A tailored machine learning approach for mortality prediction in severe COVID-19 treated with glucocorticoids.

The impact of severe COVID-19 pneumonia on healthcare systems highlighted the need for accurate predictions to improve patient outcomes. Despite the established efficacy of glucocorticoids (GCs), variable patient responses are observed, and the existing clinical scores are limited in predicting non-responders. We propose a machine learning (ML) based approach to predict mortality in COVID-19 pneumonia treated with GCs.This is an ML-driven retrospective analysis involving 825 patients. We leveraged XGBoost to select the most appropriate features from the initial 52, including clinical and laboratory data. Six different ML techniques were compared. Shapley additive explanation (SHAP) values were used to describe the influence of each feature on classification. Internal validation was performed.Nine key predictors of death were identified: increasing C-reactive protein (CRP), decreasing arterial partial pressure of oxygen to fraction of inspired oxygen ratio (PaO2/FiO2), age, coronary artery disease, invasive mechanical ventilation, acute renal failure, chronic heart failure, PaO2/FiO2 earliest value, and body mass index. Random forest achieved the highest test area under the receiver operating characteristic curve at 0.938 (95% CI 0.903-0.969). SHAP values highlighted age and PaO2/FiO2 improvement as the most influential features; the latter showed a higher impact than CRP reduction over time.The proposed ML algorithm effectively predicts the risk of hospital death in COVID-19 pneumonia patients undergoing GCs. This approach can be adapted to datasets measuring similar clinical variables..

Rigorous analysis of spheres in Gauss-Laguerre beams.

In this paper we develop a rigorous formulation of Gauss- Laguerre beams in terms of Mie scattering coefficients which permits us to quasi-analytically treat the interaction of a spherical particle located in the focal region of a possibly high numerical aperture lens illuminated by a Gauss-Laguerre beam. This formalism is used to study the scattered field as a function of the radius of a spherical scatterer, as well as the translation of a spherical scatterer through the Gauss-Laguerre illumination in the focal plane. Knowledge of the Mie coefficients provides a deeper insight to understanding the scattering process and explaining the oscillatory behaviour of the scattered intensity distribution.

Cylindrical vector beam focusing through a dielectric interface: comment.

Errors in the results and conclusions presented in the paper "Cylindrical vector beam focusing through a dielectric interface" by Biss and Brown (Opt. Express 9, 490-497 (2001)) are discussed.

Calculation of the vectorial field distribution in a stratified focal region of a high numerical aperture imaging system.

We present an algorithm for calculating the field distribution in the focal region of stratified media which is fast and easy to implement. Using this algorithm we study the effect on the electric field distribution of an air gap separating a solid immersion lens and a sample, where we analyse the maximum distance for out-of-contact operation. Also, we study how the presence of a metallic substrate affects the field distribution in the focal region; the interference effects of the reflected field could be used as an alternative for 4Pi-microscopy.