ABO Blood Groups Do Not Predict Schistosoma mansoni Infection Profiles in Highly Endemic Villages of Uganda

Schistosoma mansoni is a parasite which causes significant public-health issues, with over 240 mil-lion people infected globally. In Uganda alone, approximately 11.6 million people are affected. Despite over a decade of mass drug administration in this country, hyper-endemic hotspots persist, and individuals who are repeatedly heavily and rapidly reinfected are observed. Human blood-type antigens are known to play a role in the risk of infection for a variety of diseases, due to cross-reactivity between host antibodies and pathogenic antigens. There have been conflicting results on the effect of blood type on schistosomiasis infection and pathology. Moreover, the ef-fect of blood type as a potential intrinsic host factor on S. mansoni prevalence, intensity, clearance, and reinfection dynamics and on co-infection risk remains unknown. Therefore, the epidemio-logical link between host blood type and S. mansoni infection dynamics was assessed in three hyper-endemic communities in Uganda. Longitudinal data incorporating repeated pretreatment S. mansoni infection intensities and clearance rates were used to analyse associations between blood groups in school-aged children. Soil-transmitted helminth coinfection status and biometric parameters were incorporated in a generalised linear mixed regression model including age, gender, and body mass index (BMI), which have previously been established as significant factors influencing the prevalence and intensity of schistosomiasis. The analysis revealed no associations between blood type and S. mansoni prevalence, infection intensity, clearance, reinfection, or coinfection. Variations in infection profiles were significantly different between the villages, and egg burden significantly decreased with age. While blood type has proven to be a predictor of several diseases, the data collected in this study indicate that it does not play a significant role in S. mansoni infection burdens in these high-endemicity communities

ABO Blood Groups Do Not Predict Schistosoma mansoni Infection Profiles in Highly Endemic Villages of Uganda

From MDPI via Jisc Publications RouterHistory: accepted 2021-11-23, pub-electronic 2021-11-27Publication status: PublishedFunder: European Research Council; Grant(s): 680088 SCHISTO_PERSISTFunder: Wellcome Trust; Grant(s): 204820/Z/16/ZFunder: Engineering and Physical Sciences Research Council; Grant(s): EP/T003618/1, EP/R01437X/Funder: Medical Research Council; Grant(s): MR/P025447/1Schistosoma mansoni is a parasite which causes significant public-health issues, with over 240 million people infected globally. In Uganda alone, approximately 11.6 million people are affected. Despite over a decade of mass drug administration in this country, hyper-endemic hotspots persist, and individuals who are repeatedly heavily and rapidly reinfected are observed. Human blood-type antigens are known to play a role in the risk of infection for a variety of diseases, due to cross-reactivity between host antibodies and pathogenic antigens. There have been conflicting results on the effect of blood type on schistosomiasis infection and pathology. Moreover, the effect of blood type as a potential intrinsic host factor on S. mansoni prevalence, intensity, clearance, and reinfection dynamics and on co-infection risk remains unknown. Therefore, the epidemiological link between host blood type and S. mansoni infection dynamics was assessed in three hyper-endemic communities in Uganda. Longitudinal data incorporating repeated pretreatment S. mansoni infection intensities and clearance rates were used to analyse associations between blood groups in school-aged children. Soil-transmitted helminth coinfection status and biometric parameters were incorporated in a generalised linear mixed regression model including age, gender, and body mass index (BMI), which have previously been established as significant factors influencing the prevalence and intensity of schistosomiasis. The analysis revealed no associations between blood type and S. mansoni prevalence, infection intensity, clearance, reinfection, or coinfection. Variations in infection profiles were significantly different between the villages, and egg burden significantly decreased with age. While blood type has proven to be a predictor of several diseases, the data collected in this study indicate that it does not play a significant role in S. mansoni infection burdens in these high-endemicity communities

Translating from egg- to antigen-based indicators for Schistosoma mansoni elimination targets: A Bayesian latent class analysis study

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.Schistosomiasis is a parasitic disease affecting over 240-million people. World Health Organization (WHO) targets for Schistosoma mansoni elimination are based on Kato-Katz egg counts, without translation to the widely used, urine-based, point-of-care circulating cathodic antigen diagnostic (POC-CCA). We aimed to standardize POC-CCA score interpretation and translate them to Kato-Katz-based standards, broadening diagnostic utility in progress towards elimination. A Bayesian latent-class model was fit to data from 210 school-aged-children over four timepoints pre- to six-months-post-treatment. We used 1) Kato-Katz and established POC-CCA scoring (Negative, Trace, +, ++ and +++), and 2) Kato-Katz and G-Scores (a new, alternative POC-CCA scoring (G1 to G10)). We established the functional relationship between Kato-Katz counts and POC-CCA scores, and the score-associated probability of true infection. This was combined with measures of sensitivity, specificity, and the area under the curve to determine the optimal POC-CCA scoring system and positivity threshold. A simulation parametrized with model estimates established antigen-based elimination targets. True infection was associated with POC-CCA scores of ≥ + or ≥G3. POC-CCA scores cannot predict Kato-Katz counts because low infection intensities saturate the POC-CCA cassettes. Post-treatment POC-CCA sensitivity/specificity fluctuations indicate a changing relationship between egg excretion and antigen levels (living worms). Elimination targets can be identified by the POC-CCA score distribution in a population. A population with ≤2% ++/+++, or ≤0.5% G7 and above, indicates achieving current WHO Kato-Katz-based elimination targets. Population-level POC-CCA scores can be used to access WHO elimination targets prior to treatment. Caution should be exercised on an individual level and following treatment, as POC-CCAs lack resolution to discern between WHO Kato-Katz-based moderate- and high-intensity-infection categories, with limited use in certain settings and evaluations

Harnessing technology and portability to conduct molecular epidemiology of endemic pathogens in resource-limited settings

Improvements in genetic and genomic technology have enabled field-deployable molecular laboratories and these have been deployed in a variety of epidemics that capture headlines. In this editorial, we highlight the importance of building physical and personnel capacity in low and middle income countries to deploy these technologies to improve diagnostics, understand transmission dynamics and provide feedback to endemic communities on actionable timelines. We describe our experiences with molecular field research on schistosomiasis, trypanosomiasis and rabies and urge the wider tropical medicine community to embrace these methods and help build capacity to benefit communities affected by endemic infectious diseases

Translating From Egg- to Antigen-Based Indicators for Schistosoma mansoni Elimination Targets: A Bayesian Latent Class Analysis Study

From Frontiers via Jisc Publications RouterHistory: received 2021-11-30, collection 2022, accepted 2022-01-12, epub 2022-02-18Publication status: PublishedSchistosomiasis is a parasitic disease affecting over 240-million people. World Health Organization (WHO) targets for Schistosoma mansoni elimination are based on Kato-Katz egg counts, without translation to the widely used, urine-based, point-of-care circulating cathodic antigen diagnostic (POC-CCA). We aimed to standardize POC-CCA score interpretation and translate them to Kato-Katz-based standards, broadening diagnostic utility in progress towards elimination. A Bayesian latent-class model was fit to data from 210 school-aged-children over four timepoints pre- to six-months-post-treatment. We used 1) Kato-Katz and established POC-CCA scoring (Negative, Trace, +, ++ and +++), and 2) Kato-Katz and G-Scores (a new, alternative POC-CCA scoring (G1 to G10)). We established the functional relationship between Kato-Katz counts and POC-CCA scores, and the score-associated probability of true infection. This was combined with measures of sensitivity, specificity, and the area under the curve to determine the optimal POC-CCA scoring system and positivity threshold. A simulation parametrized with model estimates established antigen-based elimination targets. True infection was associated with POC-CCA scores of ≥ + or ≥G3. POC-CCA scores cannot predict Kato-Katz counts because low infection intensities saturate the POC-CCA cassettes. Post-treatment POC-CCA sensitivity/specificity fluctuations indicate a changing relationship between egg excretion and antigen levels (living worms). Elimination targets can be identified by the POC-CCA score distribution in a population. A population with ≤2% ++/+++, or ≤0.5% G7 and above, indicates achieving current WHO Kato-Katz-based elimination targets. Population-level POC-CCA scores can be used to access WHO elimination targets prior to treatment. Caution should be exercised on an individual level and following treatment, as POC-CCAs lack resolution to discern between WHO Kato-Katz-based moderate- and high-intensity-infection categories, with limited use in certain settings and evaluations

Anonymised raw Schistosoma mansoni and host ABO blood group data for: Francoeur et al. ABO blood group do not predict Schistosoma mansoni infection profiles in highly endemic villages of Uganda

Raw anonymised Schistosoma mansoni and human host ABO blood group dataset from school-aged children, aged 6-14, from three high endemicity villages in Mayuge District Uganda. Data include age, sex, height, weight, village, date, timepoint pre and post treatment with praziquantel and albendazole, up to three days per timepoint of duplicate Kato-Katz slides with infection intensity data for S. mansoni and soil-transmitted helminth infections, including hookworm, Ascaris lumbricoides, Trichuris trichiura, Hymenolepis nana, and Enterobius vermicularis, recorded, and ABO and Rhesus positive or negative blood group type

ABO Blood Groups Do Not Predict <i>Schistosoma mansoni</i> Infection Profiles in Highly Endemic Villages of Uganda.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-11-01, epub 2021-11-27Publication status: PublishedFunder: Medical Research Council; Grant(s): MR/P025447/1Funder: European Research Council; Grant(s): 680088 SCHISTO_PERSISTFunder: Engineering and Physical Sciences Research Council; Grant(s): EP/R01437X/, EP/T003618/1Funder: Wellcome Trust; Grant(s): 204820/Z/16/ZSchistosoma mansoni is a parasite which causes significant public-health issues, with over 240 million people infected globally. In Uganda alone, approximately 11.6 million people are affected. Despite over a decade of mass drug administration in this country, hyper-endemic hotspots persist, and individuals who are repeatedly heavily and rapidly reinfected are observed. Human blood-type antigens are known to play a role in the risk of infection for a variety of diseases, due to cross-reactivity between host antibodies and pathogenic antigens. There have been conflicting results on the effect of blood type on schistosomiasis infection and pathology. Moreover, the effect of blood type as a potential intrinsic host factor on S. mansoni prevalence, intensity, clearance, and reinfection dynamics and on co-infection risk remains unknown. Therefore, the epidemiological link between host blood type and S. mansoni infection dynamics was assessed in three hyper-endemic communities in Uganda. Longitudinal data incorporating repeated pretreatment S. mansoni infection intensities and clearance rates were used to analyse associations between blood groups in school-aged children. Soil-transmitted helminth coinfection status and biometric parameters were incorporated in a generalised linear mixed regression model including age, gender, and body mass index (BMI), which have previously been established as significant factors influencing the prevalence and intensity of schistosomiasis. The analysis revealed no associations between blood type and S. mansoni prevalence, infection intensity, clearance, reinfection, or coinfection. Variations in infection profiles were significantly different between the villages, and egg burden significantly decreased with age. While blood type has proven to be a predictor of several diseases, the data collected in this study indicate that it does not play a significant role in S. mansoni infection burdens in these high-endemicity communities

Edge Computing enabled Deep Learning for Smart Mobile DNA Malaria Diagnostics

In infectious disease diagnosis, results need to be communicated rapidly to healthcare professionals once testing has been completed so that care pathways can be implemented. This represents a particular challenge when testing in remote, low-resource rural communities, in which such diseases often create the largest burden. Here, we report a smartphone-based end-to-end platform for multiplexed DNA diagnosis of malaria. The approach uses a low-cost paper-based microfluidic diagnostic test, which is combined with deep learning algorithms for local decision support and blockchain technology for secure data connectivity and management. We validated the approach via field tests in rural Uganda, where it correctly identified more than 98% of tested cases. Our platform also provides secure geotagged diagnostic information, which creates the possibility of integrating infectious disease data within surveillance frameworks

Reconciling egg- and antigen-based estimates of Schistosoma mansoni clearance and reinfection: a modelling study.

From PubMed via Jisc Publications RouterHistory: received 2021-03-03Publication status: aheadofprint240-million people have schistosomiasis despite decades of interventions. Infections cannot be directly observed, and egg-based Kato-Katz thick smears lack sensitivity, impacting treatment efficacy and reinfection rate estimates. The Point-of-Care Circulating Cathodic Antigen test (POC-CCA) is advocated as an improvement upon the Kato-Katz, however improved estimates are limited by ambiguities in the interpretation of Trace results. We collected repeated Kato-Katz counts from 210 school-aged children and scored POC-CCAs according to manufacturer's guidelines (POC-CCA+) and the externally developed G-Score. We used Hidden Markov Models parameterised with Kato-Katz; Kato-Katz and POC-CCA+; and Kato-Katz and G-Scores, inferring latent clearance and reinfection probabilities at four timepoints over six-months through a more formal statistical reconciliation of these diagnostics than previously conducted. Our approach required minimal but robust assumptions regarding Trace interpretations. Antigen-based models estimated higher infection prevalence across all timepoints compared with the Kato-Katz model, corresponding to lower clearance and higher reinfection estimates. Specifically, pre-treatment prevalence estimates were 85% (Kato-Katz; 95% CI: 79-92%), 99% (POC-CCA+; 97-100%) and 98% (G-Score; 95-100%). Post-treatment, 93% (Kato-Katz; 88-96%), 72% (POC-CCA+; 64-79%) and 65% (G-Score; 57-73%) of those infected were estimated to clear infection. Of those who cleared infection, 35% (Kato-Katz; 27-42%), 51% (POC-CCA+; 41-62%) and 44% (G-Score; 33-55%) were estimated to have been reinfected by nine-weeks. Treatment impact was shorter-lived than only Kato-Katz-based estimates suggested, with lower clearance and rapid reinfection. Three-weeks-post-treatment captured longer-term clearance dynamics. Nine-weeks-post-treatment captured reinfection, but alone could not discern between failed clearance and rapid reinfection. Therefore, frequent sampling is required to understand these important epidemiological dynamics. [Abstract copyright: © The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.