Photocatalytic Three-Component Acylcarboxylation of Alkenes with CO2.

γ-Keto acid is a valuable chemical motif in a wide range of fields including organic, biological, and medicinal chemistry. However, its single-step synthesis is challenging because of the mismatch of the carbonyl polarity and low tolerance of carboxylic acids. Herein, we report the single-step syntheses of γ-keto acids using alkenes and CO2. Our photocatalytic system enabled the transformation of alkenes under mild conditions in high yields (up to 95%) with broad substrate generality (35 examples).

Staged Regenerative-Phenotype Modification Therapy for Multiple RT3 Gingival Recessions in Periodontally Compromised Anterior Mandibular Teeth: A Case Report.

We report the successful treatment of multiple recession type (RT) 3 gingival recessions in periodontally compromised mandibular anterior teeth with limited keratinized tissue. A 35-yearold man with stage III, grade C periodontitis underwent a two-stage intervention. Initially, a modification of the connective tissue graft (m-CTG) wall technique was used as part of phenotype modification therapy. The CTG acted as a protective 'wall,' securing space for periodontal regeneration, enhancing root coverage, soft tissue thickness, and keratinized mucosal width. Recombinant human fibroblast growth factor-2 and carbonate apatite promoted periodontal regeneration. This procedure successfully facilitated periodontal regeneration, resulting in the transition from RT3 to RT2 gingival recession and adequate keratinized mucosal width. Eighteen months later, the second surgery used a tunneled coronally advanced flap (TCAF) for root coverage. TCAF involved combining a coronally advanced flap and tunnel technique by elevating the trapezoidal surgical papilla and using a de-epithelialized CTG inserted beneath the tunneled flap. Root conditioning with ethylenediaminetetraacetic acid and enamel matrix derivative gel application were performed. Consequently, mean CAL gain was 5.3 mm, mean root coverage was 4.5 mm in height, and the gingival phenotype improved at the treated sites by the 12-month follow-up. This staged approach addresses the challenges of treating RT3 gingival recession with promising outcomes.

Resorbable bilayer membrane made of L-lactide-ε-caprolactone in guided bone regeneration: an in vivo experimental study.

PURPOSE: Guided bone regeneration (GBR) is an accepted method in dental practice that can successfully increase the bone volume of the host at sites chosen for implant placement; however, existing GBR membranes exhibit rapid absorption and lack of adequate space maintenance capabilities. We aimed to compare the effectiveness of a newly developed resorbable bilayer membrane composed of poly (L-lactic acid) and poly (-caprolactone) (PLACL) with that of a collagen membrane in a rat GBR model. METHODS: The rat calvaria was used as an experimental model, in which a plastic cylinder was placed. We operated on 40 male Fisher rats and subsequently performed micro-computed tomography and histomorphometric analyses to assess bone regeneration. RESULTS: Significant bone regeneration was observed, which was and similar across all the experimental groups. However, after 24 weeks, the PLACL membrane demonstrated significant resilience, and sporadic partial degradation. This extended preservation of the barrier effect has great potential to facilitate optimal bone regeneration. CONCLUSIONS: The PLACL membrane is a promising alternative to GBR. By providing a durable barrier and supporting bone regeneration over an extended period, this resorbable bilayer membrane could address the limitations of the current membranes. Nevertheless, further studies and clinical trials are warranted to validate the efficacy and safety of The PLACL membrane in humans.

Visible-Light-Driven Hydroacylation of Unactivated Alkenes Using Readily Available Acyl Donors.

Herein, we report visible-light-driven hydroacylation of unactivated alkenes. We employed benzimidazolines as new acyl donors and achieved perfect regioselectivity, high functional-group tolerance, and excellent substrate generality. We also performed mechanistic experiments to elucidate the detailed reaction mechanism. This is the first example of (1) hydroacylation of unactivated alkenes using (2) easily prepared acyl donors under (3) visible-light irradiation. Our findings offer a new strategy to synthesize a wide variety of ketones under mild conditions.

Visible light-driven CO2 reduction with a Ru polypyridyl complex bearing an N-heterocyclic carbene moiety.

A novel Ru polypyridyl complex with an N-heterocyclic carbene ligand was successfully synthesised and characterised. The complex exhibited an intense absorption band in the visible-light region derived from the strong electron-donating character of the carbene ligand, and efficiently catalysed the visible light-driven CO2 reduction with the reaction rate of 36.7 h-1.

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment.

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.