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Dataset results
814 results for “LPS”
A Methodology Study in Healthy Subjects to Evaluate the Effect of AZD8309 After Nasal Administration of Lipopolysaccharide (LPS)
ClinicalTrials.gov study NCT00860821. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Oxytocin Effects on Cytokines and Endocrine Parameters After LPS Induced Systemic Inflammatory Effects in Healthy Volunteers
ClinicalTrials.gov study NCT00360048. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Antioxidants on Ocular Blood Flow, Endothelial Function, and Cytokine Levels in LPS Induced Inflammatory Model in Humans.
ClinicalTrials.gov study NCT00431691. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ASSET - a Double-Blind, Randomized Placebo-Controlled Clinical Investigation With Alteco® LPS Adsorber
ClinicalTrials.gov study NCT02335723. IPD Sharing: NO. Countries: 3. Publications: 2.
Effect of Antioxidants on Oxygen Induced Vasoconstriction in Lipopolysaccharide (LPS) Induced Inflammatory Model in Humans
ClinicalTrials.gov study NCT00914576. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Comparing Clinical Outcome of 2 Different Total Knee Prostheses: Nexgen LPS-Flex Versus AGC
ClinicalTrials.gov study NCT00294528. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Oryza Sativa l Extract to LPS, ZO-1, and Intestinal Microbiota in Obese Individuals
ClinicalTrials.gov study NCT04827628. IPD Sharing: NO. Countries: 1. Publications: 11.
Development of Novel Non-invasive Inflammometry Following Lipopolysaccharide, Endotoxin (LPS) Challenge in Healthy Volunteers
ClinicalTrials.gov study NCT03044327. IPD Sharing: NO. Countries: 1. Publications: 2.
Hybrid Versus Cemented TKA Using the NexGen LPS Prosthesis
ClinicalTrials.gov study NCT02798432. IPD Sharing: NO. Countries: 1. Publications: 6.
Effect of Retaron® on Oxygen Induced Retinal Vasoconstriction in LPS Induced Inflammatory Model in Humans
ClinicalTrials.gov study NCT02221089. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Suitability of a Low Dose Lipopolysaccharide (LPS) Inhalation as a Challenge Model
ClinicalTrials.gov study NCT01400568. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Oral Dydrogesterone (OD) Versus Micronized Vaginal Progesterone (MVP) for Luteal Phase Support (LPS) in IVF/ICSI
ClinicalTrials.gov study NCT03677336. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Inflammatory Markers in Sputum After LPS Inhalation
ClinicalTrials.gov study NCT01081392. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Ex Vivo LPS Stimulation in Healthy and Compromised Subjects
ClinicalTrials.gov study NCT01656915. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Drone eviction assay raw data from Gilchrist et al for drone weights and eviction rates that have been treated with LPS or not
Open the record for dataset details and reuse information.
Data from: Phylogenomic analysis supports the ancestral presence of LPS-outer membranes in the Firmicutes
One of the major unanswered questions in evolutionary biology is when and how the transition between diderm (two membranes) and monoderm (one membrane) cell envelopes occurred in Bacteria. The Negativicutes and the Halanaerobiales belong to the classically monoderm Firmicutes, but possess outer membranes with lipopolysaccharide (LPS-OM). Here, we show that they form two phylogenetically distinct lineages, each close to different monoderm relatives. In contrast, their core LPS biosynthesis enzymes were inherited vertically, as in the majority of bacterial phyla. Finally, annotation of key OM systems in the Halanaerobiales and the Negativicutes shows a puzzling combination of monoderm and diderm features. Together, these results support the hypothesis that the LPS-OMs of Negativicutes and Halanaerobiales are remnants of an ancient diderm cell envelope that was present in the ancestor of the Firmicutes, and that the monoderm phenotype in this phylum is a derived character that arose multiple times independently through OM loss.
Data from: Microglia responses to pro-inflammatory stimuli (LPS, IFNγ+TNFα) and reprogramming by resolving cytokines (IL-4, IL-10)
Microglia respond to CNS injuries and diseases with complex reactions, often called "activation." A pro-inflammatory phenotype (also called classical or M1 activation) lies at one extreme of the reactivity spectrum. There were several motivations for this study. First, bacterial endotoxin (lipopolysaccharide, LPS) is the most commonly used pro-inflammatory stimulus for microglia, both in vitro and in vivo; however, pro-inflammatory cytokines (e.g., IFNγ, TNFα) rather than LPS will be encountered with sterile CNS damage and disease. We lack direct comparisons of responses between LPS and such cytokines. Second, while transcriptional profiling is providing substantial data on microglial responses to LPS, these studies mainly use mouse cells and models, and there is increasing evidence that responses of rat microglia can differ. Third, the cytokine milieu is dynamic after acute CNS damage, and an important question in microglial biology is: How malleable are their responses? There are very few studies of effects of resolving cytokines, particularly for rat microglia, and much of the work has focused on pro-inflammatory outcomes. Here, we first exposed primary rat microglia to LPS or to IFNγ+TNFα (I+T) and compared hallmark functional (nitric oxide production, migration) and molecular responses (almost 100 genes), including surface receptors that can be considered part of the sensome. Protein changes for exemplary molecules were also quantified: ARG1, CD206/MRC1, COX-2, iNOS, and PYK2. Despite some similarities, there were notable differences in responses to LPS and I+T. For instance, LPS often evoked higher pro-inflammatory gene expression and also increased several anti-inflammatory genes. Second, we compared the ability of two anti-inflammatory, resolving cytokines (IL-4, IL-10), to counteract responses to LPS and I+T. IL-4 was more effective after I+T than after LPS, and IL-10 was surprisingly ineffective after either stimulus. These results should prove useful in modeling microglial reactivity in vitro; and comparing transcriptional responses to sterile CNS inflammation in vivo.
Data from: Biochemical and functional analysis of Cyanobacterium Oscillatoria sp. LPS on human monocytes
Cyanobacterial blooms are an increasing source of environmental toxins that affect both human and animals. After ingestion of cyanobacteria, such as Geitlerinema sp., toxins and lipopopolysaccharide (LPS) from this organism induce fever, gastro-intestinal illness, and even death. However, little is known regarding the effects of cyanobacterial LPS on human monocytes after exposure to LPS upon ingestion. Based on our previous data using Geitlerinema sp. LPS (which was previously named Oscillatoria sp., a genus belonging to the same order as Geitlerinema), we hypothesized that Geitlerinema sp. LPS would activate human monocytes to proliferate, phagocytose particles and produce cytokines that are critical for promoting pro-inflammatory responses in the gut. Our data demonstrate that Geitlerinema sp. LPS induces monocyte proliferation and TNF-α, IL-1, and IL-6 production at high concentrations. In contrast, Geitlerinema sp. LPS is equally capable of inducing monocyte-mediated phagocytosis of FITC-Latex beads when compared to E. coli LPS, which was used as a positive control for our experiments. In order to understand the mechanism responsible for the difference in efficacy between Geitlerinema sp. LPS and E. coli LPS, we performed biochemical analysis and identified that Geitlerinema sp. LPS is comprised of significantly different sugars and fatty-acid side chains in comparison to E. coli LPS. The lipid A portion of Geitlerinema sp. LPS contains longer fatty acid side chains, such as C15:0, C16:0, and C18:0, instead of C12:0 found in E. coli LPS which may explain the decreased efficacy and toxicity of Geitlerinema sp. LPS in comparison to E. coli LPS.
Data from: Changes in the bacterial microbiota in gut, blood, and lungs following acute LPS instillation into mice lungs
Introduction: Previous reports have shown that the gastrointestinal (GI) bacterial microbiota can have profound effects on the lungs, which has been described as the "gut-lung axis". However, whether a "lung-gut" axis exists wherein acute lung inflammation perturbs the gut and blood microbiota is unknown. Methods: Adult C57/Bl6 mice were exposed to one dose of LPS or PBS instillation (n = 3 for each group) directly into lungs. Bacterial microbiota of the bronchoalveolar lavage fluid, blood, and cecum were determined using 454 pyrotag sequencing and quantitative polymerase chain reaction (qPCR) at 4 through 168 hours post-instillation. We then investigated the effects of oral neomycin and streptomycin (n = 8) on the microbiota at 4 and 24 hours post LPS instillation versus control treatment (n = 5 at baseline and 4 hours, n = 7 at 24 hours). Results: At 24 hours post LPS instillation, the total bacterial count was significantly increased in the cecum (P<0.05); whereas the total bacterial count in blood was increased at 4, 48, and 72 hours (P<0.05). Antibiotic treatment reduced the total bacteria in blood but not in the cecum. The increase in total bacteria in the blood correlated with Phyllobacteriaceae OTU 40 and was significantly reduced in the blood for both antibiotic groups (P<0.05). Conclusion: LPS instillation in lungs leads to acute changes in the bacterial microbiota in the blood and cecum, which can be modulated with antibiotics.
Low-pressure system (LPS) tracks in ERA5 over South Asia (1979-2025) v4.1
<h1>Low-Pressure System (LPS) tracks over South Asia — v4.1</h1> <p><strong>Region:</strong> South Asia (India, Sri Lanka, Pakistan; tracks may extend beyond these borders)<br><strong>Data source:</strong> 3-hourly ERA5 reanalysis<br><strong>Method:</strong> Automated LoG tracking following Hunt & Fletcher (2018, doi:10.1007/s00382-019-04744-x) and Hunt et al. (2016, doi:10.1175/MWR-D-15-0138.1)<br><br></p> <h2>Coverage</h2> <ul> <li><strong>Temporal span:</strong> <strong>1940-01-01</strong> to <strong>2025-08-31</strong> (inclusive)</li> <li><strong>Frame epoch:</strong> <code>frame</code> is hours since <strong>1940-01-01 00:00 UTC</strong></li> <li><strong>Timestep:</strong> hourly detections</li> </ul> <h3>Variables (columns)</h3> <ul> <li><code>x</code> — longitude (deg E)</li> <li><code>y</code> — latitude (deg N)</li> <li><code>frame</code> — hours since 1940-01-01 00:00 UTC (int)</li> <li><code>year</code>, <code>month</code>, <code>day</code>, <code>hour</code> — UTC components of detection time</li> <li><code>max_vort</code> — maximum relative vorticity within ~4° (~400–450 km) of the LPS centre from the <strong>850–700 hPa mean</strong>; units <strong>10⁻⁵ s⁻¹</strong></li> <li><code>mean_vort_850</code>, <code>mean_vort_700</code>, <code>mean_vort_500</code> — mean relative vorticity within ~4° at each level; units <strong>10⁻⁵ s⁻¹</strong></li> <li><code>precip_1hr</code> — mean precipitation rate within ~8° (~800–900 km) over the hour; <strong>mm h⁻¹</strong> </li> <li><code>precip_24hr</code> — mean of the <strong>calendar-day</strong> 24-h precipitation sum within ~8°; <strong>mm day⁻¹</strong></li> <li><code>mean_wind</code>, <code>max_wind</code> — 10 m wind speed within ~4° (area-mean and maximum); <strong>m s⁻¹</strong></li> <li><code>min_mslp</code> — minimum mean sea-level pressure within ~4°; <strong>hPa</strong></li> <li><code>mslp_contours</code> — count of <strong>closed</strong> 2 hPa isobars that enclose the LPS centre (dimensionless)</li> <li><code>over_land</code> — 1 if the centre is over land (Natural Earth 1:50 m), else 0</li> <li><code>category</code> — intensity class (IMD-style): 1=L, 2=D, 3=DD, 4=CS, 5=SCS, 6=VSCS, 7=SuCS</li> <li><code>track_id</code> — unique integer identifier for each track (all points with the same <code>track_id</code> belong to the same system)</li> </ul> <h3>Intensity category (<code>category</code>)</h3> <p>IMD-style classes, derived from the number of enclosing closed isobars and 10 m wind (over ocean) / combination with isobars (over land).<br>Mapping used in figures/legends:</p> <ul> <li><strong>1 = L</strong> (Low)</li> <li><strong>2 = D</strong> (Depression)</li> <li><strong>3 = DD</strong> (Deep Depression)</li> <li><strong>4 = CS</strong> (Cyclonic Storm)</li> <li><strong>5 = SCS</strong> (Severe Cyclonic Storm)</li> <li><strong>6 = VSCS</strong> (Very Severe Cyclonic Storm)</li> <li><strong>7 = SuCS</strong> (Super Cyclonic Storm)<br><br></li> </ul> <p><strong>Notes:</strong></p> <ul> <li>Over <strong>ocean</strong>, thresholds follow IMD (knots converted to m s⁻¹): 17, 28, 34, 48, 64, 119 kt breakpoints.</li> <li>Over <strong>land</strong>, the number of enclosing closed isobars (2 hPa spacing) informs classification together with wind.</li> </ul>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.