Natural and synthetic hydrogels for periodontal tissue regeneration
Bibliographic record
Abstract
Over the past few decades, there has been a great interest in periodontal regeneration therapy to restore the tissues destroyed by periodontal diseases, which are probably one of the most common bacterial infections in humans and the leading cause of tooth loss in adults. Periodontal diseases involve a set of inflammatory processes that are progressively destroying the tooth-supporting tissues (gingiva, periodontal ligament (PDL), alveolar bone and root cementum). Untreated periodontitis may cause irreversible destruction to these tissues leading to increased tooth mobility and subsequent tooth loss[1]. Currently, there is still no ideal therapeutic method to cure periodontitis or to optimally regenerate periodontal tissues in a predictable manner. Conventional mechanical or anti-infective periodontal therapies eliminate the inflammatory processes, and hinders or halts the diseases resulting generally in tissue repair without any notable signs of regeneration. Therefore, various regenerative approaches have been proposed and evaluated to restore the lost tooth-supporting tissues. These approaches included a wide range of surgical procedures and the use of various bone grafts, occlusal barrier membranes, purified protein mixtures, growth factors. Some of these approaches have achieved some success in regenerating the damaged periodontal tissues in certain ideal clinical cases; however, the outcomes are still very variable and unpredictable. Even with bone grafts, which are considered the current gold-standard material for bone regeneration, have reported failure rates up to 30% in maxillofacial and craniofacial surgeries, in addition to their drawbacks such as limited availability and donor-site morbidity. It is evident that a considerable research activity is required to improve the current periodontal therapies and to develop novel treatments to reach the ultimate goal of periodontal therapy, which is the predictable reconstruction of the lost periodontal tissues. Periodontal tissue possess the capacity to regenerate itself and substantial efforts in the tissue engineering field have been done to understand this ability in order to overcome the current limitations of therapeutic and regenerative procedures [3]. Tissue engineering is a multidisciplinary field that aims to guide body regeneration by specifically controlling the biological environment or developing biological substitutes to restore tissue functions. The damage to any tissue or organ results in the destruction and loss of extracellular matrix (ECM) with the absence of functional cells. For this reason, it is of paramount importance to restore the structure, properties, and functions of the native natural tissue. The general approach to restore the initial tissue condition is to use a three-dimensional (3D) scaffold which has the function to temporary supports the cell growth and new tissue development. The 3D scaffold may be designed as purely structural support providing with biological moieties incorporated into the scaffold to guide cell and tissue growth. Regeneration of dental/craniofacial tissues may be successfully achieved from the inimitable blend of human cells seeded biomaterial scaffolds with/without growth factors [4]. The combination of stem-cells, biomaterials, and physio-biochemical factors is the basis and major contribution of tissue engineering to regenerative medicine. In this approach, the biomaterial is critical to the regeneration of tissue since it serves as a three dimensional artificial ECM or scaffold to provide structural organization and support for the proliferation and differentiation of cells to create a neo-tissue. It is the interaction of the cells’ with the artificial ECM that is pivotal in recreating and maintaining the functional and 3D Quick Response Code www.idjsr.com
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".