PI3Kα inhibition reduces obesity in mice

Partial inhibition of PI3K is one of the best-validated and evolutionary conserved manipulations to extend longevity. The best known health beneficial effects of reduced PI3K are related to metabolism and include increased energy expenditure, reduced nutrient storage, and protection from obesity. We have previously shown that a dual chemical inhibitor of the alpha and delta PI3K isoforms (CNIO-PI3Ki) reduces obesity in mice and monkeys, without evident toxic effects after long-term treatment. Here, we dissect the role of the alpha and delta PI3K isoforms by making use of selective inhibitors against PI3Kα (BYL-719 also known as alpelisib) or PI3Kδ (GS-9820 also known as acalisib). Treatment of mice with the above mentioned inhibitors indicated that BYL-719 increases energy expenditure in normal mice and efficiently reduces body weight in obese (ob/ob) mice, whereas these effects were not observed with GS-9820. Of note, the dose of BYL-719 required to reduce obesity was 10x higher than the equivalent dose of CNIO-PI3Ki, which could suggest that simultaneous inhibition of PI3K alpha and delta is more beneficial than single inhibition of the alpha isoform. In summary, we conclude that inhibition of PI3Kα is sufficient to increase energy expenditure and reduce obesity, and suggest that concomitant PI3Kα inhibition could play an auxiliary role

Normal Proliferation and Tumorigenesis but Impaired Pancreatic Function in Mice Lacking the Cell Cycle Regulator Sei1

Sei1 is a positive regulator of proliferation that promotes the assembly of Cdk4-cyclin D complexes and enhances the transcriptional activity of E2f1. The potential oncogenic role of Sei1 is further suggested by its overexpression in various types of human cancers. To study the role of Sei1, we have generated a mouse line deficient for this gene. Sei1-null fibroblasts did not show abnormalities regarding proliferation or susceptibility to neoplastic transformation, nor did we observe defects on Cdk4 complexes or E2f activity. Sei1-null mice were viable, did not present overt pathologies, had a normal lifespan, and had a normal susceptibility to spontaneous and chemically-induced cancer. Pancreatic insulin-producing cells are known to be particularly sensitive to Cdk4-cyclin D and E2f activities, and we have observed that Sei1 is highly expressed in pancreatic islets compared to other tissues. Interestingly, Sei1-null mice present lower number of islets, decreased β-cell area, impaired insulin secretion, and glucose intolerance. These defects were associated to nuclear accumulation of the cell-cycle inhibitors p21Cip1 and p27Kip1 in islet cells. We conclude that Sei1 plays an important role in pancreatic β-cells, which supports a functional link between Sei1 and the core cell cycle regulators specifically in the context of the pancreas

Short-term effectiveness of a mobile phone app for increasing physical activity and adherence to the mediterranean diet in primary care: A randomized controlled trial (EVIDENT II study)

Background: The use of mobile phone apps for improving lifestyles has become generalized in the population, although little is still known about their effectiveness in improving health. Objective: We evaluate the effect of adding an app to standard counseling on increased physical activity (PA) and adherence to the Mediterranean diet, 3 months after implementation. Methods: A randomized, multicenter clinical trial was carried out. A total of 833 participants were recruited in six primary care centers in Spain through random sampling: 415 in the app+counseling group and 418 in the counseling only group. Counseling on PA and the Mediterranean diet was given to both groups. The app+counseling participants additionally received training in the use of an app designed to promote PA and the Mediterranean diet over a 3-month period. PA was measured with the 7-day Physical Activity Recall (PAR) questionnaire and an accelerometer; adherence to the Mediterranean diet was assessed using the Mediterranean Diet Adherence Screener questionnaire. Results: Participants were predominantly female in both the app+counseling (249/415, 60.0%) and counseling only (268/418, 64.1%) groups, with a mean age of 51.4 (SD 12.1) and 52.3 (SD 12.0) years, respectively. Leisure-time moderate-to-vigorous physical activity (MVPA) by 7-day PAR increased in the app+counseling (mean 29, 95% CI 5-53 min/week; P=.02) but not in the counseling only group (mean 17.4, 95% CI ''18 to 53 min/week; P=.38). No differences in increase of activity were found between the two groups. The accelerometer recorded a decrease in PA after 3 months in both groups: MVPA mean ''55.3 (95% CI ''75.8 to ''34.9) min/week in app+counseling group and mean ''30.1 (95% CI ''51.8 to ''8.4) min/week in counseling only group. Adherence to the Mediterranean diet increased in both groups (8.4% in app+counseling and 10.4% in counseling only group), with an increase in score of 0.42 and 0.53 points, respectively (P<.001), but no difference between groups (P=.86). Conclusions: Leisure-time MVPA increased more in the app+counseling than counseling only group, although no difference was found when comparing the increase between the two groups. Counseling accompanied by printed materials appears to be effective in improving adherence to the Mediterranean diet, although the app does not increase adherence

p21(Cip1) plays a critical role in the physiological adaptation to fasting through activation of PPARα.

Fasting is a physiological stress that elicits well-known metabolic adaptations, however, little is known about the role of stress-responsive tumor suppressors in fasting. Here, we have examined the expression of several tumor suppressors upon fasting in mice. Interestingly, p21 mRNA is uniquely induced in all the tissues tested, particularly in liver and muscle (>10 fold), and this upregulation is independent of p53. Remarkably, in contrast to wild-type mice, p21-null mice become severely morbid after prolonged fasting. The defective adaptation to fasting of p21-null mice is associated to elevated energy expenditure, accelerated depletion of fat stores, and premature activation of protein catabolism in the muscle. Analysis of the liver transcriptome and cell-based assays revealed that the absence of p21 partially impairs the transcriptional program of PPARα, a key regulator of fasting metabolism. Finally, treatment of p21-null mice with a PPARα agonist substantially protects them from their accelerated loss of fat upon fasting. We conclude that p21 plays a relevant role in fasting adaptation through the positive regulation of PPARα

Liver X Receptor Activation with an Intranasal Polymer Therapeutic Prevents Cognitive Decline without Altering Lipid Levels

The progressive accumulation of amyloid-beta (Aβ) in specific areas of the brain is a common prelude to late-onset of Alzheimer's disease (AD). Although activation of liver X receptors (LXR) with agonists decreases Aβ levels and ameliorates contextual memory deficit, concomitant hypercholesterolemia/hypertriglyceridemia limits their clinical application. DMHCA (N,N-dimethyl-3β-hydroxycholenamide) is an LXR partial agonist that, despite inducing the expression of apolipoprotein E (main responsible of Aβ drainage from the brain) without increasing cholesterol/triglyceride levels, shows nil activity in vivo because of a low solubility and inability to cross the blood brain barrier. Herein, we describe a polymer therapeutic for the delivery of DMHCA. The covalent incorporation of DMHCA into a PEG-dendritic scaffold via carboxylate esters produces an amphiphilic copolymer that efficiently self-assembles into nanometric micelles that exert a biological effect in primary cultures of the central nervous system (CNS) and experimental animals using the intranasal route. After CNS biodistribution and effective doses of DMHCA micelles were determined in nontransgenic mice, a transgenic AD-like mouse model of cerebral amyloidosis was treated with the micelles for 21 days. The benefits of the treatment included prevention of memory deterioration and a significant reduction of hippocampal Aβ oligomers without affecting plasma lipid levels. These results represent a proof of principle for further clinical developments of DMHCA delivery systems.Fil: Navas Guimaraes, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Catolica de Cuyo. Facultad de Ciencias Medicas. Instituto de Investigacion En Ciencias Biomedicas.; ArgentinaFil: Lopez Blanco, Roi. Universidad de Santiago de Compostela; EspañaFil: Correa, Juan. Universidad de Santiago de Compostela; EspañaFil: Fernandez Villamarin, Marcos. Universidad de Santiago de Compostela; EspañaFil: Bistue Millon, Maria Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Catolica de Cuyo. Facultad de Ciencias Medicas. Instituto de Investigacion En Ciencias Biomedicas.; ArgentinaFil: Martino Adami, Pamela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Morelli, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Kumar, Vijay. University of Colorado; Estados UnidosFil: Wempe, Michael F.. University of Colorado; Estados UnidosFil: Cuello, A. C.. McGill University; CanadáFil: Fernandez Megia, Eduardo. Universidad de Santiago de Compostela; EspañaFil: Bruno, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Catolica de Cuyo. Facultad de Ciencias Medicas. Instituto de Investigacion En Ciencias Biomedicas.; Argentin

On evaluating brain tissue classifiers without a ground truth

In this paper, we present a set of techniques for the evaluation of brain tissue classifiers on a large data set of MR images of the head. Due to the difficulty of establishing a gold standard for this type of data, we focus our attention on methods which do not require a ground truth, but instead rely on a common agreement principle. Three different techniques are presented: the Williams’ index, a measure of common agreement; STAPLE, an Expectation Maximization algorithm which simultaneously estimates performance parameters and constructs an estimated reference standard; and Multidimensional Scaling, a visualization technique to explore similarity data. We apply these different evaluation methodologies to a set eleven different segmentation algorithms on forty MR images. We then validate our evaluation pipeline by building a ground truth based on human expert tracings. The evaluations with and without a ground truth are compared. Our findings show that comparing classifiers without a gold standard can provide a lot of interesting information. In particular, outliers can be easily detected, strongly consistent or highly variable techniques can be readily discriminated, and the overall similarity between different techniques can be assessed. On the other hand, we also find that some information present in the expert segmentations is not captured by the automatic classifiers, suggesting that common agreement alone may not be sufficient for a precise performance evaluation of brain tissue classifiers

The relationship of the atlantic diet with cardiovascular risk factors and markers of arterial stiffness in adults without cardiovascular disease

Background: Studying the adherence of the population to the Atlantic Diet (AD) could be simplified by an easy and quickly applied dietary index. The aim of this study is to analyse the relationship of an index measuring compliance with recommendations regarding the Atlantic diet and physical activity with cardiovascular disease risk factors, cardiovascular risk factors, obesity indexes and arterial stiffness markers. Methods: We included 791 individuals from the EVIDENT study (lifestyles and arterial ageing), (52.3 ± 12 years, 61.7% women) without cardiovascular disease. Compliance with recommendations on AD was collected through the responses to a food frequency questionnaire, while physical activity was measured by accelerometer. The number of recommendations being met was estimated using a global scale between 0 and 14 points (a higher score representing greater adherence). Blood pressure, plasma lipid and glucose values and obesity rates were measured. Cardiovascular risk was estimated with the Framingham equation. Results: In the overall sample, 184 individuals (23.3%) scored between 0–3 on the 14-point index we created, 308 (38.9%) between 4 and 5 points, and 299 (37.8%) 6 or more points. The results of multivariate analysis yield a common tendency in which the group with an adherence score of at least 6 points shows lower figures for total cholesterol (p = 0.007) and triglycerides (p = 0.002). Similarly, overall cardiovascular risk in this group is the lowest (p < 0.001), as is pulse wave velocity (p = 0.050) and the mean values of the obesity indexes studied (p < 0.05 in all cases). Conclusion: The rate of compliance with the Atlantic diet and physical activity shows that greater adherence to these recommendations is linked to lower cardiovascular risk, lower total cholesterol and triglycerides, lower rates of obesity and lower pulse wave velocity values

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?Gatta, R.; Vallati, M.; Fernández Llatas, C.; Martinez-Millana, A.; Orini, S.; Sacchi, L.; Lenkowicz, J.... (2020). What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper. International Journal of Environmental research and Public Health (Online). 17(18):1-19. https://doi.org/10.3390/ijerph17186616S1191718Guyatt, G. (1992). Evidence-Based Medicine. JAMA, 268(17), 2420. doi:10.1001/jama.1992.03490170092032Hripcsak, G., Ludemann, P., Pryor, T. A., Wigertz, O. B., & Clayton, P. D. (1994). Rationale for the Arden Syntax. Computers and Biomedical Research, 27(4), 291-324. doi:10.1006/cbmr.1994.1023Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, 46(4), 744-763. doi:10.1016/j.jbi.2013.06.009Van de Velde, S., Heselmans, A., Delvaux, N., Brandt, L., Marco-Ruiz, L., Spitaels, D., … Flottorp, S. (2018). A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implementation Science, 13(1). doi:10.1186/s13012-018-0790-1Rawson, T. M., Moore, L. S. P., Hernandez, B., Charani, E., Castro-Sanchez, E., Herrero, P., … Holmes, A. H. (2017). A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clinical Microbiology and Infection, 23(8), 524-532. doi:10.1016/j.cmi.2017.02.028Greenes, R. A., Bates, D. W., Kawamoto, K., Middleton, B., Osheroff, J., & Shahar, Y. (2018). Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures. Journal of Biomedical Informatics, 78, 134-143. doi:10.1016/j.jbi.2017.12.005Garcia, C. dos S., Meincheim, A., Faria Junior, E. R., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., … Scalabrin, E. E. (2019). Process mining techniques and applications – A systematic mapping study. Expert Systems with Applications, 133, 260-295. doi:10.1016/j.eswa.2019.05.003Bhargava, A., Kim, T., Quine, D. B., & Hauser, R. G. (2019). A 20-Year Evaluation of LOINC in the United States’ Largest Integrated Health System. Archives of Pathology & Laboratory Medicine, 144(4), 478-484. doi:10.5858/arpa.2019-0055-oaLee, D., de Keizer, N., Lau, F., & Cornet, R. (2014). Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21(e1), e11-e19. doi:10.1136/amiajnl-2013-001636TROTTI, A., COLEVAS, A., SETSER, A., RUSCH, V., JAQUES, D., BUDACH, V., … COLEMAN, C. (2003). CTCAE v3.0: development of a comprehensive grading system for the adverse effects of cancer treatment. Seminars in Radiation Oncology, 13(3), 176-181. doi:10.1016/s1053-4296(03)00031-6Daniel, C., & Kalra, D. (2019). Clinical Research Informatics: Contributions from 2018. Yearbook of Medical Informatics, 28(01), 203-205. doi:10.1055/s-0039-1677921Marco-Ruiz, L., Pedrinaci, C., Maldonado, J. A., Panziera, L., Chen, R., & Bellika, J. G. (2016). Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. Journal of Biomedical Informatics, 62, 243-264. doi:10.1016/j.jbi.2016.07.011Marcos, C., González-Ferrer, A., Peleg, M., & Cavero, C. (2015). Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7’s Virtual Medical Record standard. Journal of the American Medical Informatics Association, 22(3), 587-599. doi:10.1093/jamia/ocv003Wulff, A., Haarbrandt, B., Tute, E., Marschollek, M., Beerbaum, P., & Jack, T. (2018). An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR. Artificial Intelligence in Medicine, 89, 10-23. doi:10.1016/j.artmed.2018.04.012Chen, C., Chen, K., Hsu, C.-Y., Chiu, W.-T., & Li, Y.-C. (Jack). (2010). A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Computer Methods and Programs in Biomedicine, 97(3), 280-285. doi:10.1016/j.cmpb.2009.12.004Anani, N., Mazya, M. V., Chen, R., Prazeres Moreira, T., Bill, O., Ahmed, N., … Koch, S. (2017). Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study. BMC Medical Informatics and Decision Making, 17(1). doi:10.1186/s12911-016-0401-5Eddy, D. M. (1982). Clinical Policies and the Quality of Clinical Practice. New England Journal of Medicine, 307(6), 343-347. doi:10.1056/nejm198208053070604Guyatt, G. H. (1990). Then-of-1 Randomized Controlled Trial: Clinical Usefulness. Annals of Internal Medicine, 112(4), 293. doi:10.7326/0003-4819-112-4-293CHALMERS, I. (1993). The Cochrane Collaboration: Preparing, Maintaining, and Disseminating Systematic Reviews of the Effects of Health Care. Annals of the New York Academy of Sciences, 703(1 Doing More Go), 156-165. doi:10.1111/j.1749-6632.1993.tb26345.xWoolf, S. H., Grol, R., Hutchinson, A., Eccles, M., & Grimshaw, J. (1999). Clinical guidelines: Potential benefits, limitations, and harms of clinical guidelines. BMJ, 318(7182), 527-530. doi:10.1136/bmj.318.7182.527Grading quality of evidence and strength of recommendations. (2004). BMJ, 328(7454), 1490. doi:10.1136/bmj.328.7454.1490Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924-926. doi:10.1136/bmj.39489.470347.adHill, J., Bullock, I., & Alderson, P. (2011). A Summary of the Methods That the National Clinical Guideline Centre Uses to Produce Clinical Guidelines for the National Institute for Health and Clinical Excellence. Annals of Internal Medicine, 154(11), 752. doi:10.7326/0003-4819-154-11-201106070-00007Qaseem, A. (2012). Guidelines International Network: Toward International Standards for Clinical Practice Guidelines. Annals of Internal Medicine, 156(7), 525. doi:10.7326/0003-4819-156-7-201204030-00009Rosenfeld, R. M., Nnacheta, L. C., & Corrigan, M. D. (2015). Clinical Consensus Statement Development Manual. Otolaryngology–Head and Neck Surgery, 153(2_suppl), S1-S14. doi:10.1177/0194599815601394De Boeck, K., Castellani, C., & Elborn, J. S. (2014). Medical consensus, guidelines, and position papers: A policy for the ECFS. Journal of Cystic Fibrosis, 13(5), 495-498. doi:10.1016/j.jcf.2014.06.012Clinical Practical Guidelineshttp://www.openclinical.org/guidelines.htmlHaynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A.-H. S., Dellinger, E. P., … Gawande, A. A. (2009). A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population. New England Journal of Medicine, 360(5), 491-499. doi:10.1056/nejmsa0810119Grigg, E. (2015). Smarter Clinical Checklists. Anesthesia & Analgesia, 121(2), 570-573. doi:10.1213/ane.0000000000000352Hales, B., Terblanche, M., Fowler, R., & Sibbald, W. (2007). Development of medical checklists for improved quality of patient care. International Journal for Quality in Health Care, 20(1), 22-30. doi:10.1093/intqhc/mzm062Greenfield, S. (2017). Clinical Practice Guidelines. JAMA, 317(6), 594. doi:10.1001/jama.2016.19969Vegting, I. L., van Beneden, M., Kramer, M. H. H., Thijs, A., Kostense, P. J., & Nanayakkara, P. W. B. (2012). How to save costs by reducing unnecessary testing: Lean thinking in clinical practice. European Journal of Internal Medicine, 23(1), 70-75. doi:10.1016/j.ejim.2011.07.003Drummond, M. (2016). Clinical Guidelines: A NICE Way to Introduce Cost-Effectiveness Considerations? Value in Health, 19(5), 525-530. doi:10.1016/j.jval.2016.04.020Prior, M., Guerin, M., & Grimmer-Somers, K. (2008). The effectiveness of clinical guideline implementation strategies - a synthesis of systematic review findings. Journal of Evaluation in Clinical Practice, 14(5), 888-897. doi:10.1111/j.1365-2753.2008.01014.xWatts, C. G., Dieng, M., Morton, R. L., Mann, G. J., Menzies, S. W., & Cust, A. E. (2014). Clinical practice guidelines for identification, screening and follow-up of individuals at high risk of primary cutaneous melanoma: a systematic review. British Journal of Dermatology, 172(1), 33-47. doi:10.1111/bjd.13403Woolf, S., Schünemann, H. J., Eccles, M. P., Grimshaw, J. M., & Shekelle, P. (2012). Developing clinical practice guidelines: types of evidence and outcomes; values and economics, synthesis, grading, and presentation and deriving recommendations. Implementation Science, 7(1). doi:10.1186/1748-5908-7-61Legido-Quigley, H., Panteli, D., Brusamento, S., Knai, C., Saliba, V., Turk, E., … Busse, R. (2012). Clinical guidelines in the European Union: Mapping the regulatory basis, development, quality control, implementation and evaluation across member states. Health Policy, 107(2-3), 146-156. doi:10.1016/j.healthpol.2012.08.004Rashidian, A., Eccles, M. P., & Russell, I. (2008). Falling on stony ground? A qualitative study of implementation of clinical guidelines’ prescribing recommendations in primary care. Health Policy, 85(2), 148-161. doi:10.1016/j.healthpol.2007.07.011Yang, W.-S., & Hwang, S.-Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1), 56-68. doi:10.1016/j.eswa.2005.09.003Kose, I., Gokturk, M., & Kilic, K. (2015). An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing, 36, 283-299. doi:10.1016/j.asoc.2015.07.018Pryor, T. A., Gardner, R. M., Clayton, P. D., & Warner, H. R. (1983). The HELP system. Journal of Medical Systems, 7(2), 87-102. doi:10.1007/bf00995116Shahar, Y., Miksch, S., & Johnson, P. (1998). The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine, 14(1-2), 29-51. doi:10.1016/s0933-3657(98)00015-3Boxwala, A. A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q. T., Wang, D., … Shortliffe, E. H. (2004). GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. Journal of Biomedical Informatics, 37(3), 147-161. doi:10.1016/j.jbi.2004.04.002Terenziani, P., Molino, G., & Torchio, M. (2001). A modular approach for representing and executing clinical guidelines. Artificial Intelligence in Medicine, 23(3), 249-276. doi:10.1016/s0933-3657(01)00087-2Sutton, D. R., & Fox, J. (2003). The Syntax and Semantics of the PROformaGuideline Modeling Language. Journal of the American Medical Informatics Association, 10(5), 433-443. doi:10.1197/jamia.m1264Musen, M. A., Tu, S. W., Das, A. K., & Shahar, Y. (1996). EON: A Component-Based Approach to Automation of Protocol-Directed Therapy. Journal of the American Medical Informatics Association, 3(6), 367-388. doi:10.1136/jamia.1996.97084511Ciccarese, P., Caffi, E., Quaglini, S., & Stefanelli, M. (2005). Architectures and tools for innovative Health Information Systems: The Guide Project. International Journal of Medical Informatics, 74(7-8), 553-562. doi:10.1016/j.ijmedinf.2005.02.001Shiffman, R. N., & Greenes, R. A. (1994). Improving Clinical Guidelines with Logic and Decision-table Techniques. Medical Decision Making, 14(3), 245-254. doi:10.1177/0272989x9401400306Peleg, M., Tu, S., Bury, J., Ciccarese, P., Fox, J., Greenes, R. A., … Stefanelli, M. (2003). Comparing Computer-interpretable Guideline Models: A Case-study Approach. Journal of the American Medical Informatics Association, 10(1), 52-68. doi:10.1197/jamia.m1135Karadimas, H., Ebrahiminia, V., & Lepage, E. (2018). User-defined functions in the Arden Syntax: An extension proposal. Artificial Intelligence in Medicine, 92, 103-110. doi:10.1016/j.artmed.2015.11.003Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004Marco-Ruiz, L., Moner, D., Maldonado, J. A., Kolstrup, N., & Bellika, J. G. (2015). Archetype-based data warehouse environment to enable the reuse of electronic health record data. International Journal of Medical Informatics, 84(9), 702-714. doi:10.1016/j.ijmedinf.2015.05.016Gooch, P., & Roudsari, A. (2011). Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. Journal of the American Medical Informatics Association, 18(6), 738-748. doi:10.1136/amiajnl-2010-000033Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., & Mossa, C. (2000). Guideline-based careflow systems. Artificial Intelligence in Medicine, 20(1), 5-22. doi:10.1016/s0933-3657(00)00050-6Schadow, G., Russler, D. C., & McDonald, C. J. (2001). Conceptual alignment of electronic health record data with guideline and workflow knowledge. International Journal of Medical Informatics, 64(2-3), 259-274. doi:10.1016/s1386-5056(01)00196-4González-Ferrer, A., ten Teije, A., Fdez-Olivares, J., & Milian, K. (2013). Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artificial Intelligence in Medicine, 57(2), 91-109. doi:10.1016/j.artmed.2012.08.008Shabo, A., Parimbelli, E., Quaglini, S., Napolitano, C., & Peleg, M. (2016). Interplay between Clinical Guidelines and Organizational Workflow Systems. Methods of Information in Medicine, 55(06), 488-494. doi:10.3414/me16-01-0006Mulyar, N., van der Aalst, W. M. P., & Peleg, M. (2007). A Pattern-based Analysis of Clinical Computer-interpretable Guideline Modeling Languages. Journal of the American Medical Informatics Association, 14(6), 781-787. doi:10.1197/jamia.m2389Grando, M. A., Glasspool, D., & Fox, J. (2012). A formal approach to the analysis of clinical computer-interpretable guideline modeling languages. Artificial Intelligence in Medicine, 54(1), 1-13. doi:10.1016/j.artmed.2011.07.001Kaiser, K., & Marcos, M. (2015). Leveraging workflow control patterns in the domain of clinical practice guidelines. BMC Medical Informatics and Decision Making, 16(1). doi:10.1186/s12911-016-0253-zMartínez-Salvador, B., & Marcos, M. (2016). Supporting the Refinement of Clinical Process Models to Computer-Interpretable Guideline Models. Business & Information Systems Engineering, 58(5), 355-366. doi:10.1007/s12599-016-0443-3Decision Model and Notation Version 1.0https://www.omg.org/spec/DMN/1.0Ghasemi, M., & Amyot, D. (2016). Process mining in healthcare: a systematised literature review. International Journal of Electronic Healthcare, 9(1), 60. doi:10.1504/ijeh.2016.078745Rebuge, 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.2011.01.003Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Lenkowicz, J., Gatta, R., Masciocchi, C., Casà, C., Cellini, F., Damiani, A., … Valentini, V. (2018). Assessing the conformity to clinical guidelines in oncology. Management Decision, 56(10), 2172-2186. doi:10.1108/md-09-2017-0906Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Qu, G., Liu, Z., Cui, S., & Tang, J. (2013). Study on Self-Adaptive Clinical Pathway Decision Support System Based on Case-Based Reasoning. Frontier and Future Development of Information Technology in Medicine and Education, 969-978. doi:10.1007/978-94-007-7618-0_95Van de Velde, S., Roshanov, P., Kortteisto, T., Kunnamo, I., Aertgeerts, B., Vandvik, P. O., & Flottorp, S. (2015). Tailoring implementation strategies for evidence-based recommendations using computerised clinical decision support systems: protocol for the development of the GUIDES tools. Implementation Science, 11(1). doi:10.1186/s13012-016-0393-

N-1-methylnicotinamide is a signalling molecule produced in skeletal muscle coordinating energy metabolism

Obesity is a major health problem, and although caloric restriction and exercise are successful strategies to lose adipose tissue in obese individuals, a simultaneous decrease in skeletal muscle mass, negatively effects metabolism and muscle function. To deeper understand molecular events occurring in muscle during weight-loss, we measured the expressional change in human skeletal muscle following a combination of severe caloric restriction and exercise over 4 days in 15 Swedish men. Key metabolic genes were regulated after the intervention, indicating a shift from carbohydrate to fat metabolism. Nicotinamide N-methyltransferase (NNMT) was the most consistently upregulated gene following the energy-deficit exercise. Circulating levels of N-1-methylnicotinamide (MNA), the product of NNMT activity, were doubled after the intervention. The fasting-fed state was an important determinant of plasma MNA levels, peaking at similar to 18 h of fasting and being lowest similar to 3 h after a meal. In culture, MNA was secreted by isolated human myotubes and stimulated lipolysis directly, with no effect on glucagon or insulin secretion. We propose that MNA is a novel myokine that enhances the utilization of energy stores in response to low muscle energy availability. Future research should focus on applying MNA as a biomarker to identify individuals with metabolic disturbances at an early stage.Peer reviewe