Rounds Of Testing Research Articles

Development and Initial Evaluation of a Fine Motor Subscale of a Comprehensive Developmental Scale for Children Aged 18–41 Months

ABSTRACT Identifying developmental delays in children is vital for timely intervention, yet there are limited local developmental tests in Hong Kong that include a fine motor subscale and are specifically tailored for children aged 18–41 months. To address this need, we developed a 50-item fine motor subscale, which underwent two rounds of pilot testing and expert reviews. This study investigated its internal-scale validity and reliability, comprising 340 young children in Hong Kong, including 82 children with fine motor delays and 258 typical developing children. Unidimensionality and test targeting, known-group validity, and internal consistency of this subscale were examined. The Rasch analysis supported the unidimensionality of the fine motor subscale which accounted for 63.9% of the total variance. Most items, except two, showed a good fit, and the item difficulty levels (between −5.91 and 5.63 logits) effectively covered children’s ability levels (between −7.17 and 5.04 logits). Subscale scores significantly differed across age groups and between children with and without fine motor delays. The internal consistency was high at 0.96. This study provides initial psychometric evidence for the newly developed fine motor subscale, with ongoing refinement and validation aimed at establishing the final version of the Hong Kong Comprehensive Assessment Scales for Toddlers.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

BackgroundTo evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.MethodsThe participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the ‘AI intelligent assisted diagnosis system’ teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5–10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared.ResultsThere was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1–4 and 5–7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively.ConclusionThe AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Just Care: usability testing of e-learning modules for peer caregivers living in prison

Training men and women who live in prison to assist corrections staff with the care of growing numbers of older, sicker, cognitively impaired, and dying people in prison is an innovative and viable option to help meet pressing care demands. The purpose of this paper is to report findings from the usability testing of the fully developed Just Care electronic learning program for training carefully vetted people living in prison to assist staff with geriatric and end-of-life care. Research ethics approvals and informed consent were obtained. There were two rounds of usability testing conducted via videoconference. Interdisciplinary staff (i.e., corrections specialists, supervisors, nurses, and chaplains) and people living in prison were recruited from one men’s and one women’s prison. Fifteen participants completed the testing in each round. Results include participant demographics, concerns and areas for improvement by severity level of programming, and mean scores for both participant groups on the System Usability Scale. Overall, both the incarcerated and staff participants found the program easy to navigate and the interactive content was useful, engaging, and relevant to caring for aging and dying people who are incarcerated. Findings will be used to refine Just Care before usability testing with a larger sample.

Quick and high-throughput quantification of β-agonist residues in bovine liver, meat, milk, kidney, poultry, and egg using dispersive solid phase extraction

A reliable liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry (LC-Q-Orbitrap HRMS) method was developed for the simultaneous identification and quantification of 13 β-agonist residues in bovine liver, meat, milk, kidney, poultry, and egg. Dispersive-solid phase extraction (d-SPE) using acetonitrile (ACN) was used to prepare the samples. The analyte in the extracts was separated on a reversed-phase Accucore aQ (50 mm × 2.1 mm, 2.6 μm) using a mobile phase of an aqueous solution containing 2 mM ammonium acetate and acetonitrile (ACN) 0.1 % formic acid. The method was validated in accordance with Commission Implementing Regulation (CIR) EU 2021/808 at six different concentrations ranging from 0.1 to 5 μg/kg. The mean recoveries ranged from 65 to 94 %, while repeatability and reproducibility values were all below 13 %. The linearity, as correlation coefficients (R2) ranged from 0.9955 to 0.9999. The decision limit (CCα) and detection capability (CCβ) ranges were 0.11–0.13 µg/kg and 0.12–0.15 µg/kg, respectively. The limits of detection (LOD) and limits of quantification (LOQ) were in the range of 0.004–0.048 μg/kg and 0.010–0.075 μg/kg, respectively. Of the 180 samples that were collected from local markets in Egypt, 21.11 % had β-agonist residues. The mean concentration (µg/kg) and detection frequency (%) of the most frequently found β-agonist in the samples were as follows: terbutaline (2.63 µg/kg and 90 %), ractopamine (5.14 µg/kg and 23.3 %). The method's applicability was verified by successfully completing two rounds of proficiency testing (PT).

Experience of a proficiency testing program developed in Latin America for chemical analysis of coffee

Based on ISO 17043:2010, eight (8) proficiency testing (PT) rounds were organized for moisture, ash, caffeine, and total sugar analyses using commercial ground roasted pure coffee and roasted coffee with sugar added as PT materials as part of the Interlaboratory Food Analysis Rounds Program (PRIDAA, by its acronym in Spanish). Twenty-five (25) laboratories from Latin America participated in these PT Rounds. Results were evaluated using statistical procedures based on ISO 13528: 2016. The Z/Z′-scores used to evaluate the performance of laboratories were computed using the standard deviation for PT (σp) defined based on fitness-for-purpose, while the assigned values (mean, median, or robust mean) were obtained from the consensus of PT participants’ results. For ash and caffeine, 89 % and 88 % of PT participants achieved a satisfactory performance (|Z/Z’|≤2), respectively, as average of the eight (8) rounds. Only 75 % achieved a ‘‘satisfactory’’ performance in the case of total sugars and 83 % in the case of moisture. For total sugar and caffeine, results obtained with methods other than HPLC were not comparable to the assigned value defined as the consensus of the HPLC results. Significant improvements in the methodology were applied to the PT when the ISO 17043:2010 accreditation was achieved in 2020.

Mise à jour par consensus de l’outil POPI

Medication errors are one of the causes of iatrogenic medication use in children. The POPI tool for detecting inappropriate drug prescriptions and prescription omissions in paediatrics was the first tool to be published in this field in 2014. Our aim was to update the POPI tool for French use based on current recommendations and practice. Criteria were removed, updated or added based on recommendations from learned societies and national bodies. The two-round Delphi method was used to reach a consensus of experts. The level of agreement of the healthcare professionals' proposals was rated on a 9-point Likert scale. In the first round, only proposals with a median agreement of 7 to 9 and an agreement of more than 65% were retained. In the second round, only those with a median agreement of 7 to 9 and over 75% agreement were retained. The POPI tool now includes eight categories (various, infectiology, gastroenterology, pneumonology, dermatology, neurology/pedopsychiatry, haematology and excipients). All the criteria were supported by bibliographical references. They were submitted to 20 French healthcare professionals: 9 pharmacists and 11 doctors (17 hospital-based and 3 self-employed). After two rounds of Delphi testing, 166 criteria were retained and validated (111 inappropriate prescriptions and 55 omissions). In conclusion, this study made it possible to update the POPI tool, which is still available for assessing paediatric prescriptions.

Proficiency of phenotypic drug susceptibility testing for Mycobacterium tuberculosis in China, 2008-2021.

To analyze the results of proficiency testing for anti-tuberculosis drug susceptibility testing (DST) in China. Number of laboratory participating the proficiency testing performed DST, and the sensitivity, specificity, reproducibility, and accordance rate were calculated from data of 13 rounds proficiency testing results for DST from 2008 to 2021. A total of 30 and 20 strains of Mycobacterium tuberculosis with known susceptibility results were sent to each laboratory in 2008 to 2019, 2020 and 2021, respectively. The number of participating laboratories ranged from 30 in 2009 to 546 in 2021. L-J DST was the predominant method. The specificity presented relatively higher than sensitivity. Improvement of specificity were observed for all drugs through the years, while sensitivity did not show improvement for amikacin and capreomycin. Accordance rate of pyrazinamide and kanamycin and reproducibility of capreomycin and pyrazinamide were not significantly improved through the years. Most of the participating laboratories significantly improved the quality of their DST through the consecutive rounds of proficiency testing except for second-line injectable drugs and pyrazinamide. The results highlight the importance of developing novel and/or improving existing methods for phenotypic DST for certain drugs.

Unifying quantum verification and error-detection: theory and tools for optimisations

With the advent of cloud-based quantum computing, it has become vital to provide strong guarantees that computations delegated by clients to quantum service providers have been executed faithfully. Secure—blind and verifiable—delegated quantum computing (SDQC) has emerged as one of the key approaches to address this challenge, yet current protocols lack at least one of the following three ingredients: composability, noise-robustness and modularity. To tackle this question, our paper lays out the fundamental structure of SDQC protocols, namely mixing two components: the computation which the client would like the server to perform and tests that are designed to detect a server’s malicious behaviour. Using this abstraction, our main technical result is a set of sufficient conditions on these components which imply the security and noise-robustness of generic SDQC protocols in the composable Cryptography framework. This is done by establishing a correspondence between these security properties and the error-detection capabilities of the test computations. Changing the types of tests and how they are mixed with the client’s computation automatically yields new SDQC protocols with different security and noise-robustness capabilities. This approach thereby provides the desired modularity as our sufficient conditions on test computations simplify the steps required to prove the security of the protocols and allows to focus on the design and optimisation of test rounds to specific situations. We showcase this by systematising the search for improved SDQC protocols for bounded-error quantum polynomial-time ( BQP ) computations. The resulting protocols do not require more hardware on the server’s side than what is necessary to blindly delegate the computation without verification, and they outperform all previously known results.

Secure authentication of identity information for quantum resistant IoT devices based on R-LWE and QIBE

ABSTRACT In order to improve the security of Internet of Things device authentication, a security framework is built in this paper by combining the ring-learning band error problem with quantum indistinguishable bilinear encryption. The research method meticulously designs different test scenarios and conducts multiple rounds of testing to evaluate the accuracy of the system authentication. The results indicated that the authentication accuracy was improved to 93.3% on average after using the proposed algorithm. The authentication speed is significantly improved with a shorter operation cycle. After parameter optimization, repeated tests indicated that the accuracy rate remained stable at over 90%, and the accuracy rate reached 100% in specific phases. Combining the ring learning with error problem technique with quantum indistinguishable bilinear encryption greatly improves the authentication security of Internet of Things devices against quantum computing threats. This discovery provides new strategies for Internet of Things security against quantum attacks and has far-reaching theoretical and practical implications for protecting critical infrastructure.

Development of a Self-Report Measure of Prediction in Daily Life: The Prediction-Related Experiences Questionnaire.

Predictions are complex, multisensory, and dynamic processes involving real-time adjustments based on environmental inputs. Disruptions to prediction abilities have been proposed to underlie characteristics associated with autism. While there is substantial empirical literature related to prediction, the field lacks a self-assessment measure of prediction skills related to daily tasks. Such a measure would be useful to better understand the nature of day-to-day prediction-related activities and characterize these abilities in individuals who struggle with prediction. An interdisciplinary mixed-methods approach was utilized to develop and validate a self-report questionnaire of prediction skills for adults, the Prediction-Related Experiences Questionnaire (PRE-Q). Two rounds of online field testing were completed in samples of autistic and neurotypical (NT) adults. Qualitative feedback from a subset of these participants regarding question content and quality was integrated and Rasch modeling of the item responses was applied. The final PRE-Q includes 19 items across 3 domains (Sensory, Motor, Social), with evidence supporting the validity of the measure's 4-point response categories, internal structure, and relationship to other outcome measures associated with prediction. Consistent with models of prediction challenges in autism, autistic participants indicated more prediction-related difficulties than the NT group. This study provides evidence for the validity of a novel self-report questionnaire designed to measure the day-to-day prediction skills of autistic and non-autistic adults. Future research should focus on characterizing the relationship between the PRE-Q and lab-based measures of prediction, and understanding how the PRE-Q may be used to identify potential areas for clinical supports for individuals with prediction-related challenges.

Developing and validating an assessment tool to measure climate change knowledge among middle school students: preliminary findings in a Spanish context

Considering the crucial role of education in offering mitigation and adaptation strategies for climate change, there is a clear need for objective tools to assess its impact on the understanding of the issue among secondary school students. This paper describes the methodological design used to build and validate an instrument that explores secondary education students’ climate change knowledge. The design is organized into several steps: literature and document review; item design; Expert judgment; pilot testing; and instrument quality criteria analysis. Thus, a questionnaire comprising 27 4-points Likert scale items was designed. Two rounds of pilot testing and statistical analyses were conducted on the difficulty indexes, factor analysis, Spearman coefficient (flit-half method), and alpha coefficient. The result is an instrument with a degree of feasibility, validity and reliability suitable for comparative research as was found in a Spanish macro-study involving 6398 secondary school students. Preliminary results for the Spanish context are offered.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

Effects of radiant asymmetry on the thermal comfort conditions: Experiments in a test room

Occupants' well-being in buildings is related to the thermal comfort conditions in indoor spaces. Radiant asymmetry is a parameter of localised discomfort and its evaluation is rather challenging. It should be considered when dealing with radiant systems since they could produce unacceptable asymmetric temperature fields. In this paper, the issue of radiant asymmetry in winter is experimentally investigated in a test room equipped with radiant panels. A first round of testing is carried out to study the limits in terms of supply water temperature of the heated surface in relation to the discomfort curves proposed in the current standards. Configurations with a heated wall or ceiling and the contemporary presence of one or more heat-dissipating surfaces are considered. Twelve scenarios representing real building situations are analysed, each involving three consecutive heating phases with increasing supply temperatures of 30, 35 and 40 °C. The most critical configuration is imposed in a second testing round with 8 participants checking the actual subjects' perceptions through questionnaires, with conditions of global thermal neutrality (PMV = 0). The scenario with a heated ceiling and heat-dissipating floor is the most adverse, generating a vertical asymmetry exceeding the standard limit of 4 °C when the supply temperature is above 35 °C. The survey campaign shows that uncomfortable cold sensations are prevalent with lower radiant asymmetry levels, whereas the highest ceiling temperature improves comfort perception. The results suggest that participants could not feel the asymmetry increment and analysis of radiant asymmetry discomfort should also consider surface temperatures along with plane radiant temperature difference.

The Effectiveness of ChatGPT in Translating Chunky Construction Texts in Chinese Political Discourse

From the theoretical perspective of Chinese-English temporal-spatial cognitive differences, this study employs a self-constructed bilingual parallel corpus of political discourse totaling one million words. It extracts chunky construction texts with latent agents in the source language (Chinese) and corresponding texts of target language translations (English) for a comparative experiment between ChatGPT translations and those of human translators. Driven by the “Human-AI Interaction Model”, three different prompts were set up to conduct three rounds of translation testing with ChatGPT. The study discovers: 1) ChatGPT has limitations in actively understanding the implicit elements of the source Chinese texts that are driven by a strong spatial preference, which are also reflected in the target language English translations; 2) The “Human-AI Interaction Model” can guide and train ChatGPT effectively through continuous optimization of prompts, enhancing AI abilities in understanding the source language and achieving effective target language translation; 3) Currently, ChatGPT cannot replace human translators in translating chunky discourses of Chinese, but the collaborative interaction between human translators and AI is highly effective. By optimizing the design of prompt instructions, this research offers cognitive reference and operational paradigms to improve ChatGPT’s recognition and cognitive reconstruction capabilities of Chinese-English temporal-spatial differences, providing insights for future research, translation practice, and teaching in related areas.

Acute Knee Crutch Use Provokes Changes to Postural Strategy.

Single-leg knee crutches are a relatively new, hands-free mobility assistive device with benefits over standard axillary crutches. Our main goal in this study was to evaluate balance ability in a healthy population upon first exposure to the knee crutch device. We had 20 healthy individuals (M age = 21.1; SD = 1.5years) complete baseline static and dynamic balance tests on a force plate, followed by knee crutch fitting, a self-selected duration of ambulation practice, and another round of balance testing while wearing the knee crutch. We used the BTrackS Balance Test (BBT) to measure static balance, and the BTrackS Limits of Stability (BLOS) test to measure dynamic balance, and we created a custom lateral bias score from the BLOS results. On average, participants self-selected 3.1minutes of ambulation practice. Wearing the knee crutch caused a near doubling of static balance path length and a large reduction in percentile ranking on the BBT. Dynamic balance area was more than halved (p < .001), with lateral bias scores during the BLOS, indicating that participants heavily favored their non-crutched leg (p < .001). Our results indicate that static and dynamic balance were significantly altered when wearing the knee crutch, and participants seemed to switch to a single leg stance strategy. Despite these balance changes, participants were quickly ready and willing to complete independent ambulation and balance testing procedures using the single-leg knee crutch.

Optimalisasi produktivitas pengupasan biji kopi melalui modifikasi mesin pengupas

The aim of this research is to modify the coffee bean husking machine to enhance the quality and efficiency of the husking process. The modifications include the addition of a spring mechanism in the grinding space, enabling the outer roller to move flexibly in accordance with the size of the entering coffee beans. Furthermore, these changes also consider reducing the percentage of broken coffee beans during the husking process. The machine underwent two rounds of testing, with three samples tested under two different drying conditions. The research results demonstrate the success of the modifications in achieving their objectives. The machine's capability to husk coffee beans up to the parchment layer, not just the husk, indicates an improvement in the quality of the husking process. The quantity of successfully husked coffee beans also increased, while the percentage of broken beans decreased. By enhancing the quality and quantity of coffee beans produced by this machine, the modifications can significantly benefit coffee farmers. Additionally, this study utilized Autodesk Fusion 360 software to enable virtual assembly of machine components before physical production. The outcomes of the virtual assembly aided in understanding the machine's functionality and the interactions among its components. Therefore, the modifications to the coffee bean husking machine have assisted coffee farmers in producing higher-quality beans and expediting the coffee processing process.

Evaluating the Efficacy of AI Chatbots as Tutors in Urology: A Comparative Analysis of Responses to the 2022 In-Service Assessment of the European Board of Urology

Introduction: This study assessed the potential of large language models (LLMs) as educational tools by evaluating their accuracy in answering questions across urological subtopics. Methods: Three LLMs (ChatGPT-3.5, ChatGPT-4, and Bing AI) were examined in two testing rounds, separated by 48 h, using 100 Multiple-Choice Questions (MCQs) from the 2022 European Board of Urology (EBU) In-Service Assessment (ISA), covering five different subtopics. The correct answer was defined as “formal accuracy” (FA) representing the designated single best answer (SBA) among four options. Alternative answers selected from LLMs, which may not necessarily be the SBA but are still deemed correct, were labeled as “extended accuracy” (EA). Their capacity to enhance the overall accuracy rate when combined with FA was examined. Results: In two rounds of testing, the FA scores were achieved as follows: ChatGPT-3.5: 58% and 62%, ChatGPT-4: 63% and 77%, and BING AI: 81% and 73%. The incorporation of EA did not yield a significant enhancement in overall performance. The achieved gains for ChatGPT-3.5, ChatGPT-4, and BING AI were as a result 7% and 5%, 5% and 2%, and 3% and 1%, respectively (p > 0.3). Within urological subtopics, LLMs showcased best performance in Pediatrics/Congenital and comparatively less effectiveness in Functional/BPS/Incontinence. Conclusion: LLMs exhibit suboptimal urology knowledge and unsatisfactory proficiency for educational purposes. The overall accuracy did not significantly improve when combining EA to FA. The error rates remained high ranging from 16 to 35%. Proficiency levels vary substantially across subtopics. Further development of medicine-specific LLMs is required before integration into urological training programs.

Seismic behaviour of Exterior RC beam-column joints repaired and strengthened using post-tensioned metal straps

Exterior beam-column joints are the most vulnerable part of substandard reinforced concrete (RC) buildings and are often the first to be damaged during earthquakes. This article presents an experimental and numerical investigation into the behaviour of exterior RC beam-column joints repaired and strengthened using Post-Tensioned Metal Straps (PTMS) for active confinement. The study focused on full-scale beam-column joints with an inadequate core zone detailing, thus emulating the deficiencies found in existing substandard RC buildings. Initially, four “bare” joints were subjected to cyclic tests to induce substantial damage within the core zone. Subsequently, the damaged core of the joints was repaired and recast with new concrete, and PTMS were applied to strengthen the joints, followed by another round of cyclic testing. The experimental findings were compared with predictions generated through established models from existing literature. The results revealed that ASCE/SEI 41–17 guidelines accurately predict the shear capacity of the bare joints. It is shown that recasting the core with new concrete significantly increased the joint’s shear capacity by up to 42% compared their bare counterparts. The use of PTMS strengthening further enhanced the capacity by up to 25%. A “scissors model” was employed for numerical simulations of both bare and PTMS-strengthened joints using DRAIN-2DX, which proved effective at predicting their nonlinear load-displacement envelope response. This article contributes towards the development of new cost-effective post-earthquake strengthening techniques for beam-column joints, with the potential to reduce the vulnerability of substandard RC buildings in developing countries.

Machine-Guided Discovery of Acrylate Photopolymer Compositions.

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

Activities of clinical pharmacologists across Europe: A survey by the European association of clinical pharmacology and therapeutics.

In order to explore clinical pharmacology and therapeutics (CPT) teaching and practices across continental Europe, the European Association of Clinical Pharmacology and Therapeutics (EACPT) made a survey in 2022 amongst its 27 affiliated societies. The survey was made available online to EACPT representatives, and 47 filled-in questionnaires were received from 25 countries (one to five per country), representing all geographic areas of Europe. Clinical pharmacologists (CPs) spend 25%, 30%, 15%, and 25% of their time in teaching, hospital activities, committees, and research, respectively, with large variations across and within countries. CPT courses are given at Schools of Medicine in all the countries except one, mostly organized and taught by medical doctors (MDs). In Central, Western, and Southern Europe, the teachers may have medicine or pharmacy training. Therapeutic drug monitoring and pharmacovigilance were the hospital activities most frequently reported, and clinical/forensic toxicology, rounds of visits, and pharmacogenetics the least. Two-thirds of the panel think CPs should be MDs. However, the transversal nature of CPT was underlined, with patients/diseases and drugs as gravity centres, thus calling for the complementary skills of MDs and PharmDs. Besides, most respondents reported that clinical pharmacists in their country are involved in rounds of visits, pharmacovigilance, TDM, and/or pharmacogenetic testing and that collaborations with them would be beneficial. CPT comes with a plurality of backgrounds and activities, all required to embrace the different pathologies and the whole lifecycle of medicinal products, but all of them being rarely performed in any given country. The willingness to use common CPT teaching material and prescribing exams at the European level is a good sign of increasing harmonisation of our discipline Europewide.