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Preclinical validation of human recombinant glutamate-oxaloacetate transaminase for the treatment of acute ischemic stroke

Pérez-Mato, María; Dopico-López, Antonio; Akkoc, Yunus; López-Amoedo, Sonia; Correa-Paz, Clara; Candamo-Lourido, María; Iglesias-Rey, Ramón; López-Arias, Esteban; Bugallo-Casal, Ana; da Silva-Candal, Andrés; Bravo, Susana B.; Chantada-Vázquez, María del Pilar; Arias, Susana; Santamaría-Cadavid, María; Estany-Gestal, Ana; Zaghmi, Ahlem; Gauthier, Marc A.; Gutiérrez-Fernández, María; Martin, Abraham; Llop, Jordi; Rodríguez, Cristina; Almeida, Ángeles; Migliavacca, Martina; Polo, Ester; Pelaz, Beatriz; Gozuacik, Devrim; El Yamani, Naouale; Sengupta, Tanima; Rundén-Pran, Elise; Vivancos, José; Castellanos, Mar; Díez-Tejedor, Exuperio; Sobrino, Tomás; Rabinkov, Aharon; Mirelman, David; Castillo, José; Campos, Francisco

The blood enzyme glutamate-oxaloacetate transaminase (GOT) has been postulated as an effective therapeutic to protect the brain during stroke. To demonstrate its potential clinical utility, a new human recombinant form of GOT (rGOT) was produced for medical use.

We tested the pharmacokinetics and evaluated the protective efficacy of rGOT in rodent and non-human primate models that reflected clinical stroke conditions.

We found that continuous intravenous administration of rGOT within the first 8 h after ischemic onset significantly reduced the infarct size in both severe (30%) and mild lesions (48%). Cerebrospinal fluid and proteomics analysis, in combination with positron emission tomography imaging, indicated that rGOT can reach the brain and induce cytoprotective autophagy and induce local protection by alleviating neuronal apoptosis.

Our results suggest that rGOT can be safely used immediately in patients suspected of having a stroke. This study requires further validation in clinical stroke populations.

2024

Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top-Down and Bottom-Up Methods

Hugelius, G.; Ramage, J.; Burke, E.; Chatterjee, A.; Smallman, T.L.; Aalto, T.; Bastos, A.; Biasi, C.; Canadell, J.G.; Chandra, N.; Chevallier, F.; Ciais, P.; Chang, J.; Feng, L.; Jones, M.W.; Kleinen, T.; Kuhn, M.; Lauerwald, R.; Liu, J.; López-Blanco, E.; Luijkx, I.T.; Marushchak, M.E.; Natali, S.M.; Niwa, Y.; Olefeldt, D.; Palmer, P.I.; Patra, P.K.; Peters, W.; Potter, S.; Poulter, B.; Rogers, B.M.; Riley, W.J.; Saunois, M.; Schuur, E.A.G.; Thompson, Rona Louise; Treat, C.; Tsuruta, A.; Turetsky, M.R.; Virkkala, A.-M.; Voigt, C.; Watts, J.; Zhu, Q.; Zheng, B.

Large stocks of soil carbon (C) and nitrogen (N) in northern permafrost soils are vulnerable to remobilization under climate change. However, there are large uncertainties in present-day greenhouse gas (GHG) budgets. We compare bottom-up (data-driven upscaling and process-based models) and top-down (atmospheric inversion models) budgets of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) as well as lateral fluxes of C and N across the region over 2000–2020. Bottom-up approaches estimate higher land-to-atmosphere fluxes for all GHGs. Both bottom-up and top-down approaches show a sink of CO2 in natural ecosystems (bottom-up: −29 (−709, 455), top-down: −587 (−862, −312) Tg CO2-C yr−1) and sources of CH4 (bottom-up: 38 (22, 53), top-down: 15 (11, 18) Tg CH4-C yr−1) and N2O (bottom-up: 0.7 (0.1, 1.3), top-down: 0.09 (−0.19, 0.37) Tg N2O-N yr−1). The combined global warming potential of all three gases (GWP-100) cannot be distinguished from neutral. Over shorter timescales (GWP-20), the region is a net GHG source because CH4 dominates the total forcing. The net CO2 sink in Boreal forests and wetlands is largely offset by fires and inland water CO2 emissions as well as CH4 emissions from wetlands and inland waters, with a smaller contribution from N2O emissions. Priorities for future research include the representation of inland waters in process-based models and the compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more and well-distributed in situ GHG measurements and improved resolution in upscaling techniques.

American Geophysical Union (AGU)

2024

Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention

Lepioufle, Jean-Marie; Schneider, Philipp; Hamer, Paul David; Ødegård, Rune Åvar; Vallejo, Islen; Cao, Tuan-Vu; Taherkordi, Amirhosein; Wojcikowski, Marek

In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 and to the annual hourly concentration in 2019 over the Oslo metropolitan region (1026 ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

Cambridge University Press

2024

Water quality and pollution source apportionment responses to rainfall in steppe lake estuaries: A case study of Hulun Lake in northern China

Hu, Bingtao; Liu, Yuhong; Chen, Yixue; Hao, Yipeng; Liu, Hai Ying; Wang, Zhongsheng

Hulun Lake, the largest inland steppe lake in China, is encountering severe water quality degradation. Estuaries play important roles in material and energetic exchange between rivers and lakes. The water quality at the estuaries of Hulun Lake directly reflects the impact of both human activities and natural factors on the lake’s overall water quality, especially during rainfall events. From July 28, 2021, to August 4, 2021, water samples from 62 sites were collected in the three estuaries of Hulun Lake before and after a moderate rainfall event. 13 water parameters, including dissolved oxygen (DO), Turbidity (Tur), Total Nitrogen (TN), Total Phosphorus (TP), Total Organic Nitrogen (TON), and Total Organic Phosphorus (TOP) were measured. The spatio-temporal distribution of water quality in the estuaries was assessed based on water quality index (WQI). Besides, an improved approach integrating stepwise linear regression (SLR) and principal component analysis (PCA) was utilized to construct a WQImin model for an effective assessment of water quality in these estuaries. Furthermore, the absolute principal component scores-multiple linear regression (APCS-MLR) model was employed to identify and quantify the environmental drivers underlying the water quality in the estuaries. The results of WQI indicated that the water quality of the sites in the estuaries of Hulun Lake was “medium” or “poor”, both before and after the rainfall, with a general deterioration in water quality in response to the rainfall. The simplified WQImin model consisted of 5 crucial parameters (i.e., TN, TP, ammonium (NH4+-N), Tur, and permanganate index (CODMn)), and it performed well without parameter weights. Spatial differences in some water parameters among the estuaries were detected, which were attributed to the natural factors and human activities upstream. The principal environmental factors affecting the water quality in the estuaries consisted of hydrodynamic processes, internal phosphorus release, external phosphorus input, external nitrogen input, nitrification in the estuaries, and external organic input and internal organic release. Therefore, we propose basin management strategies such as limiting grazing pressure, adopting enclosed pasture, wetland restoration, optimizing water renewal cycle in Hulun Lake, and transboundary water quality management to tackle water contamination in Hulun Lake.

Elsevier

2024

A critical review to identify data gaps and improve risk assessment of bisphenol A alternatives for human health

Mhaouty-Kodja, Sakina; Zalko, Daniel; Tait, Sabrina; Testai, Emanuela; Viguié, Catherine; Corsini, Emanuela; Grova, Nathalie; Buratti, Franca Maria; Cabaton, Nicolas J.; Coppola, Lucia; De la Vieja, Antonio; Dusinska, Maria; El Yamani, Naouale; Galbiati, Valentina; Iglesias-Hernández, Patricia; Kohl, Yvonne; Maddalon, Ambra; Marcon, Francesca; Naulé, Lydie; Rundén-Pran, Elise; Salani, Francesca; Santori, Nicoletta; Torres-Ruiz, Mónica; Turner, Jonathan D.; Adamovsky, Ondrej; Aiello-Holden, Kiara; Dirven, Hubert; Louro, Henriqueta; João Silva, Maria

Bisphenol A (BPA), a synthetic chemical widely used in the production of polycarbonate plastic and epoxy resins, has been associated with a variety of adverse effects in humans including metabolic, immunological, reproductive, and neurodevelopmental effects, raising concern about its health impact. In the EU, it has been classified as toxic to reproduction and as an endocrine disruptor and was thus included in the candidate list of substances of very high concern (SVHC). On this basis, its use has been banned or restricted in some products. As a consequence, industries turned to bisphenol alternatives, such as bisphenol S (BPS) and bisphenol F (BPF), which are now found in various consumer products, as well as in human matrices at a global scale. However, due to their toxicity, these two bisphenols are in the process of being regulated. Other BPA alternatives, whose potential toxicity remains largely unknown due to a knowledge gap, have also started to be used in manufacturing processes. The gradual restriction of the use of BPA underscores the importance of understanding the potential risks associated with its alternatives to avoid regrettable substitutions. This review aims to summarize the current knowledge on the potential hazards related to BPA alternatives prioritized by European Regulatory Agencies based on their regulatory relevance and selected to be studied under the European Partnership for the Assessment of Risks from Chemicals (PARC): BPE, BPAP, BPP, BPZ, BPS-MAE, and TCBPA. The focus is on data related to toxicokinetic, endocrine disruption, immunotoxicity, developmental neurotoxicity, and genotoxicity/carcinogenicity, which were considered the most relevant endpoints to assess the hazard related to those substances. The goal here is to identify the data gaps in BPA alternatives toxicology and hence formulate the future directions that will be taken in the frame of the PARC project, which seeks also to enhance chemical risk assessment methodologies using new approach methodologies (NAMs).

Informa Healthcare

2024

Data fusion for enhancing urban air quality modeling using large-scale citizen science data

O'Regan, Anna C.; Grythe, Henrik; Hellebust, Stig; Lopez-Aparicio, Susana; O’Dowd, Colin; Hamer, Paul David; Sousa Santos, Gabriela; Nyhan, Marguerite M.

Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.

Elsevier

2024

From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red‑based marine microplastic identification

Meyers, Nelle; De Witte, Bavo; Schmidt, Natascha; Herzke, Dorte; Fuda, Jean-Luc; Vanavermaete, David; Janssen, Colin R.; Everaert, Gert

Springer

2024

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology

Chatterjee, Ayan; Riegler, Michael; Johnson, Miriam S.; Das, Jishnu; Pahari, Nibedita; Ramachandra, Raghavendra; Ghosh, Bikramaditya; Saha, Arpan; Bajpai, Ram

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

Nature Portfolio

2024

Transitioning to building integration of photovoltaics and greenery (BIPVGREEN): case studies up-scaling from cities informal settlements

Karamanis, Dimitrios; Liu, Hai Ying; Skandalos, Nikolaos; Makis, Achilleas; Kapsalis, Vasileios; D’Agostino, Delia; Maduta, Carmen; Tolis, Athanasios; Trandafir, Simona; Parker, Danny

To achieve the objectives of COP28 for transitioning away from fossil fuels and phasing these out, both natural and technological solutions are essential, necessitating a step-change in how we implement social innovation. Given the significant CO2 emissions produced by the building sector, there is an urgent need for a transformative shift towards a net-zero building stock by mid-century. This transition to zero-energy and zero-emission buildings is difficult due to complex processes and substantial costs. Building integrated photovoltaics (BIPV) offers a promising solution due to the benefits of enhanced energy efficiency and electricity production. The availability of roof and façade space in offices and other types of buildings, especially in large cities, permits photovoltaic integration in both opaque and transparent surfaces. This study investigates the synergistic relationship between solar conversion technologies and nature-based components. Through a meta-analysis of peer-reviewed literature and critical assessment, effective BIPVs with greenery (BIPVGREEN) combinations suitable for various climatic zones are identified. The results highlight the multi-faceted benefits of this integration across a range of techno-economic and social criteria and underscore the feasibility of up-scaling these solutions for broader deployment. Applying a SWOT analysis approach, the internal strengths and weaknesses, as well as the external opportunities and threats for BIPVGREEN deployment, are investigated. The analysis reveals key drivers of synergistic effects and multi-benefits, while also addressing the challenges associated with optimizing performance and reducing investment costs. The strengths of BIPVGREEN in terms of energy efficiency and sustainable decarbonization, along with its potential to mitigate urban and climate temperature increases, enhance its relevance to the built environment, especially for informal settlements. The significance of prioritizing this BIPVGREEN climate mitigation action in low-income vulnerable regions and informal settlements is crucial through the minimum tax financing worldwide and citizen's engagement in architectural BIPVGREEN co-integration.

IOP Publishing

2024

PFAS Exposure is Associated with a Lower Spermatic Quality in an Arctic Seabird

Humann-Guilleminot, Ségolène; Blévin, Pierre; Gabrielsen, Geir W.; Herzke, Dorte; Nikiforov, Vladimir; Jouanneau, William; Moe, Børge; Parenteau, Charline; Helfenstein, Fabrice; Chastel, Olivier

Several studies have reported an increasing occurrence of poly- and perfluorinated alkyl substances (PFASs) in Arctic wildlife tissues, raising concerns due to their resistance to degradation. While some research has explored PFAS’s physiological effects on birds, their impact on reproductive functions, particularly sperm quality, remains underexplored. This study aims to assess (1) potential association between PFAS concentrations in blood and sperm quality in black-legged kittiwakes (Rissa tridactyla), focusing on the percentage of abnormal spermatozoa, sperm velocity, percentage of sperm motility, and morphology; and (2) examine the association of plasma levels of testosterone, corticosterone, and luteinizing hormone with both PFAS concentrations and sperm quality parameters to assess possible endocrine disrupting pathways. Our findings reveal a positive correlation between the concentration of longer-chain perfluoroalkyl carboxylates (PFCA; C11–C14) in blood and the percentage of abnormal sperm in kittiwakes. Additionally, we observed that two other PFAS (i.e., PFOSlin and PFNA), distinct from those associated with sperm abnormalities, were positively correlated with the stress hormone corticosterone. These findings emphasize the potentially harmful substance-specific effects of long-chain PFCAs on seabirds and the need for further research into the impact of pollutants on sperm quality as a potential additional detrimental effect on birds.

2024

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