Biomarkers for CNS injury in CSF are elevated in COVID-19 and associated with neurological symptoms and disease severity

BACKGROUND: Neurological symptoms have been frequently reported in hospitalized patients with coronavirus disease 2019 (COVID-19) and biomarkers of CNS injury are reported to be increased in plasma but not extensively studied in CSF. This study examines CSF for biomarkers of CNS injury and other pathology in relation to neurological symptoms and disease severity in patients with neurological manifestations of COVID-19. METHODS: Nineteen patients with neurological symptoms and mild to critical COVID-19 were prospectively included. Extensive analysis of CSF, including measurement of biomarkers of CNS injury (neurofilament light chain protein (NfL) glial fibrillary acidic protein (GFAp) and total tau) was performed and related to neurological features and disease severity. RESULTS: Neurological symptoms included altered mental status (42%), headache (42%), central (21%) and peripheral weakness (32%). Two patients demonstrated minor pleocytosis and four patients had increased immunoglobulin G levels in CSF. Neuronal autoantibody testing using commercial tests was negative in all patients. Increased CSF levels of NfL, GFAp and total-tau protein were seen in 63%, 37%, and 16% of patients, respectively. Increased NfL correlated with disease severity, time in intensive care and level of consciousness. NfL in CSF was higher in patients with central neurological symptoms. CONCLUSION: Although limited by small sample size, our data suggest that levels of NfL, GFAp and total tau in CSF are commonly elevated in patients with COVID-19 with neurological symptoms. This is in contrast to the standard CSF work-up where pathological findings are scarce. NfL in particular, is associated with central neurological symptoms and disease severity

Effects of amyloid pathology and the APOE ε4 allele on the association between cerebrospinal fluid Aβ38 and Aβ40 and brain morphology in cognitively normal 70-years-olds

The association between cerebrospinal fluid (CSF) amyloid beta (Aβ) Aβ38 or Aβ40 and brain grey- and white matter integrity is poorly understood. We studied this in 213 cognitively normal 70-year-olds, and in subgroups defined by presence/absence of the APOE ε4 allele and Aβ pathology: Aβ−/APOE−, Aβ+/APOE−, Aβ−/APOE+ and Aβ+/APOE+. CSF Aβ was quantified using ELISA and genotyping for APOE was performed. Low CSF Aβ42 defined Aβ plaque pathology. Brain volumes were assessed using Freesurfer-5.3, and white matter integrity using tract-based statistics in FSL. Aβ38 and Aβ40 were positively correlated with cortical thickness, some subcortical volumes and white matter integrity in the total sample, and in 3 of the subgroups: Aβ−/APOE−, Aβ+/APOE− and Aβ−/APOE+. In Aβ+/APOE+ subjects, higher Aβ38 and Aβ40 were linked to reduced cortical thickness and subcortical volumes. We hypothesize that production of all Aβ species decrease in brain regions with atrophy. In Aβ+/APOE+, Aβ-dysregulation may be linked to cortical atrophy in which high Aβ levels is causing pathological changes in the gray matter of the brain

Synthesis of Oligodeoxyribo‐ and Oligoribonucleotides According to the H‐Phosphonate Method

Oligonucleotides can be synthesized by condensing a protected nucleoside H‐phosphonate monoester with a second nucleoside in the presence of a coupling agent to produce a dinucleoside H‐phosphonate diester. This can then be converted to a dinucleoside phosphate or to a backbone‐modified analog such as a phosphorothioate or phosphoramidite. This unit discusses four alternative methods for synthesizing nucleoside H‐phosphonate monoesters. The methods are efficient and experimentally simple, and use readily available reagents. The unit describes the activation of the monoesters, as well as competing acylation and other potential side reactions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143594/1/cpnc0304.pd

Mental Health of Parents and Life Satisfaction of Children: A Within-Family Analysis of Intergenerational Transmission of Well-Being

This paper addresses the extent to which there is an intergenerational transmission of mental health and subjective well-being within families. Specifically it asks whether parents’ own mental distress influences their child’s life satisfaction, and vice versa. Whilst the evidence on daily contagion of stress and strain between members of the same family is substantial, the evidence on the transmission between parental distress and children’s well-being over a longer period of time is sparse. We tested this idea by examining the within-family transmission of mental distress from parent to child’s life satisfaction, and vice versa, using rich longitudinal data on 1,175 British youths. Results show that parental distress at year t-1 is an important determinant of child’s life satisfaction in the current year. This is true for boys and girls, although boys do not appear to be affected by maternal distress levels. The results also indicated that the child’s own life satisfaction is related with their father’s distress levels in the following year, regardless of the gender of the child. Finally, we examined whether the underlying transmission correlation is due to shared social environment, empathic reactions, or transmission via parent-child interaction

Anti-SARS-CoV2 antibody responses in serum and cerebrospinal fluid of COVID-19 patients with neurological symptoms

Antibody responses to SARS-CoV-2 in serum and CSF from 16 COVID-19 patients with neurological symptoms were assessed using two independent methods. IgG specific for the virus spike protein was found in 81% of cases in serum and in 56% in CSF. SARS-CoV-2 IgG in CSF was observed in two cases with negative serology. Levels of IgG in both serum and CSF were associated with disease severity (p<0.05). All patients with elevated markers of CNS damage in CSF also had CSF antibodies (p=0.002), and CSF antibodies had the highest predictive value for neuronal damage markers of all tested clinical variables

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research

Sex differences in CSF biomarkers for neurodegeneration and blood-brain barrier integrity.

INTRODUCTION: As cerebrospinal fluid (CSF) neurofilament light protein (NfL) and the CSF/serum albumin ratio (QAlb) are used in the clinical routine, the impact of demographic factors on these biomarkers is important to understand. METHODS: Participants were derived from two Swedish samples: the population‐based H70 Study (n = 308, age 70) and a clinical routine cohort (CSF NfL, n = 8995, QAlb, n = 39252, age 0 to 95). In the population‐based study, QAlb and NfL were examined in relation to sex, cardiovascular risk factors, and cerebral white matter lesions (WMLs). In the clinical cohort, QAlb and NfL sex differences were tested in relation to age. RESULTS: Men had higher QAlb and NfL concentrations and had higher QAlb and NfL concentrations from adolescence throughout life. NfL was not related to WML, but QAlb correlated positively with WMLs. DISCUSSION: The CSF NfL sex difference could not be explained by vascular pathology. Future studies should consider using different reference limits for men and women

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting