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Lessons from lonely flies: Molecular and neuronal mechanisms underlying social isolation

2023· review· en· W4389396064 on OpenAlexaff
R Sai Prathap Yadav, Faizah Ansari, Neha Bera, Clement F. Kent, Pavan Agrawal

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2023
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsYork University
FundersDepartment of Biotechnology, Ministry of Science and Technology, IndiaManipal Academy of Higher EducationMinistry of Science and Technology, Government of Nepal
KeywordsSocial isolationNeuroscienceIsolation (microbiology)Social behaviorBiologyDrosophila melanogasterStressorDrosophila (subgenus)PsychologyEvolutionary biologyGeneticsBioinformaticsGenePsychiatry

Abstract

fetched live from OpenAlex

Animals respond to changes in the environment which affect their internal state by adapting their behaviors. Social isolation is a form of passive environmental stressor that alters behaviors across animal kingdom, including humans, rodents, and fruit flies. Social isolation is known to increase violence, disrupt sleep and increase depression leading to poor mental and physical health. Recent evidences from several model organisms suggest that social isolation leads to remodeling of the transcriptional and epigenetic landscape which alters behavioral outcomes. In this review, we explore how manipulating social experience of fruit fly Drosophila melanogaster can shed light on molecular and neuronal mechanisms underlying isolation driven behaviors. We discuss the recent advances made using the powerful genetic toolkit and behavioral assays in Drosophila to uncover role of neuromodulators, sensory modalities, pheromones, neuronal circuits and molecular mechanisms in mediating social isolation. The insights gained from these studies could be crucial for developing effective therapeutic interventions in future.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.490
GPT teacher head0.495
Teacher spread0.005 · 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

Citations29
Published2023
Admission routes1
Has abstractyes

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