RESUMEN
Background: Implants are preferred for replacement of missing teeth by the clinicians as well as the patients. Lesser alveolar bone density doesn't preclude any individual for choosing this option but warrants for extra caution. Preclinical studies have explored the osteoinductive potential of statins, but results should be analyzed vigorously before implementing them in humans. There is no meta-analysis to document effect of statins on bone formation around implants in osteoporotic animals. Methods and material: PubMed, Embase and Cochrane were searched for studies investigating the effect of statins on bone implant contact (BIC %), bone mineral density (BMD %) and bone volume (BV %) around implants at 2, 4 and 12 weeks. Meta-analysis was performed on subgroups with osteoporotic animals which were administered statins through different routes. Results: Quantitative data from 12 studies showed favorable effect of statins on bone around implants. Positive difference was observed at 4 weeks in BIC (parenteral [SMD = 4.33 (2.89, 5.77); I 2 = 3%)], BMD (local [SMD = 1.33 (0.51, 2.15); I 2 = 0%] and BV (local [SMD = 1.58 (0.76, 2.40); I 2 = 0%]. BIC [SMD = 1.40 (0.89, 1.90); I 2 = 0%] and BV [SMD = 3.91 (2.33, 5.50); I 2 = 43%] were higher in experimental group after 12 weeks of oral administration. Conclusions: Statins can be investigated as potential bone graft materials to increase the predictability of osseointegration especially in osteoporotic individuals. Future research should focus to reproduce homogeneous data and conclusive recommendations which can be applied in clinical trials. Supplementary Information: The online version contains supplementary material available at 10.1007/s12663-023-01873-z.
RESUMEN
Twitter is one of the most popular micro-blogging and social networking platforms where users post their opinions, preferences, activities, thoughts, views, etc., in form of tweets within the limit of 280 characters. In order to study and analyse the social behavior and activities of a user across a region, it becomes necessary to identify the location of the tweet. This paper aims to predict geolocation of real-time tweets at the city level collected for a period of 30 days by using a combination of convolutional neural network and a bidirectional long short-term memory by extracting features within the tweets and features associated with the tweets. We have also compared our results with previous baseline models and the findings of our experiment show a significant improvement over baselines methods achieving an accuracy of 92.6 with a median error of 22.4 km at city level prediction.