Graphene-based nanocomposite materials to provide a surgical solution for the condition of pelvic organ prolapse
Bibliographic record
Abstract
Pelvic organ prolapse (POP) is a globally prevalent condition effecting over half of post-menopausal women. It is caused by a weakening of the soft tissue of the pelvic floor so that it is no longer able to support the organs of the pelvis. This results in a descent of the pelvic organs down the vagina, including bladder, rectum, small bowel, uterus, or vaginal vault (post-hysterectomy). Symptoms can include discomfort and pain, urinary incontinence, faecal incontinence, and dyspareunia, depending on the organ effected. This can have a significant impact on mental, social, and sexual wellbeing. Current conservative management options include lifestyle changes or the insertion of a temporary silicone pessary. The silicone pessary requires regular maintenance and replacement and can thus result in secondary pain and discomfort. Surgical options include native tissue repair or surgical augmentation with the use of a polypropylene (PP) mesh adjunct. However, the PP mesh implants have now been banned in several countries – including UK, USA, Canada and Australia, due to concerns over the safety of the material. Complications of the PP mesh included mesh exposure; chronic infection; chronic pain; and dyspareunia. These complications are thought to have occurred due to a mismatch of the biomechanical/viscoelastic properties of the PP mesh and native tissue, at the site of implantation. The alternative of native tissue repair has a high recurrence rate for POP and does not provide an effective cure to the condition. Therefore, POP is a condition with an unmet clinical need. Scientists across the globe consider graphene to be a ‘wonder material’ with superior physicochemical properties that will revolutionise every field and all industries. Graphene is a 2D single layer of carbon atoms arranged in a honeycomb lattice structure. Its properties include being 200× stronger than steel but at the same time incredibly lightweight and elastic. We developed a graphene-based nanocomposite (GBN) material that harnesses the superior properties of graphene. The material is non-toxic and biocompatible and suitable for surgical application. It is currently under development for heart valves, breast implants, and tendons, amongst other applications. The objective of this research is to use this GBN material for the development of a surgical membrane for the treatment of POP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".