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- Laj, P., et al.
(author)
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Measuring Atmospheric Composition Change
- 2009
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In: Atmospheric Environment. - : Elsevier BV. - 1873-2844 .- 1352-2310. ; 43:33, s. 5351-5414
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Journal article (peer-reviewed)abstract
- Scientific findings from the last decades have clearly highlighted the need for a more comprehensive approach to atmospheric change processes. In fact, observation of atmospheric composition variables has been an important activity of atmospheric research that has developed instrumental tools (advanced analytical techniques) and platforms (instrumented passenger aircrafts, ground-based in-situ and remote sensing stations, earth observation satellite instruments) providing essential information on the composition of the atmosphere. The variability of the atmospheric system and the extreme complexity of the atmospheric cycles for short-lived gaseous and aerosol species have led to the development of complex models to interpret observations, test our theoretical understanding of atmospheric chemistry and predict future atmospheric composition. The validation of numerical models requires accurate information concerning the variability of atmospheric composition for targeted species via comparison with observations and measurements. In this paper, we provide an overview of recent advances in instrumentation and methodologies for measuring atmospheric composition changes from space, aircraft and the surface as well as recent improvements in laboratory techniques that permitted scientific advance in the field of atmospheric chemistry. Emphasis is given to the most promising and innovative technologies that will become operational in the near future to improve knowledge of atmospheric composition. Our current observation capacity, however, is not satisfactory to understand and predict future atmospheric composition changes, in relation to predicted climate warming. Based on the limitation of the current European observing system, we address the major gaps in a second part of the paper to explain why further developments in current observation strategies are still needed to strengthen and optimise an observing system not only capable of responding to the requirements of atmospheric services but also to newly open scientific questions.
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- Cristiani, CM, et al.
(author)
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Altered Frequencies and Functions of Innate Lymphoid Cells in Melanoma Patients Are Modulated by Immune Checkpoints Inhibitors
- 2022
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In: Frontiers in immunology. - : Frontiers Media SA. - 1664-3224. ; 13, s. 811131-
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Journal article (peer-reviewed)abstract
- Monoclonal antibodies targeting immune checkpoints improved clinical outcome of patients with malignant melanoma. However, the mechanisms are not fully elucidated. Since immune check-point receptors are also expressed by helper innate lymphoid cells (ILCs), we investigated the capability of immune checkpoints inhibitors to modulate ILCs in metastatic melanoma patients as well as melanoma cells effects on ILC functions. Here, we demonstrated that, compared to healthy donors, patients showed a higher frequency of total peripheral ILCs, lower percentages of CD117+ ILC2s and CD117+ ILCs as well as higher frequencies of CD117- ILCs. Functionally, melanoma patients also displayed an impaired TNFα secretion by CD117- ILCs and CD117+ ILCs. Nivolumab therapy reduced the frequency of total peripheral ILCs but increased the percentage of CD117- ILC2s and enhanced the capability of ILC2s and CD117+ ILCs to secrete IL-13 and TNFα, respectively. Before Nivolumab therapy, high CCL2 serum levels were associated with longer Overall Survival and Progression Free Survival. After two months of treatment, CD117- ILC2s frequency as well as serum concentrations of IL-6, CXCL8 and VEGF negatively correlated with both the parameters. Moreover, melanoma cells boosted TNFα production in all ILC subsets and increased the number of IL-13 producing ILC2s in vitro. Our work shows for the first time that PD-1 blockade is able to affect ILCs proportions and functions in melanoma patients and that a specific subpopulation is associated with the therapy response.
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- Madonna, G, et al.
(author)
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Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy
- 2021
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In: Cancers. - : MDPI AG. - 2072-6694. ; 13:16
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Journal article (peer-reviewed)abstract
- The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.
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