Free radical scavenging ability of Aspalathus linearis in two in vitro models of diabetes and cancer

AbstractThe free radical scavenging activity of Aspalathus linearis (Rooibos tea) and its effect on reactive oxygen species (ROS), catalase (CAT), and superoxide dismutase (SOD) were investigated in two in vitro disease models of cancer and diabetes. Although the antioxidant activity of this tea has been reported in several studies, its effects in disease models of ROS-induced oxidative stress have not been systematically evaluated to date. The oxygen radical absorbance capacity (ORAC) assay was used in this study to quantify the antioxidant capacity of the extract, whereas the ROS scavenging ability in hyperglycemia-induced human umbilical vein endothelial cells (HUVECs) and HeLa cells were investigated. The CAT and SOD assays were also carried out in the two disease models in order to evaluate the effect of the extract in the stimulation of these two enzyme activities. The extract was observed to have reduced ROS in a dose-dependent manner in both HUVECs and HeLa cells. The stimulation of the CAT and SOD enzyme activities were observed to be dose-dependent as well. The high ORAC value of the extract indicated the presence of antioxidant compounds which could directly quench ROS, whereby this mechanism of action could be hypothesized to have been further complemented through the stimulation of CAT and SOD. Overall, the Aspalathus linearis extract was observed to have increased the CAT and SOD activities in two in vitro disease models of cancer and hyperglycemia. Given the correlation between the ORAC values, the increases in CAT and SOD activities and the reduction in ROS in a dose-dependent manner, it could be hypothesized that the extract had a significant therapeutic potential for either the prevention of the onset of the two diseases or their progression because ROS has been identified as their root causes

Kinetic modeling of supercritical fluid extraction of betel nut

Supercritical fluid extraction is an advanced extraction technique that suitable for heat sensitive and active compound material from plants and herbs. Understanding the effect of extraction parameters on mass transfer coefficient at solid and fluid phase can determine the dominating extraction regime thus performance of the extraction may be enhanced. The aim of this research was to determine the mass transfer coefficient in solid and fluid phase using kinetic modelling approach. Grounded betel nuts were treated with supercritical carbon dioxide with 5% methanol as co-solvent to determine its mass transfer coefficient in solid and fluid phase for the following extraction conditions; pressure, 20 to 30 MPa; temperature, 50 to 70 °C; and flow rate, 2 to 4 mL/min. Simplified Sovová model was coupled with Broken and Intact Cell model to determine the mass transfer coefficients. Results show the mass transfer coefficients of solid phase and liquid phase are in the ranges of 0.00015 to 0.00353 m3/min and 0.3497 to 3.9623 m3/min, respectively. The overall absolute average relative deviation was observed to be 7.39%

An Interspecific Nicotiana Hybrid as a Useful and Cost-Effective Platform for Production of Animal Vaccines

The use of transgenic plants to produce novel products has great biotechnological potential as the relatively inexpensive inputs of light, water, and nutrients are utilised in return for potentially valuable bioactive metabolites, diagnostic proteins and vaccines. Extensive research is ongoing in this area internationally with the aim of producing plant-made vaccines of importance for both animals and humans. Vaccine purification is generally regarded as being integral to the preparation of safe and effective vaccines for use in humans. However, the use of crude plant extracts for animal immunisation may enable plant-made vaccines to become a cost-effective and efficacious approach to safely immunise large numbers of farm animals against diseases such as avian influenza. Since the technology associated with genetic transformation and large-scale propagation is very well established in Nicotiana, the genus has attributes well-suited for the production of plant-made vaccines. However the presence of potentially toxic alkaloids in Nicotiana extracts impedes their use as crude vaccine preparations. In the current study we describe a Nicotiana tabacum and N. glauca hybrid that expresses the HA glycoprotein of influenza A in its leaves but does not synthesize alkaloids. We demonstrate that injection with crude leaf extracts from these interspecific hybrid plants is a safe and effective approach for immunising mice. Moreover, this antigen-producing alkaloid-free, transgenic interspecific hybrid is vigorous, with a high capacity for vegetative shoot regeneration after harvesting. These plants are easily propagated by vegetative cuttings and have the added benefit of not producing viable pollen, thus reducing potential problems associated with bio-containment. Hence, these Nicotiana hybrids provide an advantageous production platform for partially purified, plant-made vaccines which may be particularly well suited for use in veterinary immunization programs

26S Proteasome Activity Is Down-Regulated in Lung Cancer Stem-Like Cells Propagated In Vitro

Cancer stem cells (CSCs) are a small subset of cancer cells capable of self-renewal and tumor maintenance. Eradicating cancer stem cells, the root of tumor origin and recurrence, has emerged as one promising approach to improve lung cancer survival. Cancer stem cells are reported to reside in the side population (SP) of cultured lung cancer cells. We report here the coexistence of a distinct population of non-SP (NSP) cells that have equivalent self-renewal capacity compared to SP cells in a lung tumor sphere assay. Compared with the corresponding cells in monolayer cultures, lung tumor spheres, formed from human non-small cell lung carcinoma cell lines A549 or H1299, showed marked morphologic differences and increased expression of the stem cell markers CD133 and OCT3/4. Lung tumor spheres also exhibited increased tumorigenic potential as only 10,000 lung tumor sphere cells were required to produce xenografts tumors in nude mice, whereas the same number of monolayer cells failed to induce tumors. We also demonstrate that lung tumor spheres showed decreased 26S proteasome activity compared to monolayer. By using the ZsGreen–cODC (C-terminal sequence that directs degradation of Ornithine Decarboxylase) reporter assay in NSCLC cell lines, only less than 1% monolayer cultures were ZsGreen positive indicating low 26S proteasome, whereas lung tumor sphere showed increased numbers of ZsGreen-positive cells, suggesting the enrichment of CSCs in sphere cultures

Effects of the neurogranin variant rs12807809 on thalamocortical morphology in schizophrenia

10.1371/journal.pone.0085603PLoS ONE812-POLN

Spiral ligament fibrocyte-derived MCP-1/CCL2 contributes to inner ear inflammation secondary to nontypeable H. influenzae-induced otitis media

<p>Abstract</p> <p>Background</p> <p>Otitis media (OM), one of the most common pediatric infectious diseases, causes inner ear inflammation resulting in vertigo and sensorineural hearing loss. Previously, we showed that spiral ligament fibrocytes (SLFs) recognize OM pathogens and up-regulate chemokines. Here, we aim to determine a key molecule derived from SLFs, contributing to OM-induced inner ear inflammation.</p> <p>Methods</p> <p>Live NTHI was injected into the murine middle ear through the tympanic membrane, and histological analysis was performed after harvesting the temporal bones. Migration assays were conducted using the conditioned medium of NTHI-exposed SLFs with and without inhibition of MCP-1/CCL2 and CCR2. qRT-PCR analysis was performed to demonstrate a compensatory up-regulation of alternative genes induced by the targeting of MCP-1/CCL2 or CCR2.</p> <p>Results</p> <p>Transtympanic inoculation of live NTHI developed serous and purulent labyrinthitis after clearance of OM. THP-1 cells actively migrated and invaded the extracellular matrix in response to the conditioned medium of NTHI-exposed SLFs. This migratory activity was markedly inhibited by the viral CC chemokine inhibitor and the deficiency of MCP-1/CCL2, indicating that MCP-1/CCL2 is a main attractant of THP-1 cells among the SLF-derived molecules. We further demonstrated that CCR2 deficiency inhibits migration of monocyte-like cells in response to NTHI-induced SLF-derived molecules. Immunolabeling showed an increase in MCP-1/CCL2 expression in the cochlear lateral wall of the NTHI-inoculated group. Contrary to the <it>in vitro </it>data, deficiency of MCP-1/CCL2 or CCR2 did not inhibit OM-induced inner ear inflammation <it>in vivo</it>. We demonstrated that targeting MCP-1/CCL2 enhances NTHI-induced up-regulation of MCP-2/CCL8 in SLFs and up-regulates the basal expression of CCR2 in the splenocytes. We also found that targeting CCR2 enhances NTHI-induced up-regulation of MCP-1/CCL2 in SLFs.</p> <p>Conclusions</p> <p>Taken together, we suggest that NTHI-induced SLF-derived MCP-1/CCL2 is a key molecule contributing to inner ear inflammation through CCR2-mediated recruitment of monocytes. However, deficiency of MCP-1/CCL2 or CCR2 alone was limited to inhibit OM-induced inner ear inflammation due to compensation of alternative genes.</p

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages