Predictive Modeling for the Diagnosis of Gestational Diabetes Mellitus Using Epidemiological Data in the United Arab Emirates

Gestational diabetes mellitus (GDM) is a common condition with repercussions for both the mother and her child. Machine learning (ML) modeling techniques were proposed to predict the risk of several medical outcomes. A systematic evaluation of the predictive capacity of maternal factors resulting in GDM in the UAE is warranted. Data on a total of 3858 women who gave birth and had information on their GDM status in a birth cohort were used to fit the GDM risk prediction model. Information used for the predictive modeling were from self-reported epidemiological data collected at early gestation. Three different ML models, random forest (RF), gradient boosting model (GBM), and extreme gradient boosting (XGBoost), were used to predict GDM. Furthermore, to provide local interpretation of each feature in GDM diagnosis, features were studied using Shapley additive explanations (SHAP). Results obtained using ML models show that XGBoost, which achieved an AUC of 0.77, performed better compared to RF and GBM. Individual feature importance using SHAP value and the XGBoost model show that previous GDM diagnosis, maternal age, body mass index, and gravidity play a vital role in GDM diagnosis. ML models using self-reported epidemiological data are useful and feasible in prediction models for GDM diagnosis amongst pregnant women. Such data should be periodically collected at early pregnancy for health professionals to intervene at earlier stages to prevent adverse outcomes in pregnancy and delivery. The XGBoost algorithm was the optimal model for identifying the features that predict GDM diagnosis

Predictive Modeling for the Diagnosis of Gestational Diabetes Mellitus Using Epidemiological Data in the United Arab Emirates

Gestational diabetes mellitus (GDM) is a common condition with repercussions for both the mother and her child. Machine learning (ML) modeling techniques were proposed to predict the risk of several medical outcomes. A systematic evaluation of the predictive capacity of maternal factors resulting in GDM in the UAE is warranted. Data on a total of 3858 women who gave birth and had information on their GDM status in a birth cohort were used to fit the GDM risk prediction model. Information used for the predictive modeling were from self-reported epidemiological data collected at early gestation. Three different ML models, random forest (RF), gradient boosting model (GBM), and extreme gradient boosting (XGBoost), were used to predict GDM. Furthermore, to provide local interpretation of each feature in GDM diagnosis, features were studied using Shapley additive explanations (SHAP). Results obtained using ML models show that XGBoost, which achieved an AUC of 0.77, performed better compared to RF and GBM. Individual feature importance using SHAP value and the XGBoost model show that previous GDM diagnosis, maternal age, body mass index, and gravidity play a vital role in GDM diagnosis. ML models using self-reported epidemiological data are useful and feasible in prediction models for GDM diagnosis amongst pregnant women. Such data should be periodically collected at early pregnancy for health professionals to intervene at earlier stages to prevent adverse outcomes in pregnancy and delivery. The XGBoost algorithm was the optimal model for identifying the features that predict GDM diagnosis

Lokalna primjena hemina unapređuje liječenje rana u štakora s dijabetesom izazvanim streptozotocinom

Hemin may be of potential therapeutic value in wound healing management in diabetics. It is an inducer of heme oxygenase-1, an enzyme which degrades heme and participates in cellular protection against oxidative stress, inflammation and apoptosis. Thus, in the present study, hemin (0.5%) was applied topically over excision wounds, and its therapeutic effect in wound healing evaluated in diabetic rats. Topical hemin application significantly increased the percentage of wound contraction on day 2 in diabetic rats, however, povidone-iodine did the same on day 7 compared to the diabetic control. A significant increase in hydroxyproline and glucosamine content was found on day 14 in the hemin treated wounds of diabetic rats vs. the diabetic control. The histology of the hemin treated rats was in agreement with the cellular proliferation and collagen synthesis in granulation tissue. Hemin significantly increases cytokine IL-10 and decreases TNF-α in the granulation tissue of the healed wounds of diabetic rats. The finding showing the pro-healing effects of hemin was endorsed by inhibition of mRNA expression of pro-inflammatory cytokine TNF-α and adhesion molecule ICAM-1, and up-regulation of anti-inflammatory cytokine IL-10 mRNA. Hence, topical hemin application (i) helps in early and fast wound contraction (ii) enhances the hydroxyproline and glucosamine content of wounds and (iii) modulates pro-healing mRNA expression of cytokines.Hemin ima potencijalnu terapijsku vrijednost u liječenju rana u dijabetičara. On potiče hem-oksigenazu-1, enzim koji razgrađuje hem i sudjeluje u staničnoj zaštiti od oksidacijskog stresa, upale i apoptoze. U ovom je istraživanju hemin (0,5 %) primijenjen lokalno na ekscizijske rane te je procijenjen njegov terapijski učinak na cijeljenje rana u dijabetičnih štakora. Lokalna aplikacija hemina znakovito je povećala postotak zatvaranja rana 2. dan u dijabetičnih štakora, što je učinio i povidon-jod 7. dan u kontrolnoj skupini. Znakovit porast sadržaja hidroksiprolina i glukozamina pronađen je 14. dan u dijabetičnih štakora čije su rane tretirane heminom, za razliku od kontrolne skupine. Histologija je u štakora tretiranih heminom bila u skladu sa staničnom proliferacijom i sintezom kolagena u granulacijskom tkivu. Hemin je znakovito povisio citokin IL-10 i smanjio TNF-α u granulacijskom tkivu dijabetičnih štakora sa zacijeljenim ranama. Taj nalaz odgovara ljekovitom učinku hemina što je podržano inhibicijom ekspresije mRNA proupalnog citokina TNF-α i adhezijom molekule ICAM-1 te regulacijom protuupalnog cittokina IL-10 mRNA. Dakle, lokalna primjena hemina pomaže (i) u ranoj i brzoj kontrakciji rana (ii), poboljšava sadržaj hidroksiprolina i glukozamina u ranama i (iii) prilagođuje mRNA ekspresiju citokina u smjeru cijeljenja rane

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Accurate prediction of a newborn’s birth weight (BW) is a crucial determinant to evaluate the newborn’s health and safety. Infants with low BW (LBW) are at a higher risk of serious short- and long-term health outcomes. Over the past decade, machine learning (ML) techniques have shown a successful breakthrough in the field of medical diagnostics. Various automated systems have been proposed that use maternal features for LBW prediction. However, each proposed system uses different maternal features for LBW classification and estimation. Therefore, this paper provides a detailed setup for BW estimation and LBW classification. Multiple subsets of features were combined to perform predictions with and without feature selection techniques. Furthermore, the synthetic minority oversampling technique was employed to oversample the minority class. The performance of 30 ML algorithms was evaluated for both infant BW estimation and LBW classification. Experiments were performed on a self-created dataset with 88 features. The dataset was obtained from 821 women from three hospitals in the United Arab Emirates. Different performance metrics, such as mean absolute error and mean absolute percent error, were used for BW estimation. Accuracy, precision, recall, F-scores, and confusion matrices were used for LBW classification. Extensive experiments performed using five-folds cross validation show that the best weight estimation was obtained using Random Forest algorithm with mean absolute error of 294.53 g while the best classification performance was obtained using Logistic Regression with SMOTE oversampling techniques that achieved accuracy, precision, recall and F1 score of 90.24%, 87.6%, 90.2% and 0.89, respectively. The results also suggest that features such as diabetes, hypertension, and gestational age, play a vital role in LBW classification.</p

Disparities in risks of malaria associated with climatic variability among women, children and elderly in the Chittagong hill tracts of Bangladesh

Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1–7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8–7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7–27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9–25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar setting

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. “While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.</p

Impact of Energy Efficiency on CO2 Emissions: Empirical Evidence from Developing Countries

Attaining higher level of the energy efficiency is being considered as a preferred and cost-effective policy option to achieve economic propensity, environmental sustainability and improved energy security in recent years. This drive to achieve higher energy efficiency levels is mainly motivated by higher international oil prices during last two decades, the concerns regarding energy supply security and rising CO2 emissions globally. In this background, this study decomposes energy intensity into structural and activity effects, and empirically examines their impact on CO2 emissions in environmental Kuznets curve framework for the developing economies. Second generation methodological approach is adopted. The decomposed indices reflect that energy efficiency has played a key role in decreasing energy intensity, while structural shifts have caused only a minor reduction in energy intensity. The findings suggest that energy efficiency improvements have largest influence on CO2 emissions mitigation. In developing countries as a whole, energy efficiency has positive while structural shifts have negative relation with CO2 emissions in long run. The findings presented that energy efficiency is major contributor of CO2 emissions reduction. While structural shifts in developing countries tend to increase CO2 emissions because these countries are moving towards the sectors that are producing more pollution. However, the income is one of the major contributors of CO2 emissions. While renewable energy consumption has negative and industrialization has positive impact on CO2 emissions in developing countries. The study outcomes are utilized to develop a policy framework for attaining the SDG 7 and SDG 13 in the chosen countries

Role of underappreciated vectors in malaria transmission in an endemic region of Bangladesh-India border

Background Despite the efforts of the National Malaria Control Programme, malaria remains as an important public health problem in Bangladesh, particularly in the south-eastern region bordering India. Successful malaria control strategies rely on a detailed understanding of the underlying causes of malaria transmission. Here, an entomological survey was conducted in a malaria endemic area of Bangladesh bordering India to investigate the Anopheles mosquito community and assess their Plasmodium infection status. Methods Monthly entomological collections were undertaken from October 2010 to September 2011 in five villages in the Matiranga sub-district, Khagrachari district in Bangladesh, bordering the Indian State of Tripura. CDC miniature light traps were placed inside houses to collect adult Anopheles mosquitoes. Following morphological and molecular identification of the female Anopheles mosquitoes collected, they were screened for circumsporozoite proteins (CSP) of Plasmodium falciparum (Pf), Plasmodium vivax-210 (Pv-210) and Plasmodium vivax-247 (Pv-247), by ELISA to determine natural infection rates. Variation in Anopheles species composition, relative abundance and Plasmodium infection rates were analysed between sampled villages. Results A total of 2,027 female Anopheles were collected, belonging to 20 species. Anopheles nivipes was the most abundant species in our test villages during the peak malaria transmission season, and was observed sympatrically with An. philippinensis in the studied area. However, in the dry off-peak season, An. jeyporiensis was the most abundant species. Shannon’s diversity index was highest in October (2.12) and evenness was highest in May (0.91). The CSP ELISA positive rate overall was 0.44%. Anopheles karwari (n = 2), An. barbirostris s.l. (n = 1) and An. vagus (n = 1) were recorded positive for Pf. Anopheles kochi (n = 1) was positive for Pv-210 while An. umbrosus (n = 1), An. nivipes (n = 1) and An. kochi (n = 1) were positive for Pv-247. A mixed infection of Pf and Pv-247 was detected in An. barbirostris s.l.. Conclusion High diversity of Anopheles species was observed in areas close to the international border where species that were underestimated for malaria transmission significantly outnumbered principal vector species and these may play a significantly heightened role in malaria transmission