4 research outputs found
An improved multiscale method for life-cycle production optimization
No full textGeoscience and EngineeringCivil Engineering and Geoscience
Interaction of Iron Oxide Nanoparticles with Macrophages Is Influenced Distinctly by "Self" and "Non-Self" Biological Identities
No full textFundació CarrerasThe authors acknowledge the Scientific and Technical Assistance of the Proteomics, Transmission electron microscopy, Flow cytometry, and Confocal microscopy facilities at the CNB. ICP-OES analysis was carried out in the support laboratories of Instituto de Ciencia de Materiales de Madrid (CSIC). The authors are also grateful to M. Sefton for the author editing of the manuscript. This work was supported by the following grants: Grant SAF2017-82223-R (to D.F.B.) funded by MCIN/AEI/10.13039/501100011033; and an ERDF a way of making Europe and Grant PID2020-112685RB-100 (to D.F.B.) funded by the MCIN/AEI/10.13039/501100011033. V.M.-A. was a postdoctoral scholar working under a Juan de La Cierva-Incorporación Contract (IJCI-2017-31447, funded by MCIN/AEI/10.13039/501100011033), and Y. P. was first a predoctoral FPU scholar (FPU15/06170) funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future", then a predoctoral scholar funded by CSIC-COV19-012/012202020E154 intramural project and finally a postdoctoral scholar funded by the European Commission-NextGenerationEU (Regulation EU2020/2094) through the CSIC's Global Health Platform (PTI Salud Global, SGL2103021). This research work was performed in the framework of the Nanomedicine CSIC HUB (ref 202180E048).This work was supported by the following grants: Grant SAF2017-82223-R (to D.F.B.) funded by MCIN/AEI/10.13039/501100011033; and an ERDF a way of making Europe and Grant PID2020-112685RB-100 (to D.F.B.) funded by the MCIN/AEI/10.13039/501100011033. V.M.-A. was a postdoctoral scholar working under a Juan de La Cierva-Incorporación Contract (IJCI-2017-31447, funded by MCIN/AEI/10.13039/501100011033), and Y. P. was first a predoctoral FPU scholar (FPU15/06170) funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future", then a predoctoral scholar funded by CSIC-COV19-012/012202020E154 intramural project and finally a postdoctoral scholar funded by the European Commission-NextGenerationEU (Regulation EU2020/2094) through the CSIC's Global Health Platform (PTI Salud Global, SGL2103021). This research work was performed in the framework of the Nanomedicine CSIC HUB (ref 202180E048).Upon contact with biological fluids like serum, a protein corona (PC) complex forms on iron oxide nanoparticles (IONPs) in physiological environments and the proteins it contains influence how IONPs act in biological systems. Although the biological identity of PC-IONP complexes has often been studied in vitro and in vivo, there have been inconsistent results due to the differences in the animal of origin, the type of biological fluid, and the physicochemical properties of the IONPs. Here, we identified differences in the PC composition when it was derived from the sera of three species (bovine, murine, or human) and deposited on IONPs with similar core diameters but with different coatings [dimercaptosuccinic acid (DMSA), dextran (DEX), or 3-aminopropyl triethoxysilane (APS)], and we assessed how these differences influenced their effects on macrophages. We performed a comparative proteomic analysis to identify common proteins from the three sera that adsorb to each IONP coating and the 10 most strongly represented proteins in PCs. We demonstrated that the PC composition is dependent on the origin of the serum rather than the nature of the coating. The PC composition critically affects the interaction of IONPs with macrophages in self- or non-self identity models, influencing the activation and polarization of macrophages. However, such effects were more consistent for DMSA-IONPs. As such, a self biological identity of IONPs promotes the activation and M2 polarization of murine macrophages, while a non-self biological identity favors M1 polarization, producing larger quantities of ROS. In a human context, we observed the opposite effect, whereby a self biological identity of DMSA-IONPs promotes a mixed M1/M2 polarization with an increase in ROS production. Conversely, a non-self biological identity of IONPs provides nanoparticles with a stealthy character as no clear effects on human macrophages were evident. Thus, the biological identity of IONPs profoundly affects their interaction with macrophages, ultimately defining their biological impact on the immune system
At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods
No full textBy September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024
Validation of extracorporeal membrane oxygenation mortality prediction and severity of illness scores in an international COVID-19 cohort
No full textBackground: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort. Methods: We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score. Results: We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58–0.62), AUPRC (0.62–0.74), and Brier score (0.286–0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52–0.57), AURPC (0.59–0.64), and Brier Score (0.265–0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26). Conclusion: Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools
