As of April 15, 2020, the US Food and Drug Administration has granted emergency use authorization to a first saliva test for diagnosis of severe acute respiratory syndrome coronavirus 2 infection, the device developed by RUCDR Infinite Biologics laboratory, Rutgers University. A key feature that distinguishes the saliva-based test from nasopharyngeal or oropharyngeal (throat) swabs is that this kit allows self-collection and can spare healthcare professionals to be at risk during collecting nasopharyngeal or oropharyngeal samples, thereby preserving personal protective equipment for use in patient care rather than sampling and testing. Consequently, broader testing than the current methods of nasal or throat swabs will significantly increase the number of people screening, leading to more effective control of the spread of COVID-19. Nonetheless, a comparison of saliva-based assay with current swab test is needed to understand what and how we can benefit from this newly developed assay. Therefore, in this mini-review article, we aimed to summarize the current and emerging tools, focusing on diagnostic power of different clinical sampling and specimens.

Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio ( R 0 ) was calculated with the maximum likelihood “removal” method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R 0 was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R 0 and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R 0 , while nitrogen dioxide (NO 2 ), 24-h average concentration of nitrogen dioxide (NO 2 _24 h) and daily maximum temperature were inversely correlated to R 0 , with coefficients of 0.644, 0.661, −0.636, −0.657, −0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R 0 , including mean/maximum/minimum air pressure and mean wind speed, with coefficients of −0.697, −0.697, −0.697 and −0.841, respectively. There was no correlation with R 0 and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.

Coronavirus disease 2019 (COVID-19) can lead to serious illness and death, and thus, it is particularly important to predict the severity and prognosis of COVID-19. The Sequential Organ Failure Assessment (SOFA) score has been used to predict the clinical outcomes of patients with multiple organ failure requiring intensive care. Therefore, we retrospectively analyzed the clinical characteristics, risk factors, and relationship between the SOFA score and the prognosis of COVID-19 patients. We retrospectively included all patients ≥18 years old who were diagnosed with COVID-19 in the laboratory continuously admitted to Jingzhou Central Hospital from January 16, 2020 to March 23, 2020. The demographic, clinical manifestations, complications, laboratory results, and clinical outcomes of patients infected with the severe acute respiratory syndrome coronavirus-2 were collected and analyzed. Clinical variables were compared between patients with mild and severe COVID-19. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for severe COVID-19. The Cox proportional hazards model was used to analyze risk factors for hospital-related death. Survival analysis was performed by the Kaplan–Meier method, and survival differences were assessed by the log-rank test. Receiver operating characteristic (ROC) curves of the SOFA score in different situations were drawn, and the area under the ROC curve was calculated. A total of 117 patients with confirmed diagnoses of COVID-19 were retrospectively analyzed, of which 108 patients were discharged and 9 patients died. The median age of the patients was 50.0 years old (interquartile range [IQR], 35.5–62.0). 63 patients had comorbidities, of which hypertension (27.4%) was the most frequent comorbidities, followed by diabetes (8.5%), stroke (4.3%), coronary heart disease (3.4%), and chronic liver disease (3.4%). The most common symptoms upon admission were fever (82.9%) and dry cough (70.1%). Regression analysis showed that high SOFA scores, advanced age, and hypertension were associated with severe COVID-19. The median SOFA score of all patients was 2 (IQR, 1–3). Patients with severe COVID-19 exhibited a significantly higher SOFA score than patients with mild COVID-19 (3 [IQR, 2–4] vs 1 [IQR, 0–1]; P < .001). The SOFA score can better identify severe COVID-19, with an odds ratio of 5.851 (95% CI: 3.044–11.245; P < .001). The area under the ROC curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score in predicting severe COVID-19 (cutoff value = 2; AUC = 0.908 [95% CI: 0.857–0.960]; sensitivity: 85.20%; specificity: 80.40%) and the risk of death in COVID-19 patients (cutoff value = 5; AUC = 0.995 [95% CI: 0.985–1.000]; sensitivity: 100.00%; specificity: 95.40%). Regarding the 60-day mortality rates of patients in the 2 groups classified by the optimal cutoff value of the SOFA score (5), patients in the high SOFA score g...

Background Internet hospitals in China are being rapidly developed as an innovative approach to providing health services. The ongoing COVID-19 pandemic has triggered the development of internet hospitals that promote outpatient service delivery to the public via internet technologies. To date, no studies have assessed China's internet hospitals during the COVID-19 pandemic. Objective This study aimed to elucidate the characteristics of China's internet hospitals and assess the health service capacity of these hospitals. Methods Data on 711 internet hospitals were collected from official websites, the WeChat (Tencent Inc) platform, smartphone apps, and the Baidu search engine until July 16, 2020. Results As of July 16, 2020, 711 internet hospitals were developed in mainland China. More than half of these internet hospitals (421/711, 59.2%) were established during 2019 (206/711, 29%) and 2020 (215/711, 30.2%). Furthermore, about one-third (215/711, 30.2%) of internet hospitals were established at the beginning of 2020 as an emergency response to the COVID-19 epidemic. The 711 internet hospitals consisted of the following 3 types of hospitals: government-oriented (42/711, 5.91%), hospital-oriented (143/711, 20.11%), and enterprise-oriented internet hospitals (526/711, 73.98%). The vast majority of internet hospitals were traditional hospitals (526/711, 74%). Nearly 46.1% (221/711) of internet hospitals requested doctors to provide health services at a specific web clinic. Most patients (224/639, 35.1%) accessed outpatient services via WeChat. Internet hospitals’ consulting methods included SMS text messaging consultations involving the use of graphics (552/570, 96.8%), video consultations (248/570, 43.5%), and telephone consultations (238/570, 41.8%). The median number of available web-based doctors was 43, and the median consultation fees of fever clinics and other outpatient clinics were ¥0 (US $0) per consultation and ¥6 (US $0.93) per consultation, respectively. Internet hospitals have provided various services during the COVID-19 pandemic, including medical prescription, drug delivery, and medical insurance services. Conclusions The dramatic increase of internet hospitals in China has played an important role in the prevention and control of COVID-19. Internet hospitals provide different and convenient medical services for people in need.

As coronavirus disease 2019 (COVID-19) continues to spread around the world, the establishment of decentralized severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) diagnostics and point-of-care testing is invaluable. While polymerase chain reaction (PCR) has been the gold standard for COVID-19 screening, serological assays detecting anti-SARS-CoV-2 antibodies in response to past and/or current infection remain vital tools. In particular, lateral flow immunoassay devices are easy to produce, scale, distribute, and use; however, they are unable to provide quantitative information. To enable quantitative analysis of lateral flow immunoassay device results, microgating technology was used to develop an innovative spectrochip that can be integrated into a portable, palm-sized device that was capable of capturing high-resolution reflectance spectrum data for quantitative immunoassay diagnostics. Using predefined spiked concentrations of recombinant anti-SARS-CoV-2 immunoglobulin G (IgG), this spectrochip-coupled immunoassay provided extraordinary sensitivity, with a detection limit as low as 186 pg/mL. Furthermore, this platform enabled the detection of anti-SARS-CoV-2 IgG in all PCR-confirmed patients as early as day 3 after symptom onset, including two patients whose spectrochip tests would be regarded as negative for COVID-19 using a direct visual read-out without spectral analysis. Therefore, the quantitative lateral flow immunoassay with an exceptionally low detection limit for SARS-CoV-2 is of value. An increase in the number of patients tested with this novel device may reveal its true clinical potential.

Background Patients with Coronavirus disease 2019 (COVID-19) have a high mortality rate, and thus, it is particularly important to predict the severity and prognosis of COVID-19. The Sequential Organ Failure Assessment (SOFA) score has been used to predict the clinical outcomes of patients with multiple organ failure requiring intensive care. Therefore, we retrospectively analyzed the clinical characteristics, risk factors, and relationship between the SOFA score and the prognosis of COVID-19 patients. Methods Clinical variables were compared between patients with mild and severe COVID-19. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for severe COVID-19. The Cox proportional hazards model was used to analyze risk factors for hospital-related death. Survival analysis was performed by the Kaplan-Meier method, and survival differences were assessed by the log-rank test. Receiver operating characteristic (ROC) curves of the SOFA score in different situations were drawn, and the area under the ROC curve was calculated. Results The median SOFA score of all patients was 2 (IQR, 1–3). Patients with severe COVID-19 exhibited a significantly higher SOFA score than patients with mild COVID-19 [3 (IQR, 2–4) vs 1 (IQR, 0–1); P < 0.001]. The SOFA score increased the risk of severe COVID-19, with an odds ratio of 5.851 (95% CI: 3.044–11.245; P < 0.001). The area under the ROC curve (AUC) was used to evaluate the diagnostic accuracy of the SOFA score in predicting severe COVID-19 [cutoff value = 2; AUC = 0.908 (95% CI: 0.857–0.960); sensitivity: 85.20%; specificity: 80.40%] and the risk of death in COVID-19 patients [cutoff value = 5; AUC = 0.995 (95% CI: 0.985-1.000); sensitivity: 100.00%; specificity: 95.40%]. Regarding the 60-day mortality rates of patients in the two groups classified by the optimal cutoff value of the SOFA score (5), patients in the high SOFA score group (SOFA score ≥ 5) had a significantly greater risk of death than those in the low SOFA score group (SOFA score < 5). Conclusion The SOFA score could be used to evaluate the severity and 60-day mortality of COVID-19. The SOFA score may be an independent risk factor for in-hospital death.

Engineering has been playing an important role in serving and advancing healthcare. The term "Healthcare Engineering" has been used by professional societies, universities, scientific authors, and the healthcare industry for decades. However, the definition of "Healthcare Engineering" remains ambiguous. The purpose of this position paper is to present a definition of Healthcare Engineering as an academic discipline, an area of research, a field of specialty, and a profession. Healthcare Engineering is defined in terms of what it is, who performs it, where it is performed, and how it is performed, including its purpose, scope, topics, synergy, education/training, contributions, and prospects.Keywords: Healthcare engineering, definition, purpose, scope, topics, synergy, jobs, education, training, contributions, future PREAMBLEEngineering has been playing a crucial role in serving healthcare, bringing about revolutionary advances in healthcare. Contributions have been made by engineers from almost all engineering disciplines, such as Biomedical, Chemical, Civil, Computer, Electrical, Environmental, Industrial, Information, Materials, Mechanical, Software, and Systems Engineering, as well as healthcare professionals such as physicians, dentists, nurses, pharmacists, allied health professionals, and health scientists who are engaged in supporting, improving, and/or advancing any aspect of healthcare through engineering approaches. "Healthcare Engineering" is the most appropriate term to encompass such a multi-disciplinary specialty, considering that advancing healthcare is the common goal for all such efforts made through engineering approaches. However, so far, a clear, rigorous definition of "Healthcare Engineering" has never been documented.Established over 50 years ago, the American Society of Healthcare Engineering (ASHE) [1] was one of the first to publicize the term "Healthcare Engineering". ASHE, as well as its many local affiliate societies (e.g., [2]), has been mainly dedicated to the health care physical environment, which represents only one sector of what engineers do in healthcare. David and Goodman [3] first used the term "healthcare engineers" in the scientific literature in 1989, where the critical role of the engineer in the healthcare delivery system was discussed. A number of academic programs have adopted the term "Healthcare Engineering" in their names (e.g., [4][5][6][7][8][9][10][11][12][13]). However, the description/definition of "Healthcare Engineering" by these programs varies, as each institution has designed its program based on its own distinctive interest, strength, focus, and emphasis, and hence created a different description/definition accordingly. Each of these versions of description/definition excellently portrays a certain facet of Healthcare Engineering, though none reflects all dimensions of the discipline. Further, the Journal of Healthcare Engineering [14], launched in 2010, focuses on engineering involved in all aspects of healthcare delivery processes and systems....

Objectives The outbreak of the 2019 novel coronaviruses disease (COVID-2019) in areas with epidemics due to imported cases is a cause of concern in China; however, few studies have reported on the prevalence of COVID-19 in these areas. Methods The number of diagnosed cases in Fujian Province was collected, and the time distribution of these cases was analyzed. Results The results showed that the COVID-19 prevalence in areas with epidemics due to imported cases could be divided into two stages. The first stage was an outbreak dominated by imported cases, with the data showing an obviously skewed distribution. The second stage was dominated by nonimported cases with sporadic and low-level fluctuations. Moreover, the data demonstrated that the ratio of unexplained infections to nonimported cases was increasing. Conclusions A two-stage outbreak in areas with epidemics due to imported cases, effective control of the ''source of infection'' and blocking of the transmission route can significantly minimize the peak height in the first stage and the spread of the epidemic in the second stage. Control of the epidemic in the second stage requires prevention and control of the aggregation of cases caused by unexplained infections.