Analyzing the impact of spanish university funding policies on the evolution of their performance: a multi-criteria approach

The relationship between university performance and performance-based funding models has been a topic of debate for decades. Promoting performance-based funding models can create incentives for improving the educational and research effectiveness of universities, and consequently providing them with a competitive advantage over its competitors. Therefore, this paper studies how to measure the performance of a university through a mathematical multicriteria analysis and tries to link these results with certain university funding policies existing in the Spanish case. To this end, a reference point-based technique is used, which allows the consideration and aggregation of all the aspects regarded as relevant to assess university performance. The simple and easy way in which the information is provided by this technique makes it valuable for decision makers because of considering two aggregation scenarios: the fully compensatory scenario provides an idea of the overall performance, while the non-compensatory one detects possible improvement areas. This study is carried out in two stages. First, the main results of applying the proposed methodology to the performance analysis evolution of the largest three Spanish public university, over a period of five academic years, are described. Second, a discussion is carried out about some interesting features of the analysis proposed at regional level, and some policy messages are provided. The “intra” regions university performance analysis reveals some institutions with noteworthy behaviors, some with sustained trends throughout the analyzed period and other institutions with more erratic behaviors, within the same regional public university system despite having the identical funding model. However, the findings “inter” regions also reveal that only Catalonia has developed a true performance-based model, in theory and in practice, which has contributed to achieving excellent results at regional level in both teaching and researchThis research was partially funded by the Spanish Ministry of Economy and Competitiveness (Project PID2019-104263RB-C42), from the Regional Government of Andalucía (Project P18-RT-1566), and by the EU ERDF operative program (Project UMA18-FEDERJA-065

Smart zero carbon city readiness level: sistema de indicadores para el diagnostico de las ciudades en su camino hacia la descarbonizacion y su aplicacion en el Pais Vasco

Nowadays urban environments concentrate more than half the world’s population, reaching up to 70% on 2050 according to forecasts. This concentration implies that most of future challenges will take place in cities as well as the opportunities coming from their potential solutions. Current technological innovation can provide support in facing one of main challenges society is facing: reducing carbon footprint from our cities. This ambitious transition, steered by the Smart Zero Carbon City (SZCC) concept, needs a flexible characterisation method, which can be adapted to different kinds of cities to evaluate the main features of each city, hence proposing and prioritising most suitable interventions. The aim of this study is focused on the characterisation of cities according to the SZCC concept through a set of indicators: the Smart Zero Carbon City Readiness Level (SZCC Readiness Level), able to analyse key aspects of cities according to SZCC concept (Characteristics of the city; City plans and strategies; Energy; Mobility; Infrastructures and ICT services; Citizen Engagement). This characterisation enlightens the development of SZCC concept in the city, identifying its strengths and weaknesses in order to ease the alternatives’ selection towards decarbonisation, being handy at a time for those small and medium-sized municipalities, so common in the European context, which usually hold less resources than big capitals to implement decision-making support diagnoses. In order to validate this set of indicators, SZCC Readiness Level has been implemented in 5 Basque cities, which represent different urban typologies, analysing its current situation regarding SZCC concept.Los autores quieren expresar su profundo agradecimiento a las administraciones de Donostia-San Sebastián, Eibar, Irún, Sestao y Vitoria-Gasteiz por la estrecha colaboración e involucración de sus técnicos en la tarea. Del mismo modo, agradecer al Departamento de Medio Ambiente, Planificación Territorial y Vivienda del Gobierno Vasco, mediante la convocatoria Eraikal, y a la Comisión Europea, a través del proyecto SmartEnCity, por hacer posible este estudio

LA EMIGRACIÓN INTERNACIONAL, LAS REMESAS Y EL DESARROLLO ECONÓMICO EN MÉXICO

LAS REMESAS FAMILIARES CONSTITUYEN UNO DE LOS GRANDES BENEFICIOS QUE DEJA LA MIGRACIÓN INTERNACIONAL DE LOS INDIVIDUOS DE LOS DIFERENTES PAÍSES FUNDAMENTALMENTE DE AQUELLOS CONSIDERADOS EN VÍAS DE DESARROLLO Y SE CONSIDERA A AMÉRICA LATINA LA REGIÓN DE MAYOR IMPORTANCIA CON RELACIÓN A LOS MAYORES FLUJOS DE ESTAS DIVISAS. DEBIDO A LA GRAN TRASCENDENCIA QUE HA VENIDO TENIENDO PARA LAS NACIONES CONSIDERADAS EN VÍAS DE DESARROLLO, EL FONDO MULTILATERAL DE INVERSIONES (FOMIN) DEL BANCO INTERAMERICANO DE DESARROLLO (BID), EN EL AÑO 2000, SE HA DADO A LA TAREA DE RECOPILAR LA INFORMACIÓN ESTADÍSTICA CON RESPECTO A ESTE RUBRO, ASÍ COMO DE LOS COSTOS DE TRANSACCIÓN EN QUE SE INCURREN, LOS PROVEEDORES DE SERVICIOS Y EL POSIBLE IMPACTO DE ESTAS EN EL DESARROLLO DE LA ECONOMÍAS DE LOS PAÍSES QUE TIENEN POBLACIÓN MIGRANT

Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

[EN] Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities¿ relocation and increment of citizens (findings 1, 3¿4), the impact of strategies (findings 2¿3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politecnica de Valencia, project "ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MEDICOS". www.upv.es. J.M.G.G.is also partially supported by: Ministerio de Economia y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.Perez-Benito, FJ.; Sáez Silvestre, C.; Conejero, JA.; Tortajada, S.; Valdivieso, B.; Garcia-Gomez, JM. (2019). Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE. 14(8):1-19. https://doi.org/10.1371/journal.pone.0220369S119148Aguilar-Savén, R. S. (2004). Business process modelling: Review and framework. International Journal of Production Economics, 90(2), 129-149. doi:10.1016/s0925-5273(03)00102-6Poulymenopoulou, M. (2003). Journal of Medical Systems, 27(4), 325-335. doi:10.1023/a:1023701219563Dadam P, Reichert M, Kuhn K. Clinical Workflows -The Killer Application for Process-oriented Information Systems? Proceedings of the 4th International Conference on Business Information Systems. London: Springer London; 2000. pp. 36–59. doi: https://doi.org/10.1007/978-1-4471-0761-3Lenz, R., & Reichert, M. (2007). IT support for healthcare processes – premises, challenges, perspectives. Data & Knowledge Engineering, 61(1), 39-58. doi:10.1016/j.datak.2006.04.007Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Amour EAEH, Ghannouchi SA. Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE; 2017. pp. 230–237. doi: https://doi.org/10.1109/PDCAT.2017.00045Chou, Y.-C., Chen, B.-Y., Tang, Y.-Y., Qiu, Z.-J., Wu, M.-F., Wang, S.-C., … Chuang, W.-C. (2010). Prescription-Filling Process Reengineering of an Outpatient Pharmacy. Journal of Medical Systems, 36(2), 893-902. doi:10.1007/s10916-010-9553-5Leu, J.-D., & Huang, Y.-T. (2009). An Application of Business Process Method to the Clinical Efficiency of Hospital. Journal of Medical Systems, 35(3), 409-421. doi:10.1007/s10916-009-9376-4Gand K. Investigating on Requirements for Business Model Representations: The Case of Information Technology in Healthcare. 2017 IEEE 19th Conference on Business Informatics (CBI). IEEE; 2017. pp. 471–480. doi: https://doi.org/10.1109/CBI.2017.36Ferreira, G. S. A., Silva, U. R., Costa, A. L., & Pádua, S. I. D. de D. (2018). The promotion of BPM and lean in the health sector: main results. Business Process Management Journal, 24(2), 400-424. doi:10.1108/bpmj-06-2016-0115Abdulrahman Jabour RM. Cancer Reporting: Timeliness Analysis and Process. 2016; Available: https://search.proquest.com/openview/4ecf737c5ef6d2d503e948df8031fe54/1?pq-origsite=gscholar&cbl=18750&diss=yHewitt M, Simone J V. Enhancing Data Systems to Improve the Quality of Cancer Care [Internet]. National Academy Press; 2000. Available: http://www.nap.edu/catalog/9970.htmlWeiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Saez C, Robles M, Garcia-Gomez JM. Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. IEEE; 2013. pp. 3226–3229. doi: https://doi.org/10.1109/EMBC.2013.6610228Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Sáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010International Ethical Guidelines for Epidemiological Studies [Internet]. Geneva: Council for International Organizations of Medical Sciences (CIOMS) in collaboration with the World Health Organization; 2009. Available: https://cioms.ch/wp-content/uploads/2017/01/International_Ethical_Guidelines_LR.pdfResearch Ethics Committee of the Universitari i Politècnic La Fe Hospital [Internet]. Available: https://www.iislafe.es/en/research/ethics-committees/Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373-383. doi:10.1016/0021-9681(87)90171-8Schneeweiss, S., Wang, P. S., Avorn, J., & Glynn, R. J. (2003). Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations. Health Services Research, 38(4), 1103-1120. doi:10.1111/1475-6773.00165Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., … Ghali, W. A. (2005). Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data. Medical Care, 43(11), 1130-1139. doi:10.1097/01.mlr.0000182534.19832.83Sáez Silvestre C. Probabilistic methods for multi-source and temporal biomedical data quality assessment [Internet]. Thesis. Universitat Politècnica de València. 2016. doi: https://doi.org/10.4995/Thesis/10251/62188Amari S, Nagaoka H. Methods of Information Geometry [Internet]. Amer. Math. Soc. and Oxford Univ. Press. American Mathematical Society; 2000. Available: https://books.google.es/books?hl=es&lr=&id=vc2FWSo7wLUC&oi=fnd&pg=PR7&dq=Methods+of+Information+geometry&ots=4HmyCCY4PX&sig=2-dpCuwMQvEC1iREjxdfIX0yEls#v=onepage&q=MethodsofInformationgeometry&f=falseCsiszár, I., & Shields, P. C. (2004). Information Theory and Statistics: A Tutorial. Foundations and Trends™ in Communications and Information Theory, 1(4), 417-528. doi:10.1561/0100000004Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. doi:10.1109/18.61115M.Cover T. Elements Of Information Theory Notes [Internet]. 2006. Available: http://books.google.fr/books?id=VWq5GG6ycxMC&printsec=frontcover&dq=intitle:Elements+of+Information+Theory&hl=&cd=1&source=gbs_api%5Cnpapers2://publication/uuid/BAF426F8-5A4F-44A4-8333-FA8187160D9BBrandes, U., & Pich, C. (s. f.). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. Lecture Notes in Computer Science, 42-53. doi:10.1007/978-3-540-70904-6_6Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., … Talaei-Khoei, A. (2013). Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. International Journal of Medical Informatics, 82(1), 10-24. doi:10.1016/j.ijmedinf.2012.10.001Arts, D. G. T. (2002). Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework. Journal of the American Medical Informatics Association, 9(6), 600-611. doi:10.1197/jamia.m1087Bray, F., & Parkin, D. M. (2009). Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity and timeliness. European Journal of Cancer, 45(5), 747-755. doi:10.1016/j.ejca.2008.11.032Parkin, D. M., & Bray, F. (2009). Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. European Journal of Cancer, 45(5), 756-764. doi:10.1016/j.ejca.2008.11.033Fernandez-Llatas, C., Ibanez-Sanchez, G., Celda, A., Mandingorra, J., Aparici-Tortajada, L., Martinez-Millana, A., … Traver, V. (2019). Analyzing Medical Emergency Processes with Process Mining: The Stroke Case. Lecture Notes in Business Information Processing, 214-225. doi:10.1007/978-3-030-11641-5_17Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Weijters AJMM, Van Der Aalst WMP, Alves De Medeiros AK. Process Mining with the HeuristicsMiner Algorithm [Internet]. Available: https://pdfs.semanticscholar.org/1cc3/d62e27365b8d7ed6ce93b41c193d0559d086.pdfShim, S. J., & Kumar, A. (2010). Simulation for emergency care process reengineering in hospitals. Business Process Management Journal, 16(5), 795-805. doi:10.1108/14637151011076476Svolba, G., & Bauer, P. (1999). Statistical Quality Control in Clinical Trials. Controlled Clinical Trials, 20(6), 519-530. doi:10.1016/s0197-2456(99)00029-xKahn, M. G., Raebel, M. A., Glanz, J. M., Riedlinger, K., & Steiner, J. F. (2012). A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research. Medical Care, 50, S21-S29. doi:10.1097/mlr.0b013e318257dd67Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. doi:10.1145/1541880.1541883Heinrich, B., Klier, M., & Kaiser, M. (2009). A Procedure to Develop Metrics for Currency and its Application in CRM. Journal of Data and Information Quality, 1(1), 1-28. doi:10.1145/1515693.1515697Sirgo, G., Esteban, F., Gómez, J., Moreno, G., Rodríguez, A., Blanch, L., … Bodí, M. (2018). Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment. International Journal of Medical Informatics, 112, 166-172. doi:10.1016/j.ijmedinf.2018.02.007Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504-507. doi:10.1126/science.1127647Kohn LT, Corrigan JM. To err is human: building a safer health system. A report of the Committee on Quality of Health Care in America. 2000. p. 287. National Academies Press

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.This research was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed by nomination to Valencian technological innovation centres under project expedient IMDEEA/2021/100. It was also supported by grants from Instituto de Salud Carlos III FEDER (PI17/00047).S

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S

Construction of two large-size four-plane micromegas detectors

We report on the construction and initial performance studies of two micromegas detector quadruplets with an area of 0.5 m$^2$. They serve as prototypes for the planned upgrade project of the ATLAS muon system. Their design is based on the resistive-strip technology and thus renders the detectors spark tolerant. Each quadruplet comprises four detection layers with 1024 readout strips and a strip pitch of 415 $\mu$m. In two out of the four layers the strips are inclined by $\pm$1.5$^{\circ}$ to allow for the measurement of a second coordinate. We present the detector concept and report on the experience gained during the detector construction. In addition an evaluation of the detector performance with cosmic rays and test-beam data is given.Comment: 26 pages, 25 figure

A comparison of cut points for measuring risk factors for adolescent substance use and antisocial behaviors in the U.S. and Colombia

As the identification and targeting of salient risk factors for adolescent substance use become more widely used globally, an essential question arises as to whether U.S.-based cut points in the distributions of these risk factors that identify high risk can be used validly in other countries as well. This study examined proportions of youth at high risk using different empirically derived cut points in the distributions of 18 measured risk factors. Data were obtained from large-scale samples of adolescents in Colombia and the United States. Results indicated that significant (p \u3c 0.05) differences in the proportions of high risk youth were found in 38.9% of risk factors for 6th graders, 61.1% for 8th graders, and 66.6% for 10th graders. Colombian-based cut points for determining the proportion of Colombian youth at high risk were preferable to U.S.-based cut points in almost all comparisons that exhibited a significant difference. Our findings suggest that observed differences were related to the type of risk factor (e.g., drug specific vs. non-drug specific). Findings from this study demonstrate the need for collecting large-scale national data on risk factors for adolescent substance use and developing country-specific cut points based on the distributions of these measures to avoid misidentification of youth at high risk

Total and high molecular weight adiponectin have similar utility for the identification of insulin resistance

<p>Abstract</p> <p>Background</p> <p>Insulin resistance (IR) and related metabolic disturbances are characterized by low levels of adiponectin. High molecular weight adiponectin (HMWA) is considered the active form of adiponectin and a better marker of IR than total adiponectin. The objective of this study is to compare the utility of total adiponectin, HMWA and the HMWA/total adiponectin index (S_Aindex) for the identification of IR and related metabolic conditions.</p> <p>Methods</p> <p>A cross-sectional analysis was performed in a group of ambulatory subjects, aged 20 to 70 years, in Mexico City. Areas under the receiver operator characteristic (ROC) curve for total, HMWA and the S_Aindex were plotted for the identification of metabolic disturbances. Sensitivity and specificity, positive and negative predictive values, and accuracy for the identification of IR were calculated.</p> <p>Results</p> <p>The study included 101 men and 168 women. The areas under the ROC curve for total and HMWA for the identification of IR (0.664 <it>vs</it>. 0.669, <it>P </it>= 0.74), obesity (0.592 <it>vs</it>. 0.610, <it>P </it>= 0.32), hypertriglyceridemia (0.661 <it>vs</it>. 0.671, <it>P </it>= 0.50) and hypoalphalipoproteinemia (0.624 <it>vs</it>. 0.633, <it>P </it>= 0.58) were similar. A total adiponectin level of 8.03 μg/ml was associated with a sensitivity of 57.6%, a specificity of 65.9%, a positive predictive value of 50.0%, a negative predictive value of 72.4%, and an accuracy of 62.7% for the diagnosis of IR. The corresponding figures for a HMWA value of 4.25 μg/dl were 59.6%, 67.1%, 51.8%, 73.7% and 64.2%.</p> <p>The area under the ROC curve of the S_Aindex for the identification of IR was 0.622 [95% CI 0.554-0.691], obesity 0.613 [95% CI 0.536-0.689], hypertriglyceridemia 0.616 [95% CI 0.549-0.683], and hypoalphalipoproteinemia 0.606 [95% CI 0.535-0.677].</p> <p>Conclusions</p> <p>Total adiponectin, HMWA and the S_Aindex had similar utility for the identification of IR and metabolic disturbances.</p

Deciphering the quality of SARS-CoV-2 specific T-cell response associated with disease severity, immune memory and heterologous response

SARS-CoV-2 specific T-cell response has been associated with disease severity, immune memory and heterologous response to endemic coronaviruses. However, an integrative approach combining a comprehensive analysis of the quality of SARS-CoV-2 specific T-cell response with antibody levels in these three scenarios is needed. In the present study, we found that, in acute infection, while mild disease was associated with high T-cell polyfunctionality biased to IL-2 production and inversely correlated with anti-S IgG levels, combinations only including IFN-γ with the absence of perforin production predominated in severe disease. Seven months after infection, both non-hospitalised and previously hospitalised patients presented robust anti-S IgG levels and SARS-CoV-2 specific T-cell response. In addition, only previously hospitalised patients showed a T-cell exhaustion profile. Finally, combinations including IL-2 in response to S protein of endemic coronaviruses were the ones associated with SARS-CoV-2 S-specific T-cell response in pre-COVID-19 healthy donors’ samples. These results could have implications for protective immunity against SARS-CoV-2 and recurrent COVID-19 and may help for the design of new prototypes and boosting vaccine strategies