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The transmission rate of methicillin-resistant Staphylococcus aureus (MRSA) to gloves or gowns of healthcare personnel (HCP) caring for MRSA patients in a non–intensive care unit setting was 5.4%. Contamination rates were higher among HCP performing direct patient care and when patients had detectable MRSA on their body. These findings may inform risk-based contact precautions.
Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew’s correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf.
The Murchison Widefield Array (MWA) is an open access telescope dedicated to studying the low-frequency (80–300 MHz) southern sky. Since beginning operations in mid-2013, the MWA has opened a new observational window in the southern hemisphere enabling many science areas. The driving science objectives of the original design were to observe 21 cm radiation from the Epoch of Reionisation (EoR), explore the radio time domain, perform Galactic and extragalactic surveys, and monitor solar, heliospheric, and ionospheric phenomena. All together
programs recorded 20 000 h producing 146 papers to date. In 2016, the telescope underwent a major upgrade resulting in alternating compact and extended configurations. Other upgrades, including digital back-ends and a rapid-response triggering system, have been developed since the original array was commissioned. In this paper, we review the major results from the prior operation of the MWA and then discuss the new science paths enabled by the improved capabilities. We group these science opportunities by the four original science themes but also include ideas for directions outside these categories.
Clonal Mycobacterium mucogenicum isolates (determined by molecular typing) were recovered from 19 bronchoscopic specimens from 15 patients. None of these patients had evidence of mycobacterial infection. Laboratory culture materials and bronchoscopes were negative for Mycobacteria. This pseudo-outbreak was caused by contaminated ice used to provide bronchoscopic lavage. Control was achieved by transitioning to sterile ice.
We studied the association between chlorhexidine gluconate (CHG) concentration on skin and resistant bacterial bioburden. CHG was almost always detected on the skin, and detection of methicillin-resistant Staphylococcus aureus, carbapenem-resistant Enterobacteriaceae, and vancomycin-resistant Enterococcus on skin sites was infrequent. However, we found no correlation between CHG concentration and bacterial bioburden.
The Rio Grande Cone is a major fanlike depositional feature in the continental slope of the Pelotas Basin, Southern Brazil. Two representative sediment cores collected in the Cone area were retrieved using a piston core device. In this work, the organic matter (OM) in the sediments was characterized for a continental vs. marine origin using chemical proxies to help constrain the origin of gas in hydrates. The main contribution of OM was from marine organic carbon based on the stable carbon isotope (δ13C-org) and total organic carbon/total nitrogen ratio (TOC:TN) analyses. In addition, the 14C data showed important information about the origin of the OM and we suggest some factors that could modify the original organic matter and therefore mask the “real” 14C ages: (1) biological activity that could modify the carbon isotopic composition of bulk terrestrial organic matter values, (2) the existence of younger sediments from mass wasting deposits unconformably overlying older sediments, and (3) the deep-sediment-sourced methane contribution due to the input of “old” (>50 ka) organic compounds from migrating fluids.
A national need is to prepare for and respond to accidental or intentional disasters categorized as chemical, biological, radiological, nuclear, or explosive (CBRNE). These incidents require specific subject-matter expertise, yet have commonalities. We identify 7 core elements comprising CBRNE science that require integration for effective preparedness planning and public health and medical response and recovery. These core elements are (1) basic and clinical sciences, (2) modeling and systems management, (3) planning, (4) response and incident management, (5) recovery and resilience, (6) lessons learned, and (7) continuous improvement. A key feature is the ability of relevant subject matter experts to integrate information into response operations. We propose the CBRNE medical operations science support expert as a professional who (1) understands that CBRNE incidents require an integrated systems approach, (2) understands the key functions and contributions of CBRNE science practitioners, (3) helps direct strategic and tactical CBRNE planning and responses through first-hand experience, and (4) provides advice to senior decision-makers managing response activities. Recognition of both CBRNE science as a distinct competency and the establishment of the CBRNE medical operations science support expert informs the public of the enormous progress made, broadcasts opportunities for new talent, and enhances the sophistication and analytic expertise of senior managers planning for and responding to CBRNE incidents.
Background: SMA is a neurodegenerative disease caused by biallelic deletion/mutation of the survival motor neuron (SMN1) gene. In the phase 1 trial (NCT02122952), SMN GRT onasemnogene abeparvovec (AVXS-101) improved outcomes of 15 symptomatic SMA1 patients (3 at a lower dose [cohort 1] and 12 at the proposed therapeutic dose [cohort 2]). This report describes long-term follow-up study design and data from the phase 1 study. Methods: Patients in the phase 1 study could rollover into a long-term follow-up study (NCT03421977). The primary objective is to collect long-term safety data (serious adverse events, hospitalizations, and adverse events of special interest). Annual follow-up will occur for 15 years. Additionally, patient record transfers from local clinician(s) will be requested. Safety assessments include medical history and record review, physical examination, clinical laboratory evaluation, and pulmonary assessments. Efficacy assessments include physical examination to assess developmental milestones. Results: As of September 27, 2018, the oldest patients are 59.2 (cohort 1) and 52.1 (cohort 2) months old and free of permanent ventilation. Preliminary data, including survival and developmental milestones, will be presented. Conclusions: Patients treated with a one-time dose of AVXS-101 continue to gain strength, develop, and achieve new milestones, demonstrating a long-term, durable response.
Children with congenital heart disease are at high risk for malnutrition. Standardisation of feeding protocols has shown promise in decreasing some of this risk. With little standardisation between institutions’ feeding protocols and no understanding of protocol adherence, it is important to analyse the efficacy of individual aspects of the protocols.
Adherence to and deviation from a feeding protocol in high-risk congenital heart disease patients between December 2015 and March 2017 were analysed. Associations between adherence to and deviation from the protocol and clinical outcomes were also assessed. The primary outcome was change in weight-for-age z score between time intervals.
Increased adherence to and decreased deviation from individual instructions of a feeding protocol improves patients change in weight-for-age z score between birth and hospital discharge (p = 0.031). Secondary outcomes such as markers of clinical severity and nutritional delivery were not statistically different between groups with high or low adherence or deviation rates.
High-risk feeding protocol adherence and fewer deviations are associated with weight gain independent of their influence on nutritional delivery and caloric intake. Future studies assessing the efficacy of feeding protocols should include the measures of adherence and deviations that are not merely limited to caloric delivery and illness severity.
Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.
We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.
Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.
Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.
Cognitive behavioral therapy (CBT) is an effective treatment for many patients suffering from major depressive disorder (MDD), but predictors of treatment outcome are lacking, and little is known about its neural mechanisms. We recently identified longitudinal changes in neural correlates of conscious emotion regulation that scaled with clinical responses to CBT for MDD, using a negative autobiographical memory-based task.
We now examine the neural correlates of emotional reactivity and emotion regulation during viewing of emotionally salient images as predictors of treatment outcome with CBT for MDD, and the relationship between longitudinal change in functional magnetic resonance imaging (fMRI) responses and clinical outcomes. Thirty-two participants with current MDD underwent baseline MRI scanning followed by 14 sessions of CBT. The fMRI task measured emotional reactivity and emotion regulation on separate trials using standardized images from the International Affective Pictures System. Twenty-one participants completed post-treatment scanning. Last observation carried forward was used to estimate clinical outcome for non-completers.
Pre-treatment emotional reactivity Blood Oxygen Level-Dependent (BOLD) signal within hippocampus including CA1 predicted worse treatment outcome. In contrast, better treatment outcome was associated with increased down-regulation of BOLD activity during emotion regulation from time 1 to time 2 in precuneus, occipital cortex, and middle frontal gyrus.
CBT may modulate the neural circuitry of emotion regulation. The neural correlates of emotional reactivity may be more strongly predictive of CBT outcome. The finding that treatment outcome was predicted by BOLD signal in CA1 may suggest overgeneralized memory as a negative prognostic factor in CBT outcome.
The impact of dementia-related stressors and strains have been examined for their potential to threaten the well-being of either the person with dementia or the family care partner, but rarely have studies considered the dyadic nature of well-being in dementia. The purpose of this study was to examine the dyadic effects of multiple dimensions of strain on the well-being of dementia care dyads.
Using multilevel modeling to account for the inter-relatedness of individual well-being within dementia care dyads, we examined cross-sectional responses collected from 42 dyads comprised of a hospitalized patient diagnosed with a primary progressive dementia (PWD) and their family care partner (CP). Both PWDs and CPs self-reported on their own well-being using measures of quality of life (QOL-Alzheimer’s Disease scale) and depressive symptoms (Center for Epidemiological Studies Depression Scale).
In adjusted models, the PWD’s well-being (higher QOL and lower depressive symptoms) was associated with significantly less strain in the dyad’s relationship. The CP’s well-being was associated with significantly less care-related strain and (for QOL scale) less relationship strain.
Understanding the impact of dementia on the well-being of PWDs or CPs may require an assessment of both members of the dementia care dyad in order to gain a complete picture of how dementia-related stressors and strains impact individual well-being. These results underscore the need to assess and manage dementia-related strain as a multi-dimensional construct that may include strain related to the progression of the disease, strain from providing care, and strain on the dyad’s relationship quality.
The construction of future technological systems in work domains that do not yet exist, known as the envisioned world problem, is an increasingly important topic for designers, particularly given the rapid rate of technological advancement in the modern era. This paper first discusses the theoretical underpinnings of using cognitive work analysis (CWA) for developing a decision support system (DSS) situated within the envisioned world problem and recasts the problem as pathway-dependent processes. Using this pathway-dependent framework, each stage of the envisioning process is described to reveal how human factors experts can link existing work domains to envisioned instances. Finally, a case study example of the envisioning process that incorporates CWA modelling is demonstrated as it pertains to the advancement of the human spaceflight domain. As a result, this paper provides a unified treatment of the envisioned world problem with an end-to-end example of one approach to designing future technologies for future work domains.
Externalizing disorders are known to be partly heritable, but the biological pathways linking genetic risk to the manifestation of these costly behaviors remain under investigation. This study sought to identify neural phenotypes associated with genomic vulnerability for externalizing disorders.
One-hundred fifty-five White, non-Hispanic veterans were genotyped using a genome-wide array and underwent resting-state functional magnetic resonance imaging. Genetic susceptibility was assessed using an independently developed polygenic score (PS) for externalizing, and functional neural networks were identified using graph theory based network analysis. Tasks of inhibitory control and psychiatric diagnosis (alcohol/substance use disorders) were used to measure externalizing phenotypes.
A polygenic externalizing disorder score (PS) predicted connectivity in a brain circuit (10 nodes, nine links) centered on left amygdala that included several cortical [bilateral inferior frontal gyrus (IFG) pars triangularis, left rostral anterior cingulate cortex (rACC)] and subcortical (bilateral amygdala, hippocampus, and striatum) regions. Directional analyses revealed that bilateral amygdala influenced left prefrontal cortex (IFG) in participants scoring higher on the externalizing PS, whereas the opposite direction of influence was observed for those scoring lower on the PS. Polygenic variation was also associated with higher Participation Coefficient for bilateral amygdala and left rACC, suggesting that genes related to externalizing modulated the extent to which these nodes functioned as communication hubs.
Findings suggest that externalizing polygenic risk is associated with disrupted connectivity in a neural network implicated in emotion regulation, impulse control, and reinforcement learning. Results provide evidence that this network represents a genetically associated neurobiological vulnerability for externalizing disorders.
The links between low socioeconomic status and poor health are well established, yet despite adversity, some individuals with low socioeconomic status appear to avoid these negative consequences through adaptive coping. Previous research found a set of strategies, called shift-and-persist (shifting the self to stressors while persisting by finding meaning), to be particularly adaptive for individuals with low socioeconomic status, who typically face more uncontrollable stressors. This study tested (a) whether perceived social status, similar to objective socioeconomic status, would moderate the link between shift-and-persist and health, and (b) whether a specific uncontrollable stressor, unfair treatment, would similarly moderate the health correlates of shift-and-persist. A sample of 308 youth (Meanage = 13.0, range 8–17), physician diagnosed with asthma, completed measures of shift-and-persist, unfair treatment, asthma control, and quality of life in the lab, and 2 weeks of daily diaries about their asthma symptoms. Parents reported on perceived family social status. Results indicated that shift-and-persist was associated with better asthma profiles, only among youth from families with lower (vs. higher) parent-reported perceived social status. Shift-and-persist was also associated with better asthma profiles, only among youth who experienced more (vs. less) unfair treatment. These findings suggest that the adaptive values of coping strategies for youth with asthma depend on the family's perceived social status and on the stressor experienced.