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Record W4281747700 · doi:10.3390/ijms23126446

Immunity, Ion Channels and Epilepsy

2022· review· en· W4281747700 on OpenAlexaff
Tsang‐Shan Chen, Ming‐Chi Lai, Huai-Ying Ingrid Huang, Sheng‐Nan Wu, Chin‐Wei Huang

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpilepsyEpileptogenesisNeuroscienceAutoimmune encephalitisImmune systemMedicineImmunologyEncephalitisImmunotherapyAcquired immune systemDiseaseBiologyInternal medicineVirus

Abstract

fetched live from OpenAlex

Epilepsy is a common chronic neurological disorder in modern society. One of the major unmet challenges is that current antiseizure medications are basically not disease-modifying. Among the multifaceted etiologies of epilepsy, the role of the immune system has attracted considerable attention in recent years. It is known that both innate and adaptive immunity can be activated in response to insults to the central nervous system, leading to seizures. Moreover, the interaction between ion channels, which have a well-established role in epileptogenesis and epilepsy, and the immune system is complex and is being actively investigated. Some examples, including the interaction between ion channels and mTOR pathways, will be discussed in this paper. Furthermore, there has been substantial progress in our understanding of the pathophysiology of epilepsy associated with autoimmune encephalitis, and numerous neural-specific autoantibodies have been found and documented. Early recognition of immune-mediated epilepsy is important, especially in cases of pharmacoresistant epilepsy and in the presence of signs of autoimmune encephalitis, as early intervention with immunotherapy shows promise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.374
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2022
Admission routes1
Has abstractyes

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