Diagnostic potential of IL6 and other blood-based inflammatory biomarkers in mild traumatic brain injury among children.

Objectives: Inflammatory biomarkers, as indicators of biological states, provide a valuable approach for accurate and reproducible measurements, crucial for the effective management of mild traumatic brain injury (mTBI) in pediatric patients. This study aims to assess the diagnostic utility of blood-based inflammatory markers IL6, IL8, and IL10 in children with mTBI, including those who did not undergo computed tomography (CT) scans. Methods: A prospective multicentric cohort study involving 285 pediatric mTBI patients was conducted, stratified into CT-scanned and non-CT-scanned groups within 24 h post-trauma, alongside 74 control subjects. Biomarker levels were quantitatively analyzed using ELISA. Sensitivity and specificity metrics were calculated to determine the diagnostic efficacy of each biomarker. Results: A total of 223 mTBI patients (78%) did not undergo CT scan examination but were kept in observation for symptoms monitoring at the emergency department (ED) for more than 6 h (in-hospital-observation patients). Among CT-scanned patients (n = 62), 14 (23%) were positive (CT+). Elevated levels of IL6 and IL10 were found in mTBI children compared to controls. Within mTBI patients, IL6 was significantly increased in CT+ patients compared to both CT- and in-hospital-observation patients. No significant differences were observed for IL8 among the compared groups. IL6 yielded a specificity of 48% in identifying CT- and in-hospital-observation patients, with 100% sensitivity in excluding all CT+ cases. These performances were maintained whether IL6 was measured within 6 h or within 24 h after the trauma. Conclusion: The inflammatory marker IL6 emerges as a robust biomarker, showing promising stratification value for pediatric mTBI patients undergoing CT scans or staying in observation in a pediatric ED.

Management of Pediatric Mild Traumatic Brain Injury Patients: S100b, Glial Fibrillary Acidic Protein, and Heart Fatty-Acid-Binding Protein Promising Biomarkers.

Children are highly vulnerable to mild traumatic brain injury (mTBI). Blood biomarkers can help in their management. This study evaluated the performances of biomarkers, in discriminating between children with mTBI who had intracranial injuries (ICIs) on computed tomography (CT+) and (1) patients without ICI (CT-) or (2) both CT- and in-hospital-observation without CT patients. The aim was to rule out the need of unnecessary CT scans and decrease the length of stay in observation in the emergency department (ED). Newborns to teenagers (≤16 years old) with mTBI (Glasgow Coma Scale > 13) were included. S100b, glial fibrillary acidic protein (GFAP), and heart fatty-acid-binding protein (HFABP) performances to identify patients without ICI were evaluated through receiver operating characteristic curves, where sensitivity was set at 100%. A total of 222 mTBI children sampled within 6 h since their trauma were reported. Nineteen percent (n = 43/222) underwent CT scan examination, whereas the others (n = 179/222) were kept in observation at the ED. Sixteen percent (n = 7/43) of the children who underwent a CT scan had ICI, corresponding to 3% of all mTBI-included patients. When sensibility (SE) was set at 100% to exclude all patients with ICI, GFAP yielded 39% specificity (SP), HFABP 37%, and S100b 34% to rule out the need of CT scans. These biomarkers were even more performant: 52% SP for GFAP, 41% for HFABP, and 39% for S100b, when discriminating CT+ versus both in-hospital-observation and CT- patients. These markers can significantly help in the management of patients in the ED, avoiding unnecessary CT scans, and reducing length of stay for children and their families.

Antibody response in children with multisystem inflammatory syndrome related to COVID-19 (MIS-C) compared to children with uncomplicated COVID-19.

Objectives: To comprehensively analyze the quality of the antibody response between children with Multisystem inflammatory syndrome (MIS-C) and age-matched controls at one month after SARS-CoV-2 exposure, and infected in the same time-period. Methods: Serum from 20 MIS-C children at admission, and 14 control children were analyzed. Antigen specific antibody isotypes and subclasses directed against various antigens of SARS-CoV-2 as well as against human common coronavirus (HCoVs) and commensal or pathogenic microorganisms were assessed by a bead-based multiplexed serological assay and by ELISA. The functionality of these antibodies was also assessed using a plaque reduction neutralization test, a RBD-specific avidity assay, a complement deposition assay and an antibody-dependent neutrophil phagocytosis (ADNP) assay. Results: Children with MIS-C developed a stronger IgA antibody response in comparison to children with uncomplicated COVID-19, while IgG and IgM responses are largely similar in both groups. We found a typical class-switched antibody profile with high level of IgG and IgA titers and a measurable low IgM due to relatively recent SARS-CoV-2 infection (one month). SARS-CoV-2-specific IgG antibodies of MIS-C children had higher functional properties (higher neutralization activity, avidity and complement binding) as compared to children with uncomplicated COVID-19. There was no difference in the response to common endemic coronaviruses between both groups. However, MIS-C children had a moderate increase against mucosal commensal and pathogenic strains, reflecting a potential association between a disruption of the mucosal barrier with the disease. Conclusion: Even if it is still unclear why some children develop a MIS-C, we show here that MIS-C children produce higher titers of IgA antibodies, and IgG antibodies with higher functionality, which could reflect the local gastro-intestinal mucosal inflammation potentially induced by a sustained SARS-CoV-2 gut infection leading to continuous release of SARS-CoV-2 antigens.

Satellite imaging and vector-borne diseases: the approach of the French National Space Agency (CNES).

Tele-epidemiology consists in studying human and animal epidemic, the spread of which is closely tied to environmental factors, using data from earth-orbiting satellites. By combining various data originated from satellites such as SPOT (vegetation indexes), Meteosat (winds and cloud masses) and other Earth observation data from Topex/Poseidon and Envisat (wave height, ocean temperature and colour) with hydrology data (number and distribution of lakes, water levels in rivers and reservoirs) and clinical data from humans and animals (clinical cases and serum use), predictive mathematical models can be constructed. A number of such approaches have been tested in the last three years. In Senegal, for example, Rift Valley fever epidemics are being monitored using a predictive model based on the rate at which water holes dry out after the rainy season, which affects the number of mosquito eggs which carry the virus.