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Extractive electrospray ionization (EESI) is a well-known technique for high throughput online molecular characterization of chemical reaction products and intermediates, detection of native biomolecules, in vivo metabolomics, and environmental monitoring with negligible thermal and ionization-induced fragmentation for over two decades. However, the EESI extraction mechanism remains uncertain. Prior studies disagree whether particles between 20 and 400 nm diameter are fully extracted or if the extraction is limited to the surface layer. Here, we examined the analyte extraction mechanism by assessing the influence of particle size and coating thickness on the detection of the molecules therein. We find that particles are extracted fully: Organics-coated NH4NO3 particles with a fixed core volume (156 and 226 nm in diameter without coating) showed constant EESI signals for NH4NO3 independent of the shell coating thickness, while the signals of the secondary organic molecules comprising the shell varied proportionally to the shell volume. We also found that the EESI sensitivity exhibited a strong size dependence, with an increase in sensitivity by one to three orders of magnitude as particles size decreased from 300 nm to 30 nm. This dependence varied with the electrospray (ES) droplet size, the particles size and residence time for coagulation in the EESI inlet, suggesting that the EESI sensitivity was influenced by the coagulation coefficient between particles and ES droplets. Overall, our results indicate that, in the EESI, particles are fully extracted by the ES droplets regardless of the chemical composition, when they are collected by the ES droplets. However, their coalescence is not complete and depends strongly on their size. This size-dependence is especially relevant when EESI is used to probe size-varying particles as is the case in aerosol formation and growth studies with size ranges below 100 nm.

Additional file 2: Table S1. The distribution of clinical characteristics and key breast cancer risk factors in the Hong Kong breast cancer study (HKBC). Table S2. 130 immune-related genes used for the classification of luminal tumors. Table S3. The three luminal immune subtypes in relation to the luminal A/B classification in the Hong Kong breast cancer study (HKBC). Table S4. P values for comparisons of tumor MCP-counter abundance scores by immune subtype in the Hong Kong breast cancer study (HKBC). Table S5. P values for comparisons of tumor CIBERSORT fraction scores by immune subtype in the Hong Kong breast cancer study (HKBC). Table S6. The distribution of clinical characteristics and key breast cancer risk factors by luminal immune subtypes in the Hong Kong breast cancer study (HKBC). Table S7. P values for comparisons of the MCP-counter abundance scores between paired tumor and normal tissue (N = 80) by immune subtype in the Hong Kong breast cancer study (HKBC).

Extractive electrospray ionization (EESI) is a well-known technique for high throughput online molecular characterization of chemical reaction products and intermediates, detection of native biomolecules, in vivo metabolomics, and environmental monitoring with negligible thermal and ionization-induced fragmentation for over two decades. However, the EESI extraction mechanism remains uncertain. Prior studies disagree whether particles between 20 and 400 nm diameter are fully extracted or if the extraction is limited to the surface layer. Here, we examined the analyte extraction mechanism by assessing the influence of particle size and coating thickness on the detection of the molecules therein. We find that particles are extracted fully: Organics-coated NH4NO3 particles with a fixed core volume (156 and 226 nm in diameter without coating) showed constant EESI signals for NH4NO3 independent of the shell coating thickness, while the signals of the secondary organic molecules comprising the shell varied proportionally to the shell volume. We also found that the EESI sensitivity exhibited a strong size dependence, with an increase in sensitivity by one to three orders of magnitude as particles size decreased from 300 nm to 30 nm. This dependence varied with the electrospray (ES) droplet size, the particles size and residence time for coagulation in the EESI inlet, suggesting that the EESI sensitivity was influenced by the coagulation coefficient between particles and ES droplets. Overall, our results indicate that, in the EESI, particles are fully extracted by the ES droplets regardless of the chemical composition, when they are collected by the ES droplets. However, their coalescence is not complete and depends strongly on their size. This size-dependence is especially relevant when EESI is used to probe size-varying particles as is the case in aerosol formation and growth studies with size ranges below 100 nm.

Additional file 2: Table S1. The distribution of clinical characteristics and key breast cancer risk factors in the Hong Kong breast cancer study (HKBC). Table S2. 130 immune-related genes used for the classification of luminal tumors. Table S3. The three luminal immune subtypes in relation to the luminal A/B classification in the Hong Kong breast cancer study (HKBC). Table S4. P values for comparisons of tumor MCP-counter abundance scores by immune subtype in the Hong Kong breast cancer study (HKBC). Table S5. P values for comparisons of tumor CIBERSORT fraction scores by immune subtype in the Hong Kong breast cancer study (HKBC). Table S6. The distribution of clinical characteristics and key breast cancer risk factors by luminal immune subtypes in the Hong Kong breast cancer study (HKBC). Table S7. P values for comparisons of the MCP-counter abundance scores between paired tumor and normal tissue (N = 80) by immune subtype in the Hong Kong breast cancer study (HKBC).

Extractive electrospray ionization (EESI) is a well-known technique for high throughput online molecular characterization of chemical reaction products and intermediates, detection of native biomolecules, in vivo metabolomics, and environmental monitoring with negligible thermal and ionization-induced fragmentation for over two decades. However, the EESI extraction mechanism remains uncertain. Prior studies disagree whether analyte particles between 20 and 400 nm diameter are fully extracted or if the extraction is limited to the surface layer. Here, we examined the analyte extraction mechanism by assessing the influence of analyte particle size and coating thickness on the detection of the molecules therein. We find that analyte particles are extracted fully: Organics-coated NH4NO3 particles with a fixed core volume (156 and 226 nm in diameter without coating) show constant signals for NH4NO3 independent of the shell coating thickness, while the signals of the secondary organic molecules comprising the shell varied proportionally to the shell volume. We also find that the EESI sensitivity exhibits a strong size dependence, with an increase in sensitivity by one to three orders of magnitude as analyte particle size decreases from 300 nm to 30 nm. This dependence varies with the electrospray (ES) droplet size and the analyte particle residence time in the EESI inlet, suggesting that the EESI sensitivity is influenced by the coagulation rates between analyte particles and ES droplets. Overall, our results indicate that, in the EESI, analyte particles are fully extracted by the ES droplets regardless of the chemical composition, when they are collected by the ES droplets. However, their coalescence is not complete and depends strongly on their size. This size-dependence is especially relevant when EESI is used to probe size-varying analyte particles as is the case in aerosol formation and growth studies with size ranges below 100 nm, while it does not significantly influence the detection of ambient aerosol dominated by particle sizes ranging between 100 - 2500 nm, i.e. the accumulation mode.

The cumulative effects of cellular senescence and cell loss over time in various tissues and organs are considered major contributing factors to the ageing process. In various organisms, caloric restriction (CR) slows ageing and increases lifespan, at least in part, by activating nicotinamide adenine dinucleotide (NAD+)-dependent protein deacetylases of the sirtuin family. Here, we use an in vitro model of CR to study the effects of this dietary regime on replicative senescence, cellular lifespan and modulation of the SIRT1 signaling pathway in normal human diploid fibroblasts. We found that serum from calorie-restricted animals was able to delay senescence and significantly increase replicative lifespan in these cells, when compared to serum from ad libitum fed animals. These effects correlated with CR-mediated increases in SIRT1 and decreases in p53 expression levels. In addition, we show that manipulation of SIRT1 levels by either over-expression or siRNA-mediated knockdown resulted in delayed and accelerated cellular senescence, respectively. Our results demonstrate that CR can delay senescence and increase replicative lifespan of normal human diploid fibroblasts in vitro and suggest that SIRT1 plays an important role in these processes. (185 words).

Organic aerosols are ubiquitous in the atmosphere and oxygenated organics are a major driver of aerosol growth. The volatility basis set (VBS) as introduced by Donahue et al. (2006, 2011) is often used to simplify the partitioning behavior of the huge variety of atmospheric organics. Recently, the VBS was used to dynamically model aerosol growth from the smallest sizes onwards. This tutorial is intended to equip the reader with the necessary tools to facilitate organic growth modelling based on gas-phase measurements of oxygenated organics using a 2-dimensional VBS. We start with a contextualization of the VBS in partitioning theory and point out the need for dynamic modeling. We provide an overview on the most common methods to estimate the volatility of oxygenated organics and give detailed instruction on how to construct the binned VBS. We then explain the dynamic condensation model including solution and curvature effects. Furthermore, we provide a python package for VBS growth calculations and show with two examples from ambient and chamber measurements how growth rates can be calculated. Last, we summarize the limitation of this approach and outline necessary future developments.