| Abstract | In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). |
| Supervised learning: Classification | Corresponding classifiers were also built for ADCC and cytokine profiles using each of the three different learning techniques and three different feature sets; the performance of these models is also summarized in Table 1. |
| Supervised learning: Classification | The cytokine classifiers perform nearly as well as the ADCP ones, and the ADCC classifiers less accurately but still strikingly well. |
| Supervised learning: Classification | 82 Fig (ADCC) and S3 Fig ( cytokines ) detail the PLR results. |
| Supervised learning: Regression | Lars-based regression results for ADCC and cytokines are presented in S4 Fig, and SS Fig, respectively, and summarized in Table 1. |
| Supervised learning: Regression | With a mean PCC of 0.58 and a standard deviation of 0.20, the cytokine regression is comparable to that observed in predicting ADCP, though the representative scatterplot is not as pleasing to the eye due to the density of subjects With low values. |
| Supervised learning: Regression | Feature filtering achieves essentially the same performance for ADCC but a degradation in the cytokine performance as assessed by PCC, though the scatterplot appears roughly as good. |
| Unsupervised learning | For the cytokines , strong IgG1 and IgG3 correlations are observed, particularly with gp120 and V1V2. |
| Abstract | Immune responses are regulated by diffusible mediators, the cytokines , which act at sub-nanomolar concentrations. |
| Abstract | The spatial range of cytokine communication is a crucial, yet poorly understood, functional property. |
| Abstract | Both containment of cytokine action in narrow junctions between immune cells (immunological synapses) and global signaling throughout entire lymph nodes have been proposed, but the conditions under which they might occur are not clear. |
| Author Summary | The discovery that immune responses are regulated by small diffusible proteins — the cytokines — has been surprising because cytokine diffusion to ‘bystander’ cells might compromise specificity. |
| Author Summary | Measurements of cytokine concentrations with fine spatial resolution have not been achieved. |
| Analysis of cell phenotype and cytokine secretion by flow cytometry | Analysis of cell phenotype and cytokine secretion by flow cytometry |
| Analysis of cell phenotype and cytokine secretion by flow cytometry | Secreted cytokines and angiogenic factors were quantified in cell conditioned medium using FlowCytomiX technology (Bender MedSystems and RnD). |
| Estimation of the relative contribution of each cell population in the total cytokine production | Estimation of the relative contribution of each cell population in the total cytokine production |
| Estimation of the relative contribution of each cell population in the total cytokine production | For a given treatment, let Na, Nb and Nc be the relative number of cells present in each population (a = DN, b 2 SP, c 2 DP) and K be the amount of cytokine experimentally measured and expressed as a percentage of change of cytokine secretion to untreated cells. |
| Estimation of the relative contribution of each cell population in the total cytokine production | The coefficients were then used to infer the amount of cytokine by DN, SP and DP cell populations. |
| Patient and tissue specimens | TEM phenotype, cytokine secretion and pro-angiogenic activity were assessed by flow cytometry and in vivo or in vitro vascularization assay, respectively. |
| Reagents and antibodies | Common stocks of cytokines , inhibitors and assay reagents were used to minimize experimental variability. |
| Reagents and antibodies | Human recombinant cytokines were purchased from PeproTech (London, UK) and R&D Systems. |
| TEM from peripheral blood and tumor tissue of breast cancer patients show distinct pro-angiogenic phenotypes | Thus, CD11b+, CD14+ monocytes from patient blood and tumor tissue were referred to as “TEM” and compared with respect to receptor and cytokine expression. |
| TEM from peripheral blood and tumor tissue of breast cancer patients show distinct pro-angiogenic phenotypes | Blood and tumor TEM display a mixed M1-like (tumor-associated macrophages releasing inflammatory molecules) and M2-like (immunosuppressive macrophages polarized by anti-in-flammatory molecules) phenotype, with secretion of both the pro and antiinflammatory cytokines IL-12 and IL 10, respectively (Fig. |
| Blocking stable motifs may obstruct specific attractors | These T cells release specific cytokines that alter how the immune system responds to external agents, for example, by recruiting specific immune system cells to fight infection, promoting antibody production, or inhibiting the activation and proliferation of other cells. |
| Blocking stable motifs may obstruct specific attractors | Various subtypes of helper T cells are known, such as Th1, Th2, Th17 and Treg, which are distinguished by a differential expression of specific transcription factors and cytokines . |
| Blocking stable motifs may obstruct specific attractors | The reachability of each attractor depends on the presence of several external environmental signals (either cytokines or antigen), which are represented as input nodes in the network. |