Impact of a Community Pharmacy Pharmacotherapy Follow-up (PTF) service in patients using opioid analgesic.

The use of prescribed major opioid analgesics (fentanyl, tapentadol, morphine and oxycodone and combinations) for non-cancer chronic pain is fraught with risks that may generate Negative Medicine Outcomes (NMO). Among the factors associated with these risks, those related to the patient's characteristics and aberrant behavior, the treatment conditions, and the prescription health settings should be evaluated with the aim of minimizing unsafety during the health care process. The present study addresses, from a community pharmacy, the analysis of Drug Related Problems (DRP) and Negative Medicine Outcomes (NMO) in patients using these major opioid analgesics while it aims to demonstrate the role of pharmaceutical care interventions in promoting safety during the use of these molecules. A three step Pharmacotherapeutic Follow-up (PFT) protocol was designed to prevent, detect, and solve DRP and NMO associated with the use of opioid analgesics. 74.6% of the patients used opioid analgesics to treat musculoskeletal pain. Polypharmacy with benzodiazepines (61.9%); antidepressants (57.1%) and antiepileptics (30.2%) was detected in patients using these opioids. The Morisky-Green Adherence test revealed that 30.2% were nonadherent. It was observed, with statistical significance, that in all patients (63), the impact of the 14-week PFT supervised by the community pharmacist achieved an overall reduction in the prevalence of DRP and NMO. While the reduction in the number of DRPs reached 66.7%. Community pharmacies are a strategic point to promote and implement effective opioid stewardship due to both their central role in healthcare services and frequent interaction with patients.

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.