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Cited by 275

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Cerebrospinal fluid - physiology, analysis and interpretation of protein patterns for diagnosis of neurological diseases

Reiber, Hansotto·1998·Multiple Sclerosis Journal

Abstract

The state of the art in routine CSF analysis is reviewed with particular reference to multiple sclerosis regarding: (1) The physiology and pathophysiology of blood-CSF barrier function and dysfunction with the CSF flow rate as main modulator of blood- and brain-derived protein concentrations in CSF; (2) The neuroimmunological aspects regarding (a) patterns of disease-related immunoglobulin class response (IgG, lgA, IgM) in actual Reiber graphs with reference to specific parameters and optional tests, and (b) the oligoclonal, polyspecific antibody synthesis in brain; (3) Particular marker proteins in CSF and blood for differential diagnosis of neurological diseases; (4) Mathematical base for evaluations of CSF data with an example of a multiple sclerosis patient for calculation of intrathecal immunoglobulin and antibody synthesis as well as Antibody Index.

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Crossref · 10.1177/135245859800400302

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Metadata

Authors
Reiber, H.
Venue
Multiple Sclerosis Journal
Published
1998-06
Volume
4
Issue
3
Pages
99-107
ISSN
  • 1352-4585 (ISSN-L)
  • 1477-0970
OpenAlex
W2026488191
Cited by
275
References
16
FWCI
2.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Cerebrospinal fluid - physiology, analysis and interpretation of protein patterns for diagnosis of neurological diseases. https://app.cochat.ai/papers/10.1177/135245859800400302
The paper
Reiber, H. (1998). Cerebrospinal fluid - physiology, analysis and interpretation of protein patterns for diagnosis of neurological diseases. Multiple Sclerosis Journal. https://doi.org/10.1177/135245859800400302

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.