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PurposeTo quantify and compare SARS-CoV-2 transmission potential across Alabama, Louisiana, and Mississippi and selected counties with populations in the 50th, 75th, and 100th percentile.MethodsTo determine the time-varying reproduction number Rt of SARS-CoV-2, we applied the R package EpiEstim to the time series of daily incidence of confirmed cases. Median Rt percentage change when policies changed was determined. Linear regression was performed between log10-transformed cumulative incidence and log10-transformed population size at four time points.ResultsStay-at-home orders, face mask mandates, and vaccinations were associated with the most significant reductions in SARS-CoV-2 transmission in the three southern states. Rt across the three states decreased significantly by 20% following stay-at-home orders. We observed varying degrees of reductions in Rt across states following other policies. Rural Alabama counties experienced higher per capita cumulative cases relative to urban ones as of June 17 and October 17, 2020. Meanwhile, Louisiana and Mississippi saw the disproportionate impact of SARS-CoV-2 in rural counties compared to urban ones throughout the study period.ConclusionState and county policies had an impact on local pandemic trajectories. The rural-urban disparities in case burden call for evidence-based approaches in tailoring health promotion interventions and vaccination campaigns to rural residents.

Resveratrol (RSV), the most effective stilbene phytoalexin synthesized naturally or induced in plants as part of their defense mechanism, is a key component of natural phenolic compounds and is being considered as a treatment option for a variety of diseases. RSV was discovered in the skin of red grapes, mulberries, peanuts, pines, and Polygonum cuspidatum weed root extracts. It was first extracted from white hellebore (Veratrum grandiflorum O. Loes) roots in 1940, then from Polygonum cuspidatum roots in 1963. However, RSV’s use as a drug is limited due to its initial conformational strength and poor stability. The research focused on a set of RSV biological activity data. RSV has been the subject of growing concern, despite its wide range of biological and therapeutic applications. According to the literature, RSV has antioxidant, anti-cancer, cardioprotective, neuroprotective, anti- inflammatory, anti-microbial, immunomodulatory, and radioprotective properties. The current analysis summarized biological applications of RSV, their mechanisms of action, and recent scientific development in the area of their delivery. It is possible to infer that RSV has many effects on infected cells’ cellular functions.

Toll-like receptors (TLRs) are the pattern recognition receptors (PRRs) that recognize pathogen-associated molecular patterns (PAMPs) in microbial species. Among the various TLRs, TLR2 has a special place due to its ability to sense the widest repertoire of PAMPs owing to its heterodimerization with either TLR1 or TLR6, broadening its ligand diversity against pathogens. Various scaffolds are reported to activate TLR2, which include naturally occurring lipoproteins, synthetic lipopeptides, and small heterocyclic molecules. We described a detailed SAR in TLR2 agonistic scaffolds and also covered the design and chemistry for the conjugation of TLR2 agonists to antigens, carbohydrates, polymers, and fluorophores. The approaches involved in delivery of TLR2 agonists such as lipidation of antigen, conjugation to polymers, phosphonic acids, and other linkers to achieve surface adsorption, liposomal formulation, and encapsulating nanoparticles are elaborated. The crystal structure analysis and computational modeling are also included with the structural features that facilitate TLR2 activation.

BackgroundThe effectiveness of mechanical thrombectomy (MT) was demonstrated in five landmark trials published in2015.Mechanical thrombectomy is now standard of care for acute ischemic stroke and has been growing in popularity after publication of landmark trials.ObjectiveTo analyze outcomes and trends of the use of MT and intravenous thrombolysis (IVT) in patients with acute ischemic stroke in US hospitals before and after publication of these trials.MethodsPatients discharged with a diagnosis of ischemic stroke between 2012 to 2017 were diagnosed using ICD codes from the National Inpatient Sample. Thereafter, patients given acute stroke treatment were identified using the corresponding procedure codes for IVT and MT. The primary clinical outcomes of in-hospital mortality and disability were then compared between two time periods: 2012–2014 (pre-landmark trials) and 2015–2017 (post-landmark trials). Binary logistic regression and Χ2tests were used for statistical analysis.ResultsA total of 57 675 patients (median age 68.9 years (range 18-90), 50.1% female) were identified with acute procedures. Of these patients, 57.6% were from the post-landmark trials time period. Despite an increased number of cases, the rate of IVT decreased from 84.3% to 75.9% and the rate of IVT+MT decreased from 7.1% to 6.3%. After publication of the pivotal trials in 2015, the rates of MT increased from 8.7% to 17.8%. Significant reductions of in-hospital mortality (7.1% vs 8.7%, pConclusionThe analysis showed a significant increase in the proportion of patients receiving MT after 2015. This has translated into reduction of in-hospital mortality and improvement in disability.

Magnetoencephalography (MEG) is now widely used in clinical examinations and medical research in many fields. Resting-state magnetoencephalography-based brain network analysis can be used to study the physiological or pathological mechanisms of the brain. Furthermore, magnetoencephalography analysis has a significant reference value for the diagnosis of epilepsy. The scope of the proposed research is that this research demonstrates how to locate spikes in the phase locking functional brain connectivity network of the Desikan-Killiany brain region division using a neural network approach. It also improves detection accuracy and reduces missed and false detection rates. The automatic classification of epilepsy encephalomagnetic signals can make timely judgments on the patient’s condition, which is of tremendous clinical significance. The existing literature’s research on the automatic type of epilepsy EEG signals is relatively sufficient, but the research on epilepsy EEG signals is relatively weak. A full-band machine learning automatic discrimination method of epilepsy brain magnetic spikes based on the brain functional connection network is proposed. The four classifiers are comprehensively compared. The classifier with the best effect is selected, and the discrimination accuracy can reach 93.8%. Therefore, this method has a good application prospect in automatically identifying and labeling epileptic spikes in magnetoencephalography.

Background: The ensemble building is a common method to improve the performance of the model in case of regression as well as classification. Objective: In this paper we propose a weighted average ensemble model to predict the number of incidence for infectious diseases like typhoid and compare it with applied models for prediction. Methods: The Monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. The data was processed by three regressions such as support vector regression, neural network and linear regression. Results: To evaluate the prediction error and compare it with different models, different performance measures have been used such as MSE, RMSE and MAE and it was found that proposed ensemble method performed better in terms of forecast measures. Conclusion: Our main aim in this paper is to minimize the prediction error; the resulting proposed weighted average ensemble model has shown a significant result in terms of prediction errors.

The relationship between body weight and depression may have changed along with the climbing trend of obesity prevalence, but most previous studies examined the association with a single cross-sectional survey. The present study assessed the change in the association between depression and body weight, measured and perceived from 2005 to 2018, among three major racial/ethnic groups.We analyzed the data of 27,387 adults aged ≥18, collected in the National Health and Nutrition Examination Survey. Surveys from 2005 to 2010 were combined as the early period and the surveys from 2011 to 2018 as the recent period.A switching pattern was observed among white women. In the 2005-2010 survey period, only self-perceived overweight was significantly associated with depression [Odds ratio (OR) = 3.25 (95% confidence interval = 1.33, 7.90)]. However, in the 2011-2018 survey period, self-perceived overweight was not associated with depression anymore [1.32 (0.72, 2.41)], but obesity measured directly was significantly associated with depression [2.59 (1.04, 6.48)]. Among white men, self-perceived overweight and obesity measured directly were significantly associated with depression only in 2011-2018; [2.57 (1.18, 5.58)] and [0.29 (0.10, 0.80)], respectively. Obesity directly measured consistently associated with increased odds of depression among black men. No significant associations were observed in Hispanic and black women in any survey period.Significant gender and ethnic differences exist, and the associations between body weight and depression have evolved in sex-race-specific trajectories. The interventions must be consistently fine-tuned following the dynamics of the relationship between body weight and overall well-being.

Malaria, even though an avoidable and treatable disease, can be fatal if ignored. Artemisinin Combination Therapy (ACT) and RTS, S/AS01 vaccine (Mosquirix™) are the only modest means available with humans to overcome malaria, a lethal affliction wreaking havoc across the globe. Employment of ACT is associated with problems such as ‘Artemisinin Resistance’ and the ‘Hypnozoite conundrum’ that hinder the complete eradication of malaria. In this view, the natural products specifically comprising β-carboline scaffold have shown good antiplasmodial responses against different strains of malaria. Taking these observations forward, researchers have performed structure-activity relationship (SAR) studies around three different β-carboline skeletons (tetrahydro β-carbolines, dihydro β-carbolines, β-carbolines) to design new β-carboline derived heterocyclic structures or modified naturally occurring derivatives. In addition, different approaches such as dimerization and linkage to other moieties have also been adopted to enhance the antimalarial activity. The present review describes a comprehensive SAR study encapsulating various natural and synthetic β-carbolines to elaborate upon the utility of these skeletons in designing drugs to subdue this deadly disease.

Graphical abstract Fig. 2: Pictorial description of the stack based ensemble ARIMA model., Background Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely. Methods We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters. Result The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries. Conclusion Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

Background and purpose Little attention was paid to the transition of care for stroke that may partially explain the long-term trend of stroke rates. We aimed to test the trend of hospitalization attributable to stroke in US adults. Methods Data from National Hospital Discharge Surveys 1980–2010 in patients aged ≥18 (n = 6,527,304) were analyzed to examine the trend of patients with first-list diagnoses of stroke. Stroke comorbidities were classified in stroke patients with second- to seven-listed diagnoses of coronary heart disease, hypertension, diabetes, arrhythmias, or hyperlipidemia. Stroke trends by survey years and birth cohorts were analyzed using univariate, multivariate, and birth cohorts methods. Results Of the total study sample, the prevalence of hospitalization due to stroke was 22.99%, 30.00%, and 27.03% in years of 1980–1989, 1990–1999, and 2000–2010 in males, and 17.30%, 22.04%, and 19.34% in females, respectively. Overall, hospitalization rates in stroke patients significantly increased among adults aged Conclusion A significant increase in stroke hospitalization rate was observed in adults aged

Background: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter. Methods: This work addresses the classification problem for two groups; Group 1: “inter-ictal vs. ictal” for which case 1(C-E), and case 2(D-E) are included and Group 2; “activity from controlled vs. inter-ictal activity” considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered. Results: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data. Conclusion: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

Currently, there is no efficient vaccine available against clinical malaria. However, continuous efforts have been committed to develop powerful antimalarial vaccine by discovery of novel antigens with in-depth understanding of its nature, immunogenicity, and presentation (delivery adjuvants). Moreover, another important part of vaccine development includes discovery of better immunostimulatory formulation components (immunostimulants). A protective vaccine against malaria requires antigen-specific B and T helper cell responses as well as cytotoxic T lymphocyte (CTL) responses. A long-lasting B and T memory cell production is also required for effective malaria vaccine. Since activation of Toll-like receptors (TLRs) promotes both innate inflammatory responses as well as the induction of adaptive immunity, several initiatives have been mounted during the last few years for the use of TLR agonists as malaria vaccine adjuvants.The review summarizes reports related to the use and development of TLR agonists as malaria vaccine adjuvants and describes various strategies involved for the selection of specific antigens and TLR agonists.TLR agonists are promising adjuvants for the development of effective malaria vaccine, allowing for both innate inflammatory responses as well as the induction of adaptive immunity.

Angiosarcoma is a rare and aggressive tumor of the thyroid gland, mainly seen in the Alpine regions. We present such a case with literature review. We present the case of a 60-year-old man with cough, dyspnea, and hemoptysis along with slow increase in the size of his long-standing goiter. Computed tomography of the neck showed a large thyroid mass and chest imaging revealed multiple pulmonary nodules. Fine needle aspiration cytology and tru-cut biopsy of the thyroid were notable for poorly differentiated malignant cells. Diagnosis of angiosarcoma of the thyroid was made after total thyroidectomy. Patient died of continued hemoptysis and respiratory failure 3 weeks after admission. We searched the literature for previous case reports using Pubmed and Ovid. Forty-seven reported cases were identified and our case was added to make a database of 48 cases. Demographic and tumor characteristics were analyzed. Angiosarcoma was found to be more common in females and at age of 60 or above. Results were consistent with previously reported series of 14 and 17 cases from Austria. This review provides information on various characteristics angiosarcoma of the thyroid which can be used as baseline data for future reference and research studies for this cancer.

The prognosis and final outcome in patients who sustain stroke are significantly affected by medical complications occurring during the acute phase of stroke. Only limited information is available from India and other developing countries regarding acute complications of stroke. This study examined the frequency of acute stroke and the factors associated with complications of stroke in India. In this prospective multicenter study, running from March 2008 to September 2009, 6 hospitals collected information on complications of first-ever stroke during admission. Complications were defined in accordance with standard criteria. Outcome at 30 days poststroke was assessed using the modified Rankin Scale. Stroke characteristics, length of hospital stay, and stroke severity (based on the National Institutes of Health Stroke Scale) were documented. Hematologic (ie, hemoglobin) and biochemical (ie, total proteins and albumin) parameters also were obtained. A total of 449 patients out of the recruited 476 completed follow-up. The mean age was 58.1 ± 13.7 years (range, 16-96 years), and the majority were men (n = 282; 62.8%). The mean National Institutes of Stroke Scale score was 10.2 ± 5.3. Overall, 206 patients (45.9%) experienced complications during admission. In the logistic regression analysis, limb weakness (odds ratio [OR], 0.12; 95% confidence interval [CI], 0.02-0.67; P = .01), anemia (OR, 0.35; 95% CI, 0.15-0.81; P = .01), length of hospital stay (OR, 0.89; 95% CI, 0.85-0.94; P.0001), and stroke severity (OR, 0.27; 95% CI, 0.10-0.72; P = .01) were the variables associated with complications. Such complications as urinary tract infection (OR, 0.31; 95% CI, 0.13-0.78; P = .01), chest infection (OR, 1.81; 95% CI, 1.12-2.93; P = .02), bedsores (OR, 3.52; 95% CI, 1.02-12.08; P = .05), other pain (OR, 0.21; 95% CI, 0.09-0.49; P.0001), and depression (OR, 2.22; 95% CI, 1.30-3.80; P.01) were associated with poor outcome. Our study shows high rates of complication in acute stroke. Limb weakness, stroke severity, length of hospital stay, and anemia were the factors associated with complications. Other complications, such as urinary tract infection, chest infection, bedsores, other pain, and depression, can lead to poor outcome.