Royal Society Open Science最新文献

Chronic myeloid leukaemia (CML) is primarily treated using imatinib mesylate, a tyrosine kinase inhibitor (TKI) targeting the BCR::ABL1 oncoprotein. However, the development of drug resistance and adverse side effects necessitate the exploration of alternative therapeutic agents. This study presents the synthesis and characterization of a novel imatinib analogue, 3-chloro-N-(2-methyl-5-((4-(pyridin-2-yl)pyrimidin-2-yl)amino)phenyl)benzamide (PAPP1). The compound's structure was elucidated using X-ray crystallography and spectroscopic techniques, including NMR, infrared and UV-visible. Crystallographic analysis reveals that PAPP1 consists of a phenyl-amino-pyridine-pyrimidine (PAPP) scaffold with substituted aromatic rings forming a nearly coplanar geometry. Additionally, supramolecular interactions in the crystal are mediated by hydrogen bonds and dispersion forces, forming dimers and layered structures. Molecular docking studies demonstrate strong binding affinity to the ABL1 enzyme, with PAPP1 showing comparable binding energy to imatinib, indicating its potential as a lead compound for further development. Computational studies, including molecular electrostatic potential and vibrational analysis, provide further support for the structural stability and bioactivity of PAPP1. These findings suggest that PAPP could be a promising scaffold for future CML drug design, offering a potential alternative to existing TKIs, and PAPP1 is a promising lead susceptible to optimization.

As a key life-history trait, growth rates are often used to measure individual performance and to inform parameters in demographic models. Furthermore, intraspecific trait variation generates diversity in nature. Therefore, partitioning out and understanding drivers of spatiotemporal variation in growth rate is of fundamental interest in ecology and evolution. However, this has rarely been attempted owing to the amount of individual-level data required through both time and space, and issues with missing data in important covariates. Here, we implemented a Bayesian state-space model using individual-level data from 20 populations of Arctic charr (Salvelinus alpinus) across 15 capture occasions, which allowed us to: (i) integrate over the uncertainty of missing recapture records; (ii) robustly estimate size-dependence; and (iii) include a covariate (water temperature) that contained missing data. Interestingly, although there was substantial spatial, temporal and spatiotemporal variation in growth rate, this was only weakly associated with variation in water temperature and almost entirely independent of size, suggesting that spatiotemporal variation in other environmental conditions affected individuals across sizes similarly. This fine-scale spatiotemporal variation emphasizes the importance of local conditions and highlights the potential for spatiotemporal variation in a size-dependent life-history trait, even when environmental conditions are apparently very similar.

Chemistry has found broad appeal on the freely available global video-sharing platform YouTube, with some YouTube videos even being cited in the peer-reviewed chemistry literature. By applying both manual and semi-automated search methods, we identified, categorized and analysed publicly available data for 1619 chemistry YouTube channels that were available in 2023. Forty-nine per cent of channels were active in 12 months prior to sampling. The majority of channels were produced by independent content creators with no clear affiliation or background and 71% of channels were for the purposes of learning or exam revision. YouTube video production spiked in 2020, coincident with the COVID-19 global pandemic. We also examined the number of videos produced, channel lifespans, the use of features such as playlists and short-form videos, apparent revenue streams (outside of default advertising), the use of other social media and whether or not channels were exclusively producing chemistry content. This study and the associated dataset provide the first large-scale 'census' of how YouTube is being used for chemistry communication and education worldwide. We expect our findings to be of interest and use to policy makers, funding agencies, educators, content creators and the public.

Devonian ctenacanth chondrichthyans reached body sizes similar to modern great white sharks and therefore might have been apex predators of the Devonian seas. However, very little is known about the diet and feeding behaviours of these large ancestral sharks. To reconstruct their ecological properties, teeth of the large Famennian (Late Devonian) chondrichthyan Ctenacanthus concinnus from the Anti-Atlas, Morocco, were analysed. The teeth show strong tooth wear with deep horizontal as well as vertical scratches. Dental microwear texture analysis, a well-established method for the reconstruction of diet and commonly used in terrestrial vertebrates, was applied for the first time, to our knowledge, to Palaeozoic vertebrates in this study. Furthermore, finite element analysis was performed to test the biomechanical properties of the teeth. By combining both analyses, as well as palaeoenvironmental data and tooth morphology, we demonstrate that the results from only one method can be insufficient and misleading. Ctenacanthus concinnus most likely was an opportunistic feeder like many modern sharks. Direct evidence and the results of our analyses suggest that Ctenacanthus fed on ectocochleate cephalopods, other chondrichthyans and further vertebrates using a combination of head movements including lateral head shaking to cut large prey items.

In this study, three pyridine- and four thiophene-containing chalcone derivatives were synthesized via Claisen-Schmidt condensation reaction, where five derivatives were new. Different spectral analyses (IR, ¹H NMR, HRMS) clarified the structures and these proposed compounds were screened for antimicrobial activity by the agar disc diffusion technique. Compound 1c was conspicuously active against most of the bacterial and fungal strains. It displayed higher activity against Bacillus cereus (22.3 ± 0.6 mm), Shigella sonnei (43.3 ± 0.6 mm) and Shigella boydii (34.0 ± 1.0 mm) compared to the standard ceftriaxone (20.3 ± 0.6 mm, 40.3 ± 0.6 mm and 25.7 ± 0.6 mm, respectively). In addition, the exhibited inhibition zone of compound 1c against all fungal strains was higher than that of the standard amphotericin B. All the newly synthesized derivatives satisfied the ADME properties, and no toxicological risks were found. All compounds were docked against three protein receptors with the range of binding affinity of -6.3 to -9.6 kcal mol^-1. Molecular dynamics simulation was scrutinized further for compound 1c in three protein-ligand complexes where root mean square deviation and root mean square fluctuation data were below 2 Å, proposing its stability inside and minimal structural changes.

Clustering short text is a difficult problem, owing to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text. In this study, clusters are found in the embedding space using Gaussian mixture modelling. The resulting clusters are found to be more distinctive and more human-interpretable than clusters produced using the popular methods of doc2vec and latent Dirichlet allocation. The success of the clustering approach is quantified using human reviewers and through the use of a generative LLM. The generative LLM shows good agreement with the human reviewers and is suggested as a means to bridge the 'validation gap' which often exists between cluster production and cluster interpretation. The comparison between LLM coding and human coding reveals intrinsic biases in each, challenging the conventional reliance on human coding as the definitive standard for cluster validation.

Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS. Published MS MRI-based computational studies can be divided into five categories: automated diagnosis; differentiation between lesion types and/or MS stages; differential diagnosis; monitoring and predicting disease progression; and synthetic MRI dataset generation. Collectively, these approaches show promise in assisting with MS diagnosis, monitoring of disease activity and prediction of future progression, all potentially contributing to disease management. Analysis quality using ML is highly dependent on the dataset size and variability used for training. Wider public access would mean larger datasets for experimentation, resulting in higher-quality analysis, permitting for more conclusive research. This narrative review provides an outline of the fundamentals of MS pathology and pathogenesis, diagnostic techniques and data types in computational analysis, as well as collating literature pertaining to the application of computational techniques to MRI towards developing a better understanding of MS.

An open question in epidemiology is why transmission is often overdispersed, meaning that most new infections are driven by few infected individuals. For example, around 10% of COVID-19 cases cause 80% of new COVID-19 cases. This overdispersion in parasite transmission is likely driven by intrinsic heterogeneity among hosts, i.e. variable SARS-CoV-2 viral loads. However, host heterogeneity could also indirectly increase transmission dispersion by driving parasite adaptation. Specifically, transmission variation among hosts could drive parasite specialization to highly infectious hosts. Adaptation to rare, highly infectious hosts could amplify transmission dispersion by simultaneously decreasing transmission from common, less infectious hosts. This study considers whether increased transmission dispersion can be, in part, an emergent property of parasite adaptation to heterogeneous host populations. We develop a mathematical model using a Price equation framework to address this question that follows the epidemiological and evolutionary dynamics of a general host-parasite system. The results predict that parasite adaptation to heterogeneous host populations drives high transmission dispersion early in epidemics. Furthermore, parasite adaptation can maintain increased transmission dispersion at endemic equilibria if virulence differs between hosts in a heterogeneous population. More broadly, this study provides a framework for predicting how parasite adaptation determines transmission dispersion for emerging and re-emerging infectious diseases.

Numerous psychological findings have shown that incidental exposure to ideas makes those ideas seem more true, a finding commonly referred to as the 'illusory truth' effect. Under many accounts of the illusory truth effect, initial exposure to a statement provides a metacognitive feeling of 'fluency' or familiarity that, upon subsequent exposure, leads people to infer that the statement is more likely to be true. However, genuine beliefs do not only affect truth judgements about individual statements, they also imply other beliefs and drive decision-making. Here, we consider whether exposure to 'premise' statements affects people's truth ratings for novel 'implied' statements, a pattern of findings we call the 'illusory implication' effect. We argue these effects would constitute evidence for genuine belief change from incidental exposure and identify a handful of existing findings that offer preliminary support for this claim. Building upon these, we conduct three new preregistered experiments to further test this hypothesis, finding additional evidence that exposure to 'premise' statements affected participants' truth ratings for novel 'implied' statements, including for considerably more distant implications than those previously explored. Our findings suggest that the effects of incidental exposure reach further than previously thought, with potentially consequential implications for concerns around mis- and dis-information.

Gold-silver synergism has been well documented in many scientific works dealing with luminescent nanostructures that are exploitable in biomedical and environmental application. Frequently, the ratio of Au : Ag in synthetic mixtures was varied to influence the extent of Au-Ag synergism of the resulting luminescent gold-silver nanoclusters (GSNCs). However, in our approach, a new step, maturing under differing conditions using the same Au : Ag ratio (5 : 1), has been investigated systematically for the very first time. As referent systems, monometallic gold nanoclusters (AuNCs) and protein treated by the conditions of synthesis and maturing were prepared and investigated. The selected types of maturing conditions led to distinct changes in fluorescence characteristics and, consequently, Au-Ag synergism extent (evaluated as the ratio of fluorescence quantum yields of GSNCs versus AuNCs). The best synergism was obtained for GSNCs matured at 37°C for 2.5 h. The stability of luminescent signal of these GSNCs was tested in the presence of an excess (to 20 mM) of Cu(II) and/or Fe(III) ions (crucial cofactors in living systems). The same metallic ion concentration caused different extents of GSNC luminescence quenching, for which a plausible reasoning is suggested.