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Record W4285587030 · doi:10.1111/joa.13721

Investigation of heterocellular features of the mouse retinal neurovascular unit by 3D electron microscopy

2022· article· en· W4285587030 on OpenAlexfundno aff
Mona J. Albargothy, Nadhira N. Azizah, Sarah Stewart, Evan P. Troendle, David Steel, Tim M. Curtis, Michael J. Taggart

Bibliographic record

VenueJournal of Anatomy · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilPublic Health AgencyNewcastle UniversityMedical Research Council CanadaWellcome Trust
KeywordsRetinaAnatomyRetinalBiomedical engineeringPathologyComputer scienceNeuroscienceBiologyMedicine

Abstract

fetched live from OpenAlex

The retina has a complex structure with a diverse collection of component cells that work together to facilitate vision. The retinal capillaries supplying the nutritional requirements to the inner retina have an intricate system of neural, glial and vascular elements that interconnect to form the neurovascular unit (NVU). The retina has no autonomic nervous system and so relies on the NVU as an interdependent, physical and functional unit to alter blood flow appropriately to changes in the physiological environment. The importance of this is demonstrated by alterations in NVU function being apparent in the blinding disease diabetic retinopathy and other diseases of the retina. It is, therefore, imperative to understand the anatomy of the components of the NVU that underlie its functioning and in particular the nanoscale arrangements of its heterocellular components. However, information on this in three spatial dimensions is limited. In the present study, we utilised the technique of serial block-face scanning electron microscopy (SBF-SEM), and computational image reconstruction, to enable the first three-dimensional ultrastructural analysis of the NVU in mouse retinal capillaries. Mouse isolated retina was prepared for SBF-SEM and up to 150 serial scanning electron microscopy images (covering z-axes distances of 12-8 mm) of individual capillaries in the superficial plexus and NVU cellular components digitally aligned. Examination of the data in the x-, y- and z-planes was performed with the use of semi-automated computational image analysis tools including segmentation, 3D image reconstruction and quantitation of cell proximities. A prominent feature of the capillary arrangements in 3D was the extensive sheath-like coverage by singular pericytes. They appeared in close register to the basement membrane with which they interwove in a complex mesh-like appearance. Breaks in the basement membrane appeared to facilitate pericyte interactions with other NVU cell types. There were frequent, close (<10 nm) pericyte-endothelial interactions with direct contact points and peg-and-socket-like morphology. Macroglia typically intervened between neurons and capillary structures; however, regions were identified where neurons came into closer contact with the basement membrane. A software-generated analysis to assess the morphology of the different cellular components of the NVU, including quantifications of convexity, sphericity and cell-to-cell closeness, has enabled preliminary semi-quantitative characterisation of cell arrangements with neighbouring structures. This study presents new data on the nanoscale spatial characteristics of components of the murine retinal NVU in 3D that has implications for our understanding of structural integrity (e.g. pericyte-endothelial cell anchoring) and function (e.g. possible paracrine communication between macroglia and pericytes). It also serves as a platform to inform future studies examining changes in NVU characteristics with different biological and disease circumstances. All raw and processed image data have been deposited for public viewing.

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 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 score0.067
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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.

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

Citations16
Published2022
Admission routes1
Has abstractyes

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