RESUMEN
The occurrence and persistence of microplastics (MPs) in natural environments are of increasing concern. Along with this, the transport of MPs in sediments has been investigated mainly focusing on the effect of plastic size and shape, media size effect, and solution chemistry. Yet, the influence of particle density is only partially understood. Therefore, column experiments on the transport of variably buoyant MPs in saturated natural sediments and glass beads were conducted, and transport parameters were quantified using a two-site kinetic transport model with a depth-dependent blocking function (the amount of retained MPs does not decrease at a constant rate with increasing depth, the majority of MPs were retained near the column inlet). Neutral, sinking, and buoyant MPs within the same size range were selected, with stable water isotope applied as conservative tracer to explore water and MP movement in the tested sediments. The results showed that 95.5 ± 1.4% of sinking MPs remained in columns packed with gravel, followed by buoyant and neutral MPs, thus indicating that particle density does affect MP mobility. Similar recovered amounts of MPs were found in columns packed with glass beads, indicating that tested sediment types do not affect the deposition behavior of MPs. The breakthrough curves of MPs were accurately described by the selected model. However, the simulated retention profiles overestimated the observed MP amount in layers closest to the column inlet. The coupled experimental and modeled results suggest an enhanced retention of sinking MPs, while neutrally and buoyant MPs exhibit a higher mobility in comparison. Thus, neutral or buoyant MPs can potentially pose a higher contamination risk to subsurface porous media environments compared to sinking MPs. Discrepancies between observed and simulated retention profiles indicate that future model development is needed for advancing the MP deposition as affected by particle density.
RESUMEN
The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.