43 research outputs found

    The expression analysis of mouse interleukin-6 splice variants argued against their biological relevance

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    Alternative splicing generates several interleukin-6 (IL-6) isoforms; for them an antagonistic activity to the wild-type IL-6 has been proposed. In this study we quantified the relative abundance of IL-6 mRNA isoforms in a panel of mouse tissues and in C2C12 cells during myoblast differentiation or after treatment with the Ca2+ ionophore A23187, the AMP-mimetic AICAR and TNF-alpha. The two mouse IL-6 isoforrns identified, IL-6 delta 5 (deletion of the first 58 bp of exon 5) and IL-6 delta 3 (lacking exon 3), were not conserved in rat and human, did not exhibit tissue specific regulation, were expressed at low levels and their abundance closely correlated to that of full-length IL-6. Species-specific features of the IL-6 sequence, such as the presence of competitive 3' acceptor site in exon 5 and insertion of retrotransposable elements in intron 3, could explain the production of IL-6 delta 5 and IL-6 delta 3. Our results argued against biological significance for mouse IL-6 isoforms

    C2C12 MYOBLASTS RELEASE MICRO-VESICLES CONTAINING mtDNA AND PROTEINS INVOLVED IN SIGNAL TRANSDUCTION

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    none11Micro-vesicles can be released by different cell types and operate as ‘safe containers’ mediatine inter-cellular communication. In this work we investigated whether cultured myoblasts could release exosomes. The reported data demonstrate, for the first time, that C2C12 myoblasts release micro-vesicles as shown by the presence of two exosome markers (Tsg101 and Alix proteins). Using real-time PCR analysis it was shown that these micro-vesicles, like other cell types, carry mtDNA. Proteomic characterization of the released micro-vesicle contents showed the presence of many proteins involved in signal transduction. The bioinformatics assessment of the Disorder Index and Aggregation Index of these proteins suggested that C2C12 micro-vesicles mainly deliver the machinery for signal transduction to target cells rather than key proteins involved in hub functions in molecular networks. The presence of IGFBP-5 in the purified micro-vesicles represents an exception, since this binding protein can play a key role in the modulation of the IGF-1 signalling pathway. In conclusion, the present findings demonstrate that skeletal muscle cells release micro-vesicles, which probably have an important role in the communication processes within skeletal muscles and between skeletal muscles and other organs. In particular, the present findings suggest possibile new diagnostic approaches to skeletal muscle diseases.openM. GUESCINI; D. GUIDOLIN; L. VALLORANI; L. CASADEI; A.M. GIOACCHINI; P. TIBOLLO; M. BATTISTELLI; E. FALCIERI; L. BATTISTIN; L.F. AGNATI; V. STOCCHIGuescini, Michele; D., Guidolin; Vallorani, Luciana; Casadei, Lucia; Gioacchini, ANNA MARIA; P., Tibollo; Battistelli, Michela; Falcieri, Elisabetta; L., Battistin; L. F., Agnati; Stocchi, Vilbert

    A dashboard-based system for supporting diabetes care

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    [EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. J., Roze, S., Valentine, W. J., Minshall, M. E., Foos, V., Lurati, F. M., … Spinas, G. A. (2004). The CORE Diabetes Model: Projecting Long-term Clinical Outcomes, Costs and Costeffectiveness of Interventions in Diabetes Mellitus (Types 1 and 2) to Support Clinical and Reimbursement Decision-making. Current Medical Research and Opinion, 20(sup1), S5-S26. doi:10.1185/030079904x1980O’Connor, P. J., Bodkin, N. L., Fradkin, J., Glasgow, R. E., Greenfield, S., Gregg, E., … Wysham, C. H. (2011). Diabetes Performance Measures: Current Status and Future Directions. Diabetes Care, 34(7), 1651-1659. doi:10.2337/dc11-0735Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Plank, J., … Pieber, T. R. (2016). Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. International Journal of Medical Informatics, 90, 58-67. doi:10.1016/j.ijmedinf.2016.03.007Sáenz, A., Brito, M., Morón, I., Torralba, A., García-Sanz, E., & Redondo, J. (2012). Development and Validation of a Computer Application to Aid the Physician’s Decision-Making Process at the Start of and during Treatment with Insulin in Type 2 Diabetes: A Randomized and Controlled Trial. Journal of Diabetes Science and Technology, 6(3), 581-588. doi:10.1177/193229681200600313Ampudia-Blasco, F. J., Benhamou, P. Y., Charpentier, G., Consoli, A., Diamant, M., Gallwitz, B., … Stoevelaar, H. (2015). A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes. Diabetes Technology & Therapeutics, 17(3), 194-202. doi:10.1089/dia.2014.0260Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Lipton, J. A., Barendse, R. J., Akkerhuis, K. M., Schinkel, A. F. L., & Simoons, M. L. (2010). Evaluation of a Clinical Decision Support System for Glucose Control. Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, 9(3), 140-147. doi:10.1097/hpc.0b013e3181e7d7caNeubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., … Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology & Therapeutics, 17(10), 685-692. doi:10.1089/dia.2015.0027Rodbard, D., & Vigersky, R. A. (2011). Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 5(2), 402-411. doi:10.1177/193229681100500230Augstein, P., Vogt, L., Kohnert, K.-D., Heinke, P., & Salzsieder, E. (2010). Translation of Personalized Decision Support into Routine Diabetes Care. Journal of Diabetes Science and Technology, 4(6), 1532-1539. doi:10.1177/193229681000400631Reza, A. W., & Eswaran, C. (2009). A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. Journal of Medical Systems, 35(1), 17-24. doi:10.1007/s10916-009-9337-yKumar, S. J. J., & Madheswaran, M. (2012). An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images. Journal of Medical Systems, 36(6), 3573-3581. doi:10.1007/s10916-012-9833-3Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. 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M., Moore, J. L., Mehr, D. R., Wakefield, D. S., Yadamsuren, B., … Belden, J. L. (2011). A Diabetes Dashboard and Physician Efficiency and Accuracy in Accessing Data Needed for High-Quality Diabetes Care. The Annals of Family Medicine, 9(5), 398-405. doi:10.1370/afm.1286Den Ouden, H., Vos, R. C., Reidsma, C., & Rutten, G. E. (2015). Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial. BMC Family Practice, 16(1). doi:10.1186/s12875-015-0230-0Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., … Chan, D. (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Canadian Medical Association Journal, 181(1-2), 37-44. doi:10.1503/cmaj.081272O’Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2012). 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    Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

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    Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03–4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55–5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14–1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37–0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17–1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20–1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45–1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80–13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10–1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32–1.67) and 365 days (RR 1.54, 95%CI 1.21–1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section

    International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

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    Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach

    Androgens and bone mineral density in late post-menopause

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    Data Integration Technologies to Improve Clinical Decisions on T2DM Patients

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    To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system
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