65 research outputs found

    AI is a viable alternative to high throughput screening: a 318-target study

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    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNetÂź convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNetÂź model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

    Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

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    Background: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. Methods: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. Findings: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96–1·28). Interpretation: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. Funding: National Institute for Health Research Health Services and Delivery Research Programme

    Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

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    BACKGROUND: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. METHODS: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. FINDINGS: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96-1·28). INTERPRETATION: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. FUNDING: National Institute for Health Research Health Services and Delivery Research Programme

    The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

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    Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≄16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

    Analysis of humidity sensitivity of silicon strip sensors for ATLAS upgrade tracker, pre- and post-irradiation

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    The ATLAS collaboration is working on a major upgrade of the Inner-Tracker, able to withstand the extreme operational conditions expected for the forthcoming High-Luminosity Large Hadron Collider (HL-LHC) upgrade. During the prototyping phase of the new large area silicon strip sensors, the community observed a degradation of the breakdown voltage (down to 200-500 V from >= 1 kV in bias voltage) when the devices with final technology options were exposed to high humidity, recovering the electrical performance prior to the exposure after a short period in dry conditions [J. Fernandez-Tejero, et al., NIM A 978 (2020) 164406]. These findings helped to understand the humidity sensitivity of the new sensors, defining the optimal working conditions and handling recommendations during production testing. In 2020, the ATLAS strip sensor community started the pre-production phase, receiving the first sensors fabricated by Hamamatsu Photonics K.K. using the final layout design. The work presented here is focused on the analysis of the humidity sensitivity of production-like sensors with different surface properties, providing new results on their influence on the humidity sensitivity observed during the prototyping phase. Additionally, the new production strip sensors were exposed to short (days) and long (months) term exposures to high humidity. This study allows to recreate and evaluate the influence of the detector integration environment expected during the Long Shutdown 3 (LS3) in 2025, where the sensors will be exposed to ambient humidity for prolonged times. A subset of the production-like sensors were irradiated up to fluences expected at the end of the HL-LHC lifetime, allowing the study of the evolution of the humidity sensitivity and influence of the passivation layers on sensors exposed to extreme radiation conditions

    Analysis of humidity sensitivity of silicon strip sensors for ATLAS upgrade tracker, pre- and post-irradiation

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    During the prototyping phase of the new ATLAS ITk large area strip sensors, a degradation of the device breakdown voltage at high humidity was observed. Although the degradation was temporary, showing a fast recovery in dry conditions, the study of the influence of humidity on the sensor performance was critical to establish counter-measures and handling protocols during production testing in order to ensure the proper performance of the upgraded detector. The work presented here has the objective to study for the first time the breakdown voltage deterioration in presence of humidity of ATLAS ITk production layout sensors with different surface properties, before and after proton, neutron and gamma irradiations. The sensors were also exposed several days to high humidity with the aim to recreate and evaluate the influence of the detector integration environment expected during the Long Shutdown 3 (LS3) in 2025, where the sensors will be exposed to ambient humidity for prolonged times

    Test and extraction methods for the QC parameters of silicon strip sensors for ATLAS upgrade tracker

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    The Quality Control (QC) of pre-production strip sensors for the Inner Tracker (ITk) of the ATLAS Inner Detector upgrade has finished, and the collaboration has embarked on the QC test programme for production sensors. This programme will last more than 3 years and comprises the evaluation of approximately 22000 sensors. 8 Types of sensors, 2 barrel and 6 endcap, will be measured at many different collaborating institutes. The sustained throughput requirement of the combined QC processes is around 500 sensors per month in total. Measurement protocols have been established and acceptance criteria have been defined in accordance with the terms agreed with the supplier. For effective monitoring of test results, common data file formats have been agreed upon across the collaboration. To enable evaluation of test results produced by many different test setups at the various collaboration institutes, common algorithms have been developed to collate, evaluate, plot and upload measurement data. This allows for objective application of pass/fail criteria and compilation of corresponding yield data. These scripts have been used to process the data of more than 3000 sensors so far, and have been instrumental for identification of faulty sensors and monitoring of QC testing progress

    Test and extraction methods for the QC parameters of silicon strip sensors for ATLAS upgrade tracker

    No full text
    The Quality Control (QC) of pre-production strip sensors for the Inner Tracker (ITk) of the ATLAS Inner Detector upgrade has finished, and the collaboration has embarked on the QC test programme for production sensors. This programme will last more than 3 years and comprises the evaluation of approximately 22000 sensors. 8 Types of sensors, 2 barrel and 6 endcap, will be measured at many different collaborating institutes. The sustained throughput requirement of the combined QC processes is around 500 sensors per month in total. Measurement protocols have been established and acceptance criteria have been defined in accordance with the terms agreed with the supplier. For effective monitoring of test results, common data file formats have been agreed upon across the collaboration. To enable evaluation of test results produced by many different test setups at the various collaboration institutes, common algorithms have been developed to collate, evaluate, plot and upload measurement data. This allows for objective application of pass/fail criteria and compilation of corresponding yield data. These scripts have been used to process the data of more than 2500 sensors so far, and have been instrumental for identification of faulty sensors and monitoring of QC testing progress. The analysis algorithms and criteria were also used in a dedicated study of strip tests on gamma-irradiated full-size sensors
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