1
|
Caragheorgheopol R, Țucureanu C, Lazăr V, Florescu SA, Lazăr DS, Caraş I. Cerebrospinal fluid cytokines and chemokines exhibit distinct profiles in bacterial meningitis and viral meningitis. Exp Ther Med 2023; 25:204. [PMID: 37090083 PMCID: PMC10119981 DOI: 10.3892/etm.2023.11903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 02/24/2023] [Indexed: 04/25/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Ramona Caragheorgheopol
- Department of Microbiology and Immunology, Faculty of Biology, University of Bucharest, Bucharest 77206, Romania
- Immunology Laboratory, ‘Cantacuzino’ National Institute for Medico-Military Research and Development, Bucharest 050096, Romania
- Correspondence to: Mrs. Ramona Caragheorgheopol, Immunology Laboratory, ‘Cantacuzino’ National Institute for Medico-Military Research and Development, 103 Splaiul Independentei, Bucharest 050096, Romania
| | - Cătălin Țucureanu
- Immunology Laboratory, ‘Cantacuzino’ National Institute for Medico-Military Research and Development, Bucharest 050096, Romania
| | - Veronica Lazăr
- Department of Microbiology and Immunology, Faculty of Biology, University of Bucharest, Bucharest 77206, Romania
| | - Simin Aysel Florescu
- Infectious Diseases Department II, ‘Carol Davila’ University of Medicine and Pharmacy, Bucharest 050474, Romania
- Clinical Department A5 for Infectious and Tropical Diseases, ‘Dr Victor Babes’ Clinical Hospital for Infectious and Tropical Diseases, Bucharest 030303, Romania
| | - Dragoş Stefan Lazăr
- Infectious Diseases Department II, ‘Carol Davila’ University of Medicine and Pharmacy, Bucharest 050474, Romania
- Adults Department B2, ‘Dr Victor Babes’ Clinical Hospital for Infectious and Tropical Diseases, Bucharest 030303, Romania
| | - Iuliana Caraş
- Immunology Laboratory, ‘Cantacuzino’ National Institute for Medico-Military Research and Development, Bucharest 050096, Romania
| |
Collapse
|
2
|
Rath E, Palma Medina LM, Jahagirdar S, Mosevoll KA, Damås JK, Madsen MB, Svensson M, Hyldegaard O, Martins Dos Santos VAP, Saccenti E, Norrby-Teglund A, Skrede S, Bruun T. Systemic immune activation profiles in streptococcal necrotizing soft tissue infections: A prospective multicenter study. Clin Immunol 2023; 249:109276. [PMID: 36871764 DOI: 10.1016/j.clim.2023.109276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/05/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023]
Abstract
OBJECTIVE Early stages with streptococcal necrotizing soft tissue infections (NSTIs) are often difficult to discern from cellulitis. Increased insight into inflammatory responses in streptococcal disease may guide correct interventions and discovery of novel diagnostic targets. METHODS Plasma levels of 37 mediators, leucocytes and CRP from 102 patients with β-hemolytic streptococcal NSTI derived from a prospective Scandinavian multicentre study were compared to those of 23 cases of streptococcal cellulitis. Hierarchical cluster analyses were also performed. RESULTS Differences in mediator levels between NSTI and cellulitis cases were revealed, in particular for IL-1β, TNFα and CXCL8 (AUC >0.90). Across streptococcal NSTI etiologies, eight biomarkers separated cases with septic shock from those without, and four mediators predicted a severe outcome. CONCLUSION Several inflammatory mediators and wider profiles were identified as potential biomarkers of NSTI. Associations of biomarker levels to type of infection and outcomes may be utilized to improve patient care and outcomes.
Collapse
Affiliation(s)
- Eivind Rath
- Department of Medicine, Haukeland University Hospital, Bergen, Norway.
| | - Laura M Palma Medina
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Huddinge, Sweden
| | - Sanjeevan Jahagirdar
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Knut A Mosevoll
- Department of Medicine, Haukeland University Hospital, Bergen, Norway; Department of Clinical Science, University of Bergen, Norway
| | - Jan K Damås
- Department of Infectious Diseases, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway; Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin B Madsen
- Department of Intensive Care, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Mattias Svensson
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Huddinge, Sweden
| | - Ole Hyldegaard
- Department of Anaesthesia- and Surgery, Head and Orthopaedic centre, Hyperbaric Unit, Copenhagen University Hospital, Rigshospitalet, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands; LifeGlimmer GmbH, Berlin, Germany
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Anna Norrby-Teglund
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Huddinge, Sweden
| | - Steinar Skrede
- Department of Medicine, Haukeland University Hospital, Bergen, Norway; Department of Clinical Science, University of Bergen, Norway
| | - Trond Bruun
- Department of Medicine, Haukeland University Hospital, Bergen, Norway; Department of Clinical Science, University of Bergen, Norway
| |
Collapse
|
3
|
Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP. Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines. BioData Min 2021; 14:26. [PMID: 33858484 PMCID: PMC8050889 DOI: 10.1186/s13040-021-00260-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 04/07/2021] [Indexed: 01/10/2023] Open
Abstract
Background As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted “At Risk” CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of “At Risk” CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates. Results A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score. Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.
Collapse
Affiliation(s)
- Seema Singh Saharan
- Department of Statistics, University of Rajasthan, Jaipur, India. .,Voluntary Data Scientist UCSF Kane Lab, San Francisco, USA. .,UC Berkeley Extension, Berkeley, USA.
| | - Pankaj Nagar
- Department of Statistics, University of Rajasthan, Jaipur, India
| | - Kate Townsend Creasy
- Department of Medicine, Cardiovascular Research Institute, University of California, San Francisco, USA
| | - Eveline O Stock
- Department of Medicine, Cardiovascular Research Institute, University of California, San Francisco, USA
| | - James Feng
- Department of Medicine, Cardiovascular Research Institute, University of California, San Francisco, USA
| | - Mary J Malloy
- Departments of Medicine and Pediatrics, Cardiovascular Research Institute, University of California, San Francisco, USA
| | - John P Kane
- Department of Medicine, Department of Biochemistry and Biophysics, Cardiovascular Research Institute, University of California, San Francisco, USA
| |
Collapse
|
4
|
Osazuwa F, Abdul I. Association between C-reactive protein and interleukin-10 levels and malaria severity among children in Warri, Southern Nigeria. CHRISMED JOURNAL OF HEALTH AND RESEARCH 2021. [DOI: 10.4103/cjhr.cjhr_113_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
|