TLR2, TLR4 and the MYD88 Signaling Pathway Are Crucial for Neutrophil Migration in Acute Kidney Injury Induced by Sepsis

The aim of this study was to investigate the role of TLR2, TLR4 and MyD88 in sepsis-induced AKI. C57BL/6 TLR2(-/-), TLR4(-/-) and MyD88(-/-) male mice were subjected to sepsis by cecal ligation and puncture (CLP). Twenty four hours later, kidney tissue and blood samples were collected for analysis. the TLR2(-/-), TLR4(-/-) and MyD88(-/-) mice that were subjected to CLP had preserved renal morphology, and fewer areas of hypoxia and apoptosis compared with the wild-type C57BL/6 mice (WT). MyD88(-/-) mice were completely protected compared with the WT mice. We also observed reduced expression of proinflammatory cytokines in the kidneys of the knockout mice compared with those of the WT mice and subsequent inhibition of increased vascular permeability in the kidneys of the knockout mice. the WT mice had increased GR1(+low) cells migration compared with the knockout mice and decreased in GR1(+high) cells migration into the peritoneal cavity. the TLR2(-/-), TLR4(-/-), and MyD88(-/-) mice had lower neutrophil infiltration in the kidneys. Depletion of neutrophils in the WT mice led to protection of renal function and less inflammation in the kidneys of these mice. Innate immunity participates in polymicrobial sepsis-induced AKI, mainly through the MyD88 pathway, by leading to an increased migration of neutrophils to the kidney, increased production of proinflammatory cytokines, vascular permeability, hypoxia and apoptosis of tubular cells.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)National Institute of Science and Technology (INCT)Universidade Federal de São Paulo, Dept Med, Disciplina Nefrol, São Paulo, BrazilUniv São Paulo, Dept Imunol, Lab Imunobiol Transplantes, São Paulo, BrazilHosp Israelita Albert Einstein, IIEP, São Paulo, BrazilUniv Fed Triangulo Mineiro, Uberaba, BrazilUniversidade Federal de São Paulo, Dept Med, Disciplina Nefrol, São Paulo, BrazilFAPESP: 07/07139-3Web of Scienc

FAIR Charging Station Data Scripts

<p>This version was unintentionally duplicated, please refer to the following version which is properly integrated with GitHub updates:</p><p> </p><p><a href="https://doi.org/10.5281/zenodo.10201060">https://doi.org/10.5281/zenodo.10201060</a></p><h2> </h2><h2>FAIR Charging Station Data</h2><p>This repository produces two clean datasets for Charging station data based on the data provided by the german BNetzA. More info on the source can be found at their <a href="https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/E-Mobilitaet/start.html">Charging Station Website (German)</a>. Just in case it is not clear, no data is provided in this repository, you have to download it yourself. Run The download script once, it will throw an error on where to store the data to use this tool properly.</p><h2>Execution</h2><p>This script should theoretically work with any version of python able to run pandas and frictionless. If it is not obvious, you have to install the requirements.txt in your python environment.</p><p>pip install -r requirements.txt</p><p>or simply</p><p>pip install pandas requests frictionless omi openpyxl jsonschema_rs</p><p> </p><p>Each script has to be run with the directory where you want to have the data as current working directory. You run them with python normally, for example:</p><p>python src/clean.py</p><p> </p><p>To get the proper data run the scripts in the following order:</p><ol><li>load (Data has to be downloaded manually, sorry but the BNetzA website is not fond of automatic requests.)</li><li>clean</li><li>annotate</li><li>normalise</li><li>rename</li><li>evaluate</li><li>publish</li></ol><h2>Annotated CSV</h2><p>The source files are in xlsx, which is a limited format. The provider offers csv files, but it has formatting errors as it seems that it is the output of using excel directly to save as csv.</p><p>The annotate function of this repository will produce a clean dataset with minimal modification of the source material.</p><h2>Normalised CSV</h2><p>The normalised data contains the source material structured in such a way that can be better handled with relational databases. The charging stations and the connection sockets are separated in two different tables.</p><h2>Renamed CSV</h2><p>These files contain the output of the previous scripts but with column names translated to English and deprived of special characters.</p><h2>Caveats</h2><p>The cleaning script will remove duplicate entries, this was not decided lightly as it can be the case that two columns are in the same place with the exact same characteristics. It is not possible, with our resources to validate or deny this, but these duplicate entries seem to be more of a input error than actual multiple columns with similar characteristics.</p><p>It is the case that columns with different characteristics share a place, these are kept.</p><h2>Data Sources</h2><ul><li><a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/E_Mobilitaet/Ladesaeulenregister.xlsx">BNetzA Data</a></li><li><a href="https://raw.githubusercontent.com/OpenEnergyPlatform/oemetadata/develop/metadata/latest/schema.json">OEP Metadata schema</a> used in evaluate.py</li></ul&gt

Financiamento das políticas sociais

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