Hospital-wide natural language processing summarising the health data of 1 million patients

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task

Efficacy, Model of delivery, Intensity and Targets of Pragmatic Interventions for Children with Developmental Language Disorder:A Systematic Review

BACKGROUND: It is widely acknowledged that children with developmental language disorder (DLD) predominantly have difficulties in the areas of grammar and vocabulary, with preserved pragmatic skills. Consequently, few studies focus on the pragmatic skills of children with DLD, and there is a distinct lack of studies examining the effectiveness of pragmatic interventions. AIMS: To carry out a systematic review of the literature on pragmatic interventions for children with DLD. METHODS & PROCEDURES: This systematic review was registered with PROSPERO (ID = CRD42017067239). A systematic search in seven databases yielded 1031 papers, of which 11 met our inclusion criteria. The included papers focused on interventions for children with DLD (mean = 3–18 years), enhancing oral language pragmatic skills, published between January 2006 and May 2020, and were based on a group‐study design such as randomized control trial or pre‐post‐testing. Study participants were monolingual speakers. The quality of papers was appraised using the Cochrane Risk of bias tool for randomized controlled trials. OUTCOMES & RESULTS: There was a high degree of variability between the included intervention studies, especially regarding intensity, intervention targets and outcomes. The evidence suggested that pragmatic intervention is feasible for all models of delivery (individual, small and large group) and that interventions for pragmatic language are mostly focused on encouragement of conversation and narrative skills observed through parent–child interaction or shared book‐reading activities. CONCLUSIONS & IMPLICATIONS: This study highlights the importance of promoting and explicitly teaching pragmatic skills to children with DLD in structured interventions. A narrative synthesis of the included studies revealed that in addition to direct intervention, indirect intervention can also contribute to improving oral pragmatic skills of children with DLD. WHAT THIS PAPER ADDS: WHAT IS ALREADY KNOWN ON THE SUBJECT? An increasing number of studies have shown that difficulties in acquiring pragmatic language is not only present in children with autism. WHAT THIS STUDY ADDS TO EXISTING KNOWLEDGE? Interventions for pragmatic language in children with DLD are mostly focused on encouragement of conversation and narrative skills, very often through parent–child interaction or shared book‐reading activities. Interventions that target language pragmatic are feasible for all models of delivery (individual, small and large group). WHAT ARE THE POTENTIAL OR ACTUAL CLINICAL IMPLICATIONS OF THIS WORK? The efficacy of the existing studies varies, and it is difficult to give recommendations regarding the intensity and duration of the specific intervention. In addition to offering pragmatic intervention directly from a specialist, pragmatic interventions can also be carried out indirectly if the intervention is under the continuous supervision of a specialist

Design and operation of a prototype interaction point beam collision feedback system for the International Linear Collider

A high-resolution, intratrain position feedback system has been developed to achieve and maintain collisions at the proposed future electron-positron International Linear Collider (ILC). A prototype has been commissioned and tested with a beam in the extraction line of the Accelerator Test Facility at the High Energy Accelerator Research Organization in Japan. It consists of a stripline beam position monitor (BPM) with analogue signal-processing electronics, a custom digital board to perform the feedback calculation, and a stripline kicker driven by a high-current amplifier. The closed-loop feedback latency is 148 ns. For a three-bunch train with 154 ns bunch spacing, the feedback system has been used to stabilize the third bunch to 450 nm. The kicker response is linear, and the feedback performance is maintained, over a correction range of over $\pm$60 {\mu}m. The propagation of the correction has been confirmed by using an independent stripline BPM located downstream of the feedback system. The system has been demonstrated to meet the BPM resolution, beam kick, and latency requirements for the ILC

Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians’ Recommendations

Aims: To compare insulin dose adjustments made by physicians to those made by an artificial intelligence-based decision support system, the Advisor Pro, in people with type 1 diabetes (T1D) using an insulin pump and self-monitoring blood glucose (SMBG). Methods: This was a multinational, non-interventional study surveying 17 physicians from 11 countries. Each physician was asked to provide insulin dose adjustments for the settings of the pump including basal rate, carbohydrate-to-insulin ratios (CRs), and correction factors (CFs) for 15 data sets of pumps and SMBG of people with T1D (mean age 18.4 ± 4.8 years; eight females; mean glycated hemoglobin 8.2% ± 1.4% [66 ± 11mmol/mol]). The recommendations were compared among the physicians and between the physicians and the Advisor Pro. The study endpoint was the percentage of comparison points for which there was an agreement on the direction of insulin dose adjustments. Results: The percentage (mean ± SD) of agreement among the physicians on the direction of insulin pump dose adjustments was 51.8% ± 9.2%, 54.2% ± 6.4%, and 49.8% ± 11.6% for the basal, CR, and CF, respectively. The automated recommendations of the Advisor Pro on the direction of insulin dose adjustments were comparable)49.5% ± 6.4%, 55.3% ± 8.7%, and 47.6% ± 14.4% for the basal rate, CR, and CF, respectively(and noninferior to those provided by physicians. The mean absolute difference in magnitude of change between physicians was 17.1% ± 13.1%, 14.6% ± 8.4%, and 23.9% ± 18.6% for the basal, CR, and CF, respectively, and comparable to the Advisor Pro 11.7% ± 9.7%, 10.1% ± 4.5%, and 25.5% ± 19.5%, respectively, significant for basal and CR. Conclusions: Considerable differences in the recommendations for changes in insulin dosing were observed among physicians. Since automated recommendations by the Advisor Pro were similar to those given by physicians, it could be considered a useful tool to manage T1D

Inpatient COVID-19 mortality has reduced over time: Results from an observational cohort

BACKGROUND: The Covid-19 pandemic in the United Kingdom has seen two waves; the first starting in March 2020 and the second in late October 2020. It is not known whether outcomes for those admitted with severe Covid were different in the first and second waves. METHODS: The study population comprised all patients admitted to a 1,500-bed London Hospital Trust between March 2020 and March 2021, who tested positive for Covid-19 by PCR within 3-days of admissions. Primary outcome was death within 28-days of admission. Socio-demographics (age, sex, ethnicity), hypertension, diabetes, obesity, baseline physiological observations, CRP, neutrophil, chest x-ray abnormality, remdesivir and dexamethasone were incorporated as co-variates. Proportional subhazards models compared mortality risk between wave 1 and wave 2. Cox-proportional hazard model with propensity score adjustment were used to compare mortality in patients prescribed remdesivir and dexamethasone. RESULTS: There were 3,949 COVID-19 admissions, 3,195 hospital discharges and 733 deaths. There were notable differences in age, ethnicity, comorbidities, and admission disease severity between wave 1 and wave 2. Twenty-eight-day mortality was higher during wave 1 (26.1% versus 13.1%). Mortality risk adjusted for co-variates was significantly lower in wave 2 compared to wave 1 [adjSHR 0.49 (0.37, 0.65) p<0.001]. Analysis of treatment impact did not show statistically different effects of remdesivir [HR 0.84 (95%CI 0.65, 1.08), p = 0.17] or dexamethasone [HR 0.97 (95%CI 0.70, 1.35) p = 0.87]. CONCLUSION: There has been substantial improvements in COVID-19 mortality in the second wave, even accounting for demographics, comorbidity, and disease severity. Neither dexamethasone nor remdesivir appeared to be key explanatory factors, although there may be unmeasured confounding present

Epistasis and genotype-by-environment interaction of grain protein content in durum wheat

Parental, F1 , F 2 , BC 1 and BC 2 generations of four crosses involving four cultivars of durum wheat (Triticum durum Desf.) were evaluated at two sites in Tunisia. A three-parameter model was found inadequate for all cases except crosses Chili x Cocorit 71 at site Sidi Thabet and Inrat 69 x Karim at both sites. In most cases a digenic epistatic model was sufficient to explain variation in generation means. Dominance effects (h) and additive x additive epistasis (i) (when significant) were more important than additive (d) effects and other epistatic components. Considering the genotype-by-environment interaction, the non-interactive model (m, d, h, e) was found adequate. Additive variance was higher than environmental variance in three crosses at both sites. The estimated values of narrow-sense heritability were dependent upon the cross and the sites and were 0%-85%. The results indicate that appropriate choice of environment and selection in later generations would increase grain protein content in durum wheat

The ESSnuSB design study: overview and future prospects

ESSnuSB is a design study for an experiment to measure the CP violation in the leptonic sector at the second neutrino oscillation maximum using a neutrino beam driven by the uniquely powerful ESS linear accelerator. The reduced impact of systematic errors on sensitivity at the second maximum allows for a very precise measurement of the CP violating parameter. This review describes the fundamental advantages of measurement at the 2nd maximum, the necessary upgrades to the ESS linac in order to produce a neutrino beam, the near and far detector complexes, the expected physics reach of the proposed ESSnuSB experiment, concluding with the near future developments aimed at the project realization.Comment: 19 pages, 11 figures; Corrected minor error in alphabetical ordering of the authors: the author list is now fully alphabetical w.r.t. author surnames as was intended. Corrected an incorrect affiliation for two authors per their reques

TIAF1 self-aggregation in peritumor capsule formation, spontaneous activation of SMAD-responsive promoter in p53-deficient environment, and cell death

Self-aggregation of transforming growth factor β (TGF-β)1-induced antiapoptotic factor (TIAF1) is known in the nondemented human hippocampus, and the aggregating process may lead to generation of amyloid β (Aβ) for causing neurodegeneration. Here, we determined that overexpressed TIAF1 exhibits as aggregates together with Smad4 and Aβ in the cancer stroma and peritumor capsules of solid tumors. Also, TIAF1/Aβ aggregates are shown on the interface between brain neural cells and the metastatic cancer cell mass. TIAF1 is upregulated in developing tumors, but may disappear in established metastatic cancer cells. Growing neuroblastoma cells on the extracellular matrices from other cancer cell types induced production of aggregated TIAF1 and Aβ. In vitro induction of TIAF1 self-association upregulated the expression of tumor suppressors Smad4 and WW domain-containing oxidoreductase (WOX1 or WWOX), and WOX1 in turn increased the TIAF1 expression. TIAF1/Smad4 interaction further enhanced Aβ formation. TIAF1 is known to suppress SMAD-regulated promoter activation. Intriguingly, without p53, self-aggregating TIAF1 spontaneously activated the SMAD-regulated promoter. TIAF1 was essential for p53-, WOX1- and dominant-negative JNK1-induced cell death. TIAF1, p53 and WOX1 acted synergistically in suppressing anchorage-independent growth, blocking cell migration and causing apoptosis. Together, TIAF1 shows an aggregation-dependent control of tumor progression and metastasis, and regulation of cell death

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets