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

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

Deconvolution of whole blood transcriptomics identifies changes in immune cell composition in patients with systemic lupus erythematosus (SLE) treated with mycophenolate mofetil.

BackgroundSystemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use.MethodsPatients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past).ResultsPredicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains.ConclusionMMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics

Non-medical costs incurred by critically ill patients with dengue, sepsis and tetanus within a major referral hospital in Southern Vietnam: a cost of illness study

Introduction: Improving the knowledge of the costs of critical care is vital for informing health policy. However, cost data remain limited, particularly for low- and middle-income countries. The aim of this cross-sectional study is to describe the direct/indirect non-medical costs incurred by critically ill tetanus, sepsis and dengue patients and their families during their hospitalisation, using data from a major referral hospital in Vietnam. Methods: This study was conducted within the Hospital for Tropical Diseases in Ho Chi Minh City, a tertiary referral hospital specialising in infectious diseases serving Southern Vietnam. Patients who were admitted to the intensive care unit (ICU) and diagnosed with either tetanus, dengue or sepsis were enrolled between April and November 2022. In total, 94 patients (and their caregivers) were interviewed. Structured questionnaires were used to estimate the direct non-medical costs and indirect costs (costs related to productivity/time losses) incurred during their hospitalisation by the patients and their caregivers (ie, the patients’ perspective). Results: Overall, the estimated median total direct/indirect non-medical costs of the sample varied between US$511 and US$814 per patient, depending on the approach used to value the indirect costs. These total costs were broadly similar among sepsis and tetanus cases, but lower for dengue cases. The estimated indirect costs were highly sensitive to the approach used to monetise productivity losses and the valuation of informal care. Conclusion: This study demonstrates that patients admitted to the ICU with a severe infection of these diseases can incur notable direct/indirect non-medical costs. These results highlight the importance of further research in this area. These findings are particularly relevant in the context of universal health coverage targets, as even with 100% coverage of medical costs, many families are still likely to suffer financial hardship

Predicting deterioration in dengue using a low cost wearable for continuous clinical monitoring.

Close vital signs monitoring is crucial for the clinical management of patients with dengue. We investigated performance of a non-invasive wearable utilising photoplethysmography (PPG), to provide real-time risk prediction in hospitalised individuals. We performed a prospective observational clinical study in Vietnam between January 2020 and October 2022: 153 patients were included in analyses, providing 1353 h of PPG data. Using a multi-modal transformer approach, 10-min PPG waveform segments and basic clinical data (age, sex, clinical features on admission) were used as features to continuously forecast clinical state 2 h ahead. Prediction of low-risk states (17,939/80,843; 22.1%), defined by NEWS2 and mSOFA < 6, was associated with an area under the precision-recall curve of 0.67 and an area under the receiver operator curve of 0.83. Implementation of such interventions could provide cost-effective triage and clinical care in dengue, offering opportunities for safe ambulatory patient management

The A's, G's, C's, and T's of health disparities

In order to eliminate health disparities in the United States, more efforts are needed to address the breadth of social issues directly contributing to the healthy divide observed across racial and ethnic groups. Socioeconomic status, education, and the environment are intimately linked to health outcomes. However, with the tremendous advances in technology and increased investigation into human genetic variation, genomics is poised to play a valuable role in bolstering efforts to find new treatments and preventions for chronic conditions and diseases that disparately affect certain ethnic groups. Promising studies focused on understanding the genetic underpinnings of diseases such as prostate cancer or beta-blocker treatments for heart failure are illustrative of the positive contribution that genomics can have on improving minority health

Downregulation of PHEX in multibacillary leprosy patients: observational cross-sectional study

BACKGROUND: Peripheral nerve injury and bone lesions, well known leprosy complications, lead to deformities and incapacities. The phosphate-regulating gene with homologies to endopeptidase on the X chromosome (PHEX) encodes a homonymous protein (PHEX) implicated in bone metabolism. PHEX/PHEX alterations may result in bone and cartilage lesions. PHEX expression is downregulated by intracellular Mycobacterium leprae (M. leprae) in cultures of human Schwann cells and osteoblasts. M. leprae in vivo effect on PHEX/PHEX is not known. METHODS: Cross-sectional observational study of 36 leprosy patients (22 lepromatous and 14 borderline-tuberculoid) and 20 healthy volunteers (HV). The following tests were performed: PHEX flow cytometric analysis on blood mononuclear cells, cytokine production in culture supernatant, 25-hydroxyvitamin D (OHvitD) serum levels and (99m)Tc-MDP three-phase bone scintigraphy, radiography of upper and lower extremities and blood and urine biochemistry. RESULTS: Significantly lower PHEX expression levels were observed in lepromatous patients than in the other groups (χ(2) = 16.554, p < 0.001 for lymphocytes and χ(2) = 13.933, p = 0.001 for monocytes). Low levels of 25-(OHvitD) were observed in HV (median = 23.0 ng/mL) and BT patients (median = 27.5 ng/mL) and normal serum levels were found in LL patients (median = 38.6 ng/mL). Inflammatory cytokines, such as TNF, a PHEX transcription repressor, were lower after stimulation with M. leprae in peripheral blood mononuclear cells from lepromatous in comparison to BT patients and HV (χ(2) = 10.820, p < 0.001). CONCLUSION: Downregulation of PHEX may constitute an important early component of bone loss and joint damage in leprosy. The present results suggest a direct effect produced by M. leprae on the osteoarticular system that may use this mechanism. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-015-0651-5) contains supplementary material, which is available to authorized users

Improving classification of tetanus severity for patients in low-middle income countries wearing ECG sensors by using a CNN-transformer network

Tetanus is a life-threatening infectious disease, which is still common in low- and middle-income countries, including in Vietnam. This disease is characterized by muscle spasm and in severe cases is complicated by autonomic dysfunction. Ideally continuous vital sign monitoring using bedside monitors allows the prompt detection of the onset of autonomic nervous system dysfunction or avoiding rapid deterioration. Detection can be improved using heart rate variability analysis from ECG signals. Recently, characteristic ECG and heart rate variability features have been shown to be of value in classifying tetanus severity. However, conventional manual analysis of ECG is time-consuming. The traditional convolutional neural network (CNN) has limitations in extracting the global context information, due to its fixed-sized kernel filters. In this work, we propose a novel hybrid CNN-Transformer model to automatically classify tetanus severity using tetanus monitoring from low-cost wearable sensors. This model can capture the local features from the CNN and the global features from the Transformer. The time series imaging - spectrogram - is transformed from one-dimensional ECG signal and input to the proposed model. The CNN-Transformer model outperforms state-of-the-art methods in tetanus classification, achieves results with a F1 score of 0.82±0.03, precision of 0.94±0.03, recall of 0.73±0.07, specificity of 0.97±0.02, accuracy of 0.88±0.01 and AUC of 0.85±0.03. In addition, we found that Random Forest with enough manually selected features can be comparable with the proposed CNN-Transformer model