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1.
Sci Rep ; 11(1): 10820, 2021 05 24.
Article in English | MEDLINE | ID: mdl-34031480

ABSTRACT

Entrapments of the intrapelvic portions of the lumbosacral plexus are an important extraspinal cause of sciatica and pudendal neuralgia. They can be treated using Laparoscopic Neuronavigation (LANN), a minimally invasive technique that has set the foundations of an emerging field in Medicine-Neuropelveology. This retrospective-prospective study analyzes the outcomes of 63 patients treated with the LANN technique over a 10 year time period. One year after surgery, 78.3% of patients reported clinically relevant pain reduction, defined as ≥ 50% reduction in Numeric Rating Scale (NRS) score; these results were maintained for a mean follow up of 3.2 years. Preoperative chronic opioid use (≥ 4 months of ≥ 10 mg morphine equivalents/day) was a predictor of poor surgical outcome-clinically relevant pain reduction was observed in only 30.8% in this group of patients, compared to 91.5% in patients not regularly taking opioids preoperatively (p < 0.01). Perioperative complication rate was 20%. Our results indicate that the LANN technique is an effective and reproducible approach to relieve pain secondary to intrapelvic nerve entrapments and that preoperative chronic opioid therapy significantly reduces the likelihood of a successful surgical outcome. This study provides detailed information on perioperative complication and postoperative course, which is essential for patient consenting.


Subject(s)
Analgesics, Opioid/administration & dosage , Decompression, Surgical/methods , Nerve Compression Syndromes/surgery , Pudendal Neuralgia/therapy , Sciatica/therapy , Adult , Analgesics, Opioid/therapeutic use , Female , Humans , Laparoscopy , Male , Middle Aged , Nerve Compression Syndromes/complications , Neuronavigation , Pain Measurement , Prospective Studies , Pudendal Neuralgia/etiology , Retrospective Studies , Sciatica/etiology , Treatment Outcome
2.
Nucleic Acids Res ; 46(D1): D360-D370, 2018 01 04.
Article in English | MEDLINE | ID: mdl-29194489

ABSTRACT

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.


Subject(s)
Databases, Genetic , MicroRNAs/metabolism , RNA, Messenger/metabolism , Humans , RNA, Messenger/chemistry
3.
Curr Protoc Bioinformatics ; 60: 8.2.1-8.2.14, 2017 12 08.
Article in English | MEDLINE | ID: mdl-29220074

ABSTRACT

The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field-highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.


Subject(s)
Protein Interaction Maps , Animals , Computational Biology , Genomics , Humans , Machine Learning , Protein Interaction Domains and Motifs , Protein Interaction Mapping/methods , Protein Interaction Mapping/statistics & numerical data
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