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Abstract There is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive ballistocardiography (BCG) measures heart contractility and tracks CO changes. This study analyzed the feasibility of estimating CO using morphological features extracted from BCG signals. In 20 healthy subjects ECG, carotid/abdominal BCG, and invasive arterial blood pressure based CO were recorded. BCG signals were adaptively processed to isolate the circulatory component from carotid (CCc) and abdominal (CCa) BCG. Then, 66 features were computed on a beat-to-beat basis to characterize amplitude/duration/area/length of the fluctuation in CCc and CCa. Subjects’ data were split into development set (75%) to select the best feature subset with which to build a machine learning model to estimate CO and validation set (25%) to evaluate model’s performance. The model showed a mean absolute error, percentage error and 95% limits of agreement of 0.83 L/min, 30.2% and − 2.18–1.89 L/min respectively in the validation set. BCG showed potential to reliably estimate/track CO. This method is a promising first step towards an automated, non-invasive and reliable CO estimator that may be tested in prehospital emergencies.

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.

The article analyzes the variables that better explain the perceptions and attitudes of Europeans regarding science and their more recent scientific innovations. The cognitive deficit hypothesis proposes that social ambivalence arises because people have little scientific knowledge and misunderstands scientific and technical advances. The article agrees with the analytic role of contextual perspective, identifies ten myths linked to the traditional explanation of social perception of science, and proposes an alternative interpretation of the relationship between knowledge, institutional dynamics, and social trust.

We present the first rock magnetic study of archeologically-relevant chert samples from the Basque-Cantabrian basin (BCB) and surrounding regions, which was conducted in order to test the usefulness of non-destructive magnetic properties for assessing chert quality, distinguishing source areas, and identifying heated samples in the archeological record. Our results indicate that the studied BCB cherts are diamagnetic and have very low amounts of magnetic minerals. The only exception is the chert of Artxilondo, which has a mean positive magnetic susceptibility associated with larger concentrations of magnetic minerals. But even in this case, the magnetic susceptibility is within the lower range of other archeologically-relevant cherts elsewhere, which indicates that the studied BCB cherts can be considered as flint. The similar mean values for all magnetic properties, along with their associated large standard deviations, indicates that rock magnetic methods are of limited use for sourcing different types of flint except in some specific contexts involving the Artxilondo flint. With regards to the identification of chert heating in the archeological record, our results indicate only a minor magnetic enhancement of BCB natural flint samples upon heating, which we attribute to the low amount of non-silica impurities. In any case, the diamagnetic behavior of most BCB natural flints, along with the local use only of the Artxilondo type, suggests that any flint tool within the core of the BCB with positive magnetic susceptibility values is likely to have been subjected to heating for improving its knapping properties. Further studies are necessary to better identify the type, origin and grain size of magnetic minerals in BCB natural flints, and to apply non-destructive magnetic properties to flint tools in order to identify the use of heat treatment in the BCB archeological record.

Growing evidence supports a link between fatty acid metabolism and amyotrophic lateral sclerosis (ALS). Here we determined the fatty acid composition of blood lipids to identify markers of disease progression and survival. We enrolled 117 patients from two clinical centers and 48 of these were age and gender matched with healthy volunteers. We extracted total lipids from serum and blood cells, and separated fatty acid methyl esters by gas chromatography. We measured circulating biochemical parameters indicative of the metabolic status. Association between fatty acid composition and clinical readouts was studied, including ALS functional rating scale-revised (ALSFRS-R), survival, disease duration, site of onset and body mass index. Palmitoleate (16:1) and oleate (18:1) levels, and stearoyl-CoA desaturase indices (16:1/16:0 and 18:1/18:0) significantly increased in blood cells from ALS patients compared to healthy controls. Palmitoleate levels and 16:1/16:0 ratio in blood cells, but not body mass index or leptin concentrations, negatively correlated with ALSFRS-R decline over a six-month period (p

BACKGROUND:Amyotrophic lateral sclerosis (ALS), the most frequent adult onset motor neuron disease, is associated with hypermetabolism linked to defects in muscle mitochondrial energy metabolism such as ATP depletion and increased oxygen consumption. It remains unknown whether muscle abnormalities in energy metabolism are causally involved in the destruction of neuromuscular junction (NMJ) and subsequent motor neuron degeneration during ALS. METHODOLOGY/PRINCIPAL FINDINGS:We studied transgenic mice with muscular overexpression of uncoupling protein 1 (UCP1), a potent mitochondrial uncoupler, as a model of muscle restricted hypermetabolism. These animals displayed age-dependent deterioration of the NMJ that correlated with progressive signs of denervation and a mild late-onset motor neuron pathology. NMJ regeneration and functional recovery were profoundly delayed following injury of the sciatic nerve and muscle mitochondrial uncoupling exacerbated the pathology of an ALS animal model. CONCLUSIONS/SIGNIFICANCE:These findings provide the proof of principle that a muscle restricted mitochondrial defect is sufficient to generate motor neuron degeneration and suggest that therapeutic strategies targeted at muscle metabolism might prove useful for motor neuron diseases.