Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. METHODS: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. FINDINGS: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. INTERPRETATION: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. FUNDING: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

Magnitude, Patterns, and Associated Predictors of Cardiovascular Events in Tetanus: A 2-Year, Single-Center, Ambidirectional Cohort Study Involving 572 Patients.

Background: Cardiovascular events (CEs) remain the leading cause of death in patients with tetanus. We examined the incidence, patterns, and associated predictors of CEs among patients with tetanus in Vietnam. Methods: An ambidirectional cohort study was conducted on hospitalized adult patients with tetanus at the Hospital for Tropical Diseases between 2019 and 2020. Information on demographics, tetanus disease, CEs and outcomes were collected. Results: Among all 572 included patients, CEs accounted for 10.8% (95%CI 8.6-13.7%) and included Takotsubo cardiomyopathy (40.3%, 95%CI 29.0-52.8%), arrhythmia (19.4%, 95%CI 11.4-30.9%), sudden cardiac arrest (16.1%, 95%CI 9.0-27.2%), myocardial infarction (11.3%, 95%CI 5.6-21.5%), heart failure (6.5%, 95%CI 2.5-15.4%) and pulmonary embolism (6.5%, 95%CI 2.5-15.4%). CEs occurred from day 5 to 20 of illness. Among 62 CE patients, 21% (95%CI 12.7-32.6%) died and 61.3% (95%CI 48.9-72.4%) developed autonomic nervous system dysfunction (ANSD). Three-fourths (24/32) of patients with Takotsubo cardiomyopathy or myocardial infarction had ANSD. CEs were significantly associated with modified Ablett scores (AOR = 2.42, 95%CI 1.1-5.6, P = .04), underlying diseases (AOR = 2.7, 95%CI 1.1-6.8, P = .04) and overweight (AOR = 0.18, 95%CI .04-.8, P = .02). Conclusions: CEs are not rare and associated with high mortality. The most common CE is Takotsubo cardiomyopathy. CEs can occur at any stage of illness, with or without ANSD. To prevent mortality, it is pivotal to screen CEs in patients with tetanus, especially those with underlying diseases, high modified Ablett scores, and a normal or low BMI. More studies are needed to fully elucidate the impact of ANSD on the cardiovascular function and the CE associated mortality in tetanus.

Anti-cancer activity of green synthesized silver nanoparticles using Ardisia gigantifolia leaf extract against gastric cancer cells.

Using extracts from herbs for silver nanoparticle synthesis is attracting attention for its anticancer activity. Ardisia gigantifolia is a herb used in traditional Chinese medicine for treating stomach ailments, and some compounds isolated from this plant exhibit the inhibitory activity against different cancer cells. However, the synthesis of silver nanoparticle using extract of Ardisia gigantiflia leaves and their anti-cancer activity was not reported. In this report, the green synthesized silver nanoparticles using Ardisia gigantiflia extract (Arg-AgNPs) has average diameter of 6 nm with functional groups including O-H, C-H, and CO founded on the surface of these nanoparticles. The viability assays results revealed Arg-AgNPs reduced gastric cancer cell proliferation in a dose-dependent manner, with IC50 values of 1.37 and 0.65 µg/mL for AGS cells and 1.03 and 0.96 µg/mL for MKN45 cells. Arg-AgNPs caused cell cycle arrest at the G0/G1 phase and suppressed cell migration. Additionally, Arg-AgNPs significantly increased the percentage of senescent cells and promoted overproduction of reactive oxygen species (ROS) compared to the control. Thus, this study indicates that Arg-AgNPs can be considered as a promising candidate against human gastric cancer cells.

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.

Correlation between clinical characters, lymphocyte and CD4+ lymphocyte quantity in peripheral blood of HIV patients

Investigation sensitivity and specific, predicted nagative and positive value of estimation CD4 cell acording to lymphocyte in peripheral blood. Objects were ≥ 15 of ages, non-pregnant, in and out-treatment in Tropical Hospital from 1-12/2003 patients who had positive HIV serum according to strategy of Ministry of Health. The patients were tested to measure CD4 cell and total lymphocyte. In HIV/AIDS patients, reducing CD4 related to opportunistic infection. 91,7% hospitalized patients due to opportunistic infection have CD4 counts under 200/mm3. When CD4 counts was under 200mm3, 44.3% patients have over one of opportunistic infection. Relationship between lymphocyte in peripheral blood and CD4 quantity was significant. However, interrelate amount reduced as divide according to lymphocte. Estimation CD4200 according to lymphocyte was more accurate with lower of lymphocyte count. But, there are many cases with high blood lymphocyte but the number of CD4 under 200mm3.