Found 2675 publications. Showing page 33 of 268:
Nanomedicine and epigenetics: New alliances to increase the odds in pancreatic cancer survival
Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers worldwide, primarily due to its robust desmoplastic stroma and immunosuppressive tumor microenvironment (TME), which facilitate tumor progression and metastasis. In addition, fibrous tissue leads to sparse vasculature, high interstitial fluid pressure, and hypoxia, thereby hindering effective systemic drug delivery and immune cell infiltration. Thus, remodeling the TME to enhance tumor perfusion, increase drug retention, and reverse immunosuppression has become a key therapeutic strategy. In recent years, targeting epigenetic pathways has emerged as a promising approach to overcome tumor immunosuppression and cancer progression. Moreover, the progress in nanotechnology has provided new opportunities for enhancing the efficacy of conventional and epigenetic drugs. Nano-based drug delivery systems (NDDSs) offer several advantages, including improved drug pharmacokinetics, enhanced tumor penetration, and reduced systemic toxicity. Smart NDDSs enable precise targeting of stromal components and augment the effectiveness of immunotherapy through multiple drug delivery options. This review offers an overview of the latest nano-based approaches developed to achieve superior therapeutic efficacy and overcome drug resistance. We specifically focus on the TME and epigenetic-targeted therapies in the context of PDAC, discussing the advantages and limitations of current strategies while highlighting promising new developments. By emphasizing the immense potential of NDDSs in improving therapeutic outcomes in PDAC, our review paves the way for future research in this rapidly evolving field.
2023
Background Previous studies have reported associations between certain persistent organic pollutants (POPs) and type 2 diabetes mellitus (T2DM). Polybrominated diphenyl ethers (PBDEs) are a class of POPs that are found in increasing concentrations in humans. Although obesity is a known risk factor for T2DM and PBDEs are fat-soluble, very few studies have investigated associations between PBDEs and T2DM. No longitudinal studies have assessed associations between repeated measurements of PBDE and T2DM in the same individuals and compared time trends of PBDEs in T2DM cases and controls. Objectives To investigate associations between pre- and post-diagnostic measurements of PBDEs and T2DM and to compare time trends of PBDEs in T2DM cases and controls. Methods Questionnaire data and serum samples from participants in the Tromsø Study were used to conduct a longitudinal nested case-control study among 116 T2DM cases and 139 controls. All included study participants had three pre-diagnostic blood samples (collected before T2DM diagnosis in cases), and up to two post-diagnostic samples after T2DM diagnosis. We used logistic regression models to investigate pre- and post-diagnostic associations between PBDEs and T2DM, and linear mixed-effect models to assess time trends of PBDEs in T2DM cases and controls. Results We observed no substantial pre- or post-diagnostic associations between any of the PBDEs and T2DM, except for BDE-154 at one of the post-diagnostic time-points (OR = 1.65, 95% CI: 1.00, 2.71). The overall time trends of PBDE concentrations were similar for cases and controls. Discussion The study did not support PBDEs increasing the odds of T2DM, prior to or after T2DM diagnosis. T2DM status did not influence the time trends of PBDE concentrations.
2023
2023
Antarctic sea-ice low resonates in the ecophysiology of humpback whales
The past six years have been marked by some of the most dramatic climatic events observed in the Antarctic region in recent history, commencing with the 2017 sea-ice extreme low. The Humpback Whale Sentinel Programme is a circum-polar biomonitoring program for long term surveillance of the Antarctic sea-ice ecosystem. It has previously signalled the extreme La Niña event of 2010/11, and it was therefore of interest to assess the capacity of existing biomonitoring measures under the program to detect the impacts of 2017 anomalous climatic events. Six ecophysiological markers of population adiposity, diet, and fecundity were targeted, as well as calf and juvenile mortality via stranding records. All indicators, with the exception of bulk stable isotope dietary tracers, indicated a negative trend in 2017, whilst C and N bulk stable isotopes appeared to indicate a lag phase resulting from the anomalous year. The collation of multiple biochemical, chemical, and observational lines of evidence via a single biomonitoring platform provides comprehensive information for evidence-led policy in the Antarctic and Southern Ocean region.
2023
Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.
2023
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface mode
Ammonia (NH3) is an important atmospheric constituent. It plays a role in air quality and climate through the formation of ammonium sulfate and ammonium nitrate particles. It has also an impact on ecosystems through deposition processes. About 85 % of NH3 global anthropogenic emissions are related to food and feed production and, in particular, to the use of mineral fertilizers and manure management. Most global chemistry transport models (CTMs) rely on bottom-up emission inventories, which are subject to significant uncertainties. In this study, we estimate emissions from livestock by developing a new module to calculate ammonia emissions from the whole agricultural sector (from housing and storage to grazing and fertilizer application) within the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) global land surface model. We detail the approach used for quantifying livestock feed management, manure application, and indoor and soil emissions and subsequently evaluate the model performance. Our results reflect China, India, Africa, Latin America, the USA, and Europe as the main contributors to global NH3 emissions, accounting for 80 % of the total budget. The global calculated emissions reach 44 Tg N yr−1 over the 2005–2015 period, which is within the range estimated by previous work. Key parameters (e.g., the pH of the manure, timing of N application, and atmospheric NH3 surface concentration) that drive the soil emissions have also been tested in order to assess the sensitivity of our model. Manure pH is the parameter to which modeled emissions are the most sensitive, with a 10 % change in emissions per percent change in pH. Even though we found an underestimation in our emissions over Europe (−26 %) and an overestimation in the USA (+56 %) compared with previous work, other hot spot regions are consistent. The calculated emission seasonality is in very good agreement with satellite-based emissions. These encouraging results prove the potential of coupling ORCHIDEE land-based emissions to CTMs, which are currently forced by bottom-up anthropogenic-centered inventories such as the CEDS (Community Emissions Data System).
2023
2023
Marine mammals are considered sentinel species and may act as indicators of ocean health. Plastic residues are widely distributed in the oceans and are recognised as hazardous contaminants, and once ingested can cause several adverse effects on wildlife. This study aimed to identify and characterise plastic ingestion in the Guiana dolphins (Sotalia guianensis) from the Southwestern Tropical Atlantic by evaluating the stomach contents of stranded individuals through KOH digestion and identification of subsample of particles by LDIR Chemical Imaging System. Most of the individuals were contaminated, and the most common polymers identified were PU, PET and EVA. Microplastics were more prevalent than larger plastic particles (meso- and macroplastics). Smaller particles were detected during the rainy seasons. Moreover, there was a positive correlation between the stomach content mass and the number of microplastics, suggesting contamination through trophic transfer.
2023
Fine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.
2023