El tratamiento quirúrgico prenatal del mielomeningocele

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Pediatría. Fecha de lectura: 16-06-201

Security protocols for networks and Internet: a global vision

This work was supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You), by the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks), which is co-funded by European Funds (FEDER), and by the MINECO grant TIN2016-79095-C2-2-R (SMOG-DEV—Security mechanisms for fog computing: advanced security for devices)

PAgIoT - Privacy-preserving aggregation protocol for internet of things

Modern society highly relies on the use of cyberspace to perform a huge variety of activities, such as social networking or e-commerce, and new technologies are continuously emerging. As such, computer systems may store a huge amount of information, which makes data analysis and storage a challenge. Information aggregation and correlation are two basic mechanisms to reduce the problem size, for example by filtering out redundant data or grouping similar one. These processes require high processing capabilities, and thus their application in Internet of Things (IoT) scenarios is not straightforward due to resource constraints. Furthermore, privacy issues may arise when the data at stake is personal. In this paper we propose PAgIoT, a Privacy-preserving Aggregation protocol suitable for IoT settings. It enables multi-attribute aggregation for groups of entities while allowing for privacy-preserving value correlation. Results show that PAgIoT is resistant to security attacks, it outperforms existing proposals that provide with the same security features, and it is feasible in resource-constrained devices and for aggregation of up to 10 attributes in big networks.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 CIBERDINE-CM (CIBERDINE: Cybersecurity, Data, and Risks) funded by the Autonomous Community of Madrid and co-funded by European funds

Multiple myeloma and SARS-CoV-2 infection: clinical characteristics and prognostic factors of inpatient mortality

There is limited information on the characteristics, prognostic factors, and outcomes of patients with multiplemyeloma (MM) hospitalized with COVID-19. This retrospective case series investigated 167 patients reported from 73hospitals within the Spanish Myeloma Collaborative Group network in March and April, 2020. Outcomes werecompared with 167 randomly selected, contemporary, age-/sex-matched noncancer patients with COVID-19 admittedat six participating hospitals. Among MM and noncancer patients, median age was 71 years, and 57% of patients weremale; 75 and 77% of patients, respectively, had at least one comorbidity. COVID-19 clinical severity wasmoderate-severe in 77 and 89% of patients and critical in 8 and 4%, respectively. Supplemental oxygen was requiredby 47 and 55% of MM and noncancer patients, respectively, and 21%/9% vs 8%/6% required noninvasive/invasiveventilation. Inpatient mortality was 34 and 23% in MM and noncancer patients, respectively. Among MM patients,inpatient mortality was 41% in males, 42% in patients aged >65 years, 49% in patients with active/progressive MM athospitalization, and 59% in patients with comorbid renal disease at hospitalization, which were independentprognostic factors on adjusted multivariate analysis. This case series demonstrates the increased risk and identifiespredictors of inpatient mortality among MM patients hospitalized with COVID-19

MM, SARS-CoV-2 infection, and inpatient mortality

There is limited information on the characteristics, pre-admission prognostic factors, and outcomes of patients with multiple myeloma (MM) hospitalized with coronavirus disease 2019 (COVID-19). This retrospective case series investigated characteristics and outcomes of 167 MM patients hospitalized with COVID-19 reported from 73 hospitals within the Spanish Myeloma Collaborative Group network in Spain between March 1 and April 30, 2020. Outcomes were compared with a randomly selected contemporary cohort of 167 age-/sex-matched non-cancer patients with COVID-19 admitted at 6 participating hospitals. Common demographic, clinical, laboratory, treatment, and outcome variables were collected; specific disease status and treatment data were collected for MM patients. Among the MM and non-cancer patients, median age was 71 years and 57% of patients were male in each series, and 75% and 77% of patients, respectively, had at least one comorbidity. COVID-19 clinical severity was moderate-severe in 77% and 89% of patients and critical in 8% and 4%, respectively. Supplemental oxygen was required by 47% and 55% of MM and non-cancer patients, respectively, and 21%/9% vs 8%/6% required non-invasive/invasive ventilation. Inpatient mortality was 34% and 23% in MM and non-cancer patients, respectively. Among MM patients, inpatient mortality was 41% in males, 42% in patients aged >65 years, 49% in patients with active/progressive MM at hospitalization, and 59% in patients with comorbid renal disease at hospitalization, which were independent prognostic factors of inpatient mortality on adjusted multivariate analysis. This case series demonstrates the increased risk and identifies predictors of inpatient mortality among MM patients hospitalized with COVID-19.This study was supported by PETHEMA FoundationN

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification

Hacking ético para profundizar en la implementación de sistemas informáticos seguros

Memoria ID-052 Ayudas de la Universidad de Salamanca para la innovación docente, curso 2020-2021

A clinically compatible drug-screening platform based on organotypic cultures identifies vulnerabilities to prevent and treat brain metastasis

We report a medium‐throughput drug‐screening platform (METPlatform) based on organotypic cultures that allows to evaluate inhibitors against metastases growing in situ. By applying this approach to the unmet clinical need of brain metastasis, we identified several vulnerabilities. Among them, a blood–brain barrier permeable HSP90 inhibitor showed high potency against mouse and human brain metastases at clinically relevant stages of the disease, including a novel model of local relapse after neurosurgery. Furthermore, in situ proteomic analysis applied to metastases treated with the chaperone inhibitor uncovered a novel molecular program in brain metastasis, which includes biomarkers of poor prognosis and actionable mechanisms of resistance. Our work validates METPlatform as a potent resource for metastasis research integrating drug‐screening and unbiased omic approaches that is compatible with human samples. Thus, this clinically relevant strategy is aimed to personalize the management of metastatic disease in the brain and elsewhere

Jornada de Colaboración Científica en Seguridad y Defensa

Datos técnicos: 340 minutos, color, español. Ficha técnica: Gabinete de Presidencia CSIC y Departamento de Comunicación. Emitido en directo el 31 mayo 2023El Consejo Superior de Investigaciones Científicas (CSIC) organiza una JORNADA DE COLABORACIÓN CIENTÍFICA EN SEGURIDAD Y DEFENSA el próximo día 31 de mayo. En este evento se buscará potenciar las colaboraciones del CSIC en las áreas de seguridad y defensa con socios y colaboradores externos públicos y privados. La Jornada se dividirá en cuatro cápsulas en las que investigadores presentarán la investigación que se hace en el Consejo en seguridad digital y globalizada; sensores, vigilancia y teledetección para la defensa; riesgos biológicos; y las oportunidades de financiación en proyectos colaborativos competitivos. Finalizará con un coloquio en el que diversos actores debatirán sobre el avance científico en defensa y seguridad y su impacto social.Peer reviewe

Multiple myeloma and SARS-CoV-2 infection: clinical characteristics and prognostic factors of inpatient mortality

There is limited information on the characteristics, prognostic factors, and outcomes of patients with multiple myeloma (MM) hospitalized with COVID-19. This retrospective case series investigated 167 patients reported from 73 hospitals within the Spanish Myeloma Collaborative Group network in March and April, 2020. Outcomes were compared with 167 randomly selected, contemporary, age-/sex-matched noncancer patients with COVID-19 admitted at six participating hospitals. Among MM and noncancer patients, median age was 71 years, and 57% of patients were male; 75 and 77% of patients, respectively, had at least one comorbidity. COVID-19 clinical severity was moderate–severe in 77 and 89% of patients and critical in 8 and 4%, respectively. Supplemental oxygen was required by 47 and 55% of MM and noncancer patients, respectively, and 21%/9% vs 8%/6% required noninvasive/invasive ventilation. Inpatient mortality was 34 and 23% in MM and noncancer patients, respectively. Among MM patients, inpatient mortality was 41% in males, 42% in patients aged >65 years, 49% in patients with active/progressive MM at hospitalization, and 59% in patients with comorbid renal disease at hospitalization, which were independent prognostic factors on adjusted multivariate analysis. This case series demonstrates the increased risk and identifies predictors of inpatient mortality among MM patients hospitalized with COVID-19