General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species

The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neural activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.

In vivo mapping of pharmacologically induced functional reorganization onto the human brain's neurotransmitter landscape

To understand how pharmacological interventions can exert their powerful effects on brain function, we need to understand how they engage the brain's rich neurotransmitter landscape. Here, we bridge microscale molecular chemoarchitecture and pharmacologically induced macroscale functional reorganization, by relating the regional distribution of 19 neurotransmitter receptors and transporters obtained from positron emission tomography, and the regional changes in functional magnetic resonance imaging connectivity induced by 10 different mind-altering drugs: propofol, sevoflurane, ketamine, lysergic acid diethylamide (LSD), psilocybin, N,N-Dimethyltryptamine (DMT), ayahuasca, 3,4-methylenedioxymethamphetamine (MDMA), modafinil, and methylphenidate. Our results reveal a many-to-many mapping between psychoactive drugs' effects on brain function and multiple neurotransmitter systems. The effects of both anesthetics and psychedelics on brain function are organized along hierarchical gradients of brain structure and function. Last, we show that regional co-susceptibility to pharmacological interventions recapitulates co-susceptibility to disorder-induced structural alterations. Collectively, these results highlight rich statistical patterns relating molecular chemoarchitecture and drug-induced reorganization of the brain's functional architecture.

863 Improving the Outcomes of Inpatient Admissions and Optimising Referrals to a Regional Burns Unit Through Introduction of a Specialist Nurse-Led Burns Clinic

Aim

We aimed to measure the effect of a specialist nurse-led clinic and referral pathway on admission rate, patient length of stay (LOS), and total bed days. We hypothesised that this intervention would contribute to the optimisation of the patient pathway in a regional burn's unit.

Method

Retrospective analysis of inpatient records in a six-month period over three years at a regional burn's unit (June-November 2018,2019,2020). Analysis of demographics, TBSA, admissions, and length of stay before and after clinic introduction. Statistical analysis included one-way ANOVA.

Results

A reduction in admissions was observed in 2020 compared to 2019 and 2018 (7.3% and 6.7%) for a patient group with equivalent median TBSA (2%) and comparable median age (41 vs. 43.5 and 42.5). Median LOS was significantly shortened compared to 2019 (42.9%, F=4.07, p=0.046) and 2018 (42.9%, F=6.59, p=0.011). Minor burns showed reduced admission rates compared to 2019 (8.5%), and reduced median LOS (42.9% and 33.3%), and reduced total bed days (29.4% and 27.1%) compared to 2019 and 2018 respectively. Referrals to the regional burn's unit as a percentage total of regional A&E burns presentations increased in 2020 compared to 2019 and 2018 (16% vs. 14.4% and 12.1%).

Conclusions

The results suggest that the introduction of a specialist clinic has shortened in-patient stay with a significant effect on minor burns, against a backdrop of increased referrals. We suggest that continuity between clinic and inpatient care by a specialist nursing team has helped to optimise the regional burns unit.

Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network's internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4-12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli-such as default mode, dorsal attentional, and frontoparietal networks-are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain's capability for continuous switching between two modes, which is crucial for the emergence of consciousness.

Dynamic Patterns of Global Brain Communication Differentiate Conscious From Unconscious Patients After Severe Brain Injury

The neurophysiology of the subjective sensation of being conscious is elusive; therefore, it remains controversial how consciousness can be recognized in patients who are not responsive but seemingly awake. During general anesthesia, a model for the transition between consciousness and unconsciousness, specific covariance matrices between the activity of brain regions that we call patterns of global brain communication reliably disappear when people lose consciousness. This functional magnetic imaging study investigates how patterns of global brain communication relate to consciousness and unconsciousness in a heterogeneous sample during general anesthesia and after brain injury. First, we describe specific patterns of global brain communication during wakefulness that disappear during propofol (n = 11) and sevoflurane (n = 14) general anesthesia. Second, we search for these patterns in a cohort of unresponsive wakeful patients (n = 18) and unmatched healthy controls (n = 20) in order to evaluate their potential use in clinical practice. We found that patterns of global brain communication characterized by high covariance in sensory and motor areas or low overall covariance and their dynamic change were strictly associated with intact consciousness in this cohort. In addition, we show that the occurrence of these two patterns is significantly related to activity within the frontoparietal network of the brain, a network known to play a crucial role in conscious perception. We propose that this approach potentially recognizes consciousness in the clinical routine setting.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Longitudinal effects of enrofloxacin or tulathromycin use in preweaned calves at high risk of bovine respiratory disease on the shedding of antimicrobial-resistant fecal Escherichia coli

The objective of this study was to longitudinally quantify Escherichia coli resistant to ciprofloxacin and ceftriaxone in calves treated with enrofloxacin or tulathromycin for the control of bovine respiratory disease (BRD). Dairy calves 2 to 3 wk of age not presenting clinical signs of pneumonia and at high risk of developing BRD were randomly enrolled in 1 of 3 groups receiving the following treatments: (1) single label dose of enrofloxacin (ENR); (2) single label dose of tulathromycin (TUL); or (3) no antimicrobial treatment (control, CTL). Fecal samples were collected immediately before administration of treatment and at d 2, 4, 7, 14, 21, 28, 56, and 112 d after beginning treatment. Samples were used for qualification of E. coli using a selective hydrophobic grid membrane filter (HGMF) master grid. The ENR group had a significantly higher proportion of E. coli resistant to ciprofloxacin compared with CTL and TUL at time points 2, 4, and 7. At time point 28, a significantly higher proportion of E. coli resistant to ciprofloxacin was observed only compared with CTL. The TUL group had a significantly higher proportion of E. coli resistant to ciprofloxacin compared with CTL at time points 2, 4, and 7. None of the treatment groups resulted in a significantly higher proportion of E. coli isolates resistant to ceftriaxone. Our study identified that treatment of calves at high risk of developing BRB with either enrofloxacin or tulathromycin resulted in a consistently higher proportion of ciprofloxacin-resistant E. coli in fecal samples.

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.

NuDEX: A new nuclear γ-ray cascades generator

Evaluated nuclear data libraries written in ENDF-6 format are used by Monte Carlo codes such as Geant4, MCNP6 or FLUKA for the transport of low energy neutrons (up to 20 MeV). The format in which the production of γ-rays after neutron induced reactions is provided do not allow, in general, to generate these γ-ray cascades in a correlated way. This prevents, among other things, energy conservation event by event, which is crucial in many applications. We have developed a code capable to generate correlated de-excitation γ-ray cascades using as much information as possible available in the RIPL-3 and ENSDF nuclear structure data libraries, among other useful information. The code follows the same philosophy of the DICEBOX or DEGEN codes. It generates the complete level scheme and branching ratios of the nucleus by using all the information experimentally known (known level scheme and known branching ratios) and completing the missing information with the most reliable statistical models. This code is able to generate automatically cascades for a large variety of nuclei (∼300) without requiring a specific input for each particular isotope. The code has been written in C++ language and can be integrated in the Geant4 simulation toolkit framework.

Antimicrobial use and stewardship practices on Australian beef feedlots

OBJECTIVE

Improving antimicrobial stewardship in the livestock sector requires an understanding of the motivations for antimicrobial use and the quantities consumed. However,detailed information on antimicrobial use in livestock sectors is lacking. This cross-sectional study aimed to better understand antimicrobial use in the beef feedlot sector in Australia.

DESIGN

A self-administered questionnaire asking about antimicrobial use and reasons for use was designed and mailed to beef feedlot operators in Australia. Respondents were asked to report the percentage of animals treated, purpose of use, and disease conditions targeted for 26antimicrobial agents.

RESULTS

In total, 83 of 517 (16.1%) beef feedlot operators completed the survey. Monensin (61.0%of respondents) and virginiamycin (19.5%of respondents) were the most commonly reported in-feed antimicrobials. In-feed antimicrobial agents were most frequently used by respondents for treatment of gastrointestinal diseases (52.8%). Antimicrobials were used for growth promotion by 42.1% of respondents, with most (85.7%) reporting the use of ionophores(a group of compounds not used in human medicine). Short-acting penicillin(69.1%), short-acting oxytetracycline, and tulathromycin (both 57.3%) werethe most common injectable antimicrobial agents used. Injectable antimicrobials were most frequently used to treat respiratory (72.3%) and musculoskeletal (67.5%) conditions.

CONCLUSION

Overall,the use of antimicrobials was appropriate for the purpose indicated, and there was a strong preference for drugs of low-importance in human medicine. The data described here stand to be a strong influence on the implementation of an antimicrobial stewardship program in the sector.

Teaching Ordinal Patterns to a Computer: Efficient Encoding Algorithms Based on the Lehmer Code

Ordinal patterns are the common basis of various techniques used in the study of dynamical systems and nonlinear time series analysis. The present article focusses on the computational problem of turning time series into sequences of ordinal patterns. In a first step, a numerical encoding scheme for ordinal patterns is proposed. Utilising the classical Lehmer code, it enumerates ordinal patterns by consecutive non-negative integers, starting from zero. This compact representation considerably simplifies working with ordinal patterns in the digital domain. Subsequently, three algorithms for the efficient extraction of ordinal patterns from time series are discussed, including previously published approaches that can be adapted to the Lehmer code. The respective strengths and weaknesses of those algorithms are discussed, and further substantiated by benchmark results. One of the algorithms stands out in terms of scalability: its run-time increases linearly with both the pattern order and the sequence length, while its memory footprint is practically negligible. These properties enable the study of high-dimensional pattern spaces at low computational cost. In summary, the tools described herein may improve the efficiency of virtually any ordinal pattern-based analysis method, among them quantitative measures like permutation entropy and symbolic transfer entropy, but also techniques like forbidden pattern identification. Moreover, the concepts presented may allow for putting ideas into practice that up to now had been hindered by computational burden. To enable smooth evaluation, a function library written in the C programming language, as well as language bindings and native implementations for various numerical computation environments are provided in the supplements.

Anesthetists’ Heart Rate Variability as an Indicator of Performance During Induction of General Anesthesia and Simulated Critical Incidents

Abstract. In the environment of anesthesia, good performance describes the absence of threat for the patient as well as a quick reaction to challenging and possibly life-threatening circumstances. Elsewhere, performance and cognitive function have been linked to indicators of vagally-mediated heart rate variability (HRV). This exploratory study examines the correlation between anesthetists’ HRV and their performance during uneventful induction of general anesthesia and during a simulated critical incident. For this study electrocardiograms (ECG) were obtained from two different groups of anesthetists providing general anesthesia in uneventful real cases in the operation room (OR, n = 38) and during the management of a hypotension scenario in a high-fidelity human patient simulator environment (SIM, n = 23). Frequency, time domain, and nonlinear HRV metrics were calculated from 5-min ECG recordings. To separate high performing (HP) and low performing (LP) individuals, the time needed for induction (in the OR setting) and the length and depth of hypotension (in the SIM setting) were used as performance correlates. The Mann-Whitney- U-test was used to assess differences in HRV within the groups. In both settings (OR and SIM), linear and nonlinear HRV metrics did not differ significantly between the HP and LP group. Also, the anesthetists’ work experience and sex were not related to performance. While providing general anesthesia and during a simulated critical incident, high and low performing individuals do not differ with respect to HRV metrics, sex, and work experience. Further research including the HRV under resting conditions is necessary.

Updated Summation Model: An Improved Agreement with the Daya Bay Antineutrino Fluxes

A new summation method model of the reactor antineutrino energy spectrum is presented. It is updated with the most recent evaluated decay databases and with our total absorption gamma-ray spectroscopy measurements performed during the last decade. For the first time, the spectral measurements from the Daya Bay experiment are compared with the antineutrino energy spectrum computed with the updated summation method without any renormalization. The results exhibit a better agreement than is obtained with the Huber-Mueller model in the 2-5 MeV range, the region that dominates the detected flux. A systematic trend is found in which the antineutrino flux computed with the summation model decreases with the inclusion of more pandemonium-free data. The calculated flux obtained now lies only 1.9% above that detected in the Daya Bay experiment, a value that may be reduced with forthcoming new pandemonium-free data, leaving less room for a reactor anomaly. Eventually, the new predictions of individual antineutrino spectra for the ^{235}U, ^{239}Pu, ^{241}Pu, and ^{238}U are used to compute the dependence of the reactor antineutrino spectral shape on the fission fractions.

Antimicrobial ratings: the importance of importance

Debate about antimicrobial resistance (AMR) in animals and the impact on humans is often inappropriately focussed on the crude amount of antimicrobials used in animals without deference to issues of 'intensity of use', 'quality of use' and especially the class of agents being administered. In the latter case, tools for helping to manage how the use of specific antimicrobial agents impact on the risk and consequences of antimicrobial resistance in humans have been available for almost two decades. These consist of antimicrobial ratings systems, which formally define the importance of each individual agent in human health by assigning them to a position on a graduated scale of 'importance' comprising up to four categories of risk. Until recently, these published ratings have only had a modest impact on antimicrobial stewardship at the frontline of medical and veterinary practice, although they will undoubtedly have a substantial influence into the future. This article considers the evolution of the available 'antimicrobial ratings systems' applicable to Australian and international settings and their pivotal future-role in the educating of animal managers, policy makers and prescribers. Faithful application of these rating systems at all levels of decision making to do with antimicrobial use is now seen as central to the protection of animals, humans and economies from the scourge of AMR.

Large Impact of the Decay of Niobium Isomers on the Reactor ν[over ¯]_{e} Summation Calculations

Even mass neutron-rich niobium isotopes are among the principal contributors to the reactor antineutrino energy spectrum. They are also among the most challenging to measure due to the refractory nature of niobium, and because they exhibit isomeric states lying very close in energy. The β-intensity distributions of ^{100gs,100m}Nb and ^{102gs,102m}Nb β decays have been determined using the total absorption γ-ray spectroscopy technique. The measurements were performed at the upgraded Ion Guide Isotope Separator On-Line facility at the University of Jyväskylä. Here, the double Penning trap system JYFLTRAP was employed to disentangle the β decay of the isomeric states. The new data obtained in this challenging measurement have a large impact in antineutrino summation calculations. For the first time the discrepancy between the summation model and the reactor antineutrino measurements in the region of the shape distortion has been reduced.

Differentiation of Hemorrhage from Iodine Using Spectral Detector CT: A Phantom Study

BACKGROUND AND PURPOSE

Conventional CT often cannot distinguish hemorrhage from iodine extravasation following reperfusion therapy for acute ischemic stroke. We investigated the potential of spectral detector CT in differentiating these lesions.

MATERIALS AND METHODS

Centrifuged blood with increasing hematocrit (5%-85%) was used to model hemorrhage. Pure blood, blood-iodine mixtures (75/25, 50/50, and 25/75 ratios), and iodine solutions (0-14 mg I/mL) were scanned in a phantom with attenuation ranging from 12 to 75 HU on conventional imaging. Conventional and virtual noncontrast attenuation was compared and investigated for correlation with calculation of relative virtual noncontrast attenuation. Values for all investigated categories were compared using the Mann-Whitney U test. Sensitivity and specificity of virtual noncontrast, relative virtual noncontrast, conventional CT attenuation, and iodine quantification for hemorrhage detection were determined with receiver operating characteristic analysis.

RESULTS

Conventional image attenuation was not significantly different among all samples containing blood (P > .05), while virtual noncontrast attenuation showed a significant decrease with a decreasing blood component (P < .01) in all blood-iodine mixtures. Relative virtual noncontrast values were significantly different among all investigated categories (P < .01), with correct hemorrhagic component size estimation for all categories within a 95% confidence interval. Areas under the curve for hemorrhage detection were 0.97, 0.87, 0.29, and 0.16 for virtual noncontrast, relative virtual noncontrast, conventional CT attenuation, and iodine quantification, respectively. A ≥10-HU virtual noncontrast, ≥20-HU virtual noncontrast, ≥40% relative virtual noncontrast, and combined ≥10-HU virtual noncontrast and ≥40% relative virtual noncontrast attenuation threshold had a sensitivity/specificity for detecting hemorrhage of 100%/23%, 89%/95%, 100%/82%, and 100%/100%, respectively.

CONCLUSIONS

Spectral detector CT can accurately differentiate blood from iodinated contrast in a phantom setting.

Linear and non-linear heart rate metrics for the assessment of anaesthetists' workload during general anaesthesia

BACKGROUND

Excessive workload may impact the anaesthetists' ability to adequately process information during clinical practice in the operation room and may result in inaccurate situational awareness and performance. This exploratory study investigated heart rate (HR), linear and non-linear heart rate variability (HRV) metrics and subjective ratings scales for the assessment of workload associated with the anaesthesia stages induction, maintenance and emergence.

METHODS

HR and HRV metrics were calculated based on five min segments from each of the three anaesthesia stages. The area under the receiver operating characteristics curve (AUC) of the investigated metrics was calculated to assess their ability to discriminate between the stages of anaesthesia. Additionally, a multiparametric approach based on logistic regression models was performed to further evaluate whether linear or non-linear heart rate metrics are suitable for the assessment of workload.

RESULTS

Mean HR and several linear and non-linear HRV metrics including subjective workload ratings differed significantly between stages of anaesthesia. Permutation Entropy (PeEn, AUC=0.828) and mean HR (AUC=0.826) discriminated best between the anaesthesia stages induction and maintenance. In the multiparametric approach using logistic regression models, the model based on non-linear heart rate metrics provided a higher AUC compared with the models based on linear metrics.

CONCLUSIONS

In this exploratory study based on short ECG segment analysis, PeEn and HR seem to be promising to separate workload levels between different stages of anaesthesia. The multiparametric analysis of the regression models favours non-linear heart rate metrics over linear metrics.

Phenotypic and genotypic profiling of antimicrobial resistance in enteric Escherichia coli communities isolated from finisher pigs in Australia

OBJECTIVE

To assess herd-to-herd variation in antimicrobial resistance phenotypes and associated antimicrobial resistance genes (ARGs) in faecal commensal Escherichia coli communities isolated from Australian slaughter-age pigs.

METHODS

Hydrophobic grid-membrane filtration (HGMF) was used to screen populations of E. coli isolated from faecal samples obtained from pigs prior to or at slaughter. Multiplex PCRs were applied to the pooled DNA extracted from the samples to identify specific ARGs.

METHODS

Pooled faecal samples from 30 finishers, from 72 different Australian pig farms, produced 5003 isolates for screening. HGMF techniques and image analysis were used to confirm E. coli resistance phenotypes to four antimicrobial agents (ampicillin, gentamicin, florfenicol and ceftiofur) using selective agars. Multiplex PCRs were performed on DNA from pooled samples for 35 ARGs associated with seven chemical classes.

RESULTS

The prevalence of E. coli isolates showing no resistance to any of the drugs was 50.2% (95% confidence interval (CI) 41.8-58.6%). Ceftiofur resistance was very low (1.8%; CI 0.8-3.9%) and no ARGs associated with 3rd-generation cephalosporin resistance were detected. By contrast, ampicillin (29.4%, CI 22.8-37.0%), florfenicol (24.3%, CI 17.8-32.3%) and gentamicin (CI 17.5%, 10.7-27.2%) resistance prevalence varied greatly between farms and associated ARGs were common. The most common combined resistance phenotype was ampicillin-florfenicol.

CONCLUSION

The use of registered antimicrobials in Australian pigs leads to the enteric commensal populations acquiring associated ARGs. However, despite a high intensity of sampling, ARGs imparting resistance to the critically important 3rd-generation cephalosporins were not detected.

Spectral Dynamics of Resting State fMRI Within the Ventral Tegmental Area and Dorsal Raphe Nuclei in Medication-Free Major Depressive Disorder in Young Adults

Background: Dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) are major brainstem monamine nuclei consisting of serotonin and dopamine neurons respectively. Animal studies show that firing patterns in both nuclei are altered when animals exhibit depression like behaviors. Functional MRI studies in humans have shown reduced VTA activation and DRN connectivity in depression. This study for the first time aims at investigating the functional integrity of local neuronal firing concurrently in both the VTA and DRN in vivo in humans using spectral analysis of resting state low frequency fluctuation fMRI. Method: A total of 97 medication-free subjects-67 medication-free young patients (ages 18-30) with major depressive disorder and 30 closely matched healthy controls were included in the study to detect aberrant dynamics in DRN and VTA. For the investigation of altered localized dynamics we conducted power spectral analysis and above this spectral cross correlation between the two groups. Complementary to this, spectral dependence of permutation entropy, an information theoretical measure, was compared between groups. Results: Patients displayed significant spectral slowing in VTA vs. controls (p = 0.035, corrected). In DRN, spectral slowing was less pronounced, but the amount of slowing significantly correlated with 17-item Hamilton Depression Rating scores of depression severity (p = 0.038). Signal complexity as assessed via permutation entropy showed spectral alterations inline with the results on spectral slowing. Conclusion: Our results indicate that altered functional dynamics of VTA and DRN in depression can be detected from regional fMRI signal. On this basis, impact of antidepressant treatment and treatment response can be assessed using these markers in future studies.

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians

Background

It has been shown that linear and non-linear heart rate variability (HRV) metrics are suitable to assess workload of anesthetists administering anesthesia. In pre-hospital emergency care, these parameters have not yet been evaluated. We hypothesized that heart rate (HR) and HRV metrics discriminate between differing workload levels of an emergency physician.

Methods

Electrocardiograms were obtained from 13 emergency physicians. Mean HR, ten linear and seven non-linear HRV metrics were analyzed. For each sortie, four different levels of workload were defined. Mixed-effects models and the area under the receiver operating characteristics curve (AUC) were used to test and quantify the HR and HRV metrics’ ability to discriminate between levels of workload. This was conducted for mean HR and each HRV metric as well as for groups of metrics (time domain vs. frequency domain vs. non-linear metrics).

Results

The non-linear HRV metric Permutation entropy (PeEn) discriminated best between the time before the alarm and primary patient care (AUC = 0.998, 1st rank of 18 HRV metrics). In contrast, AUC of the mean HR was low (0.558, 17th rank). In the multivariable approach, the non-linear HRV metrics provided a higher AUC (0.998) compared to the frequency domain (0.677) and to the time domain metrics (0.680).

Conclusion

Non-linear heart rate metrics and, specifically, PeEn provided good validity for the assessment of different levels of a physician’s workload in the setting of pre-hospital emergency care. In contradiction to earlier findings, the physicians’ mean HR was not a valid marker of workload.

Transmission of highly virulent community-associated MRSA ST93 and livestock-associated MRSA ST398 between humans and pigs in Australia

Pigs have been recognised as a reservoir of livestock associated methicillin-resistant Staphylococcus aureus (LA-MRSA) in Europe, Asia and North America. However, little is known about the presence and distribution of MRSA in the Australian pig population and pig industry. This study describes the presence, distribution and molecular characteristics of the human adapted Australian CA-MRSA ST93 isolated from pigs, people, and the environment within a piggery. Isolates were subjected to antibiotic susceptibility testing, DNA microarray, whole genome sequencing, multi locus sequence typing, virulence and resistance gene characterization and phylogenetic analysis. MRSA were isolated from 60% (n = 52) of farm workers where 84% of isolates returned ST93 and the rest ST398. Of the thirty-one pig isolates tested further, an equal number of ST398 and ST93 (15 each) and one as ST30-V were identified. Four of six environmental isolates were identified as ST93 and two as ST398. This study has identified for the first time in Australia the occurrence of CA-MRSA ST93 and LA-MRSA ST398 amongst farm workers, pigs, and the farm environment. Comparative genome analysis indicates that ST398 is likely to have been introduced into Australia from Europe or North America. This study also reports the first linezolid resistant MRSA isolated in Australia.

Coherence of BOLD signal and electrical activity in the human brain during deep sevoflurane anesthesia

INTRODUCTION

Changes in neural activity induce changes in functional magnetic resonance (fMRI) blood oxygenation level dependent (BOLD) signal. Commonly, increases in BOLD signal are ascribed to cellular excitation.

OBJECTIVE

The relationship between electrical activity and BOLD signal in the human brain was probed on the basis of burst suppression EEG. This condition includes two distinct states of high and low electrical activity.

METHODS

Resting-state simultaneous EEG and BOLD measurements were acquired during deep sevoflurane anesthesia with burst suppression EEG in nineteen healthy volunteers. Afterwards, fMRI volumes were assigned to one of the two states (burst or suppression) as defined by the EEG.

RESULTS

In the frontal, parietal and temporal lobes as well as in the basal ganglia, BOLD signal increased after burst onset in the EEG and decreased after onset of EEG suppression. In contrast, BOLD signal in the occipital lobe was anticorrelated to electrical activity. This finding was obtained consistently in a general linear model and in raw data.

CONCLUSIONS

In human brains exhibiting burst suppression EEG induced by sevoflurane, the positive correlation between BOLD signal and electrical brain activity could be confirmed in most gray matter. The exceptional behavior of the occipital lobe with an anticorrelation of BOLD signal and electrical activity might be due to specific neurovascular coupling mechanisms that are pronounced in the deeply anesthetized brain.

Use of peanut meal as a model matrix to study the effect of composting on aflatoxin decontamination

In developing countries, aflatoxin-contaminated peanut wastes are often used as mulching materials or soil amendments, which introduce aflatoxins and aflatoxin-producing mould into subsequent farming seasons. This research evaluated the effectiveness of composting as a means of aflatoxin decontamination using highly-contaminated peanut meal as a model matrix at 40 °C for 6 w. The composting methods caused 72.2-154.9 (41.9-75.1%), 7.4-17.6 (37.9-72.0%), 1.2-6.9 (77.1-100.0%), or 0.0-2.1 (0.0-100.0%) μg/kg reduction in the levels of aflatoxin B1, B2, G1 and G2 in peanut meal, respectively. Aspergillus flavus and Aspergillus parasiticus counts and total mould counts decreased from 103-105 to <10 colony forming units/g. Composting time and the type of starters used significantly influenced aflatoxin content, while the presence of accelerator did not affect aflatoxin levels. The highest level of toxin decontamination occurred in the first week when compost temperature and ammonia concentration were high. Micronutrient contents of resulting composts were within the accepted range for fertilisers, except for calcium. Heavy metal content was below the maximum allowable levels except nickel in one of the samples. Aflatoxin reduction was also observed in an up-scale experiment using contaminated agriculture waste as raw materials. Results suggest that composting could be employed to decontaminate aflatoxin-containing agricultural waste in developing countries.

Risk Mitigation of Emerging Zoonoses: Hendra Virus and Non-Vaccinating Horse Owners

Hendra virus was identified in horses and humans in 1994, in Queensland, Australia. Flying foxes are the natural host. Horses are thought to acquire infection by direct or indirect contact with infected flying fox urine. Humans are infected from close contact with infected horses. To reduce risk of infection in horses and humans, Australian horse owners are encouraged to vaccinate horses against the virus and adopt property risk mitigation practices that focus on reducing flying fox horse contact and contamination of horses' environment with flying fox bodily fluids. This study investigates uptake of four Hendra virus risk mitigation practices in a sample of non- and partially vaccinating horse owners living close to previous Hendra virus cases. Protection motivation theory was used to develop a conceptual model to investigate risk perception and coping factors associated with uptake of risk mitigation practices. An online survey was administered via Facebook pages of veterinary clinics close to previous Hendra virus cases. Factors associated with uptake of risk mitigation practices were investigated using univariate and multivariate binary logistic regression. Belief that a risk mitigation practice would be effective in reducing Hendra virus risk was significantly associated with the uptake of that practice. Issues around the practicality of implementing risk mitigation practices were found to be the greatest barrier to uptake. Factors that relate to risk immediacy, such as nearby infection, were identified as more likely to trigger uptake of risk mitigation practices. The role of veterinarians in supporting Hendra risk mitigation was identified as more influential than that of respected others or friends. Findings from this study are being used to assist stakeholders in Australia responsible for promotion of risk mitigation practice in identifying additional pathways and reliable influencing factors that could be utilized for engaging and communicating with horse owners to promote Hendra virus risk mitigation behaviour.

Borrelia burgdorferi sensu lato and co-infections with Anaplasma phagocytophilum and Rickettsia spp. in Ixodes ricinus in Hamburg, Germany

To obtain initial data on Borrelia burgdorferi sensu lato (Spirochaetales: Spirochaetaceae) in Ixodes ricinus (Ixodida: Ixodidae) ticks in Hamburg, Germany, 1400 questing ticks were collected by flagging at 10 different public recreation areas in 2011 and analysed using probe-based quantitative real-time polymerase chain reaction. The overall rate of infection with B. burgdorferi s.l. was 34.1%; 30.0% of adults were infected (36.7% of females and 26.0% of males), as were 34.5% of nymphs. Significant differences in tick infection rates were observed between the spring and summer/autumn months, as well as among sampling locations. Borrelia genospecies identification by reverse line blotting was successful in 43.6% of positive tick samples. The most frequent genospecies was Borrelia garinii/Borrelia bavariensis, followed by Borrelia afzelii, Borrelia valaisiana, B. burgdorferi sensu stricto, Borrelia spielmanii, Borrelia bissettii and Borrelia lusitaniae. Based on previously published data, co-infection of Borrelia and Rickettsiales spp. was determined in 25.8% of ticks. Overall, 22.9% of ticks were co-infected with Rickettsia spp. (Rickettsiales: Rickettsiaceae), 1.7% with Anaplasma phagocytophilum (Rickettsiales: Anaplasmataceae), and 1.2% with both pathogens. Study results show a high prevalence of Borrelia-positive ticks in recreation areas in the northern German city of Hamburg and the potential health risk to humans in these areas should not be underestimated.

Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines

A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decoding of potentially confounding sleep patterns from rs-fMRI itself might be useful and improve data interpretation. Linear support vector machine classifiers were trained on combined rs-fMRI/EEG recordings from 25 subjects to separate wakefulness (S0) from non-rapid eye movement (NREM) sleep stages 1 (S1), 2 (S2), slow wave sleep (SW) and all three sleep stages combined (SX). Classifier performance was quantified by a leave-one-subject-out cross-validation (LOSO-CV) and on an independent validation dataset comprising 19 subjects. Results demonstrated excellent performance with areas under the receiver operating characteristics curve (AUCs) close to 1.0 for the discrimination of sleep from wakefulness (S0|SX), S0|S1, S0|S2 and S0|SW, and good to excellent performance for the classification between sleep stages (S1|S2:~0.9; S1|SW:~1.0; S2|SW:~0.8). Application windows of fMRI data from about 70 s were found as minimum to provide reliable classifications. Discrimination patterns pointed to subcortical-cortical connectivity and within-occipital lobe reorganization of connectivity as strongest carriers of discriminative information. In conclusion, we report that functional connectivity analysis allows valid classification of NREM sleep stages.

EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness

OBJECTIVE

Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity.

METHODS

We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following.

RESULTS

PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS.

CONCLUSIONS

Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness.

SIGNIFICANCE

The utilized EEG entropy analyses were able to relate to patient groups with different Disorders of Consciousness.

Electromyographic permutation entropy quantifies diaphragmatic denervation and reinnervation

Spontaneous reinnervation after diaphragmatic paralysis due to trauma, surgery, tumors and spinal cord injuries is frequently observed. A possible explanation could be collateral reinnervation, since the diaphragm is commonly double-innervated by the (accessory) phrenic nerve. Permutation entropy (PeEn), a complexity measure for time series, may reflect a functional state of neuromuscular transmission by quantifying the complexity of interactions across neural and muscular networks. In an established rat model, electromyographic signals of the diaphragm after phrenicotomy were analyzed using PeEn quantifying denervation and reinnervation. Thirty-three anesthetized rats were unilaterally phrenicotomized. After 1, 3, 9, 27 and 81 days, diaphragmatic electromyographic PeEn was analyzed in vivo from sternal, mid-costal and crural areas of both hemidiaphragms. After euthanasia of the animals, both hemidiaphragms were dissected for fiber type evaluation. The electromyographic incidence of an accessory phrenic nerve was 76%. At day 1 after phrenicotomy, PeEn (normalized values) was significantly diminished in the sternal (median: 0.69; interquartile range: 0.66-0.75) and mid-costal area (0.68; 0.66-0.72) compared to the non-denervated side (0.84; 0.78-0.90) at threshold p<0.05. In the crural area, innervated by the accessory phrenic nerve, PeEn remained unchanged (0.79; 0.72-0.86). During reinnervation over 81 days, PeEn normalized in the mid-costal area (0.84; 0.77-0.86), whereas it remained reduced in the sternal area (0.77; 0.70-0.81). Fiber type grouping, a histological sign for reinnervation, was found in the mid-costal area in 20% after 27 days and in 80% after 81 days. Collateral reinnervation can restore diaphragm activity after phrenicotomy. Electromyographic PeEn represents a new, distinctive assessment characterizing intramuscular function following denervation and reinnervation.

Bite injuries to the hand: microbiology, virology and management

Bites to the human hand, be it from a pet, a stray animal or even a fellow human, may often have dire consequences for the person suffering the insult. Bites by mammals are a common problem and they account for up to 1% of all visits to hospital emergency rooms, in the UK. Clenched fist injuries to the mouth ('fight bite') are notorious for being the worst human bites. Bite injuries of the hand and their related infections must be monitored vigilantly and managed proactively, by experts in this field of surgery. In this review article we discuss the associated microbiology and virology of these injuries as well as their management.

Comparison of antimicrobial resistance phenotypes and genotypes in enterotoxigenic Escherichia coli isolated from Australian and Vietnamese pigs

This study aimed to compare the antibiogram phenotype and carriage of antimicrobial resistance genes (ARGs) of 97 porcine multidrug-resistant (MDR) enterotoxigenic Escherichia coli (ETEC) isolates obtained from Vietnam and 117 porcine MDR-ETEC obtained from Australia, two countries with different antimicrobial regulation systems. An antimicrobial resistance index (ARI) was calculated to quantify their potential significance to public health. Both Vietnamese and Australian isolates had moderate to high levels of resistance to commonly used antibiotics (ampicillin, tetracycline and sulphonamides). None of the Australian isolates were resistant to fluoroquinolones or third-generation cephalosporins and none possessed associated plasmid-mediated ARGs. However, 23.1% of Australian isolates were resistant to gentamicin owing to ARGs associated with apramycin or neomycin resistance [e.g. aac(3)-IV] that impart cross-resistance to gentamicin. Whilst Vietnamese isolates carried aminoglycoside ARGs, 44.4% of commercial pig isolates were resistant to gentamicin in comparison with 0% of village pig isolates. The plasmid-mediated fluoroquinolone ARG qnrB was commonly detected in Vietnamese isolates (52.3% commercial, 44.1% village), but phenotypic resistance was low (3.2% and 11.8%, respectively). The mean ARI for Vietnamese isolates (26.0) was significantly different (P<0.001) from the mean ARI for Australian isolates (19.8), primarily reflecting fluoroquinolone resistance in the former collection. This comparison suggests the effectiveness of regulations that slow the dissemination of 'critical' resistance by restricting the availability of important classes of antimicrobials.

Fronto-parietal connectivity is a non-static phenomenon with characteristic changes during unconsciousness

Background

It has been previously shown that loss of consciousness is associated with a breakdown of dominating fronto-parietal feedback connectivity as assessed by electroencephalogram (EEG) recordings. Structure and strength of network connectivity may change over time. Aim of the current study is to investigate cortico-cortical connectivity at different time intervals during consciousness and unconsciousness. For this purpose, EEG symbolic transfer entropy (STEn) was calculated to indicate cortico-cortical information transfer at different transfer times.

Methods

The study was performed in 15 male volunteers. 29-channel EEG was recorded during consciousness and propofol-induced unconsciousness. EEG data were analyzed by STEn, which quantifies intensity and directionality of the mutual information flow between two EEG channels. STEn was computed over fronto-parietal channel pair combinations (10 s length, 0.5–45 Hz total bandwidth) to analyze changes of intercortical directional connectivity. Feedback (fronto → parietal) and feedforward (parieto → frontal) connectivity was calculated for transfer times from 25 ms to 250 ms in 5 ms steps. Transfer times leading to maximum directed interaction were identified to detect changes of cortical information transfer (directional connectivity) induced by unconsciousness (p<0.05).

Results

The current analyses show that fronto-parietal connectivity is a non-static phenomenon. Maximum detected interaction occurs at decreased transfer times during propofol-induced unconsciousness (feedback interaction: 60 ms to 40 ms, p = 0.002; feedforward interaction: 65 ms to 45 ms, p = 0.001). Strength of maximum feedback interaction decreases during unconsciousness (p = 0.026), while no effect of propofol was observed on feedforward interaction. During both consciousness and unconsciousness, intensity of fronto-parietal interaction fluctuates with increasing transfer times.

Conclusion

Non-stationarity of directional connectivity may play a functional role for cortical network communication as it shows characteristic changes during propofol-induced unconsciousness.

Non-stationarity of EEG during wakefulness and anaesthesia: advantages of EEG permutation entropy monitoring

Monitors evaluating the electroencephalogram (EEG) to determine depth of anaesthesia use spectral analysis approaches for analysis windows up to 61.5 s as well as additional smoothing algorithms. Stationary EEG is required to reliably apply the index algorithms. Because of rapid physiological changes, artefacts, etc., the EEG may not always fulfil this requirement. EEG analysis using permutation entropy (PeEn) may overcome this issue, since PeEn can also be applied to practically nonstationary EEG. One objective was to determine the duration of EEG sequences that can be considered stationary at different anaesthetic levels. The second, more important objective was to test the reliability of PeEn to reflect the anaesthetic levels for short EEG segments. EEG was recorded from 15 volunteers undergoing sevoflurane and propofol anaesthesia at different anaesthetic levels and for each group 10 data sets were included. EEG stationarity was evaluated for EEG sample lengths from 4 to 116 s for each level. PeEn was calculated for these sequences using different parameter settings and analysis windows from 2 to 60 s. During wakefulness EEG can only be considered stationary for sequences up to 12 s. With increasing anaesthetic level the probability and duration of stationary EEG increases. PeEn is able to reliably separate consciousness from unconsciousness for EEG segments as short as 2 s. Especially during wakefulness a conflict between stationary EEG sequence durations and methods used for monitoring may exist. PeEn does not require stationarity and functions for EEG sequences as short as 2 s. These promising results seem to support the application of non-linear parameters, such as PeEn, to depth of anaesthesia monitoring.

Brain electrical activity obeys Benford's law

BACKGROUND

Monitoring and automated online analysis of brain electrical activity are frequently used for verifying brain diseases and for estimating anesthetic depth in subjects undergoing surgery. However, false diagnosis with potentially catastrophic consequences for patients such as intraoperative awareness may result from unnoticed irregularities in the process of signal analysis. Here we ask whether Benford's Law can be applied to detect accidental or intended modulation of neurophysiologic signals. This law states that the first digits of many datasets such as atomic weights or river lengths are distributed logarithmically and not equally. In particular, we tested whether data obtained from electrophysiological recordings of human patients representing global activity and organotypic slice cultures representing pure cortical activity follow the predictions of Benford's Law in the absence and in the presence of an anesthetic drug.

METHODS

Electroencephalographic (EEG) recordings from human subjects and local field potential recordings from cultured cortical brain slices were obtained before and after administration of sevoflurane. The first digit distribution of the datasets was compared with the Benford distribution.

RESULTS

All datasets showed a Benford-like distribution. Nevertheless, distributions belonging to different anesthetic levels could be distinguished in vitro and in human EEGs. With sevoflurane, the first digit distribution of the in vitro data becomes steeper, while it flattens for EEG data. In the presence of high frequency noise, the Benford distribution falls apart.

CONCLUSIONS

In vitro and EEG data show a Benford-like distribution which is altered by sevoflurane or destroyed by noise used to simulate artefacts. These findings suggest that algorithms based on Benford's Law can be successfully used to detect sevoflurane-induced signal modulations in electrophysiological recordings.