Critical Structures of Bisphenol Analogues on Embryonic Toxicity Identified by a Computational Approach.

Safer chemical alternatives to bisphenol (BP) have been a major pursuit of modern green chemistry and toxicology. Using a chemical similarity-based approach, it is difficult to identify minor structural differences that contribute to the significant changes of toxicity. Here, we used omics and computational toxicology to identify chemical features associated with BP analogue-induced embryonic toxicity, offering valuable insights to inform the design of safer chemical alternatives.

Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein interactions and for the targeted screening of peptide- or protein-based drugs. We introduce MCGLPPI++, a meta-learning framework designed to improve the adaptability of pretrained geometric models in such scenarios. To effectively boost the meta-learning optimization by injecting prior intersample distribution knowledge, three specially designed training sample cluster splitting patterns based on protein interaction interfaces are introduced.

Bioactivity Profiling of Chemical Mixtures for Hazard Characterization.

The assessment and regulation of chemical toxicity to protect human health and the environment are done one chemical at a time and seldom at environmentally relevant concentrations. However, chemicals are found in the environment as mixtures, and their toxicity is largely unknown. Understanding the hazard posed by chemicals within the mixture is critical to enforce protective measures.

Using in-line measurement and statistical analyses to predict tablet properties compressed using a Styl'One compaction simulator: A high shear wet granulation study.

High shear wet granulation (HSWG) is widely used in tablet manufacturing mainly because of its advantages in improving flowability, powder handling, process run time, size distribution, and preventing segregation. In-line process analytical technology measurements are essential in capturing detailed particle dynamics and presenting real-time data to uncover the complexity of the HSWG process and ultimately for process control. This study is to find relationships between Lenterra in-line measurements and granule properties and tablet properties.

A web-based histology atlas for the freshwater sentinel species Daphnia magna.

Daphnia are keystone species of freshwater habitats used as model organisms in ecology and evolutionary biology. Their small size, wide geographic distribution, and sensitivity to chemicals make them useful as environmental sentinels in regulatory toxicology and chemical risk assessment. Biomolecular (-omic) assessments of responses to chemical toxicity, which reveal detailed molecular signatures, become more powerful when correlated with other phenotypic outcomes (such as behavioral, physiological, or histopathological) for comparative validation and regulatory relevance.

A Survey of the Current UK Physician Associate Educator Workforce and Recommendations for Courses and Provider Organizations.

Introduction: In the United Kingdom there are 37 physician associate (PA) training programs with limited knowledge of the educators involved, their training, and specific needs.

Method: An online questionnaire was sent to PA educators in all UK training programs requesting information on academic title and responsibilities, clinical and nonclinical background, education and qualifications before becoming a PA educator, formal and informal training received in the role, and insights into career progression.

Discussion: The questionnaire highlighted 5 specific areas that should be specific recommendations for UK training programs to support PA educators, alongside existing guidance.

Inequalities in oral health: estimating the longitudinal economic burden of dental caries by deprivation status in six countries.

Background: The recent World Health Organization (WHO) resolution on oral health urges pivoting to a preventive approach and integration of oral health into the non-communicable diseases agenda. This study aimed to: 1) explore the healthcare costs of managing dental caries between the ages of 12 and 65 years across socioeconomic groups in six countries (Brazil, France, Germany, Indonesia, Italy, UK), and 2) estimate the potential reduction in direct costs from non-targeted and targeted oral health-promoting interventions.

Methods: A cohort simulation model was developed to estimate the direct costs of dental caries over time for different socioeconomic groups.

A one health roadmap towards understanding and mitigating emerging Fungal Antimicrobial Resistance: fAMR.

The emergence of fungal antimicrobial resistance-fAMR-is having a growing impact on human and animal health, and food security. This roadmap charts inter-related actions that will enhance our ability to mitigate the risk of fAMR. As humanity's reliance on antifungal chemicals escalates, our understanding of their one-health consequences needs to scale accordingly if we are to protect our ability to manage the global spectrum of fungal disease sustainably.

Integrated computational and experimental design of fluorescent heteroatom-functionalised maleimide derivatives.

The bottom-up design and synthesis of organic molecular species with tailored photophysical properties stands as an important challenge to both computational and experimental chemical science. Overcoming this challenge presents the potential to usher in new tools and approaches to improve our ability to develop new technologies, such as molecular sensors and attuned molecular switches. Here, we report the bottom-up design and characterisation of new fluorescent maleimide derivatives using coupled computational and experimental insights.

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently.

Imbalanced intracellular nutrient stoichiometries drive the regional structural variation of microeukaryotic communities in paddy fields.

Periphytons serve as critical microbial nutrient sinks at the soil-water interface, influencing biogeochemical cycles and nutrient migration in paddy fields. Despite their importance, the impact of accumulated intracellular nutrients on the spatial dynamics and community assembly of periphytons, particularly their microeukaryote communities, remains unclear. To address this gap, we examined the nutrient accumulation potential and its effects on microeukaryotes in periphytons from 220 paddy fields spanning up to 3469 km across three temperature zones.

A New Single-Parameter Bees Algorithm.

Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex.

elicits subspecies-specific responses in human neutrophils.

as a Gram-negative anaerobe plays a key bridging role in oral biofilms. It is involved in periodontal and extraoral diseases, the most prominent being colorectal cancer. Five subspecies are recognised: and .

Advancing the Effect-Directed Identification in Combined Pollution: Using Pathways to Link Effects and Toxicants.

The difficulty in associating diverse pollutants with mixture effects has led to significant challenges in identifying toxicants in combined pollution. In this study, pathways were used to link effects and toxicants. By pathways evaluated by the concentration-dependent transcriptome, individual effects were extended to molecular mechanisms encompassing 135 pathways corresponding to 6 biological processes.

Good practice in clinical embryology laboratories: Association of Reproductive and Clinical Scientists Guidelines 2024.

Clinical embryology is a dynamic and ever-evolving field, and as such clinical practice guidelines must be regularly reviewed and updated. Accordingly, this document supersedes previous good clinical practice in clinical embryology guidance, developing, and updating where necessary, existing good practice guidelines. The Association of Reproductive and Clinical Scientists (ARCS) suggests this Good Laboratory Practice framework as appropriate and effective to ensure the best possible care for all individuals undertaking fertility treatment.

1-year health outcomes associated with systemic corticosteroids for COVID-19: a longitudinal cohort study.

Background: In patients with coronavirus disease 2019 (COVID-19) requiring supplemental oxygen, dexamethasone reduces acute severity and improves survival, but longer-term effects are unknown. We hypothesised that systemic corticosteroid administration during acute COVID-19 would be associated with improved health-related quality of life (HRQoL) 1 year after discharge.

Methods: Adults admitted to hospital between February 2020 and March 2021 for COVID-19 and meeting current guideline recommendations for dexamethasone treatment were included using two prospective UK cohort studies (Post-hospitalisation COVID-19 and the International Severe Acute Respiratory and emerging Infection Consortium).

A Digital Intervention for Capturing the Real-Time Health Data Needed for Epilepsy Seizure Forecasting: Protocol for a Formative Co-Design and Usability Study (The ATMOSPHERE Study).

Background: Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies.

pQEB1: a hospital outbreak plasmid lineage carrying .

While conducting genomic surveillance of carbapenemase-producing (CPE) from patient colonisation and clinical infections at Birmingham's Queen Elizabeth Hospital (QE), we identified an N-type plasmid lineage, pQEB1, carrying several antibiotic resistance genes, including the carbapenemase gene . The pQEB1 lineage is concerning due to its conferral of multidrug resistance, its host range and apparent transmissibility, and its potential for acquiring further resistance genes. Representatives of pQEB1 were found in three sequence types (STs) of , two STs of , and three species of .