Mitochondrial encephalocardio-myopathy with early neonatal onset due to TMEM70 mutation

Objective Mitochondrial disturbances of energy-generating systems in childhood are a heterogeneous group of disorders. The aim of this multi-site survey was to characterise the natural course of a novel mitochondrial disease with ATP synthase deficiency and mutation in the TMEM70 gene. Methods Retrospective clinical data and metabolic profiles were collected and evaluated in 25 patients (14 boys, 11 girls) from seven European countries with a c. 317-2A -> G mutation in the TMEM70 gene. Results Severe muscular hypotonia (in 92% of newborns), apnoic spells (92%), hypertrophic cardiomyopathy (HCMP; 76%) and profound lactic acidosis (lactate 5-36 mmol/l; 92%) with hyperammonaemia (100-520 mu mol/l; 86%) were present from birth. Ten patients died within the first 6 weeks of life. Most patients surviving the neonatal period had persisting muscular hypotonia and developed psychomotor delay. HCMP was non-progressive and even disappeared in some children. Hypospadia was present in 54% of the boys and cryptorchidism in 67%. Increased excretion of lactate and 3-methylglutaconic acid (3-MGC) was observed in all patients. In four surviving patients, life-threatening hyperammonaemia occurred during childhood, triggered by acute gastroenteritis and prolonged fasting. Conclusions ATP synthase deficiency with mutation in TMEM70 should be considered in the diagnosis and management of critically ill neonates with early neonatal onset of muscular hypotonia, HCMP and hypospadias in boys accompanied by lactic acidosis, hyperammonaemia and 3-MGC-uria. However, phenotype severity may vary significantly. The disease occurs frequently in the Roma population and molecular-genetic analysis of the TMEM70 gene is sufficient for diagnosis without need of muscle biopsy in affected children

GADSA: Decision Support App for Antibiotics Prescribing in Nigeria

GADSA (Gamified Antimicrobial Stewardship Decision Support App) is a decision support tool to improve evidence-based prescribing, designed to be used at the point-of-care to help clinicians comply with guidelines in their everyday practice. The app represents a novel cross-platform, mobile decision support tool, integrating principles from serious games and gamification, to improve compliance with prescription guidelines of Surgical Antibiotic Prophylaxis (SAP) in Nigeria. This paper focuses on the decision support component of the mobile application, integrating the World Health Organisation and Sanford guidelines for SAP prescriptions

Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation

A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil

A Likert Scale-Based Model for Benchmarking Operational Capacity, Organizational Resilience, and Disaster Risk Reduction

Likert scales are a common methodological tool for data collection used in quantitative or mixed-method approaches in multiple domains. They are often employed in surveys or questionnaires, for benchmarking answers in the fields of disaster risk reduction, business continuity management, and organizational resilience. However, both scholars and practitioners may lack a simple scale of reference to assure consistency across disciplinary fields. This article introduces a simple-to-use rating tool that can be used for benchmarking responses in questionnaires, for example, for assessing disaster risk reduction, gaps in operational capacity, and organizational resilience. We aim, in particular, to support applications in contexts in which the target groups, due to cultural, social, or political reasons, may be unsuitable for in-depth analyses that use, for example, scales from 1 to 7 or from 1 to 10. This methodology is derived from the needs emerged in our recent fieldwork on interdisciplinary projects and from dialogue with the stakeholders involved. The output is a replicable scale from 0 to 3 presented in a table that includes category labels with qualitative attributes and descriptive equivalents to be used in the formulation of model answers. These include examples of levels of resilience, capacity, and gaps. They are connected to other tools that could be used for in-depth analysis. The advantage of our Likert scale-based response model is that it can be applied in a wide variety of disciplines, from social science to engineering

Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics

Purpose: Dengue is considered one of the biggest public health problems in recent decades. Climate and demographic changes, the disorderly growth of cities and international trade have brought new arboviruses such as chikungunya and Zika. Control of arboviruses depends on control of the vector: the Aedes aegypti mosquito. Objective: In this work, we propose a methodology for building disease predictors capable of predicting infected cases and locations based on machine learning. We also propose an artificial experts committee based on meta-heuristic methods to detect the most relevant risk factors. Method As a case study, we applied the methodology to forecast dengue, chikungunya and Zika, with data from the City of Recife, Brazil, from 2013 to 2016. We used arboviruses cases data and climatic and environmental information: wind speeds, temperatures and precipitation. Results The best prediction results were obtained with 10-tree Random Forest regression, with Pearson’s correlation above 0.99 and RMSE (%) below 6%. Additionally, the artificial experts committee was able to present the most relevant factors for predicting cases in each two-month period. Conclusion: The spatiotemporal prediction results showed the evolution of arboviruses, pointing out as major focuses on both regions richer in urban green areas and low-income neighborhood with irregular water supply. Determining the most relevant factors for prediction, as well as the spatial distribution of cases, can be useful for the planning and execution of public policies aimed at improving the health infrastructure and planning and controlling the vector