Enhancing food supply chain in green logistics with multi-level processing strategy under disruptions

Food supply chains (FSCs) have long been exposed to environmental variability and shock events caused by various economic, political, and infrastructural factors. The outbreak of the COVID-19 pandemic has further exposed and identified the vulnerability of FSCs, and promoted integrated optimization approaches for building resilience. However, existing works focusing on general supply chains (SCs) and FSCs have not been fully aware of the distinct characteristics of FSCs in green logistics, i.e., the expiration of fresh products. In reality, perishable food materials can be processed into products of different processing levels (i.e., multi-level processing) for longer shelf lives, which can serve as a timely and economic strategy to increase safety stocks for mitigating disruption risks. Motivated by this fact, we study the problem of enhancing FSC with a multi-level processing strategy. An integrated location, inventory, and distribution planning model for a multi-echelon FSC under COVID-19-related disruptions is formulated to maximize the total profit over a finite planning horizon. Specifically, a two-stage stochastic programming model is presented to hedge against disruption risks, where scenarios are generated to characterize geographical impact induced by source-region disruptions. For small-scale problems, the model can be solved with commercial solvers. To exactly and efficiently solve the large-scale instances, we design an integer L-shaped method. Numerical experiments are conducted on a case study and randomly generated instances to show the efficiency of our model and solution method. Based on the case study, managerial insights are drawn.

The Molecular Mechanism of Cardiac Injury in SARS-CoV-2 Infection: Focus on Mitochondrial Dysfunction

Background Coronavirus disease 2019(COVID-19) caused a large number of infections worldwide. Although some patients recovered from the disease, some of the other problems that accompanied it, such as cardiac injury, could affect the patient's subsequent quality of life and prognosis. Objectives To clarify the molecular mechanism of cardiac injury in SARS-CoV-2 Infection. Methods The RNA-Seq dataset (GSE184715) comparing expression profiling of Mock human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and SARS-CoV-2-infected hiPSC-CMs was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes(DEGs) were performed by the R software. Degs were analyzed by enrichment analysis to clarify the affected pathways. Hub genes were screened out by a PPI network constructed from Degs. Finally, Connectivity Map was used to screen for the treatment of COVID-19 induced cardiac injury. Results 2705 differentially expressed genes were identified. Enrichment analysis confirmed that mitochondrial dysfunction was caused by SARS-CoV-2, meanwhile, cardiac muscle contraction was suppressed and NF-κB was activated. Based on the PPI network, 15 hub genes were identified. These 15 down-regulated hub genes were mainly involved in the reduced activity of complexes in the mitochondrial respiratory chain associated with mitochondrial dysfunction. Moreover, 5 candidate drugs were identified to treat cardiac injury. Conclusion In conclusion, SARS-CoV-2 infection of cardiomyocytes causes mitochondrial dysfunction, including reduced mitochondrial respiratory chain complex activity and decreased ATP synthesis, leading to cardiomyocyte apoptosis, while the activated NF-κB also induced cytokine storms, ultimately resulting in cardiac injury.

Efficacy and safety of Paxlovid in severe adult patients with SARS-Cov-2 infection: a multicenter randomized controlled study.

Background: Nirmatrelvir plus ritonavir (Paxlovid) reduced the risk of hospitalization or death by 89% in high-risk, ambulatory adults with COVID-19. We aimed at studying the efficacy and safety of Paxlovid in hospitalized adult patients with SARS-Cov-2 (Omicron BA.2.2 variant) infection and severe comorbidities. Methods: We conducted an open-label, multicenter, randomized controlled trial in which hospitalized adult patients with severe comorbidities were eligible and assigned in a 1:1 ratio to receive either 300 mg of nirmatrelvir plus 100 mg of ritonavir every 12 h for 5 days with standard treatment or only standard treatment. All-cause mortality on day 28, the duration of SARS-CoV-2 RNA clearance, and safety were evaluated. Findings: 264 patients (mean age, 70.35 years; 122 [46.21%] female) who met the criteria were enrolled at 5 sites in Shanghai from April 10 to May 19 in 2022. After randomization, a total of 132 patients were assigned to receive Paxlovid treatment plus standard treatment, and 132 patients were assigned to receive only standard treatment. The overall 28-day mortality was 4.92%, 8 patients died in the standard treatment group and 5 died in the Paxlovid plus standard treatment group. There was no significant difference in mortality from any cause at 28 days between the Paxlovid plus standard treatment group and the standard treatment group (absolute risk difference [ARD], 2.27; 95% CI -2.94 to 7.49, P = 0.39). There was no significant difference in the duration of SARS-CoV-2 RNA clearance among the two groups (mean days, 10 in Paxlovid plus standard treatment group and 10.50 in the standard treatment group; ARD, -0.62; 95% CI -2.29 to 1.05, P = 0.42). The incidence of adverse events that occurred during the treatment period was similar in the two groups (any adverse event, 10.61% with Paxlovid plus standard treatment vs. 7.58% with the standard, P = 0.39; serious adverse events, 4.55% vs. 3.788%, P = 0.76). Interpretation: Paxlovid showed no significant reduction in the risk of all-cause mortality on day 28 and the duration of SARS-CoV-2 RNA clearance in hospitalized adult COVID-19 patients with severe comorbidities. Funding: National Natural Science Foundation of China (grant number: 82172152, 81873944).

The molecular mechanism of cardiac injury in SARS-CoV-2 infection: Focus on mitochondrial dysfunction.

BACKGROUND: Coronavirus disease 2019(COVID-19) caused a large number of infections worldwide. Although some patients recovered from the disease, some of the other problems that accompanied it, such as cardiac injury, could affect the patient's subsequent quality of life and prognosis. OBJECTIVES: To clarify the molecular mechanism of cardiac injury in SARS-CoV-2 Infection. METHODS: The RNA-Seq dataset (GSE184715) comparing expression profiling of Mock human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and SARS-CoV-2-infected hiPSC-CMs was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes(DEGs) were performed by the R software. Degs were analyzed by enrichment analysis to clarify the affected pathways. Hub genes were screened out by a PPI network constructed from Degs. Finally, Connectivity Map was used to screen for the treatment of COVID-19 induced cardiac injury. RESULTS: 2705 differentially expressed genes were identified. Enrichment analysis confirmed that mitochondrial dysfunction was caused by SARS-CoV-2, meanwhile, cardiac muscle contraction was suppressed and NF-κB was activated. Based on the PPI network, 15 hub genes were identified. These 15 down-regulated hub genes were mainly involved in the reduced activity of complexes in the mitochondrial respiratory chain associated with mitochondrial dysfunction. Moreover, 5 candidate drugs were identified to treat cardiac injury. CONCLUSION: In conclusion, SARS-CoV-2 infection of cardiomyocytes causes mitochondrial dysfunction, including reduced mitochondrial respiratory chain complex activity and decreased ATP synthesis, leading to cardiomyocyte apoptosis, while the activated NF-κB also induced cytokine storms, ultimately resulting in cardiac injury.

Efficacy and safety of Paxlovid in severe adult patients with SARS-Cov-2 infection: a multicenter randomized controlled study

Background Nirmatrelvir plus ritonavir (Paxlovid) reduced the risk of hospitalization or death by 89% in high-risk, ambulatory adults with COVID-19. We aimed at studying the efficacy and safety of Paxlovid in hospitalized adult patients with SARS-Cov-2 (Omicron BA.2.2 variant) infection and severe comorbidities. Methods We conducted an open-label, multicenter, randomized controlled trial in which hospitalized adult patients with severe comorbidities were eligible and assigned in a 1:1 ratio to receive either 300 mg of nirmatrelvir plus 100 mg of ritonavir every 12 h for 5 days with standard treatment or only standard treatment. All-cause mortality on day 28, the duration of SARS-CoV-2 RNA clearance, and safety were evaluated. Findings 264 patients (mean age, 70.35 years;122 [46.21%] female) who met the criteria were enrolled at 5 sites in Shanghai from April 10 to May 19 in 2022. After randomization, a total of 132 patients were assigned to receive Paxlovid treatment plus standard treatment, and 132 patients were assigned to receive only standard treatment. The overall 28-day mortality was 4.92%, 8 patients died in the standard treatment group and 5 died in the Paxlovid plus standard treatment group. There was no significant difference in mortality from any cause at 28 days between the Paxlovid plus standard treatment group and the standard treatment group (absolute risk difference [ARD], 2.27;95% CI −2.94 to 7.49, P = 0.39). There was no significant difference in the duration of SARS-CoV-2 RNA clearance among the two groups (mean days, 10 in Paxlovid plus standard treatment group and 10.50 in the standard treatment group;ARD, −0.62;95% CI −2.29 to 1.05, P = 0.42). The incidence of adverse events that occurred during the treatment period was similar in the two groups (any adverse event, 10.61% with Paxlovid plus standard treatment vs. 7.58% with the standard, P = 0.39;serious adverse events, 4.55% vs. 3.788%, P = 0.76). Interpretation Paxlovid showed no significant reduction in the risk of all-cause mortality on day 28 and the duration of SARS–CoV-2 RNA clearance in hospitalized adult COVID-19 patients with severe comorbidities. Funding 10.13039/501100001809National Natural Science Foundation of China (grant number: 82172152, 81873944).

Enhancing Food Supply Chain in Green Logistics with Multi-Level Processing Strategy under Disruptions

Food supply chains (FSCs) have long been exposed to environmental variability and shock events caused by various economic, political, and infrastructural factors. The outbreak of the COVID-19 pandemic has further exposed and identified the vulnerability of FSCs, and promoted integrated optimization approaches for building resilience. However, existing works focusing on general supply chains (SCs) and FSCs have not been fully aware of the distinct characteristics of FSCs in green logistics, i.e., the expiration of fresh products. In reality, perishable food materials can be processed into products of different processing levels (i.e., multi-level processing) for longer shelf lives, which can serve as a timely and economic strategy to increase safety stocks for mitigating disruption risks. Motivated by this fact, we study the problem of enhancing FSC with a multi-level processing strategy. An integrated location, inventory, and distribution planning model for a multi-echelon FSC under COVID-19-related disruptions is formulated to maximize the total profit over a finite planning horizon. Specifically, a two-stage stochastic programming model is presented to hedge against disruption risks, where scenarios are generated to characterize geographical impact induced by source-region disruptions. For small-scale problems, the model can be solved with commercial solvers. To exactly and efficiently solve the large-scale instances, we design an integer L-shaped method. Numerical experiments are conducted on a case study and randomly generated instances to show the efficiency of our model and solution method. Based on the case study, managerial insights are drawn.

Digital Economy and Health: A Case Study of a Leading Enterprise's Value Mining Mode in the Global Big Health Market.

Coronavirus disease 2019 (COVID-19) swept across the world and posed a serious threat to human health. Health and elderly care enterprises are committed to continuously improving people's health. With the rapid development of the digital economy, many enterprises have established digital product-service ecosystems after combining "Internet +," big data, cloud computing, and the big health industry. This paper uses the case study method to analyze the overseas market value mining mode of health and elderly care enterprises through in-depth research on leading health and elderly care enterprises. This study explores the value mining mode of the leading enterprise's global big health market using a cluster analysis and Bayesian model with the support of data on geographical characteristics, users' sleep habits, and national big health. This paper theoretically summarizes the successful cases of health and elderly care enterprises through digital transformation, which provides a useful reference for the intelligent transformation of the health and elderly care industry.

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion.

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.

Non-contact screening system based for COVID-19 on XGBoost and logistic regression.

BACKGROUND: The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. OBJECTIVE: We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. METHODS: We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. RESULTS: The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. CONCLUSION: The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

Laboratory diagnosis of coronavirus disease-2019 (COVID-19).

The outbreak of Coronavirus Disease-2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has threatened health worldwide. As of the end of 2020, there were nearly 10 million confirmed cases and nearly 5 million deaths associated with COVID-19. Rapid and early laboratory diagnosis of COVID-19 is the main focus of treatment and control. Molecular tests are the basis for confirmation of COVID-19, but serological tests for SARS-CoV-2 are widely available and play an increasingly important role in understanding the epidemiology of the virus and in identifying populations at higher risk for infection. Point-of-care tests have the advantage of rapid, accurate, portable, low cost and non-specific device requirements, which provide great help for disease diagnosis and detection. This review will discuss the performance of different laboratory diagnostic tests and platforms, as well as suitable clinical samples for testing, and related biosafety protection. This review shall guide for the diagnosis of COVID-19 caused by SARS-CoV-2.