Search results
Found 13539 matches for
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
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.
The efficacy of a HUBER exercise system mediated sensorimotor training protocol on proprioceptive system, lumbar movement control and quality of life in patients with chronic non-specific low back pain.
BACKGROUND: There is a relation between deficits of the proprioceptive system and movement control dysfunction in patients with chronic low back pain (LBP) but, the exact mechanism of this relation is unknown. Exercise therapy has been recognized as an effective method for low back pain treatment. In spite of this, it is not clear which of the various exercise therapy programs lead to better results. OBJECTIVE: Therefore, the present analyze the efficacy of a HUBER study aims to exercise system mediated sensorimotor training protocol on proprioceptive system, lumbar movement control (LMC) and quality of life (QOL) in patients with chronic non-specific LBP. DESIGN: Quasi-experimental study. METHODS: 53 patients with chronic non-specific LBP (mean age 37.55 ± 6.67 years,and Body Mass Index (BMI) 22.4 ± 3.33) were selected by using Roland-Morris Disability Questionnaire (RMQ) and were assigned into two experimental (N= 27) and control groups (N= 26) The experimental group underwent a five-week (10 sessions) Sensorimotor training by using the Human Body Equalizer (HUBER) spine force under the supervision of an investigator. The movement control battery tests, the HUBER machine testing option, goniometer and visual analogue scale used for movement control, neuromuscular coordination, proprioception and LBP assessment respectively. The assessments were completed in pre-test and after five weeks. The paired and sample T tests were used for data analysis in SPSS program version 18 (Significance level were set at a P value < 0.05). RESULTS: The HUBER system mediated sensorimotor training demonstrated significant improvement in the proprioceptive system, LMC and QOL (P= 0.001). Also There was a significant reduction in the pain scores of subjects with chronic non-specific LBP in the sensorimotor group (P= 0.001). LIMITATIONS: In this study, only the short term effects of the sensorimotor training were examined. CONCLUSIONS: The results suggest that a sensorimotor training program causes significant improvement in patients with chronic non-specific LBP. Future research should be carried out with a larger sample size to examine the long term effects of the sensorimotor training program on treatment of patients with chronic non-specific LBP. Considering the efficacy of the sensorimotor training, it is recommended that this intervention should be applied to treatment of patients with chronic non-specific LBP in the future.
The Expansion of African Private Land Ownership in Isidenge, Stutterheim, Eastern Cape 1994–2024
A strong narrative persists in South Africa about the dim pace of redistribution of agricultural land since 1994. Government-published figures on the state-funded land reform programmes provide some basis for analysis; but no figures exist for land outside of such programmes. Figures based on different types of research vary. This article details evolving African private landownership in the Isidenge valley, near Stutterheim, Eastern Cape province. It is based on research in the Deeds Office supplemented by interviews. In an area of about 3700 ha, we find that most agricultural land transfers have been to black purchasers, largely without state assistance. We provide quantification of this evidence, explore profiles of purchasers, and outline their agricultural activities. We also discuss a residential site in the valley, Bongolwethu, and its impact on property relations. Most private purchasers find it difficult to operate profitably and generate income from a variety of sources. They are constrained by a lack of capital and some also by the difficulty to secure control over land. This case study provides a picture, not evident in much of the literature on land reform or rural society, of land transfers that have not been strictly reliant on the state.
Using routinely collected data to inform infection-prevention policy decisions
Measures to reduce transmission are a vital response to infectious disease epidemics. Collectively such measures are effective in reducing the burden of infectious disease but effectiveness of individual interventions is less certain. Methodologies for causal inference from observational data are well developed, but many methods have requirements that are not met by epidemic data. They may require an individual's outcome to be independent of anyone else's treatment, but the very purpose of infection-prevention measures is to break chains of transmission, benefiting both treated and untreated individuals. I combine causal inference methods, mechanistic models, and observational data to estimate effects of interventions that were used to reduce the spread of severe acute respiratory syndrome coronavirus 2 in the United Kingdom. I combine difference-in-differences methodology with a renewal-equation model. If its assumptions are met, this can detect effects of interventions on transmissibility, but if assumptions are violated, erroneous results can arise with no indication that an error is occurring. I apply the method to mass testing and mandatory use of face masks. Difference-in-differences results suggest that interventions increased incidence of detected infections. I investigate optimal timing of vaccination against respiratory viral infections with models incorporating immune boosting from re-exposure to the virus. Boosting can lead to synchrony in susceptibility and cause periodic outbreaks even without seasonal variation in infectiousness. In scenarios with more immune boosting, vaccinating sooner tends to lead to fewer infections, while in scenarios with less boosting, later vaccination is beneficial. Analyses in this thesis highlight potential problems with causal analyses that disregard mechanisms of disease transmission, and with models that oversimplify immunity. These analyses suggest that greater understanding of changing immunity over time is necessary to determine optimal approaches to reducing transmission of these respiratory viral infections.
Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.
BACKGROUND: Although statistical models have been commonly used to identify patients at risk of cardiovascular disease for preventive therapy, these models tend to over-recommend therapy. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all, as modelling in this context is currently inadequate. This study aimed to develop and validate the Transformer-based Risk assessment survival (TRisk) model, a novel deep learning model, for predicting 10-year risk of cardiovascular disease in both the primary prevention population and individuals with diabetes. METHODS: An open cohort of 3 million adults aged 25-84 years was identified using linked electronic health records from 291 general practices, for model development, and 98 general practices, for validation, across England from 1998 to 2015. Comparison against the QRISK3 score and a deep learning derivation of it was done. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status. FINDINGS: TRisk showed superior discrimination (C index in the primary prevention population 0·910; 95% CI 0·906-0·913). TRisk's performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in analyses stratified by age, sex, or socioeconomic status. All models were overall well calibrated. In decision curve analyses, TRisk showed a greater net benefit than benchmark models across the range of relevant thresholds. At the widely recommended 10% risk threshold and the higher 15% threshold, TRisk reduced both the total number of patients classified at high risk (by 20·6% and 34·6%, respectively) and the number of false negatives as compared with recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24·3% of individuals, with a small fraction of false negatives (0·2% of the cohort). INTERPRETATION: TRisk enabled a more targeted selection of individuals at risk of cardiovascular disease in both the primary prevention population and cohorts with diabetes, compared with benchmark approaches. Incorporation of TRisk into routine care could potentially reduce the number of treatment-eligible patients by approximately one-third while preventing at least as many events as with currently adopted approaches. FUNDING: None.
Determination of resilience of a panel of broadly neutralizing mAbs to emerging variants of SARS-CoV-2 generated using reverse genetics.
SARS-CoV-2 continues to evolve, and its emerging variants might escape the immune responses generated by existing vaccines and therapeutic mAbs. Accordingly, rapid analysis of their possible neutralization phenotype is essential and can be facilitated by reverse genetics (RGs) systems to regenerate viruses with variant-specific substitutions. Here, we efficiently generate a panel of recent variants of SARS-CoV-2 (Omicron XBB.1.16, EG.5.1, BA.2.86, and JN.1) using a substantially optimized circular polymerase extension reaction (CPER) RGs system. Neutralization potency was analyzed for mAbs targeting different regions of spike protein. mAbs P4-J15, C68.61, S2X259, and IY-2A IgG were able to neutralize all recent viruses. However, S309, which was previously used to treat infection and targets the outer face of RBD, showed ∼75-fold reduction in potency versus JN.1. Moreover, C68.59, which targets the SD1 region of the CTD, was unable to neutralize either BA.2.86 or JN1, which share the E554K substitution in SD1. CPER RGs system and microneutralization assays can be adopted as effective tools to evaluate the efficacy of therapeutic mAbs against emerging variants in a time-responsive manner.
Exercise Improves Myocardial Deformation But Not Cardiac Structure in Preterm-Born Adults: A Randomized Clinical Trial.
BACKGROUND: People born preterm (<37 weeks' gestation) have a potentially adverse cardiac phenotype that progresses with blood pressure elevation. OBJECTIVES: The authors investigated whether preterm-born and term-born adults exhibit similar cardiac structural and functional remodeling following a 16-week aerobic exercise intervention. METHODS: We conducted a randomized controlled trial in 203 adults (aged 18-35 years) with elevated blood pressure or stage 1 hypertension. Participants were randomized 1:1 to a 16-week aerobic exercise intervention or to a control group. In a prespecified cardiovascular magnetic resonance imaging (CMR) substudy, CMR was performed at 3.0-Tesla to assess left and right ventricular (LV and RV) structure and function before and after intervention. RESULTS: A total of 100 participants completed CMR scans at baseline and after the 16-week intervention, with n = 47 in the exercise intervention group (n = 26 term-born; n = 21 preterm-born) and n = 53 controls (n = 32 term-born; n = 21 preterm-born). In term-born participants, LV mass to end-diastolic volume ratio decreased (-3.43; 95% CI: -6.29 to -0.56; interaction P = 0.027) and RV stroke volume index increased (5.53 mL/m2; 95% CI: 2.60, 8.47; interaction P = 0.076) for those in the exercise intervention group vs controls. No significant effects were observed for cardiac structural indices in preterm-born participants. In preterm-born participants, LV basal- and mid-ventricular circumferential strain increased (-1.33; 95% CI: -2.07 to -0.60; interaction P = 0.057 and -1.54; 95% CI: -2.46 to -0.63; interaction P = 0.046, respectively) and RV global longitudinal strain increased (1.99%; 95% CI: -3.12 to -0.87; interaction P = 0.053) in the exercise intervention group vs controls. No significant effects were observed for myocardial deformation parameters in term-born participants. CONCLUSIONS: Aerobic exercise training induces improved myocardial function but not cardiac structure in preterm-born adults.
Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions.
Health research requires high-quality data, and population-based health research comes with specific opportunities and challenges for data collection. Electronic data capture can mitigate some of the challenges of working with large populations in multiple, sometimes difficult-to-reach, locations. This viewpoint paper aims to describe experiences during the implementation of two mixed methods studies in Vietnam, Nepal, and Indonesia, focusing on understanding lived experiences of the COVID-19 pandemic across 3 countries and understanding knowledge and behaviors related to antibiotic use in Vietnam. We present the opportunities, challenges, and solutions arising through using Research Electronic Data Capture (REDCap) for designing, collecting, and managing data. Electronic data capture using REDCap made it possible to collect data from large populations in different settings. Challenges related to working in multiple languages, unstable internet connections, and complex questionnaires with nested forms. Some data collectors lacked the digital skills to comfortably use REDCap. To overcome these challenges, we included regular team meetings, training, supervision, and automated error-checking procedures. The main types of errors that remained were incomplete and duplicate records due to disruption during data collection. However, with immediate access to data, we were able to identify and troubleshoot these problems quickly, while data collection was still in progress. By detailing our lessons learned-such as the importance of iterative testing, regular intersite meetings, and customized modifications-we provide a roadmap for future projects to boost productivity, enhance data quality, and effectively conduct large-scale population-based research. Our suggestions will be beneficial for research teams working with electronic data capture for population-based data.
Deep Learning-based Modelling of Complex Hypertensive Multi-Organ Damage with Uncertainty Quantification from Simple Clinical Measures
Hypertension is a leading risk factor for a number of diseases and can cause severe damage to the vital organs such as the brain and heart. However, the level of hypertension itself does not necessarily reflect the full extent of underlying end-organ changes, which may hinder the development of effective treatment strategies. While recent research has demonstrated that these end-organ changes can be measured with deep phenotyping, its clinical translation may not be feasible. In this study, we propose a state-of-art deep learning approach that can quantify multi-organ (e.g., heart, brain, vasculature) phenotypical changes due to persistent hypertension from simple and popular clinical measures such as electrocardiogram (ECG), routinely acquired clinical data (age, BMI, diastolic and systolic blood pressures), and cardiac short axis (SAX) images from the UK Biobank, one of the largest open-access biomedical databases. Our proposed approach captures the intricate patterns of hypertensive disease state without resorting to the complex measures, which is hard to obtain in practical settings. It generates a numeric score between 0 and 1 of multi-organ damage, as well as provides an estimate of the overall uncertainty. The performance of our models is evaluated in different experimental settings and compared against the reference model. The results consistently demonstrate that the proposed approach can effectively model the multi-organ phenotypical changes from simple clinical measures with high performance (best-performing model MAE=0.108, MSE=0.019, variance=0.0005), and underscores its feasibility for potential clinical use.