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Record W4411028151 · doi:10.52225/narrax.v3i1.210

Phagocytic receptors regulate Drosophila larval growth

2025· article· en· W4411028151 on OpenAlexfundno aff
Kaz Nagaosa

Bibliographic record

VenueNarra X · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
FundersRIKEN Center for Biosystems Dynamics ResearchInstitute of GeneticsJapan Society for the Promotion of ScienceRIKENMedical Research Council
KeywordsDrosophila (subgenus)LarvaBiologyReceptorCell biologyDrosophila melanogasterZoologyGeneticsEcologyGene

Abstract

fetched live from OpenAlex

Drosophila melanogaster is a key model organism for biological research due to its genetic manipulability and high degree of evolutionary conservation with humans. Phagocytic receptors play a central role in apoptotic cell clearance, a fundamental process that is highly conserved across species. Previous studies have identified two major phagocytic receptors in Drosophila: integrin αPS3βν and Draper, both of which contribute to apoptotic cell removal. However, the physiological significance of these receptors under normal developmental conditions remains unclear. Therefore, the aim of this study was to investigate the role of these receptors in developmental timing. The results demonstrated that double mutants lacking both receptors exhibited significant developmental delays, especially during the larval stage (p<0.001). Moreover, tissue-specific knockdown experiments revealed that phagocytic receptors within the fat body are mainly involved in regulating developmental timing (p=0.028). Further results established that nutrient availability influenced the extent of growth delay, suggesting that these receptors may play a role in nutrient-dependent growth regulation. Taken together, these findings suggest that phagocytic receptors contribute to maintaining proper growth timing in Drosophila larvae, potentially through energy metabolism pathways.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.346
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.295
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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