Direct isolation of small extracellular vesicles from human blood using viscoelastic microfluidics.

Small extracellular vesicles (sEVs; <200 nm) that contain lipids, nucleic acids, and proteins are considered promising biomarkers for a wide variety of diseases. Conventional methods for sEV isolation from blood are incompatible with routine clinical workflows, significantly hampering the utilization of blood-derived sEVs in clinical settings. Here, we present a simple, viscoelastic-based microfluidic platform for label-free isolation of sEVs from human blood. The separation performance of the device is assessed by isolating fluorescent sEVs from whole blood, demonstrating purities and recovery rates of over 97 and 87%, respectively. Significantly, our viscoelastic-based microfluidic method also provides for a remarkable increase in sEV yield compared to gold-standard ultracentrifugation, with proteomic profiles of blood-derived sEVs purified by both methods showing similar protein compositions. To demonstrate the clinical utility of the approach, we isolate sEVs from blood samples of 20 patients with cancer and 20 healthy donors, demonstrating that elevated sEV concentrations can be observed in blood derived from patients with cancer.

Ammonia emissions from a dairy housing and wastewater treatment plant quantified with an inverse dispersion method accounting for deposition loss.

Ammonia (NH3) emissions negatively impact air, soil, and water quality, hence human health and biodiversity. Significant emissions, including the largest sources, originate from single or multiple structures, such as livestock facilities and wastewater treatment plants (WWTPs). The inverse dispersion method (IDM) is effective in measuring total emissions from such sources, although depositional loss between the source and point of measurement is often not accounted for. We applied IDM with a deposition correction to determine total emissions from a representative dairy housing and WWTP during several months in autumn and winter in Switzerland. Total emissions were 1.19 ± 0.48 and 2.27 ± 1.53 kg NH3 d-1 for the dairy housing and WWTP, respectively, which compared well with literature values, despite the paucity of WWTP data. A concurrent comparison with an inhouse tracer ratio method at the dairy housing indicated an offset of the IDM emissions by < 20%. Diurnal emission patterns were evident at both sites mostly driven by changes in air temperature with potential lag effects such as following sludge agitation. Modeled deposition corrections to adjust the concentration loss detected at the measurement point with the associated footprint were 22-28% of the total emissions and the cumulative fraction of deposition to emission modeled with distance from the source was between 7% and 12% for the measurement distances (60-150 m). Although estimates of depositional loss were plausible, the approach is still connected with substantial uncertainty, which calls for future validation measurements. Longer measurement periods encompassing more management activities and environmental conditions are required to assess predictor variable importance on emission dynamics. Combined, IDM with deposition correction will allow the determination of emission factors at reduced efforts and costs, thereby supporting the development and assessment of emission reducing methods and expand the data availability for emission inventories.Implications: Ammonia emissions must be measured to determine emission factors and reporting national inventories. Measurements from structures like farms and industrial plants are complex due to the many different emitting surfaces and the building configuration leading to a poor data availability. Micrometeorological methods provide high resolution emission data from the entire structure, but suffer from uncertainties, as the instruments must be placed at a distance from the structure resulting in a greater loss of the emitted ammonia via dry deposition before it reaches the measurement. This study constrains such emission measurements from a dairy housing and wastewater treatment plant by applying a simple correction to account for the deposition loss and compares the results to other methods.

Targeted delivery of tumor necrosis factor in combination with CCNU induces a T cell-dependent regression of glioblastoma.

Glioblastoma is the most aggressive primary brain tumor with an unmet need for more effective therapies. Here, we investigated combination therapies based on L19TNF, an antibody-cytokine fusion protein based on tumor necrosis factor that selectively localizes to cancer neovasculature. Using immunocompetent orthotopic glioma mouse models, we identified strong anti-glioma activity of L19TNF in combination with the alkylating agent CCNU, which cured the majority of tumor-bearing mice, whereas monotherapies only had limited efficacy. In situ and ex vivo immunophenotypic and molecular profiling in the mouse models revealed that L19TNF and CCNU induced tumor DNA damage and treatment-associated tumor necrosis. In addition, this combination also up-regulated tumor endothelial cell adhesion molecules, promoted the infiltration of immune cells into the tumor, induced immunostimulatory pathways, and decreased immunosuppression pathways. MHC immunopeptidomics demonstrated that L19TNF and CCNU increased antigen presentation on MHC class I molecules. The antitumor activity was T cell dependent and completely abrogated in immunodeficient mouse models. On the basis of these encouraging results, we translated this treatment combination to patients with glioblastoma. The clinical translation is ongoing but already shows objective responses in three of five patients in the first recurrent glioblastoma patient cohort treated with L19TNF in combination with CCNU (NCT04573192).