This article has been retracted please see Elsevier Policy on Article Withdrawal (https//www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted please see Elsevier Policy on Article Withdrawal (http//www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Concern has been raised by a reader about both the inappropriateness of certain methods used to prepare Figures 1A and 3A; as well as the lack of important information including the exact age of the mice and details of the ELISA used. These issues could undermine the scientific grounds of the article. Apologies are offered to readers of the journal that this was not detected during the submission process.The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ε-constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm.
  To undertaken a systematic review of the technical success and technique efficacy rates of percutaneous image-guided radiofrequency ablation (RFA) for adrenal tumours.
 
  Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the electronic databases MEDLINE, EMBASE, and PubMed were searched for relevant studies from inception to the third week of January 2020. Only studies reporting effectiveness rates of percutaneous RFA for adrenal tumours were included. Data regarding sample size, tumours, effectiveness rates, outcomes, and complications were extracted in duplicate and recorded.
 
  A total of 15 studies evaluating 292 individuals with 305 tumours were included. Patient selection criteria included age ≥18 years, contraindication to surgical intervention, and no uncorrected coagulopathy. Cumulative technical success, primary technique efficacy, and secondary technique efficacy rates were 99%, 95.1% and 100%, respectively, indicating optimal immediate control of adrenal tumours. Technical success and technique efficacy rates of primary adrenal tumours were higher than adrenal metastases; however, formal statistical analyses were precluded due to lack of comparative studies. Local tumour progression rates for adrenal metastases were 20.3% at 3 months, 26.3% at 6 months, and 29.3% at 12 months. Overall survival rates for adrenal metastases were 81.8% at 6 months, 59.6% at 12 months, and 62.9% at 18 months. The intraprocedural complication rate was 30.2%, with the most frequency reported complication being procedural hypertensive crisis.
 
  The findings of this study suggest percutaneous image-guided RFA is a safe and efficacious procedure. Further studies are warranted to define patient selection criteria and long-term outcomes.
 The findings of this study suggest percutaneous image-guided RFA is a safe and efficacious procedure.  https://www.selleckchem.com/products/pexidartinib-plx3397.html  Further studies are warranted to define patient selection criteria and long-term outcomes.The role of imaging in clinically staging colorectal cancer has grown substantially in the 21st century with more widespread availability of multi-row detector computed tomography (CT), high-resolution magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI), and integrated positron-emission tomography (PET)/CT. In contrast to staging many other cancers, increasing colorectal cancer stage does not highly correlate with survival. As has been the case previously, clinical practice incorporates advances in staging and it is used to guide therapy before adoption into international staging guidelines. Emerging imaging techniques show promise to become part of future staging standards.
  We sought to define the prevalence and nature of patient-reported drug allergies, determine their impact on prescribing, and explore drug allergy knowledge and attitudes amongst anaesthetists.
 
  We performed a prospective cross-sectional study in 213 UK hospitals in 2018. Elective surgical patients were interviewed, with a detailed allergy history taken in those self-reporting drug allergy. Anaesthetists completed a questionnaire concerning perioperative drug allergy.
 
  Of 21 219 patients included, 6214 (29.3 %) (95% confidence interval [CI] 28.7-29.9) reported drug allergy. Antibiotics, NSAIDs, and opioids were the most frequently implicated agents. Of a total of 8755 reactions, 2462 (28.1%) (95% CI 29.2-31.1) were categorised as high risk for representing genuine allergy after risk stratification. A history suggestive of chronic spontaneous urticaria significantly increased the risk of reporting drug allergy (odds ratio 2.68; 95% CI 2.4-3; P<0.01). Of 4756 anaesthetists completing the questionnaire, 14ve prescribing through avoidance of important drugs and use of less effective alternatives. We highlight important knowledge gaps about drug allergy amongst anaesthetists, and the need for improved education around allergy.To meet the WHO vision of reducing medication errors by 50%, it is essential to know the current error rate. We undertook an integrative review of the literature, using a systematic search strategy. We included studies that provided an estimate of error rate (i.e. both numerator and denominator data), regardless of type of study (e.g. RCT or observational study). Under each method type, we categorised the error rate by type, by classification used by the primary studies (e.g. wrong drug, wrong dose, wrong time), and then pooled numerator and denominator data across studies to obtain an aggregate error rate for each method type. We included a total of 30 studies in this review. Of these, two studies were national audit projects containing relevant data, and for 28 studies we identified five discrete method types retrospective recall (6), self-reporting (7), observational (5), large databases (7), and observing for drug calculation errors (3). Of these 28 studies we included 22 for a numerical analysis and used six to inform a narrative review.