Periapical implant lesion: A systematic review.

PURPOSE: The aim of this study was to systematically review the evidence for periapical implant lesion, which makes a patient more susceptible to the periapical lesion, frequency, symptoms, signs (including radiological findings) and possible treatment options. MATERIAL AND METHODS: A systematic literature review and analysis of publications included in PubMed, Embase and Cochrane; articles published until March 2016; with a populations, exposures and outcomes (PEO) search strategy was performed, focused on the issue: "In patients with periapical lesion to the implant during the osseointegration, what symptoms, signs, and changes in complementary examination manifested, for according to that stage, be intervened with the appropriate approach?". The set criteria for inclusion were peer-reviewed articles. RESULTS: From a total of 212 papers identified, 36 studies were included in this systematic review, with 15461 implants evaluated and 183 periapical implant lesions. Which 8 papers included more than 5 cases and 28 included equal or less than 5 cases. Analysis of the papers revealed that periapical implant lesion is classified according to evolution stages into acute (non-suppurated and suppurated) and subacute (or suppurated-fistulized). In the acute stage and in the subacute if there is no loss of implant stability, the correct treatment approach is implant periapical surgery. In the subacute stage associated with implant mobility the implant must be removed. CONCLUSIONS: Evidence on the subject is very limited, there are few studies with small sample, without homogeneity of criteria for diagnosing the disease and without design of scientific evidence. Currently etiology lacks consensus. The early diagnosis of periapical implant periapical lesions during the osseointegration phase and early treatment, will lead to a higher survival rate of implants treated, hence preventing the need for implant extraction.

Highly sensitive and ultrafast read mapping for RNA-seq analysis.

As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.