Baseline symptoms of depression and anxiety negatively impact the effectiveness of CBTi in treating acute insomnia among young adults.

Background: Cognitive-behavioural therapy for insomnia (CBTi) is the first-line treatment for those with this sleep disorder. However, depressive and anxiety symptoms often co-occur with acute insomnia, which may affect the effectiveness of CBTi treatment. Aims: This study aimed to determine the impact of depressive and anxiety symptoms on the efficacy of CBTi in treating acute insomnia. Methods: A single-arm clinical trial was conducted among individuals who have acute insomnia. Participants underwent self-guided CBTi for 1-week. Their insomnia, depressive symptoms and anxiety symptoms were evaluated using the Insomnia Severity Index and the Hospital Anxiety and Depression Scale at baseline, post-treatment and 3-month follow-up. Repeated measures analysis of variance was used to assess the effectiveness of CBTi in treating insomnia, depressive symptoms and anxiety symptoms. A multivariate Cox regression model was used to determine the impact of depressive and anxiety symptoms on insomnia. Results: The study found significant reductions in insomnia, depressive symptoms and anxiety symptoms at both post-treatment and 3-month follow-up (F=17.45, p<0.001; F=36.37, p=0.001; and F=81.51, p<0.001, respectively). The duration of CBTi treatment had a positive impact on insomnia recovery (hazard ratio (HR)=0.94, p=0.018). However, baseline depressive symptoms (HR=1.83, p=0.004) and baseline anxiety symptoms (HR=1.99, p=0.001) had significant negative effects on insomnia recovery. Conclusions: The study showed that a 1-week self-guided CBTi treatment is effective in treating acute insomnia and comorbid depressive and anxiety symptoms. However, baseline depressive and anxiety symptoms negatively impact treatment effectiveness. Therefore, clinicians should assess for depressive and anxiety symptoms before treating acute insomnia with monotherapy CBTi.

Mitochondria-Targeted Fluorescent Nanoparticles with Large Stokes Shift for Long-Term BioImaging.

Mitochondria (MITO) play a significant role in various physiological processes and are a key organelle associated with different human diseases including cancer, diabetes mellitus, atherosclerosis, Alzheimer's disease, etc. Thus, detecting the activity of MITO in real time is becoming more and more important. Herein, a novel class of amphiphilic aggregation-induced emission (AIE) active probe fluorescence (AC-QC nanoparticles) based on a quinoxalinone scaffold was developed for imaging MITO. AC-QC nanoparticles possess an excellent ability to monitor MITO in real-time. This probe demonstrated the following advantages: (1) lower cytotoxicity; (2) superior photostability; and (3) good performance in long-term imaging in vitro. Each result of these indicates that self-assembled AC-QC nanoparticles can be used as effective and promising MITO-targeted fluorescent probes.

A Study of the Detection of SARS-CoV-2 by the Use of Electrochemiluminescent Biosensor Based on Asymmetric Polymerase Chain Reaction Amplification Strategy.

A new and reliable method has been constructed for detecting severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) open reading frames 1ab (ORF1ab) gene via highly sensitive electrochemiluminescence (ECL) biosensor technology based on highly efficient asymmetric polymerase chain reaction (asymmetric PCR) amplification strategy. This method uses magnetic particles coupled with biotin-labeled one complementary nucleic acid sequence of the SARS-CoV-2 ORF1ab gene as the magnetic capture probes, and [Formula: see text]-labeled amino-modified another complementary nucleic acid sequence as the luminescent probes, and then a detection model of magnetic capture probes-asymmetric PCR amplification nucleic acid products-[Formula: see text]-labeled luminescent probes is formed, which combines the advantages of highly efficient asymmetric PCR amplification strategy and highly sensitive ECL biosensor technology, enhancing the method sensitivity of detecting the SARS-CoV-2 ORF1ab gene. The method enables the rapid and sensitive detection of the ORF1ab gene and has a linear range of 1-[Formula: see text] copies/[Formula: see text], a regression equation of [Formula: see text] = [Formula: see text] + 2919.301 ([Formula: see text] = 0.9983, [Formula: see text] = 7), and a limit of detection (LOD) of 1 copy/[Formula: see text]. In summary, it can meet the analytical requirements for simulated saliva and urine samples and has the benefits of easy operation, reasonable reproducibility, high sensitivity, and anti-interference abilities, which can provide a reference for developing efficient field detection methods for SARS-CoV-2.

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

BACKGROUND: The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to developing a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method. METHOD: Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning, and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/Pneumonia). RESULT: Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVID- ensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multicategory classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images, and that the performance of this model is superior to that of several existing methods. CONCLUSION: Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection, but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.

Research progress of thrombosis in patients with COVID-19

The COVID-19 pandemic poses a huge challenge to the global economy and healthcare. Coagulation dysfunction and thrombosis are the main clinical features of severe COVID-19 patients, and closely related to the risk of death. Several mechanisms of thrombosis in COVID-19 have been proposed, such as immune inflammation, hypoxia, imbalance of angiotensin system, endothelial injury etc, and other high-risk factors such as combined with basic diseases, bed rest braking and intravenous catheterization.In this review, we summarize the mechanisms that may increase the risk of thrombosis in patients with severe COVID-19, in order to improve people's understanding of coagulation abnormalities and thrombosis complications in patients with severe COVID-19, and further explore effective methods to prevent and treat COVID-19 related thrombosis.

SCTV-UNet: a COVID-19 CT segmentation network based on attention mechanism.

The global outbreak of COVID-19 has become an important research topic in healthcare since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time is a problem. Therefore, the pathological study of COVID-19 with CT image is an important supplement to RT-RCT. The current TVLoss-based segmentation promotes the connectivity of diseased areas. However, normal pixels between some adjacent diseased areas are wrongly identified as diseased pixels. In addition, the proportion of diseased pixels in CT images is small, and the traditional BCE-based U-shaped network only focuses on the whole CT without diseased pixels, which leads to blurry border and low contrast in the predicted result. In this way, this paper proposes a SCTV-UNet to solve these problems. By combining spatial and channel attentions on the encoder, more visual layer information are obtained to recognize the normal pixels between adjacent diseased areas. By using the composite function DTVLoss that focuses on the pixels in the diseased area, the problem of blurry boundary and low contrast caused by the use of BCE in traditional U-shaped networks is solved. The experiment shows that the segmentation effect of the proposed SCTV-UNet has significantly improved by comparing with the SOTA COVID-19 segmentation networks, and can play an important role in the detection and research of clinical COVID-19.

Research on the novel medical protective clothing for COVID-19

With the outbreak of COVID-19, a large number of medical staffs have invested in the front line of anti-epidemic. Medical protective clothing (MPC) can provide a safe environment for the wearers to block bacteria and viruses. However, nowadays, the thermal performance of MPC on the market is very poor, resulting in the extremely low comfort of the wearer. Some improved MPCs were made of materials, which were not easy to obtain with high cost. Some improved MPCs were lack of thermal comfort experimental data based on real human body. Therefore, this paper proposed a novel MPC with reusable PCM and ventilation. Through simulation and experiment, human comfort of the novel MPC was compared with the other two kinds of MPCs. Five subjects were invited to carried out the comfort tests under three states of motion with three types of MPCs. The results showed the novel MPC with higher cost performance in the effective period had been proved that PPD decreased by 39.5% than the traditional MPC. Besides, the novel MPC could meet the comfort requirements of medical staffs for one shift. Furthermore, the work can provide theoretical methods and basic experimental data for the continuous improvement of the comfort and safety of MPC.

Research on the novel medical protective clothing for COVID-19.

With the outbreak of COVID-19, a large number of medical staffs have invested in the front line of anti-epidemic. Medical protective clothing (MPC) can provide a safe environment for the wearers to block bacteria and viruses. However, nowadays, the thermal performance of MPC on the market is very poor, resulting in the extremely low comfort of the wearer. Some improved MPCs were made of materials, which were not easy to obtain with high cost. Some improved MPCs were lack of thermal comfort experimental data based on real human body. Therefore, this paper proposed a novel MPC with reusable PCM and ventilation. Through simulation and experiment, human comfort of the novel MPC was compared with the other two kinds of MPCs. Five subjects were invited to carried out the comfort tests under three states of motion with three types of MPCs. The results showed the novel MPC with higher cost performance in the effective period had been proved that PPD decreased by 39.5% than the traditional MPC. Besides, the novel MPC could meet the comfort requirements of medical staffs for one shift. Furthermore, the work can provide theoretical methods and basic experimental data for the continuous improvement of the comfort and safety of MPC.

Identification of Neutrophil-Related Factor LCN2 for Predicting Severity of Patients With Influenza A Virus and SARS-CoV-2 Infection.

Background: Influenza and COVID-19 are respiratory infectious diseases that are characterized by high contagiousness and high mutation and pose a serious threat to global health. After Influenza A virus (IAV) and SARS-CoV-2 infection, severe cases may develop into acute lung injury. Immune factors act as an important role during infection and inflammation. However, the molecular immune mechanisms still remain unclear. We aimed to explore immune-related host factors and core biomarker for severe infection, to provide a new therapeutic target of host factor in patients. Methods: Gene expression profiles were obtained from Gene Expression Omnibus and the Seurat R package was used for data process of single-cell transcriptome. Differentially expressed gene analysis and cell cluster were used to explore core host genes and source cells of genes. We performed Gene Ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, and gene set enrichment analysis to explore potential biological functions of genes. Gene set variation analysis was used to evaluate the important gene set variation score for different samples. We conduct Enzyme-linked immunosorbent assay (ELISA) to test plasma concentrations of Lipocalin 2 (LCN2). Results: Multiple virus-related, cytokine-related, and chemokine-related pathways involved in process of IAV infection and inflammatory response mainly derive from macrophages and neutrophils. LCN2 mainly in neutrophils was significantly upregulated after either IAV or SARS-CoV-2 infection and positively correlated with disease severity. The plasma LCN2 of influenza patients were elevated significantly compared with healthy controls by ELISA and positively correlated with disease severity of influenza patients. Further bioinformatics analysis revealed that LCN2 involved in functions of neutrophils, including neutrophil degranulation, neutrophil activation involved in immune response, and neutrophil extracellular trap formation. Conclusion: The neutrophil-related LCN2 could be a promising biomarker for predicting severity of patients with IAV and SARS-CoV-2 infection and may as a new treatment target in severe patients.

Suicidal ideation in the general population in China after the COVID-19 pandemic was initially controlled.

BACKGROUND: The COVID-19 pandemic increases the risk of psychological problems including suicidal ideation (SI) in the general population. In this study, we investigated the risk factors of SI after the COVID-19 pandemic was initially controlled in China. METHODS: We conducted an online questionnaire via JD Health APP in China in June 2020. Demographic data, feelings and experiences related to the COVID-19 pandemic and psychological problems were collected. The participants (n = 14,690) were divided into the non-SI and SI groups. A binary logistic regression analysis was used to examine the correlates of SI. RESULTS: Nine percent of the participants (1328/14690) reported SI. The regression analysis showed that SI was positively associated with ethnic minority (OR = 1.42 [1.08-1.85]), age (e.g. 18-30 years: OR = 2.31 [1.67-3.20]), having history of mental disorders (OR = 2.75 [2.27-3.35]), daily life disturbance due to health problems (OR = 1.67 [1.38-2.01]), being around someone with the COVID-19 (OR = 1.58 [1.30-1.91]), being uncertain about effective disease control (OR = 1.23 [1.03-1.46]), and having depressive symptoms (OR = 4.40 [3.59-5.39]), insomnia symptoms (OR = 2.49 [2.13-2.90]) or psychological distress (OR = 1.87 [1.59-2.18]). LIMITATIONS: The main limitation is that the cross-sectional design of this study could not allow us to further explore the causality of SI. CONCLUSIONS: The prevalence of SI was relatively high in general population after the COVID-19 pandemic was initially controlled in China. SI should be monitored continually after the COVID-19 pandemic.

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Mental distress, quality of life and physical symptoms in Chinese women with ovarian cancer receiving olaparib treatment during the COVID-19 pandemic.

Objectives: Women with ovarian cancer (OC) have experienced unprecedented challenges since the novel coronavirus disease-2019 (COVID-19) outbreak in China. We aim to evaluate the experience of psychological status, physical symptoms and quality of life (QoL) and investigate the impact of COVID-19 pandemic on OC patients receiving olaparib. Methods: The survey was conducted online from April 22 to May 12 in 2020. Demographic and clinical questions were listed to collect general information. The degree of insomnia, depression, anxiety, stress symptoms and QoL were assessed by the Chinese versions of the Insomnia Severity Index, the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised, and the General Functional Assessment of Cancer Therapy, respectively. Multivariate logistic regression analysis was conducted to analyze the risk factors for mental distress and QoL. Results: A total of 56 respondents coming from 15 various provinces in China participated in the survey. The prevalence of insomnia, depressive, anxiety, stress symptoms and reduced QoL were 37.5, 51.8, 37.5, 30.4, and 51.8%, respectively. Unfavorable disease status, shorter period of olaparib administration, adverse events of olaparib and delay in cancer care were correlated with mental health problems. Reduced QoL was also significantly associated with psychological distress. Conclusions: This study emphasized that mental health problems and reduced QoL should gain more attention in women with OC who are receiving oral olaparib at home. Appropriate psychological healthcare strategies are necessary for OC patients during the COVID-19 pandemic.

Changes in the types of patients in Burn Unit: A single center experience

Background Burns are a common concern around the world, with the majority of cases happening in low- and middle-income nations. China is the largest developing country. With the unremitting efforts of domestic colleagues, China has taken the lead in the treatment of burn in the world. With the change of times, we have observed some noteworthy changes in the types of patients that have admitted our Burns and Plastic Surgery, the Affiliated Hospital of Yangzhou University. Methods This retrospective observational study included brought into;all patients reached to our burn unit during 2013-2021. The gathered data were descriptively examined and statistically contrasted with each other year. Results Of 4407 cases admitted to burn unit during 2013-2021, men constituted 56% of such cases, with an average age of 47.3 ± 19.3 years. Moreover, among the patients hospitalized, January and February usually admit fewer than other months. Between 2013 and 2021, both the number of patients admitted to burn unit and the expense of their hospitalization rose yearly. The percentage of burn patients admitted to burn ward of our hospital is decreasing, especially during the period of serious Coronavirus disease (COVID-19) epidemic. We also observed that during the COVID-19 pandemic, patients with superficial masses also dropped off a cliff because of government controls. Conclusion The diseases in the department show the trend of maximizing marginal disciplines, burn surgeons are facing a more complex challenge. Further research addressing the relationship between the change of patient types and economic and social development in burn department will help to foster better pinpoint hospitalization patients need, fine service for hospitalized patients.

Mental distress, quality of life and physical symptoms in Chinese women with ovarian cancer receiving olaparib treatment during the COVID-19 pandemic

Objectives Women with ovarian cancer (OC) have experienced unprecedented challenges since the novel coronavirus disease-2019 (COVID-19) outbreak in China. We aim to evaluate the experience of psychological status, physical symptoms and quality of life (QoL) and investigate the impact of COVID-19 pandemic on OC patients receiving olaparib. Methods The survey was conducted online from April 22 to May 12 in 2020. Demographic and clinical questions were listed to collect general information. The degree of insomnia, depression, anxiety, stress symptoms and QoL were assessed by the Chinese versions of the Insomnia Severity Index, the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, the Impact of Event Scale-Revised, and the General Functional Assessment of Cancer Therapy, respectively. Multivariate logistic regression analysis was conducted to analyze the risk factors for mental distress and QoL. Results A total of 56 respondents coming from 15 various provinces in China participated in the survey. The prevalence of insomnia, depressive, anxiety, stress symptoms and reduced QoL were 37.5, 51.8, 37.5, 30.4, and 51.8%, respectively. Unfavorable disease status, shorter period of olaparib administration, adverse events of olaparib and delay in cancer care were correlated with mental health problems. Reduced QoL was also significantly associated with psychological distress. Conclusions This study emphasized that mental health problems and reduced QoL should gain more attention in women with OC who are receiving oral olaparib at home. Appropriate psychological healthcare strategies are necessary for OC patients during the COVID-19 pandemic.

Research progress of thrombosis in patients with COVID-19

The COVID-19 pandemic poses a huge challenge to the global economy and healthcare.Coagulation dysfunction and thrombosis are the main clinical features of severe COVID-19 patients, and closely related to the risk of death.Several mechanisms of thrombosis in COVID-19 have been proposed, such as immune inflammation, hypoxia, imbalance of angiotensin system, endothelial injury etc, and other high-risk factors such as combined with basic diseases, bed rest braking and intravenous catheterization.In this review, we summarize the mechanisms that may increase the risk of thrombosis in patients with severe COVID-19, in order to improve people′s understanding of coagulation abnormalities and thrombosis complications in patients with severe COVID-19, and further explore effective methods to prevent and treat COVID-19 related thrombosis.

Crosstalk between gut microbiota and COVID-19 impacts pancreatic cancer progression.

Pancreatic cancer (PC) is one of the most common causes of cancer-associated death worldwide, with a low rate of 5-year survival. Currently, the pathogenesis of PC is complicated, with no efficient therapy. Coronavirus disease 2019 (COVID-19) disease caused by severe acute respiratory syndrome coronavirus 2 further exacerbates the challenge of patients with PC. The alteration of gut microbiota caused by COVID-19 infection may impact PC progression in patients via immune regulation. The expression of inflammatory immune mediators such as interleukin (IL)-6, IL-8, and IL-10 has been found to increase in both PC and COVID-19 patients, which is associated with the disease severity and prognostic outcome. Gut microbiome serves as a critical connector between viral infection and PC. It can regulate host systemic immune response and impact the efficacy of immunotherapy. Here, we first demonstrated the features of inflammatory cytokines in both diseases and their impact on disease outcomes. Then, we demonstrated the importance of immunotherapeutic strategies. This includes the immune modulation that targets a single or dual receptors using a single agent or their combinations for the treatment of PC in patients who get infected with COVID-19. Additionally, we explored the possibility of managing the disease by regulating gut microbiome. Overall, modulation of the lung-gut-pancreases axis can boost anti-cancer immunotherapy and reduce adverse prognostic outcomes.

What value does blockchain bring to the imported fresh food supply chain?

During the COVID-19 pandemic, blockchain has been widely used to trace imported fresh food information from sources to destinations. Motivated by observations of real-world practice, we studied the role that blockchain played in imported fresh food supply chains. We first developed the basic models to examine the cases without and with blockchain. We derived the optimal pricing decisions for the supply chain and the conditions under which using blockchain was profitable under each model. To assess the robustness of the results, we then analyzed how risk attitudes affected the optimal supply chain decisions. We found some interesting results: When the effect of assuaging consumers’ safety concerns brought by blockchain was not obvious, blockchain was more likely to help the manufacturer and retailer increase their profits;otherwise, the value of blockchain was not significant. Besides, the risk-averse manufacturer and retailer would decrease their prices in response to the risks of demand fluctuations. Also, the blockchain platform would benefit from the risk-averse manufacturer and retailer but suffer from risk-averse consumers.

Discovery of Epipodophyllotoxin-Derived B2 as Promising XooFtsZ Inhibitor for Controlling Bacterial Cell Division: Structure-Based Virtual Screening, Synthesis, and SAR Study.

The emergence of phytopathogenic bacteria resistant to antibacterial agents has rendered previously manageable plant diseases intractable, highlighting the need for safe and environmentally responsible agrochemicals. Inhibition of bacterial cell division by targeting bacterial cell division protein FtsZ has been proposed as a promising strategy for developing novel antibacterial agents. We previously identified 4'-demethylepipodophyllotoxin (DMEP), a naturally occurring substance isolated from the barberry species Dysosma versipellis, as a novel chemical scaffold for the development of inhibitors of FtsZ from the rice blight pathogen Xanthomonas oryzae pv. oryzae (Xoo). Therefore, constructing structure-activity relationship (SAR) studies of DMEP is indispensable for new agrochemical discovery. In this study, we performed a structure-activity relationship (SAR) study of DMEP derivatives as potential XooFtsZ inhibitors through introducing the structure-based virtual screening (SBVS) approach and various biochemical methods. Notably, prepared compound B2, a 4'-acyloxy DMEP analog, had a 50% inhibitory concentration of 159.4 µM for inhibition of recombinant XooFtsZ GTPase, which was lower than that of the parent DMEP (278.0 µM). Compound B2 potently inhibited Xoo growth in vitro (minimum inhibitory concentration 153 mg L-1) and had 54.9% and 48.4% curative and protective control efficiencies against rice blight in vivo. Moreover, compound B2 also showed low toxicity for non-target organisms, including rice plant and mammalian cell. Given these interesting results, we provide a novel strategy to discover and optimize promising bactericidal compounds for the management of plant bacterial diseases.

Establishment of a Duplex Real Time PCR Method for Detection of SARS-COV-2

To quickly and efficiently detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and prevent and control the spread of novel coronavirus disease (COVID-19), a highly sensitive duplex real-time PCR (RT-PCR) detection method has been established. In this study, the specificity of primers and probes were designed, respectively, according to the ORF1ab gene and N gene sequence of SARS-COV-2, and fluorescent probes were labeled with carboxyl fluorescein (FAM) and green fluorescent protein (VIC). The duplex RT-PCR method for detecting SARS-COV-2 with TaqMan probe was established, which has a limit of detection of 10 copies/µL, and the linear detection range of ORF1ab and N gene were 1.0 × 101-1.0 × 105 copies/µL and 1.0 × 101-1.0 × 106 copies/µL, respectively, realizing the simultaneous detection of ORF1ab and N genes in simulated SARS-COV-2 samples. The method has high sensitivity, accurate quantification, simple operation, and cost-saving, which can be used for rapid and efficient quantitative detection of SARS-COV-2.

Loss Aversion Order Strategy in Emergency Procurement during the COVID-19 Pandemic

The COVID-19 pandemic has had a serious impact on firms' sourcing strategies. Since COVID-19 disrupted the supply chain, firms have had to make emergency purchases from other suppliers. In addition, emergency ordering is one of the most effective strategies to achieve sustainable operations because such a strategy can save inventory costs. We aim to address a retailer's emergency procurement strategies during the COVID-19 pandemic. We use prospect theory and the newsvendor model to uncover the retailer's inventory decisions. In our study, we find that retailers have the choice to order items before the selling period at the normal purchase price, and, if available, they can order them before the end of the selling period at the urgent purchase price. We perform a comparison of the optimal ordering policy and margins in this case with the conventional and loss aversion models. The influence of emergency procurement on the optimal order policy and margins is investigated as well. This paper contributes in theory that we innovatively capture the uncertainty of emergency sourcing, which is a feature that has never been considered in current research.