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Record W4386418318 · doi:10.3390/ijms241713585

Identification and Classification of Long Non-Coding RNAs in the Mammary Gland of the Holstein Cow

2023· article· en· W4386418318 on OpenAlexaff
Sahar Ghulam Mohyuddin, Yan Liang, Yuxin Xia, Mengqi Wang, Huimin Zhang, Mingxun Li, Zhangping Yang, Niel A. Karrow, Yongjiang Mao

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversity of GuelphUniversité Laval
FundersNational Natural Science Foundation of China
KeywordsLactationBiologyMammary glandKEGGGeneInternal medicineGeneticsEndocrinologyGene expressionTranscriptomePregnancyMedicineCancer

Abstract

fetched live from OpenAlex

The mammary glands, responsible for milk secretion, are regulated at a local level by various hormones, growth factors, non-coding RNAs, and other elements. Recent research has discovered the presence of lncRNAs in these glands, with suggestions that they may be essential for the maintenance and function of mammary glands. Besides directly controlling the gene and protein expression, lncRNAs are believed to play a significant part in numerous physiological and pathological processes. This study focused on examining the mammary gland tissues of Chinese Holstein cows, to identify and categorize long non-coding RNAs (lncRNAs). The research intended to distinguish lncRNAs in the mammary tissues of Holstein cows and contrast them between lactation and non-lactation periods. In this study, mammary gland tissues were sampled from three Holstein cows in early lactation (n = 3, 30 days postpartum) and non-lactation (n = 3, 315 days postpartum) on a large dairy farm in Jiangsu province. Mammary tissue samples were collected during early lactation and again during non-lactation. In total, we detected 1905 lncRNAs, with 57.3% being 500 bp and 612 intronic lncRNAs. The exon count for lncRNAs varied from 2 to 10. It was observed that 96 lncRNA expressions markedly differed between the two stages, with 83 genes being upregulated and 53 downregulated. Enrichment analysis results revealed that Gene Ontology (GO) analysis was primarily abundant in cellular processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that target genes were predominantly abundant in metabolic pathways, fatty acid biosynthesis, the immune system, and glycosphingolipid biosynthesis. This study analyzed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows during both lactation and non-lactation stages, forming a foundation for further investigation into the functional roles of lncRNAs in Holstein cows throughout lactation.

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 score0.003
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
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.321
Teacher spread0.299 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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