Doping-free complementary WSe2 circuit via van der Waals metal integration.

Two-dimensional (2D) semiconductors have attracted considerable attention for the development of ultra-thin body transistors. However, the polarity control of 2D transistors and the achievement of complementary logic functions remain critical challenges. Here, we report a doping-free strategy to modulate the polarity of WSe2 transistors using same contact metal but different integration methods. By applying low-energy van der Waals integration of Au electrodes, we observed robust and optimized p-type transistor behavior, which is in great contrast to the transistors fabricated on the same WSe2 flake using conventional deposited Au contacts with pronounced n-type characteristics. With the ability to switch majority carrier type and to achieve optimized contact for both electrons and holes, a doping-free logic inverter is demonstrated with higher voltage gain of 340, at the bias voltage of 5.5 V. Furthermore, the simple polarity control strategy is extended for realizing more complex logic functions such as NAND and NOR

The RNA m6A writer METTL3 in tumor microenvironment: emerging roles and therapeutic implications

The tumor microenvironment (TME) is a heterogeneous ecosystem comprising cancer cells, immune cells, stromal cells, and various non-cellular components, all of which play critical roles in controlling tumor progression and response to immunotherapies. Methyltransferase-like 3 (METTL3), the core component of N6-methyladenosine (m6A) writer, is frequently associated with abnormalities in the m6A epitranscriptome in different cancer types, impacting both cancer cells and the surrounding TME. While the impact of METTL3 on cancer cells has been extensively reviewed, its roles in TME and anti-cancer immunity have not been comprehensively summarized. This review aims to systematically summarize the functions of METTL3 in TME, particularly its effects on tumor-infiltrating immune cells. We also elaborate on the underlying m6A-dependent mechanism. Additionally, we discuss ongoing endeavors towards developing METTL3 inhibitors, as well as the potential of targeting METTL3 to bolster the efficacy of immunotherapy

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)

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

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

Possible Luttinger liquid behavior of edge transport in monolayer transition metal dichalcogenide crystals.

In atomically-thin two-dimensional (2D) semiconductors, the nonuniformity in current flow due to its edge states may alter and even dictate the charge transport properties of the entire device. However, the influence of the edge states on electrical transport in 2D materials has not been sufficiently explored to date. Here, we systematically quantify the edge state contribution to electrical transport in monolayer MoS2/WSe2 field-effect transistors, revealing that the charge transport at low temperature is dominated by the edge conduction with the nonlinear behavior. The metallic edge states are revealed by scanning probe microscopy, scanning Kelvin probe force microscopy and first-principle calculations. Further analyses demonstrate that the edge-state dominated nonlinear transport shows a universal power-law scaling relationship with both temperature and bias voltage, which can be well explained by the 1D Luttinger liquid theory. These findings demonstrate the Luttinger liquid behavior in 2D materials and offer important insights into designing 2D electronics

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.

Data Study Group Final Report: Roche

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Roche: Personalised lung cancer treatment modelling using electronic health records and genomics Cancer immunotherapy (CIT) is a promising new type of cancer treatment that uses the patient’s own immune system to fight cancer cells. CIT drugs work to stop the cancer cells from turning off the immune system’s T-cells by inhibiting the PD-L1 produced by the tumour cells (PD-L1 is a protein that binds to PD-1 receptors on T-cells and prevents the immune system from attacking the cancer cells). CIT is currently being used to treat patients with non-small cell lung cancer (NSCLC) for whom chemotherapy or other drugs have failed. CIT is also be-ing used as part of the first-line treatment in patients with advanced NSCLC (aNSCLC - stage III and higher). Theoretically, patients with high PD-L1 ex-pression levels are more likely to respond well to CIT; however, in practice, patient outcomes vary considerably. In this data study group, we investigated different approaches for predicting survival time for patients treated with CIT as first line of treatment, using both electronic health records and tumour genomic data. We also investigated the causal effects of CIT vs other oncology treatments, and studied treatment heterogeneity. The results contribute to identifying patients who are most likely to benefit from CIT

Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal–external validation of a clinical risk prediction model

Background: The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways. Methods: Population-based cohort study of adults aged 30–85 years at type 2 diabetes diagnosis (2010–2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal–external cross-validation. Results: Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell’s C-index 0.802 (95% CI: 0.797–0.817)), the highest clinical utility, and was well calibrated. The model’s highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity. Discussion: A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging

Temporality of body mass index, blood tests, comorbidities and medication use as early markers for pancreatic ductal adenocarcinoma (PDAC): a nested case–control study

Objective Prior studies identified clinical factors associated with increased risk of pancreatic ductal adenocarcinoma (PDAC). However, little is known regarding their time-varying nature, which could inform earlier diagnosis. This study assessed temporality of body mass index (BMI), blood-based markers, comorbidities and medication use with PDAC risk .Design We performed a population-based nested case–control study of 28 137 PDAC cases and 261 219 matched-controls in England. We described the associations of biomarkers with risk of PDAC using fractional polynomials and 5-year time trends using joinpoint regression. Associations with comorbidities and medication use were evaluated using conditional logistic regression.Results Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while following a U-shaped relationship for BMI and haemoglobin. Five-year trends showed biphasic BMI decrease and HbA1c increase prior to PDAC; early-gradual changes 2–3 years prior, followed by late-rapid changes 1–2 years prior. Liver markers and blood counts (white blood cell, platelets) showed monophasic rapid-increase approximately 1 year prior. Recent diagnosis of pancreatic cyst, pancreatitis, type 2 diabetes and initiation of certain glucose-lowering and acid-regulating therapies were associated with highest risk of PDAC.Conclusion Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while followed a U-shaped relationship for BMI and haemoglobin. BMI and HbA1c derange biphasically approximately 3 years prior while liver markers and blood counts (white blood cell, platelets) derange monophasically approximately 1 year prior to PDAC. Profiling these in combination with their temporality could inform earlier PDAC diagnosis