| 1. |
- Apweiler, Rolf, et al.
(författare)
-
Approaching clinical proteomics : current state and future fields of application in cellular proteomics
- 2009
-
Ingår i: Cytometry. Part A : the journal of the International Society for Analytical Cytology. - : Wiley. - 1552-4922. ; 75A:10, s. 816-832
-
Forskningsöversikt (refereegranskat)abstract
- Recent developments in proteomics technology offer new opportunities for clinical applications in hospital or specialized laboratories including the identification of novel biomarkers, monitoring of disease, detecting adverse effects of drugs, and environmental hazards. Advanced spectrometry technologies and the development of new protein array formats have brought these analyses to a standard, which now has the potential to be used in clinical diagnostics. Besides standardization of methodologies and distribution of proteomic data into public databases, the nature of the human body fluid proteome with its high dynamic range in protein concentrations, its quantitation problems, and its extreme complexity present enormous challenges. Molecular cell biology (cytomics) with its link to proteomics is a new fast moving scientific field, which addresses functional cell analysis and bioinformatic approaches to search for novel cellular proteomic biomarkers or their release products into body fluids that provide better insight into the enormous biocomplexity of disease processes and are suitable for patient stratification, therapeutic monitoring, and prediction of prognosis. Experience from studies of in vitro diagnostics and especially in clinical chemistry showed that the majority of errors occurs in the preanalytical phase and the setup of the diagnostic strategy. This is also true for clinical proteomics where similar preanalytical variables such as inter- and intra-assay variability due to biological variations or proteolytical activities in the sample will most likely also influence the results of proteomics studies. However, before complex proteomic analysis can be introduced at a broader level into the clinic, standardization of the preanalytical phase including patient preparation, sample collection, sample preparation, sample storage, measurement, and data analysis is another issue which has to be improved. In this report, we discuss the recent advances and applications that fulfill the criteria for clinical proteomics with the focus on cellular proteomics (cytoproteomics) as related to preanalytical and analytical standardization and to quality control measures required for effective implementation of these technologies and analytes into routine laboratory testing to generate novel actionable health information. It will then be crucial to design and carry out clinical studies that can eventually identify novel clinical diagnostic strategies based on these techniques and validate their impact on clinical decision making.
|
|
| 2. |
- Apweiler, Rolf, et al.
(författare)
-
Approaching clinical proteomics : current state and future fields of application in fluid proteomics
- 2009
-
Ingår i: Clinical Chemistry and Laboratory Medicine. - 1434-6621 .- 1437-4331. ; 47:6, s. 724-744
-
Forskningsöversikt (refereegranskat)abstract
- The field of clinical proteomics offers opportunities to identify new disease biomarkers in body fluids, cells and tissues. These biomarkers can be used in clinical applications for diagnosis, stratification of patients for specific treatment, or therapy monitoring. New protein array formats and improved spectrometry technologies have brought these analyses to a level with potential for use in clinical diagnostics. The nature of the human body fluid proteome with its large dynamic range of protein concentrations presents problems with quantitation. The extreme complexity of the proteome in body fluids presents enormous challenges and requires the establishment of standard operating procedures for handling of specimens, increasing sensitivity for detection and bioinformatical tools for distribution of proteomic data into the public domain. From studies of in vitro diagnostics, especially in clinical chemistry, it is evident that most errors occur in the preanalytical phase and during implementation of the diagnostic strategy. This is also true for clinical proteomics, and especially for fluid proteomics because of the multiple pretreatment processes. These processes include depletion of high-abundance proteins from plasma or enrichment processes for urine where biological variation or differences in proteolytic activities in the sample along with preanalytical variables such as inter- and intra-assay variability will likely influence the results of proteomics studies. However, before proteomic analysis can be introduced at a broader level into the clinical setting, standardization of the preanalytical phase including patient preparation, sample collection, sample preparation, sample storage, measurement and data analysis needs to be improved. In this review, we discuss the recent technological advances and applications that fulfil the criteria for clinical proteomics, with the focus on fluid proteomics. These advances relate to preanalytical factors, analytical standardization and quality-control measures required for effective implementation into routine laboratory testing in order to generate clinically useful information. With new disease biomarker candidates, it will be crucial to design and perform clinical studies that can identify novel diagnostic strategies based on these techniques, and to validate their impact on clinical decision-making.
|
|
| 3. |
- Fjell, Anders M., et al.
(författare)
-
Is short sleep bad for the brain? : Brain structure and cognitive function in short sleepers
- 2023
-
Ingår i: Journal of Neuroscience. - 0270-6474 .- 1529-2401. ; 43:28, s. 5241-5250
-
Tidskriftsartikel (refereegranskat)abstract
- Many sleep less than recommended without experiencing daytime sleepiness. According to prevailing views, short sleep increases risk of lower brain health and cognitive function. Chronic mild sleep deprivation could cause undetected sleep debt, negatively affecting cognitive function and brain health. However, it is possible that some have less sleep need and are more resistant to negative effects of sleep loss. We investigated this using a cross-sectional and longitudinal sample of 47,029 participants of both sexes (20-89 years) from the Lifebrain consortium, Human Connectome project (HCP) and UK Biobank (UKB), with measures of self-reported sleep, including 51,295 MRIs of the brain and cognitive tests. A total of 740 participants who reported to sleep <6 h did not experience daytime sleepiness or sleep problems/disturbances interfering with falling or staying asleep. These short sleepers showed significantly larger regional brain volumes than both short sleepers with daytime sleepiness and sleep problems (n = 1742) and participants sleeping the recommended 7-8 h (n = 3886). However, both groups of short sleepers showed slightly lower general cognitive function (GCA), 0.16 and 0.19 SDs, respectively. Analyses using accelerometer-estimated sleep duration confirmed the findings, and the associations remained after controlling for body mass index, depression symptoms, income, and education. The results suggest that some people can cope with less sleep without obvious negative associations with brain morphometry and that sleepiness and sleep problems may be more related to brain structural differences than duration. However, the slightly lower performance on tests of general cognitive abilities warrants closer examination in natural settings.SIGNIFICANCE STATEMENT: Short habitual sleep is prevalent, with unknown consequences for brain health and cognitive performance. Here, we show that daytime sleepiness and sleep problems are more strongly related to regional brain volumes than sleep duration. However, participants sleeping ≤6 h had slightly lower scores on tests of general cognitive function (GCA). This indicates that sleep need is individual and that sleep duration per se is very weakly if at all related brain health, while daytime sleepiness and sleep problems may show somewhat stronger associations. The association between habitual short sleep and lower scores on tests of general cognitive abilities must be further scrutinized in natural settings.
|
|
| 4. |
- Fjell, Anders M., et al.
(författare)
-
No phenotypic or genotypic evidence for a link between sleep duration and brain atrophy
- 2023
-
Ingår i: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7:11, s. 2008-2022
-
Tidskriftsartikel (refereegranskat)abstract
- Short sleep is held to cause poorer brain health, but is short sleep associated with higher rates of brain structural decline? Analysing 8,153 longitudinal MRIs from 3,893 healthy adults, we found no evidence for an association between sleep duration and brain atrophy. In contrast, cross-sectional analyses (51,295 observations) showed inverse U-shaped relationships, where a duration of 6.5 (95% confidence interval, (5.7, 7.3)) hours was associated with the thickest cortex and largest volumes relative to intracranial volume. This fits converging evidence from research on mortality, health and cognition that points to roughly seven hours being associated with good health. Genome-wide association analyses suggested that genes associated with longer sleep for below-average sleepers were linked to shorter sleep for above-average sleepers. Mendelian randomization did not yield evidence for causal impacts of sleep on brain structure. The combined results challenge the notion that habitual short sleep causes brain atrophy, suggesting that normal brains promote adequate sleep duration—which is shorter than current recommendations.
|
|
| 5. |
- Fjell, Anders M., et al.
(författare)
-
Poor Self-Reported Sleep is Related to Regional Cortical Thinning in Aging but not Memory Decline-Results From the Lifebrain Consortium
- 2021
-
Ingår i: Cerebral Cortex. - : Oxford University Press. - 1047-3211 .- 1460-2199. ; 31:4, s. 1953-1969
-
Tidskriftsartikel (refereegranskat)abstract
- We examined whether sleep quality and quantity are associated with cortical and memory changes in cognitively healthy participants across the adult lifespan. Associations between self-reported sleep parameters (Pittsburgh Sleep Quality Index, PSQI) and longitudinal cortical change were tested using five samples from the Lifebrain consortium (n = 2205, 4363 MRIs, 18-92 years). In additional analyses, we tested coherence with cell-specific gene expression maps from the Allen Human Brain Atlas, and relations to changes in memory performance. "PSQI # 1 Subjective sleep quality" and "PSQI #5 Sleep disturbances" were related to thinning of the right lateral temporal cortex, with lower quality and more disturbances being associated with faster thinning. The association with "PSQI #5 Sleep disturbances" emerged after 60 years, especially in regions with high expression of genes related to oligodendrocytes and S1 pyramidal neurons. None of the sleep scales were related to a longitudinal change in episodic memory function, suggesting that sleep-related cortical changes were independent of cognitive decline. The relationship to cortical brain change suggests that self-reported sleep parameters are relevant in lifespan studies, but small effect sizes indicate that self-reported sleep is not a good biomarker of general cortical degeneration in healthy older adults.
|
|
| 6. |
- Fjell, Anders M., et al.
(författare)
-
Self-reported sleep relates to hippocampal atrophy across the adult lifespan : results from the Lifebrain consortium
- 2020
-
Ingår i: Sleep. - : Oxford University Press. - 0161-8105 .- 1550-9109. ; 43:5
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives: Poor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan.Methods: Self-reported sleep measures and MRI-derived hippocampal volumes were obtained from 3105 cognitively normal participants (18–90 years) from major European brain studies in the Lifebrain consortium. Hippocampal volume change was estimated from 5116 MRIs from 1299 participants for whom longitudinal MRIs were available, followed up to 11 years with a mean interval of 3.3 years. Cross-sectional analyses were repeated in a sample of 21,390 participants from the UK Biobank.Results: No cross-sectional sleep—hippocampal volume relationships were found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing 0.22% greater annual loss than low scorers. The relationship between sleep and hippocampal atrophy did not vary across age. Simulations showed that the observed longitudinal effects were too small to be detected as age-interactions in the cross-sectional analyses.Conclusions: Worse self-reported sleep is associated with higher rates of hippocampal volume decline across the adult lifespan. This suggests that sleep is relevant to understand individual differences in hippocampal atrophy, but limited effect sizes call for cautious interpretation.
|
|
| 7. |
- Gallo, Selene, et al.
(författare)
-
Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies
- 2023
-
Ingår i: Molecular Psychiatry. - : SPRINGERNATURE. - 1359-4184 .- 1476-5578. ; 28:7, s. 3013-3022
-
Tidskriftsartikel (refereegranskat)abstract
- The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
|
|
| 8. |
|
|
| 9. |
- Janssens, Stoffel D., et al.
(författare)
-
Evidence for phase separation of ethanol-water mixtures at the hydrogen terminated nanocrystalline diamond surface
- 2012
-
Ingår i: Journal of Chemical Physics. - : AIP Publishing. - 0021-9606 .- 1089-7690. ; 137:4, s. 044702-
-
Tidskriftsartikel (refereegranskat)abstract
- Interactions between ethanol-water mixtures and a hydrophobic hydrogen terminated nanocrystalline diamond surface, are investigated by sessile drop contact angle measurements. The surface free energy of the hydrophobic surface, obtained with pure liquids, differs strongly from values obtained by ethanol-water mixtures. Here, a model which explains this difference is presented. The model suggests that, due to a higher affinity of ethanol for the hydrophobic surface, when compared to water, a phase separation occurs when a mixture of both liquids is in contact with the H-terminated diamond surface. These results are supported by a computational study giving insight in the affinity and related interaction at the liquid-solid interface.
|
|
| 10. |
- Javaheripour, Nooshin, et al.
(författare)
-
Altered resting-state functional connectome in major depressive disorder : a mega-analysis from the PsyMRI consortium
- 2021
-
Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers (). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 +/- 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 +/- 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.
|
|
