Sodium-based paracetamol: impact on blood pressure, cardiovascular events, and all-cause mortality.

Background And Aims: Effervescent formulations of paracetamol containing sodium bicarbonate have been reported to associate with increased blood pressure and a higher risk of cardiovascular diseases and all-cause mortality. Given the major implications of these findings, the reported associations were re-examined.

Methods: Using linked electronic health records data, a cohort of 475 442 UK individuals with at least one prescription of paracetamol, aged between 60 and 90 years, was identified.

A comparative study of model-centric and data-centric approaches in the development of cardiovascular disease risk prediction models in the UK Biobank.

Aims: A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity.

Methods And Results: The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements.

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.

Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink.

Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk.

Objective: In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated.

Methods And Analysis: In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55-90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK.

Road Traffic Noise and Incidence of Primary Hypertension: A Prospective Analysis in UK Biobank.

Background: The quality of evidence regarding the associations between road traffic noise and hypertension is low due to the limitations of cross-sectional study design, and the role of air pollution remains to be further clarified.

Objectives: The purpose of this study was to evaluate the associations of long-term road traffic noise exposure with incident primary hypertension; we conducted a prospective population-based analysis in UK Biobank.

Methods: Road traffic noise was estimated at baseline residential address using the common noise assessment method model.

An interpretable machine learning framework for measuring urban perceptions from panoramic street view images.

The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and "black-box" nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability.

Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.

Aims: Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address.

Systolic Blood Pressure and Cardiovascular Risk in Patients With Diabetes: A Prospective Cohort Study.

Background: Whether the association between systolic blood pressure (SBP) and risk of cardiovascular disease is monotonic or whether there is a nadir of optimal blood pressure remains controversial. We investigated the association between SBP and cardiovascular events in patients with diabetes across the full spectrum of SBP.

Methods: A cohort of 49 000 individuals with diabetes aged 50 to 90 years between 1990 and 2005 was identified from linked electronic health records in the United Kingdom.

Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records.

Electronic health records (EHR) represent a holistic overview of patients' trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal.

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs).

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis.

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations.

In-vivo quantification of lactate using Near Infrared reflectance spectroscopy.

Elevated lactate levels in blood (hyperlactatemia) are indications of hypoperfusion or sepsis in critical care conditions. Quantification and monitoring of this important marker is performed using intermittent blood sampling, which fails to provide a complete scenario to aid clinicians in diagnosis. The feasibility of Near Infrared (NIR) Spectroscopy as an alternative to state-of-the-art techniques in critical care environments for non-invasive and continuous monitoring of lactate has previously been established.

Ambient Air Pollution and Respiratory Health in Sub-Saharan African Children: A Cross-Sectional Analysis.

Ambient air pollution is projected to become a major environmental risk in sub-Saharan Africa (SSA). Research into its health impacts is hindered by limited data. We aimed to investigate the cross-sectional relationship between particulate matter with a diameter ≤ 2.

An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate.

The linear relationship between optical absorbance and the concentration of analytes-as postulated by the Beer-Lambert law-is one of the fundamental assumptions that much of the optical spectroscopy literature is explicitly or implicitly based upon. The common use of linear regression models such as principal component regression and partial least squares exemplifies how the linearity assumption is upheld in practical applications. However, the literature also establishes that deviations from the Beer-Lambert law can be expected when (a) the light source is far from monochromatic, (b) the concentrations of analytes are very high and (c) the medium is highly scattering.

Design and Analysis of a Continuous and Non-Invasive Multi-Wavelength Optical Sensor for Measurement of Dermal Water Content.

Dermal water content is an important biophysical parameter in preserving skin integrity and preventing skin damage. Traditional electrical-based and open-chamber evaporimeters have several well-known limitations. In particular, such devices are costly, sizeable, and only provide arbitrary outputs.

Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection.

Near Infrared (800-2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared.

Comparison of wavelength selection methods for in-vitro estimation of lactate: a new unconstrained, genetic algorithm-based wavelength selection.

Biochemical and medical literature establish lactate as a fundamental biomarker that can shed light on the energy consumption dynamics of the body at cellular and physiological levels. It is therefore, not surprising that it has been linked to many critical conditions ranging from the morbidity and mortality of critically ill patients to the diagnosis and prognosis of acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the gold standard for the measurement of lactate requires blood sampling.

Near Infrared and Aquaphotomic analysis of water absorption in lactate containing media.

Increased concentrations of lactate levels in blood are often seen in patients with life-threatening cellular hypoperfusion or infections. State-of-the-art techniques used in clinical practice for measuring serum lactate concentrations rely on intermittent blood sampling and do not permit continuous monitoring of this all important parameter in critical care environments.In recent years, Near Infrared (NIR) Spectroscopy has been established as a possible alternative to existing methods that can mitigate these constraints and be used for non-invasive continuous monitoring of lactate.

Measurement of dermal water content using a multi-wavelength optical sensor.

Skin hydration is crucial for overall skin health. Maintaining skin hydration levels preserves skin integrity and prevents tissue damage which can lead to several debilitating conditions. Moreover, continuous monitoring of skin hydration can contribute to the diagnosis or management of serious diseases.

The efficacy of support vector machines in modelling deviations from the Beer-Lambert law for optical measurement of lactate.

Lactate is an important biomarker with a significant diagnostic and prognostic ability in relation to life-threatening conditions and diseases such as sepsis, diabetes, cancer, pulmonary and kidney diseases, to name a few. The gold standard method for the measurement of lactate relies on blood sampling, which due to its invasive nature, limits the ability of clinicians in frequent monitoring of patients' lactate levels. Evidence suggests that the optical measurement of lactate holds promise as an alternative to blood sampling.

Identification and Quantitative Determination of Lactate Using Optical Spectroscopy-Towards a Noninvasive Tool for Early Recognition of Sepsis.

Uninterrupted monitoring of serum lactate levels is a prerequisite in the critical care of patients prone to sepsis, cardiogenic shock, cardiac arrest, or severe lung disease. Yet there exists no device to continuously measure blood lactate in clinical practice. Optical spectroscopy together with multivariate analysis is proposed as a viable noninvasive tool for estimation of lactate in blood.

Recent Advances in the Scaffold Engineering of Protein Binders.

In recent years, extensive attention has been given to the generation of new classes of ligand- specific binding proteins to supplement monoclonal antibodies. A combination of protein engineering and display technologies has been used to manipulate non-human antibodies for humanization and stabilization purposes or even the generation of new binding proteins. Engineered protein scaffolds can now be directed against therapeutic targets to treat cancer and immunological disorders.

Investigations into the Effects of pH on Quantitative Measurements of Lactate in Biological Media Using ATR-FTIR Spectroscopy.

Quantification of lactate/lactic acid in critical care environments is essential as lactate serves as an important biochemical marker for the adequacy of the haemodynamic circulation in shock and of cell respiration at the onset of sepsis/septic shock. Hence, in this study, ATR-FTIR was explored as a potential tool for lactate measurement, as the current techniques depend on sample preparation and fails to provide rapid response. Moreover, the effects of pH on PBS samples (7.