Comparative assessment of phototherapy protocols for reduction of oxidative stress in partially transected spinal cord slices undergoing secondary degeneration

Background: Red/near-infrared light therapy (R/NIR-LT) has been developed as a treatment for a range of conditions, including injury to the central nervous system (CNS). However, clinical trials have reported variable or sub-optimal outcomes, possibly because there are few optimized treatment protocols for the different target tissues. Moreover, the low absolute, and wavelength dependent, transmission of light by tissues overlying the target site make accurate dosing problematic. Results: In order to optimize light therapy treatment parameters, we adapted a mouse spinal cord organotypic culture model to the rat, and characterized myelination and oxidative stress following a partial transection injury. The ex vivo model allows a more accurate assessment of the relative effect of different illumination wavelengths (adjusted for equal quantal intensity) on the target tissue. Using this model, we assessed oxidative stress following treatment with four different wavelengths of light: 450 nm (blue); 510 nm (green); 660 nm (red) or 860 nm (infrared) at three different intensities: 1.93 × 10¹⁶ (low); 3.85 × 10¹⁶ (intermediate) and 7.70 × 10¹⁶ (high) photons/cm²/s. We demonstrate that the most effective of the tested wavelengths to reduce immunoreactivity of the oxidative stress indicator 3-nitrotyrosine (3NT) was 660 nm. 860 nm also provided beneficial effects at all tested intensities, significantly reducing oxidative stress levels relative to control (p ≤ 0.05). Conclusions: Our results indicate that R/NIR-LT is an effective antioxidant therapy, and indicate that effective wavelengths and ranges of intensities of treatment can be adapted for a variety of CNS injuries and conditions, depending upon the transmission properties of the tissue to be treated.12 page(s

Improving our understanding of the in vivo modelling of psychotic disorders: a systematic review and meta-analysis

Psychotic disorders represent a severe category of mental disorders affecting about one percent of the population. Individuals experience a loss or distortion of contact with reality alongside other symptoms, many of which are still not adequately managed using existing treatments. While animal models of these disorders could offer insights into these disorders and potential new treatments, translation of this knowledge has so far been poor in terms of informing clinical trials and practice. The aim of this project was to improve our understanding of these pre-clinical studies and identify potential weaknesses underlying translational failure. I carried out a systematic search of the literature to provide an unbiased summary of publications reporting animal models of schizophrenia and other psychotic disorders. From these publications, data were extracted to quantify aspects of the field including reported quality of studies, study characteristics and behavioural outcome data. The latter of these data were then used to calculate estimates of efficacy using random-effects meta-analysis. Having identified 3847 publications of relevance, including 852 different methods used to induce the model, over 359 different outcomes tested in them and almost 946 different treatments reported to be administered. I show that a large proportion of studies use simple pharmacological interventions to induce their models of these disorders, despite the availability of models using other interventions that are arguably of higher translational relevance. I also show that the reported quality of these studies is low, and only 22% of studies report taking measures to reduce the risk of biases such as randomisation and blinding, which has been shown to affect the reliability of results drawn. Through this work it becomes apparent that the literature is incredibly vast for studies looking at animal models of psychotic disorders and that some of the relevant work potentially overlaps with studies describing other conditions. This means that drawing reliable conclusions from these data is affected by what is made available in the literature, how it is reported and identified in a search and the time that it takes to reach these conclusions. I introduce the idea of using computer-assisted tools to overcome one of these problems in the long term. Translation of results from studies looking at animals modelling uniquely-human psychotic disorders to clinical successes might be improved by better reporting of studies including publishing of all work carried out, labelling of studies more uniformly so that it is identifiable, better reporting of study design including improving on reporting of measures taken to reduce the risk of bias and focusing on models with greater validity to the human condition

Lessons learnt from registration of biomedical research

Data for all analyses presented in the Nature Human Behaviour publication

Αssessment of transparency indicators across the biomedical literature: how open is open?

Automated assessment of indicators of transparency across the entire open biomedical literature

Media and social media attention to retracted articles according to Altmetric.

The number of retracted articles has grown fast. However, the extent to which researchers and the public are made adequately aware of these retractions and how the media and social media respond to them remains unknown. Here, we aimed to evaluate the media and social media attention received by retracted articles and assess also the attention they receive post-retraction versus pre-retraction. We downloaded all records of retracted literature maintained by the Retraction Watch Database and originally published between January 1, 2010 to December 31, 2015. For all 3,008 retracted articles with a separate DOI for the original and its retraction, we downloaded the respective Altmetric Attention Score (AAS) (from Altmetric) and citation count (from Crossref), for the original article and its retraction notice on June 6, 2018. We also compared the AAS of a random sample of 572 retracted full journal articles available on PubMed to that of unretracted full articles matched from the same issue and journal. 1,687 (56.1%) of retracted research articles received some amount of Altmetric attention, and 165 (5.5%) were even considered popular (AAS>20). 31 (1.0%) of 2,953 with a record on Crossref received >100 citations by June 6, 2018. Popular articles received substantially more attention than their retraction, even after adjusting for attention received post-retraction (Median difference, 29; 95% CI, 17-61). Unreliable results were the most frequent reason for retraction of popular articles (32; 19%), while fake peer review was the most common reason (421; 15%) for the retraction of other articles. In comparison to matched articles, retracted articles tended to receive more Altmetric attention (23/31 matched groups; P-value, 0.01), even after adjusting for attention received post-retraction. Our findings reveal that retracted articles may receive high attention from media and social media and that for popular articles, pre-retraction attention far outweighs post-retraction attention

Additional file 2: of Long noncoding RNAs as novel predictors of survival in human cancer: a systematic review and meta-analysis

The studies eligible for systematic review. (DOC 444 kb

Systemic Complement Activation Profiles in Nonexudative Age-Related Macular Degeneration: A Meta-Analysis

Although complement inhibition has emerged as a possible therapeutic strategy for age-related macular degeneration (AMD), there is not a clear consensus regarding what aspects of the complement pathway are dysregulated in AMD and when this occurs relative to disease stage. We recently published a systematic review describing systemic complement activation profiles in patients with early/intermediate AMD or geographic atrophy (GA) compared to non-AMD controls. Here, we sought to meta-analyze these results to estimate the magnitude of complement dysregulation in AMD using restricted maximum likelihood estimation. The seven meta-analyzed studies included 710 independent participants with 23 effect sizes. Compared with non-AMD controls, patients with early/intermediate nonexudative AMD (N = 246) had significantly higher systemic complement activation, as quantified by the levels of complement proteins generated by common final pathway activation, and significantly lower systemic complement inhibition. In contrast, there were no statistically significant differences in the systemic levels of complement common final pathway activation products or complement inhibition in patients with GA (N = 178) versus non-AMD controls. We provide evidence that systemic complement over-activation is a feature of early/intermediate nonexudative AMD; no such evidence was identified for patients with GA. These findings provide mechanistic insights and inform future clinical trials

Calibrating the Scientific Ecosystem Through Meta-Research

While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientific ecosystem, topics that have become especially relevant amid widespread concerns about the credibility of the scientific literature. Meta-research may help calibrate the scientific ecosystem toward higher standards by providing empirical evidence that informs the iterative generation and refinement of reform initiatives. We introduce a translational framework that involves ( a) identifying problems, ( b) investigating problems, ( c) developing solutions, and ( d) evaluating solutions. In each of these areas, we review key meta-research endeavors and discuss several examples of prior and ongoing work. The scientific ecosystem is perpetually evolving; the discipline of meta-research presents an opportunity to use empirical evidence to guide its development and maximize its potential

Assessment of transparency indicators across the biomedical literature: How open is open?

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify 5 indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures, and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals, and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand, and promote transparency and reproducibility in science