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Record W3170823740 · doi:10.26355/eurrev_202105_25949

Rehabilitation training improves cognitive disorder after cerebrovascular accident by improving BDNF Bcl-2 and Bax expressions in regulating the JMK pathway.

2021· article· en· W3170823740 on OpenAlexaboutno aff
Huan‐Yu Wang, C-H Zhu, Donghu Liu, Yan Wang, Zhang Jb, S-P Wang, Y-N Song

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineRehabilitationCognitionInternal medicinePhysical therapyHippocampusAnesthesiaCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the effect of rehabilitation training on cognitive impairment after cerebrovascular accident and its potential mechanism. PATIENTS AND METHODS: 100 patients of cerebrovascular accident treated in our hospital from August 2018 to August 2019 were selected as the subjects, and 50 patients with physical examination were selected as healthy control group. The patients with cerebrovascular accident were randomly divided into control group (50 patients) and research group (50 patients). The patients in the control group were given routine medication, the patients in research group were given rehabilitation training on the basis of routine drug therapy. The blood samples were collected on admission and 6 months after admission to detect the molecular markers related to inflammation, nerve cell nutrition and function and apoptosis in the serum. The cognitive function was evaluated by scales. We established a rat cerebral ischemia model, compared the differences in the evasive latency, serum CRP, BNDF, Bcl-2, BAX, Glu, NE levels and BNDF, TrkB, pTrkB, JNK levels in hippocampus, amygdala, and prefrontal tissue between model rats after rehabilitation training and model rats without rehabilitation training. RESULTS: On admission, there were no significant differences in the scores of Barthel index (BI), Fugl-Meyer motor function scale (FM), Montreal cognitive assessment scale (MoCA) and mini-mental state examination (MMSE) (p>0.05). 6 months later, the above scores and BNDF, Bcl-2, and norepinephrine were significantly higher in the research group (p<0.05), while CRP, Bax, 5-HT and glutamate in the research group were significantly lower than those in the control group (p<0.05). CONCLUSIONS: Rehabilitation training can improve the motor function, mental state and cognitive level of patients, reduce the levels of neurotoxic factors, pro-inflammatory factors and pro-apoptotic factors, and improve the levels of inhibiting apoptotic factors, neurotrophic factors and neurotransmitters. In animal experiments, rehabilitation training can increase BDNF and its activated receptors in hippocampus, amygdala and prefrontal lobe of rats, and decrease JNK of apoptotic protein, suggesting that rehabilitation training may regulate the expression of apoptotic proteins Bcl-2 and Bax by upregulating BDNF and its receptors and acting on JNK pathway, thereby inhibiting cell apoptosis and improving cognitive impairment after cerebrovascular accident.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.219
Teacher spread0.200 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations12
Published2021
Admission routes1
Has abstractyes

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