Investigating the potential of Juglans regia phytoconstituents for the treatment of cervical cancer utilizing network biology and molecular docking approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The fourth most frequent type of cancer in women and the leading cause of mortality for females worldwide is cervical cancer. Traditionally, medicinal plants have been utilized to treat various illnesses and ailments. The molecular docking method is used in the current study to look into the phytoconstituents of Juglans regia's possible anticancer effects on cervical cancer target proteins. This work uses the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to find differentially expressed genes. Furthermore, protein-protein interactions of differentially expressed genes were constructed using network biology techniques. The top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11) are then determined by computing topological parameters with Cytohubba. In addition, molecular docking research was performed on Juglans regia phytocompounds that were extracted from the IMPPAT database versus hub genes that had been identified. Utilizing molecular dynamics, simulation confirmed that prioritized docked complexes with low binding energies were stable.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it