| 1. |
- Tamas, Andrea, et al.
(författare)
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Effect of PACAP in central and peripheral nerve injuries
- 2012
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Ingår i: International Journal of Molecular Sciences. - : MDPI. - 1661-6596 .- 1422-0067. ; 13:7, s. 8430-8448
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Forskningsöversikt (refereegranskat)abstract
- Pituitary adenylate cyclase activating polypeptide (PACAP) is a bioactive peptide with diverse effects in the nervous system. In addition to its more classic role as a neuromodulator, PACAP functions as a neurotrophic factor. Several neurotrophic factors have been shown to play an important role in the endogenous response following both cerebral ischemia and traumatic brain injury and to be effective when given exogenously. A number of studies have shown the neuroprotective effect of PACAP in different models of ischemia, neurodegenerative diseases and retinal degeneration. The aim of this review is to summarize the findings on the neuroprotective potential of PACAP in models of different traumatic nerve injuries. Expression of endogenous PACAP and its specific PAC1 receptor is elevated in different parts of the central and peripheral nervous system after traumatic injuries. Some experiments demonstrate the protective effect of exogenous PACAP treatment in different traumatic brain injury models, in facial nerve and optic nerve trauma. The upregulation of endogenous PACAP and its receptors and the protective effect of exogenous PACAP after different central and peripheral nerve injuries show the important function of PACAP in neuronal regeneration indicating that PACAP may also be a promising therapeutic agent in injuries of the nervous system.
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| 2. |
- Bukovics, Peter, et al.
(författare)
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Changes of PACAP level in cerebrospinal fluid and plasma of patients with severe traumatic brain injury
- 2014
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Ingår i: Peptides. - : Elsevier. - 0196-9781 .- 1873-5169. ; 60, s. 18-22
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Tidskriftsartikel (refereegranskat)abstract
- PACAP has well-known neuroprotective potential including traumatic brain injury (TBI). Its level is up-regulated following various insults of the CNS in animal models. A few studies have documented alterations of PACAP levels in human serum. The time course of post-ictal PACAP levels, for example, show correlation with migraine severity. Very little is known about the course of PACAP levels following CNS injury in humans and the presence of PACAP has not yet been detected in cerebrospinal fluid (CSF) of subjects with severe TBI (sTBI). The aim of the present study was to determine whether PACAP occurs in the CSF and plasma (Pl) of patients that suffered sTBI and to establish a time course of PACAP levels in the CSF and Pl. Thirty eight subjects with sTBI were enrolled with a Glasgow Coma Scale ≤8 on admission. Samples were taken daily, until the time of death or for maximum 10 days. Our results demonstrated that PACAP was detectable in the CSF, with higher concentrations in patients with TBI. PACAP concentrations markedly increased in both Pl and CSF in the majority of patients 24-48h after the injury stayed high thereafter. In cases of surviving patients, Pl and CSF levels displayed parallel patterns, which may imply the damage of the blood-brain barrier. However, in patients, who died within the first week, Pl levels were markedly higher than CSF levels, possibly indicating the prognostic value of high Pl PACAP levels.
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| 3. |
- Czeiter, Endre, et al.
(författare)
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Brain Injury Biomarkers May Improve the Predictive Power of the IMPACT Outcome Calculator
- 2012
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 29:9, s. 1770-1778
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Tidskriftsartikel (refereegranskat)abstract
- Outcome prediction following severe traumatic brain injury (sTBI) is a widely investigated field of research. A major breakthrough is represented by the IMPACT prognostic calculator based on admission data of more than 8500 patients. A growing body of scientific evidence has shown that clinically meaningful biomarkers, including glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), and alpha II-spectrin breakdown product (SBDP145), could also contribute to outcome prediction. The present study was initiated to assess whether the addition of biomarkers to the IMPACT prognostic calculator could improve its predictive power. Forty-five sTBI patients (GCS score <= 8) from four different sites were investigated. We utilized the core model of the IMPACT calculator (age, GCS motor score, and reaction of pupils), and measured the level of GFAP, UCH-L1, and SBDP145 in serum and cerebrospinal fluid (CSF). The forecast and actual 6-month outcomes were compared by logistic regression analysis. The results of the core model itself, as well as serum values of GFAP and CSF levels of SBDP145, showed a significant correlation with the 6-month mortality using a univariate analysis. In the core model, the Nagelkerke R-2 value was 0.214. With multivariate analysis we were able to increase this predictive power with one additional biomarker (GFAP in CSF) to R-2 = 0.476, while the application of three biomarker levels (GFAP in CSF, GFAP in serum, and SBDP145 in CSF) increased the Nagelkerke R-2 to 0.700. Our preliminary results underline the importance of biomarkers in outcome prediction, and encourage further investigation to expand the predictive power of contemporary outcome calculators and prognostic models in TBI.
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| 4. |
- Simonetto, Andrea, et al.
(författare)
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A Regularized Saddle-Point Algorithm for Networked Optimization with Resource Allocation Constraints
- 2012
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Ingår i: 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781467320665 ; , s. 7476-7481
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Konferensbidrag (refereegranskat)abstract
- We propose a regularized saddle-point algorithm for convex networked optimization problems with resource allocation constraints. Standard distributed gradient methods suffer from slow convergence and require excessive communication when applied to problems of this type. Our approach offers an alternative way to address these problems, and ensures that each iterative update step satisfies the resource allocation constraints. We derive step-size conditions under which the distributed algorithm converges geometrically to the regularized optimal value, and show how these conditions are affected by the underlying network topology. We illustrate our method on a robotic network application example where a group of mobile agents strive to maintain a moving target in the barycenter of their positions.
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