bluuebunny/crossref_metadata_2025_split huggingface dataset:
Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis
Authors: Wu Zhenhui , Chen Shupeng , Wang Yihao , Li Fangyang , Xu Huanhua , Li Maoxing , Zeng Yingjian , Wu Zhenfeng , Gao Yue 
Published: March 2024

Aim:
Computer-aided drug design (CADD) is a drug design technique for computing ligand–receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

Methods:
A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

Results:
A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

Conclusions:
Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.
Testing the validity of three acute care assessment tools for assessing residents’ performance during in situ simulation: the ACAT-SimSit study
Authors: Philippon Anne-Laure , Lefevre-Scelles Antoine , Eyer Xavier , Zumstein Carine , Ghazali Aiham , Audibert Simon , Le Borgne Pierrick , Triby Emmanuel , Truchot Jennifer 
Published: March 2024

Background
The assessment of technical and nontechnical skills in emergency medicine requires reliable and usable tools. Three Acute Care Assessment Tools (ACATs) have been developed to assess medical learners in their management of cardiac arrest (ACAT-CA), coma (ACAT-coma) and acute respiratory failure (ACAT-ARF).

Objective
This study aims to analyze the reliability and usability of the three ACATs when used for in situ (bedside) simulation.

Methods
This prospective multicenter validation study tested ACATs using interprofessional in situ simulations in seven emergency departments and invited training residents to participate in them. Each session was rated by two independent raters using ACAT. Intraclass correlation coefficients (ICC) were used to assess interrater reliability, and Cronbach’s alpha coefficient was used to assess internal consistency for each ACAT. The correlation between ACATs’ scores and the learners’ level of performance was also assessed. Finally, a questionnaire and two focus groups were used to assess the usability of the ACATs.

Results
A total of 104 in situ simulation sessions, including 85 residents, were evaluated by 37 raters. The ICC for ACAT-CA, ACAT-coma and ACAT-ARF were 0.95 [95% confidence interval (CI), 0.93–0.98], 0.89 (95% CI, 0.77–0.95) and 0.92 (95%CI 0.83–0.96), respectively. The Cronbach’s alphas were 0.79, 0.80 and 0.73, respectively. The ACAT-CA and ARF showed good construct validity, as third-year residents obtained significantly higher scores than first-year residents (P < 0.001; P < 0.019). The raters supported the usability of the tools, even though they expressed concerns regarding the use of simulations in a summative way.

Conclusion
This study reported that the three ACATs showed good external validity and usability.
Medical Students' Speak-Up Barriers: A Randomized Controlled Trial With Written Vignettes
Authors: Dybdal Kayser Jesper , Kjær Ersbøll Annette , Kolbe Michaela , Østergaard Doris , Dieckmann Peter 
Published: March 2024

Objectives
Little is known about medical students’ speak-up barriers upon recognizing or becoming aware of risky or deficient actions of others. Improving our knowledge on these helps in preparing student to function in actual health care organizations. The aim was to examine medical students' perceived reasons for silence in respect to different speak-up situations (i.e., vignette content) and to test if vignette difficulty had an effect on reasons indicated.

Methods
This study was a randomized, controlled, single-blind trial, with text-based vignettes to investigate speak-up barriers. Vignette contents described speak-up situations that varied systematically with respect to speak-up barrier (i.e., environmental norm, uncertainty, hierarchy) and difficulty (i.e., easy, difficult). For each vignette, participants indicated which speak-up barriers they regarded as important.
Descriptive analysis was performed for the study population, the numbers of barriers perceived and rating of vignette difficulty. Logistic regression analysis was used to examine the association between barriers perceived and vignette contents, designed vignette difficulty and subjectively rated vignette difficulty.

Results
A total of 265 students were included. The response rate was 100%. Different barriers were relevant for the different vignettes and varied in a consistent way with the theme of the vignette. Significantly more speak-up barriers were indicated for participants with the difficult version for vignette 1 (not an environmental norm) and vignette 3 (hierarchy) with odds ratio (OR) = 1.52 and 95% confidence interval (95% CI: 1.33–1.73) and OR = 1.25 (95% CI: 1.09–1.44). For (OR) estimates, confidence intervals were rather large.

Conclusions
Perceived barriers for speak-up vary consistently with the characteristics of the situation and more barriers preventing speak-up were related to the difficult versions of the vignettes.
COMPRER: A Multimodal Multi-Objective Pretraining Framework for Enhanced Medical Image Representation
Authors: Lutsker Guy , Rossman Hagai , Godneva Nastya , Segal Eran 
Published: March 2024

ABSTRACTSubstantial advances in multi-modal Artificial Intelligence (AI) facilitate the combination of diverse medical modalities to achieve holistic health assessments. We present COMPRER, a novel multi-modal, multi-objective pretraining framework which enhances medical-image representation, diagnostic inferences, and prognosis of diseases. COMPRER employs a multi-objective training framework, where each objective introduces distinct knowledge to the model. This includes a multi-modal loss that consolidates information across different imaging modalities; A temporal loss that imparts the ability to discern patterns over time; Medical-measure prediction adds appropriate medical insights; Lastly, reconstruction loss ensures the integrity of image structure within the latent space. Despite the concern that multiple objectives could weaken task performance, our findings show that this combination actually boosts outcomes on certain tasks. Here, we apply this framework to both fundus images and carotid ultrasound, and validate our downstream tasks capabilities by predicting both current and future cardiovascular conditions. COMPRER achieved higher Area Under the Curve (AUC) scores in evaluating medical conditions compared to existing models on held-out data. On the Out-of-distribution (OOD) UK-Biobank dataset COMPRER maintains favorable performance over well-established models with more parameters, even though these models were trained on 75× more data than COMPRER. In addition, to better assess our model’s performance in contrastive learning, we introduce a novel evaluation metric, providing deeper understanding of the effectiveness of the latent space pairing.
Multi-task deep latent spaces for cancer survival and drug sensitivity prediction
Authors: Rintala Teemu J. , Napolitano Francesco , Fortino Vittorio 
Published: March 2024

AbstractMotivationCancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has driven many developments in drug development, however, there are important aspects crucial to precision medicine that are often overlooked, namely the inherent differences between tumours in patients and the cell-lines used to model themin vitro. Recent developments in transfer learning methods for patient and cell-line data have shown progress in translating results from cell-lines to individual patientsin silico. However, transfer learning can be forceful and there is a risk that clinically relevant patterns in the omics profiles of patients are lost in the process.ResultsWe present MODAE, a novel deep learning algorithm to integrate omics profiles from cell-lines and patients for the purposes of exploring precision medicine opportunities. MODAE implements patient survival prediction as an additional task in a drug-sensitivity transfer learning schema and aims to balance autoencoding, domain adaptation, drugsensitivity prediction, and survival prediction objectives in order to better preserve the heterogeneity between patients that is relevant to survival. While burdened with these additional tasks, MODAE performed on par with baseline survival models, but struggled in the drug-sensitivity prediction task. Nevertheless, these preliminary results were promising and show that MODAE provides a novel AI-based method for prioritizing drug treatments for high-risk patients.Availabilityhttps://github.com/UEFBiomedicalInformaticsLab/MODAE
Timing of treatment shapes the path to androgen receptor signaling inhibitor resistance in prostate cancer
Authors: Lee Eugine , Zhang Zeda , Chen Chi-Chao , Choi Danielle , Rivera Aura C. Agudelo , Linton Eliot , Ho Yu-jui , Love Jillian , LaClair Justin , Wongvipat John , Sawyers Charles L. 
Published: March 2024

ABSTRACTThere is optimism that cancer drug resistance can be addressed through appropriate combination therapy, but success requires understanding the growing complexity of resistance mechanisms, including the evolution and population dynamics of drug-sensitive and drug-resistant clones over time. Using DNA barcoding to trace individual prostate tumor cellsin vivo, we find that the evolutionary path to acquired resistance to androgen receptor signaling inhibition (ARSI) is dependent on the timing of treatment. In established tumors, resistance occurs through polyclonal adaptation of drug-sensitive clones, despite the presence of rare subclones with known, pre-existing ARSI resistance. Conversely, in an experimental setting designed to mimic minimal residual disease, resistance occurs through outgrowth of pre-existing resistant clones andnotby adaptation. Despite these different evolutionary paths, the underlying mechanisms responsible for resistance are shared across the two evolutionary paths. Furthermore, mixing experiments reveal that the evolutionary path to adaptive resistance requires cooperativity between subclones. Thus, despite the presence of pre-existing ARSI-resistant subclones, acquired resistance in established tumors occurs primarily through cooperative, polyclonal adaptation of drug-sensitive cells. This tumor ecosystem model of resistance has new implications for developing effective combination therapy.
Advanced median-based genetic similarity analysis in Kazakh Tazy dogs: A novel approach for breed conformity assessment
Authors: Perfilyeva Anastassiya , Mussabayev Rustam , Bespalova Kira , Kuzovleva Yelena , Bespalov Sergey , Begmanova Mamura , Amirgalyeva Almira , Vishnyakova Olga , Nazarenko Inna , Perfilyeva Yuliya , Plakhov Konstantin , Djansugurova Leyla 
Published: March 2024

AbstractThe breed conformity evaluation is crucial for the preservation of the traits that characterise each dog breed. The use of genetic markers for this purpose provides a precision and objectivity that can surpass the reliability of phenotypic evaluations. In this study, we present a new simple algorithm for assessing breed conformity. The algorithm creates a similarity matrix based on genotypic data and then uses the median to calculate the percentage of genetic similarity that an individual has in relation to the genetic diversity of the breed. To validate the proposed algorithm, we applied it to the genotypic data of 18 microsatellites and 43,691 single nucleotide polymorphisms (SNPs) of the Kazakh Tazy dog, a breed of great cultural and historical importance to Kazakhstan that is now threatened with extinction due to crossbreeding. The algorithm showed a moderate correlation between the microsatellite and SNP genotyping methods, reflecting the different aspects of genetic similarity. In particular, the SNP-based evaluations agreed better with the expert judgements, highlighting their potential for accurate analysis of breed conformity. The proposed algorithm provides easily interpretable results, is flexible, adapts to different genetic markers and may provide an evaluation mechanism for breed conformity in situations where there is no reference population, incomplete pedigrees, unidentified meta-founders and high genetic diversity in the population.Author summaryOur research was initiated by the urgent concern for the possible extinction of the Kazakh Tazy dog, a breed with deep historical roots and cultural significance in Kazakhstan. Information at the DNA level may lead to faster genetic improvement of the breed than relying only on phenotypic data and pedigrees. Genotypic data can be processed to provide valuable insights into genetic diversity, relatedness and ancestry. However, existing methods do not provide a measure of percentage similarity that can be used to assess how closely a particular individual matches the typical genetic composition of the breed. These challenges have led us to propose an approach that overcomes the limitations of existing methods and allows genetic similarity to be assessed based on genotypes. It is based on a median-based approach to analyze genetic data and has been applied to microsatellite and SNP markers but can also be adapted to other genetic markers. This method provides results even in the absence of a defined reference population, complete pedigrees or identified meta-pedigrees and during high genetic diversity within a breed. It can provide breeders and researchers with a tool to maintain the genetic purity of unique breeds.
European Health Regulations Reduce Registry-Based Research
Authors: Brück Oscar , Sanmark Enni , Ponkilainen Ville , Bützow Alexander , Reito Aleksi , Kauppila Joonas H. , Kuitunen Ilari 
Published: March 2024

ABSTRACTThe European Health Data Space regulation (EHDS) has been proposed to harmonize health data processing. Given its parallels with the Act on Secondary Use of Health and Social Data (Secondary Use Act) implemented in Finland in 2020, this study examines the consequences of heightened privacy constraints on registry-based medical research. Between 2020 and 2023, a median of 5.5% fewer data permits were approved annually by Finnish university hospitals. Based on linear regression modelling, we estimated a reduction of 46.9% in new data permits nationally in 2023 compared to the expected count. Similar changes were not observed in other medical research types highlighting the consequences of excessive data privacy laws on registry-based medical research.
Examining the impact of leader's paradoxical cognition on supply chain sustainability: a moderated chain mediation model
Authors: Xu Ting , Wang Jiazhan 
Published: March 2024

PurposeThe COVID-19 pandemic has caused havoc on a global scale for supply chains, which put forward higher demand for organizations to reassess their global supply chain strategy and improve supply chain sustainability. The purpose of this paper is to understand how leader's paradoxical cognition affect supply chain sustainability.Design/methodology/approachThis study conceptualizes a research model grounded in upper echelons theory and propose a chain-mediating model under the moderating effect of big data analytics. Using PLS-SEM method, we test the hypotheses using survey data collected from supply chain managers or leaders of the supply chain team from 193 firms.FindingsThe results indicate that supply chain ambidexterity and organizational learning play a mediating role in the relationship between leaders' paradoxical cognition on supply chain sustainability, respectively, and these two variables have a chain-mediating role in the relationship above. In addition, the big data analytics negatively moderates the relationship between leader's paradoxical cognition and organizational learning, and further moderates our chain mediating model.Originality/valueThis research initiatively focuses on the micro-foundations of supply chain sustainability from managerial cognition and firstly provides empirical evidence about the impact of leader's paradoxical cognition on supply chain sustainability.
Greenwashing and sustainability assurance: a review and call for future research
Authors: Free Clinton , Jones Stewart , Tremblay Marie-Soleil 
Published: March 2024

PurposeThe purpose of this paper is to synthesize insights from the emerging work in accounting on greenwashing and sustainability assurance and propose an agenda for future research in this area.Design/methodology/approachThis article offers an original analysis of papers published on greenwashing and sustainability assurance research in the field of accounting. It adopts a systematic literature review and a narrative approach to analyse the dominant themes and key findings in this new and rapidly evolving field. From this overview, specific avenues for future research are identified.FindingsIn the past few years there has been a substantial spike in concern relating to greenwashing among academics, practitioners, regulators and society. This growing concern has only partly been reflected in the research literature. To date, research has primarily focused on: (1) the characteristics of firms adopting sustainability assurance, (2) the challenges facing sustainability auditors, (3) the development of appropriate assurance standards and regulations, and (4) capital market responses to greenwashing and sustainability auditing/assurance. Three key future research issues with respect to greenwashing are identified: (1) the future of standard-setter attempts to regulate greenwashing, (2) professional jockeying in sustainability reporting assurance, and (3) capital market opportunities and challenges relating to greenwashing and assurance.Originality/valueDespite the profound economic and reputational impact of greenwashing and the rapid development of sustainability assurance services, research in accounting remains fragmented and emergent. This review identifies avenues offering considerable scope for inter-disciplinarity and bridging the divide between academia and practice.
Characterizing the local climate of large-scale archaeological parks in the tropics
Authors: Binarti Floriberta , Pranowo Pranowo , Aditya Chandra , Matzarakis Andreas 
Published: March 2024

PurposeThis study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.Design/methodology/approachThe study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.FindingsThe study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.Practical implicationsBased on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.Originality/valueThe local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.
Beyond borders: investigating the impact of the 2023 Israeli–Palestinian conflict on global equity markets
Authors: Goyal Priyanka , Soni Pooja 
Published: March 2024

PurposeThe present research study aims to explore the impact of the most recent Israeli–Palestinian conflict, which unfolded in October 2023, on global equity markets, including a wide range of both emerging and developed markets (as per the Morgan Stanley Capital Investment country classification).Design/methodology/approachThe market model of event study methodology, with an estimation window of 200 days and 28-day event window (including event day, i.e. October 7, 2023), has been employed to investigate the event’s impact on the stock markets of different countries, with 24 emerging countries and 23 developed countries. The daily closing prices of the prominent indices of all 47 countries have been analyzed to examine the impact of the conflict on emerging markets, developed markets and overall global equity markets. Additionally, cross-sectional regression analysis has been performed to investigate the possible explanations for abnormal returns.FindingsThe findings of the study suggest the heterogeneous impact of the selected event on different markets. Notably, emerging markets and the overall global equity landscape exhibited substantial negative responses on the event day, as reflected in average abnormal returns of −0.47% and −0.397%, respectively. In contrast, developed markets displayed resilience, with no significant negative impact observed on the day of the event. A closer examination of individual countries revealed diverse reactions, with Poland, Egypt, Greece, Denmark and Portugal standing out for their positive or resilient market responses. Poland, in particular, demonstrated significantly positive cumulative abnormal returns (CARs) of 7.16% in the short-term and 8.59% in the long-term event windows (−7, +7 and −7, +20, respectively), emphasizing its robust performance amid the geopolitical turmoil. The study also found that, during various event windows, specific variables had a significant impact on the CARs.Practical implicationsThe study suggests diversification and monitoring of geopolitical risks are key strategies for investors to enhance portfolio resilience during the Israeli–Palestinian conflict. This study identifies countries such as Poland, Egypt, Greece, Denmark and Portugal with positive or resilient market reactions, providing practical insights for strategic investment decisions. Key takeaways include identifying resilient markets, leveraging opportunistic strategies and navigating market dynamics during geopolitical uncertainties.Originality/valueAs per the authors’ thorough investigation and review of the literature, the present study is the earliest attempt to explore the short-term and long-term impact of the 2023 Israeli–Palestinian conflict on equity markets worldwide using the event study approach and cross-sectional regression analysis.