An extract of Artemisia dracunculus L. stimulates insulin secretion from β cells, activates AMPK and suppresses inflammation

© 2015 Elsevier Ireland Ltd. All rights reserved. Ethnopharmacological relevance Artemisia dracunculus L. (Russian tarragon) is a perennial herb belonging to the family Compositae and has a history of medicinal use in humans, particularly for treatment of diabetes. Aim of the study: In this study a defined plant extract from A. dracunculus L. (termed PMI-5011) is used to improve beta(β) cells function and maintain β cell number in pancreatic islets as an alternative drug approach for successful treatment of diabetes. Materials and methods Mouse and human pancreatic beta cells were treated with defined plant extract of A. dracunculus L. (PMI-5011) to understand the mechanism(s) that influence beta cell function and β cell number. Results We found that the PMI-5011 enhances insulin release from primary β cells, isolated mouse and human islets and it maintains β cell number. Insulin released by PMI-5011 is associated with the activation of AMP-activated protein kinase (AMPK), and protein kinase B (PKB). Furthermore, PMI-5011 suppresses LPS/INFγ-induced inflammation and inflammatory mediator(s) in macrophages. PMI-5011 inhibited Nitric oxide (NO) production and expression of inducible nitric oxide synthase (iNOS) at the protein level and also attenuated pro-inflammatory cytokine (IL-6) production in macrophages. Conclusion PMI-5011 has potential therapeutic value for diabetes treatment via increasing insulin release from β cells and decreases capacity of macrophages to combat inflammation

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions

Cytotoxicity of juglone and thymoquinone against pancreatic cancer cells

Juglone and thymoquinone are cytotoxic to pancreatic cancer cells. The aim of this study was to investigate, using an analysis of isobolograms, the type and degree of interactions between juglone and thymoquinone on MIA PaCa-2 pancreatic cancer cells. Cell viability was evaluated using the MTT assay. Cell death was determined by flow cytometry. The IC value for juglone and TQ in combination was found to be 24.75 μM, which was higher than juglone or TQ alone. Juglone alone killed Mia Paca-2 cells by ferroptosis. At concentrations where 10, 20 or 50% of cells were affected, there existed a moderate antagonistic relationship between juglone and TQ as indicated by the combination index (CI) value determined by the Compusyn software. At concentrations that affected 75% and 90% of cells, there were nearly an additive effect with CI value of 1.09249 and 0.92391, respectively. Moderate synergism was only seen at concentration where 95% of cells were affected, and the corresponding concentration of juglone and TQ at that combination was 40.90 μM and 511.19 μM, respectively

Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km2), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km2). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide

APOBEC3 inhibits DEAD-END function to regulate microRNA activity

Abstract The RNA binding protein DEAD-END (DND1) is one of the few proteins known to regulate microRNA (miRNA) activity at the level of miRNA-mRNA interaction. DND1 blocks miRNA interaction with the 3′-untranslated region (3′-UTR) of specific mRNAs and restores protein expression. Previously, we showed that the DNA cytosine deaminase, APOBEC3 (apolipoprotein B mRNA-editing enzyme, catalytic polypeptide like 3), interacts with DND1. APOBEC3 has been primarily studied for its role in restricting and inactivating retroviruses and retroelements. In this report, we examine the significance of DND1-APOBEC3 interaction. We found that while human DND1 inhibits miRNA-mediated inhibition of P27, human APOBEC3G is able to counteract this repression and restore miRNA activity. APOBEC3G, by itself, does not affect the 3′-UTR of P27. We found that APOBEC3G also blocks DND1 function to restore miR-372 and miR-206 inhibition through the 3′-UTRs of LATS2 and CX43, respectively. In corollary experiments, we tested whether DND1 affects the viral restriction function or mutator activity of APOBEC3. We found that DND1 does not affect APOBEC3 inhibition of infectivity of exogenous retrovirus HIV (ΔVif) or retrotransposition of MusD. In addition, examination of Ter/Ter;Apobec3−/− mice, lead us to conclude that DND1 does not regulate the mutator activity of APOBEC3 in germ cells. In summary, our results show that APOBEC3 is able to modulate DND1 function to regulate miRNA mediated translational regulation in cells but DND1 does not affect known APOBEC3 function

An extract of Artemisia dracunculus L. stimulates insulin secretion from β cells, activates AMPK and suppresses inflammation

ETHNOPHARMACOLOGICAL RELEVANCE: Artemisia dracunculus L. (Russian tarragon) is a perennial herb belonging to the family Compositae and has a history of medicinal use in humans, particularly for treatment of diabetes. AIM OF THE STUDY: In this study a defined plant extract from Artemisia dracunculus L. (termed PMI-5011) is used to improve β cells function and maintain β cell number in pancreatic islets as an alternative drug approach for successful treatment of diabetes. MATERIALS AND METHODS: Mouse and human pancreatic beta cells were treated with defined plant extract of Artemisia dracunculus L. (PMI-5011) to understand the mechanism(s) that influence beta cell function and β cell number. RESULTS: We found that the PMI-5011 enhances insulin release from primary β cells, isolated mouse and human islets and it maintains β cell number. Insulin released by PMI-5011 is associated with the activation of AMP-activated protein kinase (AMPK), and protein kinase B (PKB). Furthermore, PMI-5011 suppresses LPS/INFγ-induced inflammation and inflammatory mediator(s) in macrophages. PMI-5011 inhibited Nitric oxide (NO) production and expression of inducible nitric oxide synthase (iNOS) at the protein level and also attenuated pro-inflammatory cytokine (IL-6) production in macrophages. CONCLUSION: PMI-5011 has potential therapeutic value for diabetes treatment via increasing insulin release from β cells and decreases capacity of macrophages to combat inflammation