| 1. |
- Momeni, Naghi, et al.
(författare)
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A novel blood-based biomarker for detection of autism spectrum disorders
- 2012
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Ingår i: Translational Psychiatry. - : Nature Publishing Group. - 2158-3188. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Autism Spectrum Disorders (ASD) are classified as neurological developmental disorders. Several studies have been carried out to find a candidate biomarker linked to development of these disorders, but up to date no reliable biomarker is available. Mass spectrometry techniques have been used for protein profiling of blood plasma of children with such disorders in order to identify proteins/peptides which may be used as biomarkers for detection of the disorders. Three differentially expressed peptides with mass charged (m/z) values of 2,020 ± 1, 1,864 ± 1, and 1,978 ± 1 Da in heparin plasma of children with ASD which were significantly changed as compared to the peptide pattern of the non-ASD control group are reported here. This novel set of biomarkers allows for a reliable blood based diagnostic tool that may be used in diagnosis and potentially, in prognosis of ASD.
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| 2. |
- Svensson, Jonas E., et al.
(författare)
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Serotonin transporter availability increases in patients recovering from a depressive episode
- 2021
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Ingår i: Translational Psychiatry. - : Springer. - 2158-3188. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Molecular imaging studies have shown low cerebral concentration of serotonin transporter in patients suffering from depression, compared to healthy control subjects. Whether or not this difference also is present before disease onset and after remission (i.e. a trait), or only at the time of the depressive episode (i.e. a state) remains to be explored. We examined 17 patients with major depressive disorder with positron emission tomography using [C-11]MADAM, a radioligand that binds to the serotonin transporter, before and after treatment with internet-based cognitive behavioral therapy. In all, 17 matched healthy control subjects were examined once. Cerebellum was used as reference to calculate the binding potential. Differences before and after treatment, as well as between patients and controls, were assessed in a composite cerebral region and in the median raphe nuclei. All image analyses and confirmatory statistical tests were preregistered. Depression severity decreased following treatment (p<0.001). [C-11]MADAM binding in patients increased in the composite region after treatment (p=0.01), while no change was observed in the median raphe (p=0.51). No significant difference between patients at baseline and healthy controls were observed in the composite region (p=0.97) or the median raphe (p=0.95). Our main finding was that patients suffering from a depressive episode show an overall increase in cerebral serotonin transporter availability as symptoms are alleviated. Our results suggest that previously reported cross-sectional molecular imaging findings of the serotonin transporter in depression most likely reflect the depressive state, rather than a permanent trait. The finding adds new information on the pathophysiology of major depressive disorder.
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| 3. |
- Wallert, John, et al.
(författare)
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Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data
- 2022
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Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder {MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off <= 10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and {iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.
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