IL-10 and S100B capacity to differentiate CT scan results in patients younger and older than 65.

<p>IL-10 and S100B capacity to differentiate CT scan results in patients younger and older than 65.</p

The seven significantly different expressed proteins between CT-positive and CT-negative patients, of the 92 inflammation biomarkers tested.

<p>The median protein ratio between CT-positive and CT-negative patients was calculated and the specificity (SP) of each protein was investigated with sensitivity (SE) set at 100%. All results are shown as normalised protein expression (NPX).</p

CT scan brain lesion findings.

<p>Some of the 22 CT-positive patients had more than one brain lesion type and therefore the total percentage exceed 100.</p

The classification performance of MIP-1 alpha, MCP-1 and IL-10 in the verification cohort.

<p>Predictive performance was investigated at 100% sensitivity, and the specificities reached were 0% for MIP-1 alpha (95% CI 0.0–0.0; cut-off 0 pg/mL; light grey), 6.7% for MCP-1 (95% CI 0.0–15.6; cut-off 522.3 pg/ml; dark grey) and 31.1% for IL-10 (95% CI 17.8–44.4; cut-off 0.134 pg/mL; black).</p

The two-centre validation cohort’s characteristics ≤ 6 h following a TBI event.

<p>The two-centre validation cohort’s characteristics ≤ 6 h following a TBI event.</p

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

Cohort 1 mTBI patient characteristics ≤ 6 h post trauma.

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

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