ABSTRACT
Current immune treatment directed to avoid viral replication relies mainly in convalescent plasma and monoclonal antibodies (mAbs). No clinical benefit for convalescent plasma has been reported in a meta-analysis and systematic review compared to standard of care. MAbs are recombinant proteins capable to bind with SARS-CoV-2 preventing its entrance into cells. Several mAbs have shown reduction in viral load and/or progression of the disease such as casirivimab-imdevimab, bamlanivimab-etesevimab and sotrovimab. After the apparition of Omicron variant, it has been reported that sotrovimab retained its activity whereas the other two combinations exhibited loss of neutralizing activity. Several aspects as the target population, timing and doses, serological patient status and evolution of variants still require attention, monitorization and further studies for knowledge gaps.
Subject(s)
COVID-19 Drug Treatment , COVID-19 , Spike Glycoprotein, Coronavirus , Antibodies, Monoclonal, Humanized , Antibodies, Neutralizing , Antibodies, Viral , COVID-19/therapy , Humans , Immunization, Passive , Membrane Glycoproteins , Neutralization Tests , SARS-CoV-2 , Viral Envelope Proteins , COVID-19 SerotherapyABSTRACT
Introduction: The molecular mechanisms linked to the pathology of severe COVID-19 and its outcomes are poorly described. Aim(s): To analyze the proteomic profile of bronchial aspirates (BAS) samples from critically ill COVID-19 patients in order to identify factors associated with the disease and its prognosis. Method(s): Multicenter study including 74 critically ill non-COVID-19 and COVID-19 patients. BAS was obtained by bronchoaspiration after invasive mechanical ventilation (IMV) initiation. Proximity extension assay (PEA) technology was used for proteomic profiling. Random forest (RF) statistical models were used to predict the variable importance. Result(s): After adjusting for confounding factors, CST5, NADK, SRPK2 and TGF-alpha showed differences between COVID-19 and non-COVID-19 patients. Reduced levels of ENTPD2 and PTN were observed in non-survivors, even after adjustment. AGR2, NQO2, IL-1alpha, OSM and TRAIL, were the top five strongest predictors for ICU mortality and were used to build a prediction model. PTN (HR=4.00) ENTPD2 (HR=2.14) and the prediction model (HR=6.25) were associated with higher risk of death. In survivors, FCRL1, NTF4 and THOP1 correlated with lung function (DLCO levels) 3-months after hospital discharge. Similar findings were observed for Flt3L and THOP1 and radiological features (TSS). The proteins identified are expressed in immune and non-immune lung cells. A poor control of viral infectivity and an inappropriate reparative response seems to be linked to the disease and fatal outcomes, respectively. Conclusion(s): In critically ill COVID-19 patients, specific proteomic profiles are associated with the pathology, mortality and lung sequelae.
ABSTRACT
Background: Around 80% of patients who developed COVID-19-driven ARDS present lung ailment. There is a lack of knowledge of the mechanisms that mediate the pulmonary outcomes. Aim(s): To characterize the factors linked to diffusion impairment in survivors of severe COVID-19. Method(s): Prospective cohort study including 87 COVID-19-induced ARDS survivors. A complete pulmonary evaluation was performed 3 months after hospital discharge. 364 proteins were quantified using the proximity extension assay (PEA). Partial least square-discriminant analysis (PLS-DA) and random forest (RF) were used for multivariable analyses. Result(s): Moderate to severe diffusion impairment (DLCO<60% predicted) was observed in the 30% of the cohort. 15 proteins were differentially detected [false discovery rate (FDR)<0.05] in the univariate analysis. Pleiotrophin showed the highest differences (fold change=2.22 and FDR=0.001). In continuous analysis, proteins were inversely and independently associated with DLCO, and in some cases showed a robust dose-response relationship. PLS-DA and RF identified proteomic profiles related to the severity of diffusion capacity. Clusters identified were enriched in mediators of cell proliferation and differentiation, tissue remodeling, angiogenesis, coagulation, inflammation, immune response and fibrosis. Proteins are expressed in immune and non-immune lung cells. Conclusion(s): In survivors of COVID-19-driven ARDS, lung dysfunction is linked to plasma factors involved in injury and repair mechanisms. The host proteomic profile provides a novel understanding of post-acute sequelae and may be source of therapeutic strategies and biomarkers.
ABSTRACT
La infección por el virus SARS-CoV-2, denominada COVID-19 (COronaVIrus Disease 19), fue detectada inicialmente en China en diciembre 2019, y posteriormente se ha diseminado rápidamente por todo el mundo, hasta el punto de que el 11 de marzo la Organización Mundial de la Salud declaró que el brote podría definirse como pandemia. La COVID-19 presenta un cuadro que oscila desde episodios leves seudogripales a otros graves e incluso potencialmente mortales debido, sobre todo, a insuficiencia respiratoria aguda. Es frecuente el ingreso de estos pacientes en nuestros servicios de Medicina Intensiva en relación con un síndrome de distrés respiratorio agudo. La falta de un tratamiento con evidencia científica ha llevado al empleo de diferentes pautas terapéuticas, en muchas ocasiones, con modificaciones rápidas de los protocolos. Recientes revisiones en revistas de prestigio han destacado la falta de terapias probadas y la necesidad de ensayo clínicos que permitan establecer pautas de tratamiento claras y objetivas. Este documento tiene por objeto ofrecer una actualización de la terapia que se está aplicando en la actualidad, y una ayuda en la asistencia diaria, sin pretender sustituir los protocolos adoptados en cada centro.
ABSTRACT
The outbreak of the COVID-19 pandemic has caused a notable challenge to the well-being of people all around the globe. In such times, it is of foremost importance to analyze the information posted by people on social media. In this study, a Twitter-based dataset related to COVID-19 has been analyzed, and the effect of the pandemic on societal behavior has been revealed. Tweets have been hydrated and pre-processed using the NLTK toolkit to find the most frequently posted COVID-related words. This research can help identify the social response of people to the Pandemic, realizing what people are majorly concerned about and extracting knowledge about the daily trend of sentiments around the world. It has been concluded from our analysis that rather than the expected negative trend in the use of COVID-19 terms on a daily basis, more positive figurative language has been used in the posted tweets.
ABSTRACT
The COVID-19 pandemic has led to the admission of a high number of patients to the ICU, generally due to severe respiratory failure. Since the appearance of the first cases of SARS-CoV-2 infection, at the end of 2019, in China, a huge number of treatment recommendations for this entity have been published, not always supported by sufficient scientific evidence or with methodological rigor necessary. Thanks to the efforts of different groups of researchers, we currently have the results of clinical trials, and other types of studies, of higher quality. We consider it necessary to create a document that includes recommendations that collect this evidence regarding the diagnosis and treatment of COVID-19, but also aspects that other guidelines have not considered and that we consider essential in the management of critical patients with COVID-19. For this, a drafting committee has been created, made up of members of the SEMICYUC Working Groups more directly related to different specific aspects of the management of these patients.
ABSTRACT
The COVID-19 pandemic has led to the admission of a high number of patients to the ICU, generally due to severe respiratory failure. Since the appearance of the first cases of SARS-CoV-2 infection, at the end of 2019, in China, a huge number of treatment recommendations for this entity have been published, not always supported by sufficient scientific evidence or with methodological rigor necessary. Thanks to the efforts of different groups of researchers, we currently have the results of clinical trials, and other types of studies, of higher quality. We consider it necessary to create a document that includes recommendations that collect this evidence regarding the diagnosis and treatment of COVID-19, but also aspects that other guidelines have not considered and that we consider essential in the management of critical patients with COVID-19. For this, a drafting committee has been created, made up of members of the SEMICYUC Working Groups more directly related to different specific aspects of the management of these patients.
Subject(s)
COVID-19 , Critical Illness/therapy , Humans , Intensive Care Units , Pandemics , SARS-CoV-2ABSTRACT
Industrial Big Data (IBD) and Artificial Intelligence (AI) are propelling the new era of manufacturing - smart manufacturing. Manufacturing companies can competitively position themselves amongst the most advanced and influential companies by successfully implementing Quality 4.0 practices. Despite the global impact of COVID-19 and the low deployment success rate, industrialization of the AI mega-trend has dominated the business landscape in 2020. Although these technologies have the potential to advance quality standards, it is not a trivial task. A significant portion of quality leaders do not yet have a clear deployment strategy and universally cite difficulty in harnessing such technologies. The lack of people power is one of the biggest challenges. From a career development standpoint, the higher-educated employees (such as engineers) are the most exposed to, and thus affected by, these new technologies. 79% of young professionals have reported receiving training outside of formal schooling to acquire the necessary skills for Industry 4.0. Strategically investing in training is thus important for manufacturing companies to generate value from IBD and AI. Following the path traced by Six Sigma, this article presents a certification curricula for Green, Black, and Master Black Belts. The proposed curriculum combines six areas of knowledge: statistics, quality, manufacturing, programming, learning, and optimization. These areas, along with an ad hoc 7-step problem solving strategy, must be mastered to obtain a certification. Certified professionals will be well positioned to deploy Quality 4.0 technologies and strategies. They will have the capacity to identify engineering intractable problems that can be formulated as machine learning problems and successfully solve them. These certifications are an efficient and effective way for professionals to advance in their career and thrive in Industry 4.0. © 2021 The Authors. Published by Elsevier B.V.
ABSTRACT
The COVID-19 pandemic has led to the admission of a high number of patients to the ICU, generally due to severe respiratory failure. Since the appearance of the first cases of SARS-CoV-2 infection, at the end of 2019, in China, a huge number of treatment recommendations for this entity have been published, not always supported by sufficient scientific evidence or with methodological rigor necessary. Thanks to the efforts of different groups of researchers, we currently have the results of clinical trials, and other types of studies, of higher quality. We consider it necessary to create a document that includes recommendations that collect this evidence regarding the diagnosis and treatment of COVID-19, but also aspects that other guidelines have not considered and that we consider essential in the management of critical patients with COVID-19. For this, a drafting committee has been created, made up of members of the SEMICYUC Working Groups more directly related to different specific aspects of the management of these patients.
ABSTRACT
Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of te.