Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising and estimating future COVID-19 cases to control the spread and help countries prepare their healthcare systems. In this study, time-series models- Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) are used to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%–80% of global cumulative cases are located. Initial combinations of the model parameters were selected using the auto-ARIMA model followed by finding the optimized model parameters based on the best fit between the predictions and test data. Analytical tools Auto-Correlation function (ACF), Partial Auto-Correlation Function (PACF), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. Evaluation metrics Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) were used as criteria for selecting the best model. A case study was presented where the statistical methodology was discussed in detail for model selection and the procedure for forecasting the COVID-19 cases of the USA. Best model parameters of ARIMA and SARIMA for each country are selected manually and the optimized parameters are then used to forecast the COVID-19 cases. Forecasted trends for confirmed and recovered cases showed an exponential rise for countries such as the United States, Brazil, South Africa, Colombia, Bangladesh, India, Mexico and Pakistan. Similarly, trends for cumulative deaths showed an exponential rise for countries Brazil, South Africa, Chile, Colombia, Bangladesh, India, Mexico, Iran, Peru, and Russia. SARIMA model predictions are more realistic than that of the ARIMA model predictions confirming the existence of seasonality in COVID-19 data. The results of this study not only shed light on the future trends of the COVID-19 outbreak in top-16 countries but also guide these countries to prepare their health care policies for the ongoing pandemic. The data used in this work is obtained from publicly available John Hopkins University’s COVID-19 database.

Collectively, our data demonstrate, for the first time, that a novel orally active apocynin derivative improves behavioral, inflammatory, and neurodegenerative processes in a severe progressive dopaminergic neurodegenerative model of PD. Antioxid. Redox Signal. 27, 1048-1066.

Mitochondrial dysfunction, oxidative stress and neuroinflammation have been implicated as key mediators contributing to the progressive degeneration of dopaminergic neurons in Parkinson’s disease (PD). Currently, we lack a pharmacological agent that can intervene in all key pathological mechanisms, which would offer better neuroprotective efficacy than a compound that targets a single degenerative mechanism. Herein, we investigated whether mito-apocynin (Mito-Apo), a newly-synthesized and orally available derivative of apocynin that targets mitochondria, protects against oxidative damage, glial-mediated inflammation and nigrostriatal neurodegeneration in cellular and animal models of PD. Mito-Apo treatment in primary mesencephalic cultures significantly attenuated the 1-methyl-4-phenylpyridinium (MPP+)-induced loss of tyrosine hydroxylase (TH)-positive neuronal cells and neurites. Mito-Apo also diminished MPP+-induced increases in glial cell activation and inducible nitric oxide synthase (iNOS) expression. Additionally, Mito-Apo decreased nitrotyrosine (3-NT) and 4-hydroxynonenol (4-HNE) levels in primary mesencephalic cultures. Importantly, we assessed the neuroprotective property of Mito-Apo in the MPTP mouse model of PD, wherein it restored the behavioral performance of MPTP-treated mice. Immunohistological analysis of nigral dopaminergic neurons and monoamine measurement further confirmed the neuroprotective effect of Mito-Apo against MPTP-induced nigrostriatal dopaminergic neuronal loss. Mito-Apo showed excellent brain bioavailability and also markedly attenuated MPTP-induced oxidative markers in the substantia nigra (SN). Furthermore, oral administration of Mito-Apo significantly suppressed MPTP-induced glial cell activation, upregulation of proinflammatory cytokines, iNOS and gp91phox in IBA1-positive cells of SN. Collectively, these results demonstrate that the novel mitochondria-targeted compound Mito-Apo exhibits profound neuroprotective effects in cellular and pre-clinical animal models of PD by attenuating oxidative damage and neuroinflammatory processes.

A progressive loss of neuronal structure and function is a signature of many neurodegenerative conditions including chronic traumatic encephalopathy, Parkinson’s, Huntington’s and Alzheimer’s diseases. Mitochondrial dysfunction and oxidative and nitrative stress have been implicated as key pathological mechanisms underlying the neurodegenerative processes. However, current therapeutic approaches targeting oxidative damage are ineffective in preventing the progression of neurodegeneration. Mitochondria-targeted antioxidants were recently shown to alleviate oxidative damage. In this work, we investigated the delivery of biodegradable polyanhydride nanoparticles containing the mitochondria-targeted antioxidant apocynin to neuronal cells and the ability of the nano-formulation to protect cells against oxidative stress. The nano-formulated mitochondria-targeted apocynin provided excellent protection against oxidative stress-induced mitochondrial dysfunction and neuronal damage in a dopaminergic neuronal cell line, mouse primary cortical neurons, and a human mesencephalic cell line. Collectively, our results demonstrate that nano-formulated mitochondria-targeted apocynin may offer improved efficacy of mitochondria-targeted antioxidants to treat neurodegenerative disease.

In December 2019, first case of the COVID-19 was reported in Wuhan, Hubei province in China. Soon world health organization has declared contagious coronavirus disease (a.k.a. COVID-19) as a global pandemic in the month of March 2020. Over the span of eleven months, it has rapidly spread out all over the world with total confirmed cases of ∼ 41.39 M and causing a total fatality of ∼1.13 M. At present, the entire mankind is facing serious threat and it is believed that COVID-19 may have been around for quite some time. Therefore, it has become imperative to forecast the global impact of COVID-19 in the near future. The present work proposes state-of-art deep learning Recurrent Neural Networks (RNN) models to predict the country-wise cumulative confirmed cases, cumulative recovered cases and the cumulative fatalities. The Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells along with Recurrent Neural Networks (RNN) were developed to predict the future trends of the COVID-19. We have used publicly available data from John Hopkins University's COVID-19 database. In this work, we emphasize the importance of various factors such as age, preventive measures, and healthcare facilities, population density, etc. that play vital role in rapid spread of COVID-19 pandemic. Therefore, our forecasted results are very helpful for countries to better prepare themselves to control the pandemic.