252 research outputs found
Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.
In this study, we have generated and characterized Electronic Cigarette (EC) aerosols using a combination of advanced technologies. In the gas phase, the particle number concentration (PNC) of EC aerosols was found to be positively correlated with puff duration whereas the PNC and size distribution may vary with different flavors and nicotine strength. In the liquid phase (water or cell culture media), the size of EC nanoparticles appeared to be significantly larger than those in the gas phase, which might be due to aggregation of nanoparticles in the liquid phase. By using in vitro high-throughput cytotoxicity assays, we have demonstrated that EC aerosols significantly decrease intracellular levels of glutathione in NHOKs in a dose-dependent fashion resulting in cytotoxicity. These findings suggest that EC aerosols cause cytotoxicity to oral epithelial cells in vitro, and the underlying molecular mechanisms may be or at least partially due to oxidative stress induced by toxic substances (e.g., nanoparticles and chemicals) present in EC aerosols
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E-cigarette aerosols induce unfolded protein response in normal human oral keratinocytes.
Objective: Since the introduction in 2004, global usage of e-cigarettes (ECs) has risen exponentially. However, the risks of ECs on oral health are uncertain. The purpose of this study is to understand if EC aerosol exposure impacts the gene pathways of normal human oral keratinocytes (NHOKs), particularly the unfolded protein response (UPR) pathway. Materials and methods: EC aerosols were generated reproducibly with a home-made puffing device and impinged into the culture medium for NHOKs. DNA microarrays were used to profile the gene expression changes in NHOKs treated with EC aerosols, and the Ingenuity Pathway Analysis (IPA) was used to reveal signaling pathways altered by the EC aerosols. Quantitative PCR was used to validate the expression changes of significantly altered genes. Results: DNA microarray profiling followed by IPA revealed a number of signaling pathways, such as UPR, cell cycle regulation, TGF-β signaling, NRF2-mediated oxidative stress response, PI3K/AKT signaling, NF-κB signaling, and HGF signaling, activated by EC aerosols in NHOKs. The UPR pathway genes, C/EBP homologous protein (CHOP), activating transcription factor 4 (ATF4), X box binding protein 1 (XBP1), and inositol-requiring enzyme 1 alpha (IRE1α) were all significantly up-regulated in EC aerosol-treated NHOKs whereas immunoglobulin heavy-chain binding protein (BIP) and PRKR-like ER kinase (PERK) were slightly up-regulated. qPCR analysis results were found to be well correlated with those from the DNA microarray analysis. The most significantly changed genes in EC aerosol-treated NHOKs versus untreated NHOKs were CHOP, ATF4, XBP1, IRE1α and BIP. Meanwhile, Western blot analysis confirmed that CHOP, GRP78 (BIP), ATF4, IRE1α and XBP1s (spliced XBP1) were significantly up-regulated in NHOKs treated with EC aerosols. Conclusion: Our results indicate that EC aerosols up-regulate the UPR pathway genes in NHOKs, and the induction of UPR response is mediated by the PERK - EIF2α - ATF4 and IRE1α - XBP1 pathways
Proof of User Similarity: the Spatial Measurer of Blockchain
Although proof of work (PoW) consensus dominates the current blockchain-based
systems mostly, it has always been criticized for the uneconomic brute-force
calculation. As alternatives, energy-conservation and energy-recycling
mechanisms heaved in sight. In this paper, we propose proof of user similarity
(PoUS), a distinct energy-recycling consensus mechanism, harnessing the
valuable computing power to calculate the similarities of users, and enact the
calculation results into the packing rule. However, the expensive calculation
required in PoUS challenges miners in participating, and may induce plagiarism
and lying risks. To resolve these issues, PoUS embraces the best-effort schema
by allowing miners to compute partially. Besides, a voting mechanism based on
the two-parties computation and Bayesian truth serum is proposed to guarantee
privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes
itself in recycling the computing power back to blockchain since it turns the
resource wastage to facilitate refined cohort analysis of users, serving as the
spatial measurer and enabling a searchable blockchain. We build a prototype of
PoUS and compare its performance with PoW. The results show that PoUS
outperforms PoW in achieving an average TPS improvement of 24.01% and an
average confirmation latency reduction of 43.64%. Besides, PoUS functions well
in mirroring the spatial information of users, with negligible computation time
and communication cost.Comment: 12 pages,10 figure
limma powers differential expression analyses for RNA-sequencing and microarray studies
limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously describe
Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading
cause of death in the US, underlining the importance of accurate ADRD risk
prediction. While recent advancement in ADRD risk prediction have primarily
relied on imaging analysis, yet not all patients undergo medical imaging before
an ADRD diagnosis. Merging machine learning with claims data can reveal
additional risk factors and uncover interconnections among diverse medical
codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for
ADRD risk prediction. Addressing the lack of human-interpretable reasons behind
these predictions, we introduce an innovative method to evaluate relationship
importance and its influence on ADRD risk prediction, ensuring comprehensive
interpretation.
We employed Variationally Regularized Encoder-decoder Graph Neural Network
(VGNN) for estimating ADRD likelihood. We created three scenarios to assess the
model's efficiency, using Random Forest and Light Gradient Boost Machine as
baselines. We further used our relation importance method to clarify the key
relationships for ADRD risk prediction. VGNN surpassed other baseline models by
10% in the area under the receiver operating characteristic. The integration of
the GNN model and relation importance interpretation could potentially play an
essential role in providing valuable insight into factors that may contribute
to or delay ADRD progression.
Employing a GNN approach with claims data enhances ADRD risk prediction and
provides insights into the impact of interconnected medical code relationships.
This methodology not only enables ADRD risk modeling but also shows potential
for other image analysis predictions using claims data
Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery
Street-view imagery provides us with novel experiences to explore different
places remotely. Carefully calibrated street-view images (e.g. Google Street
View) can be used for different downstream tasks, e.g. navigation, map features
extraction. As personal high-quality cameras have become much more affordable
and portable, an enormous amount of crowdsourced street-view images are
uploaded to the internet, but commonly with missing or noisy sensor
information. To prepare this hidden treasure for "ready-to-use" status,
determining missing location information and camera orientation angles are two
equally important tasks. Recent methods have achieved high performance on
geo-localization of street-view images by cross-view matching with a pool of
geo-referenced satellite imagery. However, most of the existing works focus
more on geo-localization than estimating the image orientation. In this work,
we re-state the importance of finding fine-grained orientation for street-view
images, formally define the problem and provide a set of evaluation metrics to
assess the quality of the orientation estimation. We propose two methods to
improve the granularity of the orientation estimation, achieving 82.4% and
72.3% accuracy for images with estimated angle errors below 2 degrees for CVUSA
and CVACT datasets, corresponding to 34.9% and 28.2% absolute improvement
compared to previous works. Integrating fine-grained orientation estimation in
training also improves the performance on geo-localization, giving top 1 recall
95.5%/85.5% and 86.8%/80.4% for orientation known/unknown tests on the two
datasets.Comment: This paper has been accepted by ACM Multimedia 2022. The version
contains additional supplementary material
The fast light of CsI(Na) crystals
The responds of different common alkali halide crystals to alpha-rays and
gamma-rays are tested in our research. It is found that only CsI(Na) crystals
have significantly different waveforms between alpha and gamma scintillations,
while others have not this phenomena. It is suggested that the fast light of
CsI(Na) crystals arises from the recombination of free electrons with
self-trapped holes of the host crystal CsI. Self-absorption limits the emission
of fast light of CsI(Tl) and NaI(Tl) crystals.Comment: 5 pages, 11 figures Submit to Chinese Physics
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Systematic Identification of Synergistic Drug Pairs Targeting HIV
The systematic identification of effective drug combinations has been hindered by the unavailability of methods that can explore the large combinatorial search space of drug interactions. Here we present a multiplex screening method named MuSIC (Multiplex Screening for Interacting Compounds), which expedites the comprehensive assessment of pair-wise compound interactions. We examined ~500,000 drug pairs from 1000 FDA-approved or clinically tested drugs and identified drugs that synergize to inhibit HIV replication. Our analysis reveals an enrichment of anti-inflammatory drugs in drug combinations that synergize against HIV, indicating HIV benefits from inflammation that accompanies its infection. Multiple drug pairs identified in this study, including glucocorticoid and nitazoxanide, synergize by targeting different steps of the HIV life cycle. As inflammation accompanies HIV infection, our findings indicate that inhibiting inflammation could curb HIV propagation. MuSIC can be applied to a wide variety of disease-relevant screens to facilitate efficient identification of compound combinations
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