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Berta Raventós,1,2,* Sergio Fernández-Bertolín,1,* María Aragón,1 Erica A Voss,3– 5 Clair Blacketer,3– 5 Leonardo Méndez-Boo,6 Martina Recalde,1 Elena Roel,1,2 Andrea Pistillo,1,7 Carlen Reyes,1 Sebastiaan van Sandijk,8 Lars Halvorsen,9 Peter R Rijnbeek,4,5 Edward Burn,1,10 Talita Duarte-Salles1,4 1Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 2Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain; 3Janssen Pharmaceutical Research and Development, Titusville, NJ, USA; 4Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands; 5OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA; 6Sistemes d’Informació dels Serveis d’Atenció Primària (SISAP), Institut Català de la Salut, Barcelona, Spain; 7Universitat Pompeu Fabra, Barcelona, Spain; 8Odysseus Data Services s.r.o., Prague, Czech Republic; 9edenceHealth NV, Kontich, Belgium; 10Centre for Statistics in Medicine, University of Oxford, Oxford, UK*These authors contributed equally to this workCorrespondence: Talita Duarte-Salles, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Gran Via Corts Catalanes, 587 àtic, Barcelona, 08007, Spain, Tel +34935824342, Email tduarte@idiapjgol.orgPurpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population.Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022.Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died.Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.Keywords: electronic health records, medical ontologies, secondary data use, common data model, OMOP

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

Martina Recalde,1,2,* Cyntia B Manzano-Salgado,1,2,* Yesika Díaz,1 Diana Puente,1,2 Maria Del Mar Garcia-Gil,1 Rafael Marcos-Gragera,3,4 Josefa Ribes-Puig,5,6 Jaume Galceran,7 Margarita Posso,8 Francesc Macià,8 Talita Duarte-Salles1 1Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGoL), Barcelona, Spain; 2Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Cerdanyola del Vallès, Spain; 3Unitat d’Epidemiologia i Registre de Càncer de Girona (UERCG), Pla Director d’Oncologia, Institut Català d’Oncologia, Institut d’Investigació Biomèdica de Girona (IdIBGi), Universitat De Girona, Girona, Spain; 4CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; 5Catalan Cancer Plan, Department of Health of Catalonia, Barcelona, Spain; 6Department of Clinical Sciences, University of Barcelona (UB), Barcelona, Spain; 7Registre de Càncer de Tarragona, Fundació per a la Investigació i Prevenció del Càncer (FUNCA), IISPV, Reus, Spain; 8Cancer Prevention Unit and Cancer Registry, Department of Epidemiology and Evaluation, Hospital del Mar, Barcelona, Spain*These authors contributed equally to this workCorrespondence: Talita Duarte-SallesFundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGoL) Gran Via Corts Catalanes, 587 Àtic, Barcelona 08007, SpainTel +34 93 482 43 42Email tduarte@idiapjgol.orgBackground: Electronic health records are becoming an increasingly valuable resource for epidemiology but their data quality needs to be quantified. We aimed to validate twenty-five types of incident cancer cases in the Information System for Research in Primary Care (SIDIAP) in Catalonia with the population-based cancer registries of Girona and Tarragona as the gold-standard.Methods: We calculated the sensitivity, positive predictive values (PPV), and the time-difference between the date of diagnosis entered into the SIDIAP and into the registries. We added hospital discharge cancer diagnoses to the SIDIAP to assess sensitivity changes.Results: We identified 27,046 incident cancer diagnoses in the SIDIAP from 2009–2015 among the 949,841 residents of Girona and Tarragona. The cancer types with the highest sensitivity were breast (89%, 95% CI: 88–90%), colorectal (81%, 95% CI: 80–82%), and prostate (81%, 95% CI: 80–83%). Trachea, bronchus and lung cancers had the highest PPV (76%, 95% CI: 74%-78%) followed by stomach (72%, 95% CI: 68–75%) and pancreas (71%, 95% CI: 67–75%). Most cancer diagnoses were reported with less than three months of difference between the SIDIAP and the registries. More cases were registered first in the registries than in the SIDIAP. By adding cancer diagnoses based on hospital discharge data, sensitivity increased for all cancers, especially for gallbladder and biliary tract for which the sensitivity increased by 21%.Conclusion: The SIDIAP includes 76% of the cancer diagnoses in the cancer registries but includes a considerable number of cases that are not in the registries. The SIDIAP reports most of the cancer diagnoses within a three-month period difference from the date of diagnosis in the cancer registries. Our results support the use of the SIDIAP cancer diagnoses for epidemiological research when cancer is the outcome of interest. We recommend adding hospital discharge data to the SIDIAP to increase data quality, particularly for less frequent cancer types.Keywords: validation studies, cancer, electronic health records, primary health care, population-based cancer registries

Effects of extracellular nucleotides and their hydrolysis products on regulatory volume decrease of trout hepatocytes. Am J Physiol Regul Integr Comp Physiol 287: R833-R843, 2004. First published June 24, 2004; 10.1152/ajpregu.00199.2004.--In trout hepatocytes, hypotonic swelling is followed by a compensatory shrinkage called regulatory volume decrease (RVD). It has been postulated that extracellular ATP and other nucleotides may interact with type 2 receptors ([P.sub.2]) to modulate this response. In addition, specific ectoenzymes hydrolyze ATP sequentially down to adenosine, which may bind to type 1 receptors ([P.sub.1]) and also influence RVD. Accordingly, in this study, we assessed the role of extracellular nucleoside 5'-tri- and diphosphates and of adenosine on RVD of trout hepatocytes. The extent of RVD after 40 min of maximum swelling was denoted as RV[D.sub.40], whereas the initial rate of RVD was called [V.sub.RVD]. In the presence of hypotonic medium (60% of isotonic), hepatocytes swelled 1.6 times followed by [V.sub.RVD] of 1.7 [min.sup.-1] and RV[D.sub.40] of 60.2%. ATP, UTP, UDP, or ATP[gamma]S ([P.sub.2] agonists: 5 [micro]M) increased [V.sub.RVD] 1.5-2 times, whereas no changes were observed in the values of RV[D.sub.40]. Addition of 100 [micro]M suramin or cibacron blue ([P.sub.2] antagonists) to the hypotonic medium produced no effect on [V.sub.RVD] but a 53-58% inhibition of RV[D.sub.40]. Incubation of hepatocytes in the presence of either 5 [micro]M [[[gamma].sup.-32]P]ATP or [[[alpha].sup.-32]P]ATP induced the extracellular release of [[[gamma].sup.-32]P] [P.sub.i] (0.21 nmol*[10.sup.-6] [cells.sup.-1]*[min.sup.-1]) and [[[alpha].sup.-32]P][P.sub.i] (~8 x [10.sup.-3] nmol*[10.sup.-6] [cells.sup.-1]*[min.sup.-1]), suggesting the presence of ectoenzymes capable of fully dephosphorylating ATP. Concerning the effect of [P.sub.1] activation on RVD, 5 [micro]M adenosine, both in the presence and absence of 100 [micro]M S-(4-nitrobenzil)-6-tioinosine (a blocker of adenosine uptake), decreased RV[D.sub.40] by 37-44%, whereas 8-phenyl theophylline, a [P.sub.1] antagonist, increased RV[D.sub.40] by 15%. Overall, results indicate that ATP, UTP, and UDP, acting via Pa, are important factors promoting RVD of trout hepatocytes, whereas adenosine binding to [P.sub.1] inhibits this process. fish; ectonucleoside triphosphate diphosphohydrolases; extracellular adenosine 5'-triphosphate; cyprinids

Copyright of Compendium of Veterinary Sciences / Compendio de Ciencias Veterinarias is the property of Facultad de Ciencias Veterinarias de la Universidad Nacional de Asuncion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Espanola de Antropologia Americana is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Chronic inflammation in immune-mediated conditions has been associated with an increased risk in atherosclerotic disease. There is paucity of evidence regarding the prevalence of asymptomatic atherosclerosis in patients with ulcerative colitis (UC) and its association with disease activity. We sought to compare the prevalence of asymptomatic atherosclerotic disease between young patients with UC with and without mucosal healing (MH) and healthy control individuals., Methods: An observational study was conducted in 2 hospitals in Buenos Aires, Argentina. Patients with UC 18 to 50 years of age with at least 1 previous colonoscopy in the last year were enrolled, along with age- and sex-matched healthy control individuals. Carotid and femoral ultrasound assessments were performed to determine the prevalence of atherosclerotic lesions and abnormal intima-media thickness (IMT). We compared the prevalence of atherosclerotic disease and the prevalence of abnormally increased IMT in at least 1 vascular territory., Results: Sixty patients with UC and 60 healthy control individuals were enrolled. Mean age was 38 years and 53.33% were men. Although the prevalence of atherosclerotic lesions was similar in patients with UC without MH when compared with both patients with UC with MH and control individuals (3.7% vs 0% vs 6.67%; P = .1), we found a significant increase in abnormal IMT in at least 1 vascular territory in UC patients without MH when compared with healthy control individuals (48.15% vs 26.67%; P = .05)., Conclusions: Patients with UC with active mucosal inflammation showed a significantly increased odds of asymptomatic femoral or carotid vascular disease when compared with control individuals., (© The Author(s) 2023. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

DNA methylation is crucial for chromatin structure, transcription regulation and genome stability, defining cellular identity. Aberrant hypermethylation of CpG-rich regions is common in cancer, influencing gene expression. However, the specific contributions of individual epigenetic modifications to tumorigenesis remain under investigation. In hepatocellular carcinoma (HCC), DNA methylation alterations are documented as in other tumor types. We aimed to identify hypermethylated CpGs in HCC, assess their specificity across other tumor types, and investigate their impact on gene expression. To this end, public methylomes from HCC, other liver diseases, and 27 tumor types as well as expression data from TCGA-LIHC and GTEx were analyzed. This study identified 39 CpG sites that were hypermethylated in HCC compared to control liver tissue, and were located within promoter, gene bodies, and intergenic CpG islands. Notably, these CpGs were predominantly unmethylated in healthy liver tissue and other normal tissues. Comparative analysis with 27 other tumors revealed both common and HCC-specific hypermethylated CpGs. Interestingly, the HCC-hypermethylated genes showed minimal expression in the different healthy tissues, with marginal changes in the level of expression in the corresponding tumors. These findings confirm previous evidence on the limited influence of DNA hypermethylation on gene expression regulation in cancer. It also highlights the existence of mechanisms that allow the selection of tissue-specific methylation marks in normally unexpressed genes during carcinogenesis. Overall, our study contributes to demonstrate the complexity of cancer epigenetics, emphasizing the need of better understanding the interplay between DNA methylation, gene expression dynamics, and tumorigenesis., (© 2024. The Author(s).)

Background: Metabolic syndrome (MS) has emerged as a significant global health concern. The relationship between MS and the risk of cancer doesn't seem clear, whether examining by components or in combination. The objective of this study is to examine the relationship between MS, its components, and the overall risk of cancer, including the risk of 13 specific cancer types., Methods: We included 3,918,781 individuals aged 40 years or older sourced from the SIDIAP database between 2008 and 2017. Cox models were employed with MS components and their combinations. A subsample was created using a matched cohort (by age and sex). Incidence curves were computed to determine the time elapsed between the date of having 1-5 MS components and cancer incidence, compared to matched participants with no MS components, which showed that individuals who had one MS component experienced a greater incidence of cancer over 5 and 10 years than individuals with no MS, and the incidence rose with an increase in the number of MS components., Results: Individuals exposed to MS components were diagnosed with cancer earlier than those who were not exposed to them. In the Cox model, HDL (HR 1.46, 95% CI: 1.41-1.52) and Glycemia (HR 1.40, 95% CI: 1.37-1.44) were the individual combinations with the highest risk of overall cancer. In combinations with two components, the highest HR was HDL+Glycemia (HR 1.52, 95% CI: 1.45-1.59) and Glycemia+HBP (HR 1.48, 95% CI: 1.45-1.50). In combinations with three components, the highest HR was HDL+Glycemia+HBP (HR 1.58, 95% CI: 1.55-1.62)., Conclusion: In summary, having one or more MS components raises the risk of developing at least 11 cancer types and these risk differ according to type of component included. Some sex differences are also observed. Our findings suggest that implementing prevention measures aimed at specific MS components may lower the risk of various cancer types., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)

Background & Aims: The homeostasis of the cellular transcriptome depends on transcription and splicing mechanisms. Moreover, the fidelity of gene expression, essential to preserve cellular identity and function is secured by different quality control mechanisms including nonsense-mediated RNA decay (NMD). In this context, alternative splicing is coupled to NMD, and several alterations in these mechanisms leading to the accumulation of aberrant gene isoforms are known to be involved in human disease including cancer., Methods: RNA sequencing, western blotting, qPCR and co-immunoprecipitation were performed in multiple silenced culture cell lines (replicates n ≥4), primary hepatocytes and samples of animal models (Jo2, APAP, Mdr2 -/- mice, n ≥3)., Results: Here we show that in animal models of liver injury and in human HCC (TCGA, non-tumoral = 50 vs . HCC = 374), the process of NMD is inhibited. Moreover, we demonstrate that the splicing factor SLU7 interacts with and preserves the levels of the NMD effector UPF1, and that SLU7 is required for correct NMD. Our previous findings demonstrated that SLU7 expression is reduced in the diseased liver, contributing to hepatocellular dedifferentiation and genome instability during disease progression. Here we build on this by providing evidence that caspases activated during liver damage are responsible for the cleavage and degradation of SLU7., Conclusions: Here we identify the downregulation of UPF1 and the inhibition of NMD as a new molecular pathway contributing to the malignant reshaping of the liver transcriptome. Moreover, and importantly, we uncover caspase activation as the mechanism responsible for the downregulation of SLU7 expression during liver disease progression, which is a new link between apoptosis and hepatocarcinogenesis., Impact and Implications: The mechanisms involved in reshaping the hepatocellular transcriptome and thereby driving the progressive loss of cell identity and function in liver disease are not completely understood. In this context, we provide evidence on the impairment of a key mRNA surveillance mechanism known as nonsense-mediated mRNA decay (NMD). Mechanistically, we uncover a novel role for the splicing factor SLU7 in the regulation of NMD, including its ability to interact and preserve the levels of the key NMD factor UPF1. Moreover, we demonstrate that the activation of caspases during liver damage mediates SLU7 and UPF1 protein degradation and NMD inhibition. Our findings identify potential new markers of liver disease progression, and SLU7 as a novel therapeutic target to prevent the functional decay of the chronically injured organ., (© 2024 The Authors.)

Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that are increasingly used in cancer treatments. As experience in the use of immunotherapy increases, more is known about its safety profile and immune-mediated adverse effects. Among them is diabetic ketoacidosis (DKA), a rare but serious fatal complication of treatment. In this paper we describe the cases of three patients who presented with episodes of DKA during treatment with ICIs, two of which manifested with fulminant forms, leading to an acute course with initially normal glycosylated hemoglobin values. In addition, we conducted a review of the literature on DKA associated with ICIs in order to highlight the importance of noticing these potentially fatal complications and promptly establishing appropriate therapy.

Background: Whether cancer risk associated with a higher body mass index (BMI), a surrogate measure of adiposity, differs among adults with and without cardiovascular diseases (CVD) and/or type 2 diabetes (T2D) is unclear. The primary aim of this study was to evaluate separate and joint associations of BMI and CVD/T2D with the risk of cancer., Methods: This is an individual participant data meta-analysis of two prospective cohort studies, the UK Biobank (UKB) and the European Prospective Investigation into Cancer and nutrition (EPIC), with a total of 577,343 adults, free of cancer, T2D, and CVD at recruitment. We used Cox proportional hazard regressions to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between BMI and incidence of obesity-related cancer and in turn overall cancer with a multiplicative interaction between BMI and the two cardiometabolic diseases (CMD). HRs and 95% CIs for separate and joint associations for categories of overweight/obesity and CMD status were estimated, and additive interaction was quantified through relative excess risk due to interaction (RERI)., Results: In the meta-analysis of both cohorts, BMI (per ~ 5 kg/m 2 ) was positively associated with the risk of obesity-related cancer among participants without a CMD (HR: 1.11, 95%CI: 1.07,1.16), among participants with T2D (HR: 1.11, 95% CI: 1.05,1.18), among participants with CVD (HR: 1.17, 95% CI: 1.11,1.24), and suggestively positive among those with both T2D and CVD (HR: 1.09, 95% CI: 0.94,1.25). An additive interaction between obesity (BMI ≥ 30 kg/m 2 ) and CVD with the risk of overall cancer translated into a meta-analytical RERI of 0.28 (95% CI: 0.09-0.47)., Conclusions: Irrespective of CMD status, higher BMI increased the risk of obesity-related cancer among European adults. The additive interaction between obesity and CVD suggests that obesity prevention would translate into a greater cancer risk reduction among population groups with CVD than among the general population., (© 2023. The Author(s).)