In vitro alpha-amylase and alpha-glucosidase inhibitory activity and in vivo antidiabetic activity of Quercus coccifera (Oak tree) leaves extracts

Quercus species are group of plants known as oak which represent important genus of Fagaceae family. These species are widely distributed in Mediterranean countries. Many of those species used in traditional medicine to treat and prevent various human disorders such as diabetes. Exhausted extraction for Quercus coccifera leaves were carried out using n-hexane, chloroform, methanol, boiled water and microwaved water. Extracts were subjected to phytochemical screening, acute toxicity study, and in vitro and in vivo animal model to evaluate antidiabetic activity of the produced extracts. The highest in vitro activity against α-amylase and α-glucosidase activity was obtained from methanolic extract with an IC₅₀ of 0.17 and 0.38 µg/ml respectively and better than the positive control acarbose. While the rest of the extract was either with moderate or low activity. Similarly, in the in vivo study, methanolic extract with a concentration of 200 mg/kg/day was able to reduce the blood glucose level for the diabetic mice to 146.8 mg/dL with normal bodyweight and biochemical signs when compared to the normal mice group. While the rest of the extracts were either with moderate or low ability to maintain blood glucose level for diabetic mice with few signs of hepatic and renal toxicity and weight loss. All data were statistically significantly different with p-value of less than 0.001 at confidence interval of 95% with high variance homogeneity. In conclusion, methanolic plant leaves extract of Q. coccifera can possibly be used alone to control the elevation of blood glucose level with a renal and hepatic protective property.

A guide to appropriately planning and conducting meta-analyses: part 2-effect size estimation, heterogeneity and analytic approaches

Meta-analyses by definition are a subtype of systematic review intended to quantitatively assess the strength of evidence present on an intervention or treatment. Such analyses may use individual-level data or aggregate data to produce a point estimate of an effect, also known as the combined effect, and measure precision of the calculated estimate. The current article will review several important considerations during the analytic phase of a meta-analysis, including selection of effect estimators, heterogeneity and various sub-types of meta-analytic approaches.

Changes in NO2 and O3 levels due to the pandemic lockdown in the industrial cities of Tehran and Arak, Iran using Sentinel 5P images, Google Earth Engine (GEE) and statistical analysis

Air pollution has very damaging effects on human health. In recent years the Coronavirus disease (COVID-19) pandemic has created a worldwide economic disaster. Although the consequences of the COVID-19 lockdowns have had severe effects on economic and social conditions, these lockdowns also have also left beneficial effects on improving air quality and the environment. This research investigated the impact of the COVID-19 lockdown on NO2 and O3 pollutants changes in the industrial and polluted cities of Arak and Tehran in Iran. Based on this, the changes in NO2 and O3 levels during the 2020 lockdown and the same period in 2019 were investigated in these two cities. For this purpose, the Sentinel-5P data of these two pollutants were used during the lockdown period from November 19 to December 05, 2020, and at the same time before the pandemic from November 19 to December 05, 2019. For better results, the effect of climatic factors such as rain and wind in reducing pollution was removed. The obtained results indicate a decrease in NO2 and O3 levels by 3.5% and 6.8% respectively in Tehran and 20.97% and 5.67% in Arak during the lockdown of 2020 compared to the same time in 2019. This decrease can be caused by the reduction in transportation and socio-economic and industrial activities following the lockdown measures. This issue can be a solid point to take a step toward controlling and reducing pollution in non-epidemic conditions by implementing similar standards and policies in the future.

OntoBioStat: Supporting Causal Diagram Design and Analysis

Suitable causal inference in biostatistics can be best achieved by knowledge representation thanks to causal diagrams or directed acyclic graphs. However, necessary and sufficient causes are not easily represented. Since existing ontologies do not fill this gap, we designed OntoBioStat in order to enable covariate selection support based on causal relation representations. OntoBioStat automatic ontological causal diagram construction and inferences are detailed in this study. OntoBioStat inferences are allowed by Semantic Web Rule Language rules and axioms. First, statements made by the users include outcome, exposure, covariate, and causal relation specification. Then, reasoning enable automatic construction using generic instances of Meta_Variable and Necessary_Variable classes. Finally, inferred classes highlighted potential bias such as confounder-like. Ontological causal diagram built with OntoBioStat was compared to a standard causal diagram (without OntoBioStat) in a theoretical study. It was found that confounding and bias were not completely identified by the standard causal diagram, and erroneous covariate sets were provided. Further research is needed in order to make OntoBioStat more usable.

ACL Surgery Necessity in Non-Acute Patients (ACL SNNAP): a statistical analysis plan for a randomised controlled trial

Background

Rupture of the anterior cruciate ligament (ACL) is a common injury, primarily affecting young, active individuals. Despite surgical intervention being the more common treatment for patients suffering ACL ruptures, current management is based on limited and generally low-quality evidence. We describe a statistical analysis plan (SAP) for the ACL SNNAP randomised controlled trial, which aims to investigate the necessity of surgical management in patients with ACL injuries.

Methods/design

ACL SNNAP is a pragmatic, multi-centre, superiority, parallel-group randomised controlled trial in participants with a symptomatic non-acute ACL deficient knee. Participants are allocated in a 1:1 ratio to either non-surgical management (rehabilitation) or surgical management (reconstruction) with the aim of assessing the efficacy and cost-effectiveness. The primary outcome of the study is the Knee Injury and Osteoarthritis Outcome Score (KOOS4) at 18 months post-randomisation. The KOOS4 score at 18 months will be evaluated using a linear regression model adjusting for recruitment centre and baseline KOOS4 scores, allowing for intra-centre correlation. A secondary analysis of the primary outcome will be carried out using an area under the curve (AUC) approach using treatment estimates obtained from a mixed model using baseline, 6 months, 12 months, and 18 months post-randomisation outcome data. Secondary outcomes will be measured at 18 months and will include return to activity/level of sport participation, intervention-related complications, the EQ-5D-5L questionnaire, all 5 individual subscales of the KOOS questionnaire, the ACL-QOL score, expectations of return to activity and cost-effectiveness of the interventions. Missing primary outcome data will be investigated through a sensitivity analysis. Full details of the planned methods for the statistical analysis of clinical outcomes are presented in this paper. The study protocol for the ACL SNNAP trial has been published previously.

Discussion

The methods of analysis for the ACL SNNAP trial have been described here to minimise the risk of data-driven results and reporting bias. Any deviations from the analysis methods described in this paper will be described in full and justified in the publications of the trial results.

Trial registration

ISRCTN ISRCTN10110685. Registered on 16 November 2016

Comparisons of PNEC derivation logic flows under example regulatory schemes and implications for ecoTTC

Derivation of Predicted No Effect Concentrations (PNECs) for aquatic systems is the primary deterministic form of hazard extrapolation used in environmental risk assessment. Depending on the data availability, different regulatory jurisdictions apply application factors (AFs) to the most sensitive measured endpoint to derive the PNEC for a chemical. To assess differences in estimated PNEC values, two PNEC determination methodologies were applied to a curated public database using the EnviroTox Platform (www.EnviroToxdatabase.org). PNECs were derived for 3647 compounds using derivation procedures based on example US EPA and a modified European Union chemical registration procedure to allow for comparisons. Ranked probability distributions of PNEC values were developed and 5th percentile values were calculated for the entire dataset and scenarios where full acute or full chronic data sets were available. The lowest PNEC values indicated categorization based on chemical attributes and modes of action would lead to improved extrapolations. Full acute or chronic datasets gave measurably higher 5th percentile PNEC values. Algae were under-represented in available ecotoxicity data but drove PNECs disproportionately. Including algal inhibition studies will be important in understanding chemical hazards. The PNEC derivation logic flows are embedded in the EnviroTox Platform providing transparent and consistent PNEC derivations and PNEC distribution calculations.

Lies, Damn Lies, and Bad Statistics?

A wide spectrum of research such as experimental, randomized trials, cohort or epidemiological studies, technical or control case reports, systematic reviews, and meta-analyses has resulted in a huge amount of publications. These studies and publications may be subject to errors due to poor application of statistical tests, which can lead to misinformation, misinterpretation, and erroneous conclusions, sometimes even considered as lies. In this article, some ideas about this issue are discussed in order to adopt new directions in the future and thus avoid lies and bad statistics.

Should the nail plate be replaced or discarded after nail bed repair in children? Nail bed INJury Analysis (NINJA) randomised controlled trial: a health economic and statistical analysis plan

Background

Nail bed trauma is one of the most common surgically treated paediatric hand injuries in the UK. Despite surgeons generally expressing a preference to replace the nail plate after repairing the nail bed, there is limited evidence to support this practice. We describe a statistical and health economic analysis plan (SHEAP) for the Nail bed INJury Analysis (NINJA) randomised controlled trial.

Methods/design

NINJA is a multicentre, pragmatic, superiority, parallel group randomised controlled trial of the treatment of nail bed injury in participants 16 years old or younger. The study aims to evaluate the efficacy and cost-effectiveness of replacing the nail plate compared to discarding it following the repair of a nail bed injury. Surgical site infection at 7–10 days post-randomisation and cosmetic appearance of the nail are the co-primary outcomes for NINJA. Surgical site infection at 7–10 days post-randomisation will be evaluated using a logistic regression model adjusting for site as the sole stratification factor and allowing for intra-site correlation. Cosmetic appearance will be assessed via the newly developed Oxford Finger Nail Appearance Score and will be evaluated by use of a Mann-Whitney U test. An ordinal logistic regression model will also be used to assess the Oxford Finger Nail Appearance Score, adjusting for site and allowing for intra-site correlation. Secondary outcomes are measured at 7–10 days and 4 months and include the EQ-5D-Y questionnaire, pain at first dressing change, cost-effectiveness, late surgical site infection, and participant/parent satisfaction with nail healing. Missing primary outcome data will be summarised by treatment arm and investigated through a sensitivity analysis. Full details of the planned methods of analysis and descriptive statistics are described in this paper. The NINJA study protocol has been published previously.

Discussion

The planned analysis strategy for the NINJA trial has been set out here to reduce the risk of reporting bias and data-driven analysis. Any deviations from the SHEAP described in this paper will be detailed and justified fully in the final report of the trial.

Trial registration

ISRCTN, ISRCTN44551796. Registered on 23 April 2018.

Dataset of computed N-value and factual N-value traced for Soil Subsurface Profiling

Soil requires load bearing impact assessment for stability. Therefore, this study aims to utilize the multi-channel analysis surface wave (MASW) for soil subsurface investigation and profiling around Peninsular Malaysia. The standard penetration test (SPT) was conducted for comparison between factual N-value and computed N-value from shear wave velocity (V_s) obtained from MASW using the Imai and Tonouchi equation. The correlation coefficient (R) and coefficient of determination, (R²), showed strong relationship between factual N-value and computed N-value. The model of V_s and factual N-value data distribution is non-normal but the analyzed relationship shows a significant level of p-value < 0.05. The R² for each location of V_s-N-value relationship are ranging from 0.5 to 0.9.

Sulfur removal from model fuel by Zn impregnated retorted shale and with assistance of design of experiments

There is global concern about acid rain and other pollution which is caused by the consumption of oil. By decreasing sulfur content in the oil, we can reduce unwanted emissions and acid rain. Shale was used which is a solid waste generated in the pyrolysis of shale, impregnated with Zn as an adsorbent which removes sulfur present in fuels from the hexane/toluene model solution. An influence of the agitation time (60-180 min), temperature (25-35 °C), adsorbent mass (0.1-0.25 g), and initial sulfur concentration (100-250 ppm) factorial 24 with three central points totaling 19 experiments was applied to investigate the effect of the variables on the efficiency of sulfur removal in fuels. The values of the parameters tested for maximum sulfur removal were obtained as follows: contact time = 180 min, temperature = 35 °C, adsorbent mass = 0.25 g, and initial sulfur concentration = 100 ppm. The mathematical model proposed with R² 99.97% satisfied the experimental data. This may provide a theoretical basis for new research and alternative uses for tailings of schist industrialization in order to evaluate its potential.

Epidemiology of Clostridium Difficile Infection in a Large Hospital in Northern Italy: Questioning the Ward-Based Transmission

Background:

Clostridium Difficile infection (CDI) is considered a ward-based nosocomial infection, due to contagion among patients. Molecular studies recently questioned ward-based contact for disease spread.

Objective:

To investigate whether it is plausible that CDI spread in San Martino Hospital of Genoa was due to a ward-based contact and patient-to-patient diffusion.

Methods:

We conducted a retrospective cohort study of CDI cases from April 2010 to March 2015. We referred to Hospital data set and Admission Service. Multilevel modelling approach and ecological analysis were used to assess C. difficile infection risk according to wards and time of occurrence. Six representative CD strains were ribotyped to assess a possible equivalence.

Results:

The assessment of 514 CDI cases showed that the risk of disease and rate of incidence in wards were independent, while frequency of cases and number of wards involved exhibited a positive relationship, excluding the typical epidemic pattern of contagious diffusion, i.e., many cases in few wards. The extra-binomial variability due to ward clustering was not significant, indicating homogeneity in the probability of CDI occurrence across all wards. Three hundred sixty-eight patients changed ward, without showing connection between the frequency of cases in new wards and incidence among new subjects. Trigonometric components described a significant contribution of seasonality, with excess of CDI cases during the winter months. Molecular analysis showed different ribotypes of CD strains from the same ward.

Conclusion:

From our results it seems unlikely that in our institution CDI occurrence is due to ward-based contact and inter-human contagion of the organism.

A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs

Connectome mapping using techniques such as functional magnetic resonance imaging (fMRI) has become a focus of systems neuroscience. There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series. One key statistic for any time series is its (effective) degrees of freedom, df, which will generally be less than the number of time points (or nominal degrees of freedom, N). If we know the df, then probabilistic inference on other fMRI statistics, such as the correlation between two voxel or regional time series, is feasible. However, we currently lack good estimators of df in fMRI time series, especially after the degrees of freedom of the “raw” data have been modified substantially by denoising algorithms for head movement. Here, we used a wavelet-based method both to denoise fMRI data and to estimate the (effective) df of the denoised process. We show that seed voxel correlations corrected for locally variable df could be tested for false positive connectivity with better control over Type I error and greater specificity of anatomical mapping than probabilistic connectivity maps using the nominal degrees of freedom. We also show that wavelet despiked statistics can be used to estimate all pairwise correlations between a set of regional nodes, assign a P value to each edge, and then iteratively add edges to the graph in order of increasing P. These probabilistically thresholded graphs are likely more robust to regional variation in head movement effects than comparable graphs constructed by thresholding correlations. Finally, we show that time-windowed estimates of df can be used for probabilistic connectivity testing or dynamic network analysis so that apparent changes in the functional connectome are appropriately corrected for the effects of transient noise bursts. Wavelet despiking is both an algorithm for fMRI time series denoising and an estimator of the (effective) df of denoised fMRI time series. Accurate estimation of df offers many potential advantages for probabilistically thresholding functional connectivity and network statistics tested in the context of spatially variant and non-stationary noise. Code for wavelet despiking, seed correlational testing and probabilistic graph construction is freely available to download as part of the BrainWavelet Toolbox at www.brainwavelet.org.

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We lack good estimators of degrees of freedom (df) in denoised fMRI time series.

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Wavelet despiking can simultaneously denoise and estimate effective df post-denoising.

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We show Type I error controlled single-subject probabilistic inference for seed connectivity.

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We describe new methods for building and probabilistically thresholding brain graphs.

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We extend these methods to probabilistic inference for time-varying connectomics.