Supplements purchase intention: young consumer's perspective

PurposeThis study aims to investigate the factors that influence young consumers' purchase intention towards dietary supplements (DS) in Malaysia.Design/methodology/approachThe supplement industry in Malaysia has been growing rapidly recently due to a paradigm shift in healthcare management, from curative to prevention. Thus, it has sparked interest to conduct a study on the factors that influence young consumers' purchase intentions towards DS. With a response rate of 74.5%, a survey questionnaire was used to elicit data from 149 Gen-Y respondents who consume supplements on a regular basis.FindingsThe results revealed that the influencing factors towards purchase decisions among these Gen-Y respondents aged between 17 and 25 are based on product knowledge and product quality. The implications of these findings and directions for future research are outlined at the end of this paper.Originality/valueThis study offers empirical insights from the perspective of an emerging digital economy on the factors that influence young consumers' purchase intentions towards DS in Malaysia.

Impaired potency of neutralizing antibodies against cell-cell fusion mediated by SARS-CoV-2.

The SARS-CoV-2 Omicron subvariants have dominated the pandemic due to their high transmissibility and immune evasion conferred by the spike mutations. The Omicron subvariants can spread by cell-free virus infection and cell-cell fusion, the latter of which is more effective but has not been extensively investigated. In this study, we developed a simple and high-throughput assay that provides a rapid readout to quantify cell-cell fusion mediated by the SARS-CoV-2 spike proteins without using live or pseudotyped virus. This assay can be used to identify variants of concern and to screen for prophylactic and therapeutic agents. We further evaluated a panel of monoclonal antibodies (mAbs) and vaccinee sera against D614G and Omicron subvariants, finding that cell-cell fusion is substantially more resistant to mAb and serum inhibition than cell-free virus infection. Such results have important implications for the development of vaccines and antiviral antibody drugs against cell-cell fusion induced by SARS-CoV-2 spikes.

N-Butanol Extract of Glycyrrhizae Radix et Rhizoma Inhibits Dengue Virus through Targeting Envelope Protein.

BACKGROUND: At present, about half of the world's population is at risk of being infected with dengue virus (DENV). However, there are no specific drugs to prevent or treat DENV infection. Glycyrrhizae Radix et Rhizome, a well-known traditional Chinese medicine, performs multiple pharmacological activities, including exerting antiviral effects. The aim of this study was to investigate the anti-DENV effects of n-butanol extract from Glycyrrhizae Radix et Rhizome (GRE). METHODS: Compounds analysis of GRE was conducted via ultra-performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). The antiviral activities of GRE were determined by the CCK-8 assay, plaque assay, qRT-PCR, Western blotting, and the immunofluorescence assay. The DENV-infected suckling mice model was constructed to explore the antiviral effects of GRE in vivo. RESULTS: Four components in GRE were analyzed by UHPLC-MS/MS, including glycyrrhizic acid, glycyrrhetnic acid, liquiritigenin, and isoliquiritigenin. GRE inhibited the attachment process of the virus replication cycle and reduced the expression of the E protein in cell models. In the in vivo study, GRE significantly relieved clinical symptoms and prolong survival duration. GRE also significantly decreased viremia, reduced the viral load in multiple organs, and inhibited the release of pro-inflammatory cytokines in DENV-infected suckling mice. CONCLUSIONS: GRE exhibited significant inhibitory activities in the adsorption stage of the DENV-2 replication cycle by targeting the envelope protein. Thus, GRE might be a promising candidate for the treatment of DENV infection.

The Short- and Long-Run Impacts of Air Pollution on Human Health: New Evidence from China.

Under the background of the far-reaching impact of the COVID-19 epidemic on global economic development, the interactive effect of economic recovery and pollution rebound makes the research topic of air pollution and human health receive attention again. Matching a series of new datasets and employing thermal inversion as an instrumental variable, this study investigates the physical and mental health effect of air pollution jointly in China. We find that in the short run, the above inference holds for both physical and mental health. These short-run influences are credible after a series of robustness checks and vary with different individual characteristics and geographical locations. We also find that in the long run, air pollution only damages mental health. Finally, this study calculates the health cost of air pollution. The above findings indicate that in China, the effect of air pollution on physical and mental health cannot be ignored. The government needs to consider the heterogeneity and long-run and short-run differences in the health effects of air pollution when formulating corresponding environmental and medical policies.

Strong Baseline and Bag of Tricks for COVID-19 Detection of CT Scans (preprint)

This paper investigates the application of deep learning models for lung Computed Tomography (CT) image analysis. Traditional deep learning frameworks encounter compatibility issues due to variations in slice numbers and resolutions in CT images, which stem from the use of different machines. Commonly, individual slices are predicted and subsequently merged to obtain the final result; however, this approach lacks slice-wise feature learning and consequently results in decreased performance. We propose a novel slice selection method for each CT dataset to address this limitation, effectively filtering out uncertain slices and enhancing the model's performance. Furthermore, we introduce a spatial-slice feature learning (SSFL) technique\cite{hsu2022} that employs a conventional and efficient backbone model for slice feature training, followed by extracting one-dimensional data from the trained model for COVID and non-COVID classification using a dedicated classification model. Leveraging these experimental steps, we integrate one-dimensional features with multiple slices for channel merging and employ a 2D convolutional neural network (CNN) model for classification. In addition to the aforementioned methods, we explore various high-performance classification models, ultimately achieving promising results.

Epidemiological, clinical, and household transmission characteristics of children and adolescents infected with SARS-CoV-2 Omicron variant in Shanghai, China: a retrospective, multicenter observational study.

OBJECTIVES: To describe the epidemiological, clinical, and household transmission characteristics of pediatric COVID-19 cases in Shanghai, China. METHODS: Pediatric patients with COVID-19 hospitalized in Shanghai from March-May 2022 were enrolled in this retrospective, multicenter cohort study. The symptoms and the risk factors associated with disease severity were analyzed. RESULTS: In total, 2620 cases (age range, 24 days-17 years) were enrolled in this study. Of these, 1011 (38.6%) were asymptomatic, whereas 1415 (54.0%), 190 (7.3%), and 4 (0.2%) patients developed mild, moderate, and severe illnesses, respectively. Household infection rate was negatively correlated with household vaccination coverage. Children aged 0-3 years, those who are unvaccinated, those with underlying diseases, and overweight/obese children had a higher risk of developing moderate to severe disease than children aged 12-17 years, those who were vaccinated, those without any underlying disease, and those with normal weight, respectively (all P <0.05). A prolonged duration of viral shedding was associated with disease severity, presence of underlying diseases, vaccination status, and younger age (all P <0.05). CONCLUSION: Children aged younger than 3 years who were not eligible for vaccination had a high risk of developing moderate to severe COVID-19 with a prolonged duration of viral shedding. Vaccination could protect children from COVID-19 at the household level.

Alarming antibody evasion properties of rising SARS-CoV-2 BQ and XBB subvariants.

The BQ and XBB subvariants of SARS-CoV-2 Omicron are now rapidly expanding, possibly due to altered antibody evasion properties deriving from their additional spike mutations. Here, we report that neutralization of BQ.1, BQ.1.1, XBB, and XBB.1 by sera from vaccinees and infected persons was markedly impaired, including sera from individuals boosted with a WA1/BA.5 bivalent mRNA vaccine. Titers against BQ and XBB subvariants were lower by 13- to 81-fold and 66- to 155-fold, respectively, far beyond what had been observed to date. Monoclonal antibodies capable of neutralizing the original Omicron variant were largely inactive against these new subvariants, and the responsible individual spike mutations were identified. These subvariants were found to have similar ACE2-binding affinities as their predecessors. Together, our findings indicate that BQ and XBB subvariants present serious threats to current COVID-19 vaccines, render inactive all authorized antibodies, and may have gained dominance in the population because of their advantage in evading antibodies.

Antigenic characterization of the SARS-CoV-2 Omicron subvariant BA.2.75.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariant BA.2.75 emerged recently and appears to be spreading. It has nine mutations in spike compared with the currently circulating BA.2, raising concerns that it may further evade vaccine-elicited and therapeutic antibodies. We found BA.2.75 to be moderately more neutralization resistant to sera from vaccinated/boosted individuals than BA.2 (1.8-fold), similar to BA.2.12.1 (1.1-fold), but more neutralization sensitive than BA.4/5 (0.6-fold). Relative to BA.2, BA.2.75 showed heightened resistance to class 1 and class 3 monoclonal antibodies targeting the spike-receptor-binding domain while gaining sensitivity to class 2 antibodies. Resistance was largely conferred by G446S and R460K mutations. BA.2.75 was slightly resistant (3.7-fold) to bebtelovimab, a therapeutic antibody with potent activity against all Omicron subvariants. BA.2.75 also exhibited a higher binding affinity to host receptor ACE2 than other Omicron subvariants. BA.2.75 provides further insight into SARS-CoV-2 evolution as it gains transmissibility while incrementally evading antibody neutralization.

Hourly organic tracers-based source apportionment of PM2.5 before and during the Covid-19 lockdown in suburban Shanghai, China: Insights into regional transport influences and response to urban emission reductions.

During the Covid-19 outbreak, strict lockdown measures led to notable reductions in transportation-related emissions and significantly altered atmospheric pollution characteristics in urban and suburban areas. In this work, we compare comprehensive online measurements of PM2.5 major components and organic molecular markers in a suburban location in Shanghai, China before lockdown (Dec. 28, 2019 to Jan. 23, 2020) and during lockdown (Jan. 24 to Feb. 9, 2020). The NOx levels declined sharply by 59% from 44 to 18 ppb during the lockdown, while O3 rose two times higher to 42 ppb. The PM2.5 level dropped from 64 to 49 µg m-3 (-24%). The major components all showed reductions, with the reduction of nitrate most prominent at -58%, followed by organics at -19%, and sulfate at -17%. Positive matrix factorization analysis identifies fourteen source factors, including nine primary sources and five secondary sources. The secondary sources consist of sulfate-rich factor, nitrate-rich factor, and three secondary organic aerosol (SOA) factors, with SOA_I being anthropogenic SOA, SOA_II associated with later generation products of organic oxidation, and SOA_III being biogenic SOA. The combined secondary sources contributed to 69% and 63% (40 and 22 µg m-3) of PM2.5 before and during lockdown, respectively, among which the reductions in the nitrate-rich (-55%) factor was the most prominent. Among primary sources, large reductions (>80%) were observed in contributions from industrial, cooking, and vehicle emissions. Unlike some studies reporting that the restriction during the Covid-19 resulted in enhanced secondary sulfate and SOA formation, we observed decreases in both secondary inorganic and SOA formation despite the overall elevated oxidizing capacity in the suburban site. Our results indicate that the formation change in secondary inorganic and organic compounds in response to substantial reductions in urban primary precursors are different for urban and suburban environments.

Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach.

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 µg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 µg/m3, accounting for a marginal reduction of 18.6% of the overall NO2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 µg/m3 NO2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.

Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5.

SARS-CoV-2 Omicron subvariants BA.2.12.1 and BA.4/5 have surged notably to become dominant in the United States and South Africa, respectively1,2. These new subvariants carrying further mutations in their spike proteins raise concerns that they may further evade neutralizing antibodies, thereby further compromising the efficacy of COVID-19 vaccines and therapeutic monoclonals. We now report findings from a systematic antigenic analysis of these surging Omicron subvariants. BA.2.12.1 is only modestly (1.8-fold) more resistant to sera from vaccinated and boosted individuals than BA.2. However, BA.4/5 is substantially (4.2-fold) more resistant and thus more likely to lead to vaccine breakthrough infections. Mutation at spike residue L452 found in both BA.2.12.1 and BA.4/5 facilitates escape from some antibodies directed to the so-called class 2 and 3 regions of the receptor-binding domain3. The F486V mutation found in BA.4/5 facilitates escape from certain class 1 and 2 antibodies but compromises the spike affinity for the viral receptor. The R493Q reversion mutation, however, restores receptor affinity and consequently the fitness of BA.4/5. Among therapeutic antibodies authorized for clinical use, only bebtelovimab retains full potency against both BA.2.12.1 and BA.4/5. The Omicron lineage of SARS-CoV-2 continues to evolve, successively yielding subvariants that are not only more transmissible but also more evasive to antibodies.

Nationwide Trends of Pediatric Obesity and BMI z-Score From 2017-2021 in China: Comparable Findings From Real-World Mobile- and Hospital-Based Data.

Introduction: Lifestyle changes including COVID-19 lockdown cause weight gain and may change obesity trends; however, timely changes are largely unknown and monitoring measures are usually lack. This first large-scale study aimed to analyze the real-world national trends of obesity prevalence of Chinese children in the past five years, and the impact of COVID-19 pandemic on pediatric obesity development through both mobile- and hospital-based data. Methods: This study included children aged 3 to 19 years old all over China from January 2017 to April 2021. Hospital-measured and parent-reported cases from XIGAO database were analyzed. Body mass index (BMI) z-score calculation and obesity status evaluation were made according to Chinese standards. We evaluated obesity/overweight prevalence over the past five years and the changes of BMI z-score during COVID-19 lockdown. Results: A total of 656396 children from 31 provinces were involved, including 447481 hospital-measured cases and 208915 parent-reported cases. The obesity and overweight prevalence were 8.05% (95%CI 7.76%-8.39%) and 10.06% (95%CI 10.79%-11.55%), comparable to those of China National Nutrition Surveys during 2015-2019. Northern China had the highest obesity prevalence. Parent-reported data had higher obesity/overweight prevalence than hospital-measured data (18.3% [95%CI 17.7%-18.9%] vs. 21.7% [95%CI 20.7%-23.0%]). The trend of obesity prevalence remained stable with slight decrease, but COVID-19 lockdown caused a significant increase of 1.86% in 2020. Both mobile- and hospital-based data showed weight gain in the first half of 2020. High BMI z-score increase were found among primary and junior middle school children, and children in northeast area during lockdown. Conclusion: Weight gain during COVID-19 among Chinese children had regional differences and mainly affect primary and junior middle school children, thus warrants targeted interventions. The mobile growth assessment based on parent-reported data was a feasible, efficient and timely way for obesity monitoring among Chinese children, especially during epidemic.

An antibody class with a common CDRH3 motif broadly neutralizes sarbecoviruses.

The devastation caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made clear the importance of pandemic preparedness. To address future zoonotic outbreaks due to related viruses in the sarbecovirus subgenus, we identified a human monoclonal antibody, 10-40, that neutralized or bound all sarbecoviruses tested in vitro and protected against SARS-CoV-2 and SARS-CoV in vivo. Comparative studies with other receptor-binding domain (RBD)-directed antibodies showed 10-40 to have the greatest breadth against sarbecoviruses, suggesting that 10-40 is a promising agent for pandemic preparedness. Moreover, structural analyses on 10-40 and similar antibodies not only defined an epitope cluster in the inner face of the RBD that is well conserved among sarbecoviruses but also uncovered a distinct antibody class with a common CDRH3 motif. Our analyses also suggested that elicitation of this class of antibodies may not be overly difficult, an observation that bodes well for the development of a pan-sarbecovirus vaccine.

Potent Anti-SARS-CoV-2 Efficacy of COVID-19 Hyperimmune Globulin from Vaccine-Immunized Plasma.

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine). COVID-HIG shows high-affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein, the receptor-binding domain (RBD), the N-terminal domain of the S protein, and the nucleocapsid protein; and blocks RBD binding to human angiotensin-converting enzyme 2 (hACE2). Pseudotyped and authentic virus-based assays show that COVID-HIG displays broad-spectrum neutralization effects on a wide variety of SARS-CoV-2 variants, including D614G, Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in vitro. However, a significant reduction in the neutralization titer is detected against Beta, Delta, and Omicron variants. Additionally, assessments of the prophylactic and treatment efficacy of COVID-HIG in an Adv5-hACE2-transduced IFNAR-/- mouse model of SARS-CoV-2 infection show significantly reduced weight loss, lung viral loads, and lung pathological injury. Moreover, COVID-HIG exhibits neutralization potency similar to that of anti-SARS-CoV-2 hyperimmune globulin from pooled convalescent plasma. Overall, the results demonstrate the potential of COVID-HIG against SARS-CoV-2 infection and provide reference for subsequent clinical trials.

Antibody evasion properties of SARS-CoV-2 Omicron sublineages.

The identification of the Omicron (B.1.1.529.1 or BA.1) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Botswana in November 20211 immediately caused concern owing to the number of alterations in the spike glycoprotein that could lead to antibody evasion. We2 and others3-6 recently reported results confirming such a concern. Continuing surveillance of the evolution of Omicron has since revealed the rise in prevalence of two sublineages, BA.1 with an R346K alteration (BA.1+R346K, also known as BA.1.1) and B.1.1.529.2 (BA.2), with the latter containing 8 unique spike alterations and lacking 13 spike alterations found in BA.1. Here we extended our studies to include antigenic characterization of these new sublineages. Polyclonal sera from patients infected by wild-type SARS-CoV-2 or recipients of current mRNA vaccines showed a substantial loss in neutralizing activity against both BA.1+R346K and BA.2, with drops comparable to that already reported for BA.1 (refs. 2,3,5,6). These findings indicate that these three sublineages of Omicron are antigenically equidistant from the wild-type SARS-CoV-2 and thus similarly threaten the efficacies of current vaccines. BA.2 also exhibited marked resistance to 17 of 19 neutralizing monoclonal antibodies tested, including S309 (sotrovimab)7, which had retained appreciable activity against BA.1 and BA.1+R346K (refs. 2-4,6). This finding shows that no authorized monoclonal antibody therapy could adequately cover all sublineages of the Omicron variant, except for the recently authorized LY-CoV1404 (bebtelovimab).

Establishing reference sequences for each clade of SARS-CoV-2 to provide a basis for virus variation and function research.

Coronavirus disease 2019 (COVID-19) is a severe respiratory disease caused by the highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As the COVID-19 pandemic continues, mutations of SARS-CoV-2 accumulate. These mutations may not only make the virus spread faster, but also render current vaccines less effective. In this study, we established a reference sequence for each clade defined using the GISAID typing method. Homology analysis of each reference sequence confirmed a low mutation rate for SARS-CoV-2, with the latest clade GRY having the lowest homology with other clades (99.89%-99.93%), and the homology between other clade being greater than or equal to 99.95%. Variation analyses showed that the earliest genotypes S, V, and G had 2, 3, and 3 characterizing mutations in the genome respectively. The G-derived clades GR, GH, and GV had 5, 6, and 13 characterizing mutations in the genome respectively. A total of 28 characterizing mutations existed in the genome of the latest clades GRY. In addition, we found differences in the geographic distribution of different clades. G, GH, and GR are popular in the USA, while GV and GRY are common in the UK. Our work may facilitate the custom design of antiviral strategies depending on the molecular characteristics of SARS-CoV-2.

Striking antibody evasion manifested by the Omicron variant of SARS-CoV-2.

The B.1.1.529/Omicron variant of SARS-CoV-2 was only recently detected in southern Africa, but its subsequent spread has been extensive, both regionally and globally1. It is expected to become dominant in the coming weeks2, probably due to enhanced transmissibility. A striking feature of this variant is the large number of spike mutations3 that pose a threat to the efficacy of current COVID-19 vaccines and antibody therapies4. This concern is amplified by the findings of our study. Here we found that B.1.1.529 is markedly resistant to neutralization by serum not only from patients who recovered from COVID-19, but also from individuals who were vaccinated with one of the four widely used COVID-19 vaccines. Even serum from individuals who were vaccinated and received a booster dose of mRNA-based vaccines exhibited substantially diminished neutralizing activity against B.1.1.529. By evaluating a panel of monoclonal antibodies against all known epitope clusters on the spike protein, we noted that the activity of 17 out of the 19 antibodies tested were either abolished or impaired, including ones that are currently authorized or approved for use in patients. Moreover, we also identified four new spike mutations (S371L, N440K, G446S and Q493R) that confer greater antibody resistance on B.1.1.529. The Omicron variant presents a serious threat to many existing COVID-19 vaccines and therapies, compelling the development of new interventions that anticipate the evolutionary trajectory of SARS-CoV-2.

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.

BACKGROUND: Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. METHODS: In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. RESULTS: Statistical measures-Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)-are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.