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A global study to test if either chloroquine or hydroxychloroquine can prevent COVID-19 in vital frontline healthcare workers will open to UK participants at hospital sites in Brighton and Oxford today.
Early neurological deterioration in minor stroke caused by small artery occlusion: Incidence, risk factors and treatment impact.
IntroductionEarly neurological deterioration (END) is a forecast factor in poor outcomes in minor strokes. END's prevalence and forecast factors in minor strokes caused by small artery occlusion (SAO) are still unclear.Patients and methodWe retrospectively analyzed 451 patients with minor stroke (NIHSS ≤ 5) caused by SAO hospitalized within an initial 24 h at BachMai Hospital's stroke center. END was defined as conditions with an elevated two or more NIHSS points within an initial 72 h. The primary outcome included the determination of the END incidence. The secondary outcome identified forecast factors for END through multivariate logistic regression analyses, and therapeutic impacts of antiplatelet and thrombolytic treatments.ResultsEND occurred in 9.5 % (43/451) of patients (62.7 % male, mean age 63.8 ± 11.8 years). Independent forecast included admission SBP ≥ 150 mmHg (OR = 1.99; 95 % CI: 1.01 - 3.94; p = 0.048), diabetes history (OR = 0.58; 95 % CI: 1.05 - 4.33; p = 0.036), admission blood glucose ≥ 14mmol/L (OR = 2.99; 95 % CI: 1.05 - 8.54; p = 0.04), and internal capsule infarction (OR = 2.23; 95 % CI: 1.01 - 4.92; p = 0.048). The patients group admitted within 4.5 h, DAPT has significantly lower END risk compared to SAPT (OR = 0.079; 95 % CI: 0.007 - 0.939; p = 0.04) and altepase (OR = 0.013; 95 % CI: 0.01 - 0.12; p < 0.01). END risk was similar between SAPT and altepase (p = 0.074).Discussion and conclusionEND is a 9.5 % incidence in minor acute ischemic stroke due to SAO. Independent forecasts are admission SBP and blood glucose, diabetes history, and internal capsule infarction. The DAPT group has significantly lower END risk than the SAPT and alteplase groups.
Prevalence of common autosomal recessive and X-linked conditions in pregnant women in Vietnam: a cross-sectional study.
The prevalence of recessive disorder carriers among Vietnamese women is still indistinct. This study aims to assess the prevalence of carriers for common autosomal recessive and X-linked conditions among Vietnamese pregnant women and to identify common mutations within these genes. A cross-sectional study was conducted with 8,464 Vietnamese pregnant women with indications for carrier screening tests for recessive disorders from November 2022 to August 2023 at the Institute of DNA Technology and Genetic Analysis. The survey includes demographic information, and the genetic screening was conducted using next-generation sequencing (NGS) techniques, focusing on 13 specific recessive conditions. 8,464 Vietnamese pregnant women's records were involved in this study. 1,928 of them carried at least one genetic recessive condition, representing the frequency of a recessive disorder was 22.8%. The highest recessive disorders rate among pregnant women was found for the G6PD gene mutation (G6PD deficiency) at a rate of about 1 in 20 individuals, followed by the HBA1 and HBA2 gene mutations (Alpha Thalassemia) at a rate of about 1 in 25. Other common recessive carrier genes included SRD5A2 (5-alpha reductase deficiency) at a rate of about 1 in 27, HBB (Beta Thalassemia) at a rate of about 1 in 28, ATP7B (Wilson's disease) at a rate of about 1 in 40, PAH (Phenylketonuria) at a rate of about 1 in 40, and SLC25A13 (Citrin deficiency) at a rate of about 1 in 45. The prevalence of recessive carriers among Vietnamese pregnant women is high, and at least 1 in 5 pregnant women carries one recessive gene. It is essential to encourage Vietnamese pregnant women to conduct recessive carrier screening tests to reduce mortality rates among children and to implement effective pregnancy planning and childbirth.
Early neurological deterioration in patients with minor stroke: A single-center study conducted in Vietnam
A minor ischemic stroke is associated with a higher likelihood of poor clinical outcomes at 90 days when there is early neurological deterioration (END). The objective of this case-control study conducted in a comprehensive stroke facility in Vietnam is to examine the frequency, forecast, and outcomes of patients with END in minor strokes. The study employs a descriptive observational design, longitudinally tracking patients with minor strokes admitted to Bach Mai Hospital’s Stroke Center between December 1, 2023, and August 31, 2024. Hospitalized within 24 hours of symptom onset, minor stroke patients with National Institutes of Health Stroke Scale (NIHSS) scores ≤ 5 and items 1a, 1b, and 1c on the NIHSS scale, each equal to 0, were included in the study. The primary measure of interest is the END rate, defined as a rise of 2 or more points in the NIHSS score during the first 72 hours after admission. We conduct a logistic regression analysis to identify forecasting factors for END. Out of 839 patients, 88 (10.5%) had END. In the END group, we found that most patients had complications within the first 24 hours of stroke, accounting for 43.2%; the 24 – 48-hour window accounted for 35.2%, and the 48 – 72-hour window accounted for 21.6%. END was associated with a higher likelihood of poor outcomes (mRS 2 – 6) at discharge (OR = 22.76; 95% CI 11.22 – 46.20; p < 0.01), 30 days post-stroke(OR = 24.38; 95% CI 14.40 – 41.29; p < 0.01), and 90 days post-stroke (OR = 21.74; 95% CI 12.63 – 37.43; p < 0.01). Some of the prognostic factors for END were admission NIHSS score (OR = 1.24; 95% CI 1.03 – 1.49; p = 0.02), admission systolic blood pressure greater than 150mmHg (OR = 1.70; 95% CI 1.03 – 2.81; p = 0.04), admission blood glucose (OR = 1.07; 95% CI 1.01 – 1.14; p = 0.02), reperfusion therapy (OR = 3.35; 95% CI 1.50 – 7.49; p < 0.01), use of antiplatelet monotherapy (OR = 3.69; 95% CI 2.24 – 6.08; p < 0.01), internal capsule infarction (OR = 2.54; 95% CI 1.37 – 4.71; p < 0.01), hemorrhagic transformation (OR = 5.72; 95% CI 1.07 – 30.45; p = 0.04), corresponding extracranial carotid artery occlusion (OR = 4.84; 95% CI 1.26 – 18.65; p = 0.02), and middle cerebral artery occlusion OR = 3.06; 95% CI 1.29 – 7.30; p = 0.01). END in minor stroke patients accounts for 10.5% and is a risk factor for poor neurological outcomes. Admission NIHSS score, higher systolic blood pressure, admission blood glucose, reperfusion therapy, use of antiplatelet monotherapy, internal capsule infarction, hemorrhagic transformation, corresponding extracranial carotid artery occlusion, and middle cerebral artery occlusion were some of the prognostic factors for END in our observational study.
Comparative Analysis of the Net Clinical Benefit of Direct Oral Anticoagulants in Atrial Fibrillation: Systematic Review and Network Meta-analysis of Randomised Controlled Trials.
BackgroundDirect oral anticoagulants (DOACs) are the standard treatment for stroke prevention in AF. However, high-quality head-to-head comparisons of DOACs are lacking. This study compared oral anticoagulants in patients with AF.MethodsData were retrieved from eligible randomised controlled trials (RCTs). Interventions were ranked using the surface under the cumulative ranking curve (SUCRA) and the frequentist random effects model was applied. Efficacy outcomes included stroke, systemic embolism, MI, and all-cause mortality; the safety outcome was major bleeding. A composite outcome of efficacy and net clinical benefit was also evaluated.ResultsFrom 23,152 records, 11 eligible RCTs were identified and included in the study. Rivaroxaban was superior to vitamin K antagonists (VKA) in net clinical benefit (RR 0.75; 95% CI [0.59-0.94]; p=0.0133), but there were no significant differences between other DOACs and VKA or among the DOACs themselves. Rivaroxaban reduced the risk of the composite outcome of efficacy compared with dabigatran (RR 0.85; 95% CI [0.75-0.98]; p=0.02) and edoxaban (RR 0.84; 95% CI [0.75-0.95]; p=0.0051), but not apixaban (RR 0.89; 95% CI [0.89-1.02]; p=0.087). All DOACs showed superiority over VKA in efficacy, without an increased risk of major bleeding. Based on the SUCRA, rivaroxaban showed a favourable risk-benefit profile compared with the other anticoagulants.ConclusionThis study showed that DOACs are superior to VKA in efficacy without increasing major bleeding risk, with rivaroxaban demonstrating the most balanced risk-benefit profile. Well-designed RCTs are needed to validate these findings.
A systematic review of machine learning models for management, prediction and classification of ARDS.
AimAcute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS.MethodIn this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research.ResultsGradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times.ConclusionFor databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
Quantifying heterogeneity in an animal model of acute respiratory distress syndrome, a comparison of inspired sinewave technique to computed tomography
AbstractThe inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluate the reproducibility and predicted clinical outcomes of IST heterogeneity values, a comparison with a quantitative lung computed tomography (CT) scan is performed. Six anaesthetised pigs were studied after surfactant depletion by saline-lavage. Paired measurements of lung heterogeneity were then taken with both the IST and CT. Lung heterogeneity measured by the IST was calculated by (a) the ratio of tracer gas outputs measured at oscillation periods of 180 s and 60 s, and (b) by the standard deviation of the modelled log-normal distribution of ventilations and perfusions in the simulation lung. In the CT images, lungs were manually segmented and divided into different regions according to voxel density. A quantitative CT method to calculate the heterogeneity (the Cressoni method) was applied. The IST and CT show good Pearson correlation coefficients in lung heterogeneity measurements (ventilation: 0.71, and perfusion, 0.60, p < 0.001). Within individual animals, the coefficients of determination average ventilation (R2 = 0.53) and perfusion (R2 = 0.68) heterogeneity. Strong concordance rates of 98% in ventilation and 89% when the heterogeneity changes were reported in pairs measured by CT scanning and IST methods. This quantitative method to identify heterogeneity has the potential to replicate CT lung heterogeneity, and to aid individualised care in ARDS.
Study protocol: Early neurological deterioration in patients with minor stroke, frequency, predictors, and outcomes in Vietnam single-centre study.
Early neurological deterioration (END) is progressive neurological deterioration with an increase in NIHSS score of 2 points or more in the first 72 hours from the onset of acute ischemic stroke. END increases the risk of poor clinical outcomes at day 90 of ischemic stroke. We will study the frequency, predictors, and outcomes of patients with END in a case-control study at a comprehensive stroke centre in Vietnam. of the design is a descriptive observational study, longitudinal follow-up of patients with minor stroke hospitalized at the Stroke Center of Bach Mai Hospital from December 1, 2023, to December 1, 2024. Minor stroke patients characterized by NIHSS score ≤ 5 hospitalized within 24 hours of symptom onset will be recruited. The estimated END rate is about 30%, relative accuracy ε = 0.11, 95% reliability, expected 5% of patients lost data or follow-up, and an estimated sample size of 779 patients. This study will help determine the END rate in patients with minor stroke and related factors, thereby building a prognostic model for END. Our study determined the END rate in patients with minor stroke in Vietnam and also proposed risk factors for minor stroke management and treatment.
Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study.
The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage have not been well established among patients in Vietnam. 2885 consecutive diagnosed patients with ischemic stroke and intracerebral hemorrhage at ten stroke centres across Vietnam were involved in this prospective study. Posthoc analyses were performed in 2209 subjects (age was 65.4 ± 13.7 years, with 61.4% being male) to explore the clinical characteristics and prognostic factors associated with 90-day mortality following treatment. An explainable machine learning model using extreme gradient boosting and SHapley Additive exPlanations revealed the correlation between original clinical research and advanced machine learning methods in stroke care. In the 90 days following treatment, the mortality rate for ischemic stroke was 8.2%, while for intracerebral hemorrhage, it was higher at 20.5%. Atrial fibrillation was an elevated risk of 90-day mortality in the ischemic stroke patient (OR 3.09; 95% CI 1.90-5.02, p<0.001). Among patients with intracerebral hemorrhage, there was no statistical significance in those with hypertension compared to their counterparts without hypertension (OR 0.65, 95% CI 0.41-1.03, p > 0.05). The baseline NIHSS score was a significant predictor of 90-day mortality in both patient groups. The machine learning model can predict a 0.91 accuracy prediction of death rate after 90 days. Age and NIHSS score were in the top high risks with other features, such as consciousness, heart rate, and white blood cells. Stroke severity, as measured by the NIHSS, was identified as a predictor of mortality at discharge and the 90-day mark in both patient groups.
L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging.
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.
A survey of fatigue measures and models
In long, stressful operational periods, military personnel face numerous challenges that may compromise their performance, an especially important one being fatigue. Current literature supports the view that behavioral, physiological, and cognitive factors are all predictive of the level of fatigue in individuals. However, much of the work on modeling fatigue has taken a narrow approach, relying only on a handful of modalities to measure fatigue. This paper aims to fill the void by providing an extensive overview of the current literature on both computationally measuring and modeling fatigue. We provide up-to-date and practical advice on which models are best suited for different situations and highlight directions for future work.
An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using Gammatone and Continuous Wavelet transformation. This step aims to transform the respiratory sound input into a two-dimensional spectrogram where both spectral and temporal features are presented. Then, our proposed system integrates Inception-residual-based backbone models combined with multi-head attention and multi-objective loss to classify respiratory anomalies. Instead of applying a simple concatenation approach by combining results from various spectrograms, we propose a linear combination, which has the ability to regulate equally the contribution of each individual spectrogram throughout the training process. To evaluate the performance, we conducted experiments over the benchmark dataset of SPRSound (The Open-Source SJTU Paediatric Respiratory Sound) proposed by the IEEE BioCAS 2022 challenge. As regards the Score computed by an average between the average score and harmonic score, our proposed system gained significant improvements of 9.7%, 15.8%, 17.8%, and 16.1% in Task 1-1, Task 1-2, Task 2-1, and Task 2-2, respectively, compared to the challenge baseline system. Notably, we achieved the Top-1 performance in Task 2-1 and Task 2-2 with the highest Score of 74.5% and 53.9%, respectively.
A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuous Wavelet transformation. This transformation converts the respiratory sound input into a two-dimensional spectrogram where both spectral and temporal features are presented. Then, our proposed deep learning architecture inspired by the Inception-residual-based backbone performs the spatio-temporal-focusing and multi-head attention mechanism to classify respiratory anomalies. In this work, we evaluate our proposed models on the benchmark SPRSound (The Open-Source SJTU Paediatric Respiratory Sound) database proposed by the IEEE BioCAS 2023 challenge. As regards the Score computed by an average between the average score and harmonic score, our robust system has achieved Top-1 performance with Scores of 0.810, 0.667, 0.744, and 0.608 in Tasks 1-1, 1-2, 2-1, and 2-2, respectively.
Continuous measurement of arterial oxygenation in mechanically ventilated horses
Summary Background The possibility of accurately and continuously measuring arterial oxygen partial pressure (PaO 2 ) in horses may facilitate the management of hypoxaemia during general anaesthesia. Objectives The aim of this study was to evaluate the ability of a novel fibreoptic sensor to measure PaO 2 (PaO 2Sensor ) continuously and in real time in horses undergoing ventilatory manoeuvres during general anaesthesia. Study design In vivo experimental study. Methods Six adult healthy horses were anaesthetised and mechanically ventilated in dorsal recumbency. A fibreoptic sensor was placed in one of the facial arteries through a catheter to continuously measure and record PaO 2Sensor . After an alveolar recruitment manoeuvre, a decremental positive end‐expiratory pressure (PEEP) titration using 20‐minute steps of 5 cm H 2 O from 20 to 0 cm H 2 O was performed. An arterial blood sample was collected at 15 minutes of ventilation at each PEEP level for PaO 2 measurement using an automated blood gas machine (PaO 2Ref ). The agreement between PaO 2Sensor and PaO 2Ref was assessed by Pearson's correlation, Bland‐Altman plot and four‐quadrant plot analysis. In the last minute of ventilation at each PEEP level, a slow tidal inflation/deflation manoeuvre was performed. Results The mean relative bias between PaO 2Sensor and PaO 2Ref was 4% with limits of agreement between −17% and 29%. The correlation coefficient between PaO 2Sensor and PaO 2Ref was 0.98 ( P < .001). The PaO 2Sensor and PaO 2Ref concordance rate for changes was 95%. Measurements of PaO 2Sensor during the slow inflation/deflation manoeuvre at PEEP 15 and 10 cm H 2 O were not possible because of significant noise on the PaO 2 signal generated by a small blood clot. Main limitations Small sample size. Conclusion The tested fibreoptic probe was able to accurately and continuously measure PaO 2Sensor in anaesthetised horses undergoing ventilatory manoeuvres. A heparinised system in the catheter used by the fibreoptic sensor should be used to avoid blood clots and artefacts in the PaO 2 measurements.
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging.
Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. However, learning from multi-view inputs (e.g., both the raw signals and the time-frequency images) for sleep staging is difficult and not well understood. This work proposes a sequence-to-sequence sleep staging model, XSleepNet,1 that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. In simple terms, the learning on a particular view is speeded up when it is generalizing well and slowed down when it is overfitting. View-specific generalization/overfitting measures are computed on-the-fly during the training course and used to derive weights to blend the gradients from different views. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases.
Bedside monitoring of lung volume available for gas exchange
Abstract Background Bedside measurement of lung volume may provide guidance in the personalised setting of respiratory support, especially in patients with the acute respiratory distress syndrome at risk of ventilator-induced lung injury. We propose here a novel operator-independent technique, enabled by a fibre optic oxygen sensor, to quantify the lung volume available for gas exchange. We hypothesised that the continuous measurement of arterial partial pressure of oxygen (PaO2) decline during a breath-holding manoeuvre could be used to estimate lung volume in a single-compartment physiological model of the respiratory system. Methods Thirteen pigs with a saline lavage lung injury model and six control pigs were studied under general anaesthesia during mechanical ventilation. Lung volumes were measured by simultaneous PaO2 rate of decline (VPaO2) and whole-lung computed tomography scan (VCT) during apnoea at different positive end-expiratory and end-inspiratory pressures. Results A total of 146 volume measurements was completed (range 134 to 1869 mL). A linear correlation between VCT and VPaO2 was found both in control (slope = 0.9, R2 = 0.88) and in saline-lavaged pigs (slope = 0.64, R2 = 0.70). The bias from Bland–Altman analysis for the agreement between the VCT and VPaO2 was − 84 mL (limits of agreement ± 301 mL) in control and + 2 mL (LoA ± 406 mL) in saline-lavaged pigs. The concordance for changes in lung volume, quantified with polar plot analysis, was − 4º (LoA ± 19°) in control and − 9° (LoA ± 33°) in saline-lavaged pigs. Conclusion Bedside measurement of PaO2 rate of decline during apnoea is a potential approach for estimation of lung volume changes associated with different levels of airway pressure.