Enhancing the Understanding of Resilience in Health Systems of Low- and Middle-income Countries: A Qualitative Evidence Synthesis

BACKGROUND: A country's health system faces pressure when hit by an unexpected shock, such as what we observe in the midst of the coronavirus disease 2019 (COVID-19) pandemic. The concept of resilience is highly relevant in this context and is a prerequisite for a health system capable of withstanding future shocks. By exploring how the key dimensions of the resilient health system framework are applied, the present systematic review synthesizes the vital features of resilient health systems in low- and middle-income countries. The aim of this review is to ascertain the relevance of health system resilience in the context of a major shock, through better understanding its dimensions, uses and implications. METHODS: The review uses the best-fit framework synthesis approach. An a priori conceptual framework was selected and a coding framework created. A systematic search identified 4284 unique citations from electronic databases and reports by non-governmental organisations, 12 of which met the inclusion criteria. Data were extracted and coded against the pre-existing themes. Themes outside of the a priori framework were collated to form a refined list of themes. Then, all twelve studies were revisited using the new list of themes in the context of each study. RESULTS: Ten themes were generated from the analysis. Five confirmed the a priori conceptual framework that capture the dynamic attributes of a resilient system. Five new themes were identified as foundational for achieving resilience: realigned relationships, foresight and motivation as drivers, and emergency preparedness and change management as organisational mechanisms. CONCLUSION: The refined conceptual model shows how the themes inter-connect. The foundations of resilience appear to be critical especially in resource-constrained settings to unlock the dynamic attributes of resilience. This review prompts countries to consider building the foundations of resilience described here as a priority to better prepare for future shocks

Molecular basis for the regulation of human glycogen synthase by phosphorylation and glucose-6-phosphate

\ua9 2022, The Author(s). Glycogen synthase (GYS1) is the central enzyme in muscle glycogen biosynthesis. GYS1 activity is inhibited by phosphorylation of its amino (N) and carboxyl (C) termini, which is relieved by allosteric activation of glucose-6-phosphate (Glc6P). We present cryo-EM structures at 3.0–4.0 \uc5 resolution of phosphorylated human GYS1, in complex with a minimal interacting region of glycogenin, in the inhibited, activated and catalytically competent states. Phosphorylations of specific terminal residues are sensed by different arginine clusters, locking the GYS1 tetramer in an inhibited state via intersubunit interactions. The Glc6P activator promotes conformational change by disrupting these interactions and increases the flexibility of GYS1, such that it is poised to adopt a catalytically competent state when the sugar donor UDP-glucose (UDP-glc) binds. We also identify an inhibited-like conformation that has not transitioned into the activated state, in which the locking interaction of phosphorylation with the arginine cluster impedes subsequent conformational changes due to Glc6P binding. Our results address longstanding questions regarding the mechanism of human GYS1 regulation

Metabolomics in Early Alzheimer's Disease: Identification of Altered Plasma Sphingolipidome Using Shotgun Lipidomics

The development of plasma biomarkers could facilitate early detection, risk assessment and therapeutic monitoring in Alzheimer's disease (AD). Alterations in ceramides and sphingomyelins have been postulated to play a role in amyloidogensis and inflammatory stress related neuronal apoptosis; however few studies have conducted a comprehensive analysis of the sphingolipidome in AD plasma using analytical platforms with accuracy, sensitivity and reproducibility.We prospectively analyzed plasma from 26 AD patients (mean MMSE 21) and 26 cognitively normal controls in a non-targeted approach using multi-dimensional mass spectrometry-based shotgun lipidomics to determine the levels of over 800 molecular species of lipids. These data were then correlated with diagnosis, apolipoprotein E4 genotype and cognitive performance. Plasma levels of species of sphingolipids were significantly altered in AD. Of the 33 sphingomyelin species tested, 8 molecular species, particularly those containing long aliphatic chains such as 22 and 24 carbon atoms, were significantly lower (p<0.05) in AD compared to controls. Levels of 2 ceramide species (N16:0 and N21:0) were significantly higher in AD (p<0.05) with a similar, but weaker, trend for 5 other species. Ratios of ceramide to sphingomyelin species containing identical fatty acyl chains differed significantly between AD patients and controls. MMSE scores were correlated with altered mass levels of both N20:2 SM and OH-N25:0 ceramides (p<0.004) though lipid abnormalities were observed in mild and moderate AD. Within AD subjects, there were also genotype specific differences.In this prospective study, we used a sensitive multimodality platform to identify and characterize an essentially uniform but opposite pattern of disruption in sphingomyelin and ceramide mass levels in AD plasma. Given the role of brain sphingolipids in neuronal function, our findings provide new insights into the AD sphingolipidome and the potential use of metabolomic signatures as peripheral biomarkers

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Tuning fulleride electronic structure and molecular ordering via variable layer index

C60 fullerides are uniquely flexible molecular materials that exhibit a rich variety of behavior, including superconductivity and magnetism in bulk compounds, novel electronic and orientational phases in thin films, and quantum transport in a single-C60 transistor. The complexity of fulleride properties stems from the existence of many competing interactions, such as electron-electron correlations, electron-vibration coupling, and intermolecular hopping. The exact role of each interaction is controversial due to the difficulty of experimentally isolating the effects of a single interaction in the intricate fulleride materials. Here we report a unique level of control of the material properties of KxC60 ultra-thin films through well-controlled atomic layer indexing and accurate doping concentrations. Using STM techniques, we observe a series of electronic and structural phase transitions as the fullerides evolve from two-dimensional monolayers to quasi-threedimensional multilayers in the early stages of layer-by-layer growth. These results demonstrate the systematic evolution of fulleride electronic structure and molecular ordering with variable KxC60 film layer index, and shed new light on creating novel molecular structures and devices.Comment: 16 pages, 4 figures, to appear in Nature Material

Silica-Encapsulated Efficient and Stable Si Quantum Dots with High Biocompatibility

A facile fabrication method to produce biocompatible semiconductor Quantum Dots encapsulated in high quality and thick thermal oxide is presented. The process employs sonication of porous Si/SiO2 structures to produce flakes with dimension in the 50–200 nm range. These flakes show a coral-like SiO2 skeleton with Si nanocrystals embedded in and are suitable for functionalization with other diagnostic or therapeutic agents. Silicon is a biocompatible material, efficiently cleared from the human body. The Photoluminescence emission falls in the transparency window for living tissues and is found to be bright and stable for hours in the aggressive biological environment

Comparison of the Commercial Color LCD and the Medical Monochrome LCD Using Randomized Object Test Patterns

Workstations and electronic display devices in a picture archiving and communication system (PACS) provide a convenient and efficient platform for medical diagnosis. The performance of display devices has to be verified to ensure that image quality is not degraded. In this study, we designed a set of randomized object test patterns (ROTPs) consisting of randomly located spheres with various image characteristics to evaluate the performance of a 2.5 mega-pixel (MP) commercial color LCD and a 3 MP diagnostic monochrome LCD in several aspects, including the contrast, resolution, point spread effect, and noise. The ROTPs were then merged into 120 abdominal CT images. Five radiologists were invited to review the CT images, and receiver operating characteristic (ROC) analysis was carried out using a five-point rating scale. In the high background patterns of ROTPs, the sensitivity performance was comparable between both monitors in terms of contrast and resolution, whereas, in the low background patterns, the performance of the commercial color LCD was significantly poorer than that of the diagnostic monochrome LCD in all aspects. The average area under the ROC curve (AUC) for reviewing abdominal CT images was 0.717±0.0200 and 0.740±0.0195 for the color monitor and the diagnostic monitor, respectively. The observation time (OT) was 145±27.6 min and 127±19.3 min, respectively. No significant differences appeared in AUC (p = 0.265) and OT (p = 0.07). The overall results indicate that ROTPs can be implemented as a quality control tool to evaluate the intrinsic characteristics of display devices. Although there is still a gap in technology between different types of LCDs, commercial color LCDs could replace diagnostic monochrome LCDs as a platform for reviewing abdominal CT images after monitor calibration

From covalent bonding to coalescence of metallic nanorods

Growth of metallic nanorods by physical vapor deposition is a common practice, and the origin of their dimensions is a characteristic length scale that depends on the three-dimensional Ehrlich-Schwoebel (3D ES) barrier. For most metals, the 3D ES barrier is large so the characteristic length scale is on the order of 200 nm. Using density functional theory-based ab initio calculations, this paper reports that the 3D ES barrier of Al is small, making it infeasible to grow Al nanorods. By analyzing electron density distributions, this paper shows that the small barrier is the result of covalent bonding in Al. Beyond the infeasibility of growing Al nanorods by physical vapor deposition, the results of this paper suggest a new mechanism of controlling the 3D ES barrier and thereby nanorod growth. The modification of local degree of covalent bonding, for example, via the introduction of surfactants, can increase the 3D ES barrier and promote nanorod growth, or decrease the 3D ES barrier and promote thin film growth