Cerebrospinal fluid cytokines and chemokines exhibit distinct profiles in bacterial meningitis and viral meningitis.

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-1ß, 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-1ß (r=0.64) and IL-1ß/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.

Relationship between chemokines and T lymphocytes in the context of respiratory allergies (Review).

Allergic diseases have been classified in the last decades using various theories. The main classes of the newest classification in allergic respiratory diseases focus on the characterization of the endotype (which takes into account biomarkers related to determinant pathophysiological mechanisms) and of the phenotype (based on the description of the disease). Th2, Th1 and Th17 lymphocytes and the type of inflammatory response mediated by them represent the basis for Th2 and non-Th2 endotype classification. In addition, new lymphocytes were also used to characterize allergic diseases: Th9 lymphocytes, Th22 lymphocytes, T follicular helper cells (TFH) lymphocytes and invariant natural killer T (iNKT) lymphocytes. In the last decade, a growing body of evidence focused on chemokines, chemoattractant cytokines, which seems to have an important contribution to the pathogenesis of this pathology. This review presents the interactions between chemokines and Th lymphocytes in the context of Th2/non-Th2 endotype classification of respiratory allergies.

The Relationship between Chemokine Ligand 3 and Allergic Rhinitis.

Background Allergic rhinitis (AR) is a chronic and frequent condition characterized by an excessive response of the immune system to innocent substances encountered in the nasal mucosa. These reactions are mediated by many factors, including chemokines. Chemokine ligand 3 (CCL3, a macrophage inflammatory protein 1α) is a chemokine implicated in the activation of mast cells - white cells shown to be highly involved in orchestrating allergic reactions. The present study evaluated the role of CCL3 in AR. Material and methods Thirty-nine participants, including 24 patients with AR and 15 healthy controls, were evaluated for allergies to dust mites, cat and dog danders, cockroaches (Blatella germanica), molds, grasses, weeds, and tree pollen using skin prick tests. Participants were also evaluated for inflammatory conditions by measuring total blood count with differential; concentrations of rheumatoid factor, fibrinogen, and C-reactive protein; and erythrocyte sedimentation rate. CCL3 in blood samples was measured at the Immunology Laboratory, Cantacuzino National Institute for Military Medical Research and Development, Bucharest, Romania, using Human Multianalyte Profiling Base Kits (R&D Systems Inc., Minneapolis, MN). Results Mean serum CCL3 concentration was significantly higher in patients with AR than in controls (15.03 ± 7.11 pg/ml vs. 8.34 ± 4.46 pg/ml, p = 0.001 [t-test] and p = 0.026 [Mann-Whitney test]). CCL3 concentrations correlated with polysensitization, defined as two or more positive prick tests per patient (r = 0.325, p = 0.046) and seasonal AR (r = 0.482, p = 0.002). Conclusions Elevated levels of CCL3 were seen in our patients with AR. We have observed correlations with polysensitization and seasonal allergies. These results suggest that chemokines might play an important role in the pathogenesis of AR. In the future, chemokines might be used in endotype classification of patients with AR and as a possible target in the treatment of AR.