A High-Resolution Earth Observations and Machine Learning-Based Approach to Forecast Waterborne Disease Risk in Post-Disaster Settings

Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches

Multi-drug resistant gram negative infections and use of intravenous polymyxin B in critically ill children of developing country: Retrospective cohort study

Background: Patients in pediatric intensive care Units (PICU) are susceptible to infections with antibiotic resistant organisms which increase the morbidity, mortality and cost of care. To describe the clinical characteristics and mortality in patients with Multi-Drug Resistant (MDR) gram negative organisms. We also report safety of Polymyxin B use in these patients.Methods: Files of patients admitted in PICU of Aga Khan University Hospital, from January 2010 to December 2011, one month to 15 years of age were reviewed. Demographic and clinical features of patients with MDR gram negative infections, antibiotic susceptibility pattern of isolates, discharge disposition and adverse effects of Polymyxin B were recorded.Results: A total of 44.8/1000(36/803) admitted patients developed MDR gram negative infections, of which 47.2%(17/36) were male, with mean age of 3.4 yrs(+/-4.16). Acinetobacter Species (25.5%) was the most frequently isolated MDR organisms followed by Klebsiella Pneumoniae (17%). Sensitivity of isolates was 100% to Polymyxin B, followed by Imipenem (50%), and piperacillin/tazobactem (45%). The crude mortality rate of patients with MDR gram negative infections was 44.4% (16/36). Fourteen of 36 patients received Polymyxin B and 57.1%; (8/14) of them were cured. Nephrotoxicity was observed in 21.4% (3/14) cases, none of the patients showed signs of neuropathy.Conclusion: Our study highlights high rates of Carbapenem resistant gram negative isolates, leading to increasing use of Polymyxin B as the only drug to combat against these critically ill children. Therefore, we emphasizeon Stewardship of Antibiotics and continuous surveillance system as strategies in overall management of these critically ill children

Red blood cell transfusion in critically-ill children and its association with outcome

Objective: To determine the indications and threshold of haemoglobin levels for packed red blood cell transfusion and its association with outcomes in a paediatric intensive care setting.Methods: The retrospective study was conducted in the paediatric intensive care unit of the Aga Khan University Hospital, Karachi, and comprised medical records of all inpatients with age between 1 month and 16 years who received packed red blood cell transfusions between January and December 2017. Data was retrieved from the hospital database and was analyzed using SPSS 22.Results: Of the 147 subjects with a mean age of 67.89±65.8 months, 76(51.7%) were males. Mean paediatric risk of mortality score was 11.72±7.86. Major admitting diagnosis included sepsis and multiorgan dysfunction 50(34%), respiratory diseases 26(17.7%) and haematology/oncology diseases 22(15%). The indications for transfusion was low haemoglobin in 90(61.2%) patients, shock 29(19.7%) and hypoxia 28(19%). Acute transfusion reaction was observed in 1(0.7%) patient; 120(82%) required mechanical ventilation; and 94(64%) required inotropic support. Of the total, 88(59.9%) patients survived. Paediatric risk of mortality score, need for inotropic support and mechanical ventilation were associated with mortality (p\u3c0.05).Conclusions: Packed red blood cell transfusion, which is frequently prescribed in intensive care settings, was not found to be associated with favourable outcome

Perception of tomorrow’s Health-Care connoisseur and front-runners of their educational environment utilizing DREEM inventory in Bahasa Melayu version, the native language of Malaysia

Background There have been a lot of reports throughout the world that medical students were abused during their undergraduate education and clerkship training. Thereafter, calls for intensifying the evaluation of medical and health schools’ curricula based on students’ perceptions of their educational environment. Several studies, methods, and instruments were developed including the Dundee Ready Education Environment Measure (DREEM) inventory, to evaluate the medical educational environment in last five decades. The DREEM inventory has been translated into minimum eight different native tongues namely Arabic, Chinese, Japanese, Persian, Portuguese, Spanish, Swedish, and Turkish. Aims The objective of this study was to assess the educational environment of the UniSZA undergraduate medical program from the students’ perspective utilizing the DREEM inventory translated in Bahasa Melayu. Methods This was a descriptive cross-sectional survey conducted among the medical students of session 2015-2016 to assess educational environment of the Faculty of Medicine, UniSZA. The study was conducted from December 2015 to January 2016. Universal sampling technique was adopted. Results A total of 277 (95.5 per cent) out of 290 students responded to the questionnaire; among them 27.4 per cent were male and 72.6 per cent were female respondents. The overall mean DREEM scores for both preclinical and clinical students were 67.41±24.06. The scores for pre-clinical and clinical were 64.02±25.10 and 69.65±23.15 respectively; however, no statistically significant (p=0.57) differences was observed between two phases. A significant difference was observed between gender of the respondents in students’ perceptions of teachers (p=0.005) and students’ social self-perceptions (p=0.046)

Salt tolerance QTLs of an endemic rice landrace, \u3ci\u3eHorkuch\u3c/i\u3e at seedling and reproductive stages

Salinity has a significant negative impact on production of rice. To cope with the increased soil salinity due to climate change, we need to develop salt tolerant rice varieties that can maintain their high yield. Rice landraces indigenous to coastal Bangladesh can be a great resource to study the genetic basis of salt adaptation. In this study, we implemented a QTL analysis framework with a reciprocal mapping population developed from a salt tolerant landrace Horkuch and a high yielding rice variety IR29. Our aim was to detect genetic loci that contributes to the salt adaptive responses of the two different developmental stages of rice which are very sensitive to salinity stress. We identified 14 QTLs for 9 traits and found that most are unique to specific developmental stages. In addition, we detected a significant effect of the cytoplasmic genome on the QTL model for some traits such as leaf total potassium and filled grain weight. This underscores the importance of considering cytoplasm-nuclear interaction for breeding programs. Finally, we identified QTLs co-localization for multiple traits that highlights the possible constraint of multiple QTL selection for breeding programs due to different contributions of a donor allele for different traits

Continuous-time quantum walks for MAX-CUT are hot

By exploiting the link between time-independent Hamiltonians and thermalisation, heuristic predictions on the performance of continuous-time quantum walks for MAX-CUT are made. The resulting predictions depend on the number of triangles in the underlying MAX-CUT graph. We extend these results to the time-dependent setting with multi-stage quantum walks and Floquet systems. The approach followed here provides a novel way of understanding the role of unitary dynamics in tackling combinatorial optimisation problems with continuous-time quantum algorithms.Comment: 25 pages, 29 figure

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Perceived risk of infection and death from COVID-19 among community members of low- and middle-income countries: A cross-sectional study [version 1; peer review: awaiting peer review]

Background: Risk perceptions of coronavirus disease 2019 (COVID-19) are considered important as they impact community health behaviors. The aim of this study was to determine the perceived risk of infection and death due to COVID-19 and to assess the factors associated with such risk perceptions among community members in low- and middle-income countries (LMICs) in Africa, Asia, and South America. Methods: An online cross-sectional study was conducted in 10 LMICs in Africa, Asia, and South America from February to May 2021. A questionnaire was utilized to assess the perceived risk of infection and death from COVID-19 and its plausible determinants. A logistic regression model was used to identify the factors associated with such risk perceptions. Results: A total of 1,646 responses were included in the analysis of the perceived risk of becoming infected and dying from COVID-19. Our data suggested that 36.4% of participants had a high perceived risk of COVID-19 infection, while only 22.4% had a perceived risk of dying from COVID-19. Being a woman, working in healthcare-related sectors, contracting pulmonary disease, knowing people in the immediate social environment who are or have been infected with COVID-19, as well as seeing or reading about individuals infected with COVID-19 on social media or TV were all associated with a higher perceived risk of becoming infected with COVID-19. In addition, being a woman, elderly, having heart disease and pulmonary disease, knowing people in the immediate social environment who are or have been infected with COVID-19, and seeing or reading about individuals infected with COVID-19 on social media or TV had a higher perceived risk of dying from COVID-19. Conclusions: The perceived risk of infection and death due to COVID-19 are relatively low among respondents; this suggests the need to conduct health campaigns to disseminate knowledge and information on the ongoing pandemic

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa.

BACKGROUND: Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature. METHODS: A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes. RESULTS: With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature. CONCLUSIONS: In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs