A review on post-COVID-19 impacts and opportunities of agri-food supply chain in Malaysia.

Background: Malaysia is strongly supported by the agriculture sector as the backbone to drive the economy. However, COVID-19 has significantly affected agriculture across the production, supply, and marketing chains. It also disturbs the balance of food supply and demand in Malaysia. COVID-19 was an unexpected pandemic that resulted in shock and panic and caused a huge global impact. However, the impacts of this pandemic on the agriculture sector in Malaysia, particularly in the production and supply chains, are still unclear and scarce. This review offers insights into the challenges, particularly in sustaining agri-food production and supply chains. It also highlights the opportunity and relevant measures towards sustainability in agriculture to avoid agri-food disasters in the future. Methods: This study was carried out through a desk review of the secondary source of information covering the impact of COVID-19 in Malaysia particularly in the agri-food aspect, and a wide range of strategies and initiatives as the effective measures to overcome the crisis of this pandemic. Online desk research of the government published data and customer desk research were utilized to complete this study. Search engines such as Google Scholar and the statistical data from the official websites including the Department of Statistics Malaysia (DOSM) and the Food and Fertilizer Technology Center for the Asian and Pacific Region (FFTC-AP), were utilized. Keywords such as impact of COVID-19, pandemic, and agri-food supply chain were used to conduct the searches. The articles identified to be related to the study's objective were then downloaded and included in the study. Descriptive methods were used as the primary analysis technique following the descriptive analysis and visual data analysis in performing the sources obtained. Results: This devastating impact damages the lives by causing 4.3 million confirmed infections and more than 290,000 deaths. This disease presents an unprecedented challenge to the public health. The lockdown restriction under the movement control order (MCO), for more than of the world's population in the year 2020 to control the virus from spreading, has disrupted most of the economic sectors. The agriculture industry was seen as one of the essential industries and allowed to operate under strict standard operating procedures (SOP). Working under strict regulations came with a huge price paid for almost all industries. Conclusion: This pandemic has affected the national agri-food availability and accessibility in Malaysia. This outbreak created a reflection of opportunity for sharing a more flexible approaches in handling emergencies on agricultural food production and supply chains. Therefore, the government should be ready with the roadmap and enforce the measures to control the pandemic without disrupting the agri-food supply chain in the near future.

Clinical characteristics and risks of the convalescent COVID-19 patients with re-detectable positive RNA test: a 430 patients with Omicron infected cross-sectional survey in Tianjin, China.

BACKGROUND: The outbreak of severe acute respiratory syndrome coronavirus (SARS-CoV-2) Omicron variant occurred in Tianjin, China, at the beginning of 2022. In the present study, we identified risk factors that may affect positive (RP) RNA re-testing in recovered patients infected with Omicron variants during recovery in hospital. METHODS: We retrospectively analyzed the medical records of 425 patients with Omicron variant infection admitted to our medical center from January 21, 2022 to February 24, 2022, based on the recurrence of RT-PCR positive results for SARS-CoV-2 after cure and discharge. Patients were divided into re-tested positive (RP) and non-re-detectable positive patients (NRP) groups, and clinical data from both groups were analyzed to investigate the characteristics and risk factors of RP patients. RESULTS: Univariate analysis showed significant differences in age, vaccination rate and dose, partial signs and symptoms, most co-existing disorders, and levels of CRP and IL-6 between the RP and NRP groups (all P < 0.05), while multifactorial logistic regression analysis showed that vaccination status and levels of IL-6 were independent risk factors for RP patients. CONCLUSION: Our results suggested that clinicians should assess the probability of "re-positive" nucleic acid tests after discharge, taking the following indicators into account: pre-admission underlying diseases, unvaccinated status, and high levels of CRP and IL-6. Post-discharge isolation and follow-up should also be strengthened.

Feasibility Studies of Nebulized SARS-CoV-2 Neutralizing Antibody in Mice and Cynomolgus Monkeys.

PURPOSE: Neutralizing antibodies, administrated through intravenous infusion, have shown to be highly efficacious in treating mild and moderate COVID-19 caused by SARS-CoV-2 infection in the lung. However, antibodies do not transport across the plasma-lung barrier efficiently, and up to 100 mg/kg dose was used in human causing significant supply and cost burdens. This study was to explore the feasibility of nebulized antibodies inhalation delivery as an alternative route. METHODS: HB27, a potent RBD-specific humanized monoclonal antibody (Zhu et al. in National Sci Rev. 8:nwaa297, 2020), showed excellent protection against SARS-CoV-2 in animal model and good safety profile in clinical studies. The pharmacokinetics and preliminary safety of HB27 administrated through the respiratory tract were studied in mice and cynomolgus monkeys here. RESULTS: At a single 5 mg/kg dose, the peak HB27 concentration in mice pulmonary epithelial lining fluid (ELF) reached 857.8 µg/mL, 670-fold higher than the PRNT90 value of 1.28 µg/mL, and maintained above PRNT90 over 240 h. In contrast, when administrated by intravenous injection at a 5 mg/kg dose, the antibody concentrations in mice ELF were below PRNT90 value throughout, and were about 50-fold lower than that in the serum. In cynomolgus monkeys administrated with a single dose through inhalation, the antibody concentration in ELF remained high within 3 days. No drug-related safety concerns were observed in the studies. CONCLUSIONS: The study demonstrated that nebulized neutralizing antibody delivery though inhalation could be a more efficient and efficacious alternative approach for treating COVID-19 and other respiratory infectious diseases, and warrants further evaluation in clinical studies.

Lysine ß-Hydroxybutyrylation Improves Stability of COVID-19 Antibody.

ß-Hydroxybutyrate (3HB) is a small molecule produced as a ketone body in mammalian animals. It has been found that 3HB provides not only energy for a body, it also participates in cell signal transduction events as a signal molecule. This study focuses on investigation of 3HB immunomodulatory mechanisms. Proteomic analysis indicates a new post-translational modification of ß-hydroxybutyrylation (Kbhb) on antibodies. Because of the low level of Kbhb antibodies and the associated difficulty in purifying them, simulated Kbhb antibody was produced using chemical modification in vitro. The chemically modified Kbhb antibody was shown to improve the stability of antibodies to protease and heat treatments. Furthermore, Kbhb of antibodies stabilizes the antibodies in plasma. As a remarkable example, COVID-19 neutralizing antibody B38 produced by 293T cells was Kbhb modified and stabilized in vivo, providing a strategy for the possibility of extending the protection effects of COVID-19 antibodies.

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.