.     

Associative Learning during Early Adulthood Enhances Later Memory Retention in Honeybees

Background

Cognitive experiences during the early stages of life play an important role in shaping the future behavior in mammals but also in insects, in which precocious learning can directly modify behaviors later in life depending on both the timing and the rearing environment. However, whether olfactory associative learning acquired early in the adult stage of insects affect memorizing of new learning events has not been studied yet.

Methodology

Groups of adult honeybee workers that experienced an odor paired with a sucrose solution 5 to 8 days or 9 to 12 days after emergence were previously exposed to (i) a rewarded experience through the offering of scented food, or (ii) a non-rewarded experience with a pure volatile compound in the rearing environment.

Principal Findings

Early rewarded experiences (either at 1–4 or 5–8 days of adult age) enhanced retention performance in 9–12-day-conditioned bees when they were tested at 17 days of age. The highest retention levels at this age, which could not be improved with prior rewarded experiences, were found for memories established at 5–8 days of adult age. Associative memories acquired at 9–12 days of age showed a weak effect on retention for some pure pre-exposed volatile compounds; whereas the sole exposure of an odor at any younger age did not promote long-term effects on learning performance.

Conclusions

The associative learning events that occurred a few days after adult emergence improved memorizing in middle-aged bees. In addition, both the timing and the nature of early sensory inputs interact to enhance retention of new learning events acquired later in life, an important matter in the social life of honeybees.     

Improved search heuristics find 20 000 new alignments between human and mouse genomes

Sequence similarity search is a fundamental way of analyzing nucleotide sequences. Despite decades of research, this is not a solved problem because there exist many similarities that are not found by current methods. Search methods are typically based on a seed-and-extend approach, which has many variants (e.g. spaced seeds, transition seeds), and it remains unclear how to optimize this approach. This study designs and tests seeding methods for inter-mammal and inter-insect genome comparison. By considering substitution patterns of real genomes, we design sets of multiple complementary transition seeds, which have better performance (sensitivity per run time) than previous seeding strategies. Often the best seed patterns have more transition positions than those used previously. We also point out that recent computer memory sizes (e.g. 60 GB) make it feasible to use multiple (e.g. eight) seeds for whole mammal genomes. Interestingly, the most sensitive settings achieve diminishing returns for human–dog and melanogaster–pseudoobscura comparisons, but not for human–mouse, which suggests that we still miss many human–mouse alignments. Our optimized heuristics find ∼20 000 new human–mouse alignments that are missing from the standard UCSC alignments. We tabulate seed patterns and parameters that work well so they can be used in future research.     

Dynamics of the Anatomical Changes That Occur in the Brains of Schoolchildren as They Learn to Read

Although the functional brain network involved in reading for adults and children is now well documented, a critical lack of knowledge still exists about the structural development of these brain areas. To provide a better overview of the structural dynamics of the brain that sustain reading acquisition, we acquired anatomical MRI brain images from 55 children that were divided into two groups: one prior to the formal learning of reading (n = 33, 5–6 years old) and the second a few years after formal learning (n = 22, 9–10 years old). Reading performances were collected based on the “Alouette-R” test, a standardized test for reading text in French. Voxel-based morphometry analysis of gray matter showed that only the right insula volume was different between the two groups. Moreover, the reading group showed that the volumes of the left fusiform gyrus (corresponding to the well-known visual word form area, VWFA), the anterior part of the left inferior occipital gyrus and the left thalamus were significantly modulated by reading performance. This study reinforces the crucial role of the Visual Word Form Area in reading and correlation analyses performed between ROIs volumes suggesting that the VWFA is fully connected with the traditional left-hemispheric language brain network.     

A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems

Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven ‘Delta’ rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.     

Decentralized Opportunistic Spectrum Resources Access Model and Algorithm toward Cooperative Ad-Hoc Networks

Limited communication resources have gradually become a critical factor toward efficiency of decentralized large scale multi-agent coordination when both system scales up and tasks become more complex. In current researches, due to the agent’s limited communication and observational capability, an agent in a decentralized setting can only choose a part of channels to access, but cannot perceive or share global information. Each agent’s cooperative decision is based on the partial observation of the system state, and as such, uncertainty in the communication network is unavoidable. In this situation, it is a major challenge working out cooperative decision-making under uncertainty with only a partial observation of the environment. In this paper, we propose a decentralized approach that allows agents cooperatively search and independently choose channels. The key to our design is to build an up-to-date observation for each agent’s view so that a local decision model is achievable in a large scale team coordination. We simplify the Dec-POMDP model problem, and each agent can jointly work out its communication policy in order to improve its local decision utilities for the choice of communication resources. Finally, we discuss an implicate resource competition game, and show that, there exists an approximate resources access tradeoff balance between agents. Based on this discovery, the tradeoff between real-time decision-making and the efficiency of cooperation using these channels can be well improved.     

The CLIP-170 N-terminal domain binds directly to both F-actin and microtubules in a mutually exclusive manner

The cooperation between the actin and microtubule (MT) cytoskeletons is important for cellular processes such as cell migration and muscle cell development. However, a full understanding of how this cooperation occurs has yet to be sufficiently developed. The MT plus-end tracking protein CLIP-170 has been implicated in this actin–MT coordination by associating with the actin-binding signaling protein IQGAP1 and by promoting actin polymerization through binding with formins. Thus far, the interactions of CLIP-170 with actin were assumed to be indirect. Here, we demonstrate using high-speed cosedimentation assays that CLIP-170 can bind to filamentous actin (F-actin) directly. We found that the affinity of this binding is relatively weak but strong enough to be significant in the actin-rich cortex, where actin concentrations can be extremely high. Using CLIP-170 fragments and mutants, we show that the direct CLIP-170–F-actin interaction is independent of the FEED domain, the region that mediates formin-dependent actin polymerization, and that the CLIP-170 F-actin-binding region overlaps with the MT-binding region. Consistent with these observations, in vitro competition assays indicate that CLIP-170–F-actin and CLIP-170–MT interactions are mutually exclusive. Taken together, these observations lead us to speculate that direct CLIP-170–F-actin interactions may function to reduce the stability of MTs in actin-rich regions of the cell, as previously proposed for MT end-binding protein 1.     

Quasi-Monopolar Stimulation: A Novel Electrode Design Configuration for Performance Optimization of a Retinal Neuroprosthesis

In retinal neuroprostheses, spatial interaction between electric fields from various electrodes – electric crosstalk – may occur in multielectrode arrays during simultaneous stimulation of the retina. Depending on the electrode design and placement, this crosstalk can either enhance or degrade the functional characteristics of a visual prosthesis. To optimize the device performance, a balance must be satisfied between the constructive interference of crosstalk on dynamic range and power consumption and its negative effect on artificial visual acuity. In the present computational modeling study, we have examined the trade-off in these positive and negative effects using a range of currently available electrode array configurations, compared to a recently proposed stimulation strategy – the quasi monopolar (QMP) configuration – in which the return current is shared between local bipolar guards and a distant monopolar electrode. We evaluate the performance of the QMP configuration with respect to the implantation site and electrode geometry parameters. Our simulation results demonstrate that the beneficial effects of QMP are only significant at electrode-to-cell distances greater than the electrode dimensions. Possessing a relatively lower activation threshold, QMP was found to be superior to the bipolar configuration in terms of providing a relatively higher visual acuity. However, the threshold for QMP was more sensitive to the topological location of the electrode in the array, which may need to be considered when programming the manner in which electrode are simultaneously activated. This drawback can be offset with a wider dynamic range and lower power consumption of QMP. Furthermore, the ratio of monopolar return current to total return can be used to adjust the functional performance of QMP for a given implantation site and electrode parameters. We conclude that the QMP configuration can be used to improve visual information-to-stimulation mapping in a visual prosthesis, while maintaining low power consumption.     

Prehospital Lactated Ringer's Solution Treatment and Survival in Out-of-Hospital Cardiac Arrest: A Prospective Cohort Analysis

Background

No studies have evaluated whether administering intravenous lactated Ringer''s (LR) solution to patients with out-of-hospital cardiac arrest (OHCA) improves their outcomes, to our knowledge. Therefore, we examined the association between prehospital use of LR solution and patients'' return of spontaneous circulation (ROSC), 1-month survival, and neurological or physical outcomes at 1 month after the event.

Methods and Findings

We conducted a prospective, non-randomized, observational study using national data of all patients with OHCA from 2005 through 2009 in Japan. We performed a propensity analysis and examined the association between prehospital use of LR solution and short- and long-term survival. The study patients were ≥18 years of age, had an OHCA before arrival of EMS personnel, were treated by EMS personnel, and were then transported to hospitals. A total of 531,854 patients with OHCA met the inclusion criteria. Among propensity-matched patients, compared with those who did not receive pre-hospital intravenous fluids, prehospital use of LR solution was associated with an increased likelihood of ROSC before hospital arrival (odds ratio [OR] adjusted for all covariates [95% CI] = 1.239 [1.146–1.339] [p<0.001], but with a reduced likelihood of 1-month survival with minimal neurological or physical impairment (cerebral performance category 1 or 2, OR adjusted for all covariates [95% CI] = 0.764 [0.589–0.992] [p = 0.04]; and overall performance category 1 or 2, OR adjusted for all covariates [95% CI] = 0.746 [0.573–0.971] [p = 0.03]). There was no association between prehospital use of LR solution and 1-month survival (OR adjusted for all covariates [95% CI] = 0.960 [0.854–1.078]).

Conclusion

In Japanese patients experiencing OHCA, the prehospital use of LR solution was independently associated with a decreased likelihood of a good functional outcome 1 month after the event, but with an increased likelihood of ROSC before hospital arrival. Prehospital use of LR solution was not associated with 1-month survival. Further study is necessary to verify these findings. Please see later in the article for the Editors'' Summary