RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning.

BACKGROUND: Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pharmaceutical developments to be used during the prenatal and perinatal periods aimed at decreasing the RSV burden, there is a pressing need to identify infants at risk of severe disease. We aimed to stratify the risk of developing a clinically severe RSV infection in infants under 1 year of age. METHODS: This retrospective observational study was conducted at the Hospices Civils de Lyon, France, involving infants born between 2014 and 2018. This study focused on infants hospitalized with severe and very severe acute lower respiratory tract infections associated with RSV (SARI-WI group). Data collection included perinatal information and clinical data, with machine-learning algorithms used to discriminate SARI-WI cases from nonhospitalized infants. RESULTS: Of 42,069 infants, 555 developed SARI-WI. Infants born in November were very likely (>80%) predicted SARI-WI. Infants born in October were very likely predicted SARI-WI except for births at term by vaginal delivery and without siblings. Infants were very unlikely (<10%) predicted SARI-WI when all the following conditions were met: born in other months, at term, by vaginal delivery and without siblings. Other infants were possibly (10-30%) or probably (30-80%) predicted SARI-WI. CONCLUSIONS: Although RSV preventive measures are vital for all infants, and specific recommendations exist for patients with high-risk comorbidities, in situations where prioritization becomes necessary, infants born just before or within the early weeks of the epidemic should be considered as a risk group.

Survival of bronchopulmonary cancers according to radon exposure.

Introduction: Residential exposure is estimated to be responsible for nearly 10% of lung cancers in 2015 in France, making it the second leading cause, after tobacco. The Auvergne-Rhône-Alpes region, in the southwest of France, is particularly affected by this exposure as 30% of the population lives in areas with medium or high radon potential. This study aimed to investigate the impact of radon exposure on the survival of lung cancer patients. Methods: In this single-center study, patients with a histologically confirmed diagnosis of lung cancer, and newly managed, were prospectively included between 2014 and 2020. Univariate and multivariate survival analyses were carried out using a non-proportional risk survival model to consider variations in risk over time. Results: A total of 1,477 patients were included in the analysis. In the multivariate analysis and after adjustment for covariates, radon exposure was not statistically associated with survival of bronchopulmonary cancers (HR = 0.82 [0.54-1.23], HR = 0.92 [0.72-1.18], HR = 0.95 [0.76-1.19] at 1, 3, and 5 years, respectively, for patients residing in category 2 municipalities; HR = 0.87 [0.66-1.16], HR = 0.92 [0.76-1.10], and HR = 0.89 [0.75-1.06] at 1, 3, and 5 years, respectively, for patients residing in category 3 municipalities). Discussion: Although radon exposure is known to increase the risk of lung cancer, in the present study, no significant association was found between radon exposure and survival of bronchopulmonary cancers.

Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap.

Characterization of immune responses is currently hampered by the lack of systems enabling quantitative and dynamic phenotypic characterization of individual cells and, in particular, analysis of secreted proteins such as cytokines and antibodies. We recently developed a simple and robust microfluidic platform, DropMap, to measure simultaneously the kinetics of secretion and other cellular characteristics, including endocytosis activity, viability and expression of cell-surface markers, from tens of thousands of single immune cells. Single cells are compartmentalized in 50-pL droplets and analyzed using fluorescence microscopy combined with an immunoassay based on fluorescence relocation to paramagnetic nanoparticles aligned to form beadlines in a magnetic field. The protocol typically takes 8-10 h after preparation of microfluidic chips and chambers, which can be done in advance. By contrast, enzyme-linked immunospot (ELISPOT), flow cytometry, time-of-flight mass cytometry (CyTOF), and single-cell sequencing enable only end-point measurements and do not enable direct, quantitative measurement of secreted proteins. We illustrate how this system can be used to profile downregulation of tumor necrosis factor-α (TNF-α) secretion by single monocytes in septic shock patients, to study immune responses by measuring rates of cytokine secretion from single T cells, and to measure affinity of antibodies secreted by single B cells.

Evaluation of integrative clustering methods for the analysis of multi-omics data.

Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sensitivity and their ability to recover both the correct number of clusters and the simulated clustering at the common and data-specific levels. Standard non-integrative approaches were also included to quantify the added value of integrative methods. For most matrix factorization methods and one Bayesian approach (BCC), the shared and specific structures were successfully recovered with high and moderate accuracy, respectively. An opposite behavior was observed on non-integrative approaches, i.e. high performances on specific structures only. Finally, we applied the methods on the Cancer Genome Atlas breast cancer data set to check whether results based on experimental data were consistent with those obtained in the simulations.

Survival model construction guided by fit and predictive strength.

Survival model construction can be guided by goodness-of-fit techniques as well as measures of predictive strength. Here, we aim to bring together these distinct techniques within the context of a single framework. The goal is how to best characterize and code the effects of the variables, in particular time dependencies, when taken either singly or in combination with other related covariates. Simple graphical techniques can provide an immediate visual indication as to the goodness-of-fit but, in cases of departure from model assumptions, will point in the direction of a more involved and richer alternative model. These techniques appear to be intuitive. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Measures of predictive strength are used in conjunction with these goodness-of-fit techniques and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. Illustrations from studies in breast cancer show how these tools can be of help in guiding the practical problem of efficient model construction for survival data.