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Background Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. Methods Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. Results Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83–0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were Conclusion CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018

Direct measurements of reaction cross-sections at astrophysical energies often require the use of solid targets able to withstand high ion beam currents for extended periods of time. Thus, monitoring target thickness, isotopic composition, and target stoichiometry during data taking is critical to account for possible target modifications and to reduce uncertainties in the final cross-section results. A common technique used for these purposes is the Nuclear Resonant Reaction Analysis (NRRA), which however requires that a narrow resonance be available inside the dynamic range of the accelerator used. In cases when this is not possible, as for example the $$^{13}\hbox {C}(\alpha ,\hbox {n})^{16}\hbox {O}$$ reaction recently studied at low energies at the Laboratory for Underground Nuclear Astrophysics (LUNA) in Italy, alternative approaches must be found. Here, we present a new application of the shape analysis of primary $$\gamma $$ rays emitted by the $$^{13}\hbox {C}(\hbox {p},\gamma )^{14}\hbox {N}$$ radiative capture reaction. This approach was used to monitor $$^{13}\hbox {C}$$ target degradation in situ during the $$^{13}\hbox {C}(\alpha ,\hbox {n})^{16}\hbox {O}$$ data taking campaign. The results obtained are in agreement with evaluations subsequently performed at Atomki (Hungary) using the NRRA method. A preliminary application for the extraction of the $$^{13}\hbox {C}(\alpha ,\hbox {n})^{16}\hbox {O}$$ reaction cross-section at one beam energy is also reported.

The nucleosynthesis of light elements, from helium up to silicon, mainly occurs in Red Giant and Asymptotic Giant Branch stars and Novae. The relative abundances of the synthesized nuclides critically depend on the rates of the nuclear processes involved, often through non-trivial reaction chains, combined with complex mixing mechanisms. In this review, we summarize the contributions made by LUNA experiments in furthering our understanding of nuclear reaction rates necessary for modeling nucleosynthesis in AGB stars and Novae explosions.