Equivalent titanium dioxide nanoparticle deposition by intratracheal instillation and whole body inhalation: the effect of dose rate on acute respiratory tract inflammation

BACKGROUND: The increased production of nanomaterials has caused a corresponding increase in concern about human exposures in consumer and occupational settings. Studies in rodents have evaluated dose–response relationships following respiratory tract (RT) delivery of nanoparticles (NPs) in order to identify potential hazards. However, these studies often use bolus methods that deliver NPs at high dose rates that do not reflect real world exposures and do not measure the actual deposited dose of NPs. We hypothesize that the delivered dose rate is a key determinant of the inflammatory response in the RT when the deposited dose is constant. METHODS: F-344 rats were exposed to the same deposited doses of titanium dioxide (TiO(2)) NPs by single or repeated high dose rate intratracheal instillation or low dose rate whole body aerosol inhalation. Controls were exposed to saline or filtered air. Bronchoalveolar lavage fluid (BALF) neutrophils, biochemical parameters and inflammatory mediator release were quantified 4, 8, and 24 hr and 7 days after exposure. RESULTS: Although the initial lung burdens of TiO(2) were the same between the two methods, instillation resulted in greater short term retention than inhalation. There was a statistically significant increase in BALF neutrophils at 4, 8 and 24 hr after the single high dose TiO(2) instillation compared to saline controls and to TiO(2) inhalation, whereas TiO(2) inhalation resulted in a modest, yet significant, increase in BALF neutrophils 24 hr after exposure. The acute inflammatory response following instillation was driven primarily by monocyte chemoattractant protein-1 and macrophage inflammatory protein-2, mainly within the lung. Increases in heme oxygenase-1 in the lung were also higher following instillation than inhalation. TiO(2) inhalation resulted in few time dependent changes in the inflammatory mediator release. The single low dose and repeated exposure scenarios had similar BALF cellular and mediator response trends, although the responses for single exposures were more robust. CONCLUSIONS: High dose rate NP delivery elicits significantly greater inflammation compared to low dose rate delivery. Although high dose rate methods can be used for quantitative ranking of NP hazards, these data caution against their use for quantitative risk assessment

Autonomous decision-making against induced seismicity in deep fluid injections

The rise in the frequency of anthropogenic earthquakes due to deep fluid injections is posing serious economic, societal, and legal challenges to geo-energy and waste-disposal projects. We propose an actuarial approach to mitigate this risk, first by defining an autonomous decision-making process based on an adaptive traffic light system (ATLS) to stop risky injections, and second by quantifying a "cost of public safety" based on the probability of an injection-well being abandoned. The ATLS underlying statistical model is first confirmed to be representative of injection-induced seismicity, with examples taken from past reservoir stimulation experiments (mostly from Enhanced Geothermal Systems, EGS). Then the decision strategy is formalized: Being integrable, the model yields a closed-form ATLS solution that maps a risk-based safety standard or norm to an earthquake magnitude not to exceed during stimulation. Finally, the EGS levelized cost of electricity (LCOE) is reformulated in terms of null expectation, with the cost of abandoned injection-well implemented. We find that the price increase to mitigate the increased seismic risk in populated areas can counterbalance the heat credit. However this "public safety cost" disappears if buildings are based on earthquake-resistant designs or if a more relaxed risk safety standard or norm is chosen.Comment: 8 pages, 4 figures, conference (International Symposium on Energy Geotechnics, 26-28 September 2018, Lausanne, Switzerland

Open Access

Equivalent titanium dioxide nanoparticle deposition by intratracheal instillation and whole body inhalation: the effect of dose rate on acute respiratory tract inflammatio

Prognostic implications of various models for calculation of S-phase fraction in 259 patients with soft tissue sarcoma

The S-phase fraction (SPF) in flow cytometric DNA histograms in soft tissue sarcoma (STS) can be calculated in various ways. The traditional planimetric method of Baisch has been shown to be prognostic, but is hampered by a failure rate of around 40%. We therefore tested other models to see if this rate could be decreased with retained prognostic value. In 259 STS of the locomotor system the SPF was calculated according to Baisch and with commercial parametric MultiCycle software using different corrections for background. Using the Baisch model, 159 histograms could be evaluated for SPF. The 5-year metastasis-free survival rate (MFSR) was 0.94 for the low-risk group (defined with SPF), and 0.53 for the high-risk group. In the low-risk group, four of the seven patients who developed metastasis did so after 5 years. Using the MultiCycle software, SPF could be calculated in 253 tumours. Depending on type of background correction used, the 5-year MFSR varied between 0.67 and 0.82 for the low-risk group, and between 0.47 and 0.53 for the high-risk group. The late metastasis pattern in the low-risk group was never seen using the MultiCycle software. We conclude that in paraffin archival material, calculation of SPF according to Baisch is preferable in clinical use due to better separation between low-risk and high-risk groups, and also the possibility to identify patients who metastasize late. © 1999 Cancer Research Campaig

Non invasive ventilation after extubation in paediatric patients: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Non-invasive ventilation (NIV) may be useful after extubation in children. Our objective was to determine postextubation NIV characteristics and to identify risk factors of postextubation NIV failure.</p> <p>Methods</p> <p>A prospective observational study was conducted in an 8-bed pediatric intensive care unit (PICU). Following PICU protocol, NIV was applied to patients who had been mechanically ventilated for over 12 hours considered at high-risk of extubation failure -elective NIV (eNIV), immediately after extubation- or those who developed respiratory failure within 48 hours after extubation -rescue NIV (rNIV)-. Patients were categorized in subgroups according to their main underlying conditions. NIV was deemed successful when reintubation was avoided. Logistic regression analysis was performed in order to identify predictors of NIV failure.</p> <p>Results</p> <p>There were 41 episodes (rNIV in 20 episodes). Success rate was 50% in rNIV and 81% in eNIV (p = 0.037). We found significant differences in univariate analysis between success and failure groups in respiratory rate (RR) decrease at 6 hours, FiO₂at 1 hour and PO₂/FiO₂ratio at 6 hours. Neurologic condition was found to be associated with NIV failure. Multiple logistic regression analysis identified no variable as independent NIV outcome predictor.</p> <p>Conclusions</p> <p>Our data suggest that postextubation NIV seems to be useful in avoiding reintubation in high-risk children when applied immediately after extubation. NIV was more likely to fail when ARF has already developed (rNIV), when RR at 6 hours did not decrease and if oxygen requirements increased. Neurologic patients seem to be at higher risk of reintubation despite NIV use.</p

A qualitative exploration of Malaysian cancer patients' perspectives on cancer and its treatment

<p>Abstract</p> <p>Background</p> <p>Cancer patients' knowledge about cancer and experiences with its treatment play an important role in long-term adherence in their disease management. This study aimed to explore cancer patients' knowledge about cancer, their perceptions of conventional therapies and the factors that contribute to medication adherence in the Malaysian population.</p> <p>Methods</p> <p>A qualitative research approach was adopted to gain a better understanding of the current perceptions and knowledge held by cancer patients. Twenty patients were interviewed using a semi-structured interview guide. A saturation point was reached after the 18^thinterview, and no new information emerged with the subsequent 2 interviews. All interviews were transcribed verbatim and analysed by means of a standard content analysis framework.</p> <p>Results</p> <p>The majority of patients related the cause of their cancer to be God's will. Participants perceived conventional therapies as effective due to their scientific methods of preparations. A fear of side effects was main reasons given for delay in seeking treatment; however, perceptions were reported to change after receiving treatment when effective management to reduce the risk of side effects had been experienced.</p> <p>Conclusions</p> <p>This study provides basic information about cancer patients' perceptions towards cancer and its treatment. These findings can help in the design of educational programs to enhance awareness and acceptances of cancer screening. Priorities for future research should focus on patients who refused the conventional therapies at any stage.</p

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

abstract: Background The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable. Results We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes. Conclusions The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.The electronic version of this article is the complete one and can be found online at: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-48

Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register