ROSC rates and live discharge rates after cardiopulmonary resuscitation by different CPR teams - a retrospective cohort study

Background

Previous studies have reported that the quality of cardiopulmonary resuscitation (CPR) is closely associated with patient outcomes. The aim of this study was to compare patient CPR outcomes across resident, emergency medicine, and rapid response teams.

Methods

The records of patients who underwent CPR at the Seoul National University Bundang Hospital from January 1, 2013 to December 31, 2016 were analyzed retrospectively. Return of spontaneous circulation, 10- and 30-day survival, and live discharge after return of spontaneous circulation were compared across patients treated by the three CPR teams.

Results

Of the 1145 CPR cases, 444 (39%) were conducted by the resident team, 431 (38%) by the rapid response team, and 270 (23%) by the emergency medicine team. The adjusted odds ratios for the return of spontaneous circulation and subsequent 10-day survival among patients who received CPR from the resident team compared to the rapid response team were 0.59 (P?=?0.001) and 0.71 (P?=?0.037), respectively. There were no significant differences in the 30-day survival and rate of live discharge between patients who received CPR from the rapid response and resident teams; likewise, no significant differences were observed between patients who received CPR from the emergency medicine and rapid response teams.

Conclusions

Patients receiving CPR from the rapid response team may have higher 10-day survival and return of spontaneous circulation rates than those who receive CPR from the resident team. However, our results are limited by the differences in approach, time of CPR, and room settings between teams.

     

A multi-context learning approach for EEG epileptic seizure detection

Background

Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroencephalogram (EEG) is the most commonly used biological measurement to effectively capture the abnormalities of different brain areas during the EEG seizures. To avoid manual visual inspection from long-term EEG readings, automatic epileptic EEG seizure detection has become an important research issue in bioinformatics.

Results

We present a multi-context learning approach to automatically detect EEG seizures by incorporating a feature fusion strategy. We generate EEG scalogram sequences from the EEG records by utilizing waveform transform to describe the frequency content over time. We propose a multi-stage unsupervised model that integrates the features extracted from the global handcrafted engineering, channel-wise deep learning, and EEG embeddings, respectively. The learned multi-context features are subsequently merged to train a seizure detector.

Conclusions

To validate the effectiveness of the proposed approach, extensive experiments against several baseline methods are carried out on two benchmark biological datasets. The experimental results demonstrate that the representative context features from multiple perspectives can be learned by the proposed model, and further improve the performance for the task of EEG seizure detection.

     

Medical students and physical education students as CPR instructors: an appropriate solution to the CPR-instructor shortage in secondary schools?

Background

Integrating cardiopulmonary resuscitation (CPR) training in secondary schools will increase the number of potential CPR providers. However, currently too few certified instructors are available for this purpose. Training medical students and physical education student teachers to become CPR instructors could decrease this shortage.

Aim

Examine whether medical students and physical education student teachers can provide CPR training for secondary school pupils as well as (i.?e., non-inferior to) registered nurses.

Methods

A total of 144 secondary school pupils were randomly assigned to CPR training by a registered nurse (n = 12), a  medical student (n = 17) or a physical education student teacher (n = 15). CPR performance was assessed after training and after eight weeks in a simulated cardiac arrest scenario on a resuscitation manikin, using manikin software and video recordings.

Results

No significant differences were found between the groups on the overall Cardiff Test scores and the correctness of the CPR techniques during the post-training and retention test. All pupils showed sufficient CPR competence, even after eight weeks.

Conclusion

Training by medical students or physical education student teachers is non-inferior to training by a registered nurse, suggesting that school teachers, student teachers and medical students can be recruited for CPR training in secondary schools.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Quantification of angiogenesis in estrogen receptor-positive and negative breast carcinoma

Background

The objective of this study was to evaluate angiogenesis according to CD34 antigen expression in estrogen receptor (ER)-positive and negative breast carcinomas.

Methods

This study comprised 64 cases of infiltrating ductal carcinoma in postmenopausal women divided into two groups: Group A: ER-positive, n = 35; and Group B: ER-negative, n = 29. The anti-CD34 monoclonal antibody was used as a marker for endothelial cells. Microvessel count was carried out in 10 fields per slide using a 40× objective lens (magnification 400×). Statistical analysis of the data was performed using Student's t-test (p < 0.05).

Results

The mean number of vessels stained with the anti-CD34 antibody in the estrogen receptor-positive and negative tumors was 23.51 ± 1.15 and 40.24 ± 0.42, respectively. The number of microvessels was significantly greater in the estrogen receptor-negative tumors (p < 0.001).

Conclusion

ER-negative tumors have significantly greater CD34 antigen expression compared to ER-positive tumors.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer

Introduction

Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).

Objectives

An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.

Methods

In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.

Results

The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).

Conclusion

These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

     

Extraction of natural products from bark of <Emphasis Type="Italic">Betula pendula</Emphasis> using ionic liquids

Introduction

Aqueous–methanol mixtures have successfully been applied to extract a broad range of metabolites from plant tissue. However, a certain amount of material remains insoluble.

Objectives

To enlarge the metabolic compendium, two ionic liquids were selected to extract the methanol insoluble part of trunk from Betula pendula.

Methods

The extracted compounds were analyzed by LC/MS and GC/MS.

Results

The results show that 1-butyl-3-methylimidazolium acetate (IL-Ac) predominantly resulted in fatty acids, whereas 1-ethyl-3-methylimidazolium tosylate (IL-Tos) mostly yielded phenolic structures. Interestingly, bark yielded more ionic liquid soluble metabolites compared to interior wood.

Conclusion

From this one can conclude that the application of ionic liquids may expand the metabolic snapshot.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets

Background

In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works.

Methods

By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad.

Results

The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper.

Conclusions

This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

     

Portable wireless neurofeedback system of EEG alpha rhythm enhances memory

Background

Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group.

Methods

The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8–12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively.

Results

The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8–12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group.

Conclusions

Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Longitudinal data methods for evaluating genome-by-epigenome interactions in families

Background

Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.

Results

Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score.

Conclusions

Comparison of all modeling approaches indicated a need for bias correction in marginal models and similar power for each method, with quadratic inference functions providing a minor decrement in power compared to generalized estimating equations and linear mixed-effects models.

     

Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter

Background

The electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull. In this paper, we compute the potential distribution over the closed surface covering the brain (cortex) from the EEG scalp potential. We compare two methods – L-curve and generalised cross validation (GCV) used to obtain the regularisation parameter and also investigate the feasibility in applying such techniques to N170 component of the visually evoked potential (VEP) data.

Methods

Using the image data set of the visible human man (VHM), a finite difference method (FDM) model of the head was constructed. The EEG dataset (256-channel) used was the N170 component of the VEP. A forward transfer matrix relating the cortical potential to the scalp potential was obtained. Using Tikhonov regularisation, the potential distribution over the cortex was obtained.

Results

The cortical potential distribution for three subjects was solved using both L-curve and GCV method. A total of 18 cortical potential distributions were obtained (3 subjects with three stimuli each – fearful face, neutral face, control objects).

Conclusions

The GCV method is a more robust method compared to L-curve to find the optimal regularisation parameter. Cortical potential imaging is a reliable method to obtain the potential distribution over cortex for VEP data.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

Early recognition of sudden cardiac arrest in athletes during sports activity

Introduction

Sudden cardiac arrest (SCA) in athletes is an unexpected life-threatening event, which is often not recognised early and cardiopulmonary resuscitation (CPR) is not always initiated immediately. We describe key features to rapidly recognise non-traumatic SCA in athletes during sports activity.

Methods

We reviewed videos and images of athletes suffering from non-traumatic SCA during sports activity. We searched Google images, Google videos and YouTube.com using the keywords ‘sudden cardiac death athlete’ and ‘resuscitation athlete’. We analysed (1) the athlete’s performance before syncope, (2) the athlete’s performance at the start of syncope, (3) the position of the body, and (4) the athlete’s facial expressions before CPR. We analysed our data by describing these four features to answer our research question.

Results

We analysed the sequence of events in six well-known soccer players in whom a camera-witnessed non-traumatic SCA occurred during their athletic activity. All six athletes showed no changes before syncope. Four became unstable while standing and unexpectedly collapsed falling on their back. Two suddenly ‘dropped dead’ and fell face down. All six had their eyes wide open with a fixed gaze and fixed pupils.

Conclusions

Sudden unexpected loss of consciousness in an athlete in action and a fixed gaze eye position are key features of SCA. Immediate cardiac massage should follow. The described features to immediately recognise SCA in athletes during sports activity should be taught to everyone involved in athletic activity leading to earlier recognition of SCA followed by earlier CPR.

     

Exploring strategies to prevent post-lobectomy space: transient diaphragmatic paralysis using Botulinum Toxin Type A (BTX-A)

Objective

Various techniques to reduce air space after pulmonary lobectomy especially for lung cancer have been an important concern in thoracic surgical practice. The aim of this study was to assess the effectiveness of Botulinum toxin A (BTX-A) injection into the diaphragm to reduce air space after right lower pulmonary lobectomy in an animal model.

Methods

Twelve male New Zealand rabbits were randomly allocated into two groups. All animals underwent right lower lobectomy. Then, normal saline of 0,1 ml and 10 units of 0,1 ml Botulinum toxin type A were injected into the muscular part of the right hemidiaphragm in control (n = 6) and BTX-A groups (n = 6) respectively. Residual air space and diaphragmatic elevation were evaluated with chest X-ray pre- and postoperatively. Diaphragmatic elevation was measured as a distance in millimetre from the line connecting the 10th ribs to the midpoint of the right hemidiaphragm.

Results

The mean diaphragmatic elevation in BTX-A and control groups were 7.0 ± 2.5 and 1.3 ± 1.2 millimetres respectively. Diaphragmatic elevations were significantly higher in BTX-A group (p = 0.0035).

Conclusion

Intraoperative Botulinum toxin type A injection may reduce postlobectomy spaces effectively via hemidiaphragmatic paralysis in rabbits. Further studies are needed to validate the safe use of Botulinum toxin type A in human beings.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.