70 research outputs found
A biomarker approach to syndrome-based treatment of severe childhood illness in malaria-endemic areas
This opinion article deals with the diagnostic clinical challenges faced by clinicians or health care workers in malaria-endemic areas when a severely sick child presents to the clinic with fever, coma or respiratory distress. Indeed, the coexistence of malaria with other severe infections like meningitis, invasive bacterial infection or pneumonia makes appropriate treatment allocation a matter of life and death. The use of biomarkers has been proposed as a potential solution to this problem. The arrival of high-throughput technologies allowed thousands of molecules (transcripts, proteins and metabolites) to be been screened in clinical samples from large cohorts of well/characterised patients. The major aim of these studies was to identify biomarkers that inform important decisions: should this child be referred to hospital? Should antibiotics, anti-malarials, or both, be administered? There is a large discrepancy between the number of biomarker discovery studies published and the number of biomarkers that have been clinically validated, let alone implemented. This article reflects on the many opportunities and obstacles encountered in biomarker research in malaria-endemic areas
PfHPRT: a new biomarker candidate of acute Plasmodium falciparum infection.
Plasmodium falciparum is a protozoan parasite that causes human malaria. This parasitic infection accounts for approximately 655,000 deaths each year worldwide. Most deaths could be prevented by diagnosing and treating malaria promptly. To date, few parasite proteins have been developed into rapid diagnostic tools. We have combined a shotgun and a targeted proteomic strategy to characterize the plasma proteome of Gambian children with severe malaria (SM), mild malaria, and convalescent controls in search of new candidate biomarkers. Here we report four P. falciparum proteins with a high level of confidence in SM patients, namely, PF10_0121 (hypoxanthine phosphoribosyltransferase, pHPRT), PF11_0208 (phosphoglycerate mutase, pPGM), PF13_0141 (lactate dehydrogenase, pLDH), and PF14_0425 (fructose bisphosphate aldolase, pFBPA). We have optimized selected reaction monitoring (SRM) assays to quantify these proteins in individual patients. All P. falciparum proteins were higher in SM compared with mild cases or control subjects. SRM-based measurements correlated markedly with clinical anemia (low blood hemoglobin concentration), and pLDH and pFBPA were significantly correlated with higher P. falciparum parasitemia. These findings suggest that pHPRT is a promising biomarker to diagnose P. falciparum malaria infection. The diagnostic performance of this marker should be validated prospectively
Identification of a novel clinical phenotype of severe malaria using a network-based clustering approach
The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM highlights important differences in disease pathogenesis that often require specific therapeutic options. The clinical heterogeneity of SM is largely unresolved. Here we report a network-based analysis of clinical phenotypes associated with SM in 2,915 Gambian children admitted to hospital with Plasmodium falciparum malaria. We used a network-based clustering method which revealed a strong correlation between disease heterogeneity and mortality. The analysis identified four distinct clusters of SM and respiratory distress that departed from the WHO definition. Patients in these clusters characteristically presented with liver enlargement and high concentrations of brain natriuretic peptide (BNP), giving support to the potential role of circulatory overload and/or right-sided heart failure as a mechanism of disease. The role of heart failure is controversial in SM and our work suggests that standard clinical management may not be appropriate. We find that our clustering can be a powerful data exploration tool to identify novel disease phenotypes and therapeutic options to reduce malaria-associated mortality
The power of data mining in diagnosis of childhood pneumonia
Childhood pneumonia is the leading cause of death of children under the age of five globally. Diagnostic information on presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia, and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (Creactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (Lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians
Gut microbiota composition in travellers is associated with faecal lipocalin-2, a mediator of gut inflammation
IntroductionWe examined the gut microbiota of travellers returning from tropical areas with and without travellerâs diarrhoea (TD) and its association with faecal lipocalin-2 (LCN2) levels.MethodsParticipants were recruited at the Hospital Clinic of Barcelona, Spain, and a single stool sample was collected from each individual to perform the diagnostic of the etiological agent causing gastrointestinal symptoms as well as to measure levels of faecal LCN2 as a biomarker of gut inflammation. We also characterised the composition of the gut microbiota by sequencing the region V3-V4 from the 16S rRNA gene, and assessed its relation with the clinical presentation of TD and LCN2 levels using a combination of conventional statistical tests and unsupervised machine learning approaches.ResultsAmong 61 participants, 45 had TD, with 40% having identifiable etiological agents. Surprisingly, LCN2 levels were similar across groups, suggesting gut inflammation occurs without clinical TD symptoms. Differential abundance (DA) testing highlighted a microbial profile tied to high LCN2 levels, marked by increased Proteobacteria and Escherichia-Shigella, and decreased Firmicutes, notably Oscillospiraceae. UMAP analysis confirmed this profileâs association, revealing distinct clusters based on LCN2 levels. The study underscores the discriminatory power of UMAP in capturing meaningful microbial patterns related to clinical variables. No relevant differences in the gut microbiota composition were found between travellers with or without TD.DiscussionThe findings suggest a correlation between gut microbiome and LCN2 levels during travel, emphasising the need for further research to discern the nature of this relationship
Lactate levels in severe malarial anaemia are associated with haemozoin-containing neutrophils and low levels of IL-12
BACKGROUND: Hyperlactataemia is often associated with a poor outcome in severe malaria in African children. To unravel the complex pathophysiology of this condition the relationship between plasma lactate levels, parasite density, pro- and anti-inflammatory cytokines, and haemozoin-containing leucocytes was studied in children with severe falciparum malarial anaemia. METHODS: Twenty-six children with a primary diagnosis of severe malarial anaemia with any asexual Plasmodium falciparum parasite density and Hb < 5 g/dL were studied and the association of plasma lactate levels and haemozoin-containing leucocytes, parasite density, pro- and anti-inflammatory cytokines was measured. The same associations were measured in non-severe malaria controls (N = 60). RESULTS: Parasite density was associated with lactate levels on admission (r = 0.56, P < 0.005). Moreover, haemozoin-containing neutrophils and IL-12 were strongly associated with plasma lactate levels, independently of parasite density (r = 0.60, P = 0.003 and r = -0.46, P = 0.02, respectively). These associations were not found in controls with uncomplicated malarial anaemia. CONCLUSION: These data suggest that blood stage parasites, haemozoin and low levels of IL-12 may be associated with the development of hyperlactataemia in severe malarial anaemia
Positive direct antiglobulin test in post-artesunate delayed haemolysis: more than a coincidence?
Background: Delayed haemolysis is a frequent adverse event after treatment with artesunate (AS). Removing onceinfected âpittedâ erythrocytes by the spleen is the most accepted mechanism of haemolysis in these cases. However,
an increasing number of cases with positive direct antiglobulin test (DAT) haemolysis after AS have been reported.
Methods: All malaria cases seen at Hospital Clinic of Barcelona between 2015 and 2017 were retrospectively
reviewed. Clinical, parasitological and laboratory data from patients treated with intravenous artesunateâspecifcally
looking for delayed haemolysis and DATâwas collected.
Results: Among the 36 severe malaria patients treated with artesunate at the hospital, 10 (27.8%) developed postartesunate delayed haemolysis. Out of these, DAT was performed in six, being positive in four of them (at least 40%).
DAT was positive only for complementâwithout IgGâsuggesting drug-dependent immune-haemolytic anaemia of
the immune-complex type. Three of the four patients were treated with corticosteroids and two also received blood
transfusion, with a complete recovery.
Conclusions: Drug-induced auto-immune phenomena in post-artesunate delayed haemolysis may be underreâ
ported and must be considered. The role of corticosteroids should be reassessed
Positive direct antiglobulin test in post-artesunate delayed haemolysis: more than a coincidence?
BACKGROUND: Delayed haemolysis is a frequent adverse event after treatment with artesunate (AS). Removing once-infected 'pitted' erythrocytes by the spleen is the most accepted mechanism of haemolysis in these cases. However, an increasing number of cases with positive direct antiglobulin test (DAT) haemolysis after AS have been reported. METHODS: All malaria cases seen at Hospital Clinic of Barcelona between 2015 and 2017 were retrospectively reviewed. Clinical, parasitological and laboratory data from patients treated with intravenous artesunate-specifically looking for delayed haemolysis and DAT-was collected. RESULTS: Among the 36 severe malaria patients treated with artesunate at the hospital, 10 (27.8%) developed post-artesunate delayed haemolysis. Out of these, DAT was performed in six, being positive in four of them (at least 40%). DAT was positive only for complement-without IgG-suggesting drug-dependent immune-haemolytic anaemia of the immune-complex type. Three of the four patients were treated with corticosteroids and two also received blood transfusion, with a complete recovery. CONCLUSIONS: Drug-induced auto-immune phenomena in post-artesunate delayed haemolysis may be underreported and must be considered. The role of corticosteroids should be reassessed
Characterization of vaginal microbiota in women with preterm labor with intra-amniotic inflammation
This study aims to investigate the relation between
vaginal microbiota and exposition to intra-amniotic inflammation
(IAI). We conducted a prospective cohort study in women with
preterm labor <34 weeks who had undergone amniocentesis to
rule out IAI. Vaginal samples were collected after
amniocentesis. Women with IAI included those with positive
amniotic fluid (AF) for a microorganism identified by specific
culture media and Sanger sequencing 16S ribosomal RNA gene
and/or high AF interleukin (IL)-6 levels. Vaginal microbiota was
characterized by 16S ribosomal RNA gene amplicon sequencing.
Specific quantitative PCR targeted to Lactobacillus spp. was
also performed. Regression models were used to evaluate
associations between vaginal microbiota and exposition to IAI.
Concerning our results, 64 women were included. We observed an
inverse association between AF IL-6 levels and load of
Lactobacillus spp. Depletion in Lactobacillus spp. load was
significantly associated with an early gestational age at
delivery and a short latency to delivery. Microbial-diversity
was found to be a risk factor for the subsequent occurrence of
clinical chorioamnionitis. To the contrary, higher Lactobacillus
spp. load had a protective role. In conclusion, the study
identifies reduced bacterial load of Lactobacillus spp. in women
exposed to IAI and found microbial-diversity and Lactobacillus
spp. depletion to be associated with a worse perinatal outcome
Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
More than 400,000 deaths from severe malaria (SM) are
reported every year, mainly in African children. The diversity
of clinical presentations associated with SM indicates important
differences in disease pathogenesis that require specific
treatment, and this clinical heterogeneity of SM remains poorly
understood. Here, we apply tools from machine learning and
model-based inference to harness large-scale data and dissect
the heterogeneity in patterns of clinical features associated
with SM in 2904 Gambian children admitted to hospital with
malaria. This quantitative analysis reveals features predicting
the severity of individual patient outcomes, and the dynamic
pathways of SM progression, notably inferred without requiring
longitudinal observations. Bayesian inference of these pathways
allows us assign quantitative mortality risks to individual
patients. By independently surveying expert practitioners, we
show that this data-driven approach agrees with and expands the
current state of knowledge on malaria progression, while
simultaneously providing a data-supported framework for
predicting clinical risk
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