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Objectives: To determine the cause of stillbirth after application of relevant condition at death (ReCoDe) classification system. Methods: This was a retrospective cross sectional study of 207 women diagnosed with stillbirth after 24 completed weeks of pregnancy at the Aga Khan University Hospital (AKUH), Karachi between 1st January 2015 and 31st December 2019. The primary objective was to find the cause of stillbirth according to the new classification of relevant condition at death (ReCoDe). Results: There were a total of 32413 live births and 207 stillbirths during the study period thus stillbirth rate of 6 per 1000 live births. In this study, 80% of women were in the age group of 20-35 years, 16% had advanced maternal age while 3.8% of women accounted for less than 20 years. Among the maternal factors; 54.5% cases were booked and the remaining were were un-booked cases. Pre-eclampsia was the most common associated maternal condition (14.9%). Fetal cause accounted for 34.7% of stillbirths and the fetal growth restriction (FGR) was the most common; 23.6%. After application of ReCoDe classification, in 81% of stillbirth cases associated condition were found and only 18.8% of cases were categorized unexplained. Conclusion: Application of ReCoDe classification is easy to understand and applicable, especially in low resource settings with associated causes identified in vast majority of cases. doi: https://doi.org/10.12669/pjms.38.1.4470 How to cite this:Kashif U, Bhamani S, Patel A, Shamsul Islam Z. Still Birth classification: Application of Relevant Condition at Death (ReCoDe) classification system in a tertiary care hospital of Pakistan. Pak J Med Sci. 2022;38(1):133-137. doi: https://doi.org/10.12669/pjms.38.1.4470 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.

Access to safely managed drinking water (SMDW) remains a global challenge, and affects 2.2 billion people1,2. Solar-driven atmospheric water harvesting (AWH) devices with continuous cycling may accelerate progress by enabling decentralized extraction of water from air3–6, but low specific yields (SY) and low daytime relative humidity (RH) have raised questions about their performance (in litres of water output per day)7–11. However, to our knowledge, no analysis has mapped the global potential of AWH12 despite favourable conditions in tropical regions, where two-thirds of people without SMDW live2. Here we show that AWH could provide SMDW for a billion people. Our assessment—using Google Earth Engine13—introduces a hypothetical 1-metre-square device with a SY profile of 0.2 to 2.5 litres per kilowatt-hour (0.1 to 1.25 litres per kilowatt-hour for a 2-metre-square device) at 30% to 90% RH, respectively. Such a device could meet a target average daily drinking water requirement of 5 litres per day per person14. We plot the impact potential of existing devices and new sorbent classes, which suggests that these targets could be met with continued technological development, and well within thermodynamic limits. Indeed, these performance targets have been achieved experimentally in demonstrations of sorbent materials15–17. Our tools can inform design trade-offs for atmospheric water harvesting devices that maximize global impact, alongside ongoing efforts to meet Sustainable Development Goals (SDGs) with existing technologies., Mapping of the global potential of atmospheric water harvesting using solar energy shows that it could provide safely managed drinking water for a billion people worldwide based on climate suitability.

Personalized medicine (PM) has revolutionized oncology management in high human development indexed countries. By interrogating both disease and host factors through a variety of tools, oncologists have been able to better target an individual's cancer, leading to improved outcomes. But both the tools used to define these variables, such as next generation sequencing, large immunohistochemical and fluorescence in situ hybridization (FISH) panels, and the weapons employed against each target are extremely expensive. The expenses have to be measured as not only the direct cost to the patient but also the cost to the system to develop and deploy the necessary infrastructure to optimally use them. However, the concepts of predictive, timely prevention and PM have demonstrated improvement in patient's satisfaction and cost effectiveness. In this paper we will summarize the relevant barriers and challenges that limit the implementation of PM in the developing world with an emphasis on the challenges in Nigeria and Nepal. World J Oncol. 2021;12(2-3):50-60 doi: https://doi.org/10.14740/wjon1345

To evaluate prevalence and risk factors of moderate-severe Bronchopulmonary dysplasia (BPD)/Death in extremely low gestation age neonates (ELGANs). Study of 266 ELGANs born at gestational age (GA) ≤ 28 weeks (w). Primary Outcome measure—composite outcome of moderate-severe BPD/Death using the National Institute of Child Health and Human Development NICHD’s (2001) BPD definition. Cohort’s mean GA and birth-weight (BW) were 25.3 ± 1.4w and 724 ± 14 g respectively with an overall mortality of 19% and moderate-severe BPD of 67%. Prevalence of moderate-severe BPD/death decreased significantly with increasing GA (86–93%) at 23–24 w; to 25%(OR 1.06; 95% CI; 1.01–1.11); and mechanical ventilation(MV) on Day7 (OR5.5; 95% CI; 2.8–10.8). Only need for Day7 MV was independently predictive of composite outcome (OR1.97; 95% CI; 1.3–3.1). Risk factor identification will enable initiatives to implement lung protective strategies and develop prospective models for BPD prediction and prognostication.

Naturally-occurring membranes in the xylem tissue of gymnosperm sapwood enable its use as an abundantly-available material to construct filters, with potential to facilitate access to safe drinking water in resource-constrained settings. However, the material’s behavior as a filter is poorly understood, and challenges such as short shelf life have not been addressed. Here, we characterize the operational attributes of xylem filters and show that the material exhibits a highly non-linear dependence of flow resistance on thickness upon drying, and a tendency for self-blocking. We develop guidelines for the design and fabrication of xylem filters, demonstrate gravity-operated filters with shelf life >2 years, and show that the filters can provide >3 log removal of E. coli, MS-2 phage, and rotavirus from synthetic test waters and coliform bacteria from contaminated spring, tap, and ground waters. Through interviews and workshops in India, we use a user-centric approach to design a prototype filtration device with daily- to weekly-replaceable xylem filters, and uncover indicators of social acceptance of xylem as a natural water filter. Our work enhances the understanding of xylem as a filtration material, and opens opportunities for engineering a diverse range of low-cost, biodegradable xylem-based filtration products on a global scale., Gymnosperm sapwood is an abundantly available material to construct water filters but the material’s behaviour as a filter is poorly understood and challenges such as short shelf live have not been addressed. Here, the authors develop guidelines for the design and fabrication of xylem filters and demonstrate gravity-operated filters with a shelf life of more than two years for the removal of contaminants from spring, tap and ground water.

Perceptual visual dysfunction (PVD) comprises a group of vision disorders resulting from dysfunction of the posterior parietal and/or temporal lobes. Often, affected children have normal/near normal visual acuities and/or visual fields, but have difficulties in activities of daily living involving the use of vision. PVDs are known to be common among children with risk factors such as a history of prematurity and/or neurodevelopmental disorders. The inferior temporal lobes and ventral stream transform visual signals into perception, while the posterior parietal lobes and dorsal stream transform visual signals to non-consciously map the scene to guide action and facilitate attention. Dysfunction of these can lead to specific visual impairments that need to be identified during history taking, triggering ascertainment of further details by a structured inventory approach. Clinical tests to elicit dorsal and ventral stream visual dysfunctions have good specificity but low sensitivity. Neuropsychologists are rarely available in the developing world to perform detailed assessments, but there are a few tests that can be used by eye care professionals with some training. Optical coherence tomography (OCT) showing thinning of the ganglion cell layer and retinal nerve fiber layer is being explored as a potential tool for rapid assessment in the clinic. The behavioral outcomes of PVD can mimic psychological conditions including autism spectrum disorder, attention deficit hyperactivity disorder, specific learning disability, and intellectual impairment, and one needs to be aware of overlap among these differential diagnoses. A practical functional approach providing working solutions for each child's set of difficulties in day-to-day activities is needed.

Household air pollution generated from the use of polluting cooking fuels and technologies is a major source of disease and environmental degradation in low- and middle-income countries. Using a novel modelling approach, we provide detailed global, regional and country estimates of the percentages and populations mainly using 6 fuel categories (electricity, gaseous fuels, kerosene, biomass, charcoal, coal) and overall polluting/clean fuel use – from 1990-2020 and with urban/rural disaggregation. Here we show that 53% of the global population mainly used polluting cooking fuels in 1990, dropping to 36% in 2020. In urban areas, gaseous fuels currently dominate, with a growing reliance on electricity; in rural populations, high levels of biomass use persist alongside increasing use of gaseous fuels. Future projections of observed trends suggest 31% will still mainly use polluting fuels in 2030, including over 1 billion people in Sub-Saharan African by 2025., Household air pollution derived from cooking fuels is a major source of health and environmental problems. Here, the authors provide detailed global, regional and country estimates of cooking fuel usage from 1990 to 2030 and project that 31% of people will still be mainly using polluting fuels in 2030.

University-industry collaboration (UIC) is increasingly seen as an important driver of economic growth, but knowledge on its implementation mechanisms is still limited especially in developing countries. This constrains the capacity of those countries to design adequate policies and strategies to foster such interactions. This paper provides information about the extent to which academic staff from Tertiary Education Institutions (TEIs) in Rwanda interact with industry. It identifies the factors underlying their propensity to engage in interactions and the barriers or challenges faced. Data were collected from 334 academic staff permanently employed by 17 Higher Education Institutions in Rwanda. Results indicate a low level of interaction except in some informal channels arguably less inclined to foster relational learning and knowledge creation. Regression analyses indicated that the type of institution and the academic grades are significantly associated with the propensity of academic staff to interact with industry. In addition, four main barriers to interaction are identified and they are all linked to poor support from the government and the universities. Therefore, the study recommends the elaboration of explicit policies and strategies at country and institutional levels to set the stage for such indispensable interactions.

The atmospheric pressure that decreases with altitude affects lung physiology. However, these changes in physiology are not usually considered in ventilator design and testing. We argue that high altitude human populations require special attention to access the international supply of ventilators.

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC–AUC of 0.89 (95% CI: 0.87–0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p, Diabetic eye disease is a cause of preventable blindness and accurate and timely referral of patients with diabetic macular edema is important to start treatment. Here the authors present a deep learning model that can predict the presence of diabetic macular edema from color fundus photographs with superior specificity and positive predictive value compared to retinal specialists.

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards., Artificial intelligence (AI) is becoming more and more common in people’s lives. Here, the authors use an expert elicitation method to understand how AI may affect the achievement of the Sustainable Development Goals.

This paper presents a dataset of yearly land use and land cover classification maps for Mato Grosso State, Brazil, from 2001 to 2017. Mato Grosso is one of the world’s fast moving agricultural frontiers. To ensure multi-year compatibility, the work uses MODIS sensor analysis-ready products and an innovative method that applies machine learning techniques to classify satellite image time series. The maps provide information about crop and pasture expansion over natural vegetation, as well as spatially explicit estimates of increases in agricultural productivity and trade-offs between crop and pasture expansion. Therefore, the dataset provides new and relevant information to understand the impact of environmental policies on the expansion of tropical agriculture in Brazil. Using such results, researchers can make informed assessments of the interplay between production and protection within Amazon, Cerrado, and Pantanal biomes., Measurement(s)land • land useTechnology Type(s)computational modeling techniqueFactor Type(s)year • geographic location • land use and cover classSample Characteristic - EnvironmentlandSample Characteristic - LocationMato Grosso State Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11440461

More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 2904 Gambian children admitted to hospital with malaria. This quantitative analysis reveals features predicting the severity of individual patient outcomes, and the dynamic pathways of SM progression, notably inferred without requiring longitudinal observations. Bayesian inference of these pathways allows us assign quantitative mortality risks to individual patients. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.

Penile cancer is relatively frequent in developing world compared with developed countries. Majority of histologies are of squamous origin and spread through lymphatics or vascular channels. Metastases from penile carcinoma mainly involves lung, liver, bone, and rarely other sites. The patient reported here is a treated case of penile cancer who presented with lesion in right breast after treatment for primary disease. He responded well to the adjuvant therapy.

Spatially explicit data on electricity access and use are essential for effective policy-making and infrastructure planning in low-income, data-scarce regions. We present and validate a 1-km resolution electricity access dataset covering sub-Saharan Africa built on gridded nighttime light, population, and land cover data. Using light radiance probability distributions, we define electricity consumption tiers for urban and rural areas and estimate the by-tier split of consumers living in electrified areas. The approach provides new insight into the spatial distribution and temporal evolution of electricity access, and a measure of its quality beyond binary access. We find our estimates to be broadly consistent with recently published province- and national-level statistics. Moreover, we demonstrate consistency between the estimated electricity access quality indicators and survey-based consumption levels defined in accordance with the World Bank Multi-Tier Framework. The dataset is readily reproduced and updated using an open-access scientific computing framework. The data and approach can be applied for improving the assessment of least-cost electrification options, and examining links between electricity access and other sustainable development objectives., Design Type(s)modeling and simulation objective • observational design • data integration objectiveMeasurement Type(s)ElectricityTechnology Type(s)digital curationFactor Type(s)temporal_interval • geographic locationSample Characteristic(s)Sub-Saharan Africa • anthropogenic habitat Machine-accessible metadata file describing the reported data (ISA-Tab format)

A significant proportion of the African continent is conducive for animal agricultural production, due to its historical experience and available resources to accommodate and nurture various indigenous and exotic animal species and breeds. With food security being a global challenge, animal products can play an important role as nutrient dense food sources in human diets, particularly in Africa. However, this does not seem to reach its full potential in practice, due to numerous reasons that have not been adequately addressed. Animal welfare reservations can be highlighted as one of the major contributing factors to the curbed progress. The consequences have been scientifically proven to affect product quality and market access. However, in the African community, the concept of animal welfare has not been fully embraced. While there are international animal welfare standards in the developed world, there are inherent factors that hinder adoption of such initiatives in most developing regions, particularly among communal farmers. These include cultural norms and practices, social ranking, socio-economic status, available resources, information dissemination and monitoring tools. Therefore, there is need to harmonize what is internationally required and what is feasible to accommodate global variability. The protocols followed to ensure and evaluate farm animal welfare require regular investigation, innovation and a sustainable approach to enhance animal productivity, efficiency and product quality. Additionally, investing in animal wellbeing and health, as well as empowering communities with significant knowledge, has a potential to improve African livelihoods and contribute to food security. This review seeks to highlight the concept of animal welfare in relation to livestock and food production in African conditions.

Infectious diseases are emerging globally at an unprecedented rate while global food demand is projected to increase sharply by 2100. Here, we synthesize the pathways by which projected agricultural expansion and intensification will influence human infectious diseases and how human infectious diseases might likewise affect food production and distribution. Feeding 11 billion people will require substantial increases in crop and animal production that will expand agricultural use of antibiotics, water, pesticides and fertilizer, and contact rates between humans and both wild and domestic animals, all with consequences for the emergence and spread of infectious agents. Indeed, our synthesis of the literature suggests that, since 1940, agricultural drivers were associated with >25% of all — and >50% of zoonotic — infectious diseases that emerged in humans, proportions that will likely increase as agriculture expands and intensifies. We identify agricultural and disease management and policy actions, and additional research, needed to address the public health challenge posed by feeding 11 billion people., Population growth and economic development affect and are affected by infectious diseases and food production. This Review synthesizes understanding about the links between emerging infectious diseases and food production, finding strong associations worldwide.

Several intervention strategies are available to reduce micronutrient deficiencies, but uncoordinated implementation of multiple interventions may result in excessive intakes. We reviewed relevant data collection instruments and available information on excessive intakes for selected micronutrients and considered possible approaches for weighing competing risks of intake above tolerable upper intake levels (ULs) versus insufficient intakes at the population level. In general, population‐based surveys in low‐ and middle‐income countries suggest that dietary intakes greater than the UL are uncommon, but simulations indicate that fortification and supplementation programs could lead to high intakes under certain scenarios. The risk of excessive intakes can be reduced by considering baseline information on dietary intakes and voluntary supplement use and continuously monitoring program coverage. We describe a framework for comparing risks of micronutrient deficiency and excess, recognizing that critical information for judging these risks is often unavailable. We recommend (1) assessing total dietary intakes and nutritional status; (2) incorporating rapid screening tools for routine monitoring and surveillance; (3) addressing critical research needs, including evaluations of the current ULs, improving biomarkers of excess, and developing methods for predicting and comparing risks and benefits; and (4) ensuring that relevant information is used in decision‐making processes.

The expansion of health insurance in emerging countries raises concerns about the unintended negative effects of health insurance on labour supply. This article examines the labour supply effects of the Health Care Fund for the Poor (HCFP) in Vietnam in terms of the number of work hours per month and labour force participation (the probability of employment). Employing various matching methods combined with a Difference-in-Differences approach on the Vietnam Household Living Standard Surveys 2002–2006, we show that the HCFP, which aims to provide poor people and disadvantaged minority groups with free health insurance, has a negative effect on labour supply. This is manifested in both the average number of hours worked per month and the probability of employment, suggesting the income effect of the HCFP. Interestingly, the effects are mainly driven by the non-poor recipients living in rural areas, raising the question of the targeting strategy of the programme. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Objective: To determine the stillbirth rate in 2017 at Christian Medical College, a tertiary care perinatal center in South India, and to identify causes for the various stillbirths that occurred using the Relevant Condition at Death (ReCoDe) classification. Material and Methods: Medical records of the women with stillbirths between January 1st, to December 31st, 2017, were retrieved and analyzed using the SPSS software (IBM, version 23). The study was approved by the institutional review board (minute no: 11273, retro dated: 28/3/2018). Results: Of the total 14696 deliveries between January 1st, 2017, to December 31st, 2017, there were 247 stillbirths, a rate of 16.8 per 1000 births. Maternal factors: 156 (64.2%) women were booked and the rest were un-booked. Hypertensive disorders of pregnancy were detected in 27.5% (n=67). A greater number of un-booked women had gestational hypertension as compared with booked women (41% vs 24%, p=0.005). Fetal characteristics: still births secondary to lethal congenital anomalies were seen in 18.2% (n=45). Lethal congenital anomalies were diagnosed 10 times more in the booked patients than un-booked ones (24.7% vs 2.3%, p=0.001). Obstetric factors: one or two previous miscarriages were seen in 29.5% cases. Seventeen women (6.9%) had a prior stillbirth. ReCoDe Classification: we were able to successfully classify 84.2% of the stillbirths, leaving 15.78% unclassified. Fetal growth restriction secondary to uteroplacental insufficiency was found in 25.9% cases. Of the placental causes, abruption accounted for 10.9% of cases. Medical co-morbidities were seen in 46.5% pregnancies. Conclusion: The ReCoDe method of classifying stillbirths is useful in the developing world. It helped to elucidate the cause for stillbirths in 84.2% of cases. The majority of cases in our set were due to fetal growth restriction, hypertensive disorders of pregnancy, and uteroplacental insufficiency. Stillbirths can be prevented by a comprehensive antenatal care system, early recognition, and close monitoring of high-risk pregnancies.

Objective To assess the effects of new-born care intervention through self-help groups in improving new-born healthcare practices in rural India. Methods A quasi-experimental design was used to evaluate behavioral change intervention integrated in >25,000 microfinance-based self-help groups in rural Bihar. Three rounds of cross-sectional surveys were conducted to understand the impact of intervention on new-born healthcare practices by talking to women who delivered a baby in the last 6 months. Results Intervention groups showed greater improvement than control groups in the timely initiation of breastfeeding (adjusted odds ratio (AOR) = 6.3, 95% CI: 2.8, 14.3), exclusive breastfeeding on day 1 (AOR = 4.3, 95% CI: 1.9, 9.9), initiation of skin-to-skin care (AOR = 1.9, CI: 1.0, 3.8), and delayed bathing (AOR = 2.8, 95% CI: 1.4, 5.9) with greater effect of on home deliveries where clinical care is often absent. Conclusion Sharing messages on appropriate new-born practices through self-help groups improve new-born care practices.

The symbiosis between smart minigrids and electric mobility has the potential to improve the cost and reliability of energy access for off-grid communities while providing low-carbon transport services. This study explores the commercial viability of using electric vehicles (EVs), recharged by solar minigrids, to provide transport services in off-grid communities. Preliminary findings are presented from a field trial in The Gambia that aims to assess the techno-economic feasibility of integrating sustainable energy and transport infrastructures in sun-rich regions of the Global South. As a dispatchable anchor load, an EV can improve the technical and economic performance of a minigrid by providing demand-side response services. In the developing world, rural communities are often among the poorest, and inadequate transport services remain a major barrier to wealth-creation. Some solutions to this situation may be transferrable to island communities, which share similar challenges in terms of access to energy and fuel. The first of its kind in Africa, this field trial uses an electric minivan, operating from an off-grid village where it has access to a minigrid whose 4.5 kWp of photovoltaic modules form the roof of a parking shelter for the vehicle. While there, the taxi can recharge, ideally during sunny periods when the photovoltaic array produces surplus power, thus allowing the EV’s battery to recharge while bypassing the minigrid’s own accumulator. This improves system reliability and cost effectiveness, while providing pollution-free energy for the taxi. Ultimately, the intention is to test different vehicles in a variety of circumstances, but this paper outlines only the preliminary findings of the first of these trials. Early results provide convincing new evidence that commercial viability of such a concept is possible in Sub-Saharan Africa. Some promising scenarios for commercial viability are identified, which warrant further investigation, since they suggest that a taxi driver’s earnings could be increased between 250 and 1,300% in local operations, and even 20-fold in tourist markets, depending on vehicle type, minigrid configuration and target market. It is hoped that these may encourage the rollout of solar-recharged EVs where the nexus of sustainable energy and transport systems are likely to make the greatest contribution to addressing the UN’s Sustainable Development Goals, by helping to solve the trilemma of providing energy security, social benefit and environmental sustainability in low-income countries.

Hodgkin lymphoma (HL) accounts for roughly 10% of all lymphomas and 0.6% of all malignant tumours analysed worldwide yearly. Data regarding HL in developing world are exceptionally constrained. The main objective of this research is to investigate the incidence patterns of HL within the Republic of Armenia and to portray disease distribution according to age and sex. There is a very strict evidence on the frequency of HL in Armenia. The results of our research find out that the frequency of HL in Armenia has not changed altogether over the past 15 years and is comparable to that detailed from the USA and Europe.

Choosing to do an elective in a developing country can be a gamble. Everyone has heard stories of unsuspecting students being deposited in a remote hospital and told that actually they are the doctor, and here are the hordes of patients they must look after for the next eight weeks. As if this isn't enough, the students often find themselves suffering severe culture shock, working in a health care service that bears no resemblance to the one they are accustomed to and encountering patients who frequently present with either unfamiliar diseases or unfamiliar presentations of familiar diseases.

Livestock support the livelihoods of one billion people in Africa, Asia and Latin America, but the productivity of animals remains low, reducing the potential of the sector to support higher incomes and better nutrition. Improved livestock feeding has been identified as the most important step towards higher productivity. This scoping review assessed the evidence for the uptake of improved ruminant livestock feed options, the effect of this uptake on livestock productivity and the degree to which this improves smallholder farmer livelihoods. In total, 22,981 papers were identified, of which 73 papers were included in the final analysis after a rigorous double-blind screening review. Only papers that reported farmers’ decision to use a new feed intervention were selected, thereby excluding feeding trials and participatory feed assessments. Of the 73 papers, only 6 reported combined evidence of adoption, effect on productivity and livelihood changes. A total of 58 papers looked at adoption, 19 at productivity change and 22 at livelihood change. This scoping review highlights the gap in evidence for the adoption of new livestock feeding practices and provides recommendations to support farmers’ uptake of feed interventions., Livestock support the livelihoods of a billion people in Africa, Asia and Latin America, and improved feeding could increase productivity. However, this scoping review highlights serious gaps in evidence around the adoption of new feeding practices.

Background Worldwide, the epidemiological and demographic transitions have resulted in nutrition shift characterized by an increased consumption of high energy fast food products. In just over 3 decades, overweight and obesity rates have nearly tripled to currently affecting over a third of the global population. Notwithstanding the ever present under-nutrition burden, sub Saharan Africa (SSA) is witnessing a drastic escalation of overweight and obesity. We aimed to explore the prevalence and associated factors for obesity among residents of Dar es Salaam city in Tanzania. Methods Participants from this study were recruited in a community screening conducted during the Dar es Salaam International Trade Fair. Sociodemographic and clinical data were gathered using a structured questionnaire during enrollment. Dietary habits and anthropometric measurements were assessed using standard methods. All statistical analyses utilized STATA v11.0 software. Pearson Chi square and Student’s T-test were used to compare categorical and continuous variables respectively. Logistic regression analyses were used to assess for factors associated with BMI ≥ 25. All tests were 2-sided and p Results A total of 6691 participants were enrolled. The mean age was 43.1 years and males constituted 54.2% of all participants. Over two-thirds of participants were alcohol consumers and 6.9% had a positive smoking history. 88.3% of participants were physically inactive, 4.7% had a history of diabetes mellitus and 18.1% were known to have elevated blood pressure. Overweight and obesity were observed in 34.8 and 32.4% of participants respectively. Among overweight and obese participants, 32.8% had a misperception of having a healthy weight. Age ≥ 40, female gender, a current working status, habitual breakfast skipping, poor water intake, high soft drink consumption, regular fast food intake, low vegetable and fruit consumption, alcohol consumption and hypertension were found to be independent associated factors for obesity. Conclusion Amidst the ever present undernutrition in SSA, a significant proportion of participants had excess body weight. Concomitantly, the rates of physical inactivity and unhealthy eating are disproportionately high in Dar es Salaam. In view of this, community-based and multilevel public health strategies to promote and maintain healthy eating and physical activity require an urgent step-up in urban Tanzania.

The COVID‐19 pandemic has shaken the world through its first wave, and we have yet to experience the second wave. Even resourceful countries have failed to adequately prevent epidemics in their country, and for countries like Bangladesh, which already has strained an ineffective healthcare system, the challenges to contain the SARS‐CoV‐2 virus are that much more severe. Due to the scarcity of resources and systematic failures the Bangladeshi people deeply mistrust the healthcare system. The mistrust is further magnified as healthcare providers are hesitant to treat the patients because of the lack of proper protective gear. Physicians have a moral obligation to serve and treat patients; however, they have a moral obligation to protect their families. This dilemma places healthcare providers in situations where they experience moral distress. This article specifically discusses the importance of interpersonal relationships in driving change, using the framework of Responsiveness, while stating the need for complementary systematic change in order to rebuild trust in the Bangladeshi healthcare system.

Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response., Measurement(s) electricity access • urban area • rural area Technology Type(s) digital curation Factor Type(s) Administrative boundaries • Population • Night-time lights Sample Characteristic - Environment populated place Sample Characteristic - Location Sub-Saharan Africa Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13516283

Summary Background Indiscriminate antimicrobial use is one of the greatest contributors to antimicrobial resistance. A low level of asepsis in hospitals and inadequate laboratory support have been adduced as reasons for indiscriminate use of antimicrobials among surgical patients. At present, there are no guidelines for presumptive antibiotic use in Nigeria and sub-Saharan Africa. Aim Surgical inpatients at the study hospital were surveyed to determine the level of antimicrobial use and degree of compliance with prescription quality indicators. Methods A cross-sectional survey was conducted among all surgical inpatients in May 2019 using a standardized tool developed by the University of Antwerp to assess the point prevalence of antimicrobials. Inpatients who were admitted from 08:00 h on the day of the survey were included. Data on patients' demographics, indication for antimicrobial use, reason for antimicrobial use, stop/review date, adherence to guidelines and laboratory use were collected. The prevalence of antimicrobial use in the surgical department was estimated. Results Eighty-two inpatients were included in the survey. Of these, 97.6% were receiving at least one antimicrobial agent. Only 5.4% of the prescriptions were targeted, and 37.6% of prescriptions were for empirical treatment of infections. Approximately half (50.7%) of the patients were receiving presumptive antibiotics, and 6% were receiving prophylactic antibiotics. In total, 58.7% of prescriptions were administered parenterally, and 98.2% of patients had documentation of a stop/review date. Metronidazole (P=32.3%, T=29.2%), ceftriaxone (P=28.4%, T=19.8%) and ciprofloxacin (P=14.2%, T=14.6%) were the most common antimicrobials used. Conclusions There is a high rate of antimicrobial use among surgical inpatients, and the rate of indiscriminate antimicrobial prescribing among these patients needs to be reduced. This can be achieved by developing antimicrobial guidelines for presumptive antimicrobial therapy.

Electrowetting on dielectric-based digital microfluidic platforms (EWOD-DMF) have a potential to impact point-of-care diagnostics. Conventionally, EWOD-DMF platforms are manufactured in cleanrooms by expert technicians using costly and time consuming micro-nanofabrication processes such as optical lithography, depositions and etching. However, such high-end microfabrication facilities are extremely challenging to establish in resource-poor and low-income countries, due to their high capital investment and operating costs. This makes the fabrication of EWOD-DMF platforms extremely challenging in low-income countries, where such platforms are most needed for many applications such as point-of-care testing applications. To address this challenge, we present a low-cost and simple fabrication procedure for EWOD-DMF electrode arrays, which can be performed anywhere with a commercial office inkjet printer without the need of expensive cleanroom facilities. We demonstrate the utility of our platform to move and mix droplets of different reagents and physiologically conductive buffers, thereby showing its capability to potentially perform a variety of biochemical assays. By combining our low-cost, inkjet-printed EWOD-DMF platform with smartphone imaging technology and a compact control system for droplet manipulation, we also demonstrate a portable and hand-held device which can be programmed to potentially perform a variety of biochemical assays.

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good., The AI for Social Good movement aims to apply AI/ML tools to help in delivering on the United Nations’ sustainable development goals (SDGs). Here, the authors identify the challenges and propose guidelines for designing and implementing successful partnerships between AI researchers and application - domain experts.

Few studies have evaluated the impact of nutritional supplementation among pregnant adolescents. We examined the effects of the Rang Din Nutrition Study (RDNS) interventions on children born to mothers, Few studies have evaluated the impact of nutritional supplementation among pregnant adolescents. This paper examines the effects of nutritional interventions on children born to mothers

With more than 820 million undernourished people living in rural areas of low- and middle-income countries (LMICs), ending hunger and ensuring access to food by all is a global priority. In the past few decades, the adoption of technological innovations in the agricultural sector and related crop yield improvements have not led to expected improvements in the nutritional status of rural households in many LMICs. The increased energy expenditure associated with the adoption of productivity-enhancing innovations may provide an important explanation of the disconnect between agricultural productivity enhancements and improved nutritional outcomes. We develop a methodology for generating reliable livelihood energy/calorie expenditure profiles for rural agricultural households using research-grade accelerometer devices. We integrate the data on physical activity and energy expenditure in rural households with data on time-use and food intakes to generate a data set that provides a unique window into rural livelihoods. This can be a valuable resource to analyse agriculture-nutrition impact pathways and improve the welfare of rural and agricultural households., Measurement(s)physical activityTechnology Type(s)Accelerometer • SurveyFactor Type(s)geographic location • community typeSample Characteristic - Environmentrural areaSample Characteristic - LocationGhana • Nepal • India Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11871537

Postharvest loss exacerbates the food insecurity and welfare loss of farming households in developing countries. This paper analyzes the impact of improved storage technologies on food and nutrition security and welfare using nationally representative data from Ethiopia. Endogenous switching regression models are employed to control for unobserved heterogeneity. The study finds that the use of improved storage technologies increases dietary diversity and reduces child malnutrition and self-reported food insecurity. We also find that non-user households would have experienced these benefits had they used improved storage technologies. Overall, the study suggests that improved storage technologies can enhance food and nutrition security, and could play a key role in alleviating the challenges of feeding a growing population.

Evolutionary Medicine (EM) is a fundamental science exploring why our bodies are plagued with disease and hindered by limitations. EM views the body as an assortment of benefits, mistakes, and compromises molded over millennia. It highlights the role of evolution in numerous diseases encountered in community and family medicine clinics of developing countries. It enables us to ask informed questions and develop novel responses to global health problems. An understanding of the field is thus crucial for budding doctors, but its study is currently limited to a handful of medical schools in high-income countries. For the developing world, Pakistan's medical schools may be excellent starting posts as the country is beset with communicable and non-communicable diseases that are shaped by evolution. Remarkably, Pakistani medical students are open to studying and incorporating EM into their training. Understanding the principles of EM could empower them to tackle growing health problems in the country. Additionally, some difficulties that western medical schools face in integrating EM into their curriculum may not be a hindrance in Pakistan. We propose solutions for the remaining challenges, including obstinate religious sentiments. Herein, we make the case that incorporating EM is particularly important in developing countries such as Pakistan and that it is achievable in its medical student body.

Limited research has examined lesbian and bisexual women's sexual health practices in the Caribbean, where lesbian and bisexual women experience sexual stigma that may reduce sexual healthcare utilisation. We conducted a sequential multi-method research study, including semi-structured individual interviews (n = 20) and a focus group (n = 5) followed by a cross-sectional survey (n = 205) with lesbian and bisexual women in Kingston, Montego Bay, and Ocho Rios, Jamaica. Binary logistic analyses and ordinal logistic regression were conducted to estimate the odds ratios for social-ecological factors associated with lifetime STI testing, sex work involvement, and the last time of STI testing. Over half of participants reported a lifetime STI test and of these, 6.1% reported an STI diagnosis. One-fifth of the sample reported ever selling sex. Directed content analysis of women's narratives highlighted that stigma and discrimination from healthcare providers, in combination with low perceived STI risk, limited STI testing access and safer sex practices. Participants described how safer sex self-efficacy increased their safer sex practices. Quantitative results revealed that a longer time since last STI test was positively associated with depression, sexual stigma, and forced sex, and negatively associated with residential location, perceived STI risk, safer sex self-efficacy, and LGBT connectedness. Selling sex was associated with perceived STI risk, relationship status, sexual stigma, food insecurity, and forced sex. Sexual health practices among lesbian and bisexual women in Jamaica are associated with intrapersonal, interpersonal, and structural factors, underscoring the urgent need for multi-level interventions to improve sexual health and advance sexual rights among lesbian and bisexual women in Jamaica.

The delay between first-line antiretroviral therapy (ART) failure and initiation of second-line ART in resource-limited settings can be prolonged. Increasing evidence links delayed antiretroviral switch with increased risk for opportunistic infection (OI) and death, particularly in patients with advanced HIV at the time of first-line failure. As access to viral load (VL) monitoring widens beyond a few countries, mechanisms are needed to optimize the use of routine virologic monitoring and assure that first-line regimen failure results in prompt second-line switch. For patients with advanced HIV or OI at the time of first-line failure, a targeted fast track to second-line ART should be considered, involving a switch to second-line ART during a single visit. To derive the maximum benefit from both the current expansion of VL monitoring and the falling costs of second-line ART, clinics and healthcare workers should be given the tools and training to detect and switch patients with regimen failure before HIV disease progression.

Acute kidney injury (AKI) is independently associated with new onset chronic kidney disease (CKD), end-stage kidney disease, cardiovascular disease, and all-cause mortality. However, only a minority of patients receive follow-up care after an episode of AKI in the developing world, and the optimal strategies to promote rehabilitation after AKI are ill-defined. On this background, a working group of the 18th Acute Dialysis Quality Initiative (ADQI) applied the consensus-building process informed by a PubMed review of English language articles to address questions related to rehabilitation after AKI. The consensus statements propose that all patients should be offered follow-up within three months of an AKI episode, with more intense follow-up (e.g., < one month) considered based upon patient risk factors, characteristics of the AKI event, and the degree of kidney recovery. Patients should be monitored for renal and non-renal events post-AKI, and we suggest the minimum level of monitoring consist of an assessment of kidney function and proteinuria within three months of the AKI episode. Care should be individualized for higher risk patients, particularly patients who are still dialysis-dependent to promote renal recovery. While evidence-based treatments for survivors of AKI are lacking and some outcomes may not be modifiable, we recommend simple interventions such as lifestyle changes, medication reconciliation, blood pressure control, and education, including the documentation of AKI on the patient’s medical record. In conclusion, survivors of AKI represent a high-risk population and these consensus statements should provide clinicians with guidance on the care of patients after an episode of AKI.

There are numerous examples in which the introduction of expensive medical equipment into the developing world fail for lack of a basic understanding of the device operation, lack of spare parts and poor maintenance. This paper describes the development of a Fleisch pneumotachograph and cellphone based telespirometer that can easily be built from &ldquo, junk box&rdquo, medical and electronic components available in the developing world. This approach should introduce a sense of local ownership to the project as well as encouraging participation by the local electronics repair industry. Experimental results confirm that the forced expiratory flow data are reliably modulated onto an audio signal and transmitted by cellphone to a base station for examination by a district nurse or doctor. Flow measurement and data transmission accuracies are sufficiently good for remote diagnoses of chronic obstructive pulmonary disease.

Increasing cost of generating energy through conventional sources coupled with environmental pollution concerns has led to the need to find more sustainable, clean and cheaper sources of energy generation. This paper evaluates two forms of energy: nuclear and solar energy for developing countries with a case study on Ghana. The research found out that Ghana like other several other developing countries have the opportunity to reduce their cost of electricity significantly should any of the under studied technologies be deployed. Obtained LCOE for the 20 MW modelled Solar power plant (SPP) in Navrongo ranges between 5.74 and 9.41 ¢/kWh for real discount and annual interest rates ranging between 1 and 25%. LCOEs of 125.53–125.55 $/MWh for discount rates between 3% and 15%, respectively were obtained for the 1200 MW nuclear power plant (NPP) expected to be constructed in Ghana. The research concluded with a suggestion that since the NPP project is capital intensive, development of SPP should be given the needed attention in the short to medium term to help build a resilient economy upon which NPP can be considered in the long term. Some financing models were also suggested for the construction of such capital-intensive projects. © 2020 Elsevier Ltd

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance., This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.

Twelve years after cardiologists and cardiac surgeons from all over the world issued the ‘Drakensberg Declaration on the Control of Rheumatic Fever and Rheumatic Heart Disease in Africa’, calling on the world community to address the prevention and treatment of rheumatic heart disease (RHD) through improving living conditions, to develop pilot programmes at selected sites for control of rheumatic fever and RHD, and to periodically review progress made and challenges that remain, RHD still accounts for a major proportion of cardiovascular diseases in children and young adults in low- and middle-income countries, where more than 80% of the world population live. Globally equal in prevalence to human immunodeficiency virus infection, RHD affects 33 million people worldwide. Prevention efforts have been important but have failed to eradicate the disease. At the present time, the only effective treatment for symptomatic RHD is open heart surgery, yet that life-saving cardiac surgery is woefully absent in many endemic regions. In this declaration, we propose a framework structure to create a co-ordinated and transparent international alliance to address this inequality.

Objectives Child and adolescent marriage are widely prevalent in some developing countries, and yet evidence on the maternal healthcare utilization of married adolescents is sparse. This study sought to identify the determinants of maternal healthcare utilization among married adolescents in 13 countries with the highest adolescent marriage rates in Sub-Saharan Africa. Study design This is a multicountry cross-sectional analysis. Methods Demographic and Health Survey data on 4288 married adolescents were used. Multivariable logistic regressions were used to identify the individual- and household-level predictors of the utilization of antenatal care, safe delivery and postnatal care. Results Wealth and access to media were positively associated with the utilization of all types of maternal healthcare services. Female education and partner education were positively associated with antenatal care. Predictors of safe delivery included urban residence (odds ratio [OR] = 1.87; 95% confidence interval [CI] = 1.15–3.03), female education (OR = 1.37; 95% CI = 1.16–1.60) and number of living children (OR = 1.25; 95% CI = 1.01,1.54), whereas positive correlates of postnatal care were urban residence (OR = 1.35; 95% CI = 1.00–1.82), partner education (OR = 1.32; 95% CI = 1.12–1.56) and employment (OR = 1.43; 95% CI = 1.07,1.89). Full antenatal care and safe delivery utilization were associated with increased postnatal care utilization. Second or third birth order and associated birth intervals were strong barriers to maternal healthcare utilization. Although autonomy in decision-making over financial spending and relationships were positively associated with full antenatal care (OR = 1.09; 95% CI = 1.03–1.14), the results suggest that autonomy over personal healthcare decisions had an opposite relationship (OR = 0.76; 95% CI = 0.58–1.01). Conclusions Living conditions and autonomy in decision-making over resources and relationships are facilitators of maternal healthcare utilization among married adolescents. Determinants observed in this multicountry study can help shape maternal healthcare interventions in context with high child and teenage marriage rates.

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations., A high-resolution, global atlas of mortality of children under five years of age between 2000 and 2017 highlights subnational geographical inequalities in the distribution, rates and absolute counts of child deaths by age.

Implementing genetic and genomic medicine is dependent to a large extent on the successful training of a genomics workforce with expertise in interpreting, communicating, and integrating genomic information in a clinical setting. In order to effectively implement genomic medicine at different levels of healthcare delivery, strategies for establishing training in core competencies of genetics and genomics targeted at the undergraduate, postgraduate, and continuing professional development levels need to be in place. Several approaches have been adopted in Western countries like the UK and USA to ensure that their healthcare workforce is adequately trained and competent to effectively use genetic and genomic information in their professional practice. However, this is not the case in most low- and middle-income countries (LMICs) located in regions of East Asia and the Pacific, Central and South Asia, Latin America, and the Caribbean, North and Sub-Saharan Africa, with a gross national income of $1,026–$3,995. In many of these countries, this necessity has been plagued by numerous challenges stemming from the lack of local capacity to plan and carry out the required training of the healthcare workforce. The other contributory factors are the scarcity of adequate funding for training as well as establishing core facilities needed for delivering these services around which such training programs could be implemented and delivered (Sirisena and Dissanayake, 2018). Herein, we provide a concise overview of the various challenges faced in achieving genomic literacy for integrating genomic medicine into the healthcare setting in LMICs and potential strategies for overcoming such limitations.

Background Delays that postpone the evaluation and management of malignancy may lead to considerable morbidity. The primary objective of this study was to assess the time required to diagnose and treat lung cancer at an Indian public referral center that predominantly serves lower-income patients. Methods A review of patients diagnosed with lung cancer between January 2008 and December 2016 was completed. We computed the median time intervals and inter-quartile ranges between symptom onset, definitive diagnostic investigation, confirmed histologic diagnosis, and chemotherapy initiation. Median intervals were correlated with baseline demographics and disease characteristics using Kruskal-Wallis test. Results One thousand, three hundred and-seventy patients were selected. A majority (94.5%) with non-small cell lung cancer were diagnosed with advanced disease. After developing symptoms, patients required 101 [56–168] days to undergo a definitive diagnostic study, 107 [60–173] days to confirm a diagnosis, and 126 [85–196.8] days to initiate treatment. Patients who were previously treated for tuberculosis required more time to receive chemotherapy compared to those who were not (187 [134–261.5] days vs. 113 [75–180] days, p Conclusion Among lower-income Indian patients, the most prominent delays occur prior to diagnosis. Efforts should be directed toward encouraging physicians to maintain a high index of clinical suspicion and educating patients to report concerning symptoms as early as possible.

Health facilities form a central component of health systems, providing curative and preventative services and structured to allow referral through a pyramid of increasingly complex service provision. Access to health care is a complex and multidimensional concept, however, in its most narrow sense, it refers to geographic availability. Linking health facilities to populations has been a traditional per capita index of heath care coverage, however, with locations of health facilities and higher resolution population data, Geographic Information Systems allow for a more refined metric of health access, define geographic inequalities in service provision and inform planning. Maximizing the value of spatial heath access requires a complete census of providers and their locations. To-date there has not been a single, geo-referenced and comprehensive public health facility database for sub-Saharan Africa. We have assembled national master health facility lists from a variety of government and non-government sources from 50 countries and islands in sub Saharan Africa and used multiple geocoding methods to provide a comprehensive spatial inventory of 98,745 public health facilities., Design Type(s)data integration objective • database creation objectiveMeasurement Type(s)Healthcare FacilityTechnology Type(s)digital curationFactor Type(s)geographic location • Facility • organizationSample Characteristic(s)Sub-Saharan Africa • public infrastructure Machine-accessible metadata file describing the reported data (ISA-Tab format)

Over the past 50 years, the development of information and communications technology has provided unprecedented support to the steady economic growth of developed countries. In recent years, some of the largest growth has been reported in emerging economies, which, however, often lack adequate telecommunications infrastructure to further sustain their development. Although a number of service providers and system vendors have started to address the issue, the challenges they encounter are substantially different from those in the developed world, including an unreliable electricity grid, poor fiber infrastructure, low revenue expectations, and often a harsh climate environment. This paper reports use cases and solutions pertinent to the development of the networking infrastructure in emerging economies, provided by organizations directly involved in such activities. After providing some background information on the current state of network infrastructure and the main challenges for Africa and rural China, the paper provides details on two proposed solutions. The first focuses on the provisioning of services and network infrastructure through the development of low-cost data centers, whereas the second proposes cost-effective adaptation of both fiber and hybrid copper–fiber technology to rural areas. The article is concluded with a brief discussion on the complementarity of the two approaches.

Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with that of human graders. A total of 25,326 gradable retinal images of patients with diabetes from the community-based, nationwide screening program of DR in Thailand were analyzed for DR severity and referable diabetic macular edema (DME). Grades adjudicated by a panel of international retinal specialists served as the reference standard. Relative to human graders, for detecting referable DR (moderate NPDR or worse), the deep learning algorithm had significantly higher sensitivity (0.97 vs. 0.74