Improving the clinical management of traumatic brain injury through the pharmacokinetic modeling of peripheral blood biomarkers
© The Author(s) 2016
Received: 7 October 2016
Accepted: 15 November 2016
Published: 30 November 2016
Blood biomarkers of neurovascular damage are used clinically to diagnose the presence severity or absence of neurological diseases, but data interpretation is confounded by a limited understanding of their dependence on variables other than the disease condition itself. These include half-life in blood, molecular weight, and marker-specific biophysical properties, as well as the effects of glomerular filtration, age, gender, and ethnicity. To study these factors, and to provide a method for markers’ analyses, we developed a kinetic model that allows the integrated interpretation of these properties.
The pharmacokinetic behaviors of S100B (monomer and homodimer), Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase L1 were modeled using relevant chemical and physical properties; modeling results were validated by comparison with data obtained from healthy subjects or individuals affected by neurological diseases. Brain imaging data were used to model passage of biomarkers across the blood–brain barrier.
Our results show the following: (1) changes in biomarker serum levels due to age or disease progression are accounted for by differences in kidney filtration; (2) a significant change in the brain-to-blood volumetric ratio, which is characteristic of infant and adult development, contributes to variation in blood concentration of biomarkers; (3) the effects of extracranial contribution at steady-state are predicted in our model to be less important than suspected, while the contribution of blood–brain barrier disruption is confirmed as a significant factor in controlling markers’ appearance in blood, where the biomarkers are typically detected; (4) the contribution of skin to the marker S100B blood levels depends on a direct correlation with pigmentation and not ethnicity; the contribution of extracranial sources for other markers requires further investigation.
We developed a multi-compartment, pharmacokinetic model that integrates the biophysical properties of a given brain molecule and predicts its time-dependent concentration in blood, for populations of varying physical and anatomical characteristics. This model emphasizes the importance of the blood–brain barrier as a gatekeeper for markers’ blood appearance and, ultimately, for rational clinical use of peripherally-detected brain protein.
KeywordsPhysiologically-based pharmacokinetic model Precision medicine Traumatic brain injury Glomerular filtration Serum markers
Peripheral biomarkers have myriad potential uses for prognostication, treatment and pharmacovigilance in many diseases, including those of neurological nature. For example, levels of the brain-derived glial fibrillary acidic protein (GFAP), S100B, tau and Ubiquitin C-Terminal Hydrolase L1 (UCHL-1) in biological fluids have been shown to correlate with presence and severity of many neurological disorders. Steady-state blood levels of these biomarkers are measurable, albeit at low concentrations, and increase rapidly after head injury. The most common use for peripheral biomarkers has been in the field of traumatic brain injury (TBI). The possibility of using serum S100B as a diagnostic tool for patients with mild head injury (MHI) was first reported in 1995 . It was first thought that S100B release was a biomarker of subtle brain damage after MHI, although data suggests that an equally relevant mechanism may involve the release of S100B through a disrupted blood–brain barrier (BBB), without necessarily involving actual cellular damage [2–5]. Comparable results were obtained with GFAP and UCHL-1  which suggests that these markers also appear in blood when the BBB is compromised.
The brain parenchyma is protected by a vascular barrier, referred to as BBB. The system of capillaries forming the human BBB has approximately 20 m2 of exchange surface with brain tissue, and is separated from neurons by only a few microns. The BBB maintains a strict compartmentalization of brain-and-blood-specific substances through the presence of a tight-junctioned endothelial cell layer. During blood–brain barrier disruption (BBBD), proteins normally present in high concentrations in the CNS are free to diffuse into the blood following their concentration gradients . An ideal and clinically significant biomarker should be: (1) present at low or undetectable levels in serum of normal subjects under steady-state conditions; (2) present in brain and cerebrospinal fluid (CSF) at higher concentrations than in blood; (3) susceptible to extravasation in the event of BBBD; (4) further released by brain cells in response to brain damage (e.g., during reactive gliosis).
Among the several reasons that made the use of brain biomarkers a holy grail for neurology is the minimally-invasive nature of the process required to obtain blood samples. While a venipuncture is typically required, such a procedure is clearly less morbid than CSF sampling or the use of intravascular contrast agents (e.g., gadolinium or iodinated contrast agents). In addition, imaging modalities such as computed tomography (CT) scans expose the patient to radiation. Last, but perhaps not least, is the cost differential between state-of-the-art medical imaging and a simple blood test.
While the advantages of peripheral biomarkers are well understood, their widespread use has been confounded by several factors including inter-individual variability in “reference values”, the effect of age on markers’ presence or levels, ethnic differences, etc. Many groups have described this variability, and most of the data presented so far has focused on the astrocytic protein S100B . This biomarker has been studied for several years, and investigated as a tool to diagnose non-CNS conditions, mainly malignant melanoma . Other markers are being investigated, and it’s very likely that a number of new markers will become available in the next decade. We hypothesize that one of the obstacles in the acceptance of peripheral biomarker detection as a diagnostic approach for neurological diseases is the lack of understanding on how serum biomarker levels are holistically controlled by other physiological functions and parameters. For example, it has been suggested that S100B levels directly depend on body mass index (BMI) , while others have suggested that the increased BBB permeability in diabetes or conditions associated with obesity are the underlying factors contributing to this variability . We developed a computer model that mimics, for a range of biomarker proteins, the key physiological features (e.g., BBB permeability, extracranial contribution) and pharmacokinetic properties (e.g., biomarker size and distribution, renal elimination) that contribute to changes in serum biomarker levels irrespective of neurological triggers.
Literature review for initial assignments of the model
Initial parameter values used within model
Brain biomarker concentration
S100b monomer (10.7 kD)
10.0 ng/ml = 1.0 nM
S100b dimer (21.0 kD)
10.0 ng/ml = 0.5 nM
GFAP (26.0 kD)
1.0 ng/ml = 0.038 nM
UCHL-1 (26.0 kD)
7.6 ng/ml = 0.292 nM
10% of maximal BBBD
1–5% of maximal BBBD
Central nervous system
Brain volume, newborn
Brain volume, adult
1.42 l (male) 1.05 l (female)
Blood volume, newborn
Blood volume, adult
Skin volume, adult
S100b in light skin
S100b in dark skin
Glomerular filtration rate
GFR (ml/min) = ((A*((SrCr/B)) ^ 1.209) * (0.993 ^ Age)
Coefficient A (Caucasian)
141 (male) 144 (female)
Coefficient A (African American)
163 (male) 166 (female)
0.9 (male) 0.7 (female)
Physiologically-based biomarker kinetic model development
Our model was developed using the SimBiology extension of MatLab (MathWorks, Natick MA), and results were analyzed in the Origin Pro 9.0 (Northampton, MA) and JMP 11 (SAS) programs. Our methods were derived from a generic Physiologically-Based Pharmacokinetic (PBPK) model developed by others , which was further based on a previous multi-compartment system in which several organs were represented with realistic dimensions [15, 16]. In the aforementioned model, the organs were connected by arterial and venous circulation with appropriate hemodynamic values, also obtained from the literature. For the model described herein, we simplified this arterial-to-venous transfer of biomarkers by assuming a homogeneous distribution of the biomarker in the systemic circulation, and that the volume of this idealized vascular compartment was equal to the total volemia. An additional consideration was made for the cerebral circulation, where permeability across the BBB was incorporated as a governing factor to free diffusion of brain-specific biomarkers. This dynamic range theoretically extends from a biomarker diffusivity (cm2/s) of zero to a diffusivity that equals the concentration-driven diffusion of a given molecule in bodily fluids. This spectrum of values is biologically unrealistic, but was established for convenience (see also Eq. 3 and paragraphs below). The extent of “opening” for the BBB was based on clinical observations (see Fig. 2b), and the kinetic property of molecule extravasation was based on empirical results (see Fig. 2a) [4, 5, 17, 18]. While there is a large difference between measurements based on contrast-enhancement versus diffusion of a molecule from brain to blood, we suggest that this “Radiologic Index” is currently the best comparative approach to model the behavior of a diffusible marker against clinically acceptable means. Please note that markers’ concentrations in the blood were set to 0 ng/ml at the beginning of the simulation so that a kinetic progression toward steady-state levels could be observed.
BBB disruption in patients
All patients signed an informed consent according to institutional review protocols of The Cleveland Clinic Foundation and the Declaration of Helsinki. Eight patients with the histologically-proven, non-acquired immunodeficiency syndrome Primary Central Nervous System lymphoma (PCNSL) consented to participate in an institutional, review board-approved protocol for the management of this disease at the Cleveland Clinic Foundation. This protocol involved the concurrent administration of intravenous chemotherapy and a treatment that included BBB disruption  followed by the instillation of intra-arterial chemotherapy (IAC). This subset of patients also agreed to additional blood draws for serum S100B sampling. The appropriate inclusion and exclusion of patients on this protocol was documented previously . Specifically, these patients were treated with intra-arterial injection of mannitol causing a temporary disruption of the BBB, followed by a selective, intra-carotid chemotherapeutic injection. The procedure consisted of the following steps: (1) patient is taken to the operating room and general thiopental anesthesia is induced; (2) catheterization of a selected intracranial artery (either an internal carotid or vertebral artery) is performed via a percutaneous, trans-femoral puncture on a given treatment day; (3) mannitol (25%; osmolarity 1372) is administered intra-arterially via the catheter at a predetermined rate of 3–12 cc/s for 30 s; (4) after the BBB is “opened” with mannitol, intra-arterial methotrexate is infused. Immediately following delivery of chemotherapy, non-ionic contrast dye is given intravenously; (5) the patient is transported, still anesthetized, for a CT scan. This step is essential to determine and document the extent of BBB opening since better disruption portends better delivery of chemotherapeutic drugs across the barrier. Methods for grading the degree of BBBD and correlation of these grades with Hounsfield units were previously described ; degree of BBBD was graded by visual inspection as nil, fair, good, or excellent; (6) after the CT scan is completed the patient is awakened, extubated and monitored in the hospital overnight. Blood samples were drawn 10 min prior to mannitol injection and 2–5 min after mannitol injection. S100B was measured on all available blood samples by techniques described elsewhere [5, 13]. A total of 102 BBBD procedures in eight patients were studied. The results in Fig. 1 refer to 14 procedures consisting of intra-arterial chemotherapy not preceded by BBBD.
Serum S100B measurements
Serum samples of S100B were obtained after induction of anesthesia, immediately prior to and immediately after intra-arterial mannitol infusion (Fig. 2a). At each time point, blood samples were collected and immediately centrifuged at 1200×g for 10 min, and the supernatant sera were stored at −80 °C. The S100B concentration was measured by the Sangtec 100 ELISA method (Diasorin, Stillwater, MN) using high and low level manufacturer-provided controls to ensure proper assay performance. A total of 267 apparently healthy subjects were prospectively enrolled in compliance with IRB regulations. Serum samples were collected in different seasons (summer and winter), from different regions of the USA (North, Central, and South), and of light and dark skin color. Dark skin color was defined according to FDA guidance (“dark skinned” is defined as Black or African–American, “light skinned” is defined as White, Hispanic, Asian, American Indian, Alaska Native, Native Hawaiian, and other Pacific Islander).
Age-related differences in blood biomarkers dynamics
Since one of our goals was to expand this model to include other markers, we added a variable that takes into account protein excretion, at a given GFR, for different MWs. The results are shown in Fig. 3b and c while Eq. 5 shows the modeling relationship used to extrapolate kidney filtration for a marker’s MW. In newborn (Fig. 3b), the steady-state and post-BBBD values for two brain markers with different MWs are shown alongside the kinetic curves of monomeric vs. homodimeric S100B . Note that increased MW resulted in pronounced increases in clearance time, which translated into longer persistence of the signals. Similar results were obtained in adults (Fig. 3c). Please note that, although neonates and adults were modeled using physiological values for body size and kidney function, the initial concentration of brain markers in neonates was set equal to adults. These results emphasize how age-related differences in steady-state and post-BBBD serum levels of each marker may be explained by anatomic (e.g., brain volume) or physiological (e.g., steady-state BBB permeability) variations.
Gender-related differences in blood biomarker levels
This model predicted minimal physiological changes in serum biomarker levels between an adult male and female. This is consistent with previously reported data showing no gender-specific variations in steady-state levels of S100B . Although the Cockroft–Gault formula for estimating glomerular filtration rate provides a lower rate of elimination for females than males, extent of contribution by the brain is also decreased due to a smaller brain-to-blood volumetric ratio . This deviation from the physiology of the adult male resulted in a slightly varied kinetic curve, due to reduced clearance of biomarkers from female subjects’ serum. The difference predicted by the model is not clinically relevant as gender-driven differences have not been reported.
Ethnicity-related differences in blood biomarker levels
An obvious limitation of this approach is that one needs to input an initial concentration for dermal S100B or any other organ contributing to serum levels. We therefore measured levels of S100B by ELISA in freshly resected surgical samples from normal access tissue (Fig. 1) and these values were added to an appropriate volume of skin . Only adult males were considered for this portion of the simulation. The results confirmed our hypothesis: when using the measured values of skin [S100B] and the appropriate volumetric ratios, the model accurately predicted increases in serum S100B based on sun exposure or skin pigmentation differences due to race. Note that sun exposure resulted in different levels of S100B even within a light (or dark) skinned population. Unlike in the modeling results presented in Fig. 3, changes in BBBD-induced S100B were minimally effected (not shown). This is to be expected, given that BBBD only effects cerebral vasculature permeability.
The main outcome of this study was the implementation of a MatLab-based pharmacokinetic model that allows to study or interpret the fate and excretion, levels and half-life of markers derived from the CNS but sampled in the blood compartment. A corollary set of hypotheses, which were largely confirmed by cross-validation of the model with existing data, implicated the variation of markers’ levels due to: (1) physiological parameters (e.g., GFR); (2) somatic properties (volumetric size of different organs during development); and (3) environmental factors such as sun exposure.
Strengths of the model
One of the key strengths of this model, and the results presented herein, is the extent to which these results can be validated by empirical data. These data were primarily obtained from our own work but we also used findings by others in the public domain. In addition, we used a realistic model of the human body, based on the success of PBPK analysis of drug AMDE . In these models, and in the variation adopted by us, the body is represented as a network of intercommunicating compartments; each organ has an adjustable volume to accommodate anatomical variations, and the organs are interconnected by a realistic vascular tree with arteries and veins. However, the capillary compartment is not included.
The main strength and uniqueness of this approach resides in the clinical data we used to model permeability of the blood-brain barrier. Our results are based on uncommon inter-arterial procedures used to treat brain neoplasms. For details and rationale of this procedure, see . Pertinent to this effort is the fact that “opening” of the BBB was clinically measured at time of blood testing by contrast-enhanced CT scans. Figure 2b shows the quantitative relationship between radiological measurements of BBBD and associated changes in blood S100B. Please note that because of the clinical nature of this trial and the large number of subjects enrolled, the data are not as clear-cut as one desires. Human studies were still utilized over available data from animal studies, however, due to increased translatability and clinical relevance.
Another significant feature of our modeling effort is the presence of excretive systems. This may come as a surprise given that the main focus of our research is in neurosciences. However, the modeling results demonstrate that one of the chief regulators of markers’ presence in blood is the level of GFR. We were able to show that kidney function (both physiologic and pathologic; Fig. 2d) also affects markers’ half-life in a size-dependent manner. In other words, with physiologic kidney function, half-life was linearly related to markers’ molecular size. However, when approaching kidney failure, the effect was overwhelmingly shifted toward markers with higher (over 40 kD) molecular weight. This is important because markers of brain and BBB damage can be very small (S100B, 10 kD), of intermediate size (tau, 46 kD), or large (autoreactive IgG, 140 kD). We underscore that without adjusting for molecular weight and kidney function, one may misinterpret the true clinical meaning of a given marker. For example, if one wishes to determine the delayed sequelae of a given event (e.g., stroke, TBI) it is best to use a marker with a longer half-life (higher molecular weight).
An additional aspect that we wish to discuss is the use of accepted values for the markers’ initial levels in the brain (Fig. 1a). We also modeled the relative changes in brain-to-blood volume due to changes in age and gender, as well as extracranial biomarker sources. In the case of S100B, it is widely reported that skin and fat contain substantial levels of S100B [10, 34]. In our model we used measured values for fat and skin S100B content (Fig. 1b). By doing so, we were able to show that skin levels directly affect steady-state serum S100B levels, and what is more important, they also reproduce changes in basal S100B levels due to ethnicity, exposure to sun and skin complexion. As in the other modeling efforts, we used real data to confirm or disprove the output of the model (Fig. 4). Fat tissue, when measured in a broad range of BMI, has been reported not to influence blood S100B . This may be surprising since the measured levels of S100B in skin were in fact lower than levels in fat. This discrepancy can be explained by two mechanisms, namely the high cellular turnover and death rate of dermal cells  and the poor vascularization of adipose tissue compared to dermal tissue .
In every modeling effort, the source of modeling inputs is essential. Despite our efforts to use meaningful input values, some aspects of this approach require further studies to improve output accuracy. For example, MRI is the recognized quantitative tool to measure BBBD and yet we used CT. This was due to the fact that, at the time of our experiments, not only was intraoperative MRI not available, the velocity of acquisition in CT scanning made their use more amenable for fast-paced, intra-arterial procedures. Furthermore, the length of time required for MRI signal acquisition was inconsistent with the time resolution required by the model (minutes, see Fig. 2a).
Another limitation of this approach is the fact that the transfer of intracellular markers to the extracellular space is not fully understood, and certainly not known for the biomarkers discussed herein. We used as a surrogate for the movements of S100B across the plasma membrane data from melanoma cell lines expressing high levels of S100B (see Additional file 1: Figure S1 and Reference ). However, since none of the markers studied or modeled appear to have endocrine or exocrine functions, we believe it is safe to assume that their rate of intracellular-to-extracellular transfer is low in healthy tissue. By the same token, it is reasonable to predict that physical trauma will mobilize the marker from soft tissues such as skin and fat, and that under condition of traumatic events, the contribution of extracranial sources may well be different than at steady-state. In addition, while every effort was made to use available knowledge on brain and body development and aging, we lacked quantitative values for the brain concentration of various biomarkers in the newborn population.
In conclusion, we developed a multi-compartment, pharmacokinetic model that integrates the biophysical properties of a given brain molecule and predicts its time-dependent concentration in blood, for populations of varying physical and anatomical characteristics.
central nervous system
mild head injury
traumatic brain injury
brain-derived glial fibrillary acidic protein
Ubiquitin C-Terminal Hydrolase L1
absorption, distribution, metabolism, and excretion
DJ and NM designed the study and analyzed results, as acknowledged in their published work. DJ, AD and JW developed the Matlab program that was used for the modeling of markers’ dynamic behavior in the human body. All authors read and approved the final manuscript.
Dr. Damir Janigro holds a patent for the use of S100B in neurological diseases.
Availability of data and materials
All data were published by us or by others as described in Table 1 and in the main text.
Consent for publication
As part of the approved consent form the participants signed the following: “The medical and research information recorded about you will be used within the Cleveland Clinic and/or disclosed outside the Cleveland Clinic as part of this research. Some of the tests and procedures done solely for this research study also may be placed in your medical record so your other doctors know you are in this study. Upon completion of the study, you may have access to the research information that is contained in the medical chart.”
Ethics approval and consent to participate
All studies involving human subjects were approved by the IR committee at the Cleveland Clinic Foundation. See approved IRB studies listed below:
IRB # 4406: S100B as a Serum Marker for Early Detection of Brain Tumors.
IRB # 4302: S100B as a Serum Marker for Disruption of the Blood–Brain Barrier (BBBD).
IRB #5468: S100B as a Serum Marker for Early Detection of Brain Metastases.
The work was supported by R01NS078307 (NM, DJ). UH4TR000491, awarded to DJ. R01NS43284, R41MH093302, R21NS077236, R42MH093302, and R21HD057256, awarded to DJ.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Schiavi P, Laccarino C, Servadei F. The value of the calcium binding protein S100 in the management of patients with traumatic brain injury. Acta Biomed. 2012;83(1):5–20.PubMedGoogle Scholar
- Janigro D, Barnett G, Mayberg M, Inventors. Peripheral marker of blood brain barrier permeability. US Patent 20030170747 A1. 2003. http://www.google.ch/patents/US20030170747.
- Kanner AA, Marchi N, Fazio V, Mayberg MR, Koltz MT, Siomin V, et al. Serum S100beta: a noninvasive marker of blood-brain barrier function and brain lesions. Cancer. 2003;97(11):2806–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Kapural M, Krizanac-Bengez L, Barnett G, Perl J, Masaryk T, Apollo D, et al. Serum S-100beta as a possible marker of blood-brain barrier disruption. Brain Res. 2002;940(1–2):102–4.View ArticlePubMedGoogle Scholar
- Marchi N, Rasmussen PA, Kapural M, Fazio V, Cavaglia M, Janigro D. Peripheral markers of brain damage and blood-brain barrier dysfunction. Restorative Neurol Neurosci. 2003;21(3–4):109–21.Google Scholar
- Welch RD, Ayaz SI, Lewis LM, Unden J, Chen JY, Mika VH, et al. Ability of serum glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and S100B to differentiate normal and abnormal head computed tomography findings in patients with suspected mild or moderate traumatic brain injury. J Neurotrauma. 2016;33(2):203–14.View ArticlePubMedPubMed CentralGoogle Scholar
- Reiber H. Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta. 2001;310(2):173–86.View ArticlePubMedGoogle Scholar
- Heidari K, Vafaee A, Rastekenari AM, Taghizadeh M, Shad EG, Eley R, et al. S100B protein as a screening tool for computed tomography findings after mild traumatic brain injury: systematic review and meta-analysis. Brain Inj. 2015;11:1–12.Google Scholar
- Ghanem G, Loir B, Morandini R, Sales F, Lienard D, Eggermont A, et al. On the release and half-life of S100B protein in the peripheral blood of melanoma patients. Int J Cancer. 2001;94(4):586–90.View ArticlePubMedGoogle Scholar
- Steiner J, Schiltz K, Walter M, Wunderlich MT, Keilhoff G, Brisch R, et al. S100B serum levels are closely correlated with body mass index: an important caveat in neuropsychiatric research. Psychoneuroendocrinology. 2010;35(2):321–4.View ArticlePubMedGoogle Scholar
- Pham N, Fazio V, Cucullo L, Teng Q, Biberthaler P, Bazarian JJ, et al. Extracranial sources of S100B do not affect serum levels. PLoS ONE. 2010;5(9):e12691.View ArticlePubMedPubMed CentralGoogle Scholar
- Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20(11):2305–13.View ArticlePubMedGoogle Scholar
- Marchi N, Fazio V, Cucullo L, Kight K, Masaryk TJ, Barnett G, et al. Serum transthyretin as a possible marker of blood-to-CSF barrier disruption. J Neurosci. 2003;23(5):1949–55.PubMedGoogle Scholar
- Peters SA. Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis. Clin Pharmacokinet. 2008;47(4):261–75.View ArticlePubMedGoogle Scholar
- Bernareggi A. Clinical pharmacokinetics of nimesulide. Clin Pharmacokinet. 1998;35(4):247–74.View ArticlePubMedGoogle Scholar
- Bernareggi A, Rowland M. Physiologic modeling of cyclosporin kinetics in rat and man. J Pharmacokinet Biopharm. 1991;19(1):21–50.View ArticlePubMedGoogle Scholar
- Marchi N, Angelov L, Masaryk T, Fazio V, Granata T, Hernandez N, et al. Seizure-promoting effect of blood-brain barrier disruption. Epilepsia. 2007;48(4):732–42.View ArticlePubMedPubMed CentralGoogle Scholar
- Angelov L, Doolittle ND, Kraemer DF, Siegal T, Barnett GH, Peereboom DM, et al. Blood-brain barrier disruption and intra-arterial methotrexate-based therapy for newly diagnosed primary CNS lymphoma: a multi-institutional experience. J Clin Oncol. 2009;27(21):3503–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Boron WF, Boulpaep EL. Glomerular filtration and renal blood flow. In: Medical physiology. Philadelphia, PA: Elsevier; 2003. p. 757–73.
- Kroll RA, Neuwelt EA. Outwitting the blood-brain barrier for therapeutic purposes: osmotic opening and other means. Neurosurgery. 1998;42(5):1083–99.View ArticlePubMedGoogle Scholar
- Neuwelt EA, Goldman DL, Dahlborg SA, Crossen J, Ramsey F, Roman Goldstein S, et al. Primary CNS lymphoma treated with osmotic blood-brain barrier disruption: prolonged survival and preservation of cognitive function. J Clin Oncol. 1991;9:1580–90.View ArticlePubMedGoogle Scholar
- Roman-Goldstein S, Clunie DA, Stevens J, Hogan R, Monard J, Ramsey F, et al. Osmotic blood-brain barrier disruption: CT and radionuclide imaging. AJNR Am J Neuroradiol. 1994;15(3):581–90.PubMedGoogle Scholar
- Bouvier D, Duret T, Rouzaire P, Jabaudon M, Rouzaire M, Nourrisson C, et al. Preanalytical, analytical, gestational and pediatric aspects of the S100B immuno-assays. Clin Chem Lab Med. 2016;54(5):833–42.View ArticlePubMedGoogle Scholar
- Vogelbaum MA, Masaryk T, Mazzone P, Mekhail T, Fazio V, McCartney S, et al. S100beta as a predictor of brain metastases: brain versus cerebrovascular damage. Cancer. 2005;104(4):817–24.View ArticlePubMedGoogle Scholar
- Biberthaler P, Mussack T, Wiedemann E, Kanz KG, Mutschler W, Linsenmaier U, et al. Rapid identification of high-risk patients after minor head trauma (MHT) by assessment of S-100B: ascertainment of a cut-off level. Eur J Med Res. 2002;7(4):164–70.PubMedGoogle Scholar
- Astor BC, Levey AS, Stevens LA, Van LF, Selvin E, Coresh J. Method of glomerular filtration rate estimation affects prediction of mortality risk. J Am Soc Nephrol. 2009;20(10):2214–22.View ArticlePubMedPubMed CentralGoogle Scholar
- Hostetter TH, Levey AS, Stevens LA. Clinical impact of reporting estimated glomerular filtration rates. Clin Chem. 2010;56(9):1381–3.View ArticlePubMedGoogle Scholar
- Stevens LA, Claybon MA, Schmid CH, Chen J, Horio M, Imai E, et al. Evaluation of the chronic kidney disease epidemiology collaboration equation for estimating the glomerular filtration rate in multiple ethnicities. Kidney Int. 2011;79(5):555–62.View ArticlePubMedGoogle Scholar
- Nylen K, Ost M, Csajbok LZ, Nilsson I, Hall C, Blennow K, et al. Serum levels of S100B, S100A1B and S100BB are all related to outcome after severe traumatic brain injury. Acta Neurochir (Wien). 2008;150(3):221–7.View ArticleGoogle Scholar
- Ben AO, Vally J, Adem C, Foglietti MJ, Beaudeux JL. Reference values for serum S-100B protein depend on the race of individuals. Clin Chem. 2003;49(5):836–7.View ArticleGoogle Scholar
- Allen JS, Damasio H, Grabowski TJ. Normal neuroanatomical variation in the human brain: an MRI-volumetric study. Am J Phys Anthropol. 2002;118(4):341–58.View ArticlePubMedGoogle Scholar
- Morera-Fumero AL, Abreu-Gonzalez P, Henry-Benitez M, Yelmo-Cruz S, Diaz-Mesa E. Summer/winter changes in serum S100B protein concentration as a source of research variance. J Psychiatr Res. 2013;47(6):791–5.View ArticlePubMedGoogle Scholar
- Bazarian JJ, Pope C, McClung J, Cheng YT, Flesher W. Ethnic and racial disparities in emergency department care for mild traumatic brain injury. Acad Emerg Med. 2003;10(11):1209–17.View ArticlePubMedGoogle Scholar
- Bargerstock E, Puvenna V, Iffland P, Falcone T, Hossain M, Vetter S, et al. Is peripheral immunity regulated by blood-brain barrier permeability changes? PLoS ONE. 2014;9(7):e101477.View ArticlePubMedPubMed CentralGoogle Scholar
- Weiss L, Haydock K, Pickren JW, Lane WW. Organ vascularity and metastatic frequency. Am J Pathol. 1980;101(1):101–14.PubMedPubMed CentralGoogle Scholar
- Mayer CA, Brunkhorst R, Niessner M, Pfeilschifter W, Steinmetz H, Foerch C. Blood levels of glial fibrillary acidic protein (GFAP) in patients with neurological diseases. PLoS ONE. 2013;8(4):e62101.View ArticlePubMedPubMed CentralGoogle Scholar
- Mondello S, Kobeissy F, Vestri A, Hayes RL, Kochanek PM, Berger RP. Serum concentrations of ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein after pediatric traumatic brain injury. Sci Rep. 2016;6:28203.View ArticlePubMedPubMed CentralGoogle Scholar
- Orasanu E, Melbourne A, Cardoso MJ, Modat M, Taylor AM, Thayyil S, et al. Brain volume estimation from post-mortem newborn and fetal MRI. Neuroimage Clin. 2014;6:438–44.View ArticlePubMedPubMed CentralGoogle Scholar
- Sisson TR, Lund CJ, Whalen LE, Telek A. The blood volume of infants. I. The full-term infant in the first year of life. J Pediatr. 1959;55(2):163–79.View ArticlePubMedGoogle Scholar
- Falcone T, Fazio V, Lee C, Simon B, Franco K, Marchi N, et al. Serum S100B: a potential biomarker for suicidality in adolescents? PLoS ONE. 2010;5(6):e11089.View ArticlePubMedPubMed CentralGoogle Scholar