Understanding the harmful effects of polyethylene microplastics on Eisenia fetida: A toxicological evaluation

Microplastics, measuring less than 5 mm, are pervasive environmental pollutants raising concerns about their toxic effects on terrestrial ecosystems, especially earthworms.A comprehensive toxicological evaluation of polyethylene microplastics on earthworms will be beneficial for determining the detrimental impacts of these ubiquitous pollutants on soil ecosystem. Therefore, in the present study, the best representative soil organism, earthworms (Eisenia fetida), were opted for examining the toxicological effect of polyethylene microplastic. E. fetida were subjected to different concentrations of polyethylene microplastic (200, 400, 600, 800, and 1000 mg/kg) in soil and randomly picked out on days 7 to 56. Earthworms exposed to higher concentration of polyethylene (1000 mg/kg of artificial soil) showed a significant reduction in body weight and cocoon formation after 35th days of incubation. A consistent decrease in the concentration of carbohydrates, lipids, and protein was observed when the worms were exposed to the higher concentration of polyethylene. Further, antioxidant enzymes like superoxide dismutase, glutathione S-transferase, peroxidase, catalase, and malondialdehyde were determined for antioxidant stress.Exposure of 200 mg/kg to 1000 mg/kg of artificial soil caused a prominent amplification in the build-up of malonedialdehyde (a biological marker of oxidative stress) by 1.29-fold. It also considerably augmented the activity of the antioxidant enzymes viz., glutathione S-transferase (1.54-fold), superoxide dismutase (1.51-fold), peroxidase (1.25-fold), and catalase (1.87-fold). The present study's findings provide a new understanding of the toxic effect of microplastic on earthworm E. fetida, presenting a foundation for its risk evaluation on soil ecosystems and non-target biological toxicity.

CURRENT GOOD MANUFACTURING GUIDELINES FOR MEDICINAL PRODUCT

The holder of a manufacturing  authorisation must manufacture medicinal products so as to ensure that they are fit for their intended use, comply with the requirements of the Marketing Authorisation and do not place patients at risk due to inadequate safety, quality or efficacy. The attainment of this quality objective is the responsibility of senior management and requires the participation and commitment by staff in many different departments and at all levels within the company, by the company’s suppliers and by the distributors. To achieve the quality objective reliably there must be a comprehensively designed and correctly implemented system of Quality Assurance Incorporating Good Manufacturing Practice, and thus Quality Control and Quality Risk Management. It should be fully documented and its effectiveness monitored. All parts of the Quality Assurance systems should be adequately resourced with competent personnel, and suitable and sufficient premises, equipment and facilities. There are additional legal responsibilities for the holder of the manufacturing  authorisation and for the  authorised person Keywords: Good Manufacturing Practice, Quality control, Quality assurance, authorise

COVID-19, SARS and MERS:A neurological perspective

Central to COVID-19 pathophysiology is an acute respiratory infection primarily manifesting as pneumonia. Two months into the COVID-19 outbreak, however, a retrospective study in China involving more than 200 participants revealed a neurological component to COVID-19 in a subset of patients. The observed symptoms, the cause of which remains unclear, included impaired consciousness, skeletal muscle injury and acute cerebrovascular disease, and appeared more frequently in severe disease. Since then, findings from several studies have hinted at various possible neurological outcomes in COVID-19 patients. Here, we review the historical association between neurological complications and highly pathological coronaviruses including SARS-CoV, MERS-CoV and SARS-CoV-2. We draw from evidence derived from past coronavirus outbreaks, noting the similarities and differences between SARS and MERS, and the current COVID-19 pandemic. We end by briefly discussing possible mechanisms by which the coronavirus impacts on the human nervous system, as well as neurology-specific considerations that arise from the repercussions of COVID-19.</p

COVID-19, SARS and MERS:A neurological perspective

Central to COVID-19 pathophysiology is an acute respiratory infection primarily manifesting as pneumonia. Two months into the COVID-19 outbreak, however, a retrospective study in China involving more than 200 participants revealed a neurological component to COVID-19 in a subset of patients. The observed symptoms, the cause of which remains unclear, included impaired consciousness, skeletal muscle injury and acute cerebrovascular disease, and appeared more frequently in severe disease. Since then, findings from several studies have hinted at various possible neurological outcomes in COVID-19 patients. Here, we review the historical association between neurological complications and highly pathological coronaviruses including SARS-CoV, MERS-CoV and SARS-CoV-2. We draw from evidence derived from past coronavirus outbreaks, noting the similarities and differences between SARS and MERS, and the current COVID-19 pandemic. We end by briefly discussing possible mechanisms by which the coronavirus impacts on the human nervous system, as well as neurology-specific considerations that arise from the repercussions of COVID-19.</p

Markedly disturbed sleep in medically refractory compared to controlled epilepsy – A clinical and polysomnography study

AbstractPurposeTo evaluate sleep disturbances or sleep related events and their characteristics among patients with medically refractory epilepsy, compared to those with controlled epilepsy.MethodsIn a prospective case-controlled study, patients of medically refractory and controlled epilepsy were recruited and history pertaining to epilepsy and sleep related events and Epworth sleepiness scores were recorded and all patients underwent over night polysomnography.ResultsAmong 40 patients, 20 with medically refractory (Group 1) and 20 with controlled epilepsy (Group 2) (median age 18, range 10–35 years), the self reported sleep parameters in Group 1 patients were found to be significantly different as compared to Group 2, in terms of the duration of night time sleep, day time sleep, day time nap frequency, total sleep hours per day, excessive daytime sleepiness (EDS)(45% vs. 15%) and average sleep hours over the week prior to polysomnography. On PSG, Group 1 patients showed significantly less total sleep time [340.4min (147–673) vs. 450.3min (330–570)] with delayed sleep latency and REM latency, poor sleep efficiency [80.45 (40.5–98.0) vs. 95.45 (88.4–99.7)] and frequent arousals and wake after sleep onset (WASO) compared to Group 2 patients. Four patients (20%) in Group 1 compared to none in Group 2 were found to have mild obstructive sleep apnea.ConclusionsOur results indicate that medically refractory epilepsy patients believe that they spend more time sleeping, in contrast to the documented shorter sleep duration on polysomnography. This difference between perceived and actual sleep seems, by their data, to arise mainly from sleep fragmentation, disturbed architecture and the interesting finding of associated sleep apnea among the medically refractory epilepsy patients

Seasonal Variability in Fine Particulate Matter Water Content and Estimated pH over a Coastal Region in the Northeast Arabian Sea

The acidity of atmospheric particles can promote specific chemical processes that result in the production of extra condensed phases from lesser volatile species (secondary fine particulate matter), change the optical and water absorption characteristics of particles, and enhance trace metal solubility that can function as essential nutrients in nutrient-limited environments. In this study, we present an estimated pH of fine particulate matter (FPM) through a thermodynamic model and assess its temporal variability over a coastal location in the northeast Arabian Sea. Here, we have used the chemical composition of FPM (PM2.5) collected during the period between 2017–2019. Chemical composition data showed large variability in water-soluble ionic concentrations (WSIC; range: 2.3–39.9 μg m−3) with higher and lower average values during the winter and summer months, respectively. SO42− ions were predominant among anions, while NH4+ was a major contributor among cations throughout the season. The estimated pH of FPM from the forward and reverse modes exhibits a moderate correlation for winter and summer samples. The estimated pH of FPM is largely regulated by SO42− content and strongly depends on the relative ambient humidity, particularly in the forward mode. Major sources of FPM assessed based on Positive matrix factorization (PMF) and air-mass back trajectory analyses demonstrate the dominance of natural sources (sea salt and dust) during summer months, anthropogenic sources in winter months and mixed sources during the post-monsoon season

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

537-542Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results