Fighting the Progress of COVID-19 by Enhancing Immunity: A Review of Traditional Sudanese Natural Products Containing Immune-Boosting Elements

The World Health Organization has classified the coronavirus disease outbreak as a worldwide pandemic as a result of the COVID-19 expansion. According to medical professionals, individuals with strong immunity often outlive infections more frequently than those with poor immunity. The COVID-19 pandemic has prompted the need for novel approaches to treating the illness and its symptoms. Natural products from plants are increasingly being seen favorably in comparison to synthetic ones in the fight against diseases. As a result, in order to avoid contracting any unanticipated illnesses, individuals must increase their immunity by eating more dietary supplements and by taking drugs that have immune-boosting properties. This review aimed to give a general overview of some traditional Sudanese foods and drinks that are rich in immune-boosting elements, and accordingly, they could be safely recommended as an adjuvant dietary supplement to improve the immune system\u27s ability to fight such infections as COVID-19. Also, this review aims to bring attention to the fact that immune boosters may be found in natural sources, which will help pharmaceutical companies by taking some of the load off of them. Electronic databases, including Google Scholar, Scopus, and the Web of Science, were searched for relevant material. The selected articles underwent independent eligibility and information extraction reviews. The review focused on certain traditional Sudanese herbs and their derivatives that are rich in immune-stimulating vitamins and minerals and therefore could possibly be recommended as immune-boosting dietary supplements to help fight COVID-19. This review highlights the fact that the pharmaceutical sector, especially community and hospital pharmacists, could play a vital role in supporting the healthcare system by encouraging their communities to add plants and their products that are rich in immune-boosting vitamins and minerals to their diet

Strategy for the management of diabetic macular edema: the European Vitreo-Retinal Society macular edema study

Objective. To compare the efficacy of different therapies in the treatment of diabetic macular edema (DME). Design. Nonrandomized, multicenter clinical study. Participants. 86 retina specialists from 29 countries provided clinical information on 2,603 patients with macular edema including 870 patients with DME. Methods. Reported data included the type and number of treatment(s) performed, the pre-and posttreatment visual acuities, and other clinical findings.The results were analyzed by the French INSEE (National Institute of Statistics and Economic Studies). Main Outcome Measures. Mean change of visual acuity and mean number of treatments performed. Results.The change in visual acuity over time in response to each treatment was plotted in second order polynomial regression trend lines. Intravitreal triamcinolone monotherapy resulted in some improvement in vision. Treatmentwith threshold or subthreshold grid laser also resulted in minimal vision gain. Anti-VEGF therapy resulted in more significant visual improvement. Treatment with pars plana vitrectomy and internal limiting membrane (ILM) peeling alone resulted in an improvement in vision greater than that observed with anti-VEGF injection alone. In our DME study, treatment with vitrectomy and ILM peeling alone resulted in the better visual improvement compared to other therapies

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers

Performance of text classification models tends to drop over time due to changes in data, which limits the lifetime of a pretrained model. Therefore an ability to predict a model’s ability to persist over time can help design models that can be effectively used over a longer period of time. In this paper, we provide a thorough discussion into the problem, establish an evaluation setup for the task. We look at this problem from a practical perspective by assessing the ability of a wide range of language models and classification algorithms to persist over time, as well as how dataset characteristics can help predict the temporal stability of different models. We perform longitudinal classification experiments on three datasets spanning between 6 and 19 years, and involving diverse tasks and types of data. By splitting the longitudinal datasets into years, we perform a comprehensive set of experiments by training and testing across data that are different numbers of years apart from each other, both in the past and in the future. This enables a gradual investigation into the impact of the temporal gap between training and test sets on the classification performance, as well as measuring the extent of the persistence over time. Through experimenting with a range of language models and algorithms, we observe a consistent trend of performance drop over time, which however differs significantly across datasets; indeed, datasets whose domain is more closed and language is more stable, such as with book reviews, exhibit a less pronounced performance drop than open-domain social media datasets where language varies significantly more. We find that one can estimate how a model will retain its performance over time based on (i) how well the model performs over a restricted time period and its extrapolation to a longer time period, and (ii) the linguistic characteristics of the dataset, such as the familiarity score between subsets from different years. Findings from these experiments have important implications for the design of text classification models with the aim of preserving performance over time

Opinions are Made to be Changed: Temporally Adaptive Stance Classification

Given the rapidly evolving nature of social media and people's views, word usage changes over time. Consequently, the performance of a classifier trained on old textual data can drop dramatically when tested on newer data. While research in stance classification has advanced in recent years, no effort has been invested in making these classifiers have persistent performance over time. To study this phenomenon we introduce two novel large-scale, longitudinal stance datasets. We then evaluate the performance persistence of stance classifiers over time and demonstrate how it decays as the temporal gap between training and testing data increases. We propose a novel approach to mitigate this performance drop, which is based on temporal adaptation of the word embeddings used for training the stance classifier. This enables us to make use of readily available unlabelled data from the current time period instead of expensive annotation efforts. We propose and compare several approaches to embedding adaptation and find that the Incremental Temporal Alignment (ITA) model leads to the best results in reducing performance drop over time

Planning Cooperation in Inter-Organizational Systems

The purpose of this chapter is to present a systematic reasoning framework (called e-Planning) to plan cooperation between organizations in a network. Thorough assessment of opportunities for and obstacles to cooperation is of paramount importance, as setting up electronic networks usually requires considerable up-front investments in information technology (IT) specific for this cooperation. E-Planning offers an action plan for decision makers to determine with whom to establish cooperation first, and on which topic to cooperate more closely. Following a method for critical problem solving, e-Planning provides guidance to analyze different potential partners and to reason about obstacles to and opportunities for cooperation. To illustrate and validate the framework, it is applied in the area of cooperation between customs organizations of member states of the European Union. Applying the reasoning framework in practice revealed several potential benefits, such as fast and successful assessment of cooperation needs that result in increased re-use of knowledge and software applications. In particular, this way of reasoning may prove to help decision makers cut down unnecessary expenses by, for instance, avoiding duplicated projects

Comparison of Oleocanthal-Low EVOO and Oleocanthal against Amyloid-β and Related Pathology in a Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is characterized by several pathological hallmarks, including the deposition of amyloid-β (Aβ) plaques, neurofibrillary tangles, blood–brain barrier (BBB) dysfunction, and neuroinflammation. Growing evidence support the neuroprotective effects of extra-virgin olive oil (EVOO) and oleocanthal (OC). In this work, we aimed to evaluate and compare the beneficial effects of equivalent doses of OC-low EVOO (0.5 mg total phenolic content/kg) and OC (0.5 mg OC/kg) on Aβ and related pathology and to assess their effect on neuroinflammation in a 5xFAD mouse model with advanced pathology. Homozygous 5xFAD mice were fed with refined olive oil (ROO), OC-low EVOO, or OC for 3 months starting at the age of 3 months. Our findings demonstrated that a low dose of 0.5 mg/kg EVOO-phenols and OC reduced brain Aβ levels and neuroinflammation by suppressing the nuclear factor-κB (NF-κB) pathway and reducing the activation of NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasomes. On the other hand, only OC suppressed the receptor for advanced glycation endproducts/high-mobility group box 1 (RAGE/HMGB1) pathway. In conclusion, our results indicated that while OC-low EVOO demonstrated a beneficial effect against Aβ-related pathology in 5xFAD mice, EVOO rich with OC could provide a higher anti-inflammatory effect by targeting multiple mechanisms. Collectively, diet supplementation with EVOO or OC could prevent, halt progression, and treat AD