Background: Gingival recession has a multifactorial etiology, involving various predisposing and precipitating factors. Non-carious cervical lesions (NCCLs) are often associated with gingival recession and pose challenges due to their complex pathodynamics. There is limited evidence regarding tunnel-based procedures combined with connective tissue grafts (CTGs) for treating recession-associated NCCLs. This case study illustrates a periodontal-restorative approach for addressing multiple adjacent recession-associated NCCLs.
Methods: A healthy, non-smoking 55-year-old Asian male presented with generalized mucogingival conditions, including gingival recessions and lack of keratinized tissue on the facial aspect of the maxillary right first molar through the left first molar, accompanied by physiological gingival pigmentation. The recessions were classified as Cairo RT I. NCCLs were evident in all six maxillary anterior teeth. After restoring the NCCLs to the maximum root coverage (MRC) level with composite resin, the patient underwent a mucogingival procedure via a double-vestibular incision subperiosteal tunnel access (double-VISTA) approach with autogenous CTG for treating the recession defects. Due to limited CTG availability, the maxillary left side was treated first, followed by the right side 3 months later.
Results: Post-surgical healing was uneventful, except for mild facial swelling for 5 days, which subsided within a week. After 12 months, all treated teeth exhibited complete coverage to the predetermined MRC level, with a thick gingival phenotype. At the 2-year follow-up, all treated teeth maintained a stable root coverage outcome with harmonious gingival margins.
Conclusion: This case study demonstrates that the double-VISTA technique for treating recession-associated NCCLs is promising in achieving complete coverage at sites where the MRC level was predetermined.
Key Points: This case study introduces a modified vestibular incision subperiosteal tunnel access (VISTA) technique, termed double-VISTA, which employs two distant vestibular incisions to enhance access for instrumentation and recipient site preparation in the treatment of multiple adjacent recession defects. A periodontal-restorative approach is detailed in this case study, demonstrating a step-by-step process for managing multiple adjacent recession-associated non-carious cervical lesions using the double-VISTA technique with a promising treatment outcome. The advantages and disadvantages of the double-VISTA technique are further discussed in this case study. Limitations of this technique include shallow vestibular depth and close proximity to the mental nerve, necessitating careful attention during recipient site preparation under these conditions.
Plain Language Summary: Gingival recession, where gums recede and expose tooth roots, can result from various causes. Non-cavity lesions on the tooth necks (NCCLs) are often associated with this condition and are challenging to treat. Limited evidence exists on using tunnel-based procedures with connective tissue grafts (CTGs) for NCCLs. This case study presents a method to treat multiple adjacent NCCLs in a healthy 55-year-old Asian man with gum recession on his upper teeth and pigmented gums. The patient had non-cavity lesions on all six upper front teeth. Initially, the exposed root necks were covered with composite resin, followed by a double-vestibular incision subperiosteal tunnel access (double-VISTA) surgical approach with CTGs to address the recession. Due to limited graft availability, the left side was treated first, then the right side 3 months later. The patient experienced mild facial swelling for 5 days, which resolved within a week. After 12 months, all treated teeth showed complete coverage with thickened gums, and the results remained stable with even gum margins at the 2-year check-up. This case study suggests that the double-VISTA technique for treating NCCLs shows promising results for achieving complete coverage, offering a viable treatment option for these dental conditions.
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http://dx.doi.org/10.1002/cap.10333 | DOI Listing |
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Memorial University of Newfoundland, Chemistry, Department of Chemistry, 230 Elizabeth avenue, A1B 3X7, St. John's, CANADA.
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Department of Chemistry, Birla Institute of Technology and Science Pilani, Hyderabad 500078, India.
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Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
The study of land cover dynamics and the valuation of ecosystem services in coastal cities is pivotal for guiding sustainable urban development and conserving natural resources amidst the unique challenges posed by their geographical and ecological contexts. This study utilizes a 30 m × 30 m land use/cover change (LUCC) dataset to elucidate the spatiotemporal evolution of LUCC and ecosystem service value (ESV) and the trade-offs and synergistic relationships among ecosystem services in the coastal city of Qingdao under three different scenarios over the past 35 years and in the future based on the dual perspective of the past-future by using the equivalent factor approach (EFA), the PLUS model, and Spearman's rank correlation coefficient. The findings reveal a pronounced expansion in built-up areas in Qingdao from 1985 to 2020, with a concomitant significant reduction in cropland, leading to a fluctuation in the total ESV, which initially increased and then declined.
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Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.
Introduction: Variable selection is a common technique to identify the most predictive variables from a pool of candidate predictors. Low prevalence predictors (LPPs) are frequently found in clinical data, yet few studies have explored their impact on model performance during variable selection. This study compared the Random Forest (RF) algorithm and stepwise regression (SWR) for variable selection using data from a paediatric sepsis screening tool, where 18 out of 32 predictors had a prevalence < 10%.
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