Dataset Information

Cerebrospinal fluid lens-free microscopy: a new tool for the laboratory diagnosis of meningitis.

ABSTRACT: Cerebrospinal fluid cytology is performed by operator-dependant light microscopy as part of the routine laboratory work-flow diagnosis of meningitis. We evaluated operator-independent lens-free microscopy numeration of erythrocytes and leukocytes for the cytological diagnosis of meningitis. In a first step, prospective optical microscopy counts of leukocytes done by five different operators yielded an overall 16.7% misclassification of 72 cerebrospinal fluid specimens in meningitis/non-meningitis categories using a 10?leukocyte/?L cut-off. In a second step, the lens-free microscopy algorithm adapted for counting cerebrospinal fluid cells and discriminating leukocytes from erythrocytes was modified step-by-step in the prospective analysis of 215 cerebrospinal fluid specimens. The definite algorithm yielded a 100% sensitivity and a 86% specificity compared to confirmed diagnostics. In a third step, a blind lens-free microscopic analysis of 116 cerebrospinal fluid specimens, including six cases of microbiology-confirmed infectious meningitis, yielded a 100% sensitivity and a 79% specificity. Adapted lens-free microscopy is thus emerging as an operator-independent technique for the rapid numeration of leukocytes and erythrocytes in cerebrospinal fluid. In particular, this technique is well suited to the rapid diagnosis of meningitis at point-of-care laboratories.

SUBMITTER: Delacroix R

PROVIDER: S-EPMC5206666 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cerebrospinal fluid lens-free microscopy: a new tool for the laboratory diagnosis of meningitis.

Delacroix Robin R Morel Sophie Nhu An SN Hervé Lionel L Bordy Thomas T Dinten Jean-Marc JM Drancourt Michel M Allier Cédric C

Scientific reports 20170103

Cerebrospinal fluid cytology is performed by operator-dependant light microscopy as part of the routine laboratory work-flow diagnosis of meningitis. We evaluated operator-independent lens-free microscopy numeration of erythrocytes and leukocytes for the cytological diagnosis of meningitis. In a first step, prospective optical microscopy counts of leukocytes done by five different operators yielded an overall 16.7% misclassification of 72 cerebrospinal fluid specimens in meningitis/non-meningiti ...[more]

PMID: 28045084

Similar Datasets

Project description:Differential diagnosis of bacterial meningitis (BM) and viral meningitis (VM) is a critical clinical challenge, as the early and accurate identification of the causative agent determines the appropriate treatment regimen and markedly improves patient outcomes. Clinical and experimental studies have demonstrated that the pathogen and the host immune response contribute to mortality and neurological sequelae. As BM is associated with the activation of an inflammatory cascade, the patterns of pro- and anti-inflammatory cytokines/chemokines (CTs/CKs) present in the cerebrospinal fluid (CSF) in response to the immune assault may be useful as sensitive markers for differentiating BM from VM. In the present study, the ability of CTs/CKs in the CSF to differentiate between BM and VM was investigated. For this, biochemical markers and CT/CK profiles were analysed in 145 CSF samples, divided into three groups: BM (n=61), VM (n=58) and the control group (C; n=26) comprising patients with meningism. The CSF concentrations of monocyte chemoattractant protein-1, interleukin (IL)-8, IL-1β, IL-6, macrophage inflammatory protein-1α (MIP-1α), epithelial-neutrophil activating peptide, IL-10, tumour necrosis factor-α (TNF-α), proteins and white blood cells were significantly higher and the CSF glucose level was significantly lower in the BM group compared with the VM and C groups (P<0.01). Correlation analysis identified 28 significant correlations between various CTs/CKs in the BM group (P<0.01), with the strongest positive correlations being for TNF-α/IL-6 (r=0.75), TNF-α/MIP-1α (r=0.69), TNF-α/IL-1β (r=0.64) and IL-1β/MIP-1α (r=0.64). To identify the optimum CT/CK patterns for predicting and classifying BM and VM, a dataset of 119 BM and VM samples was divided into training (n=90) and testing (n=29) subsets for use as input for a Random Forest (RF) machine learning algorithm. For the 29 test samples (15 BM and 14 VM), the RF algorithm correctly classified 28 samples, with 92% sensitivity and 93% specificity. The results show that the patterns of CT/CK levels in the CSF can be used to aid discrimination of BM and VM.

Dataset Information

Cerebrospinal fluid lens-free microscopy: a new tool for the laboratory diagnosis of meningitis.

Publications

Cerebrospinal fluid lens-free microscopy: a new tool for the laboratory diagnosis of meningitis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets