scholarly journals In Search of the Most Relevant Parameter for Quantifying Lung Inflammatory Response to Nanoparticle Exposure: Particle Number, Surface Area, or What?

2007 ◽  
Vol 115 (2) ◽  
pp. 187-194 ◽  
Author(s):  
Klaus Wittmaack
Keyword(s):  
Inflammatory Response ◽  
Surface Area ◽  
Particle Number ◽  
Relevant Parameter ◽  
Nanoparticle Exposure

scholarly journals Simple Equations Pertaining to the Particle Number and Surface Area of Metallic, Polymeric, Lipidic and Vesicular Nanocarriers

2021 ◽  
Vol 89 (2) ◽  
pp. 15
Author(s):  
M. R. Mozafari ◽  
E. Mazaheri ◽  
K. Dormiani
Keyword(s):  
Drug Delivery ◽  
Surface Area ◽  
Particle Number ◽  
Analytical Techniques ◽  
Metallic Particles ◽  
Nutraceutical Compounds ◽  
Mathematical Formulas ◽  
Simple Equations

Introduction: Bioactive encapsulation and drug delivery systems have already found their way to the market as efficient therapeutics to combat infections, viral diseases and different types of cancer. The fields of food fortification, nutraceutical supplementation and cosmeceuticals have also been getting the benefit of encapsulation technologies. Aim: Successful formulation of such therapeutic and nutraceutical compounds requires thorough analysis and assessment of certain characteristics including particle number and surface area without the need to employ sophisticated analytical techniques. Solution: Here we present simple mathematical formulas and equations used in the research and development of drug delivery and controlled release systems employed for bioactive encapsulation and targeting the sites of infection and cancer in vitro and in vivo. Systems covered in this entry include lipidic vesicles, polymeric capsules, metallic particles as well as surfactant- and tocopherol-based micro- and nanocarriers.

scholarly journals How do particle number, surface area, and mass correlate with toxicity of diesel particle emissions as measured in chemical and cellular assays?

Chemosphere ◽  
2019 ◽  
Vol 229 ◽  
pp. 559-569 ◽  
Author(s):  
Jian Xue ◽  
Shaohua Hu ◽  
David Quiros ◽  
Alberto Ayala ◽  
Heejung S. Jung
Keyword(s):  
Surface Area ◽  
Particle Number ◽  
Particle Emissions ◽  
Cellular Assays

Effect of change in particle number on pulmonary clearance of aerosolized 99mTc-DTPA

1989 ◽  
Vol 66 (6) ◽  
pp. 2750-2755 ◽  
Author(s):  
S. Groth ◽  
J. Mortensen ◽  
P. Lange ◽  
S. Vest ◽  
N. Rossing ◽  
...  
Keyword(s):  
Surface Area ◽  
Absorption Rate ◽  
Particle Number ◽  
The Other ◽  
Inhaled Particles ◽  
Diffusion Limited ◽  
Alveolar Surface Area ◽  
Pulmonary Clearance ◽  
Jet Nebulizers ◽  
Alveolar Surface

Pulmonary clearance (PCl) of inhaled aerosolized 99mTc-diethylenetriamine pentaacetic acid (DTPA) across the alveolocapillary membrane is diffusion limited. Therefore, if the mixing of the 99mTc-DTPA in the aqueous hypophase underlying surfactant is slow or incomplete or if there were no hypophase, an increase in the alveolar surface area occupied by 99mTc-DTPA particles would increase the absorption rate. The aim of this study was to examine whether there is an effect on PCl of changing the number of inhaled particles. The change in particle number was accomplished by a setup of four parallel jet nebulizers feeding a central delivery chamber of 400 cm3. We performed two kinds of experiments in eight healthy nonsmokers between 28 and 52 yr of age. In the first experiment, 99mTc-DTPA in saline was nebulized in one nebulizer, while saline was nebulized in the other three. In the second experiment the number of inhaled particles containing 99mTc-DTPA was increased by a factor of four by nebulizing 99mTc-DTPA in saline in all four nebulizers simultaneously. Increasing the number of inhaled 99mTc-DTPA particles caused an increase in PCl of 24.2% (P less than 0.01). We conclude that there is a slight but significant effect of changing the number of DTPA particles on PCl and that this is probably due to an uneven mixing of the 99mTc-DTPA in the aqueous hypophase underlying the surfactant lining and the alveoli.

Factor XII Promotes Leukocyte Inflammation and Its Deficiency Results in Faster Wound Healing

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 368-368 ◽  
Author(s):  
Evi Stavrou ◽  
Gretchen A. LaRusch ◽  
Matthew J. Fullana ◽  
Gary E. Wnek ◽  
Cheng-Kui Qu ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Wound Healing ◽  
Inflammatory Response ◽  
Surface Area ◽  
Inflammatory Cells ◽  
Total Surface Area ◽  
Factor Xii ◽  
Wound Area ◽  
Cell Recruitment ◽  
Leukocyte Response

Abstract Abstract 368 Background: Factor XII (FXII) participates in inflammation. FXII deficient patients have reduced leukocyte migration into skin windows. Recently, we have determined that FXII signals through uPAR, β1 integrin and the EGFR to stimulate ERK1/2 and Akt phosphorylation (Blood 115:5111, 2010). The downstream consequences of this pathway are FXII-induced cell proliferation and post-natal angiogenesis. Present studies examined if this pathway also influences leukocyte response to injury. Methods: Exon 3- to 8-deleted FXII knockout mice (FXII KO), generously provided by Dr. Frank Castellino, have impaired inflammatory response to injury. FXII KO were wounded by creating full thickness (5 mm) punch biopsies on their backs. The total surface area of inflammatory cells recruited at the injury site/20 × high power field (HPF) was determined and analyzed by Image J (NIH). We observed that FXII KO on Day 1 exhibit significantly decreased recruitment of CD11b-labeled inflammatory cells to injury sites compared with wild type mice (WT) [mean ± SEM, FXII KO: 40220 ± 3732 vs. WT: 59740 ± 6318 total surface area of CD11b positive cells (pixels)/HPF, p=0.0136]. Similar results are observed when uPAR KO were compared to WT mice [39380 ± 5234 vs. 73310 ± 4688, p=0.0001], suggesting that leukocyte migration is mediated through uPAR. On Day 1 of thioglycolate (TG)-induced peritonitis, FXII KO have a mean of 12.62 × 105/mL peritoneal exudate cells (PEC) vs. 25.94 × 105/mL (p=0.0072) observed in WT. Additionally on Day 7, FXII KO have 10.63 × 105/mL PEC vs. WT 25.38 × 105/mL (p< 0.0001). Likewise on Day 7, uPAR KO have 16.34 × 105/mL peritoneal leukocytes recruited vs. WT 25.38 × 105/mL (p=0.0124). These combined studies indicate that FXII influences the degree of leukocyte inflammation and the inflammatory response is partially mediated through uPAR. Studies next determined if leukocyte recruitment promotes wound healing. On Days 3–5 the rate of wound closure of back punch biopsies is faster in FXII KO than WT (p < 0.0327 by one way ANOVA). Again, we observed that FXII KO on Days 2 and 5 exhibit significantly less CD11b-labeled inflammatory cells to the injury site compared to WT (Day 2: p=0.0004, Day 5: p= 0.0075). Leukocyte subpopulation analysis reveals decreased neutrophil migration (Gr-1 positive cells) into the wound area of FXII KO on Day 2 [FXII KO 5669 ± 1844 vs. WT 40490 ± 8564 total surface area of Gr-1 positive cells (pixels)/ HPF, p=0.0018] and Day 5 [FXII KO 6216 ± 3829 vs. WT 34890 ± 7629 total surface area of Gr-1 positive cells (pixels)/HPF, p=0.0176]. Macrophage recruitment into the wound area, as determined by F4-80 antigenicity, increases over time, but remains significantly reduced in FXII KO mice on Day 2 [FXII KO 3111 ± 1115 vs. WT 19140 ± 5767 total surface area of F4-80 positive cells (pixels)/HPF, p=0.0348] and Day 5 [FXII KO 9377 ± 4772 vs. WT 28340 ± 4411 total surface area of F4-80 positive cells (pixels)/HPF, p=0.0171]. These studies indicate that, in FXII deficiency, there is less neutrophil and macrophage-mediated inflammation and this observation correlates with faster wound healing. Finally studies determined if the reduced inflammation seen in FXII KO is the result of host factors or bone marrow-derived cells. Adoptive bone marrow transplant (BMT) experiments were performed where WT or FXII KO bone marrow was transplanted into FXII KO hosts. Eight weeks following the BMT, mice were subjected to TG-induced peritonitis. KO/KO recipients, have significantly decreased number of PEC on day 1 (17.3 × 105/mL ± 3.894 × 105/mL cells) and day 7 (18.6 × 107/mL ± 4.0 × 107/mL cells) when compared to WT/KO recipients [Day 1: 68.25 × 105/mL ± 13.83 × 105/mL cells (p=0.041), Day 7: 190.5 × 107/mL ± 51.80 × 107/mL cells]. These data indicate that FXII interacts with neutrophils and macrophages to promote the inflammatory response; its absence causes decreased inflammatory cell recruitment. Conclusions: Our data indicate that FXII deficiency disrupts the leukocyte response to injury by reducing inflammatory cell recruitment in two murine models. Paradoxically, reduced leukocyte infiltration into skin wounds promotes healing. These investigations indicate a novel role for FXII in inflammation and wound healing and indicate a unique potential target for inflammation therapeutics. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Modification, calibration, and performance of the Ultra-High Sensitivity Aerosol Spectrometer for particle size distribution and volatility measurements during the Atmospheric Tomography Mission (ATom) airborne campaign

2018 ◽  
Vol 11 (1) ◽  
pp. 369-383 ◽  
Author(s):  
Agnieszka Kupc ◽  
Christina Williamson ◽  
Nicholas L. Wagner ◽  
Mathews Richardson ◽  
Charles A. Brock
Keyword(s):  
Refractive Index ◽  
Ammonium Sulfate ◽  
Surface Area ◽  
Particle Number ◽  
High Sensitivity ◽  
Counting Efficiency ◽  
Number Concentration ◽  
Particle Number Concentration ◽  
And Performance ◽  
Counting Statistics

Abstract. Atmospheric aerosol is a key component of the chemistry and climate of the Earth's atmosphere. Accurate measurement of the concentration of atmospheric particles as a function of their size is fundamental to investigations of particle microphysics, optical characteristics, and chemical processes. We describe the modification, calibration, and performance of two commercially available, Ultra-High Sensitivity Aerosol Spectrometers (UHSASs) as used on the NASA DC-8 aircraft during the Atmospheric Tomography Mission (ATom). To avoid sample flow issues related to pressure variations during aircraft altitude changes, we installed a laminar flow meter on each instrument to measure sample flow directly at the inlet as well as flow controllers to maintain constant volumetric sheath flows. In addition, we added a compact thermodenuder operating at 300 ∘C to the inlet line of one of the instruments. With these modifications, the instruments are capable of making accurate (ranging from 7 % for Dp < 0.07 µm to 1 % for Dp > 0.13 µm), precise (< ±1.2 %), and continuous (1 Hz) measurements of size-resolved particle number concentration over the diameter range of 0.063–1.0 µm at ambient pressures of > 1000 to 225 hPa, while simultaneously providing information on particle volatility. We assessed the effect of uncertainty in the refractive index (n) of ambient particles that are sized by the UHSAS assuming the refractive index of ammonium sulfate (n= 1.52). For calibration particles with n between 1.44 and 1.58, the UHSAS diameter varies by +4/−10 % relative to ammonium sulfate. This diameter uncertainty associated with the range of refractive indices (i.e., particle composition) translates to aerosol surface area and volume uncertainties of +8.4/−17.8 and +12.4/−27.5 %, respectively. In addition to sizing uncertainty, low counting statistics can lead to uncertainties of < 20 % for aerosol surface area and < 30 % for volume with 10 s time resolution. The UHSAS reduction in counting efficiency was corrected for concentrations > 1000 cm−3. Examples of thermodenuded and non-thermodenuded aerosol number and volume size distributions as well as propagated uncertainties are shown for several cases encountered during the ATom project. Uncertainties in particle number concentration were limited by counting statistics, especially in the tropical upper troposphere where accumulation-mode concentrations were sometimes < 20 cm−3 (counting rates ∼ 5 Hz) at standard temperature and pressure.

In vivo inflammatory response to polymethylmethacrylate particulate debris: Effect of size, morphology, and surface area

1994 ◽  
Vol 12 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Howard Gelb ◽  
H. Ralph Schumacher ◽  
John Cuckler ◽  
Daniel G. Baker
Keyword(s):  
Inflammatory Response ◽  
Surface Area ◽  
Particulate Debris

Supervised regression learning for predictions of aerosol particle size distributions from PM2.5, total particle number and meteorological parameters at Helsinki SMEAR3 station

2020 ◽  
Author(s):  
Juha Kangasluoma ◽  
Yusheng Wu ◽  
Runlong Cai ◽  
Joel Kuula ◽  
Hilkka Timonen ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Surface Area ◽  
Aerosol Particle ◽  
Particle Number ◽  
Meteorological Parameters ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Aerosol Particle Size ◽  
Regression Learning

&lt;p&gt;Supervised regression learning for predictions of aerosol particle size distributions from PM2.5, total particle number and meteorological parameters at Helsinki SMEAR3 station&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;J. Kangasluoma&lt;sup&gt;1&lt;/sup&gt;, Y. Wu&lt;sup&gt;1&lt;/sup&gt;, R. Cai&lt;sup&gt;1&lt;/sup&gt;, J. Kuula&lt;sup&gt;2&lt;/sup&gt;, H. Timonen&lt;sup&gt;2&lt;/sup&gt;, P. P. Aalto&lt;sup&gt;1&lt;/sup&gt;, M. Kulmala&lt;sup&gt;1&lt;/sup&gt;, T. Pet&amp;#228;j&amp;#228;&lt;sup&gt;1&lt;/sup&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;sup&gt;1&lt;/sup&gt; Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland&lt;/p&gt;&lt;p&gt;&lt;sup&gt;2 &lt;/sup&gt;Finnish Meteorological Institute, Erik Palm&amp;#233;nin aukio 1, 00560 Helsinki, Finland&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Atmospheric particulate material is a significant pollutant and causes millions premature deaths yearly especially in urban city environments. To conduct epidemiological studies and quantify of the role of sub-micron particles, especially role of the ultrafine particles (&lt;100 nm), in mortality caused by the particulate matter, long-term monitoring of the particle number, surface area, mass and chemical composition are needed. Such monitoring on large scale is currently done only for particulate mass, namely PM2.5 (mass of particulates smaller than 2.5 &amp;#956;m), while large body of evidence suggests that ultrafine particles, which dominate the number of the aerosol distribution, cause significant health effects that do not originate from particle mass.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The chicken-egg-problem here is that monitoring of particle number or surface area is not required from the authorities due to lack of epidemiological evidence showing the harm and suitable instrumentation (although car industry already voluntarily limits the ultrafine particle number emissions), while these epidemiological studies are lacking because of the suitable lack of data. Here we present the first step in solving this &amp;#8220;lack of data issue&amp;#8221; by predicting aerosol particle size distributions based on PM2.5, particle total number and meteorological measurements, from which particle size distribution, and subsequently number, surface area and mass exposure can be calculated.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;We use baggedtree supervised regression learning (from Matlab toolbox) to train an algorithm with one full year data from SMEAR3 station at 10 min time resolution in Helsinki during 2018. The response variable is the particle size distribution (each bin separately) and the training variables are PM2.5, particle number and meteorological parameters. The trained algorithm is then used with the same training variables data, but from 2019 to predict size distributions, which are directly compared to the measured size distributions by a differential mobility particle sizer.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;To check the model performance, we divide the predicted distributions to three size bins, 3-25, 25-100 and 100-1000 nm, and calculate the coefficient of determination (r&lt;sup&gt;2&lt;/sup&gt;) between the measured and predicted number concentration at 10 min time resolution, which are 0.79, 0.60 and 0.50 respectively. We also calculate r&lt;sup&gt;2&lt;/sup&gt; between the measured and predicted number, surface area and mass exposure, which are 0.87, 0.79 and 0.74, respectively. Uncertainties in the prediction are mostly random, thus the r&lt;sup&gt;2&lt;/sup&gt; values will increase at longer averaging times.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Our results show that an algorithm that is trained with particle size distribution data, and particle number, PM2.5 and meteorological data can predict particle size distributions and number, surface area and mass exposures. In practice, these predictions can be realized e.g. in air pollution monitoring networks by implementing a condensation particle counter at each site, and circulating a differential mobility size spectrometer around the sites.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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