Deprescribing in primary care without deterioration of health-related outcomes: A real-life, quality improvement project.

Medication reviews focusing on deprescribing can reduce potentially inappropriate medication; however, evidence regarding effects on health-related outcomes is sparse. In a real-life quality improvement project using a newly developed chronic care model, we investigated how a general practitioner-led medication review intervention focusing on deprescribing affected health-related outcomes. We performed a before-after intervention study including care home residents and community-dwelling patients affiliated with a large Danish general practice. The primary outcomes were changes in self-reported health status, general condition and functional level from baseline to 3-4 months follow-up. Of the 105 included patients, 87 completed the follow-up. From baseline to follow-up, 255 medication changes were made, of which 83% were deprescribing. Mean self-reported health status increased (0.55 [95% CI: 0.22 to 0.87]); the proportion with general condition rated as 'average or above' was stable (0.06 [95% CI: -0.02 to 0.14]); and the proportion with functional level 'without any disability' was stable (-0.05 [95% CI: -0.09 to 0.001]). In conclusion, this general practitioner-led medication review intervention was associated with deprescribing and increased self-reported health status without the deterioration of general condition or functional level in real-life primary care patients. The results should be interpreted carefully given the small sample size and lack of control group.

Prognosis of breast cancer patients with brain metastasis treated with radiotherapy.

BACKGROUND: Life expectancy for patients diagnosed with metastatic breast cancer (BC) has improved in recent years, especially due to better systemic treatment. This has led to an increased incidence of brain metastases (BM), and BC is now the leading cause of BM in women. Treatment of BM primarily consists of surgery and/or radiotherapy. We aimed to investigate survival time and prognostic factors for BC patients treated with radiotherapy for BM. MATERIAL & METHODS: During the period 1st of January 2015 to 1st of June 2020, 144 consecutive BC patients treated for BM from one centre were retrospectively analyzed. The patients were either diagnosed with BM as the first metastatic lesion, or developed BM during palliative therapy for distant non-brain metastasis. The study was approved by the Central Denmark Region. RESULTS: Median age at BM diagnosis was 66 years, and 90% of the patients already had extracranial metastatic disease at BM diagnosis. Median overall survival after diagnosis of BM was 6.1 months. Short survival was observed for patients with poor performance status, leptomeningeal metastasis or more than three solid BM. Several of these factors were overrepresented in patients with estrogen receptor-positive (ER+) tumours who had poorer survival than patients with different receptor status. CONCLUSION: The number of metastatic BC patients developing BM is high, and survival following local treatment remains poor. Several prognostic factors appear to influence survival after radiotherapy. Treatment of BC patients with BM should be individualized according to performance status, leptomeningeal disease, number of BM, and receptor status of the disease.

Efficient de novo assembly of large and complex genomes by massively parallel sequencing of Fosmid pools.

BACKGROUND: Sampling genomes with Fosmid vectors and sequencing of pooled Fosmid libraries on the Illumina platform for massive parallel sequencing is a novel and promising approach to optimizing the trade-off between sequencing costs and assembly quality. RESULTS: In order to sequence the genome of Norway spruce, which is of great size and complexity, we developed and applied a new technology based on the massive production, sequencing, and assembly of Fosmid pools (FP). The spruce chromosomes were sampled with ~40,000 bp Fosmid inserts to obtain around two-fold genome coverage, in parallel with traditional whole genome shotgun sequencing (WGS) of haploid and diploid genomes. Compared to the WGS results, the contiguity and quality of the FP assemblies were high, and they allowed us to fill WGS gaps resulting from repeats, low coverage, and allelic differences. The FP contig sets were further merged with WGS data using a novel software package GAM-NGS. CONCLUSIONS: By exploiting FP technology, the first published assembly of a conifer genome was sequenced entirely with massively parallel sequencing. Here we provide a comprehensive report on the different features of the approach and the optimization of the process.We have made public the input data (FASTQ format) for the set of pools used in this study:ftp://congenie.org/congenie/Nystedt_2013/Assembly/ProcessedData/FosmidPools/.(alternatively accessible via http://congenie.org/downloads).The software used for running the assembly process is available at http://research.scilifelab.se/andrej_alexeyenko/downloads/fpools/.

Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling.

MOTIVATION: Phylogenetic profiling methods can achieve good accuracy in predicting protein-protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly available, identifying the most-informative RT is becoming increasingly difficult. Previous studies on the selection of RT have provided guidelines for manual taxon selection, and for eliminating closely related taxa. However, no general strategy for automatic selection of RT is currently available. RESULTS: We present three novel methods for automating the selection of RT, using machine learning based on known protein-protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting phylogenetic profiles often require very different RT sets to support high prediction accuracy.

Gene Ontology-driven inference of protein-protein interactions using inducers.

MOTIVATION: Protein-protein interactions (PPIs) are pivotal for many biological processes and similarity in Gene Ontology (GO) annotation has been found to be one of the strongest indicators for PPI. Most GO-driven algorithms for PPI inference combine machine learning and semantic similarity techniques. We introduce the concept of inducers as a method to integrate both approaches more effectively, leading to superior prediction accuracies. RESULTS: An inducer (ULCA) in combination with a Random Forest classifier compares favorably to several sequence-based methods, semantic similarity measures and multi-kernel approaches. On a newly created set of high-quality interaction data, the proposed method achieves high cross-species prediction accuracies (Area under the ROC curve ≤ 0.88), rendering it a valuable companion to sequence-based methods. AVAILABILITY: Software and datasets are available at http://bioinformatics.org.au/go2ppi/ CONTACT: m.ragan@uq.edu.au.