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External Quality Assessment (EQA) schemes are an important quality assurance tool and aim to ensure consistency among histopathologists. In this study, we use Shannon entropy as a novel metric to evaluate linguistic variability in the UK Liver Pathology EQA scheme. Analysing free‐text responses by participants over a decade, we aimed to quantify language trends in morphological assessments and clinicopathological diagnoses. Accounting for an increasing word count and when pathologists joined the scheme, our findings reveal a significant increase in entropy of morphological assessments over time, indicating growing linguistic diversity that may reflect the increasing complexity of liver pathology. Entropy of clinicopathological diagnoses over the same period did not provide clear evidence for convergent diagnostic language. High entropy corresponded to cases that elicited more diverse responses and could be considered more challenging, highlighting the utility of this method to identify potential areas for targeted education. We demonstrate entropy as a novel tool to analyse pathologist language and enhance quality assurance in the evolving pathology landscape.

Background: Rectal cancer is common and frequently treated with neoadjuvant radiotherapy prior to surgery to reduce the risk of tumour recurrence. However, the therapeutic benefits and side effects of radiotherapy can vary between patients, and there are currently no validated biomarkers to predict treatment response. Tumour cell density (TCD) and tumour-infiltrating lymphocyte (TIL) density are proven prognostic biomarkers in colorectal cancer; however, their utility in predicting radiotherapy response remains unclear. We assessed the prognostic and predictive value of TCD and TIL density in rectal cancer patients treated with radiotherapy. Methods: TCD was quantified using a manual point-counting method in 253 pre-treatment biopsies and across the entire tumour area of 569 resection specimens from the MRC CR07 clinical trial, which randomised patients to either neoadjuvant short-course radiotherapy (SCRT) or straight to surgery (control). TIL density was measured in 102 biopsies and matched resection specimens (73 SCRT, 29 control) across different tumour areas using deep learning-based cell detection in MIM (HeteroGenius Ltd., Leeds, UK). Cutoffs for low/high-TCD and TIL density were both pre-defined and derived from survival data using the survminer R package. Survival analyses were performed to evaluate the predictive and prognostic value of TCD/TIL in relation to overall and cancer-specific survival. Results: TCD in the resection specimens was lower in the SCRT group (19.9%, IQR 12.9–26.7%) than the control group (34.3%, IQR 27.7–40.5%, p < 0.001). In control resections, low-TCD was associated with a higher risk of all-cause mortality (HR 2.20, 95% CI 1.41–3.44, p < 0.001) and cancer-related death (HR 2.69, 95% CI 1.41–5.13, p = 0.0026). In contrast, after SCRT, low resection TCD was associated with a reduced risk of death (HR 0.63, 95% CI 0.40–0.98, p = 0.04). In the SCRT group, low biopsy TCD prior to radiotherapy was associated with a reduced risk of cancer-related death (HR 0.34, 95% CI 0.13–0.89, p = 0.028). Across both trial arms, TIL density was higher in pre-treatment biopsies than resections (2492 vs. 1304/mm2, p < 0.001). Low biopsy TIL density was associated with an increased risk of all-cause mortality (HR 2.43, 95% CI 1.24–4.76, p = 0.01). The SCRT group had lower TIL density in the resection compared with controls (1210 vs. 1615/mm2, p < 0.001), and low resection TIL density across the whole tumour area was associated with a higher risk of death (HR 2.55, 95% CI 1.11–5.87, p = 0.027). Conclusions: Our findings support the role of TCD and TIL density as quantitative biomarkers in rectal cancer patients. TCD can be used to assess the degree of response to radiotherapy, and contrasting survival associations are observed between straight-to-surgery and SCRT-treated patients. This study raises the possibility of using TCD as both a prognostic and predictive biomarker. TIL density failed to show predictive value but demonstrated expected prognostic associations.

Cancer is a leading cause of disease burden globally, with more than 19·3 million cases and 10 million deaths recorded in 2020. Research is crucial to understanding the determinants of cancer and the effects of interventions, and to improving outcomes. We aimed to analyse global patterns of public and philanthropic investment in cancer research. In this content analysis, we searched the UberResearch Dimensions database and Cancer Research UK data for human cancer research funding awards from public and philanthropic funders between Jan 1, 2016, and Dec 31, 2020. Included award types were project and programme grants, fellowships, pump priming, and pilot projects. Awards focused on operational delivery of cancer care were excluded. Awards were categorised by cancer type, cross-cutting research theme, and research phase. Funding amount was compared with global burden of specific cancers, measured by disability-adjusted life-years, years lived with disability, and mortality using data from the Global Burden of Disease study. We identified 66 388 awards with total investment of about US$24·5 billion in 2016–20. Investment decreased year-on-year, with the largest drop observed between 2019 and 2020. Pre-clinical research received 73·5% of the funding across the 5 years ($18 billion), phase 1–4 clinical trials received 7·4% ($1·8 billion), public health research received 9·4% ($2·3 billion), and cross-disciplinary research received 5·0% ($1·2 billion). General cancer research received the largest investment ($7·1 billion, 29·2% of the total funding). The most highly funded cancer types were breast cancer ($2·7 billion [11·2%]), haematological cancer ($2·3 billion [9·4%]), and brain cancer ($1·3 billion [5·5%]). Analysis by cross-cutting theme revealed that 41·2% of investment ($9·6 billion) went to cancer biology research, 19·6% ($4·6 billion) to drug treatment research, and 12·1% ($2·8 billion) to immuno-oncology. 1·4% of the total funding ($0·3 billion) was spent on surgery research, 2·8% ($0·7 billion) was spent on radiotherapy research, and 0·5% ($0·1 billion) was spent on global health studies. Cancer research funding must be aligned with the global burden of cancer with more equitable funding for cancer research in low-income and middle-income countries (which account for 80% of cancer burden), both to support research relevant to these settings, and build research capacity within these countries. There is an urgent need to prioritise investment in surgery and radiotherapy research given their primacy in the treatment of many solid tumours. None.

Pre-existing immunity to seasonal endemic coronaviruses could have profound consequences for antibody responses to SARS-CoV-2, induced from natural infection or vaccination. A first step to establish whether pre-existing responses can impact SARS-CoV-2 infection is to understand the nature and extent of cross-reactivity in humans to coronaviruses. Here we compare serum antibody and memory B cell responses to coronavirus spike proteins from pre-pandemic and SARS-CoV-2 convalescent donors using binding and functional assays. We show weak evidence of pre-existing SARS-CoV-2 cross-reactive serum antibodies in pre-pandemic donors. However, we find evidence of pre-existing cross-reactive memory B cells that are activated during SARS-CoV-2 infection. Monoclonal antibodies show varying degrees of cross-reactivity with betacoronaviruses, including SARS-CoV-1 and endemic coronaviruses. We identify one cross-reactive neutralizing antibody specific to the S2 subunit of the S protein. Our results suggest that pre-existing immunity to endemic coronaviruses should be considered in evaluating antibody responses to SARS-CoV-2. Pre-existing immune responses between antigenically related viruses can influence responses in viral infections or vaccinations. Here the authors assess and characterize the presence of antibody and memory B cell populations specific to SARS-CoV2 and endemic human coronaviruses.

The habenula is a small, epithalamic brain structure situated between the mediodorsal thalamus and the third ventricle. It plays an important role in the reward circuitry of the brain and is implicated in psychiatric conditions, such as depression. The importance of the habenula for human cognition and mental health make it a key structure of interest for neuroimaging studies. However, few studies have characterised the physical properties of the human habenula using magnetic resonance imaging because its challenging visualisation in vivo, primarily due to its subcortical location and small size. To date, microstructural characterization of the habenula has focused on quantitative susceptibility mapping. In this work, we complement this previous characterisation with measures of longitudinal and effective transverse relaxation rates, proton density and magnetisation transfer saturation using a high-resolution quantitative multi-parametric mapping protocol at 3T, in a cohort of 26 healthy participants. The habenula had consistent boundaries across the various parameter maps and was most clearly visualised on the longitudinal relaxation rate maps. We have provided a quantitative multi-parametric characterisation that may be useful for future sequence optimisation to enhance visualisation of the habenula, and additionally provides reference values for future studies investigating pathological differences in habenula microstructure.

Anthropogenic habitat modification is a leading contributor to biodiversity change, but it is unclear what factors, including scale, influence the magnitude of change. Changes in species richness and its scaling relationship across an anthropogenic gradient can be influenced by changes in the total number of individuals in each sample, the species abundance distribution, and/or the spatial arrangement of conspecific individuals. Here, we integrated continental‐scale citizen science data on bird occurrences across the contiguous United States – from eBird – with an analytical framework capable of dissecting the aforementioned biodiversity components to quantify bird diversity changes along an anthropogenic landscape habitat modification gradient. We found an overall decline in bird diversity along an anthropogenic modification gradient, with peak levels of bird diversity at low to moderate levels of modification. The magnitude of biodiversity change was greater at gamma than at alpha scales and was most strongly associated with a declining number of individuals along the anthropogenic gradient. Spatial species turnover was lower at higher impacted sites, but this was also due to the sampling of fewer individuals rather than changes in spatial species patchiness. Our results suggest that local‐scale management can promote bird diversity, especially at the natural–rural–suburban interface. Management efforts (e.g. managing natural habitat or preserving urban greenspaces against development) should be focused on creating, restoring, and preserving resources (e.g. nesting habitat, foraging resources) necessary for a large number of individuals, as this is the primary influence of diversity change along an anthropogenic gradient.

Medical devices, such as contact lenses, bring bacteria in direct contact with human cells. Consequences of these host-pathogen interactions include the alteration of mammalian cell surface architecture and induction of cellular death that renders tissues more susceptible to infection. Gram-negative bacteria known to induce cellular blebbing by mammalian cells, Pseudomonas and Vibrio species, do so through a type III secretion system-dependent mechanism. This study demonstrates that a subset of bacteria from the Enterobacteriaceae bacterial family induce cellular death and membrane blebs in a variety of cell types via a type V secretion-system dependent mechanism. Here, we report that ShlA-family cytolysins from Proteus mirabilis and Serratia marcescens were required to induce membrane blebbling and cell death. Blebbing and cellular death were blocked by an antioxidant and RIP-1 and MLKL inhibitors, implicating necroptosis in the observed phenotypes. Additional genetic studies determined that an IgaA family stress-response protein, GumB, was necessary to induce blebs. Data supported a model where GumB and shlBA are in a regulatory circuit through the Rcs stress response phosphorelay system required for bleb formation and pathogenesis in an invertebrate model of infection and proliferation in a phagocytic cell line. This study introduces GumB as a regulator of S. marcescens host-pathogen interactions and demonstrates a common type V secretion system-dependent mechanism by which bacteria elicit surface morphological changes on mammalian cells. This type V secretion-system mechanism likely contributes bacterial damage to the corneal epithelial layer, and enables access to deeper parts of the tissue that are more susceptible to infection.

Broad‐scale biodiversity monitoring relies, at least in part, on the efforts of citizen, or community, scientists. To ensure robust inferences from citizen science data, it is important to understand the spatial pattern of sampling effort by citizen scientists and how it deviates from an optimal pattern. Here, we develop a generalized workflow to estimate the optimal distribution of sampling effort for inference of species diversity (e.g., species richness, Shannon diversity, and Simpson's diversity) patterns using the relationship between species diversity and land cover. We used data from the eBird citizen science project that was collected across heterogeneous landscapes in Florida (USA) to illustrate this workflow across different grain sizes. We found that a relatively small number of samples are needed to meet 95% sampling completeness when diversity estimation is focused on dominant species: 43, 64, 96, 123, 172, and 176 for 5 × 5, 10 × 10, 15 × 15, 20 × 20, 25 × 25, and 30 × 30‐km2 grain sizes, respectively. In contrast, three to five times more samples are necessary to infer species diversity when estimation is focused on rare species. However, in both cases, the optimal distribution of effort was spatially heterogeneous, with more effort needed in regions of higher diversity. Our results highlight the potential of citizen science data to make informed comparisons of species diversity in space and time, as well as how sampling effort inherently depends on monitoring goals, such as whether dominant or rare species are targeted. Our general workflow allows for the quantification of sampling effort needed to estimate species diversity with citizen science data and can guide future adaptive sampling by citizen science participants.

The coordinated regulation of gene expression is required for homeostasis, growth and development in all organisms. Such coordination may be partly achieved at the level of messenger RNA stability 1 , in which the targeted destruction of subsets of transcripts generates the potential for cross-regulating metabolic pathways. In Escherichia coli , the balance and composition of the transcript population is affected by RNase E, an essential endoribonuclease that not only turns over RNA but also processes certain key RNA precursors 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . RNase E cleaves RNA internally, but its catalytic power is determined by the 5′ terminus of the substrate, even if this lies at a distance from the cutting site 11 , 12 , 13 , 14 . Here we report crystal structures of the catalytic domain of RNase E as trapped allosteric intermediates with RNA substrates. Four subunits of RNase E catalytic domain associate into an interwoven quaternary structure, explaining why the subunit organization is required for catalytic activity. The subdomain encompassing the active site is structurally congruent to a deoxyribonuclease, making an unexpected link in the evolutionary history of RNA and DNA nucleases. The structure explains how the recognition of the 5′ terminus of the substrate may trigger catalysis and also sheds light on the question of how RNase E might selectively process, rather than destroy, specific RNA precursors.

The objective was to determine the extent to which the external peak knee adduction moment (KAM) and cumulative knee adductor load explained variation in medial cartilage morphology of the tibia and femur in knee osteoarthritis (OA). Sixty-two adults with clinical knee OA participated (61.5±6.2 years). To determine KAM, inverse dynamics was applied to motion and force data of walking. Cumulative knee adductor load reflected KAM impulse and loading frequency. Loading frequency was captured from an accelerometer. Magnetic resonance imaging scans were acquired with a coronal fat-saturated sequence using a 1.0T peripheral scanner. Scans were segmented for medial cartilage volume, surface area of the bone–cartilage interface, and thickness. Forward linear regressions assessed the relationship of loading variables with cartilage morphology unadjusted, then adjusted for covariates. In the medial tibia, age and peak KAM explained 20.5% of variance in mean cartilage thickness (p<0.001). Peak KAM alone explained 12.3% of the 5th percentile of medial tibial cartilage thickness (i.e., thinnest cartilage region) (p=0.003). In the medial femur, sex, BMI, age, and peak KAM explained 44% of variance in mean cartilage thickness, with peak KAM contributing 7.9% (p<0.001). 20.7% of variance in the 5th percentile of medial femoral cartilage thickness was explained by BMI and peak KAM (p=0.001). In these models, older age, female sex, greater BMI, and greater peak KAM related with thinner cartilage. Models of KAM impulse produced similar results. In knee OA, KAM peak and impulse, but not loading frequency, were associated with cartilage thickness of the medial tibia and femur.