Survival Function According to Number of Health Behaviours in Men and Women Aged 45–79 Years without Known Cardiovascular Disease or Cancer, Adjusted for Age, Sex, Body Mass Index and Social Class, EPIC-Norfolk 1993–2006

<p>Survival Function According to Number of Health Behaviours in Men and Women Aged 45–79 Years without Known Cardiovascular Disease or Cancer, Adjusted for Age, Sex, Body Mass Index and Social Class, EPIC-Norfolk 1993–2006</p

MOESM1 of Cost effectiveness and resource allocation of Plasmodium falciparum malaria control in Myanmar: a modelling analysis of bed nets and community health workers

Additional file 1. Cost effectiveness and resource allocation of malaria control in Myanmar: further sensitivity and scenario analyses

Additional file 2: of Beliefs and practices during pregnancy, post-partum and in the first days of an infant’s life in rural Cambodia

Semi structured interview topic guide. (PDF 89 kb

Additional file 1: of Beliefs and practices during pregnancy, post-partum and in the first days of an infant’s life in rural Cambodia

Focus group discussion topic guide. (PDF 89 kb

MOESM3 of Genetic polymorphisms in the circumsporozoite protein of Plasmodium malariae show a geographical bias

Additional file 3. NAPG tetrapeptide repeats in Plasmodium malariae field isolates from Thailand, Myanmar, Lao PDR, and Bangladesh

MOESM1 of Genetic polymorphisms in the circumsporozoite protein of Plasmodium malariae show a geographical bias

Additional file 1. Frequency distribution of the NAAG tetrapeptide repeat unit in the central repeat region of pmcsp. (a) Frequency distribution of the repeat unit in isolates collected from Thailand, Myanmar, Kenya, and Cameroon. (b) Frequency distribution of the repeat unit in isolates collected from Asia and Africa. X-axis represents the number of repeat units, and Y-axis indicates the number of samples corresponding to each repeat unit

MOESM4 of Genetic polymorphisms in the circumsporozoite protein of Plasmodium malariae show a geographical bias

Additional file 4. Average number of the tetrapeptide repeats: NAAG and NDAG, between the Asian and African samples (A) at the country level and (B) at the continent level

MOESM2 of Genetic polymorphisms in the circumsporozoite protein of Plasmodium malariae show a geographical bias

Additional file 2. Frequency distribution of the NDAG tetrapeptide repeat unit in the central repeat region of pmcsp. (a) Frequency distribution of the repeat unit in isolates collected from Thailand, Myanmar, Kenya, and Cameroon. (b) Frequency distribution of the repeat unit in isolates collected from Asia and Africa

MOESM1 of Community perceptions of targeted anti-malarial mass drug administrations in two provinces in Vietnam: a quantitative survey

Additional file 1: Figure S1. Comparison of what respondents do to prevent malaria with number of doses ingested. Figure S2. What kind of complaints do people with malaria have

MOESM1 of Acidosis and acute kidney injury in severe malaria

Additional file 1: Figure S1. Principal Component Analysis (PCA) results of plasma concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA) and p-hydroxyphenyllactic acid (pHPLA) with plasma creatinine of severe malaria patients with AKI (in blue) and without AKI(in red). Figure S2. Principal Component Analysis (PCA) results of corrected urine concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA), p-hydroxyphenyllactic acid (pHPLA), methylmalonic acid (MMA), ethylmalonic acid, (EMA) and α-ketoglutaric acid (αKGA) with urinary creatinine of severe malaria patients with AKI (in blue) and without AKI (in red). Figure S3. Principal Component Analysis (PCA) results of plasma concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA) and p-hydroxyphenyllactic acid (pHPLA) of severe malaria patients with coma (in blue) and without coma (in red). Figure S4. Principal Component Analysis (PCA) results of corrected urine concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA), p-hydroxyphenyllactic acid (HPLA), methylmalonic acid (MMA), ethylmalonic acid (EMA) and α-ketoglutaric acid (αKGA) of severe malaria patients with coma (in blue) and without coma (in red). Figure S5. Principal Component Analysis (PCA) results of plasma concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA) and p-hydroxyphenyllactic acid (HPLA) of severe malaria patients with high parasite biomass (in blue) and without high parasite biomass (in red). Figure S6. Principal Component Analysis (PCA) results of corrected urine concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA), p-hydroxyphenyllactic acid (HPLA), methylmalonic acid (MMA), ethylmalonic acid (EMA) and α-ketoglutaric acid (αKGA) of severe malaria patients with high parasite biomass (in blue) and without high parasite biomass (in red). Figure S7. Principal Component Analysis (PCA) results of uncorrected urine concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA), p-hydroxyphenyllactic acid (HPLA), methylmalonic acid (MMA), ethylmalonic acid (EMA) and α-ketoglutaric acid (αKGA) of severe malaria patients with AKI (in blue) and without AKI (in red). Figure S8. Principal Component Analysis (PCA) results of uncorrected urine concentrations of L-lactic acid (LA), α-hydroxybutyric acid (αHBA), β-hydroxybutyric acid (βHBA), p-hydroxyphenyllactic acid (HPLA), methylmalonic acid (MMA), ethylmalonic acid (EMA) and α-ketoglutaric acid (αKGA) with urinary creatinine of severe malaria patients with AKI (in blue) and without AKI (in red). Table S1a. Plasma and urine concentration range of organic acids detected in patients with severe malaria with coma (+/-). Table S1b. Plasma and urine concentration range of organic acids detected in patients with severe malaria with high biomass (+/-). Table S2. Summary of linear regression models in patients with severe falciparum malaria (N = 90), with plasma PfHRP2 concentrations and plasma or urinary acid concentrations as independent variables, and plasma or urinary creatinine concentrations as dependent variable. Table S3. Summary of linear regression models in patients with sepsis (N = 19), with plasma PfHRP2 concentrations and plasma or urinary acid concentrations as independent variables, and plasma or urinary creatinine concentrations as dependent. Table S4a. Partial Least Square Discriminant Analysis classification results of plasma concentration of 4 acids. Table S4b. Partial Least Square Discriminant Analysis classification results of corrected urine concentration of 7 acids