The importance of place-kicking in Women's International Rugby Union.

Despite the growing popularity of women's rugby, there is a lack of research understanding the contribution of place-kicking to match outcomes. This study aims to establish the characteristics and contribution of place-kicking to women's international Rugby Union and evaluate the performance of place-kickers while accounting for factors that contribute to kick difficulty. Data from 674 place-kicks across 80 matches were analysed. A binomial generalised linear mixed model (GLMM) was used to predict the probability of kick success. 60.5% of place-kicks were successful, and they contributed 23.9% of all points scored; conversions accounted for 16.8% and penalties 7.1%. Kick success percentages for conversions (56.9%) and penalties (78.3%) significantly differed (p < 0.01). Kick distance and angle were significant (p < 0.01) predictors of kick success and the GLMM had a prediction accuracy of 73.6%. The performance rankings of kickers changed when comparing observed and expected success, highlighting the need to consider contextual factors contributing to kick difficulty when evaluating performance. The GLMM results provide valuable insights for coaches and players to make informed decisions, for example, whether to attempt a place-kick when a penalty is awarded, by enabling predictions of place-kick success. This could enhance a team's chances of winning matches.

Elevated CO2 does not alter behavioural lateralization in free-swimming juvenile European sea bass (Dicentrarchus labrax) tested in groups.

The authors investigated left-right turning preferences of n = 260 juvenile European sea bass (Dicentrarchus labrax) reared in ambient conditions and ocean acidification (OA) conditions or in ambient conditions but tested in OA water. Groups of 10 individuals were observed alone in a circular tank, and individuals' left and right turning during free-swimming was quantified using trajectory data from the video. The authors showed that near-future OA levels do not affect the number of turns made, or behavioural lateralization (turning preference), in juvenile D. labrax tested in groups.

Multiple-endpoint in vitro carcinogenicity test in human cell line TK6 distinguishes carcinogens from non-carcinogens and highlights mechanisms of action.

Current in vitro genotoxicity tests can produce misleading positive results, indicating an inability to effectively predict a compound's subsequent carcinogenic potential in vivo. Such oversensitivity can incur unnecessary in vivo tests to further investigate positive in vitro results, supporting the need to improve in vitro tests to better inform risk assessment. It is increasingly acknowledged that more informative in vitro tests using multiple endpoints may support the correct identification of carcinogenic potential. The present study, therefore, employed a holistic, multiple-endpoint approach using low doses of selected carcinogens and non-carcinogens (0.001-770 µM) to assess whether these chemicals caused perturbations in molecular and cellular endpoints relating to the Hallmarks of Cancer. Endpoints included micronucleus induction, alterations in gene expression, cell cycle dynamics, cell morphology and bioenergetics in the human lymphoblastoid cell line TK6. Carcinogens ochratoxin A and oestradiol produced greater Integrated Signature of Carcinogenicity scores for the combined endpoints than the "misleading" in vitro positive compounds, quercetin, 2,4-dichlorophenol and quinacrine dihydrochloride and toxic non-carcinogens, caffeine, cycloheximide and phenformin HCl. This study provides compelling evidence that carcinogens can successfully be distinguished from non-carcinogens using a holistic in vitro test system. Avoidance of misleading in vitro outcomes could lead to the reduction and replacement of animals in carcinogenicity testing.

Collective action reduces androgen responsiveness with implications for shoaling dynamics in stickleback fish.

Androgens, traditionally viewed as hormones that regulate secondary sexual characteristics and reproduction in male vertebrates, are often modulated by social stimuli. High levels of the 'social hormone' testosterone (T) are linked to aggression, dominance, and competition. Low T levels, in contrast, promote sociopositive behaviours such as affiliation, social tolerance, and cooperation, which can be crucial for group-level, collective behaviours. Here, we test the hypothesis that - in a collective context - low T levels should be favourable, using non-reproductive male and female stickleback fish (Gasterosteus aculeatus) and non-invasive waterborne hormone analysis. In line with our predictions, we show that the fishes' T levels were significantly lower during shoaling compared to when alone, with high-T individuals showing the largest decrease. Ruling out stress-induced T suppression and increased T conversion into oestradiol, we find evidence that shoaling directly affects androgen responsiveness. We also show that groups characterized by lower mean T exhibit less hierarchical leader-follower dynamics, suggesting that low T promotes egalitarianism. Overall, we show that collective action results in lower T levels, which may serve to promote coordination and group performance. Our study, together with recent complementary findings in humans, emphasizes the importance of low T for the expression of sociopositive behaviour across vertebrates, suggesting similarities in endocrine mechanisms.

A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells.

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70-90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens' adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention.

Nanoparticle vesicle encoding for imaging and tracking cell populations.

For phenotypic behavior to be understood in the context of cell lineage and local environment, properties of individual cells must be measured relative to population-wide traits. However, the inability to accurately identify, track and measure thousands of single cells via high-throughput microscopy has impeded dynamic studies of cell populations. We demonstrate unique labeling of cells, driven by the heterogeneous random uptake of fluorescent nanoparticles of different emission colors. By sequentially exposing a cell population to different particles, we generated a large number of unique digital codes, which corresponded to the cell-specific number of nanoparticle-loaded vesicles and were visible within a given fluorescence channel. When three colors are used, the assay can self-generate over 17,000 individual codes identifiable using a typical fluorescence microscope. The color-codes provided immediate visualization of cell identity and allowed us to track human cells with a success rate of 78% across image frames separated by 8 h.

Quantifying the cellular uptake of semiconductor quantum dot nanoparticles by analytical electron microscopy.

Semiconductor quantum dot nanoparticles are in demand as optical biomarkers yet the cellular uptake process is not fully understood; quantification of numbers and the fate of internalized particles are still to be achieved. We have focussed on the characterization of cellular uptake of quantum dots using a combination of analytical electron microscopies because of the spatial resolution available to examine uptake at the nanoparticle level, using both imaging to locate particles and spectroscopy to confirm identity. In this study, commercially available quantum dots, CdSe/ZnS core/shell particles coated in peptides to target cellular uptake by endocytosis, have been investigated in terms of the agglomeration state in typical cell culture media, the traverse of particle agglomerates across U-2 OS cell membranes during endocytosis, the merging of endosomal vesicles during incubation of cells and in the correlation of imaging flow cytometry and transmission electron microscopy to measure the final nanoparticle dose internalized by the U-2 OS cells. We show that a combination of analytical transmission electron microscopy and serial block face scanning electron microscopy can provide a comprehensive description of the internalization of an initial exposure dose of nanoparticles by an endocytically active cell population and how the internalized, membrane bound nanoparticle load is processed by the cells. We present a stochastic model of an endosome merging process and show that this provides a data-driven modelling framework for the prediction of cellular uptake of engineered nanoparticles in general.

Poisson-event-based analysis of cell proliferation.

A protocol for the assessment of cell proliferation dynamics is presented. This is based on the measurement of cell division events and their subsequent analysis using Poisson probability statistics. Detailed analysis of proliferation dynamics in heterogeneous populations requires single cell resolution within a time series analysis and so is technically demanding to implement. Here, we show that by focusing on the events during which cells undergo division rather than directly on the cells themselves a simplified image acquisition and analysis protocol can be followed, which maintains single cell resolution and reports on the key metrics of cell proliferation. The technique is demonstrated using a microscope with 1.3 µm spatial resolution to track mitotic events within A549 and BEAS-2B cell lines, over a period of up to 48 h. Automated image processing of the bright field images using standard algorithms within the ImageJ software toolkit yielded 87% accurate recording of the manually identified, temporal, and spatial positions of the mitotic event series. Analysis of the statistics of the interevent times (i.e., times between observed mitoses in a field of view) showed that cell division conformed to a nonhomogeneous Poisson process in which the rate of occurrence of mitotic events, λ exponentially increased over time and provided values of the mean inter mitotic time of 21.1 ± 1.2 hours for the A549 cells and 25.0 ± 1.1 h for the BEAS-2B cells. Comparison of the mitotic event series for the BEAS-2B cell line to that predicted by random Poisson statistics indicated that temporal synchronisation of the cell division process was occurring within 70% of the population and that this could be increased to 85% through serum starvation of the cell culture.

Measurement of molecular mixing at a conjugated polymer interface by specular and off-specular neutron scattering.

Measurements have been performed on thermally equilibrated conjugated-polymer/insulating-polymer bilayers, using specular and off-specular neutron reflectivity. While specular reflectivity is only sensitive to the structure normal to the sample, off-specular measurements can probe the structure of the buried polymer/polymer interface in the plane of the sample. Systematic analysis of the scattering from a set of samples with varying insulating-polymer-thickness, using the distorted-wave Born approximation (DWBA), has allowed a robust determination of the intrinsic width at the buried polymer/polymer interface. The quantification of this width (12 Å ± 4 Å) allows us to examine aspects of the conjugated polymer conformation at the interface, by appealing to self-consistent field theory (SCFT) predictions for equilibrium polymer/polymer interfaces in the cases of flexible and semi-flexible chains. This analysis enables us to infer that mixing at this particular interface cannot be described in terms of polymer chain segments that adopt conformations similar to a random walk. Instead, a more plausible explanation is that the conjugated polymer chain segments become significantly oriented in the plane of the interface. It is important to point out that we are only able to reach this conclusion following the extensive analysis of reflectivity data, followed by comparison with SCFT predictions. It is not simply the case that conjugated polymers would be expected to adopt this kind of oriented conformation at the interface, because of their relatively high chain stiffness. It is the combination of a high stiffness and a relatively narrow intrinsic interfacial width that results in a deviation from flexible chain behaviour.

Statistical prediction of nanoparticle delivery: from culture media to cell.

The application of nanoparticles (NPs) within medicine is of great interest; their innate physicochemical characteristics provide the potential to enhance current technology, diagnostics and therapeutics. Recently a number of NP-based diagnostic and therapeutic agents have been developed for treatment of various diseases, where judicious surface functionalization is exploited to increase efficacy of administered therapeutic dose. However, quantification of heterogeneity associated with absolute dose of a nanotherapeutic (NP number), how this is trafficked across biological barriers has proven difficult to achieve. The main issue being the quantitative assessment of NP number at the spatial scale of the individual NP, data which is essential for the continued growth and development of the next generation of nanotherapeutics. Recent advances in sample preparation and the imaging fidelity of transmission electron microscopy (TEM) platforms provide information at the required spatial scale, where individual NPs can be individually identified. High spatial resolution however reduces the sample frequency and as a result dynamic biological features or processes become opaque. However, the combination of TEM data with appropriate probabilistic models provide a means to extract biophysical information that imaging alone cannot. Previously, we demonstrated that limited cell sampling via TEM can be statistically coupled to large population flow cytometry measurements to quantify exact NP dose. Here we extended this concept to link TEM measurements of NP agglomerates in cell culture media to that encapsulated within vesicles in human osteosarcoma cells. By construction and validation of a data-driven transfer function, we are able to investigate the dynamic properties of NP agglomeration through endocytosis. In particular, we statistically predict how NP agglomerates may traverse a biological barrier, detailing inter-agglomerate merging events providing the basis for predictive modelling of nanopharmacology.

Ghrelin inhibits LPS-induced release of IL-6 from mouse dopaminergic neurones.

BACKGROUND: Ghrelin is an orexigenic stomach hormone that acts centrally to increase mid-brain dopamine neurone activity, amplify dopamine signaling and protect against neurotoxin-induced dopamine cell death in the mouse substantia nigra pars compacta (SNpc). In addition, ghrelin inhibits the lipopolysaccharide (LPS)-induced release of pro-inflammatory cytokines from peripheral macrophages, T-cells and from LPS stimulated microglia. Here we sought to determine whether ghrelin attenuates pro-inflammatory cytokine release from dopaminergic neurones. FINDINGS: The dopaminergic SN4741 cell-line, which derives from the mouse substantia nigra (SN) and expresses the ghrelin-receptor (growth hormone secretagogue receptor (GHS-R)) and the ghrelin-O-acyl transferase (GOAT) enzyme, was used to determine the neuro-immunomodulatory action of ghrelin. We induced innate immune activation via LPS challenge (1 µg/ml) of SN4741 neurones that had been pre-cultured in the presence or absence of ghrelin (1, 10, 100 nM) for 4 h. After 24 h supernatants were collected for detection of IL-1 beta (IL-1ß ), TNF alpha (TNF-α) and IL-6 cytokines via enzyme linked immunosorbent assay (ELISA) analysis. Nuclear translocation of the transcription factor nuclear factor kappa B (NF-κB) was analyzed by Western blotting, and to determine viability of treatments a cell viability assay and caspase-3 immunohistochemistry were performed.We provide evidence that while IL-1ß and TNF-α were not detectable under any conditions, SN4741 neurones constitutively released IL-6 under basal conditions and treatment with LPS significantly increased IL-6 secretion. Pre-treatment of neurones with ghrelin attenuated LPS-mediated IL-6 release at 24 h, an affect that was inhibited by the GHS-R antagonist [D-Lys3]-GHRP-6. However, while ghrelin pre-treatment attenuated the LPS-mediated increase in NF-κB, there was no alteration in its nuclear translocation. Cell viability assay and caspase-3 immunocytochemistry demonstrated that the results were independent from activation of cytotoxic and/or apoptotic mechanisms in the neuronal population, respectively. CONCLUSION: Our results provide evidence that the gut-hormone, ghrelin, attenuates IL-6 secretion to LPS challenge in mid-brain dopaminergic neurones. These data suggest that ghrelin may protect against dopaminergic SN nerve cell damage or death via modulation of the innate immune response.

Performance indicators associated with match outcome within the United Rugby Championship.

OBJECTIVES: The aims of this study were to: i) identify performance indicators associated with match outcomes in the United Rugby Championship; ii) compare the efficacy of isolated and relative datasets to predict match outcome; and iii) investigate whether reduced statistical models can reproduce predictive accuracy. DESIGN: Retrospective analysis of key performance indicators in the United Rugby Championship. METHODS: Twenty-seven performance indicators were selected from 96 matches (2020-21 United Rugby Championship). Random forest classification was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy performance indicator selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy. RESULTS: Within the 2020-21 datasets, the full models correctly classified 83% of match performances for the relative dataset and 64% for isolated data, the equivalent reduced models classified 85% and 66% respectively. The reduced relative model successfully predicted 90% of match performances in the 21-22 season, highlighting that five performance indicators were significant: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties. CONCLUSIONS: Relative performance indicators were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship.

Classifying Winning Performances in International Women's Rugby Union.

PURPOSE: The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union. METHODS: Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. RESULTS: The isolated full model correctly classified 75% of outcomes (CI, 65%-82%), whereas the relative full model correctly classified 78% (CI, 69%-86%). Reduced respective models correctly classified 74% (CI, 65%-82%) and 76% (CI, 67%-84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant. CONCLUSIONS: Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby.

Interoperability of time series cytometric data: a cross platform approach for modeling tumor heterogeneity.

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell proliferation-while abnormal control and modulation of the cell cycle are characteristic of tumor cells. The principal aim in cancer biology is to seek an understanding of the origin and nature of innate and acquired heterogeneity at the cellular level, driven principally by temporal and functional asynchrony. A major bottleneck when mathematically modeling these biological systems is the lack of interlinked structured experimental data. This often results in the in silico models failing to translate the specific hypothesis into parameterized terms that enable robust validation and hence would produce suitable prediction tools rather than just simulation tools. The focus has been on linking data originating from different cytometric platforms and cell-based event analysis to inform and constrain the input parameters of a compartmental cell cycle model, hence partly measuring and deconvolving cell cycle heterogeneity within a tumor population. Our work has addressed the concept that the interoperability of cytometric data, derived from different cytometry platforms, can complement as well as enhance cellular parameters space, thus providing a more broader and in-depth view of the cellular systems. The initial aim was to enable the cell cycle model to deliver an improved integrated simulation of the well-defined and constrained biological system. From a modeling perspective, such a cross platform approach has provided a paradigm shift from conventional cross-validation approaches, and from a bioinformatics perspective, novel computational methodology has been introduced for integrating and mapping continuous data with cross-sectional data. This establishes the foundation for developing predictive models and in silico tracking and prediction of tumor progression

Flow-based cytometric analysis of cell cycle via simulated cell populations.

We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. Here we link the "real" data to initialise a computer simulation of the cell cycle that mimics the evolution of individual cells within a larger population and simulates the associated changes in fluorescence intensity of functional reporters. The model is based on stochastic formulations of cell cycle progression and cell division and uses evolutionary algorithms, allied to further experimental data sets, to optimise the system variables. At the population level, the in-silico cells provide the same statistical distributions of fluorescence as their real counterparts; in addition the model maintains information at the single cell level. The cell model is demonstrated in the analysis of cell cycle perturbation in human osteosarcoma tumour cells, using the topoisomerase II inhibitor, ICRF-193. The simulation gives a continuous temporal description of the pharmacodynamics between discrete experimental analysis points with a 24 hour interval; providing quantitative assessment of inter-mitotic time variation, drug interaction time constants and sub-population fractions within normal and polyploid cell cycles. Repeated simulations indicate a model accuracy of +/-5%. The development of a simulated cell model, initialized and calibrated by reference to experimental data, provides an analysis tool in which biological knowledge can be obtained directly via interrogation of the in-silico cell population. It is envisaged that this approach to the study of cell biology by simulating a virtual cell population pertinent to the data available can be applied to "generic" cell-based outputs including experimental data from imaging platforms.

Data Driven Cell Cycle Model to Quantify the Efficacy of Cancer Therapeutics Targeting Specific Cell-Cycle Phases From Flow Cytometry Results.

Many chemotherapeutic drugs target cell processes in specific cell cycle phases. Determining the specific phases targeted is key to understanding drug mechanism of action and efficacy against specific cancer types. Flow cytometry experiments, combined with cell cycle phase and division round specific staining, can be used to quantify the current cell cycle phase and number of mitotic events of each cell within a population. However, quantification of cell interphase times and the efficacy of cytotoxic drugs targeting specific cell cycle phases cannot be determined directly. We present a data driven computational cell population model for interpreting experimental results, where in-silico populations are initialized to match observable results from experimental populations. A two-stage approach is used to determine the efficacy of cytotoxic drugs in blocking cell-cycle phase transitions. In the first stage, our model is fitted to experimental multi-parameter flow cytometry results from untreated cell populations to identify parameters defining probability density functions for phase transitions. In the second stage, we introduce a blocking routine to the model which blocks a percentage of attempted transitions between cell-cycle phases due to therapeutic treatment. The resulting model closely matches the percentage of cells from experiment in each cell-cycle phase and division round. From untreated cell populations, interphase and intermitotic times can be inferred. We then identify the specific cell-cycle phases that cytotoxic compounds target and quantify the percentages of cell transitions that are blocked compared with the untreated population, which will lead to improved understanding of drug efficacy and mechanism of action.

Long-term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal.

The monitoring of cells labeled with quantum dot endosome-targeted markers in a highly proliferative population provides a quantitative approach to determine the redistribution of quantum dot signal as cells divide over generations. We demonstrate that the use of time-series flow cytometry in conjunction with a stochastic numerical simulation to provide a means to describe the proliferative features and quantum dot inheritance over multiple generations of a human tumor population. However, the core challenge for long-term tracking where the original quantum dot fluorescence signal over time becomes redistributed across a greater cell number requires accountability of background fluorescence in the simulation. By including an autofluorescence component, we are able to continue even when this signal predominates (i.e., >80% of the total signal) and obtain valid readouts of the proliferative system. We determine the robustness of the technique by tracking a human osteosarcoma cell population over 8 days and discuss the accuracy and certainty of the model parameters obtained. This systems biology approach provides insight into both cell heterogeneity and division dynamics within the population and furthermore informs on the lineage history of its members.

Automated Quantification of Mitochondrial Fragmentation in an In Vitro Parkinson's Disease Model.

Neuronal mitochondrial fragmentation is a phenotype exhibited in models of neurodegeneration such as Parkinson's disease. Delineating the dysfunction in mitochondrial dynamics found in diseased states can aid our understanding of underlying mechanisms of disease progression and possibly identify novel therapeutic approaches. Advances in microscopy and the availability of intuitive open-access software have accelerated the rate of image acquisition and analysis, respectively. These developments allow routine biology researchers to rapidly turn hypotheses into results. In this protocol, we describe the utilization of cell culture techniques, high-content imaging (HCI), and the subsequent open-source image analysis pipeline for the quantification of mitochondrial fragmentation in the context of a rotenone-based in vitro Parkinson's disease model. © 2020 The Authors. Basic Protocol 1: SN4741 neuron culture and treatment in a rotenone-based model of Parkinson's disease Basic Protocol 2: Identification of cell nuclei, measurement of mitochondrial membrane potential, and measurement of mitochondrial fragmentation in mouse-derived midbrain dopaminergic neurons.

Abnormal clot microstructure formed in blood containing HIT-like antibodies.

INTRODUCTION: Thrombosis is a severe and frequent complication of heparin-induced thrombocytopenia (HIT). However, there is currently no knowledge of the effects of HIT-like antibodies on the resulting microstructure of the formed clot, despite such information being linked to thrombotic events. We evaluate the effect of the addition of pathogenic HIT-like antibodies to blood on the resulting microstructure of the formed clot. MATERIALS AND METHODS: Pathogenic HIT-like antibodies (KKO) and control antibodies (RTO) were added to samples of whole blood containing Unfractionated Heparin and Platelet Factor 4. The formed clot microstructure was investigated by rheological measurements (fractal dimension; df) and scanning electron microscopy (SEM), and platelet activation was measured by flow cytometry. RESULTS AND CONCLUSIONS: Our results revealed striking effects of KKO on clot microstructure. A significant difference in df was found between samples containing KKO (df = 1.80) versus RTO (df = 1.74; p < 0.0001). This increase in df was often associated with an increase in activated platelets. SEM images of the clots formed with KKO showed a network consisting of a highly branched and compact arrangement of thin fibrin fibres, typically found in thrombotic disease. This is the first study to identify significant changes in clot microstructure formed in blood containing HIT-like antibodies. These observed alterations in clot microstructure can be potentially exploited as a much-needed biomarker for the detection, management and monitoring of HIT-associated thrombosis.

The origin of heterogeneous nanoparticle uptake by cells.

Understanding nanoparticle uptake by biological cells is fundamentally important to wide-ranging fields from nanotoxicology to drug delivery. It is now accepted that the arrival of nanoparticles at the cell is an extremely complicated process, shaped by many factors including unique nanoparticle physico-chemical characteristics, protein-particle interactions and subsequent agglomeration, diffusion and sedimentation. Sequentially, the nanoparticle internalisation process itself is also complex, and controlled by multiple aspects of a cell's state. Despite this multitude of factors, here we demonstrate that the statistical distribution of the nanoparticle dose per endosome is independent of the initial administered dose and exposure duration. Rather, it is the number of nanoparticle containing endosomes that are dependent on these initial dosing conditions. These observations explain the heterogeneity of nanoparticle delivery at the cellular level and allow the derivation of simple, yet powerful probabilistic distributions that accurately predict the nanoparticle dose delivered to individual cells across a population.