345 research outputs found
sj-xls-3-nmi-10.1177_11786388221125107 – Supplemental material for Predictors of Stunting and Underweight Among Children Aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: A Cross-Sectional Study
No full textSupplemental material, sj-xls-3-nmi-10.1177_11786388221125107 for Predictors of Stunting and Underweight Among Children Aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: A Cross-Sectional Study by Edirisa Juniour Nsubuga, Ivan Arinda Kato, Seungwon Lee, Muzafaru Ssenyondo and John Bosco Isunju in Nutrition and Metabolic Insights</p
sj-docx-1-nmi-10.1177_11786388221125107 – Supplemental material for Predictors of Stunting and Underweight Among Children Aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: A Cross-Sectional Study
No full textSupplemental material, sj-docx-1-nmi-10.1177_11786388221125107 for Predictors of Stunting and Underweight Among Children Aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: A Cross-Sectional Study by Edirisa Juniour Nsubuga, Ivan Arinda Kato, Seungwon Lee, Muzafaru Ssenyondo and John Bosco Isunju in Nutrition and Metabolic Insights</p
sj-docx-2-nmi-10.1177_11786388221125107 – Supplemental material for Predictors of Stunting and Underweight Among Children Aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: A Cross-Sectional Study
No full textSupplemental material, sj-docx-2-nmi-10.1177_11786388221125107 for Predictors of Stunting and Underweight Among Children Aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: A Cross-Sectional Study by Edirisa Juniour Nsubuga, Ivan Arinda Kato, Seungwon Lee, Muzafaru Ssenyondo and John Bosco Isunju in Nutrition and Metabolic Insights</p
sj-docx-1-tai-10.1177_20499361241247467 – Supplemental material for Malaria vaccine acceptance among next of kin of children under 5 years of age in Gulu, northern Uganda in 2023: a community-based study
No full textSupplemental material, sj-docx-1-tai-10.1177_20499361241247467 for Malaria vaccine acceptance among next of kin of children under 5 years of age in Gulu, northern Uganda in 2023: a community-based study by Felix Bongomin, Fiona Jollyne Megwera, Jerry Mundua, Nabirah Naluwooza, Frank Ayesiga, Yakobo Nsubuga, Grace Madraa, Winnie Kibone and Jerom Okot in Therapeutic Advances in Infectious Disease</p
Corrigendum to “Back rubs or foot flicks for neonatal stimulation at birth in a low-resource setting: A randomized controlled trial”. [Resuscitation 167 (2021) 137–143] (Resuscitation (2021) 167 (137–143), (S0300957221003282), (10.1016/j.resuscitation.2021.08.028))
No full textThe authors regret that in the acknowledgments section, the sentence “We are very grateful to the parents of the enrolled babies, to the midwives and to all the local CUAMM staff of the St. Luke Catholic Hospital in Wolisso (Ethiopia) for their support during this study.” should have read “We are very grateful to the parents of the enrolled babies, to the midwives and to all the local CUAMM staff of the St. Kizito Hospital in Matany (Uganda) for their support during this study.” The authors would like to apologise for any inconvenience caused
sj-pdf-3-tai-10.1177_20499361241247467 – Supplemental material for Malaria vaccine acceptance among next of kin of children under 5 years of age in Gulu, northern Uganda in 2023: a community-based study
No full textSupplemental material, sj-pdf-3-tai-10.1177_20499361241247467 for Malaria vaccine acceptance among next of kin of children under 5 years of age in Gulu, northern Uganda in 2023: a community-based study by Felix Bongomin, Fiona Jollyne Megwera, Jerry Mundua, Nabirah Naluwooza, Frank Ayesiga, Yakobo Nsubuga, Grace Madraa, Winnie Kibone and Jerom Okot in Therapeutic Advances in Infectious Disease</p
sj-docx-2-tai-10.1177_20499361241247467 – Supplemental material for Malaria vaccine acceptance among next of kin of children under 5 years of age in Gulu, northern Uganda in 2023: a community-based study
No full textSupplemental material, sj-docx-2-tai-10.1177_20499361241247467 for Malaria vaccine acceptance among next of kin of children under 5 years of age in Gulu, northern Uganda in 2023: a community-based study by Felix Bongomin, Fiona Jollyne Megwera, Jerry Mundua, Nabirah Naluwooza, Frank Ayesiga, Yakobo Nsubuga, Grace Madraa, Winnie Kibone and Jerom Okot in Therapeutic Advances in Infectious Disease</p
The Tanzania Field Epidemiology and Laboratory Training Program: Building and Transforming the Public Health Workforce.
No full textThe Tanzania Field Epidemiology and Laboratory Training Program (TFELTP) was established in 2008 as a partnership among the Ministry of Health and Social Welfare (MOHSW), Muhimbili University of Health and Allied Sciences, National Institute for Medical Research, and local and international partners. TFELTP was established to strengthen the capacity of MOHSW to conduct public health surveillance and response, manage national disease control and prevention programs, and to enhance public health laboratory support for surveillance, diagnosis, treatment and disease monitoring. TFELTP is a 2-year full-time training program with approximately 25% time spent in class, and 75% in the field. TFELTP offers two tracks leading to an MSc degree in either Applied Epidemiology or, Epidemiology and Laboratory Management. Since 2008, the program has enrolled a total of 33 trainees (23 males, 10 females). Of these, 11 were enrolled in 2008 and 100% graduated in 2010. All 11 graduates of cohort 1 are currently employed in public health positions within the country. Demand for the program as measured by the number of applicants has grown from 28 in 2008 to 56 in 2011. While training the public health leaders of the country, TFELTP has also provided essential service to the country in responding to high-profile disease outbreaks, and evaluating and improving its public health surveillance systems and diseases control programs. TFELTP was involved in the country assessment of the revised International Health Regulations (IHR) core capabilities, development of the Tanzania IHR plan, and incorporation of IHR into the revised Tanzania Integrated Disease Surveillance and Response (IDSR) guidelines. TFELTP is training a competent core group of public health leaders for Tanzania, as well as providing much needed service to the MOHSW in the areas of routine surveillance, outbreak detection and response, and disease program management. However, the immediate challenges that the program must address include development of a full range of in-country teaching capacity for the program, as well as a career path for graduates
A machine learning approach to predict E. coli antibacterial resistance using whole-genome sequencing data
No full textBackground: Antimicrobial resistance (AMR) is a significant global health threat, particularly impacting low- and middle-income countries(LMICS) such as Uganda, where reliable and rapid methods for detecting AMR in E. coli and other pathogens are scarce. This lack can lead to inappropriate treatment and the spread of drug-resistant infections. This thesis undertakes a comprehensive study, where various machine learning models to predict AMR in E. coli for ciprofloxacin(CIP), ampicillin(AMP), and cefotaxime(CTX) were trained on whole genome sequencing (WGS) data from England where such data is more readily available. A separate Ugandan dataset was used for validation purposes, further demonstrating the generalizability and effectiveness of the models in LMICS.
Methods: 1496 (CIP), 1428 (CTX), and 1396 (AMP) sequences from England were divided into training and testing. 42 from Uganda were used for validation. Eight different machine learning models were trained and tested: Logistic Regression(LR), Random Forest(RF), Gradient Boosting(GB), XGBoost(XGB), LightGBM(LGBM), CatBoost(CB), Feed-Forward Neural Network(FFNN), and Support Vector Machine(SVM). The models were evaluated based on precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Upsampling techniques were implemented to address class imbalance in the data.
Results: Model predictive performance varied significantly across different antibiotics, underlining the critical role of model selection and dataset characteristics. Notably, the FFNN model demonstrated superior performance during testing for CIP (accuracy 84%; F1 0.55; AUC 91%), LR for CTX (accuracy 91%; F1 0.37; AUC 83%) and GB for AMP (accuracy 57%; F1 0.62, AUC 53%), while the LGBM and RF models outperformed others in same scenarios (p < 0.001). Upsampling did not significantly improve the models' performance, underscoring the complexity and high-dimensionality of SNP data. Despite high accuracy scores with the Ugandan validation dataset(FFNN with CIP accuracy 95%, LR with AMP accuracy 98% and GB with CTX accuracy 65%), the models struggled with the recall metric due to severe class imbalance. Key mutations associated with antimicrobial resistance were identified for these antibiotics.
Conclusion: As the threat of AMR continues to rise, the successful application of these models - particularly on the Ugandan dataset, signals a promising avenue for improving AMR detection and treatment strategies in LMICS were genomic data is scarce. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against antimicrobial resistance.The author was funded by the East African Network for Bioinformatics Training (EANBIT) under Fogarty International Center at the U.S. National Institutes of Health (NIH) under award number U2RTW010677 as a Masters scholar.
The author would also like to acknowledge the Open Science Grid (OSG) consortium which provided computational resources to carry out this study. The OSG is supported by the National Science Foundation award number 2030508 and 1836650
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