Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 7 de 7
Filter
1.
J Biosaf Biosecur ; 3(1): 51-55, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1284239

ABSTRACT

The origin of SARS-CoV-2 is still an unresolved mystery. In this study, we systematically reviewed the main research progress of wild animals carrying virus highly homologous to SARS-CoV-2 and analyzed the natural foci characteristics of SARS-CoV-2. The complexity of SARS-CoV-2 origin in wild animals and the possibility of SARS-CoV-2 long-term existence in human populations are also discussed. The joint investigation of corona virus carried by wildlife, as well as the ecology and patho-ecology of bats and other wildlife, are key measures to further clarify the characteristics of natural foci of SARS-CoV-2 and actively defend against future outbreaks of emerging zoonotic diseases.

2.
Sci Adv ; 7(20)2021 05.
Article in English | MEDLINE | ID: covidwho-1226704

ABSTRACT

Soft, skin-integrated electronic sensors can provide continuous measurements of diverse physiological parameters, with broad relevance to the future of human health care. Motion artifacts can, however, corrupt the recorded signals, particularly those associated with mechanical signatures of cardiopulmonary processes. Design strategies introduced here address this limitation through differential operation of a matched, time-synchronized pair of high-bandwidth accelerometers located on parts of the anatomy that exhibit strong spatial gradients in motion characteristics. When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.


Subject(s)
Accelerometry/instrumentation , Accelerometry/methods , Electrocardiography, Ambulatory/instrumentation , Electrocardiography, Ambulatory/methods , Wearable Electronic Devices , Body Temperature , COVID-19 , Exercise/physiology , Heart Rate , Humans , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , SARS-CoV-2
3.
Proc Natl Acad Sci U S A ; 118(19)2021 05 11.
Article in English | MEDLINE | ID: covidwho-1203480

ABSTRACT

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.


Subject(s)
COVID-19/physiopathology , Heart Rate , Respiratory Rate , Respiratory Sounds , SARS-CoV-2 , Wireless Technology , Biomarkers , Humans , Monitoring, Physiologic
4.
IEEE J Transl Eng Health Med ; 9: 4900311, 2021.
Article in English | MEDLINE | ID: covidwho-1189590

ABSTRACT

OBJECTIVE: Controlling the spread of the COVID-19 pandemic largely depends on scaling up the testing infrastructure for identifying infected individuals. Consumer-grade wearables may present a solution to detect the presence of infections in the population, but the current paradigm requires collecting physiological data continuously and for long periods of time on each individual, which poses limitations in the context of rapid screening. Technology: Here, we propose a novel paradigm based on recording the physiological responses elicited by a short (~2 minutes) sequence of activities (i.e. "snapshot"), to detect symptoms associated with COVID-19. We employed a novel body-conforming soft wearable sensor placed on the suprasternal notch to capture data on physical activity, cardio-respiratory function, and cough sounds. RESULTS: We performed a pilot study in a cohort of individuals (n=14) who tested positive for COVID-19 and detected altered heart rate, respiration rate and heart rate variability, relative to a group of healthy individuals (n=14) with no known exposure. Logistic regression classifiers were trained on individual and combined sets of physiological features (heartbeat and respiration dynamics, walking cadence, and cough frequency spectrum) at discriminating COVID-positive participants from the healthy group. Combining features yielded an AUC of 0.94 (95% CI=[0.92, 0.96]) using a leave-one-subject-out cross validation scheme. Conclusions and Clinical Impact: These results, although preliminary, suggest that a sensor-based snapshot paradigm may be a promising approach for non-invasive and repeatable testing to alert individuals that need further screening.


Subject(s)
COVID-19/physiopathology , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/methods , Adult , Aged , Area Under Curve , COVID-19/diagnosis , Case-Control Studies , Cough/diagnosis , Exercise , Female , Heart Rate , Humans , Male , Middle Aged , Pilot Projects , Quarantine , Walking , Wearable Electronic Devices
5.
Preprint | SSRN | ID: ppcovidwho-5719

ABSTRACT

Background: There had been a preliminary occurrence of human-to-human transmissions between healthcare workers (HCWs), but risk factors in the susceptibility

6.
Sleep Med X ; 2: 100028, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-857168

ABSTRACT

Background: Healthcare workers (HCWs) are at the forefront of fighting against the COVID-19 pandemic. However, they are at high risk of acquiring the pathogen from infected patients and transmitting to other HCWs. We aimed to investigate risk factors for nosocomial COVID-19 infection among HCWs in a non-COVID-19 hospital yard. Methods: Retrospective data collection on demographics, lifestyles, contact status with infected subjects for 118 HCWs (including 12 COVID-19 HCWs) at Union Hospital of Wuhan, China. Sleep quality and working pressure were evaluated by the Pittsburgh Sleep Quality Index (PSQI) and The Nurse Stress Index (NSI), respectively. The follow-up duration was from Dec 25, 2019, to Feb 15, 2020. Results: A high proportion of COVID-19 HCWs had engaged in night shift-work (75.0% vs. 40.6%) and felt working under pressure (66.7% vs. 32.1%) than uninfected HCWs. SARS-CoV-2 infected HCWs had significantly higher scores of PSQI and NSI than uninfected HCWs (P < 0.001). Specifically, scores of 5 factors (sleep quality, sleep time, sleep efficiency, sleep disorder, and daytime dysfunction) in PSQI were higher among infected HCWs. For NSI, its 5 subscales (nursing profession and work, workload and time allocation, working environment and resources, patient care, management and interpersonal relations) were all higher in infected than uninfected nurse. Furthermore, total scores of PSQI (HR = 2.97, 95%CI = 1.86-4.76; P <0.001) and NSI (HR = 4.67, 95%CI = 1.42-15.45; P = 0.011) were both positively associated with the risk of SARS-CoV-2 infection. Conclusion: Our analysis shows that poor sleep quality and higher working pressure may increase the risk of nosocomial SARS-CoV-2 infection among HCWs.

SELECTION OF CITATIONS
SEARCH DETAIL
...