Circulating Glycated Albumin and Glomerular Anionic Charges

Aiming to discern the mechanisms by which circulating glycated albumin alters the glomerular filtration properties that lead to glomerular dysfunction in diabetes, the authors studied the distribution and densities of anionic charges through the rat glomerular wall upon intravascular infusion of Amadori products, as well as in various conditions of increased glomerular permselectivity. Polylysine-gold was used as the probe to reveal the anionic charges. The study was carried on renal tissue sections of bovine serum albumin (BSA)- and glycated BSA–injected, normoglycemic animals. Results were generated through morphometrical evaluations of the gold labeling. Changes in glomerular anionic distribution were corroborated on renal tissue sections of short- and long-term diabetic rats and of normal newborn rats, situations known for abnormal glomerular filtration. Altered renal function in these conditions was clearly associated with changes in glomerular anionic charges. On the other hand, the infusion of glycated albumin in the circulation of normal rats, though altering glomerular filtration properties, did not modify the distribution and density of the polylysine-gold labeling through the glomerular basement membrane. Thus, anionic charges seem not to be the factor involved in the early changes of glomerular permeability induced by circulating glycated albumin

Social support from the closest person and sleep quality in later life: evidence from a British birth cohort study

ObjectivesSupportive social relationships have been found to be related to fewer sleep problems and better sleep quality. We examined associations between positive and negative support from the nominated close person across 15 years of follow-up with sleep quality in older age.MethodsMRC National Survey of Health and Development study members reported sleep quality at age 68 (n = 2446). Cumulative exposure to and changes in positive and negative support were derived from data at age 53, 60–64 and 68 years. Pittsburgh Sleep Quality Index scores were regressed on social support measures adjusted for i) gender only then additionally ii) education, marital status, number in household, limiting illness, body mass index, caregiving, iii) and affective symptoms.ResultsGreater exposure to positive support and lower exposure to negative support over 15 years were independently associated with better sleep quality at age 68. Sleep quality was poorer for those who experienced declining positive support or increasing negative support. Those who nominated their spouse/partner as their closest person at age 53 but not at age 68 had poorer sleep quality than those who nominated their spouse on both occasions. These associations were not explained by the covariates, including affective symptoms.ConclusionsBased on repeat data on support from the closest person, this study finds a link between declining social relationship quality and poor sleep quality. Whilst acknowledging that the association may be bi-directional, the study suggests that interventions to improve older people's social relationships may have benefits for sleep

MedCATTrainer: A biomedical free text annotation interface with active learning and research use case specific customisation

We present MedCATTrainer1 an interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. NER+L is often used as a first step in deriving value from clinical text. Collecting labelled data for training models is difficult due to the need for specialist domain knowledge. MedCATTrainer offers an interactive web-interface to inspect and improve recognised entities from an underlying NER+L model via active learning. Secondary use of data for clinical research often has task and context specific criteria. MedCATTrainer provides a further interface to define and collect supervised learning training data for researcher specific use cases. Initial results suggest our approach allows for efficient and accurate collection of research use case specific training data

Apolipoprotein-E (ApoE) ε4 and cognitive decline over the adult life course

We tested the association between APOE-ε4 and processing speed and memory between ages 43 and 69 in a population-based birth cohort. Analyses of processing speed (using a timed letter search task) and episodic memory (a 15-item word learning test) were conducted at ages 43, 53, 60–64 and 69 years using linear and multivariable regression, adjusting for gender and childhood cognition. Linear mixed models, with random intercepts and slopes, were conducted to test the association between APOE and the rate of decline in these cognitive scores from age 43 to 69. Model fit was assessed with the Bayesian Information Criterion. A cross-sectional association between APOE-ε4 and memory scores was detected at age 69 for both heterozygotes and homozygotes (β = −0.68 and β = −1.38, respectively, p = 0.03) with stronger associations in homozygotes; no associations were observed before this age. Homozygous carriers of APOE-ε4 had a faster rate of decline in memory between ages 43 and 69, when compared to non-carriers, after adjusting for gender and childhood cognition (β = −0.05, p = 0.04). There were no cross-sectional or longitudinal associations between APOE-ε4 and processing speed. We conclude that APOE-ε4 is associated with a subtly faster rate of memory decline from midlife to early old age; this may be due to effects of APOE-ε4 becoming manifest around the latter stage of life. Continuing follow-up will determine what proportion of this increase will become clinically significant

PGE1 nebulisation during caesarean section for Eisenmenger's syndrome: a case report

<p>Abstract</p> <p>Introduction</p> <p>Eisenmenger's syndrome in pregnancy can lead to death in 50% to 65% of parturients. Expensive invasive monitoring and medication have improved management and outcomes. Cheaper alternatives for the management of high-risk patients who present with no prenatal care are still not available.</p> <p>Case presentation</p> <p>We describe the obstetric anaesthesia management of a 34-year-old, 34-weeks pregnant woman who presented with a recent diagnosis of severe Eisenmenger's syndrome. A combined spinal epidural anaesthesia was used together with invasive cardiac monitoring as well as PGE1 nebulisation after delivery of the baby. This helped achieve a reduction of shunt, improvement of hypoxia and reduction of pulmonary pressures.</p> <p>Conclusion</p> <p>We found this to be a cheaper and safe alternative in the management of such patients who present with no adequate prior management.</p

Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

Background: An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments). / Methods: Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall. / Findings: Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91–100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required. / Interpretation: Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. / Funding: National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK

Investigating the Association between Physical Health Comorbidities and Disability in Individuals with Severe Mental Illness

BACKGROUND: Research suggests that an increased risk of physical comorbidities might have a key role in the association between severe mental illness (SMI) and disability. We examined the association between physical multimorbidity and disability in individuals with SMI. METHODS: Data were extracted from the clinical record interactive search system at South London and Maudsley Biomedical Research Centre. Our sample (n = 13,933) consisted of individuals who had received a primary or secondary SMI diagnosis between 2007 and 2018 and had available data for Health of Nations Outcome Scale (HoNOS) as disability measure. Physical comorbidities were defined using Chapters II–XIV of the International Classification of Diagnoses (ICD-10). RESULTS: More than 60 % of the sample had complex multimorbidity. The most common organ system affected were neurological (34.7%), dermatological (15.4%), and circulatory (14.8%). All specific comorbidities (ICD-10 Chapters) were associated with higher levels of disability, HoNOS total scores. Individuals with musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders were found to be associated with significant difficulties associated with more than five HoNOS domains while others had a lower number of domains affected. CONCLUSIONS: Individuals with SMI and musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders are at higher risk of disability compared to those who do not have those comorbidities. Individuals with SMI and physical comorbidities are at greater risk of reporting difficulties associated with activities of daily living, hallucinations, and cognitive functioning. Therefore, these should be targeted for prevention and intervention programs