Updated version of final design and of the architecture of SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Analysis of dose–TSH response effect of levothyroxine soft-gel formulation

BackgroundHypothyroidism is treated with daily levothyroxine (LT4). In recent years, soft gel caps of LT4 (LT4-C) have been commercialized, and their performance has been optimized. Since guidelines recommend dose LT4 according to the tablet preparation efficacy, the present study was undertaken to obtain data about the daily requirement, normalized per body weight, of LT4-C.MethodsPatients undergoing LT4-C after total thyroidectomy and radioiodine treatment for differentiated thyroid carcinoma were selected. There was no specific indication of suppression of TSH (i.e., <0.5 or <0.1 mIU/L). Patients were required to maintain a stable LT4 dose during the study period. Patients with interfering factors were excluded from this study.ResultsThirty patients were enrolled (18 females and 12 males; median age, 50 years; median body weight, 71 kg; median LT4-C dose, 1.71 µg/kg/day). The analysis of patient age did not reveal any differences. The LT4-C dose correlated with free-T4 p = 0.03), but not with TSH (p = 0.42) and free-T3 (p = 0.13). TSH was <1.0 mIU/L in 90% of the cases. The LT4-C dose–TSH response effect was analysed by probit regression model: the probability to achieve TSH <1.0 mIU/l was 99% with a dose of 1.84 (95%CI 1.57–2.12) µg/kg/day, 75% with a dose of 1.38 µg/kg/day (95%CI 1.17–1.59), and 50% with a dose of 1.20 (95%CI 0.96–1.43). At ROC curve analysis, the most accurate cut-off of LT4-C dose to achieve TSH <1.0 mIU/l was 1.53 ug/kg/day with 70% sensitivity and 100% specificity.ConclusionsAthyreotic patients can be initially treated with an LT4-C dose lower than previously stated. Therefore, further prospective studies are warranted

P1245 Polymorphic Variants of HSD3B1 Gene Confer Different Outcome in Specific Subgroups of Patients Infected With SARS-CoV-2

Introduction: Severe respiratory syndrome coronavirus 2 (SARS-CoV-2) uses the androgen receptor (AR), through ACE2 receptor and TMPRSS2, to enter nasal and upper airways epithelial cells. Genetic analyses revealed that HSD3B1 P1245C polymorphic variant increases dihydrotestosterone production and upregulation of TMPRSS2 with respect to P1245A variant, thus possibly influencing SARS-CoV-2 infection. Our aim was to characterize the HSD3B1 polymorphism status and its potential association with clinical outcomes in hospitalized patients with COVID-19 in Southern Switzerland. Materials and Methods: The cohort included 400 patients hospitalized for COVID-19 during the first wave between February and May 2020 in two different hospitals of Canton Ticino. Genomic DNA was extracted from formalin-fixed paraffin-embedded tissue blocks, and HSD3B1 gene polymorphism was evaluated by Sanger sequencing. Statistical associations were verified using different test. Results: HSD3B1 polymorphic variants were not associated with a single classical factor related to worse clinical prognosis in hospitalized patients with SARS-CoV-2. However, in specific subgroups, HSD3B1 variants played a clinical role: intensive care unit admission was more probable in patients with P1245C diabetes compared with P1245A individuals without this comorbidity and death was more associated with hypertensive P1245A>C cases than patients with P1245A diabetes without hypertension. Discussion: This is the first study showing that HSD3B1 gene status may influence the severity of SARS-CoV-2 infection. If confirmed, our results could lead to the introduction of HSD3B1 gene status analysis in patients infected with SARS-CoV-2 to predict clinical outcome. Keywords: HSD3B1 gene polymorphism; Likelihood-ratio tests; SARS-CoV-2; androgen receptor; direct sequencing

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Pre-existing cardiovascular conditions as clinical predictors of myocarditis reporting with immune checkpoint inhibitors: a VigiBase study

Up to 50% of myocarditis events developed in cancer patients upon treatment with immune checkpoint inhibitors (ICIs) are fatal. Therefore, identification of clinical risk factors predicting myocarditis onset during treatment with ICIs is important for the purpose of cardiac surveillance of high-risk patients. The aim of this retrospective matched case-control study was to assess whether pre-existing cardiovascular conditions were associated with the reporting of myocarditis with ICIs in VigiBase, the World Health Organization global database of suspected adverse drug reactions. Taking drugs labelled for the treatment of cardiovascular conditions as a proxy for concomitant cardiovascular risk factors and/or cardiovascular diseases, we found an association of moderate size between pre-existing cardiovascular conditions and the reporting of myocarditis with ICIs. Future prospective pharmacoepidemiological studies should assess the causal relationship between pre-existing cardiovascular conditions and myocarditis onset in a cohort of cancer patients followed during treatment with ICIs. Although rare, immune checkpoint inhibitor (ICI)-related myocarditis can be life-threatening, even fatal. In view of increased ICI prescription, identification of clinical risk factors for ICI-related myocarditis is of primary importance. This study aimed to assess whether pre-existing cardiovascular (CV) patient conditions are associated with the reporting of ICI-related myocarditis in VigiBase, theWHO global database of suspected adverse drug reactions (ADRs). In a (retrospective) matched case-control study, 108 cases of ICI-related myocarditis and 108 controls of ICI-related ADRs other than myocarditis were selected from VigiBase. Drugs labeled as treatment for CV conditions (used as a proxy for concomitant CV risk factors and/or CV diseases) were found to be associated more strongly with the reporting of ICI-related myocarditis than with other ICI-related ADRs (McNemar’s chi-square test of marginal homogeneity: p = 0.026, Cramer’s coefficient of effect size: F = 0.214). No significant associationwas found between pre-existing diabetes and ICI-relatedmyocarditis reporting (McNemar’s test of marginal homogeneity: p = 0.752). These findings offer an invitation for future prospective pharmacoepidemiological studies to assess the causal relationship between pre-existing CV conditions and myocarditis onset in a cohort of cancer patients followed during ICI treatment

Artificial intelligence in thyroid field: a comprehensive review

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient

A data-driven approach to identify risk profiles and protective drugs in COVID-19

As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin−angiotensin−aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19