Comorbidities and their association with COVID-19 mortality in Mexico between January 2020 and August 2021.

By August 17, 2021, 4.3 million people had died globally as a result of SARS-CoV-2 infection. While data collection is ongoing, it is abundantly obvious that this is one of the most significant public health crises in modern history. Consequently, global efforts are being made to attain a greater understanding of this disease and to identify risk factors associated with more severe outcomes. The goal of this study is to identify clinical characteristics and risk factors associated with COVID-19 mortality in Mexico. The dataset used in this study was released by Sistema Nacional de Vigilancia Epidemiologica de Enfermedades Respiratorias (SISVER) de la Secretaría de Salud and contains 2.9 million COVID-19 cases. The effects of risk factors on COVID-19 mortality were estimated using multivariable logistic regression models with generalized estimation equation and Kaplan-Meier curves. Case fatality rates, case hospitalization rates are also reported using the Centers for Disease Control and Prevention (CDC) USA death-to-case ratio method. In general, older males with pre-existing conditions had higher odds of death. Age greater than 40, male sex, hypertension, diabetes, and obesity are associated with higher COVID-19 mortality. End-stage renal disease, chronic obstructive pulmonary disease, and immunosuppression are all linked with COVID-19 patient fatalities. Smoking and Asthma are associated with lower COVID-19 mortality which is consistent with findings from the article published in Nature based on National Health Service (NHS) of UK dataset (17 million cases). Intensive care unit (ICU), patient intubation, and pneumonia diagnosis are shown to substantially increase mortality risk for COVID-19 patients.

Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management.

The COVID-19 pandemic has caused a global crisis with 47,209,305 confirmed cases and 1,209,505 confirmed deaths worldwide as of November 2, 2020. Forecasting confirmed cases and understanding the virus dynamics is necessary to provide valuable insights into the growth of the outbreak and facilitate policy-making regarding virus containment and utilization of medical resources. In this study, we applied a mathematical epidemic model (MEM), statistical model, and recurrent neural network (RNN) variants to forecast the cumulative confirmed cases. We proposed a reproducible framework for RNN variants that addressed the stochastic nature of RNN variants leveraging z-score outlier detection. We incorporated heterogeneity in susceptibility into the MEM considering lockdowns and the dynamic dependency of the transmission and identification rates which were estimated using Poisson likelihood fitting. While the experimental results demonstrated the superiority of RNN variants in forecasting accuracy, the MEM presented comprehensive insights into the virus spread and potential control strategies.

Epileptic Seizure Detection for Imbalanced Datasets Using an Integrated Machine Learning Approach.

Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.

Microwave Application and Anhydrous Cu(OAc)2 Mediated O-Arylation of Aliphatic Amino Alcohols.

Anhydrous Cu(OAc)2 mediated efficient protocol has been developed in the area of C-O coupling from potassium aryltrifluoroborates and aliphatic amino alcohols such as ß-hydroxy, γ-hydroxy, and δ-hydroxy amines. The scope of this transformation focuses on direct O-arylation and Ostyrylation. The reaction vial loaded with reactants under argon atmosphere is microwaved at 140°C for 30 min to furnish the corresponding cross-coupling product, amino ethers, in good yields.

Catalyst free, base free microwave irradiated synthesis of aryl nitrites from potassium aryltrifluoroborates and bismuth nitrate.

A mixture of bismuth nitrate pentahydrate and potassium aryltrifluoroborate in toluene under microwave heating at 120 °C for 20 min provides an interesting and mild reaction protocol for the synthesis of aryl nitrite. The conversion to aryl nitrites from aryltrifluoroborates without transition metal catalyst and base in high yields is remarkable.

A novel route to organonitrites by Pd-catalyzed cross-coupling of sodium nitrite and potassium organotrifluoroborates.

Microwave irradiated palladium-catalyzed cross-coupling reaction of potassium styryltrifluoroborates and sodium nitrite gives the corresponding styryl nitrites in high yields. Potassium aryltrifluoroborates also furnish aryl nitrites under same reaction condition. This unprecedented cross-coupling is an interesting development and has the potential to lead to new nitration protocols.

Microwave-enhanced palladium-catalyzed cross-coupling reactions of potassium vinyltrifluoroborates and allyl acetates: a new route to 1,4-pentadienes.

[reaction: see text] A novel and straightforward microwave synthesis of 1,4-pentadienes has been developed involving the cross-coupling of potassium vinyltrifluoroborates and allyl acetates in the presence of a palladium catalyst.