Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually—H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10—for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CT-negative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins—H-FABP, IL-10, S100B and GFAP—showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23–40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36–55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43–61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients

Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually-H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10-for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CTnegative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins-H-FABP, IL-10, S100B and GFAP-showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23-40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36-55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43-61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients

H-FABP: A new biomarker to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

The majority of patients with mild traumatic brain injury (mTBI) will have normal Glasgow coma scale (GCS) of 15. Furthermore, only 5%±8% of them will be CT-positive for an mTBI. Having a useful biomarker would help clinicians evaluate a patient's risk of developing intracranial lesions. The S100B protein is currently the most studied and promising biomarker for this purpose. Heart fatty-acid binding protein (H-FABP) has been highlighted in brain injury models and investigated as a biomarker for stroke and severe TBI, for example. Here, we evaluate the performances of S100B and H-FABP for differentiating between CT-positive and CT-negative patients. A total of 261 patients with a GCS score of 15 and at least one clinical symptom of mTBI were recruited at three different European sites. Blood samples from 172 of them were collected 6 h after trauma. Patients underwent a CT scan and were dichotomised into CT-positive and CT-negative groups for statistical analyses. HFABP and S100B levels were measured using commercial kits, and their capacities to detect all CT-positive scans were evaluated, with sensitivity set to 100%. For patients recruited 6 h after trauma, the CT-positive group demonstrated significantly higher levels of both H-FABP (p = 0.004) and S100B (p = 0.003) than the CT-negative group. At 100% sensitivity, specificity reached 6% (95% CI 2.8±10.7) for S100B and 29% (95% CI 21.4± 37.1) for H-FABP. Similar results were obtained when including all the patients recruited, i.e. hospital arrival within 24 h of trauma onset. H-FABP out-performed S100B and thus seems to be an interesting protein for detecting all CT-positive mTBI patients with a GCS score of 15 and at least one clinical symptom

The Typology оf Non-Verbal Signals іn Forming оf Linguosociocultural Competence іn the Process оf Reading іn English

Статтю присвячено науково-методичним основам формування лінгвосоціокультурної компетентності у процесі англомовного читання. Проаналізовано підходи до визначення поняття «невербальні засоби комунікації». Визначено основні критерії класифікації невербальних засобів комунікації та подано типології невербальних засобів комунікації відповідно до фізичної природи їх продукування й відповідно до їх значення. Запропоновано методичну типологію невербальних засобів комунікації для формування в майбутніх філологів лінгвосоціокультурної компетентності у процесі англомовного читання, яка враховує основні труднощі формування лінгвосоціокультурної компетентності, пов’язані з розумінням невербальних засобів комунікації.The article deals with the scientific and methodological basics of forming linguosociocultural competence as a сomplex and multicomponent phenomenon in the process of teaching reading in English the students of universities who learn English as the target language. The abundance of different definitions of “non-verbal signals” necessitates the analyses of the approaches to defining the notion. Thus the main approaches to defining the notion of “non-verbal signals” have been analyzed. Non-verbal signals have been defined as the meaningful movements of a person, which include gestures, mimics, pantomimics, changes of personal space, distance, particularities of voice and its modulations and such specific static details as clothing, hairdo styles, accessories, jewelry, tattoos, the smell of a person, etc. The basic criteria of non-verbal signals classification have been singled out. The typologies of non-verbal signals have been introduced. Among them are 1) the typology which is suggested in accordance to the physical nature of producing the non-verbal signals and 2) the typology which is suggested in accordance to the meaning of non-verbal signals. The typology which is based upon physical nature of producing of non-verbal signals includes body language, distance and physical appearance, voice, touch, the use of time, eye contact and the actions of looking while talking and listening, frequency of glances, patterns of fixation, pupil dilation, and blink rate. The typology which is based upon the meaning of non-verbal signals accounts that context non-verbal signals are found within (standard non-verbal signals and situational non-verbal signals) and the degree of universality of their meaning (universal, national-biased, individual/author’s non-verbal signals). The methodological typology of non-verbal signals in forming linguosociocultural competence in the process of reading in English, which embraces and accounts the difficulties of forming of linguocultural competence connected with the interpreting and understanding non-verbal signals has been specified. Thus the difficulties of forming of linguocultural competence connected with the interpreting and understanding non-verbal signals are influenced by the abundance of various types of non-verbal signals, their specific universal/nationally-biased/individual meaning, and also their contextual meaning

H-FABP: A new biomarker to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

<div><p>The majority of patients with mild traumatic brain injury (mTBI) will have normal Glasgow coma scale (GCS) of 15. Furthermore, only 5%–8% of them will be CT-positive for an mTBI. Having a useful biomarker would help clinicians evaluate a patient’s risk of developing intracranial lesions. The S100B protein is currently the most studied and promising biomarker for this purpose. Heart fatty-acid binding protein (H-FABP) has been highlighted in brain injury models and investigated as a biomarker for stroke and severe TBI, for example. Here, we evaluate the performances of S100B and H-FABP for differentiating between CT-positive and CT-negative patients. A total of 261 patients with a GCS score of 15 and at least one clinical symptom of mTBI were recruited at three different European sites. Blood samples from 172 of them were collected ≤ 6 h after trauma. Patients underwent a CT scan and were dichotomised into CT-positive and CT-negative groups for statistical analyses. H-FABP and S100B levels were measured using commercial kits, and their capacities to detect all CT-positive scans were evaluated, with sensitivity set to 100%. For patients recruited ≤ 6 h after trauma, the CT-positive group demonstrated significantly higher levels of both H-FABP (p = 0.004) and S100B (p = 0.003) than the CT-negative group. At 100% sensitivity, specificity reached 6% (95% CI 2.8–10.7) for S100B and 29% (95% CI 21.4–37.1) for H-FABP. Similar results were obtained when including all the patients recruited, i.e. hospital arrival within 24 h of trauma onset. H-FABP out-performed S100B and thus seems to be an interesting protein for detecting all CT-positive mTBI patients with a GCS score of 15 and at least one clinical symptom.</p></div

The proteins performances at classifying mTBI CT-positive and CT-negative patients.

<p>Performance was investigated at 100% sensitivity, and the specificity (dots) reached 31.5% for H-FABP (95% CI 23.4–39.6; cut-off: 1.99 ng/mL), 10.8% for S100B (95% CI 5.4–17.1; cut-off: 0.06 ug/L), 21.6% for IL-10 (95% CI 14.4–28.8; cut-off: 0.12 pg/mL) and 30.6% for GFAP (95% CI 22.5–39.6; cut-off: 97.31 pg/mL).</p

Blood concentrations of 13 biomarkers in 48 CT-negative and 14 CT-positive mTBI patients, and the individual specificity performances.

<p>Blood concentrations of 13 biomarkers in 48 CT-negative and 14 CT-positive mTBI patients, and the individual specificity performances.</p

Cohort 1 mTBI patient characteristics ≤ 6 h post trauma.

<p>Cohort 1 mTBI patient characteristics ≤ 6 h post trauma.</p

The best two-biomarker panel, combining H-FABP and GFAP, at correctly classifying mTBI CT-positive and CT-negative patients compared to the individual best-performing protein: H-FABP.

<p>Performance was investigated at 100% sensitivity, and the panel’s specificity reached 46% (dot, cut-off: 2 parameters, black), compared to the best-performing single marker, H-FABP, which reached 32% specificity (triangle, cut-off: 1.99 ng/mL, grey).</p

The performances of H-FABP and S100B biomarkers at 100% sensitivity, ≤ 6 h post-trauma, dichotomised into younger and older patients.

<p>The performances of H-FABP and S100B biomarkers at 100% sensitivity, ≤ 6 h post-trauma, dichotomised into younger and older patients.</p