Résumé
Objectives: A LSR is a systematic review that is continually updated, incorporating new evidence as it becomes available. They are conducted in research areas where new evidence is constantly emerging on diagnostic methods, treatments, and outcomes. The objective of this study was to understand the current application of LSRs across research areas. Method(s): Embase, MEDLINE, and the Cochrane Database of Systematic Reviews were searched to identify LSRs. Only the most recent update of a LSR was included. Data regarding the indication, intervention, methods, frequency of updates, and funding were extracted. Result(s): Of the 1,243 records identified, 126 LSRs were included for analysis. The first LSR was published in 2015, with a significant increase in the number of LSRs published starting in 2020, coinciding with the COVID-19 pandemic. The most common indication represented by LSRs was COVID-19 (72%), followed by oncology (10%). Other indications with LSRs included chronic pain, traumatic brain injury, and skin disorders, among others. While most oncology LSRs identified interventional randomized-controlled trials (RCTs) (85%), only 54% of COVID-19 LSRs were restricted to interventional studies, including a combination of RCTS and real-world observational studies. Oncology LSRs included common cancers such as prostate, renal, or multiple myeloma. Of the reviews that reported update frequency, 28% planned monthly, 12% yearly, and 12% weekly updates. Only 46% of LSRs were registered. The majority of LSRs were funded by government or research organizations. Objectives of LSRs varied, with most stating the need to maintain up-to-date databases;however, several studies used LSRs to facilitate network meta-analysis or mixed treatment comparisons. Conclusion(s): While LSRs were introduced over five years ago, their frequency increased during the COVID-19 pandemic. Apart from COVID-19, LSRs are commonly used in oncology settings. LSRs provide high-level, relevant, and up-to-date evidence, making them a useful tool for clinical and real-world research.Copyright © 2023
Résumé
The emergence of infectious diseases has put on an alarming threat to human health and progress. The World Health Organization (WHO) claims that infectious diseases continue to spread and emerge and that efforts being made globally to combat newly emerging and drug-resistant infections are jeopardized by a lack of new antimicrobials (2020). Covid- 19, a recent pandemic that has affected the entire world and claimed the lives of 2.7 million people, has hundreds of millions of documented cases (2021). Outbreaks of infectious disease can have detrimental social, political, and economic ramifications, and emerging and overlooking infectious diseases pose a real threat to public health. Since the historic IOM report that emphasized the significance of emerging infectious diseases, much has been taught from previous outbreaks and significant strides have been made. Yet, preparing for pandemics continues to be a significant global challenge. It has been discovered that a wide range of factors, including human behavior and activities, pathogen evolution, poverty, environmental changes, and dynamic human interactions with animals, all contributed to the emergence and spread of infectious diseases. Furthermore, the emergence of pathogens that are resistant to antibiotics has reduced the number of available treatments and caused incurable infections, necessitating the development of novel antibiotics. There is a global need to find novel sources of antibiotics. Natural products produced by endophytic actinobacteria (EA) act as a source of potential new antibiotics. In addition, they seem to be a source of brandnew, potent compounds to fight the rising tide of pathogens with multidrug resistance. Endophytic actinobacteria reside within plant tissues and interact with the host to produce diverse bioactive metabolites. Exploration of endophytic actinobacteria from untouched ecosystems is going on in the search for new bioactive molecules. The current review highlights the isolation and discovery of potent novel bioactive metabolites produced by endophytic actinobacteria associated with diverse ecosystems. © 2023 Nova Science Publishers, Inc.
Résumé
Coronaviridae belongs to an enveloped RNA virus family and is kenned to cause the common cold and sometimes astringent illnesses. The most recently discovered coronavirus is COVID-19, referred to as severe acute respiratory syndrome caused by SARS-CoV-2. Current classification criteria for moderate and severe disease are respiratory rate, oxygen saturation, and PaO2 /FiO2 . These markers are significant but have no COVID-19 specificity. NLR is suggested as a simple marker of the systemic inflammatory response in critically ill patients and is an independent indicator of both short-term and long-termmortality in critical patients. The ease of using NLR as a systemic inflammatory marker and a potential predictor of clinical risk and outcome in critically ill patients reinforce its use in the COVID scenario. The aim of our study was to evaluate NLR as a COVID-19 disease severity marker and to evaluate the role of NLR in COVID-19 disease outcome. We included the demographics and clinical characteristics of 117 admitted patients who were RT-PCR positive for COVID-19. As per age and gender-wise distribution, 74 patients were male, and 43 were female, with a mean age of 49.11 +/- 18.63 years. Mild patients had a mean NLR of 4.76 (2.03 to 7.77), the moderate disease had a mean NLR of 5.21 (2.00 to 9.88), and severe disease had a mean NLR of 6.19 (0.2 to 25) at admission. Our results show a strong relationship between higher NLR values with mortality (AUC = 97.4) with a sensitivity of 92.3% and specificity of 86.6% and is statistically significant. We recommend that NLR can be a quick, inexpensive, accessible, reproducible marker for gauging severity and outcome in COVID-19. Copyright © 2022, Indian Academy of Clinical Medicine. All rights reserved.
Résumé
One of the main contributors to air pollution is particulate matter (PMxy), which causes several Covid-19 related diseases such as respiratory problems and cardiovascular disorders. Therefore, the spatial and temporal trend analysis of particulate matter and the mass concentration of all aerosol particles <= 2.5 mu m in diameter (PM2.5) have become critical to control the risk factors of co-morbidity of a patient. Lockdown plays a significant role in reducing Covid-19 cases as well as air pollution, including particulate matter concentration. This study aims to analyse the effect of the lockdown on controlling air pollution in metropolitan cities in India through various statistical modelling approaches. Most research articles in the literature assume a linear relationship between responses and covariates and take independent and identically distributed error terms in the model, which may not be appropriate for analysing such air pollution data. In this study, a pattern analysis of PM2.5 daily emissions in different main activity zones during 2019 and 2020 was performed. The seasonal effect was also taken into account when measuring the lockdown effect. The PM2.5 values at the unobserved location were predicted using three popular spatial interpolation techniques: (i) inverse distance weight (IDW), (ii) ordinary kriging (OK), and (iii) random forest regression kriging (RFK), and their root mean square error (RMSE) was compared. Subsequently, the spatio-temporal intervention of lock down on air pollution was estimated using the difference-in-difference (DID) estimator. In winter, the transport zones, namely Anand Vihar and ITO airport, were the most affected regions. The northwestern part of Delhi is the most sensitive zone in terms of air pollution. Due to the lockdown, the weekly PM2.5 emission decreased by 62.15%, the mass concentration of all aerosol particles <= 10 mu m in diameter (PM10) decreased by 53.14%, and the air quality index (AQI) improved by 22.40%. A proposal is made to adopt corrective measures to maintain the air pollution index, taking into account the spatial and temporal variability in the responses.
Résumé
Purpose Universities across the USA are facing challenging decision-making problems amid the COVID-19 pandemic. The purpose of this study is to facilitate universities in planning disease mitigation interventions as they respond to the pandemic. Design/methodology/approach An agent-based model is developed to mimic the virus transmission dynamics on campus. Scenario-based experiments are conducted to evaluate the effectiveness of various interventions including course modality shift (from face-to-face to online), social distancing, mask use and vaccination. A case study is performed for a typical US university. Findings With 10%, 30%, 50%, 70% and 90% course modality shift, the number of total cases can be reduced to 3.9%, 20.9%, 35.6%, 60.9% and 96.8%, respectively, comparing against the baseline scenario (no interventions). More than 99.9% of the total infections can be prevented when combined social distancing and mask use are implemented even without course modality shift. If vaccination is implemented without other interventions, the reductions are 57.1%, 90.6% and 99.6% with 80%, 85% and 90% vaccine efficacies, respectively. In contrast, more than 99% reductions are found with all three vaccine efficacies if mask use is combined. Practical implications This study provides useful implications for supporting universities in mitigating transmissions on campus and planning operations for the upcoming semesters. Originality/value An agent-based model is developed to investigate COVID-19 transmissions on campus and evaluate the effectiveness of various mitigation interventions.