Objectives: Accidental displacement of endosseous implants into the maxillary sinus is an unusual but potential complication in implantology procedures due to the special features of the posterior aspect of the maxillary bone; there is also a possibility of migration throughout the upper paranasal sinuses and adjacent structures. The aim of this paper is to review the published literature about accidental displacement and migration of dental implants into the maxillary sinus and other adjacent structures. Study Design: A review has been done based on a search in the main on-line medical databases looking for papers about migration of dental implants published in major oral surgery, periodontal, dental implant and ear-nose-throat journals, using the keywords “implant,” “migration,” “complication,” “foreign body” and “sinus.” Results: 24 articles showing displacement or migration to maxillary, ethmoid and sphenoid sinuses, orbit and cranial fossae, with different degrees of associated symptoms, were identified. Techniques found to solve these clinical issues include Cadwell-Luc approach, transoral endoscopy approach via canine fossae and transnasal functional endoscopy surgery. Conclusion: Before removing the foreign body, a correct diagnosis should be done in order to evaluate the functional status of the ostiomeatal complex and the degree of affectation of paranasal sinuses and other involved structures, determining the size and the exact location of the foreign body. After a complete diagnosis, an indicated procedure for every case would be decided. Key words:Implant, oral surgery, foreign body, paranasal sinuses, displacement, migration.

Aim: (i) to compare the effects of two different low-volume resistance priming sessions, where the external load is modified on neuromuscular performance after 6 h of rest; and (ii) to identify the effects on psychological readiness in participants with resistance training experience. Methods: Eleven participants (Body mass: 77.0 ± 8.9 kg; Body height: 1.76 ± 0.08 m; Half squat repetition maximum: 139.8 ± 22.4 kg) performed the priming session under three experimental conditions in a randomized and cross-over design during the morning. The control (CON) condition: no resistance training, "optimal load" (OL) condition: two half-squat sets with a velocity loss of around 20% were performed with the "optimal load", and 80% of repetition maximum (80% RM) condition: 2 half-squat sets with a velocity loss of around 20% were performed with the 80% RM. Countermovement jump (CMJ), mean power with OL (MP OL) and 80% RM (MP 80RM), and mean velocity with OL (MV OL) and 80% RM (MV 80RM) were assessed six hours after the intervention. Subjective readiness was also recorded prior to resistance training and evaluation. Significance was set at p < 0.05. Results: CMJ was higher after the 80% RM intervention than CON (p < 0.001; Δ = 6.5% [3.4-9.5]). MP OL and MV OL seemed to be unaffected by both morning sessions. Higher MP 80RM (p = 0.044; Δ = 9.7% [4.0-15.6]; d = 0.24[0.10-0.37]) and MV 80RM (p = 0.004; Δ = 8.1% [3.2-13.3]; d = 0.32[0.13-0.52]) after 80% RM than after CON were observed. No effect was observed on psychological readiness. Conclusions: 80% RM priming session increased CMJ height and the capacity to generate power and velocity under a high-load condition without any effect on psychological readiness.

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.