A Silicon-Based PDMS-PEG Copolymer Microfluidic Chip for Real-Time Polymerase Chain Reaction Diagnosis.

Polydimethylsiloxane (PDMS) has been widely used to make lab-on-a-chip devices, such as reactors and sensors, for biological research. Real-time nucleic acid testing is one of the main applications of PDMS microfluidic chips due to their high biocompatibility and transparency. However, the inherent hydrophobicity and excessive gas permeability of PDMS hinder its applications in many fields. This study developed a silicon-based polydimethylsiloxane-polyethylene-glycol (PDMS-PEG) copolymer microfluidic chip, the PDMS-PEG copolymer silicon chip (PPc-Si chip), for biomolecular diagnosis. By adjusting the modifier formula for PDMS, the hydrophilic switch occurred within 15 s after contact with water, resulting in only a 0.8% reduction in transmittance after modification. In addition, we evaluated the transmittance at a wide range of wavelengths from 200 nm to 1000 nm to provide a reference for its optical property study and application in optical-related devices. The improved hydrophilicity was achieved by introducing a large number of hydroxyl groups, which also resulted in excellent bonding strength of PPc-Si chips. The bonding condition was easy to achieve and time-saving. Real-time PCR tests were successfully conducted with higher efficiency and lower non-specific absorption. This chip has a high potential for a wide range of applications in point-of-care tests (POCT) and rapid disease diagnosis.

Does human resource system strength help employees act proactively? The roles of crisis strength and work engagement

Many empirical studies have elucidated the antecedents and psychological mechanisms of employees' proactive behaviors. However, there is limited knowledge about how a human resource (HR) system helps employees proactively adjust to their changing work environment. Drawing on social exchange theory and event system theory, we developed a theoretical model to examine whether, how, and when perceptions of the HR system strength impact employee proactive behavior during crises. Results from a three‐wave time‐lagged survey of 305 employees in 65 teams in eight Chinese companies indicate that HR system strength creates a strong situation by alleviating employees' uncertainty about how to behave during crises, which stimulates employees' work engagement and subsequent proactive behaviors. Moreover, employees' perceptions of HR system strength are more likely to influence work engagement when employees perceive the COVID‐19 crisis as more severe. We discuss the theoretical and practical implications of the findings and outline important future research directions.

An Aluminum-Based Microfluidic Chip for Polymerase Chain Reaction Diagnosis.

Real-time polymerase chain reaction (real-time PCR) tests were successfully conducted in an aluminum-based microfluidic chip developed in this work. The reaction chamber was coated with silicone-modified epoxy resin to isolate the reaction system from metal surfaces, preventing the metal ions from interfering with the reaction process. The patterned aluminum substrate was bonded with a hydroxylated glass mask using silicone sealant at room temperature. The effect of thermal expansion was counteracted by the elasticity of cured silicone. With the heating process closely monitored, real-time PCR testing in reaction chambers proceeded smoothly, and the results show similar quantification cycle values to those of traditional test sets. Scanning electron microscope (SEM) and atomic force microscopy (AFM) images showed that the surface of the reaction chamber was smoothly coated, illustrating the promising coating and isolating properties. Energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma-optical emission spectrometer (ICP-OES) showed that no metal ions escaped from the metal to the chip surface. Fourier-transform infrared spectroscopy (FTIR) was used to check the surface chemical state before and after tests, and the unchanged infrared absorption peaks indicated the unreacted, antifouling surface. The limit of detection (LOD) of at least two copies can be obtained in this chip.

Role of the extracellular matrix in COVID-19.

An outbreak of coronavirus disease 2019 (COVID-19) has spread globally, with over 500 million cases and 6 million deaths to date. COVID-19 is associated with a systemic inflammatory response and abnormalities of the extracellular matrix (ECM), which is also involved in inflammatory storms. Upon viral infection, ECM proteins are involved in the recruitment of inflammatory cells and interference with target organ metabolism, including in the lungs. Additionally, serum biomarkers of ECM turnover are associated with the severity of COVID-19 and may serve as potential targets. Consequently, understanding the expression and function of ECM, particularly of the lung, during severe acute respiratory syndrome of the coronavirus 2 infection would provide valuable insights into the mechanisms of COVID-19 progression. In this review, we summarize the current findings on ECM, such as hyaluronic acid, matrix metalloproteinases, and collagen, which are linked to the severity and inflammation of COVID-19. Some drugs targeting the extracellular surface have been effective. In the future, these ECM findings could provide novel perspectives on the pathogenesis and treatment of COVID-19.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection.

Background: SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods: This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results: Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion: Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection

Background SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities;lower Ct value at recurrence;more persistent cough;and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Design of UVC Surface Disinfection Robot with Coverage Path Planning Using Map-Based Approach At-The-Edge

In response to the issue of virus contamination in the cold-chain warehouse or hospital environment under the influence of the COVID-19, we propose the design work of a disinfection robot based on the UVC radiation mechanism using the low-computational path optimization at-the-edge. To build a surface disinfection robot with less computing power to generate a collision-free path with shorter total distance in studies, a 2D map is used as a graph-based approach to automatically generate a closed-loop disinfection path to cover all the accessible surfaces. The discrete disinfection points from the map are extracted with effective disinfection distances and sorted by a nearest-neighbor (NN) search over historical trajectory data and improved A * algorithm to obtain an efficient coverage path to all accessible boundaries of the entire area. The purpose of improved A * algorithm with NN is not to find the optimal path solution but to optimize one with reasonable computing power. The proposed algorithm enhances the path-finding efficiency by a dynamically weighted heuristic function and reduces the path turning angles, which improves the path smoothness significantly requiring less computing power. The Gazebo simulation is conducted, and the prototype disinfection robot has been built and tested in a real lab environment. Compared with the classic A * algorithm, the improved A * algorithm with NN has improved the path-finding efficiency and reduced the path length while covering the same area. Both the simulation and experimental results show that this approach can provide the design to balance the tradeoffs among the path-finding efficiency, smoothness, disinfection coverage, and computation resources.

Does human resource system strength help employees act proactively? The roles of crisis strength and work engagement

Many empirical studies have elucidated the antecedents and psychological mechanisms of employees' proactive behaviors. However, there is limited knowledge about how a human resource (HR) system helps employees proactively adjust to their changing work environment. Drawing on social exchange theory and event system theory, we developed a theoretical model to examine whether, how, and when perceptions of the HR system strength impact employee proactive behavior during crises. Results from a three-wave time-lagged survey of 305 employees in 65 teams in eight Chinese companies indicate that HR system strength creates a strong situation by alleviating employees' uncertainty about how to behave during crises, which stimulates employees' work engagement and subsequent proactive behaviors. Moreover, employees' perceptions of HR system strength are more likely to influence work engagement when employees perceive the COVID-19 crisis as more severe. We discuss the theoretical and practical implications of the findings and outline important future research directions. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Investigation of mouse hepatitis virus strain A59 inactivation under both ambient and cold environments reveals the mechanisms of infectivity reduction following UVC exposure.

The surface contamination of SARS-CoV-2 is becoming a potential source of virus transmission during the pandemic of COVID-19. Under the cold environment, the infection incidents would be more severe with the increase of virus survival time. Thus, the disinfection of contaminated surfaces in both ambient and cold environments is a critical measure to restrain the spread of the virus. In our study, it was demonstrated that the 254 nm ultraviolet-C (UVC) is an efficient method to inactivate a coronavirus, mouse hepatitis virus strain A59 (MHV-A59). The inactivation rate to MHV-A59 coronavirus was up to 99.99% when UVC doses were 2.90 and 14.0 mJ/cm2 at room temperature (23 °C) and in cold environment (-20 °C), respectively. Further mechanistic study demonstrated that UVC could induce spike protein damage to partly impede virus attachment and genome penetration processes, which contributes to 12% loss of viral infectivity. Additionally, it can induce genome damage to significantly interrupt genome replication, protein synthesis, virus assembly and release processes, which takes up 88% contribution to viral inactivation. With these mechanistic understandings, it will greatly contribute to the prevention and control of the current SARS-CoV-2 transmissions in cold chains (low temperature-controlled product supply chains), public area such as airport, school, and warehouse.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study.

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.

Point-of-care testing detection methods for COVID-19.

COVID-19 is an acute respiratory disease caused by SARS-CoV-2, which has high transmissibility. People infected with SARS-CoV-2 can develop symptoms including cough, fever, pneumonia and other complications, which in severe cases could lead to death. In addition, a proportion of people infected with SARS-CoV-2 may be asymptomatic. At present, the primary diagnostic method for COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR), which tests patient samples including nasopharyngeal swabs, sputum and other lower respiratory tract secretions. Other detection methods, e.g., isothermal nucleic acid amplification, CRISPR, immunochromatography, enzyme-linked immunosorbent assay (ELISA) and electrochemical sensors are also in use. As the current testing methods are mostly performed at central hospitals and third-party testing centres, the testing systems used mostly employ large, high-throughput, automated equipment. Given the current situation of the epidemic, point-of-care testing (POCT) is advantageous in terms of its ease of use, greater approachability on the user's end, more timely detection, and comparable accuracy and sensitivity, which could reduce the testing load on central hospitals. POCT is thus conducive to daily epidemic control and achieving early detection and treatment. This paper summarises the latest research advances in POCT-based SARS-CoV-2 detection methods, compares three categories of commercially available products, i.e., nucleic acid tests, immunoassays and novel sensors, and proposes the expectations for the development of POCT-based SARS-CoV-2 detection including greater accessibility, higher sensitivity and lower costs.

An integrative analysis identifying transcriptional features and key genes involved in COVID-19.

Aim: To elucidate the transcriptional characteristics of COVID-19. Materials & methods: We utilized an integrative approach to comprehensively analyze the transcriptional features of both COVID-19 patients and SARS-CoV-2 infected cells. Results: Widespread infiltration of immune cells was observed. We identified 233 genes that were codifferentially expressed in both bronchoalveolar lavage fluid and lung samples of COVID-19 patients. Functional analysis suggested upregulated genes were related to immune response such as neutrophil activation and antivirus response, while downregulated genes were associated with cell adhesion. Finally, we identified LCN2, STAT1 and UBE2L6 as core genes during SARS-CoV-2 infection. Conclusion: The identification of core genes involved in COVID-19 can provide us with more insights into the molecular features of COVID-19.

COVID-19 in Children: Clinical Characteristics and Follow-Up Study.

Since December 2019, the coronavirus disease 2019 (COVID-19) has spread globally. But the clinical symptoms and detailed follow-up of children with COVID-19 infection are lacking. Here, we conducted a retrospective study including children with confirmed COVID-19. We recorded patients' epidemiological, clinical features, and follow-up data after discharging in order to improve the awareness and treatment of children with COVID-19.

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.