A logística humanitária no gerenciamento de desastres naturais: Revisão Sistemática da Literatura (RSL) orientada ao COVID-19

Desastres são cada vez mais frequentes ao redor do mundo e geram perdas materiais e humanas incontáveis, demonstrando a vulnerabilidade da população mundial ao lhe dar com desastres, sejam eles naturais ou causados pelo homem. Ter um bom gerenciamento das operações logísticas e da cadeia de suprimentos após a ocorrência de um desastre é um ponto importante para garantir uma operação eficaz na logística humanitária. A partir do final de 2019, um desastre natural começou a chamar atenção do mundo todo: uma doença que é conhecida como novo Coronavírus (COVID-19 / SARS-CoV-2) que está gerando grandes perdas, tanto materiais quanto imateriais, aos seres humanos. Portanto, é importante verificar e analisar como a logística humanitária pode auxiliar em casos de desastres como este. Mediante a isso, este trabalho aplica a revisão sistemática da literatura (RSL) para desenvolver uma abordagem conceitual, como também apresentar o conceito e a aplicação Logística Humanitária (LH) e Cadeia de Suprimentos Humanitária (CSH) no gerenciamento de desastres naturais mais especificamente orientada a desastres biológicos, com foco no novo Coronavirus. Os resultados apontam que há muitos estudos relacionados a revisões bibliográficas da LH e da CSH e que há uma carência de trabalhos correlacionando COVID-19 a LH e/ou a CSH como forma de auxiliar no combate dos danos que essa doença está causando na vida dos seres humanos. Ao se entender como aplicar a LH e o gerenciamento da CSH aos desastres naturais pode-se tentar aliviar o sofrimento das vítimas.Alternate abstract:Disasters are increasingly frequent around the world and generate uncountable material and human losses, demonstrating the vulnerability of the world population when dealing with disasters, whether natural or man-made. Having a good management of logistics operations and supply chain after a disaster occurs is an important point to ensure an effective operation in humanitarian logistics. As of the end of 2019, a natural disaster started to draw attention worldwide: a disease that is known as new Coronavirus (COVID-19 / SARS-CoV-2) that is generating great losses, both material and immaterial, to humans. Therefore, it is important to verify and analyze how humanitarian logistics can help in cases of disasters like this. Therefore, this paper applies the systematic literature review (SLR) to develop a conceptual approach, as well as to present the concept and application of Humanitarian Logistics (HL) and Humanitarian Supply Chain (HSC) in natural disasters management, more specifically oriented to biological disasters, focusing on the new Coronavirus. The results point out that there are many studies related to literature reviews of LH and CSH and that there is a lack of works correlating COVID-19 to LH and/or CSH as a way to help combat the damage that this disease is causing in the lives of human beings. By understanding how to apply LH and CSH management to natural disasters one can try to alleviate the suffering of the victims.

A Machine Learning Approach for Diagnosing and Identifying Symptoms of COVID-19

Coronavirus 2019 (COVID-19) is a pandemic that hit the world and was responsible for the death of millions and the life disruption of billions of people. One of the most critical challenges faced during the earlier breakthrough of the diseases was identifying symptoms confused with colds, flu, and other common infections. Nevertheless, despite all the effort and research conducted for this purpose, this challenge continues as more strains, variants, and mutations appear. This work presents a solution for this problem based on machine learning classification and variable importance algorithms. A public dataset of 274,957 cases has been classified into typical and COVID-19 cases based on the reported symptoms and other variables. The dataset was used for classifying the reported cases using K-nearest neighbor (KNN), Naïve Bayes, and Decision Trees (DT) algorithms and identifying the significant symptoms that were decisive in classifying the patients using Gini, Information Gain, and Information Gain Ratio algorithms. Naïve Bayes and Decision Trees performed best with a Classification Accuracy (CA) score of 95.2% and 96.3%, respectively. The Naïve Bayes classifier scored an Area Under the Curve (AUC) of 88.75%. In addition, the applied variable importance algorithms identified headache, fever, and sore throat as the most important symptoms. © 2022 IEEE.

Ovarian follicular function is not altered by SARS-CoV-2 infection or BNT162b2 mRNA COVID-19 vaccination.

STUDY QUESTION: Does the immune response to coronavirus disease 2019 (COVID-19) infection or the BNT162b2 mRNA vaccine involve the ovarian follicle, and does it affect its function? SUMMARY ANSWER: We were able to demonstrate anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG in follicular fluid (FF) from both infected and vaccinated IVF patients, with no evidence for compromised follicular function. WHAT IS KNOWN ALREADY: No research data are available yet. STUDY DESIGN, SIZE, DURATION: This is a cohort study, composed of 32 consecutive IVF patients, either infected with COVID-19, vaccinated or non-exposed, conducted between 1 February and 10 March 2021 in a single university hospital-based IVF clinic. PARTICIPANTS/MATERIALS, SETTING, METHODS: A consecutive sample of female consenting patients undergoing oocyte retrieval was recruited and assigned to one of the three study groups: recovering from confirmed COVID-19 (n = 9); vaccinated (n = 9); and uninfected, non-vaccinated controls (n = 14). Serum and FF samples were taken and analyzed for anti-COVID IgG as well as estrogen, progesterone and heparan sulfate proteoglycan 2 concentration, as well as the number and maturity of aspirated oocytes and day of trigger estrogen and progesterone measurements. Main outcome measures were follicular function, including steroidogenesis, follicular response to the LH/hCG trigger, and oocyte quality biomarkers. MAIN RESULTS AND THE ROLE OF CHANCE: Both COVID-19 and the vaccine elicited anti-COVID IgG antibodies that were detected in the FF at levels proportional to the IgG serum concentration. No differences between the three groups were detected in any of the surrogate parameters for ovarian follicle quality. LIMITATIONS, REASONS FOR CAUTION: This is a small study, comprising a mixed fertile and infertile population, and its conclusions should be supported and validated by larger studies. WIDER IMPLICATIONS OF THE FINDINGS: This is the first study to examine the impact of SARS-Cov-2 infection and COVID-19 vaccination on ovarian function and these early findings suggest no measurable detrimental effect on function of the ovarian follicle. STUDY FUNDING/COMPETING INTEREST(S): The study was funded out of an internal budget. There are no conflicts of interest for any of the authors. TRIAL REGISTRATION NUMBER: CinicalTrials.gov registry number NCT04822012.

In silico identification of available drugs targeting cell surface BiP to disrupt SARS-CoV-2 binding and replication: Drug repurposing approach.

AIMS: Cell surface binding immunoglobin protein (csBiP) is predicted to be susceptible to SARS-CoV-2 binding. With a substrate-binding domain (SBD) that binds to polypeptides and a nucleotide-binding domain (NBD) that can initiate extrinsic caspase-dependent apoptosis, csBiP may be a promising therapeutic target for COVID-19. This study aims to identify FDA-approved drugs that can neutralize viral binding and prevent viral replication by targeting the functional domains of csBiP. METHODS: In silico screening of 1999 FDA-approved drugs against the functional domains of BiP were performed using three molecular docking programs to avoid bias from individual docking programs. Top ligands were selected by averaging the ligand rankings from three programs. Interactions between top ligands and functional domains of BiP were analyzed. KEY FINDINGS: The top 10 SBD-binding candidates are velpatasvir, irinotecan, netupitant, lapatinib, doramectin, conivaptan, fenoverine, duvelisib, irbesartan, and pazopanib. The top 10 NBD-binding candidates are nilotinib, eltrombopag, grapiprant, topotecan, acetohexamide, vemurafenib, paritaprevir, pixantrone, azosemide, and piperaquine-phosphate. Among them, Velpatasvir and paritaprevir are antiviral agents that target the protease of hepatitis C virus. Netupitant is an anti-inflammatory drug that inhibits neurokinin-1 receptor, which contributes to acute inflammation. Grapiprant is an anti-inflammatory drug that inhibits the prostaglandin E2 receptor protein subtype 4, which is expressed on immune cells and triggers inflammation. These predicted SBD-binding drugs could disrupt SARS-CoV-2 binding to csBiP, and NBD-binding drugs may falter viral attachment and replication by locking the SBD in closed conformation and triggering apoptosis in infected cells. SIGNIFICANCE: csBiP appears to be a novel therapeutic target against COVID-19 by preventing viral attachment and replication. These identified drugs could be repurposed to treat COVID-19 patients.