There is now Class A RCT evidence that DRGS provides superior pain relief to SCS for CRPS and causalgia of the lower limb. In the coming years we hope that randomized controlled trials will be performed on an indication-by-indication basis, which, together with the publication of longer term follow-up data, will provide a more complete understanding of the role of DRGS in the treatment of neuropathic pain syndromes.

In this article, we characterized roles for CLAVATA in the development of a moss, Physcomitrella patens, focusing on the 2D to 3D growth transition. Ongoing work to further characterize mutant phenotypes identified some phenotype discrepancies among the Ppclv1a and Ppclv1b mutant lines published in the original paper. For this reason, we implemented further checks of the published manuscript and fully sequenced both PpCLV1a and PpCLV1b loci in all mutants originally reported in the Methods S1 figure ''CRISPR/Cas9 strategy for generating Ppclv1 mutants.'' Although the conclusions of the paper remain valid, our investigations revealed errors that we wish to correct. We found that Ppclv1a line 18 (Figures 4D and 4K; Methods S1, CRISPR figure, panel E) contained an 805 bp deletion at the PpCLV1a locus, but while Ppclv1a line 18 plants had phenotypes resembling WT plants, there was also a 9 bp deletion at PpCLV1b. We found no mutations at PpCLV1a or PpCLV1b in Ppclv1a line 29 or Ppclv1a line 32 (Methods S1, CRISPR figure, panels F and G), and these lines were indistinguishable from WT plants. The genotype of Ppclv1b line 2 (Figures 4E and 4L; Methods S1, CRISPR figure, panel H) was reported as a 2 bp deletion, but genome walking now confirms that there is a 4 bp deletion and >6 kb insertion at PpCLV1b, and the insertion comprises sequence integrated from the pACT::Cas9 expression vector used to engineer the lines [1]. The genotypes of Ppclv1b line 9 and Ppclv1b line 33 (Methods S1, CRISPR figure, panels I and J) were not previously reported, and while Ppclv1b line 9 has a 47 bp deletion at the PpCLV1b locus, Ppclv1b line 33 has no mutation at PpCLV1a or PpCLV1b, and an indistinguishable phenotype from WT plants. The reported genotypes of Ppclv1a1b lines 6, 8, and 12 were accurate (Methods S1, CRISPR figure, panels K-M). Consequently, we have re-engineered and fully sequenced PpCLV1a and PpCLV1b in three independent Ppclv1a and Ppclv1b mutant lines to verify the mutant phenotypes reported in Figure 4. The Ppclv1a and Ppclv1b mutant phenotypes previously reported hold true, and the genotypes and phenotypes of lines now in use are shown in Figure 1 below.

SummaryPseudomonas fluorescens SBW25 is a plant growth-promoting bacterium that efficiently colonises the leaf surfaces and rhizosphere of a range of plants. Previous studies have identified a putative plant-induced nitrilase gene (pinA) in P. fluorescens SBW25 that is expressed in the rhizosphere of sugar beet plants. Nitrilase enzymes have been characterised in plants, bacteria and fungi and are thought to be important in detoxification of nitriles, utilisation of nitrogen and synthesis of plant hormones. We reveal that pinA is a NIT4-type nitrilase that catalyses the hydrolysis of b-cyano-L-alanine, a nitrile common in the plant environment and an intermediate in the cyanide detoxification pathway in plants. In plants cyanide is converted to b-cyano-L-alanine, which is subsequently detoxified to aspartic acid and ammonia by NIT4. In P. fluorescens SBW25 pinA is induced in the presence of b-cyano-L-alanine, and the b-cyano-L-alanine precursors cyanide and cysteine. pinA allows P. fluorescens SBW25 to use b-cyano-L-alanine as a nitrogen source and to tolerate toxic concentrations of this nitrile. In addition, pinA is shown to complement a NIT4 mutation in Arabidopsis thaliana, enabling plants to grow in concentrations of b-cyano-L-alanine that would otherwise prove lethal. Interestingly, over-expression of pinA in wild-type A. thaliana not only resulted in increased growth in high concentrations of b-cyano-L-alanine, but also resulted in increased root elongation in the absence of exogenous b-cyano-L-alanine, demonstrating that b-cyano-L-alanine nitrilase activity can have a significant effect on root physiology and root development.

Background Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of written text into interpretable datasets that can be analysed by statistical and machine learning (ML) models. The purpose of this paper is to provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software. Methods We performed three NLP experiments using publicly-available data obtained from medicine review websites. First, we conducted lexicon-based sentiment analysis on open-text patient reviews of four drugs: Levothyroxine, Viagra, Oseltamivir and Apixaban. Next, we used unsupervised ML (latent Dirichlet allocation, LDA) to identify similar drugs in the dataset, based solely on their reviews. Finally, we developed three supervised ML algorithms to predict whether a drug review was associated with a positive or negative rating. These algorithms were: a regularised logistic regression, a support vector machine (SVM), and an artificial neural network (ANN). We compared the performance of these algorithms in terms of classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results Levothyroxine and Viagra were reviewed with a higher proportion of positive sentiments than Oseltamivir and Apixaban. One of the three LDA clusters clearly represented drugs used to treat mental health problems. A common theme suggested by this cluster was drugs taking weeks or months to work. Another cluster clearly represented drugs used as contraceptives. Supervised machine learning algorithms predicted positive or negative drug ratings with classification accuracies ranging from 0.664, 95% CI [0.608, 0.716] for the regularised regression to 0.720, 95% CI [0.664,0.776] for the SVM. Conclusions In this paper, we present a conceptual overview of common techniques used to analyse large volumes of text, and provide reproducible code that can be readily applied to other research studies using open-source software.