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Record W3202091166 · doi:10.1136/jnnp-2021-326258

Reduced blood flow velocity in lenticulostriate arteries of patients with CADASIL assessed by PC-MRA at 7T

2021· letter· en· W3202091166 on OpenAlexfundno aff
Chengyue Sun, Yue Wu, Ling Chen, Zhiying Xie, Yunchuang Sun, Zhihao Xie, Zhixin Li, Xiaojing Fang, Qingle Kong, Jing An, Bo Wang, Yan Zhuo, Wei Zhang, Zhaoxia Wang, Yun Yuan, Zihao Zhang

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2021
Typeletter
Languageen
FieldMedicine
TopicCerebrovascular and genetic disorders
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Beijing MunicipalityNational Natural Science Foundation of ChinaCapital Health
KeywordsCADASILCardiologyMedicineInternal medicineBlood flowLeukoencephalopathy

Abstract

fetched live from OpenAlex

Previous studies reported cerebral hypoperfusion ocurred in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL).1 2 As a cerebral small vessels disease, the reduction of blood flow velocity in cerebral small arteries may precede cerebral hypoperfusion and lesions formation. Due to the limitations of spatial resolution, conventional methods failed to detect the velocity of cerebral small vessels. Ultrahigh field MRI provided us a non-invasive way to investigate the velocity in lenticulostriate arteries (LSAs) of patients with CADASIL.3–5 Assessing blood flow velocity of small arteries can monitor the progress of the disease in long-term follow-up of patients. We enrolled 32 patients with CADASIL and 34 healthy controls from September 2019 to September 2020 at Peking University First Hospital. Details of subject selection, clinical assessment, MRI acquisition and analysis were described in online supplemental methods, online supplemental figure 1, online supplemental tables 1 and 2. The phase contrast magnetic resonance angiography (PC-MRA) was acquired at a 7 T MR scanner with high resolution (0.35×0.35×0.40 mm3) and low velocity-encoding (VENC) value (15.00 cm/s) was acquired to measure blood flow velocities of LSAs. The analysis pipeline of LSA velocities includes: (1) intensity bias corrections; (2) LSA region extraction on maximum intensity projection images; (3) threshold-based noise masking; (4) velocity components reconstruction from phase-difference data; (5) extracting dominant branches of the LSAs and calculating mean velocity. Test–retest experiments and Bland-Altman analysis were performed to validate the precision of the measurement method (online supplemental figure 2).5 ### Supplementary data [jnnp-2021-326258supp001.pdf] ### Demographic and clinical features One patient and three healthy controls were excluded for excessive head motion causing large artefacts and poor image quality. Ultimately, we included 31 patients from 24 pedigrees and 31 healthy controls in this study. The heterozygous mutations affecting the patients with CADASIL were listed in online supplemental table 3 …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.376
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 teacher head, not a consensus.

Study designObservational
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

Citations20
Published2021
Admission routes1
Has abstractyes

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