Dynamical self-assembly of dipolar active Brownian particles in two dimensions

Based on Brownian Dynamics (BD) simulations, we study the dynamical self-assembly of active Brownian particles with dipole–dipole interactions, stemming from a permanent point dipole at the particle center. The propulsion direction of each particle is chosen to be parallel to its dipole moment. We explore a wide range of motilities and dipolar coupling strengths and characterize the corresponding behavior based on several order parameters. At low densities and low motilities, the most important structural phenomenon is the aggregation of the dipolar particles into chains. Upon increasing the particle motility, these chain-like structures break, and the system transforms into a weakly correlated isotropic fluid. At high densities, we observe that the motility-induced phase separation is strongly suppressed by the dipolar coupling. Once the dipolar coupling dominates the thermal energy, the phase separation disappears, and the system rather displays a flocking state, where particles form giant clusters and move collective along one direction. We provide arguments for the emergence of the flocking behavior, which is absent in the passive dipolar system.TU Berlin, Open-Access-Mittel - 2020DFG, 65143814, GRK 1524: Self-Assembled Soft-Matter Nanostructures at Interface

The Modern Corporation Statement on Company Law: Summary: Fundamental Rules of Corporate Law

Corporations play a central role in modern economies. Certain beliefs about corporations and corporate law are widely held and relied upon by business experts, the financial press, and economists who study the firm. Unfortunately, some of these widely-held beliefs are mistaken. This has led to numerous common errors in the way corporate law concepts are understood and applied. The authors of this Summary are experts versed in a variety of national legal systems, including those of the U.S. and U.K. as well as the E.U. We provide this simple Summary of certain fundamentals of corporate law, applicable in almost all jurisdictions, in an effort to help prevent analytical errors which can have severe and damaging effects on corporations and corporate governance

Disparities in care and outcomes for primary liver cancer in England during 2008–2018: a cohort study of 8.52 million primary care population using the QResearch database

Background Liver cancer has one of the fastest rising incidence and mortality rates among all cancers in the UK, but it receives little attention. This study aims to understand the disparities in epidemiology and clinical pathways of primary liver cancer and identify the gaps for early detection and diagnosis of liver cancer in England. Methods This study used a dynamic English primary care cohort of 8.52 million individuals aged ≥25 years in the QResearch database during 2008–2018, followed up to June 2021. The crude and age-standardised incidence rates, and the observed survival duration were calculated by sex and three liver cancer subtypes, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (CCA), and other specified/unspecified primary liver cancer. Regression models were used to investigate factors associated with an incident diagnosis of liver cancer, emergency presentation, late stage at diagnosis, receiving treatments, and survival duration after diagnosis by subtype. Findings 7331 patients were diagnosed with primary liver cancer during follow-up. The age-standardised incidence rates increased over the study period, particularly for HCC in men (increased by 60%). Age, sex, socioeconomic deprivation, ethnicity, and geographical regions were all significantly associated with liver cancer incidence in the English primary care population. People aged ≥80 years were more likely to be diagnosed through emergency presentation and in late stages, less likely to receive treatments and had poorer survival than those aged <60 years. Men had a higher risk of being diagnosed with liver cancer than women, with a hazard ratio (HR) of 3.9 (95% confidence interval 3.6–4.2) for HCC, 1.2 (1.1–1.3) for CCA, and 1.7 (1.5–2.0) for other specified/unspecified liver cancer. Compared with white British, Asians and Black Africans were more likely to be diagnosed with HCC. Patients with higher socioeconomic deprivation were more likely to be diagnosed through the emergency route. Survival rates were poor overall. Patients diagnosed with HCC had better survival rates (14.5% at 10-year survival, 13.1%–16.0%) compared to CCA (4.4%, 3.4%–5.6%) and other specified/unspecified liver cancer (12.5%, 10.1%–15.2%). For 62.7% of patients with missing/unknown stage in liver cancer, their survival outcomes were between those diagnosed in Stages III and IV. Interpretation This study provides an overview of the current epidemiology and the disparities in clinical pathways of primary liver cancer in England between 2008 and 2018. A complex public health approach is needed to tackle the rapid increase in incidence and the poor survival of liver cancer. Further studies are urgently needed to address the gaps in early detection and diagnosis of liver cancer in England. Funding The Early Detection of Hepatocellular Liver Cancer (DeLIVER) project is funded by Cancer Research UK (Early Detection Programme Award, grant reference: C30358/A29725)

Dosimetric Selection for Helical Tomotherapy Based Stereotactic Ablative Radiotherapy for Early-Stage Non-Small Cell Lung Cancer or Lung Metastases

Background No selection criteria for helical tomotherapy (HT) based stereotactic ablative radiotherapy (SABR) to treat early stage non-small cell lung cancer (NSCLC) or solitary lung metastases has been established. In this study, we investigate the dosimetric selection criteria for HT based SABR delivering 70 Gy in 10 fractions to avoid severe toxicity in the treatment of centrally located lesions when adequate target dose coverage is desired. Materials and Methods 78 HT-SABR plans for solitary lung lesions were created to prescribe 70 Gy in 10 fractions to the planning target volume (PTV). The PTV was set to have ≥95% PTV receiving 70 Gy in each case. The cases for which dose constraints for ≥1 OAR could not be met without compromising the target dose coverage were compared with cases for which all target and OAR dose constraints were met. Results There were 23 central lesions for which OAR dose constraints could not be met without compromising PTV dose coverage. Comparing to cases for which optimal HT-based SABR plans were generated, they were associated with larger tumor size (5.72±1.96 cm vs. 3.74±1.49 cm, p\u3c0.0001), higher lung dose, increased number of immediately adjacent OARs ( 3.45±1.34 vs. 1.66±0.81, p\u3c0.0001), and shorter distance to the closest OARs (GTV: 0.26±0.22 cm vs. 0.88±0.54 cm, p\u3c0.0001; PTV 0.19±0.18 cm vs. 0.48±0.36 cm, p = 0.0001). Conclusion Delivery of 70 Gy in 10 fractions with HT to meet all the given OAR and PTV dose constraints are most likely when the following parameters are met: lung lesions ≤3.78 cm (11.98 cc), ≤2 immediately adjacent OARs which are ≥0.45 cm from the gross lesion and ≥0.21 cm from the PTV

Development and validation of personalised risk prediction models for early detection and diagnosis of primary liver cancer among the English primary care population using the QResearch database: research protocol and statistical analysis plan

BACKGROUND AND RESEARCH AIM: The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings. METHODS: This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25–84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008–30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal–external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated. DISCUSSION: The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00133-x

HCV genotype 6 prevalence, spontaneous clearance and diversity amongst elderly members of the Li ethnic minority in Baisha County, China

The epidemiology of hepatitis C virus varies widely across geographical regions and ethnic groups. Our previous study showed that 6 strains isolated from Baisha County, Hainan Island, China, were all new genotype 6 (gt6) subtypes which differed significantly from subtypes of other regions. In the current study, we conducted a comprehensive epidemiological survey of HCV in the Li ethnic group, native to Baisha County. Anti‐HCV antibodies were detected by 2 independent ELISAs in all participants, and positive results confirmed by the recombinant immunoblot assay (RIBA) and HCV RNA viral loads were measured. Univariate chi‐square test and multivariable logistic regression analyses were used to determine the risk factors for HCV infection and spontaneous clearance rates. Indeterminate RIBA results were excluded or included in analyses; consequently, findings were expressed as a range. Direct sequencing of partial regions within NS5B and E1 was employed for genotyping. Among 1682 participants, 117 to 153 were anti‐HCV positive (7.0%‐9.1%), with 42.7%‐52.6% confirmed to have cleared infection. Anti‐HCV positivity was associated with older age (≥60 years) (OR = 0.02, 95% CI 0.01‐0.05, P &lt; 0.01) and surgery (OR = 2.75, 95% CI 1.36‐5.57, P &lt; 0.01), with no significant difference found between the HCV infection group and the HCV spontaneous clearance group. The gt6 subtype distribution characteristics of Baisha County were unique, complex and diverse. The sequences did not cluster with known gt6 subtypes but formed 4 Baisha community‐specific groups. HCV infection in members of the Li minority ethnic group is characterized by high prevalence rates in the elderly, high spontaneous clearance rates and broad gt6 diversity

Identification of symptoms associated with the diagnosis of pancreatic exocrine and neuroendocrine neoplasms: a nested case-control study of the UK population

Background: Pancreatic cancer has the worst survival rate among all cancers. Almost 70% of patients were diagnosed at Stage IV. Aim: This study aimed to investigate the symptoms associated with the diagnoses of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine neoplasms (PNEN), comparatively characterise the symptomatology between the two tumour types to inform earlier diagnosis. Design and Setting: A nested case-control study was conducted using data from the QResearch database. Patients aged ≥25 years and diagnosed with PDAC or PNEN during 2000-2019 were the cases. Up to 10 controls from the same general practice were matched with each case by age, sex, and calendar year using incidence density sampling. Methods: Conditional logistic regression was used to investigate the association between the forty-two shortlisted symptoms and the diagnoses of PDAC/PNEN in different timeframes relative to the index date, adjusting for patients’ sociodemographic characteristics, lifestyle, and relevant comorbidities. Results: There were 23,640 patients diagnosed with PDAC and 596 with PNEN. Twenty-three symptoms were significantly associated with PDAC, and nine symptoms with PNEN. Jaundice and gastrointestinal bleeding were the two alarm symptoms for both tumours. Thirst and dark urine were the two new identified symptoms for PDAC. The risk of unintentional weight loss may be longer than two years before the diagnosis of PNEN. Conclusion: PDAC and PNEN have overlapping symptom profiles. The QCancer (Pancreas) risk prediction model could be updated by including the newly identified symptoms and comorbidities, which could help GP identify high-risk patients for timely investigation in primary care

Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R ]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP] , LLP , Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO] , PLCO , Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R in both sexes in the QResearch validation cohort and 59% of the R in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP and PLCO ), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. Innovate UK (UK Research and Innovation). For the Chinese translation of the abstract see Supplementary Materials section. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.