A Declaratory Model of Generalized Regression Neural Network (GRNN) for Estimating Sleep Apnea Index in the Elderly Suffering from Sleep Disturbance
Bibliographic record
Abstract
Background: Tinospora cordifolia and Gynura procumbens are important medicinal plants native to South and Southeast Asia. This study integrates metagenomic and metabolomic approaches to compare microbial communities and bioactive compound profiles in wild and cultivated populations of T. cordifolia and G. procumbens.Methods: Microbiome analysis using 16S rRNA amplicon sequencing and metabolomic profiling via LC-MS/MS were performed on root and leaf of both plants.Results: Bacterial communities in T. cordifolia and G. procumbens varied significantly between leaf and root. Proteobacteria dominated all samples, while roots harbored higher bacterial diversity, including Actinobacteria, Firmicutes, and Chloroflexi, particularly in wild populations. Metabolomic analysis revealed distinct profiles between organs, with leaves showing greater population-dependent variability, especially in T. cordifolia, where 482 metabolites differed significantly between wild and cultivated plants. Notably, turmerone was upregulated in wild leaves, while cinnamic acid was downregulated. Root metabolomes were more stable but still exhibited population-specific patterns in G. procumbens. In T. cordifolia, bis(4-ethylbenzylidene)sorbitol positively correlated with Actinobacteria and Chloroflexi, while vicenin and 2-methoxyestradiol showed negative correlations with several phyla, suggesting antimicrobial potential. In G. procumbens, ethamivan positively correlated with Firmicutes and Chloroflexi.Conclusion: These findings highlight the role of ecological and organ identity in driving plant-microbiome-metabolite dynamics, with implications for medicinal plant quality, bioactivity, and cultivation.Keywords: Bioactive Compounds, Biological Diversity, Gynura procumbens, Metagenomics, Microbial Communities, Tinospora cordifolia
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".