We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.

Background Atrial fibrillation (AF) is common and associated with adverse health outcomes. Timely detection of AF can be challenging using traditional diagnostic tools. Smartphone use is increasing and may provide an inexpensive and user-friendly means to diagnose AF. Objective To test the hypothesis that a smartphone-based application could detect an irregular pulse from AF. Methods 76 adults with persistent AF were consented for participation in our study. We obtained pulsatile time series recordings before and after cardioversion using an iPhone 4S camera. A novel smartphone application conducted real-time pulse analysis using 2 statistical methods [Root Mean Square of Successive RR Differences (RMSSD/mean); Shannon Entropy (ShE)]. We examined the sensitivity, specificity, and predictive accuracy of both algorithms using the 12-lead electrocardiogram as the gold standard. Results RMSDD/mean and ShE were higher in participants in AF compared with sinus rhythm. The 2 methods were inversely related to AF in regression models adjusting for key factors including heart rate and blood pressure (β coefficients per SD-increment in RMSDD/mean and ShE were −0.20 and −0.35; p<0.001). An algorithm combining the 2 statistical methods demonstrated excellent sensitivity (0.962), specificity (0.975), and accuracy (0.968) for beat-to-beat discrimination of an irregular pulse during AF from sinus rhythm. Conclusions In a prospectively recruited cohort of 76 participants undergoing cardioversion for AF, we found that a novel algorithm analyzing signals recorded using an iPhone 4S accurately distinguished pulse recordings during AF from sinus rhythm. Data are needed to explore the performance and acceptability of smartphone-based applications for AF detection.

A self-powered wearable electrocardiography (ECG) system is demonstrated. The ECG sensing circuit was fabricated on a flexible PCB and powered by a wearable thermoelectric generator (w-TEG) using body heat as the energy source. To allow the TEG to obtain a large temperature difference for high power generation and also be wearable, a polymer-based flexible heat sink (PHS) comprised of a superabsorbent polymer (SAP) and a fiber that promotes liquid evaporation was devised. Parametric studies on the PHS were conducted, and the structure of the w-TEG was also optimized for the PHS. The output power density from the w-TEG with the PHS was over 38 μW/cm2 for the first 10 min and over 13 μW/cm2 even after 22 consecutive hours of driving the circuits. This power level is high enough to continuously drive the wearable ECG system, including the sensors and the power management circuits.

Background Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Atrial fibrillation (AF) affects three to five million Americans and is associated with significant morbidity and mortality. Existing methods to diagnose this paroxysmal arrhythmia are cumbersome and/or expensive. We hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4S for AF detection, we first used two databases, the MIT-BIH AF and normal sinus rhythm (NSR) to derive discriminatory threshold values between two rhythms. Both databases include RR time series originating from 250 Hz sampled ECG recordings. We rescaled the RR time series to 30 Hz so that the RR time series resolution is 1/30 (s) which is equivalent to the resolution from an iPhone 4S. We investigated three statistical methods consisting of the root mean square of successive differences (RMSSD), the Shannon entropy (ShE) and the sample entropy (SampE), which have been proved to be useful tools for AF assessment. Using 64-beat segments from the MIT-BIH databases, we found the beat-to-beat accuracy value of 0.9405, 0.9300, and 0.9614 for RMSSD, ShE, and SampE, respectively. Using an iPhone 4S, we collected 2-min pulsatile time series from 25 prospectively recruited subjects with AF pre- and postelectrical cardioversion. Using derived threshold values of RMSSD, ShE and SampE from the MIT-BIH databases, we found the beat-to-beat accuracy of 0.9844, 0.8494, and 0.9522, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF in the data. Using this criterion, we achieved an accuracy of 100% for both the MIT-BIH AF and iPhone 4S databases.

The therapeutic potential of stem cells in chronic obstructive pulmonary disease is not well known although stem cell therapy is effective in models of other pulmonary diseases. We tested the capacities of bone marrow cells (BMCs), mesenchymal stem cells (MSCs), and conditioned media of MSCs (MSC-CM) to repair cigarette smoke-induced emphysema. Inbred female Lewis rats were exposed to cigarette smoke for 6 mo and then received BMCs, MSCs, or MSC-CM from male Lewis rats. For 2 mo after injection, the BMC treatment gradually alleviated the cigarette smoke-induced emphysema and restored the increased mean linear intercept. The BMC treatment significantly increased cell proliferation and the number of small pulmonary vessels, reduced apoptotic cell death, attenuated the mean pulmonary arterial pressure, and inhibited muscularization in small pulmonary vessels. However, only a few male donor cells were detected from 1 day to 1 mo after BMC administration. The MSCs and cell-free MSC-CM also induced the repair of emphysema and increased the number of small pulmonary vessels. Our data show that BMC, MSCs, and MSC-CM treatment repaired cigarette smoke-induced emphysema. The repair activity of these treatments is consistent with a paracrine effect rather than stem cell engraftment because most of the donor cells disappeared and because cell-free MSC-CM also induced the repair.

The present study investigated the antifatigue effects of Panax ginseng C.A. Meyer in 90 subjects (21 men and 69 women) with idiopathic chronic fatigue (ICF) in a randomised, double-blind, placebo-controlled and parallel designed trial. A bespoke 20% ethanol extract of P. ginseng (1 g or 2 g day–1) or a placebo was administered to each group for 4 weeks, and then fatigue severity was monitored using a self-rating numeric scale (NRS) and a visual analogue scale (VAS) as a primary endpoint. Serum levels of reactive oxygen species (ROS), malondialdehyde (MDA), total glutathione (GSH) contents and glutathione reductase (GSH-Rd) activity were determined. After 4-week, P. ginseng administration decreased the total NRS score, but they were not statistically significant compared with placebo (P>0.05). Mental NRS score was significantly improved by P. ginseng administrations as 20.4±5.0 to 15.1±6.5 [95% CI 2.3∼8.2] for 1 g and 20.7±6.3 to 13.8±6.2 [95% CI −0.1∼4.2] for 2 g compared with placebo 20.9±4.5 to 18.8±2.9 [95% CI 4.1∼9.9, P<0.01]. Only 2 g P. ginseng significantly reduced the VAS score from 7.3±1.3 to 4.4±1.8 [95% CI 0.7∼1.8] compared with the placebo 7.1±1.0 to 5.8±1.3 [95% CI 2.2 ∼3.7, P<0.01]. ROS and MDA levels were lowered by P. ginseng compared to placebo. P. ginseng 1 g increased GSH concentration and GSH-Rd activity. Our results provide the first evidence of the antifatigue effects of P. ginseng in patients with ICF, and we submit that these changes in antioxidant properties contribute in part to its mechanism.Trial RegistrationClinical Research Information Service (CRIS) KCT0000048

Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.