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- Camporesi, Elena, et al.
(författare)
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Neuroligin-1 in brain and CSF of neurodegenerative disorders: investigation for synaptic biomarkers
- 2021
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Ingår i: Acta Neuropathologica Communications. - : Springer Science and Business Media LLC. - 2051-5960. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Synaptic pathology is a central event in Alzheimer's disease (AD) and other neurodegenerative conditions, and investigation of synaptic proteins can provide valuable tools to follow synaptic dysfunction and loss in these diseases. Neuroligin-1 (Nlgn1) is a postsynaptic cell adhesion protein, important for synapse stabilization and formation. Nlgn1 has been connected to cognitive disorders, and specifically to AD, as target of the synaptotoxic effect of amyloid-beta (A beta) oligomers and A beta fibrils. To address changes in Nlgn1 expression in human brain, brain regions in different neurological disorders were examined by Western blot and mass spectrometry. Brain specimens from AD (n = 23), progressive supranuclear palsy (PSP, n = 11), corticobasal degeneration (CBD, n = 10), and Pick's disease (PiD, n = 9) were included. Additionally, cerebrospinal fluid (CSF) samples of AD patients (n = 43) and non-demented controls (n = 42) were analysed. We found decreased levels of Nlgn1 in temporal and parietal cortex (similar to 50-60% reductions) in AD brains compared with controls. In frontal grey matter the reduction was not seen for AD patients; however, in the same region, marked reduction was found for PiD (similar to 77%), CBD (similar to 66%) and to a lesser extent for PSP (similar to 43%), which could clearly separate these tauopathies from controls. The Nlgn1 level was reduced in CSF from AD patients compared to controls, but with considerable overlap. The dramatic reduction of Nlgn1 seen in the brain extracts of tauopathies warrants further investigation regarding the potential use of Nlgn1 as a biomarker for these neurodegenerative diseases.
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| 2. |
- Smart, Sophie E., et al.
(författare)
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Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium
- 2022
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Ingår i: Schizophrenia Research. - : Elsevier. - 0920-9964 .- 1573-2509. ; 250
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionOur aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.MethodsWe combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.ResultsOur sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).ImplicationsOur findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.
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