Contact profilometry and correspondence analysis to correlate surface properties and cell adhesion in vitro of uncoated and coated Ti and Ti6Al4V disks.

A fundamental goal in the field of implantology is the design of specific devices able to induce a controlled and rapid \u201cosseointegration\u201d. This result has been achieved by means of surface modifications aimed at optimizing implant-to-bone contact; furthermore, bone cell adhesion on implant surface has been directly improved by the application of biomolecules that stimulate new tissue formation, thus controlling interactions between biological environment and implanted materials. Actually, methods for biochemical factor delivery at the interface between implant surface and biological tissues are under investigation; a reliable technique is represented by the inclusion of biologically active molecules into biocompatible and biodegradable materials used for coating implant surface. This paper focuses the application of three polymeric materials already acknowledged in the clinical practice, i.e. poly-l-lactic acid (PLLA), poly-dl-lactic acid (PDLA), and sodium alginate hydrogel. They have been used to coat Ti (Ti2) and Ti6Al4V (Ti5) disks; their characteristics have been determined and their performances compared, with specific regard to the ability in allowing osteoblast adhesion in vitro. Moreover, profilometry data analysis permitted to identify a specific roughness parameter (peak density) which mainly controls the amount of osteoblast adhesion

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Background: Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results: Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered model