The Relationship between Routine Blood Parameters and the Prognosis of COVID-19 Patients in the Emergency Department

Imidacloprid-induced lung injury in mice: Activation of the PI3K/AKT/NF-κB signaling pathway via TLR4 receptor engagement

Significant impairment of pulmonary function has been demonstrated through long-term exposure to neonicotinoid insecticides, such as imidacloprid (IMI). However, the underlying mechanisms of lung injury induced by IMI remain unclear. In this study, a mouse model of IMI-induced pulmonary injury was established, and the toxicity and lung damage were assessed through mouse body weight, organ index, hematological parameters, and histopathological analysis of lung tissues. Furthermore, metabolomics and transcriptomics techniques were employed to explore the mechanistic aspects. Results from the toxicity assessments indicated that mouse body weight was significantly reduced by IMI, organ index was disturbed, and hematological parameters were disrupted, resulting in pulmonary injury. The mechanistic experimental results indicate that the differences in metabolites and gene expression in mouse lungs could be altered by IMI. Validation of the results through combined analysis of metabolomics and transcriptomics revealed that the mechanism by which IMI induces lung injury in mice might be associated with the activation of the TLR4 receptor, thereby activating the PI3K/AKT/NF-κB signaling pathway to induce inflammation in mouse lungs. This study provided valuable insights into the mechanisms underlying IMI-induced pulmonary damage, potentially contributing to the development of safer pest control strategies. The knowledge gained served as a robust scientific foundation for the prevention and treatment of IMI-related pulmonary injuries.

Evaluation of hematological changes and immune response biomarkers as a prognostic factor in critical patients with COVID-19

COVID-19 disease has been a challenge for health systems worldwide due to its high transmissibility, morbidity, and mortality. Severe COVID-19 is associated with an imbalance in the immune response, resulting in a cytokine storm and a hyperinflammation state. While hematological parameters correlate with prognosis in COVID patients, their predictive value has not been evaluated specifically among those severely ill. Therefore, we aim to evaluate the role of hematological and immune response biomarkers as a prognostic factor in critically ill patients with COVID-19 admitted to the intensive care unit. From May 2020 to July 2021, a retrospective cohort study was conducted in a reference hospital in Manaus, which belongs to the Brazilian public health system. This study was carried out as single-center research. Clinical and laboratory parameters were analyzed to evaluate the association with mortality. We also evaluated the role of neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein-to-lymphocyte ratio (CLR). We gathered information from medical records, as well as from prescriptions and forms authorizing the use of antimicrobial medications. During the study period, 177 patients were included, with a mean age of 62.58 ± 14.39 years. The overall mortality rate was 61.6%. Age, mechanical ventilation (MV) requirement, leukocytosis, neutrophilia, high c-reactive protein level, NLR, and CLR showed a statistically significant association with mortality in the univariate analysis. In the multivariate logistic regression analysis, only MV (OR 35.687, 95% CI: 11.084-114.898, p< 0.001) and NLR (OR 1.026, 95% CI: 1.003-1.050, p = 0.028) remained statistically associated with the outcome of death (AUC = 0.8096). While the need for mechanical ventilation is a parameter observed throughout the hospital stay, the initial NLR can be a primary risk stratification tool to establish priorities and timely clinical intervention in patients with severe COVID-19 admitted to the ICU.

Anti-Viral Surfaces in the Fight against the Spread of Coronaviruses

This review is conducted against the background of nanotechnology, which provides us with a chance to effectively combat the spread of coronaviruses, and which primarily concerns polyelectrolytes and their usability for obtaining protective function against viruses and as carriers for anti-viral agents, vaccine adjuvants, and, in particular, direct anti-viral activity. This review covers nanomembranes in the form of nano-coatings or nanoparticles built of natural or synthetic polyelectrolytes--either alone or else as nanocomposites for creating an interface with viruses. There are not a wide variety of polyelectrolytes with direct activity against SARS-CoV-2, but materials that are effective in virucidal evaluations against HIV, SARS-CoV, and MERS-CoV are taken into account as potentially active against SARS-CoV-2. Developing new approaches to materials as interfaces with viruses will continue to be relevant in the future.

Clinical Significance and Diagnostic Utility of NLR, LMR, PLR and SII in the Course of COVID-19: A Literature Review

Nowadays, society is increasingly struggling with infectious diseases that are characterized by severe course and even death. Recently, the whole world has faced the greatest epidemiological threat, which is COVID-19 caused by SARS CoV-2 virus. SARS CoV-2 infection is often accompanied by severe inflammation, which can lead to the development of different complications. Consequently, clinicians need easily interpreted and effective markers of inflammation that can predict the efficacy of the treatment and patient prognosis. Inflammation is associated with changes in many biochemical and hematological parameters, including leukocyte counts and their populations. In COVID-19, changes in leukocytes count populations such as neutrophils, lymphocytes or monocytes are observed. The numerous research confirm that indicators like neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelets-to-lymphocyte ratio (PLR) and systemic inflammatory index (SII) may prove effective in assessment patient prognosis and choosing optimal therapy. Therefore, in this review, we would like to summarize the latest knowledge about the diagnostic utility of systemic inflammatory ratios - NLR, LMR, PLR and SII in patients with COVID-19. We focused on the papers evaluating the diagnostic utility of inflammatory ratios using ROC curve published in the recent 3 years. Identification of biomarkers associated with inflammation would help the selection of patients with severe course of COVID-19 and high risk of death.

Jusvinza, an anti-inflammatory drug derived from the human heat-shock protein 60, for critically ill COVID-19 patients. An observational study

This paper presents the results of an observational and retrospective study on the therapeutic effects of Jusvinza, an immunomodulatory peptide with anti-inflammatory properties for critically ill COVID-19 patients. This peptide induces regulatory mechanisms on the immune response in experimental systems and in patients with Rheumatoid Arthritis. Exploratory research in COVID-19 patients revealed that Jusvinza promotes clinical and radiological improvement. The aim of this study is to describe the clinical outcome and variations of several inflammatory biomarkers in a cohort of critically ill COVID-19 patients, divided into two groups during the observational research: one group received Jusvinza and the other did not. Research physicians extracted the patients´ data from their hospital's clinical records. The study analyzed 345 medical records, and 249 records from critically ill patients were included. The data covered the demographic characteristics, vital signs, ventilatory parameters and inflammatory biomarkers. Survival outcome was significantly higher in the group receiving Jusvinza (90.4%) compared to the group without Jusvinza (39.5%). Furthermore, in patients treated with Jusvinza there was a significant improvement in ventilatory parameters and a reduction in inflammation and coagulation biomarkers. Our findings show that Jusvinza could control the extent of inflammation in COVID-19 patients. This study indicates that Jusvinza is a helpful drug for the treatment of diseases characterized by hyperinflammation.

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model's performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.

Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section

Background. COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a virus belonging to the Coronaviridae family. This disease has spread rapidly around the world and soon became an international public health emergency leading to an unpredicted pressure on the hospital emergency units. Early routine blood biomarkers could be key predicting factors of COVID-19 morbidity and mortality as suggested for C-reactive protein (CRP), IL-6, prothrombin and D-dimer. This study aims to identify other early routine blood biomarkers for COVID-19 severity prediction disclosed directly into the emergency section. Methods. Our research was conducted on 156 COVID-19 patients hospitalized at the Sapienza University Hospital “Policlinico Umberto I” of Rome, Italy, between March 2020 and April 2020 during the paroxysm’s initial phase of the pandemic. In this retrospective study, patients were divided into three groups according to their outcome: (1) emergency group (patients who entered the emergency room and were discharged shortly after because they did not show severe symptoms); (2) intensive care unit (ICU) group (patients who attended the ICU after admission to the emergency unit); (3) the deceased group (patients with a fatal outcome who attended the emergency and, afterward, the ICU units). Routine laboratory tests from medical records were collected when patients were admitted to the emergency unit. We focused on Aspartate transaminase (AST), Alanine transaminase (ALT), Lactate dehydrogenase (LDH), Creatine kinase (CK), Myoglobin (MGB), Ferritin, CRP, and D-dimer. Results. As expected, ANOVA data show an age morbidity increase in both ICU and deceased groups compared with the emergency group. A main effect of morbidity was revealed by ANOVA for all the analyzed parameters with an elevation between the emergency group and the deceased group. Furthermore, a significant increase in LDH, Ferritin, CRP, and D-dimer was also observed between the ICU group and the emergency group and between the deceased group and ICU group. Receiver operating characteristic (ROC) analyses confirmed and extended these findings. Conclusions. This study suggests that the contemporaneous presence of high levels of LDH, Ferritin, and as expected, CRP, and D-dimer could be considered as potential predictors of COVID-19 severity and death.