MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models.

The exponential growth of data is challenging for humans because their ability to analyze data is limited. Especially in chemistry, there is a demand for tools that can visualize molecular datasets in a convenient graphical way. We propose a new, ready-to-use, multi-tool, and open-source framework for visualizing and navigating chemical space. This framework adheres to the low-code/no-code (LCNC) paradigm, providing a KNIME node, a web-based tool, and a Python package, making it accessible to a broad cheminformatics community. The core technique of the MolCompass framework employs a pre-trained parametric t-SNE model. We demonstrate how this framework can be adapted for the visualisation of chemical space and visual validation of binary classification QSAR/QSPR models, revealing their weaknesses and identifying model cliffs. All parts of the framework are publicly available on GitHub, providing accessibility to the broad scientific community. Scientific contributionWe provide an open-source, ready-to-use set of tools for the visualization of chemical space. These tools can be insightful for chemists to analyze compound datasets and for the visual validation of QSAR/QSPR models.

The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

Evaluation of the applicability of hospital-affiliated green spaces to patient recovery using the entropy weight method and grey relational analysis.

Introduction: Hospital affiliated green spaces can help patients recover and recover their physical functions, promote physical and mental relaxation, enhance health awareness, and improve overall health. However, there are still significant questions about how to scientifically construct hospital affiliated green spaces. This study examines the impact of hospital green spaces on patient rehabilitation through scientific evaluation methods, providing reference for the scientific construction of hospital affiliated green spaces. Applicability evaluation was conducted on the affiliated green spaces of three hospitals in Harbin. An evaluation system covering plants, space, accessibility, rehabilitation functions, and promotional and educational functions has been constructed. The entropy weight method is used to determine the weight of indicators, and the grey correlation analysis method is used to evaluate the suitability of green space for patient rehabilitation. Methods: The experimental results showed that the landscape accessibility index had the highest weight (0.3005) and the plant index had the lowest weight (0.1628), indicating that caring for special needs is the foundation of hospital landscapes, and plants have subtle and long-term effects on physical and mental health. In the evaluation of the rehabilitation applicability of the affiliated green spaces of various hospitals, the second hospital has the highest grey correlation degree (0.8525), followed by the tumor hospital (0.5306) and the fifth hospital (0.4846). It can be seen that the green space of the second hospital has high applicability for patient rehabilitation, but the green space of the tumor hospital and the fifth hospital needs to be improved and developed. Results and discussion: The evaluation criteria used in this study are comprehensive. The landscaping at the Third Hospital is well-planned with good plant configuration and reasonable spatial layout. However, there is insufficient consideration for accessibility in the landscape design, and the details are lacking. The rehabilitation and educational functions of the landscape are inadequate, with limited outdoor activities and low road safety. The hospital's affiliated green spaces should adhere to the principle of "appropriate scale, comprehensive functionality, and educational leisure," integrating rehabilitation and educational functions while increasing the variety of outdoor activities. In the future, emphasis should be placed on exploring the integration of landscape and rehabilitation to provide a functional site that is convenient for visiting, with improved rehabilitation facilities and an educational and enjoyable environment. The design should incorporate elements that contribute to a sense of well-being, including roads and.

A Comparative Review of Solidified Floating Organic Drop Microextraction Methods for Metal Separation: recent Developments, Enhanced co-Factors, Challenges, and Environmental Assessment.

The current work is devoted to a comparative analysis of enhanced co-factors in solidified floating organic drop microextraction methods (SFODME) and an environmental assessment. Also, the description of SFODME, with a focus on its applications in the determination of metals in different matrices, was explained. The impact of several parameters, developments, and greenness evaluations was introduced. Especially, the review provides a concise overview of the multiple approaches to SFODME, with an emphasis on environmentally friendly, supported co-factors. These mods include ultrasound, vortex, and air-assisted SFODME procedures. The selectivity and sensitivity increase when co-factors are added to SFODME. Lastly, the analysis also aims to select tools (Analytic GREEnness Metric Approach (AGREE), Red-Green-Blue (RGB12), and Blue Applicability Grade Index (BAGI)) that have been described as environmentally friendly. Additionally provide an explanation of the data collected, compare, and emphasize the advantages of certain characteristics in each tool. Furthermore, case studies and comparisons for three tools were shown.

Multi-task pretrained language model with novel application domains enables more comprehensive health and ecological toxicity prediction.

In silico models for screening substances of healthy and ecological concern are essential for effective chemical management. However, current data-driven toxicity prediction models confront formidable challenges related to expressive capacity, data scarcity, and reliability issues. Thus, this study introduces TOX-BERT, a SMILES-based pretrained model for screening health and ecological toxicity. Results show that masked atom recovery pretraining and multi-task learning offer promising solutions to enhance model capacity and address data scarcity issues. Two novel application domain (AD) parameters, termed PCA-AD and LDS, were proposed to improve prediction reliability of TOX-BERT with accuracy surpassing 90 % and mean absolute error (MAE) below 0.52. TOX-BERT was applied to 18,905 IECSC chemicals, revealing distinct toxicity relationships that align with experimental studies such as those between cardiotoxicity and acute ecotoxicity. In addition to previous PBT screening, 156 potential high-risk chemicals for specific endpoint were identified covering 7 categories. Furthermore, a SMILES-based toxicity site detection approach was developed for structural toxicity analysis. These advancements carry profound implications to address challenges faced by current data-driven toxicity prediction models. TOX-BERT emerges as a valuable tool for more comprehensive, reliable, and applicable predictions of health and ecological toxicity in chemical risk assessment and management.

New horizons in clinical practice guidelines for use with older people.

Globally, more people are living into advanced old age, with age-associated frailty, disability and multimorbidity. Achieving equity for all ages necessitates adapting healthcare systems. Clinical practice guidelines (CPGs) have an important place in adapting evidence-based medicine and clinical care to reflect these changing needs. CPGs can facilitate better and more systematic care for older people. But they can also present a challenge to patient-centred care and shared decision-making when clinical and/or socioeconomic heterogeneity or personal priorities are not reflected in recommendations or in their application. Indeed, evidence is often lacking to enable this variability to be reflected in guidance. Evidence is more likely to be lacking about some sections of the population. Many older adults are at the intersection of many factors associated with exclusion from traditional clinical evidence sources with higher incidence of multimorbidity and disability compounded by poorer healthcare access and ultimately worse outcomes. We describe these challenges and illustrate how they can adversely affect CPG scope, the evidence available and its summation, the content of CPG recommendations and their patient-centred implementation. In all of this, we take older adults as our focus, but much of what we say will be applicable to other marginalised groups. Then, using the established process of formulating a CPG as a framework, we consider how these challenges can be mitigated, with particular attention to applicability and implementation. We consider why CPG recommendations on the same clinical areas may be inconsistent and describe approaches to ensuring that CPGs remain up to date.

Delphi method applicability in drug foresight.

BACKGROUND: The aim of the current study was to assess the accuracy of expert predictions, which were derived using a Delphi panel foresight study between 2009 and 2011, on a variety of drug-related topics in Finland in 2020. METHODS: The material used to evaluate the accuracy of the predictions consists of published reports on statistics, survey results, official register data, wastewater analyses and official documents. Whenever possible, we used multiple information sources to ascertain possible changes related to the predictions. RESULTS: Between 2009 and 2011, the majority - but not all - of the experts accurately predicted an increase in drug use. Indeed, more people experimented with or used drugs, and more drug residues were found in wastewater monitoring. The experts also correctly predicted an increase in population-level approval of drug use, but this development has been rather slow. Contrary to predictions, there was no marked increase in the use of new synthetic drugs. However, the misuse of buprenorphine increased during the 2010s. In the drug market, unit prices were surprisingly stable over the ten-year period. There were no changes in legislation related to the legal status of drugs, as was foreseen by the experts. However, enforcement moved in the direction foreseen by the experts: more lenient measures have been taken against users. Drug care system reforms favored a combination of mental health and addiction care units between 2009 and 2011, and 2020, as foreseen by the experts. CONCLUSIONS: It seems to have been easier for the experts to foresee the continuation of existing trends, e.g., increasing use of drugs or widening approval of drugs, than to predict possible changes in the popularity of distinct groups of drugs such as new psychoactive substances (NPS). Even armed with the prediction that drug imports and wholesale would increasingly fall into the domain of organized crime, this undesirable development could not be stopped. Expert disagreement can also be seen as a valuable indication of uncertainty regarding the future. Foresight related to drug-related issues can produce relatively accurate and realistic views of the future at least up to ten years ahead.

Development of a greener and sustainable method to determine acrylamide in corn products by LC-MS/MS: Evaluation of levels in corn-based products.

Acrylamide (AA) is produced through the reaction between sugars and amino acids present in starchy foods cooked at high temperature. It is classified as probably carcinogenic to humans. In 2019, the European Commission reported a list of foods for monitoring the presence of AA, which includes cereal snacks. This study presents the development and validation of an analytical approach for detecting AA in popcorn and corn-based snacks. It includes solid-liquid extraction and clean-up with dispersive solid phase extraction followed by analysis through liquid chromatography coupled with tandem mass spectrometry. The proposed method was characterized in terms of recoveries (84-105%), and precision (< 16.1%). Limits of quantification were 17 and 60 µg kg-1 for corn and popcorn, respectively. Sustainability of the methodology was evaluated using AGREEprep and BAGI, providing values of 0.43 and 65.0, respectively. Twenty-four corn-based products were analyzed, with AA levels from 219 to 418 µg kg-1.

Study on the path of high-quality development of the construction industry and its applicability.

Exploring the influencing factors and enhancement paths of high-quality development of the construction industry is crucial for promoting sustainable development of the construction industry. Based on the concepts of "five development", this paper takes the construction industry data of 29 provinces (autonomous regions and municipalities) in China as a sample, utilizes comprehensively the combination method of NCA and fsQCA to build a high-quality development driving model of the construction industry, and explores the coupling effect of factors, like the level of scientific and technological innovation, structural degree, precast building model, external market vitality, resources, and environment, on the development of the industry, revealing the path of high-quality development of the construction industry and analyze its applicability. These findings demonstrate that: (1) The level of scientific and technological innovation, the degree of structure, and the vitality of the external market are the core conditions, and a single factor does not constitute the necessary conditions for the high-quality development of the construction industry; (2) There are three paths for the high-quality development of the construction industry, among which the number of representative cases of linkage development led by openness innovation coordination is the largest, and has strong applicability; (3) There are two non-high-quality development paths in the construction industry, and there is a non-simple opposition relationship with the three high-quality development paths in the construction industry.

A Review on Small Organic Colorimetric and Fluorescent Hosts for the Detection of Cobalt and Nickel Ion.

In recent years, there has been a notable increase in efforts to advance efficient hosts for detecting cobalt and nickel ions, driven by their extensive industrial applications and environmental significance. This review meticulously examines the progress made in small organic colorimetric and fluorescent hosts tailored specifically for the sensitive and selective detection of cobalt and nickel ions. It delves into a diverse range of molecular architectures, including organic ligands, elucidating their unique attributes such as sensitivity, selectivity, and response time. Moreover, the review precisely explores the underlying principles governing the colorimetric and fluorescent mechanisms employed by these hosts, shedding light on the intricate interactions between the sensing moieties and the target metal ions. Furthermore, it critically evaluates the practical applicability of these hosts, considering crucial factors such as detection limits, recyclability, and compatibility with complex sample matrices. Additionally, exploration extends to potential challenges and prospects in the field, emphasizing the imperative for ongoing innovation to address emerging environmental and analytical demands. Eventually, through this comprehensive examination, the review seeks to contribute to the ongoing endeavor to develop robust and efficient tools for monitoring and detecting cobalt and nickel metal ions in diverse analytical scenarios.

Enhancing multi-scenario applicability of freeway variable speed limit control strategies using continual learning.

Variable speed limit (VSL) control benefits freeway operations through dynamic speed limit adjustment strategies for specific operation scenarios, such as traffic jams, secondary crash prevention, etc. To develop optimal strategies, deep reinforcement learning (DRL) has been employed to map the traffic operation status to speed limits with the corresponding control effects. Then, VSL control strategies were obtained based upon memories of these complex mapping relationships. However, under multi-scenario conditions, DRL trained VSL faces the challenge of performance decay, where the control strategy effects drop sharply for early trained "old scenarios". This so-called scenario forgetting problem is attributed to the fact that DRL would forget the learned old scenario mapping memories after new scenario trainings. To tackle this issue, a continual learning approach has been introduced in this study to enhance the multi-scenario applicability of VSL control strategies. Specifically, a gradient projection memory (GPM) based neural network parameter updating method was proposed to keep the mapping memories of old scenarios during new scenario trainings by imposing constraints on the direction of gradient updates for new tasks. The proposed method was evaluated using three typical freeway operation scenarios developed in the simulation platform SUMO. Experimental results showed that the continual learning approach has substantially reduced the performance decay in old scenarios by 17.76% (valued using backward transfer metrics). Furthermore, the multi-scenario VSL control strategies successfully reduced the speed standard deviation and average travel time by 28.77% and 7.25% respectively. Moreover, the generalization of the proposed continual learning based VSL approach were evaluated and discussed.

[Feasibility of a screening instrument for adult attention-deficit/hyperactivity disorder (ASRS-5) in general practice: A qualitative study]. / Anwendbarkeit eines Screening-Instruments für die Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung bei Erwachsenen (ASRS-5) in der Hausarztpraxis ­ eine qualitative Studie.

OBJECTIVES: The aim of the study was to explore the subjective views of general practitioners on the applicability of the Adult ADHD Self-Report Screening Scale for DSM-5 (ASRS-5) as a screening tool for attention-deficit/hyperactivity disorder (ADHD) in adults in general practice. METHOD: Eleven general practitioners, who had participated in the validation study of the German version of the ASRS-5, were interviewed. For this purpose, a semi-structured interview guide was designed using the Consolidated Framework for Implementation Research (CFIR). The interviews were audio-recorded, transcribed, and analyzed using qualitative content analysis according to Kuckartz. RESULTS: The ASRS-5 seems to work well in general medical practice. But there is evidence for a lack of knowledge about ADHD in adults among general practitioners and a demand for further training in this area. Moreover, insufficient possibilities for subsequent treatment after a positive ADHD screening were claimed. DISCUSSION: In general medicine, the introduction of a screening using ASRS-5 in cases of clinical suspicion could be the first step towards improving the management of adult patients with ADHD. CONCLUSION: Optimizing the management of adults with ADHD requires additional information and training initiatives to support early diagnosis especially in the primary care setting, and to reveal treatment options and care concepts for adults with ADHD.

The suboptimal clinical applicability of prognostic prediction models for severe postpartum hemorrhage: a meta-epidemiological study.

OBJECTIVES: To systematically investigate clinical applicability of the current prognostic prediction models for severe postpartum hemorrhage (SPPH). STUDY DESIGN AND SETTING: A meta-epidemiological study of prognostic prediction models was conducted for SPPH. A pre-designed structured questionnaire was adopted to extract the study characteristics, predictors and the outcome, modeling methods, predictive performance, the classification ability for high-risk individuals, and clinical use scenarios. The risk of bias among studies was assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: Twenty-two studies containing 27 prediction models were included. The number of predictors in the final models varied from 3 to 53. However, one-third of the models (11) did not clearly specify the timing of predictor measurement. Calibration was found to be lacking in 10 (37.0%) models. Among the 20 models with an incidence rate of predicted outcomes below 15.0%, none of the models estimated the area under the precision-recall curve, and all reported positive predictive values were below 40.0%. Only two (7.4%) models specified the target clinical setting, while seven (25.9%) models clarified the intended timing of model use. Lastly, all 22 studies were deemed to be at high risk of bias. CONCLUSION: Current SPPH prediction models have limited clinical applicability due to methodological flaws, including unclear predictor measurement, inadequate calibration assessment, and insufficient evaluation of classification ability. Additionally, there is a lack of clarity regarding the timing for model use, target users, and clinical settings. These limitations raise concerns about the reliability and usefulness of these models in real-world clinical practice.

Applicability of STOPP/START prescribing criteria in integrated Swedish administrative health registries and a Swedish population-based cohort.

PURPOSE: The STOPP/START criteria are frequently applied in observational studies to assess potentially inappropriate prescribing in older adults. This study aimed to assess the applicability of the three available STOPP/START versions in two distinct data sources. METHODS: To evaluate the applicability of the three versions of STOPP/START criteria, we used two observational data sources: (i) Integrated Swedish administrative health registries (ISHR) encompassing routinely collected health data and (ii) the population-based Swedish National study on Aging and Care in Kungsholmen (SNAC-K), based on health professional-led clinical assessments. The Anatomical Therapeutic Classification code (ATC) was used to categorise medications. Diseases were categorised using the international classification of diseases version 10 (ICD10). RESULTS: The first STOPP/START version demonstrated an applicability rate of 80% in ISHR and 84% in SNAC-K. The second version demonstrated an applicability of 64% in ISHR and 74% in SNAC-K. The third version showed an applicability of 66% in ISHR and 77% in SNAC-K. Challenges in applicability included broad definitions, vague terminology, and the lack of information on disease severity, symptomatic traits, and stability of certain conditions. CONCLUSION: The applicability of the STOPP/START criteria in observational studies seems to have decreased in more recent versions of the tool. Population-based studies with comprehensive clinical assessments may offer higher applicability compared to studies based on administrative data. Future versions of the STOPP/START criteria should prioritise clear and unambiguous definitions to improve their applicability in research and promote result generalisability and comparability.

New approach methods to assess developmental and adult neurotoxicity for regulatory use: a PARC work package 5 project.

In the European regulatory context, rodent in vivo studies are the predominant source of neurotoxicity information. Although they form a cornerstone of neurotoxicological assessments, they are costly and the topic of ethical debate. While the public expects chemicals and products to be safe for the developing and mature nervous systems, considerable numbers of chemicals in commerce have not, or only to a limited extent, been assessed for their potential to cause neurotoxicity. As such, there is a societal push toward the replacement of animal models with in vitro or alternative methods. New approach methods (NAMs) can contribute to the regulatory knowledge base, increase chemical safety, and modernize chemical hazard and risk assessment. Provided they reach an acceptable level of regulatory relevance and reliability, NAMs may be considered as replacements for specific in vivo studies. The European Partnership for the Assessment of Risks from Chemicals (PARC) addresses challenges to the development and implementation of NAMs in chemical risk assessment. In collaboration with regulatory agencies, Project 5.2.1e (Neurotoxicity) aims to develop and evaluate NAMs for developmental neurotoxicity (DNT) and adult neurotoxicity (ANT) and to understand the applicability domain of specific NAMs for the detection of endocrine disruption and epigenetic perturbation. To speed up assay time and reduce costs, we identify early indicators of later-onset effects. Ultimately, we will assemble second-generation developmental neurotoxicity and first-generation adult neurotoxicity test batteries, both of which aim to provide regulatory hazard and risk assessors and industry stakeholders with robust, speedy, lower-cost, and informative next-generation hazard and risk assessment tools.

Identifying uncertainty in physical-chemical property estimation with IFSQSAR.

This study describes the development and evaluation of six new models for predicting physical-chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol-water (KOW), octanol-air (KOA), and air-water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure-Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for "novel chemicals" in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7-1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.

Chemoinformatic regression methods and their applicability domain.

The growing interest in chemoinformatic model uncertainty calls for a summary of the most widely used regression techniques and how to estimate their reliability. Regression models learn a mapping from the space of explanatory variables to the space of continuous output values. Among other limitations, the predictive performance of the model is restricted by the training data used for model fitting. Identification of unusual objects by outlier detection methods can improve model performance. Additionally, proper model evaluation necessitates defining the limitations of the model, often called the applicability domain. Comparable to certain classifiers, some regression techniques come with built-in methods or augmentations to quantify their (un)certainty, while others rely on generic procedures. The theoretical background of their working principles and how to deduce specific and general definitions for their domain of applicability shall be explained.

How the Xenopus eleutheroembryonic thyroid assay compares to the amphibian metamorphosis assay for detecting thyroid active chemicals.

The Xenopus Eleutheroembryonic Thyroid Assay (XETA) was recently published as an OECD Test Guideline for detecting chemicals acting on the thyroid axis. However, the OECD validation did not cover all mechanisms that can potentially be detected by the XETA. This study was therefore initiated to investigate and consolidate the applicability domain of the XETA regarding the following mechanisms: thyroid hormone receptor (THR) agonism, sodium-iodide symporter (NIS) inhibition, thyroperoxidase (TPO) inhibition, deiodinase (DIO) inhibition, glucocorticoid receptor (GR) agonism, and uridine 5'-diphospho-glucuronosyltransferase (UDPGT) induction. In total, 22 chemicals identified as thyroid-active or -inactive in Amphibian Metamorphosis Assays (AMAs) were tested using the XETA OECD Test Guideline. The comparison showed that both assays are highly concordant in identifying chemicals with mechanisms of action related to THR agonism, DIO inhibition, and GR agonism. They also consistently identified the UDPGT inducers as thyroid inactive. NIS inhibition, investigated using sodium perchlorate, was not detected in the XETA. TPO inhibition requires further mechanistic investigations as the reference chemicals tested resulted in opposing response directions in the XETA and AMA. This study contributes refining the applicability domain of the XETA, thereby helping to clarify the conditions where it can be used as an ethical alternative to the AMA.

Supporting the applicability and transferability of the "Sortir Ensemble & Se Respecter" program in France / Accompagner l'applicabilité et la transférabilité du programme « Sortir Ensemble & Se Respecter ¼ en Frances

INTRODUCTION: The prevention of intimate partner violence (IPV) among young people is a major challenge for public policies. Nevertheless, there are a few prevention programs that have proven effective in France. "Sortir Ensemble & Se Respecter" (SE&SR) is a Swiss adaptation of "Safe Dates," an American intervention program that has reduced violent behavior by young perpetrators and victims of IPV alike. The aim of this article is to analyze the applicability and "potential transferability" of SE&SR in France. METHODS: We described the SE&SR intervention by explaining the intervention theory, the key functions (i.e., the "ingredients" allowing the SE&SR program to work), and we commented on its applicability from a perspective of adapting and transferring it to the French context. We used the ASTAIRE tool and the FIC (key functions, implementation, context) approach. RESULTS: The intervention theory highlighted various factors, acting at the individual level (i.e., beliefs/representations, knowledge, life skills) and at the level of the living environment (i.e., facilities welcoming young people; families; public policies; networks of actors), that can prevent IPV among young people. Ten key functions have been identified, revealing the "skeleton" of the Swiss intervention. We drew on these results to comment on the intervention's applicability, with a view to transferability, specifying the contextual elements to consider before implementing SE&SR in France. CONCLUSION: This study aims to make the process of evaluating applicability, with a view to transferring an evidence-based program to the French context, more accessible.

Introduction: La prévention des violences dans les relations amoureuses (VRA) chez les jeunes est un enjeu fort des politiques publiques. Néanmoins, il existe peu de programmes de prévention ayant fait la preuve de leur efficacité en France. « Sortir Ensemble & Se Respecter ¼ (SE&SR) est une adaptation suisse de « Safe Dates ¼, un programme d'intervention américain qui a montré des résultats en matière de réduction des comportements violents tant du côté des jeunes victimes que des auteurs. L'objectif de cet article est d'analyser l'applicabilité et la « potentielle transférabilité ¼ de SE&SR en France. Méthodes: L'approche adoptée consistait à décrire l'intervention SE&SR en explicitant la théorie d'intervention, les fonctions clés (soit les « ingrédients ¼ permettant que le programme SE&SR fonctionne) et en proposant des commentaires d'applicabilité en vue d'une transférabilité adaptée au contexte français. L'outil ASTAIRE et la démarche FIC ont été utilisés. Résultats: La théorie d'intervention a mis en évidence différents facteurs, agissant au niveau individuel (i.e. connaissances, croyances/représentations, compétences psychosociales) et au niveau des milieux de vie (i.e. structures accueillant les jeunes, familles, politiques publiques/réseaux d'acteurs), qui peuvent prévenir les VRA chez les jeunes. Dix fonctions clés ont été identifiées, dégageant le « squelette ¼ de l'intervention suisse. À la suite de ces résultats, des commentaires d'applicabilité en vue d'une transférabilité ont permis de préciser les éléments de contexte à prendre en compte avant la mise en œuvre de SE&SR en France. Conclusion: Cette étude souhaite rendre accessibles les process d'applicabilité en vue d'une transférabilité d'un programme probant en contexte français.