Ambient fine particulate matter exposures and oxidative protein damage in early pregnant women.

The association between oxidative protein damage in early pregnant women and ambient fine particulate matter (PM2.5) is unknown. We estimated the effect of PM2.5 exposures within seven days before blood collection on serum 3-nitrotyrosine (3-NT) and advanced oxidation protein products (AOPP) in 100 women with normal early pregnancy (NEP) and 100 women with clinically recognized early pregnancy loss (CREPL). Temporally-adjusted land use regression model was applied for estimation of maternal daily PM2.5 exposure. Daily nitrogen dioxide (NO2) exposure of each participant was estimated using city-level concentrations of NO2. Single-day lag effect of PM2.5 was analyzed using multivariable linear regression model. Net cumulative effect and distributed lag effect of PM2.5 and NO2 within seven days were analyzed using distributed lag non-linear model. In all 200 subjects, the serum 3-NT were significantly increased with the single-day lag effects (4.72%-8.04% increased at lag 0-2), distributed lag effects (2.32%-3.49% increased at lag 0-2), and cumulative effect within seven days (16.91% increased). The single-day lag effects (7.41%-10.48% increased at lag 0-1), distributed lag effects (3.42%-5.52% increased at lag 0-2), and cumulative effect within seven days (24.51% increased) of PM2.5 significantly increased serum 3-NT in CREPL group but not in NEP group. The distributed lag effects (2.62%-4.54% increased at lag 0-2) and cumulative effect within seven days (20.25% increased) of PM2.5 significantly increased serum AOPP in early pregnant women before the coronavirus disease (COVID-19) pandemic but not after that, similarly to the effects of NO2 exposures. In conclusion, PM2.5 exposures were associated with oxidative stress to protein in pregnant women in the first trimester, especially in CREPL women. Analysis of NO2 exposures suggested that combustion PM2.5 was the crucial PM2.5 component. Wearing masks may be potentially preventive in PM2.5 exposure and its related oxidative protein damage.

Biological chemotaxis-guided self-thermophoretic nanoplatform augments colorectal cancer therapy through autonomous mucus penetration.

Oral drug delivery systems have great potential to treat colorectal cancer (CRC). However, the drug delivery efficiency is restricted by limited CRC-related intestine positioning and dense mucus barrier. Here, we present a biological chemotaxis-guided self-thermophoretic nanoplatform that facilitates precise intestinal positioning and autonomous mucus penetration. The nanoplatform introduces asymmetric platinum-sprayed mesoporous silica to achieve autonomous movement in intestinal mucus. Furthermore, inspired by the intense interaction between pathogenic microbes and CRC, the nanoplatform is camouflaged by Staphylococcus aureus membrane to precisely anchor in CRC-related intestine. Owing to 4.3-fold higher biological chemotactic anchoring of CRC-related intestine and 14.6-fold higher autonomous mucus penetration performance, the nanoplatform vastly improves the oral bioavailability of cisplatin, leading to a tumor inhibition rate of 99.1% on orthotopic CRC-bearing mice. Together, the exquisitely designed nanoplatform to overcome multiple physiological barriers provides a new horizon for the development of oral drug delivery systems.

Ambient PM2.5 exposures and systemic inflammation in women with early pregnancy.

The association between ambient fine particulate matter (PM2.5) and systemic inflammation in women with early pregnancy is unclear. This study estimated the effects of PM2.5 exposures on inflammatory biomarkers in women with normal early pregnancy (NEP) or clinically recognized early pregnancy loss (CREPL). Serum interleukin-1beta (IL-1ß), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) were measured in 228 early pregnant women recruited in Tianjin, China. Maternal PM2.5 exposures at lag 0 through lag 30 before blood collection were estimated using temporally-adjusted land use regression models. Daily exposures to ambient PM10, NO2, SO2, CO and 8-hours maximum ozone were estimated using city-level concentrations. Single-day lag effects at lag 0 through lag 7 were estimated using multivariable linear regression models. Distributed lag effects and cumulative effects over the preceding seven days and 30 days were estimated using distributed lag non-linear models. Serum IL-1ß (8.0% increase at lag 3), IL-6 (33.9% increase at lag 5) and TNF-α (12.7% increase at lag 5) in early pregnant women were significantly increased with an interquartile range increase in PM2.5 exposures adjusted for temporal confounders and demographic characteristics. These effects were robust in several two-pollutant models. Distributed lag effects over the preceding 30 days also showed that the three cytokines were significantly increased with PM2.5 on some lag days. Among all cumulative effects of PM2.5 on the three cytokines in all subjects or in the two groups, only IL-6 was significantly increased in CREPL women over the preceding seven days and 30 days. No significant cumulative effect of PM2.5 was observed in NEP women. In conclusion, exposure to ambient PM2.5 may induce systemic inflammation in women in the first trimester of pregnancy. Whether the PM2.5-related cumulative increase in maternal IL-6 is involved in the pathogenic mechanisms of early pregnancy loss needs to be identified in future research.

Ambient fine particulate matter exposures and human early placental inflammation.

The effect of fine particulate matter (PM2.5) on human early maternal-fetal interface is unknown. We explored the association between maternal exposure to ambient PM2.5 and inflammation in placental villus of 114 women with clinically recognized early pregnancy loss (CREPL) and 114 women with normal early pregnancy (NEP). Temporally-adjusted land use regression models were used to estimate maternal daily PM2.5 exposure during pregnancy. Villus interleukin-1beta (IL-1ß), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured using multiplex cytokines detection platform. Single-day lag effect of PM2.5 exposure within ten days before early placental villus collection was estimated using multivariable linear regression model. Distributed lag and net cumulative effects of PM2.5 exposures within ten and 30 days before villus collection, as well as five single weeks during the periovulatory period, were estimated using distributed lag non-linear models. In all 228 subjects, after adjusting for group (CREPL or NEP), temporal confounders, and demographic characteristics, both single-day and distributed lag effects of PM2.5 exposure at lag 8 significantly increased villus IL-6; distributed lag effects of PM2.5 exposure in the first and second weeks before ovulation increased IL-1ß, and PM2.5 exposure in the third week after ovulation increased IL-6 and TNF-α. In CREPL, single-day lag effect significantly increased IL-1ß (at lag 1), IL-6 (at lag 8), and TNF-α (at lag 5); distributed lag effect increased IL-6 (at lag 4-lag 8) and TNF-α (at lag 4-lag 6); and cumulative effect within ten days before villus collection increased IL-6. There was no statistically significant cumulative effect in NEP. In summary, maternal PM2.5 exposure was associated with placental inflammation in human early pregnancy, particularly with increased villus IL-6 in CREPL. Whether maternal-fetal interface inflammation related to PM2.5 exposure during the periovulatory period or later contributes to CREPL or other adverse pregnancy outcomes requires further study.

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution.

Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing this number lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multidimensional fields, for applications such as image super-resolution. In this article, we investigate the use of such architectures for learning the upscaling of a low resolution sampling of an isosurface to a higher resolution, with reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating smooth, edge-aware depth and normal fields as well as ambient occlusions from a low resolution depth and normal field. By adding a frame-to-frame motion loss into the learning stage, upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We assess the quality of inferred results and compare it to bi-linear and cubic upscaling. We do this for isosurfaces which were never seen during training, and investigate the improvements when the network can train on the same or similar isosurfaces. We discuss remote visualization and foveated rendering as potential applications.