A model for polymer membranes

Separation processes are widely used throughout the chemical and pharmaceutical industries. Polymer membranes have the potential to significantly improve both energy usage and the costs of separation processes by reducing reliance on fractional distillation. For this to occur methods to control the porosity of the polymer membranes must be identified. The P84 molecule is a relatively complex co-polymer with numerous strongly interacting rigid groups, with a persistence length of over 1.1 nm, and the region in which filtration pores form in the membrane is typically 50–80nm thick, whilst the pores of interest within the membrane are typically less than 0.5 nm in size. P84 membranes are used commercially to separate molecules from organic solvents, in a process called organic solvent nanofiltration. Recent experiments with membranes produced from the P84 polyimide molecule found that altering the solvent used in the initial stage of manufacture radically altered the size of the sub-nanometre pores in the filtration region of the membrane. This effect was not expected, and could not be explained by the available models for polymer membrane formation. I present here a model as well as key results developed during my investigation of the formation of P84 polymer membranes. The model uses a mixture of fully atomistic molecular dynamics simulations of a single P84 molecule in solvent and coarse grained Monte Carlo simulations containing hundreds of complete polymer molecules. It demonstrates that the experimentally observed changes in pore sizes in P84 membranes can be explained by the differing interaction energies between the solvents and the polymers. I further present a new method for coarse graining aromatic polymers in molecular dynamics simulations which has been shown to permit the time step to be increased from 1 fs to 5 fs whilst maintaining all-atom accuracy.Open Acces

Orthodontists? preference on type of rigid fixed functional appliance for skeletal Class II correction : a survey study

Rigid fixed functional appliances are most commonly used to correct skeletal Class II malocclusions. The objective of this study was to assess orthodontists? preference of different rigid fixed functional appliances used in the U.S.A for correction of skeletal Class II malocclusions. A survey on use and preference of rigid fixed functional appliances for skeletal Class II correction was emailed to 2,227 members of the American Association of Orthodontists (AAO) in the U.S.A. Frequency distribution of different responses and their association with demographic factors was assessed. Out of 140 orthodontists completing the survey, 110 responded as using rigid fixed functional appliances. Eight incomplete responses were eliminated from data analysis. 51.5% (68/132) orthodontists used rigid fixed functional appliances. The most preferred rigid fixed functional appliance was the Herbst appliance with 72% response followed by Mandibular Anterior Repositioning Appliance (24%) and AdvanSync (4%). There was no statistically significant difference in use of rigid fixed functional appliances between different age groups (p=0.284). However, the 40-54 age group used the most rigid fixed functional appliances in practice, followed by the 25-39 year age group and the 55-69 age group using these appliances the least. There was statistical significance between the type of practice setting one works in and the use of rigid fixed functional appliances in practice (p=0.022). About 52% of orthodontists use rigid fixed functional appliances to correct skeletal Class II malocclusions. The Herbst appliance is the most commonly used and most preferred amongst all rigid fixed functional appliances with a 72% preferred rate

Classical simulation of entanglement swapping with bounded communication

Entanglement appears under two different forms in quantum theory, namely as a property of states of joint systems and as a property of measurement eigenstates in joint measurements. By combining these two aspects of entanglement, it is possible to generate nonlocality between particles that never interacted, using the protocol of entanglement swapping. We show that even in the more constraining bilocal scenario where distant sources of particles are assumed to be independent, i.e. to share no prior randomness, this process can be simulated classically with bounded communication, using only 9 bits in total. Our result thus provides an upper bound on the nonlocality of the process of entanglement swapping.Comment: 6 pages, 1 figur

A cohort study on mental disorders, stage of cancer at diagnosis and subsequent survival.

OBJECTIVES: To assess the stage at cancer diagnosis and survival after cancer diagnosis among people served by secondary mental health services, compared with other local people. SETTING: Using the anonymised linkage between a regional monopoly secondary mental health service provider in southeast London of four London boroughs, Croydon, Lambeth, Lewisham and Southwark, and a population-based cancer register, a historical cohort study was constructed. PARTICIPANTS: A total of 28 477 cancer cases aged 15+ years with stage of cancer recorded at diagnosis were identified. Among these, 2206 participants had been previously assessed or treated in secondary mental healthcare before their cancer diagnosis and 125 for severe mental illness (schizophrenia, schizoaffective or bipolar disorders). PRIMARY AND SECONDARY OUTCOME MEASURES: Stage when cancer was diagnosed and all-cause mortality after cancer diagnosis among cancer cases registered in the geographical area of southeast London. RESULTS: Comparisons between people with and without specific psychiatric diagnosis in the same residence area for risks of advanced stage of cancer at diagnosis and general survival after cancer diagnosed were analysed using logistic and Cox models. No associations were found between specific mental disorder diagnoses and beyond local spread of cancer at presentation. However, people with severe mental disorders, depression, dementia and substance use disorders had significantly worse survival after cancer diagnosis, independent of cancer stage at diagnosis and other potential confounders. CONCLUSIONS: Previous findings of associations between mental disorders and cancer mortality are more likely to be accounted for by differences in survival after cancer diagnosis rather than by delayed diagnosis

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records

Background: Electronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research. Methods: We describe development, implementation and evaluation of a bespoke de-identification algorithm used to create the register. It is designed to create dictionaries using patient identifiers (PIs) entered into dedicated source fields and then identify, match and mask them (with ZZZZZ) when they appear in medical texts. We deemed this approach would be effective, given high coverage of PI in the dedicated fields and the effectiveness of the masking combined with elements of a security model. We conducted two separate performance tests i) to test performance of the algorithm in masking individual true PIs entered in dedicated fields and then found in text (using 500 patient notes) and ii) to compare the performance of the CRIS pattern matching algorithm with a machine learning algorithm, called the MITRE Identification Scrubber Toolkit – MIST (using 70 patient notes – 50 notes to train, 20 notes to test on). We also report any incidences of potential breaches, defined by occurrences of 3 or more true or apparent PIs in the same patient’s notes (and in an additional set of longitudinal notes for 50 patients); and we consider the possibility of inferring information despite de-identification. Results: True PIs were masked with 98.8% precision and 97.6% recall. As anticipated, potential PIs did appear, owing to misspellings entered within the EHRs. We found one potential breach. In a separate performance test, with a different set of notes, CRIS yielded 100% precision and 88.5% recall, while MIST yielded a 95.1% and 78.1%, respectively. We discuss how we overcome the realistic possibility – albeit of low probability – of potential breaches through implementation of the security model. Conclusion: CRIS is a de-identified psychiatric database sourced from EHRs, which protects patient anonymity and maximises data available for research. CRIS demonstrates the advantage of combining an effective de-identification algorithm with a carefully designed security model. The paper advances much needed discussion of EHR de-identification – particularly in relation to criteria to assess de-identification, and considering the contexts of de-identified research databases when assessing the risk of breaches of confidential patient information

Unraveling ethnic disparities in antipsychotic prescribing among patients with psychosis: A retrospective cohort study based on electronic clinical records

BACKGROUND: Previous studies have shown mixed evidence on ethnic disparities in antipsychotic prescribing among patients with psychosis in the UK, partly due to small sample sizes. This study aimed to examine the current state of antipsychotic prescription with respect to patient ethnicity among the entire population known to a large UK mental health trust with non-affective psychosis, adjusting for multiple potential risk factors. METHODS: This retrospective cohort study included all patients (N = 19,291) who were aged 18 years or over at their first diagnoses of non-affective psychosis (identified with the ICD-10 codes of F20-F29) recorded in electronic health records (EHRs) at the South London and Maudsley NHS Trust until March 2021. The most recently recorded antipsychotic treatments and patient attributes were extracted from EHRs, including both structured fields and free-text fields processed using natural language processing applications. Multivariable logistic regression models were used to calculate the odds ratios (OR) for antipsychotic prescription according to patient ethnicity, adjusted for multiple potential contributing factors, including demographic (age and gender), clinical (diagnoses, duration of illness, service use and history of cannabis use), socioeconomic factors (level of deprivation and own-group ethnic density in the area of residence) and temporal changes in clinical guidelines (date of prescription). RESULTS: The cohort consisted of 43.10 % White, 8.31 % Asian, 40.80 % Black, 2.64 % Mixed, and 5.14 % of patients from Other ethnicity. Among them, 92.62 % had recorded antipsychotic receipt, where 24.05 % for depot antipsychotics and 81.72 % for second-generation antipsychotic (SGA) medications. Most ethnic minority groups were not significantly different from White patients in receiving any antipsychotic. Among those receiving antipsychotic prescribing, Black patients were more likely to be prescribed depot (adjusted OR 1.29, 95 % confidence interval (CI) 1.14-1.47), but less likely to receive SGA (adjusted OR 0.85, 95 % CI 0.74-0.97), olanzapine (OR 0.82, 95 % CI 0.73-0.92) and clozapine (adjusted OR 0.71, 95 % CI 0.6-0.85) than White patients. All the ethnic minority groups were less likely to be prescribed olanzapine than the White group. CONCLUSIONS: Black patients with psychosis had a distinct pattern in antipsychotic prescription, with less use of SGA, including olanzapine and clozapine, but more use of depot antipsychotics, even when adjusting for the effects of multiple demographic, clinical and socioeconomic factors. Further research is required to understand the sources of these ethnic disparities and eliminate care inequalities

ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals. The pipeline uses characteristics specific to Psychiatric EHRs to guide the annotation process, and distinguishes: a) the temporal value associated with the ADE mention (whether it is historical or present), b) the categorical value of the ADE (whether it is assertive, hypothetical, retrospective or a general discussion) and c) the implicit contextual value where the status of the ADE is deduced from surrounding indicators, rather than explicitly stated. We manually created the rulebase in collaboration with clinicians and pharmacists by studying ADE mentions in various types of clinical notes. We evaluated the open-source Adverse Drug Event annotation Pipeline (ADEPt) using 19 ADEs specific to antipsychotics and antidepressants medication. The ADEs chosen vary in severity, regularity and persistence. The average F-measure and accuracy achieved by our tool across all tested ADEs were 0.83 and 0.83 respectively. In addition to annotation power, the ADEPT pipeline presents an improvement to the state of the art context-discerning algorithm, ConText