Changing Face of Inflammatory Activation in Complex Coronary Artery Disease during the COVID-19 Pandemic.

INTRODUCTION: The COVID-19 pandemic has changed the immunological status of the population, indicating increased activation. The aim of the study was to compare the degree of inflammatory activation in patients admitted for surgical revascularization in the period before and during the COVID-19 pandemic. MATERIALS AND METHODS: This retrospective analysis included an analysis of inflammatory activation assessed on the basis of whole blood counts in 533 patients (435 (82%) male and 98 (18%) female) with a median age of 66 (61-71) years who underwent surgical revascularization, including 343 and 190 patients operated on in 2018 and 2022, respectively. RESULTS: The compared groups were matched by propensity score matching analysis, obtaining 190 patients in each group. Significantly higher values of preoperative monocyte count (p = 0.015), monocyte-to-lymphocyte ratio (p = 0.004) and systemic inflammatory response index (p = 0.022) were found in the during-COVID subgroup. The perioperative and 12-month mortality rates were comparable, with 1% (n = 4) in 2018 vs. 1% (n = 2) in 2022 (p = 0.911), and 5.6 % (n = 11 patients) vs. 7% (n = 13 patients) (p = 0.413), in the pre-COVID and during-COVID subgroups, respectively. CONCLUSIONS: Simple whole blood analysis in patients with complex coronary artery disease performed before and during the COVID-19 pandemic indicates excessive inflammatory activation. However, the immune variation did not interfere with one-year mortality rate after surgical revascularization.

Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania.

COVID-19 revealed some major weaknesses and threats that are related to the level of territorial development. In Romania, the manifestation and the impact of the pandemic were not homogenous, which was influenced, to a large extent, by a diversity of sociodemographic, economic, and environmental/geographic factors. The paper is an exploratory analysis focused on selecting and integrating multiple indicators that could explain the spatial differentiation of COVID-19-related excess mortality (EXCMORT) in 2020 and 2021. These indicators include, among others, health infrastructure, population density and mobility, health services, education, the ageing population and distance to the closest urban center. We analyzed the data from local (LAU2) and county level (NUTS3) by applying multiple linear regression and geographically weighted regression models. The results show that mobility and lower social distancing were far more critical factors for higher mortality than the intrinsic vulnerability of the population, at least in the first two years of COVID-19. However, the highly differentiated patterns and specificities of different areas of Romania resulting from the modelling of EXCMORT factors drive to the conclusion that the decision-making approaches should be place-specific in order to have more efficiency in case of pandemics.

Characterization and Source Apportionment of PM in Handan—A Case Study during the COVID-19

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10;the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. © 2023 by the authors.

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach.

The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models-specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)-were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission

COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.

Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique.

Diabetes is one of the most common and serious diseases affecting human health. Early diagnosis and treatment are vital to prevent or delay complications related to diabetes. An automated diabetes detection system assists physicians in the early diagnosis of the disease and reduces complications by providing fast and precise results. This study aims to introduce a technique based on a combination of multiple linear regression (MLR), random forest (RF), and XGBoost (XG) to diagnose diabetes from questionnaire data. MLR-RF algorithm is used for feature selection, and XG is used for classification in the proposed system. The dataset is the diabetic hospital data in Sylhet, Bangladesh. It contains 520 instances, including 320 diabetics and 200 control instances. The performance of the classifiers is measured concerning accuracy (ACC), precision (PPV), recall (SEN, sensitivity), F1 score (F1), and the area under the receiver-operating-characteristic curve (AUC). The results show that the proposed system achieves an accuracy of 99.2%, an AUC of 99.3%, and a prediction time of 0.04825 seconds. The feature selection method improves the prediction time, although it does not affect the accuracy of the four compared classifiers. The results of this study are quite reasonable and successful when compared with other studies. The proposed method can be used as an auxiliary tool in diagnosing diabetes.

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries.

The use of titanium dioxide (TiO2) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO2 nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO2 into the tissues, and the results showed that the kidney had a significant TiO2 buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO2 NP treatment may cause kidney damage in experimental samples.

Major ions and potentially toxic elements in atmospheric precipitation during the COVID-19 lockdown in Moscow megacity

For the first time, the change in the physicochemical properties, ions composition, concentrations, and ratio of soluble and insoluble potentially toxic elements (PTEs), as well as sources contribution to the content of PTEs using principal component analysis with multiple linear regression (PCA-MLR) were determined in the Moscow precipitation before, during and after the lockdown (January–July 2020). The impact of the lockdown on the precipitation composition was ambivalent. The decrease in the precipitation pollution with PTEs (by 10–99% for soluble PTEs and 9–61% for insoluble ones) was caused by the purification of the atmosphere from aerosols during their long-term washing out by precipitation and a decrease in anthropogenic emissions. Air advection to Moscow from the suburbs, where wood, coal, household and agricultural wastes were burned, on the contrary, contributed to the growth of precipitation pollution with insoluble P, Pb, Cd, soluble P, Ag, Pb, Sb, As, Cd, as well as [Cl−] and [K+]. After the lockdown, the restoration of the level of precipitation pollution by PTEs occurred gradually due to the time lag between the increase in atmospheric pollution and the washing out of aerosols by precipitation, as well as dilution by exceptional rainfall amount. Faster restoration rates of insoluble PTEs compared to soluble ones are associated with the rapid increase in the activity of the urban source (road and construction dust, industrial and traffic emissions). The lifting lockdown restrictions reduced the contribution of industrial sources to the content of soluble PTEs forms (from 38–66% to 6%) due to an increase in the contribution of road dust and non-exhaust emissions, soil particles resuspension, waste and fuel combustion, and vehicle emissions. A decrease in the contribution of vehicle emissions, road dust resuspension, and construction dust to the content of insoluble PTEs from winter to summer due to the lockdown influence and a large amount of precipitation in late spring and early summer was confirmed. The results highlighted the need for further studies of the chemical composition and properties of precipitation in the city in similar periods for the correct separation of the influence of social, economic, emission, meteorological, and physicochemical factors on the content and the ratio of PTEs forms.

Hemograms and serial hemogram-derived ratios in survivors and non-survivors of COVID-19 in Campinas, Brazil.

Introduction: The hemogram and hemogram-derivative ratios (HDRs) are becoming markers of the severity and mortality of COVID-19. We evaluated the hemograms and serial weekly HDRs [neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), neutrophil-platelet ratio (NPR) and systemic immune-inflammatory index (SII)] in the survivors and non-survivors of COVID-19. Methods: We retrospectively reviewed the medical notes and serial hemograms of real-time reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 adults hospitalized from April 2020 to March 2021 from the time of diagnosis to the 3rd week of diagnosis. Results: Of the 320 adults, 257 (80.3%) were survivors and had a lower mean age than the non-survivors (57.73 vs. 64.65 years, p < 0.001). At diagnosis, the non-survivors had lower lymphocyte (p = 0.002) and basophil (p = 0.049) counts and the hematocrit showed a p-value (Is this what you meant???) of 0.021); higher NLR (p < 0.001), PLR (p = 0.047), NPR (p = 0.022) and SII (p = 0.022). Using general linear models, the survivors and non-survivors showed significant variations with weekly lymphocyte count (p < 0.001), neutrophil count (p = 0.005), NLR (p = 0.009), MLR (p = 0.010) and PLR (p = 0.035). All HDRs remained higher in the non-survivors in the 2nd week and 3rd week of diagnosis and the HDRs were higher in the intubated patients than in the non-intubated patients. The NLR and SII were more efficient predictors of mortality in COVID-19 patients. Conclusions: This study shows that serial lymphocyte and neutrophil counts, NLR, PLR, MLR, NPR and SII could serve as good and easily accessible markers of severity and predictors of outcomes in COVID-19 patients and should be used for the monitoring of treatment response.

Platelet to Lymphocyte Ratio and Neutrophil to Lymphocyte Ratio as a Severity Marker in Moderate to Severe COVID-19 Patients

The world is facing COVID-19 pandemic which has created havoc amongst the mankind. It has created huge burden on health care facilities. The COVID-19 disease is caused by a newly emerged mutant of corona virus that is SARS-CoV-2. The virus is highly contagious and infects through respiratory route. It invades the respiratory tract mainly lungs causing coronavirus pneumonia. Patients usually present with fever, non-productive cough, breathlessness, myalgia, fatigue. In severe cases, disease can rapidly progress to ARDS (Acute Respiratory Distress Syndrome), septic shock, MODS (Multi-Organ Dysfunction Syndrome). Death may occur due to the complications. Furthermore, early diagnosis of severe cases and early interventions help in decreasing the burden on intensive healthcare facilities. HRCT scans are being used to assess the disease severity and CT score were calculated which was graded as mild, moderate and severe with score 0-8, 9-15 and 16-25 respectively. But this is highly expensive for general population of a developing country like India. Interleukins, D-dimer, ferritin, pro-calcitonin tests have also been used to assess the severity but again they pose a financial constraint for the population. So we needed a basic investigation which could let us assess the severity of disease and prognosis of the patient early for effective and early management of the patient. This might help provide better intensive care management for the patients at early stage and decrease the morbidity and mortality in COVID-19 patients. We have tried to unfold the CBC as prognostic marker for COVID-19 patients.

Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients.

BACKGROUND: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of thromboembolic events and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)] and the CT Severity Score in acute limb ischemia (ALI) risk, intensive unit care (ICU) admission, and mortality in COVID-19 patients.; Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients older than 18 years of age with a diagnosis of COVID-19 infection, confirmed through real time-polymerase chain reaction (RT-PCR), and admitted to the County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST "George Emil Palade" of Targu Mures, Romania between January 2020 and December 2021. RESULTS: Non-Survivors and "ALI" patients were associated with higher incidence of cardiovascular disease [atrial fibrillation (AF) p = 0.0006 and p = 0.0001; peripheral arterial disease (PAD) p = 0.006 and p < 0.0001], and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, PLR, SII, SIRI, AISI, and the CT Severity Score independent predictor of adverse outcomes for all recruited patients (all p < 0.0001). Moreover, the presence of AF and PAD was an independent predictor of ALI risk and mortality. CONCLUSIONS: According to our findings, higher MLR, NLR, PLR, SII, SIRI, AISI, and CT Severity Score values at admission strongly predict ALI risk, ICU admission, and mortality. Moreover, patients with AF and PAD had highly predicted ALI risk and mortality but no ICU admission.

Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients' Mortality.

BACKGROUND: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. METHODS: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST "George Emil Palade" of Targu Mures, Romania between January 2021 and December 2021. RESULTS: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. CONCLUSIONS: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.

QSAR study of a series of peptidomimetic derivatives towards MERS-CoV inhibitors

In this study, we report the quantitative structure activity relationships (QSAR) investigation to determine the relationship between the anti-MERS-CoV activity and a set of chemical descriptors computed using ChemSketch, MarvinSketch and ChemOffice software. Herein, the principal components analysis (PCA), multiple linear regression (MLR) and multiple non-linear regression (MNLR) methods were used with the intention to obtain a reliable QSAR model with good predictive capacity. The original data set of 43 peptidomimetic compounds was randomly divided into training and test set of 35 and 8 compounds, respectively. The values obtained by MLR and MNLR for the determination coefficient are 0.777 and 0.813, respectively. The predictive ability of the MLR model was assessed by external validation using the eight compounds of the test set with predicted determination coefficients R2test of 0.655.

Toward a secure global contact tracing app for Covid-19

The outbreak of the covid-19 pandemic has devastated many sectors of each country and led to the development of contact tracing applications for controlling its spread. Contact tracing apps have been promoted to track infected contacts. However, contact tracing has gained significant debate due to its security and privacy concerns. The goal of this study is to examine the most popular contact tracing apps, their impact on pandemic control, as well security and privacy concerns. The multivocal literature review (MLR) brings the results from the state-of-the-art literature. We extracted 23 studies from both formal and grey literature to achieve the research objectives and found several security and privacy threats in the existing contact tracing applications. Additionally, the best practices to address these threats were also identified. We further proposed a preliminary structure of a secure global contact tracing app using blockchain technology © 2022 ACM.

A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients.

BACKGROUND: Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention. METHODS: This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients' outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model. RESULTS: The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88-0.98) and AUC 0.90 (95% CI 0.85-0.96) for classic regression models, respectively. CONCLUSIONS: Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

Prediction of electricity energy consumption including COVID-19 precautions using the hybrid MLR-FFANN optimized with the stochastic fractal search with fitness distance balance algorithm.

The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression-feedforward artificial neural network (MLR-FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID-19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR-FFANN approach was to simultaneously optimize the ß polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR-FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID-19 precautions.

MLR-1023 Treatment in Mice and Humans Induces a Thermogenic Program, and Menthol Potentiates the Effect.

A glucose-lowering medication that acts by a different mechanism than metformin, or other approved diabetes medications, can supplement monotherapies when patients fail to meet blood glucose goals. We examined the actions underlying the effects of an insulin sensitizer, tolimidone (MLR-1023) and investigated its effects on body weight. Diet-induced obesity (CD1/ICR) and type 2 diabetes (db/db) mouse models were used to study the effect of MLR-1023 on metabolic outcomes and to explore its synergy with menthol. We also examined the efficacy of MLR-1023 alone in a clinical trial (NCT02317796), as well as in combination with menthol in human adipocytes. MLR-1023 produced weight loss in humans in four weeks, and in mice fed a high-fat diet it reduced weight gain and fat mass without affecting food intake. In human adipocytes from obese donors, the upregulation of Uncoupling Protein 1, Glucose (UCP)1, adiponectin, Glucose Transporter Type 4 (GLUT4), Adipose Triglyceride Lipase (ATGL), Carnitine palmitoyltransferase 1 beta (CPT1ß), and Transient Receptor Potential Melastin (TRPM8) mRNA expression suggested the induction of thermogenesis. The TRPM8 agonist, menthol, potentiated the effect of MLR-1023 on the upregulation of genes for energy expenditure and insulin sensitivity in human adipocytes, and reduced fasting blood glucose in mice. The amplification of the thermogenic program by MLR-1023 and menthol in the absence of adrenergic activation will likely be well-tolerated, and bears investigation in a clinical trial.

Dynamic Changes of the Neutrophil-to-Lymphocyte Ratio, Systemic Inflammation Index, and Derived Neutrophil-to-Lymphocyte Ratio Independently Predict Invasive Mechanical Ventilation Need and Death in Critically Ill COVID-19 Patients.

BACKGROUND: Hematological indices can predict disease severity, progression, and death in patients with coronavirus disease-19 (COVID-19). OBJECTIVES: To study the predictive value of the dynamic changes (first 48 h after ICU admission) of the following ratios: neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), monocyte-to-lymphocyte (MLR), systemic inflammation index (SII), and derived neutrophil-to-lymphocyte (dNLR) for invasive mechanical ventilation (IMV) need and death in critically ill COVID-19 patients. METHODS: Observational, retrospective, and multicentric analysis on 272 patients with severe or critical COVID-19 from two tertiary centers. Hematological indices were adjusted for confounders through multivariate analysis using Cox regression. RESULTS: Patients comprised 186 males and 86 females with no difference across groups (p > 0.05). ΔNLR > 2 had the best independent predictive value for IMV need (HR = 5.05 (95% CI, 3.06-8.33, p < 0.0001)), followed by ΔSII > 340 (HR = 3.56, 95% CI 2.21-5.74, p < 0.0001) and ΔdNLR > 1 (HR = 2.61, 95% CI 1.7-4.01, p < 0.0001). Death was also best predicted by an NLR > 11 (HR = 2.25, 95% CI: 1.31-3.86, p = 0.003) followed by dNLR > 6.93 (HR = 1.89, 95% CI: 1.2-2.98, p = 0.005) and SII > 3700 (HR = 1.68, 95% CI: 1.13-2.49, p = 0.01). CONCLUSIONS: Dynamic changes of NLR, SII, and dNLR independently predict IMV need and death in critically ill COVID-19 patients.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Learning it the hard way - how enjoying life and positive appraisal buffer the negative effects of stressors on mental health in the COVID-19 pandemic.

BACKGROUND: Higher levels of stress and negative emotions such as anxiety and depression have been reported since the beginning of the COVID-19 pandemic, but it remains less clear how positive emotions, such as hedonic capacity, may be affected. Further, during lockdowns, the ability to learn new pleasurable activities (hedonic learning) may be particularly relevant. Here, we investigated if state hedonia and/or hedonic learning mediated the relationship between COVID-19 stress and mental health. Moreover, we explored whether positive appraisal style (PAS), a major resilience factor, influenced these relationships. METHODS: Using a cross-sectional design, 5000 German-speaking participants filled out online questionnaires targeting stressors, mental health, state hedonia, hedonic learning, and PAS between April 9 and May 15, 2020. After confirming the factor structure of our constructs, we applied latent structural equation modeling to test mediation as well as moderated mediation models. RESULTS: Stress showed a positive association with mental health symptoms, which was buffered by both state hedonia and hedonic learning. While higher stress was related to lower state hedonia, participants reported more hedonic learning with greater stressor load. The latter effect was greater for individuals with high PAS. LIMITATIONS: The present results should be replicated in longitudinal designs with representative samples to confirm the directionality and generalizability of effects. CONCLUSIONS: Both state hedonia and hedonic learning buffered the effect of stress on mental health in an early phase of the COVID-19 pandemic. Learning new rewarding activities in combination with a PAS may be especially relevant for maintaining mental health during lockdowns.