Missed nursing care in newborn units: a cross-sectional direct observational study

Background: Improved hospital care is needed to reduce newborn mortality in low/middle-income countries (LMIC). Nurses are essential to the delivery of safe and effective care, but nurse shortages and high patient workloads may result in missed care. We aimed to examine nursing care delivered to sick newborns and identify missed care using direct observational methods. Methods: A cross-sectional study using directobservational methods for 216 newborns admitted in six health facilities in Nairobi, Kenya, was used to determine which tasks were completed. We report the frequency of tasks done and develop a nursing care index (NCI), an unweighted summary score of nursing tasks done for each baby, to explore how task completion is related to organisational and newborn characteristics. Results: Nursing tasks most commonly completed were handing over between shifts (97%), checking and where necessary changing diapers (96%). Tasks with lowest completion rates included nursing review of newborns (38%) and assessment of babies on phototherapy (15%). Overall the mean NCI was 60% (95% CI 58% to 62%), at least 80% of tasks were completed for only 14% of babies. Private sector facilities had a median ratio of babies to nurses of 3, with a maximum of 7 babies per nurse. In the public sector, the median ratio was 19 babies and a maximum exceeding 25 babies per nurse. In exploratory multivariable analyses, ratios of ≥12 babies per nurse were associated with a 24-point reduction in the mean NCI compared with ratios of ≤3 babies per nurse. Conclusion: A significant proportion of nursing care is missed with potentially serious effects on patient safety and outcomes in this LMIC setting. Given that nurses caring for fewer babies on average performed more of the expected tasks, addressing nursing is key to ensuring delivery of essential aspects of care as part of improving quality and safety

Lessons from a Health Policy and Systems Research programme exploring the quality and coverage of newborn care in Kenya.

There are global calls for research to support health system strengthening in low-income and middle-income countries (LMICs). To examine the nature and magnitude of gaps in access and quality of inpatient neonatal care provided to a largely poor urban population, we combined multiple epidemiological and health services methodologies. Conducting this work and generating findings was made possible through extensive formal and informal stakeholder engagement linked to flexibility in the research approach while keeping overall goals in mind. We learnt that 45% of sick newborns requiring hospital care in Nairobi probably do not access a suitable facility and that public hospitals provide 70% of care accessed with private sector care either poor quality or very expensive. Direct observations of care and ethnographic work show that critical nursing workforce shortages prevent delivery of high-quality care in high volume, low-cost facilities and likely threaten patient safety and nurses' well-being. In these challenging settings, routines and norms have evolved as collective coping strategies so health professionals maintain some sense of achievement in the face of impossible demands. Thus, the health system sustains a functional veneer that belies the stresses undermining quality, compassionate care. No one intervention will dramatically reduce neonatal mortality in this urban setting. In the short term, a substantial increase in the number of health workers, especially nurses, is required. This must be combined with longer term investment to address coverage gaps through redesign of services around functional tiers with improved information systems that support effective governance of public, private and not-for-profit sectors

Postnatal Changes in the Expression Pattern of the Imprinted Signalling Protein XLαs Underlie the Changing Phenotype of Deficient Mice

The alternatively spliced trimeric G-protein subunit XLαs, which is involved in cAMP signalling, is encoded by the Gnasxl transcript of the imprinted Gnas locus. XLαs deficient mice show neonatal feeding problems, leanness, inertia and a high mortality rate. Mutants that survive to weaning age develop into healthy and fertile adults, which remain lean despite elevated food intake. The adult metabolic phenotype can be attributed to increased energy expenditure, which appears to be caused by elevated sympathetic nervous system activity. To better understand the changing phenotype of Gnasxl deficient mice, we compared XLαs expression in neonatal versus adult tissues, analysed its co-localisation with neural markers and characterised changes in the nutrient-sensing mTOR1-S6K pathway in the hypothalamus. Using a newly generated conditional Gnasxl lacZ gene trap line and immunohistochemistry we identified various types of muscle, including smooth muscle cells of blood vessels, as the major peripheral sites of expression in neonates. Expression in all muscle tissues was silenced in adults. While Gnasxl expression in the central nervous system was also developmentally silenced in some midbrain nuclei, it was upregulated in the preoptic area, the medial amygdala, several hypothalamic nuclei (e.g. arcuate, dorsomedial, lateral and paraventricular nuclei) and the nucleus of the solitary tract. Furthermore, expression was detected in the ventral medulla as well as in motoneurons and a subset of sympathetic preganglionic neurons of the spinal cord. In the arcuate nucleus of Gnasxl-deficient mice we found reduced activity of the nutrient sensing mTOR1-S6K signalling pathway, which concurs with their metabolic status. The expression in these brain regions and the hypermetabolic phenotype of adult Gnasxl-deficient mice imply an inhibitory function of XLαs in energy expenditure and sympathetic outflow. By contrast, the neonatal phenotype of mutant mice appears to be due to a transient role of XLαs in muscle tissues

Enhancement of COPD biological networks using a web-based collaboration interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks

BCAT1 controls metabolic reprogramming in activated human macrophages and is associated with inflammatory diseases

This work was supported by the Medical Research Council (MR/M004716/1 and MR/N01121X/1 to J.B.) and by Kidney Research UK (RP9/2013 to J.B.). D.C. is supported by the Institute Pasteur, Fondazione Cenci Bolognetti. C.M. is supported by the British Hearth Foundation Fellowship (FS/12/3829640)

Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based method