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

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

Comparative genomics-informed design of two LAMP detection assays for detection of the kiwifruit pathogen Pseudomonas syringae pv. actinidiae and discrimination of isolates belonging to the pandemic biovar 3

The aim of this study was to develop a rapid, sensitive and reliable field-based assay for detection of the quarantine pathogen Pseudomonas syringae pv. actinidiae, the causal agent of the most destructive and economically important bacterial disease of kiwifruit. A comparative genomic approach was used on the publicly available P. syringae pv. actinidiae genomic data to select unique target regions for the development of two loop-mediated isothermal amplification (LAMP) assays able to detect P. syringae pv. actinidiae and to discriminate strains belonging to the highly virulent and globally spreading P. syringae pv. actinidiae biovar 3. Both LAMP assays showed specificity in accordance to their target and were able to detect reliably 125 CFU per reaction in less than 30 min. The developed assays were able to detect the presence of P. syringae pv. actinidiae in symptomatic as well as in asymptomatic naturally infected kiwifruit material, thus increasing the potential of the LAMP assays for phytosanitary purposes

NIFTP-adjusted risk estimation of Bethesda thyroid cytology categories should consider the indication for FNA according to TIRADS.

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was firstly described in 2016. Since NIFTP is thought a non-malignant tumor, the Bethesda system for thyroid cytology proposes two estimations of risk of malignancy of the diagnostic categories, one considering NIFTP as cancer and another one considering it as a benign neoplasm. The present study aimed to review NIFTPs in a single center, re-assess them across categories of three Thyroid Imaging Reporting and Data Systems (TIRADSs), and define the indication for biopsy according to the category-specific size cut-offs. The study period was from 2017 to 2023. The institutional database was searched for histologically proven NIFTPs with preoperative ultrasound images. NIFTPs were re-assessed according to the American College of Radiology (ACR), European (EU), and Korean (K) TIRADSs. The indication for biopsy was defined according to TIRADS category-specific size threshold. Twenty NIFTPs from 19 patients were included. The median size of the NIFTPs was 23 mm. According to ultrasound, 80-85% of NIFTPs were at low-intermediate risk and 5-15% at high risk without significant difference among the tree TIRADSs (p = 0.91). The indication for FNA, according to three TIRADSs, was found in 52-58% of cases with no significant difference among systems (p = 0.96). NIFTPs have heterogeneous presentation according to TIRADSs with very low indication rate for FNA

Integration of models, data and expert knowledge by means of indicators: the Seamless Project Experience

International audienc

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 angiotensinconverting 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

Une méthode pour augmenter la flexibilité de l'évaluation intégrée des impacts de politiques publiques en agriculture

International audienc