Cellular interactions with polystyrene nanoplastics-The role of particle size and protein corona.

Plastic waste is ubiquitously spread across the world and its smaller analogs-microplastics and nanoplastics-raise particular health concerns. While biological impacts of microplastics and nanoplastics have been actively studied, the chemical and biological bases for the adverse effects are sought after. This work explores contributory factors by combining results from in vitro and model mammalian membrane experimentation to assess the outcome of cell/nanoplastic interactions in molecular detail, inspecting the individual contribution of nanoplastics and different types of protein coronae. The in vitro study showed mild cytotoxicity and cellular uptake of polystyrene (PS) nanoplastics, with no clear trend based on nanoplastic size (20 and 200 nm) or surface charge. In contrast, a nanoplastic size-dependency on bilayer disruption was observed in the model system. This suggests that membrane disruption resulting from direct interaction with PS nanoplastics has little correlation with cytotoxicity. Furthermore, the level of bilayer disruption was found to be limited to the hydrophilic headgroup, indicating that transmembrane diffusion was an unlikely pathway for cellular uptake-endocytosis is the viable mechanism. In rare cases, small PS nanoplastics (20 nm) were found in the vicinity of chromosomes without a nuclear membrane surrounding them; however, this was not observed for larger PS nanoplastics (200 nm). We hypothesize that the nanoplastics can interact with chromosomes prior to nuclear membrane formation. Overall, precoating PS particles with protein coronae reduced the cytotoxicity, irrespective of the corona type. When comparing the two types, the extent of reduction was more apparent with soft than hard corona

Long COVID clinical evaluation, research and impact on society: a global expert consensus

\ua9 2025. The Author(s). BACKGROUND: Long COVID is a complex, heterogeneous syndrome affecting over four hundred million people globally. There are few recommendations, and no formal training exists for medical professionals to assist with clinical evaluation and management of patients with Long COVID. More research into the pathology, cellular, and molecular mechanisms of Long COVID, and treatments is needed. The goal of this work is to disseminate essential information about Long COVID and recommendations about definition, diagnosis, treatment, research and social issues to physicians, researchers, and policy makers to address this escalating global health crisis. METHODS: A 3-round modified Delphi consensus methodology was distributed internationally to 179 healthcare professionals, researchers, and persons with lived experience of Long COVID in 28 countries. Statements were combined into specific areas: definition, diagnosis, treatment, research, and society. RESULTS: The survey resulted in 187 comprehensive statements reaching consensus with the strongest areas being diagnosis and clinical assessment, and general research. We establish conditions for diagnosis of different subgroups within the Long COVID umbrella. Clear consensus was reached that the impacts of COVID-19 infection on children should be a research priority, and additionally on the need to determine the effects of Long COVID on societies and economies. The consensus on COVID and Long COVID is that it affects the nervous system and other organs and is not likely to be observed with initial symptoms. We note, biomarkers are critically needed to address these issues. CONCLUSIONS: This work forms initial guidance to address the spectrum of Long COVID as a disease and reinforces the need for translational research and large-scale treatment trials for treatment protocols

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses

Outcome measures in clinical trials of treatments for acute severe haemorrhage

BACKGROUND: Acute severe haemorrhage is a common complication of injury, childbirth, surgery, gastrointestinal pathologies and other medical conditions. Bleeding is a major cause of death, but patients also die from non-bleeding causes, the frequency of which varies by the site of haemorrhage and between populations. Because patients can bleed to death within hours, established interventions inevitably take priority over randomisation into a trial. These circumstances raise challenges in selecting appropriate outcome measures for clinical trials of haemostatic interventions. MAIN BODY: We use data from three large randomised controlled trials in acute severe haemorrhage (CRASH-2, WOMAN and HALT-IT) to explore the strengths and limitations of outcome measures commonly used in trials of haemostatic treatments, including all-cause and cause-specific mortality, blood transfusion and surgical interventions. Many deaths following acute severe haemorrhage are due to patient comorbidities or complications rather than bleeding. If non-bleeding deaths are unaffected by a haemostatic intervention, even large trials will have low power to detect an effect on all-cause mortality. Due to the dilution from deaths unaffected or reduced by the trial treatment, all-cause mortality can also obscure important harmful effects. Additionally, because the relative contributions of different causes of death vary within and between patient populations, all-cause mortality is not generalisable. Different causes of death occur at different time intervals from bleeding onset, with bleeding deaths generally occurring early. Time-specific mortality can therefore be used as a proxy for cause in un-blinded trials where bias is a concern or in situations where cause of death cannot be assessed. Urgent treatment is critical, and so post-randomisation blood transfusion and surgery are often planned before or at the time of randomisation and therefore cannot be influenced by the trial treatment. CONCLUSIONS: All-cause mortality has low power, lacks generalisability and can obscure harmful effects. Cause-specific mortality, such as death due to bleeding or thrombosis, avoids these drawbacks. In certain scenarios, time-specific mortality can be used as a proxy for cause-specific mortality. Blood transfusion and surgical procedures have limited utility as outcome measures in trials of haemostatic treatments