Innovative seated vertical lumbar traction allows simultaneous computer work while inducing spinal height changes similar to supine lying.

BACKGROUND: Lumbar intervertebral disc height loss has been associated with spinal height change (SHC) and low back pain (LBP), including stenosis. Non-invasive methods to improve disc height loss require forms of lying down, which are unconducive to computer work. OBJECTIVE: Intermittent vertical traction (VT) integrated with seated computer work may provide ergonomic alternatives for increasing SHC to promote LBP relief. The primary aim was to develop and introduce a safe VT prototype and dosage to induce and measure SHC. Prototype comfort and LBP ratings were exploratory secondary aims. METHODS: Forty-one participants were stadiometry-measured for pre- and post-intervention SHC from seated VT at 35% body weight removed, supine lying (SL), and sitting at a computer (SIT) without VT. Pain ratings were recorded for those self-reporting LBP. VT prototype evaluations were compiled from a 3-question, 7-point Likert-style survey. RESULTS: SHC increased by 3.9 ± 3.4 mm in VT, 1.7 ± 3.4 mm in SIT, and 4.3 ± 3.1 mm in SL (P< 0.000). Post hoc findings were significant between VT and SIT (P< 0.000), and SL and SIT (P< 0.000). VT and SL LBP ratings both decreased, but not SIT. CONCLUSION: Intermittent seated VT is a promising alternative for postural relief during seated computer work, producing SHC similar to lying down without compromising workflow.

Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells.

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.

Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).

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.

Biodegradable polyanhydride-based nanomedicines for blood to brain drug delivery.

An urgent need to deliver therapeutics across the blood-brain barrier (BBB) underlies a paucity of effective therapies currently available for treatment of degenerative, infectious, traumatic, chemical, and metabolic disorders of the nervous system. With an eye toward achieving this goal, an in vitro BBB model was employed to simulate biodegradable polyanhydride nanoparticle-based drug delivery to the brain. Using a combination of confocal microscopy, flow cytometry, and high performance liquid chromatography, we examined the potential of polyanhydride nanoparticles containing the anti-oxidant, mito-apocynin, to be internalized and then transferred from monocytes to human brain microvascular endothelial cells. The efficacy of this nanoparticle-based delivery platform was demonstrated by neuronal protection against oxidative stress. Taken together, this polyanhydride nanoparticle-based delivery system holds promise for enhancing neuroprotection by facilitating drug transport across the BBB. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 2881-2890, 2018.

Neuronal protection against oxidative insult by polyanhydride nanoparticle-based mitochondria-targeted antioxidant therapy.

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.

Nano-enabled delivery of diverse payloads across complex biological barriers.

Complex biological barriers are major obstacles for preventing and treating disease. Nanocarriers are designed to overcome such obstacles by enhancing drug delivery through physiochemical barriers and improving therapeutic indices. This review critically examines both biological barriers and nanocarrier payloads for a variety of drug delivery applications. A spectrum of nanocarriers is discussed that have been successfully developed for improving tissue penetration for preventing or treating a range of infectious, inflammatory, and degenerative diseases.

Enabling nanomaterial, nanofabrication and cellular technologies for nanoneuromedicines.

Nanoparticulate delivery systems represent an area of particular promise for nanoneuromedicines. They possess significant potential for desperately needed therapies designed to combat a range of disorders associated with aging. As such, the field was selected as the focus for the 2014 meeting of the American Society for Nanomedicine. Regenerative, protective, immune modulatory, anti-microbial and anti-inflammatory products, or imaging agents are readily encapsulated in or conjugated to nanoparticles and as such facilitate the delivery of drug payloads to specific action sites across the blood-brain barrier. Diagnostic imaging serves to precisely monitor disease onset and progression while neural stem cell replacement can regenerate damaged tissue through control of stem cell fates. These, taken together, can improve disease burden and limit systemic toxicities. Such enabling technologies serve to protect the nervous system against a broad range of degenerative, traumatic, metabolic, infectious and immune disorders. From the clinical editor: Nanoneuromedicine is a branch of nanomedicine that specifically looks at the nervous system. In the clinical setting, a fundamental hurdle in nervous system disorders is due to an inherent inability of nerve cells to regenerate after damage. Nanotechnology can offer new approaches to overcome these challenges. This review describes recent developments in nanomedicine delivery systems that would affect stem cell repair and regeneration in the nervous system.

Pulmonary biodistribution and cellular uptake of intranasally administered monodisperse particles.

PURPOSE: For the rational design of nanovaccines against respiratory pathogens, careful selection of optimal particle size and chemistry is paramount. This work investigates the impact of these properties on the deposition, biodistribution, and cellular interactions of nanoparticles within the lungs. METHOD: In this work, biodegradable poly(sebacic anhydride) (poly(SA)) nanoparticles of multiple sizes were synthesized with narrow particle size distributions. The lung deposition and retention as well as the internalization by phagocytic cells of these particles were compared to that of non-degradable monodisperse polystyrene nanoparticles of similar sizes. RESULTS: The initial deposition of intranasally administered particles in the lungs was dependent on primary particle size, with maximal deposition occurring for the 360-470 nm particles, regardless of chemistry. Over time, both particle size and chemistry affected the frequency of particle-positive cells and the specific cell types taking up particles. The biodegradable poly(SA) particles associated more closely with phagocytic cells and the dynamics of this association impacted the clearance of these particles from the lung. CONCLUSIONS: The findings reported herein indicate that both size and chemistry control the fate of intranasally administered particles and that the dynamics of particle association with phagocytic cells in the lungs provide important insights for the rational design of pulmonary vaccine delivery vehicles.

Pulmonary function responses to ozone in smokers with a limited smoking history.

In non-smokers, ozone (O3) inhalation causes decreases in forced expiratory volume (FEV1) and dead space (VD) and increases the slope of the alveolar plateau (SN). We previously described a population of smokers with a limited smoking history that had enhanced responsiveness to brief O3 boluses and aimed to determine if responsiveness to continuous exposure was also enhanced. Thirty smokers (19M, 11F, 24±4 years, 6±4 total years smoking,4±2 packs/week) and 30 non-smokers (17M, 13F, 25±6 years) exercised for 1h on a cycle ergometer while breathing 0.30ppm O3. Smokers and non-smokers were equally responsive in terms of FEV1 (-9.5±1.8% vs -8.7±1.9%). Smokers alone were responsive in terms of VD (-6.1±1.2%) and SN (9.1±3.4%). There was no difference in total delivered dose. Dead space ventilation (VD/VT) was not initially different between the two groups, but increased in the non-smokers (16.4±2.8%) during the exposure, suggesting that the inhaled dose may be distributed more peripherally in smokers. We also conclude that these cigarette smokers retain their airway responsiveness to O3 and, uniquely, experience changes in VD that lead to heterogeneity in airway morphometry and an increase in SN.

Longitudinal distribution of ozone absorption in the lung: comparison of cigarette smokers and nonsmokers.

In nonsmokers, ozone (O(3)) is removed primarily by the epithelial lining fluid (ELF) of the conducting airways. We hypothesized that cigarette smokers, whose ELF antioxidant capacity may be limited by smoking, would remove less O(3) from their conducting airways than nonsmokers. We recruited 29 nonsmokers (17M, 12F) and 30 smokers (19M, 11F, 4+/-4 pack-years) with similar anthropometric characteristics and measured the longitudinal distribution of O(3) using the bolus inhalation method. We also assessed the physiological effect of this transient exposure regimen using forced spirometry and capnography. Contrary to our hypothesis, the penetration volume at which 50% of a bolus was absorbed was not different between smokers and nonsmokers (97.1+/-5.4 mL versus 97.9+/-5.8 mL, p=0.92). However, smokers did experience an increase in the slope of the alveolar plateau of the capnogram (S(N)) (8.1+/-3.2%, p=0.02) and a small decrease in FEV(1) (-1.3+/-0.6%, p=0.03), whereas nonsmokers did not (DeltaFEV(1) -0.1+/-0.5% and DeltaS(N) -0.2+/-2.5%, p>0.10). Thus, smokers are more sensitive to inhaled O(3) boluses than nonsmokers, despite a similar internal dose distribution.