Sequence-dependent histone variant positioning signatures

Background: Nucleosome, the fundamental unit of chromatin, is formed by wrapping nearly 147bp of DNA around an octamer of histone proteins. This histone core has many variants that are different from each other by their biochemical compositions as well as biological functions. Although the deposition of histone variants onto chromatin has been implicated in many important biological processes, such as transcription and replication, themechanisms of how they are deposited on target sites are still obscure. Results: By analyzing genomic sequences of nucleosomes bearing different histone variants from human, including H2A.Z, H3.3 and both (H3.3/H2A.Z, so-called double variant histones), we found that genomic sequencecontributes in part to determining target sites for different histone variants. Moreover, dinucleotides CA/TG are remarkably important in distinguishing target sites of H2A.Z-only nucleosomes with those of H3.3-containing (both H3.3-only and double variant) nucleosomes. Conclusions: There exists a DNA-related mechanism regulating the deposition of different histone variants onto chromatin and biological outcomes thereof. This provides additional insights into epigenetic regulatory mechanisms of many important cellular processes

vital_sqi: A Python package for physiological signal quality control

Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs

Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer

Tetanus, a life-threatening bacterial infection prevalent in low- and middle-income countries like Vietnam, impacts the nervous system, causing muscle stiffness and spasms. Severe tetanus often involves dysfunction of the autonomic nervous system (ANS). Timely detection and effective ANS dysfunction management require continuous vital sign monitoring, traditionally performed using bedside monitors. However, wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative. While machine learning-based ECG analysis can aid in tetanus severity classification, existing methods are excessively time-consuming. Our previous studies have investigated the improvement of tetanus severity classification using ECG time series imaging. In this study, our aim is to explore an alternative method using ECG data without relying on time series imaging as an input, with the aim of achieving comparable or improved performance. To address this, we propose a novel approach using a 1D-Vision Transformer, a pioneering method for classifying tetanus severity by extracting crucial global information from 1D ECG signals. Compared to 1D-CNN, 2D-CNN, and 2D-CNN + Dual Attention, our model achieves better results, boasting an F1 score of 0.77 ± 0.06, precision of 0.70 ± 0. 09, recall of 0.89 ± 0.13, specificity of 0.78 ± 0.12, accuracy of 0.82 ± 0.06 and AUC of 0.84 ± 0.05

2D-WinSpatt-Net: a dual spatial self-attention Vision Transformer boosts classification of tetanus severity for patients wearing ECG sensors in low- and middle-income countries

Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging—continuous wavelet transforms—is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00

The potential of combining UAV and remote sensing in supporting precision mapping of irrigation systems for paddy land in urban agricultural areas: study case in the Hoa Vang district, Danang city, Central Vietnam

This research was carried out to test the potential of combining unmanned aerial vehicle (UAV) and remote sensing (RS) to support precision mapping of irrigation systems for paddy land. The study area is an urban/agricultural area of Central Vietnam. The Sentinel-2A imagery acquired on 30 June 2018 was interpreted according an object-based classification method aiming to map paddy land and irrigation systems for the Hoa Vang district; the total accuracy was 91.33% with a Kappa coefficient of 0.87. However, with the spatial resolution from the Sentinel-2A images (20 meters x 20 meters) it was difficult to classify paddy land and water from other objects within small and scattered parcel areas. This research was designed on five experimental flying zones, collecting 2,085 images by the UAV. With the very high spatial resolution data of the UAV, it was possible to clearly identify the boundaries of paddy land parcels, water sources such as rivers and lakes, and other objects such as canals and concrete irrigation systems. This classification derived from the orthogonal images from the five experimental zones using an object-based classification method, correcting the interpretation results of the Sentinel 2A images. Outcomes indicate that, the combination of UAV and RS can be applied to support precision mapping of irrigation systems for paddy land in urban agricultural areas.Nghiên cứu này được thực hiện nhằm thử nghiệm khả năng kết hợp giữa UAV với viễn thám trong hỗ trợ độ chính xác của bản đồ hệ thống nước tưới cho đất trồng lúa ở vùng nông nghiệp đô thị tại Miền trung Việt Nam. Ảnh viễn thám Sentinel- 2A thu nhận vào 30/6/2018 đã được giải đoán bằng phương pháp định hướng đối hướng để thành lập bản đồ hệ thống nguồn nước tưới cho huyện Hòa Vang vào năm 2018, với kết quả độ chính xác tổng số là 91,33% và hệ số kappa là 0,87. Mặc dù với kết quả giải đoán có độ chính xác cao nhưng với độ phân giải không gian của ảnh Sentinel-2A là 20m x 20m rất khó để phân loại được các vùng đất lúa có diện tích nhỏ và phân bố phân tán. Nghiên cứu này đã thiết kế 5 khu vực bay thử nghiệm với 2.085 ảnh để thu thập dữ liệu từ UAV. Có thể thấy rằng dữ liệu ảnh từ UAV với độ phân giải siêu cao có thể nhận diện và phân biệt được một cách rõ ràng không chỉ ranh giới của các thửa đất lúa, hệ thống nguồn nước như sông hồ, mà còn cả những đối tượng kênh mương thủy lợi nhỏ. Kết quả giải đoán các ảnh bay chụp bằng UAV sử dụng dụng phương pháp định hướng đối tượng, nghiên cứu này đã hiệu chỉnh được kết quả giải đoán ảnh Sentinel 2A. Kết quả cho thấy việc kết hợp dữ liệu viễn thám với UAV là hoàn toàn có khả năng sử dụng để hỗ trợ độ chính xác thành lập bản đồ hệ thống nguồn nước cho đất trồng lúa ở vùng nông nghiệp đô thị

Heart rate variability measured from wearable devices as a marker of disease severity in tetanus

Tetanus is a disease associated with significant morbidity and mortality. Heart rate variability (HRV) is an objective clinical marker with potential value in tetanus. This study aimed to investigate the use of wearable devices to collect HRV data and the relationship between HRV and tetanus severity. Data were collected from 110 patients admitted to the intensive care unit in a tertiary hospital in Vietnam. HRV indices were calculated from 5-minute segments of 24-hour electrocardiogram recordings collected using wearable devices. HRV was found to be inversely related to disease severity. The standard deviation of NN intervals and interquartile range of RR intervals (IRRR) were significantly associated with the presence of muscle spasms; low frequency (LF) and high frequency (HF) indices were significantly associated with severe respiratory compromise; and the standard deviation of differences between adjacent NN intervals, root mean square of successive differences between normal heartbeats, LF to HF ratio, total frequency power, and IRRR, were significantly associated with autonomic nervous system dysfunction. The findings support the potential value of HRV as a marker for tetanus severity, identifying specific indices associated with clinical severity thresholds. Data were recorded using wearable devices, demonstrating this approach in resource-limited settings where most tetanus occurs

An artificial intelligence-based approach to identify volume status in patients with severe dengue using wearable PPG data

Dengue shock syndrome (DSS) is a serious complication of dengue infection which occurs when critical plasma leakage results in haemodynamic shock. Treatment is challenging as fluid therapy must balance the risk of hypoperfusion with volume overload. In this study, we investigate the potential utility of wearable photoplethysmography (PPG) to determine volume status in DSS. In this prospective observational study, we enrolled 250 adults and children with a clinical diagnosis of dengue admitted to the Hospital for Tropical Diseases, Ho Chi Minh City. PPG monitoring using a wearable device was applied for a 24-hour period. Clinical events were then matched to the PPG data by date and time. We predefined two clinical states for comparison: (1) the 2-hour period before a shock event was an “empty” volume state and (2) the 2-hour period between 1 and 3 hours after a fluid initiation event was a “full” volume state. PPG data were sampled from these states for analysis. Variability and waveform morphology features were extracted and analyzed using principal components analysis and random forest. Waveform images were used to develop a computer vision model. Of the 250 patients enrolled, 90 patients experienced the predefined outcomes, and had sufficient data for the analysis. Principal components analysis identified four principal components (PCs), from the 23 pulse wave features. Logistic regression using these PCs showed that the empty state is associated with PCs 1 (p = 0.016) and 4 (p = 0.036) with both PCs denoting increased sympathetic activity. Random forest showed that heart rate and the LF-HF ratio are the most important features. A computer vision model had a sensitivity of 0.81 and a specificity of 0.70 for the empty state. These results provide proof of concept that an artificial intelligence-based approach using continuous PPG monitoring can provide information on volume states in DSS

Wearable devices for remote monitoring of hospitalized patients with COVID-19 in Vietnam

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments

Urinary catecholamine excretion, cardiovascular variability, and outcomes in tetanus

Severe tetanus is characterized by muscle spasm and cardiovascular system disturbance. The pathophysiology of muscle spasm is relatively well understood and involves inhibition of central inhibitory synapses by tetanus toxin. That of cardiovascular disturbance is less clear, but is believed to relate to disinhibition of the autonomic nervous system. The clinical syndrome of autonomic nervous system dysfunction (ANSD) seen in severe tetanus is characterized principally by changes in heart rate and blood pressure which have been linked to increased circulating catecholamines. Previous studies have described varying relationships between catecholamines and signs of ANSD in tetanus, but are limited by confounders and assays used. In this study, we aimed to perform detailed characterization of the relationship between catecholamines (adrenaline and noradrenaline), cardiovascular parameters (heart rate and blood pressure) and clinical outcomes (ANSD, mechanical ventilation required, and length of intensive care unit stay) in adults with tetanus, as well as examine whether intrathecal antitoxin administration affected subsequent catecholamine excretion. Noradrenaline and adrenaline were measured by ELISA from 24-h urine collections taken on day 5 of hospitalization in 272 patients enrolled in a 2 × 2 factorial-blinded randomized controlled trial in a Vietnamese hospital. Catecholamine results measured from 263 patients were available for analysis. After adjustment for potential confounders (i.e., age, sex, intervention treatment, and medications), there were indications of non-linear relationships between urinary catecholamines and heart rate. Adrenaline and noradrenaline were associated with subsequent development of ANSD, and length of ICU stay

The ability of a 3-gene host signature in blood to distinguish tuberculous meningitis from other brain infections

Background: Tuberculous meningitis (TBM) is difficult to diagnose. We investigated whether a 3-gene host response signature in blood can distinguish TBM from other brain infections. Methods: The expression of 3 genes (dual specificity phosphatase 3 [DUSP3], guanylate-binding protein [GBP5], krupple-like factor 2 [KLF2]) was analyzed by RNA sequencing of archived whole blood from 4 cohorts of Vietnamese adults: 281 with TBM, 279 with pulmonary tuberculosis, 50 with other brain infections, and 30 healthy controls. Tuberculosis scores (combined 3-gene expression) were calculated following published methodology and discriminatory performance compared using area under a receiver operator characteristic curve (AUC). Results: GBP5 was upregulated in TBM compared to other brain infections (P < .001), with no difference in DUSP3 and KLF2 expression. The diagnostic performance of GBP5 alone (AUC, 0.74; 95% confidence interval [CI], .67–.81) was slightly better than the 3-gene tuberculosis score (AUC, 0.66; 95% CI, .58–.73) in TBM. Both GBP5 expression and tuberculosis score were higher in participants with human immunodeficiency virus (HIV; P < .001), with good diagnostic performance of GBP5 alone (AUC, 0.86; 95% CI, .80–.93). Conclusions: The 3-gene host signature in whole blood has the ability to discriminate TBM from other brain infections, including in individuals with HIV. Validation in large prospective diagnostic study is now required