Complement consumption in children with Plasmodium falciparum malaria

<p>Abstract</p> <p>Background</p> <p>Complement (C) can be activated during malaria, C components consumed and inflammatory mediators produced. This has potential to impair host innate defence.</p> <p>Methods</p> <p>In a case-control study, C activation was assessed by measuring serum haemolytic activity (CH50), functional activity of each pathway and levels of C3a, C4a and C5a in children presenting at Kisumu District Hospital, western Kenya, with severe malarial anaemia (SMA) or uncomplicated malaria (UM).</p> <p>Results</p> <p>CH50 median titers for lysis of sensitized sheep erythrocytes in SMA (8.6 U/mL) were below normal (34–70 U/mL) and were one-fourth the level in UM (34.6 U/mL (<it>P </it>< 0.001). Plasma C3a median levels were 10 times higher than in normals for</p> <p>SMA (3,200 ng/ml) and for UM (3,500 ng/ml), indicating substantial C activation in both groups. Similar trends were obtained for C4a and C5a. The activities of all three C pathways were greatly reduced in SMA compared to UM (9.9% vs 83.4% for CP, 0.09% vs 30.7% for MBL and 36.8% vs 87.7% for AP respectively, <it>P </it>< 0.001).</p> <p>Conclusion</p> <p>These results indicate that, while C activation occurs in both SMA and UM, C consumption is excessive in SMA. It is speculated that in SMA, consumption of C exceeds its regeneration.</p

Targeting remaining pockets of malaria transmission in Kenya to hasten progress towards national elimination goals: an assessment of prevalence and risk factors in children from the Lake endemic region

Background: With an overall decline of malaria incidence, elimination of malaria is gradually becoming the next target for many of countries affected by the disease. In Kenya the national malaria control strategy is aiming to reach pre-elimination for most parts of the country. However, considerable heterogeneity in prevalence of the disease within the country and especially the remaining high prevalent region of the Lake endemic region is likely to slow progress towards this target. To achieve a sustained control and an eventual elimination, a clear understanding of drivers of ongoing malaria transmission in remaining hotspots is needed. Methods: Data from the 2015 Malaria Indicator Survey (MIS) were analysed for prevalence of malaria parasitaemia in children (6 months to 14 years) of different countries within the highly endemic Lake region. Univariate and multivariate logistic regression analysis were preformed to explore associations between selected risk factors and being parasitaemic. A predictive model was built for the association between malaria and the risk factors with the aim of identifying heterogeneities of the disease at the lower administrative levels. Results: Overall, 604/2253 (27%, 95% CI 21.8-32.2) children were parasitaemic. The highest prevalence was observed in Busia County (37%) and lowest in Bungoma County (18%). Multivariate logistic regression analysis showed that the 10-14 years age group (OR = 3.0, 95% CI 2.3-4.1), households in the poorest socio-economic class (OR = 2.1, 95% CI 1.3-3.3), farming (OR = 1.4, 95% CI 1.2-2.5) and residence in Busia (OR = 4.6, 95% CI 2.1-8.2), Kakamega (OR = 2.6, 95% CI 1.3-5.4), and Migori counties (OR = 4.6 95% CI 2.1-10.3) were associated with higher risk of parasitaemia. Having slept under a long-lasting insecticide-treated bed net (LLIN) was associated with a lower risk (OR = 0.7, 95% CI 0.6-0.9). No association were found between malaria infection and the gender of the child, the household head, and the education status of the household head. Discussion and conclusion: Detailed analysis of malaria prevalence data in a hotspot area can identify new threats and avail opportunities for directing intervention. In the Lake endemic region of Kenya, interventions should be focused more on counties with the highest prevalence, and should target older children as well as children from the lower socio-economic strata. Precisely targeting interventions in remaining hotspots and high-risk populations will likely make impact and accelerate progress towards pre-elimination targets

Targeting remaining pockets of malaria transmission in Kenya to hasten progress towards national elimination goals: an assessment of prevalence and risk factors in children from the Lake endemic region

Background: With an overall decline of malaria incidence, elimination of malaria is gradually becoming the next target for many of countries affected by the disease. In Kenya the national malaria control strategy is aiming to reach pre-elimination for most parts of the country. However, considerable heterogeneity in prevalence of the disease within the country and especially the remaining high prevalent region of the Lake endemic region is likely to slow progress towards this target. To achieve a sustained control and an eventual elimination, a clear understanding of drivers of ongoing malaria transmission in remaining hotspots is needed. Methods: Data from the 2015 Malaria Indicator Survey (MIS) were analysed for prevalence of malaria parasitaemia in children (6 months to 14 years) of different countries within the highly endemic Lake region. Univariate and multivariate logistic regression analysis were preformed to explore associations between selected risk factors and being parasitaemic. A predictive model was built for the association between malaria and the risk factors with the aim of identifying heterogeneities of the disease at the lower administrative levels. Results: Overall, 604/2253 (27%, 95% CI 21.8-32.2) children were parasitaemic. The highest prevalence was observed in Busia County (37%) and lowest in Bungoma County (18%). Multivariate logistic regression analysis showed that the 10-14 years age group (OR = 3.0, 95% CI 2.3-4.1), households in the poorest socio-economic class (OR = 2.1, 95% CI 1.3-3.3), farming (OR = 1.4, 95% CI 1.2-2.5) and residence in Busia (OR = 4.6, 95% CI 2.1-8.2), Kakamega (OR = 2.6, 95% CI 1.3-5.4), and Migori counties (OR = 4.6 95% CI 2.1-10.3) were associated with higher risk of parasitaemia. Having slept under a long-lasting insecticide-treated bed net (LLIN) was associated with a lower risk (OR = 0.7, 95% CI 0.6-0.9). No association were found between malaria infection and the gender of the child, the household head, and the education status of the household head. Discussion and conclusion: Detailed analysis of malaria prevalence data in a hotspot area can identify new threats and avail opportunities for directing intervention. In the Lake endemic region of Kenya, interventions should be focused more on counties with the highest prevalence, and should target older children as well as children from the lower socio-economic strata. Precisely targeting interventions in remaining hotspots and high-risk populations will likely make impact and accelerate progress towards pre-elimination targets

Frequency of Epstein - Barr Virus in Patients Presenting with Acute Febrile Illness in Kenya.

BACKGROUND:Most acute febrile illnesses (AFI) are usually not associated with a specific diagnosis because of limitations of available diagnostics. This study reports on the frequency of EBV viremia and viral load in children and adults presenting with febrile illness in hospitals in Kenya. METHODOLOGY/PRINCIPAL FINDINGS:A pathogen surveillance study was conducted on patients presenting with AFI (N = 796) at outpatient departments in 8 hospitals located in diverse regions of Kenya. Enrollment criterion to the study was fever without a readily diagnosable infection. All the patients had AFI not attributable to the common causes of fever in Kenyan hospitals, such as malaria or rickettsiae, leptospira, brucella and salmonella and they were hence categorized as having AFI of unknown etiology. EBV was detected in blood using quantitative TaqMan-based qPCR targeting a highly conserved BALF5 gene. The overall frequency of EBV viremia in this population was 29.2%, with significantly higher proportion in younger children of <5years (33.8%, p = 0.039) compared to patients aged ≥5 years (26.3% for 5-15 years or 18.8% for >15 years). With respect to geographical localities, the frequency of EBV viremia was higher in the Lake Victoria region (36.4%), compared to Kisii highland (24.6%), Coastal region (22.2%) and Semi-Arid region (25%). Furthermore, patients from the malaria endemic coastal region and the Lake Victoria region presented with significantly higher viremia than individuals from other regions of Kenya. CONCLUSIONS/SIGNIFICANCE:This study provides profiles of EBV in patients with AFI from diverse eco-regions of Kenya. Of significant interest is the high frequency of EBV viremia in younger children. The observed high frequencies of EBV viremia and elevated viral loads in residents of high malaria transmission areas are probably related to malaria induced immune activation and resultant expansion of EBV infected B-cells

Dot plots showing complement activation by fractions eluted at different EDTA concentrations.

<p>The 50 mM fraction (Panel D) had maximal activity that was comparable to that of the crude culture supernatant (Panel A). Sham supernatant eluted at 50 mM EDTA had no complement activity.</p

EBV viral load in different age groups.

<p>EBV viral load in patients with AFI were determined by quantitative real time PCR as described in the methods section. The geometric mean viral load were significantly different between the <5 year vs. 5–15 year age categories.</p

Silver stained PAGE gel showing three distinct proteins of sizes 40–64 kDa.

<p>Lanes marked MW =  molecular weight marker, 1 =  crude <i>P. falciparum</i> culture supernatant, 2 =  MBL binding proteins eluted with 50 mM EDTA.</p

Demographic and clinical findings of the patients in this study.

<p>Demographic and clinical findings of the patients in this study.</p

Map of Kenya showing the locations of various surveillance hospitals.

<p>Lake Victoria basin: Kisumu District Hospital, New Nyanza Provincial Hospital and Alupe District Hospital. Kisii highland: Kisii District Hospital. Semi-arid: Marigat District Hospital on floor of the Rift Valley. Arid north eastern Kenya: Garissa District Hospital and Iftin sub-District Hospital. Coast of Indian Ocean: Malindi District Hospital. The map was generated in house using the ArcView^®10.0 application (Environmental Systems Research Institute, Redlands, CA, USA). Elevation base data was downloaded from the world resources institute website (<a href="http://www.wri.org/" target="_blank">http://www.wri.org/</a>).</p