Effectiveness of COVID-19 Vaccination Against SARS-CoV-2 Omicron Variant Infection and Symptoms - China, December 2022-February 2023.

What is already known about this topic?: A considerable percentage of the population has received both primary and booster vaccinations, which could potentially provide protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron infections and related symptoms. What is added by this report?: The self-reported infection rate, as determined from an online survey, reached its peak (15.5%) between December 19 and 21, 2022, with an estimated 82.4% of individuals in China being infected as of February 7, 2023. During the epidemic, the effectiveness of booster vaccinations against SARS-CoV-2 Omicron infection was found to be 49.0% within three months of vaccination and 37.9% between 3 and 6 months following vaccination. Furthermore, the vaccine effectiveness of the booster vaccination in relation to symptom prevention varied from 48.7% to 83.2% within three months and from 25.9% to 69.0% between 3 and 6 months post-booster vaccination. What are the implications for public health practice?: The development and production of efficacious vaccines, together with prompt vaccinations or emergency vaccinations, have the potential to mitigate the epidemic's impact and safeguard public health.

In Silico Discovery of Small Molecule Modulators Targeting the Achilles' Heel of SARS-CoV-2 Spike Protein.

The spike protein of SARS-CoV-2 has been a promising target for developing vaccines and therapeutics due to its crucial role in the viral entry process. Previously reported cryogenic electron microscopy (cryo-EM) structures have revealed that free fatty acids (FFA) bind with SARS-CoV-2 spike protein, stabilizing its closed conformation and reducing its interaction with the host cell target in vitro. Inspired by these, we utilized a structure-based virtual screening approach against the conserved FFA-binding pocket to identify small molecule modulators of SARS-CoV-2 spike protein, which helped us identify six hits with micromolar binding affinities. Further evaluation of their commercially available and synthesized analogs enabled us to discover a series of compounds with better binding affinities and solubilities. Notably, our identified compounds exhibited similar binding affinities against the spike proteins of the prototypic SARS-CoV-2 and a currently circulating Omicron BA.4 variant. Furthermore, the cryo-EM structure of the compound SPC-14 bound spike revealed that SPC-14 could shift the conformational equilibrium of the spike protein toward the closed conformation, which is human ACE2 (hACE2) inaccessible. Our identified small molecule modulators targeting the conserved FFA-binding pocket could serve as the starting point for the future development of broad-spectrum COVID-19 intervention treatments.

Working from Home in Urban China during the COVID-19 Pandemic: Assemblages of Work-Family Interference.

During the COVID-19 pandemic, millions of workers globally have been forced to work from home. Empirical data from Chinese cities in the Hubei province reveal work productivity decreased among many respondents working from home in 2020, primarily due to family interference with work. Such interference stems not only from the domain of daily life but also from other family members' e-working and e-learning. Conversely, respondents' work interferes with family; thus, interference operates bi-directionally. This article proposes an analytical framework of work-family interference along three dimensions: work-daily life, work-work, work-study, and each dimension can be understood through four distinct aspects: temporality, physicality, vocality, digitality. Remote workers encounter 'assemblages of work-family interference', consisting of a heterogeneous mixture of these dimensions and aspects. Furthermore, some factors (e.g., living patterns, work culture, digital infrastructure) constrain effective work-family boundary management among urban households.

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China.

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province.

Impact of the COVID-19 on electricity consumption of open university campus buildings - The case of Twente University in the Netherlands.

Since the COVID-19 outbreak, the restrictive policies enacted by countries in response to the epidemic have led to changes in the movement of people in public places, which has had a direct impact on the use and energy consumption of various public buildings. This study was based on electricity consumption data for 25 on-campus public buildings at 1-hour intervals between January 2020 and June 2022 at Tewnte University in the Netherlands, and after the data were climate-corrected by multiple regression analysis, the changes in EU and EUI for various types of buildings were compared for different restriction periods using ANOVA, LSD and t-tests. And additionally, further analyzed the changes and reasons for the electricity consumption of various public buildings on campus and customers' electricity consumption behavior in a period of time after the lifting of the epidemic restriction policy. The results of ANOVA analysis show that the restriction policy has a significant effect on teaching, sports, and cultural buildings, and the electricity intensity of the three types of buildings is reduced by 0.28, 0.09, and 0.07 kwh/m2/day respectively under the strict restriction policy; The t-test results show that during the restriction period, all building types, except for living and academic buildings, show a significant decreasing trend, with the teaching buildings having the greatest energy saving potential, with an average daily EU reduction of 1088kwh/day and an EUI reduction of 0.075kwh/ m2/day. The above findings provide a case study of a complete cycle of energy consumption changes in university buildings under similar epidemic restriction policies before and after the epidemic restriction, and inform the electricity allocation policies of university and government energy management authorities.

Serum Proteomic Analysis for New Types of Long-Term Persistent COVID-19 Patients in Wuhan.

The emergence of a new type of COVID-19 patients, who were retested positive after hospital discharge with long-term persistent SARS-CoV-2 infection but without COVID-19 clinical symptoms (hereinafter, LTPPs), poses novel challenges to COVID-19 treatment and prevention. Why was there such a contradictory phenomenon in LTPPs? To explore the mechanism underlying this phenomenon, we performed quantitative proteomic analyses using the sera of 12 LTPPs (Wuhan Pulmonary Hospital), with the longest carrying history of 132 days, and mainly focused on 7 LTPPs without hypertension (LTPPs-NH). The results showed differential serum protein profiles between LTPPs/LTPPs-NH and health controls. Further analysis identified 174 differentially-expressed-proteins (DEPs) for LTPPs, and 165 DEPs for LTPPs-NH, most of which were shared. GO and KEGG analyses for these DEPs revealed significant enrichment of "coagulation" and "immune response" in both LTPPs and LTPPs-NH. A unity of contradictory genotypes in the 2 aspects were then observed: some DEPs showed the same dysregulated expressed trend as that previously reported for patients in the acute phase of COVID-19, which might be caused by long-term stimulation of persistent SARS-CoV-2 infection in LTPPs, further preventing them from complete elimination; in contrast, some DEPs showed the opposite expression trend in expression, so as to retain control of COVID-19 clinical symptoms in LTPPs. Overall, the contrary effects of these DEPs worked together to maintain the balance of LTPPs, further endowing their contradictory steady-state with long-term persistent SARS-CoV-2 infection but without symptoms. Additionally, our study revealed some potential therapeutic targets of COVID-19. Further studies on these are warranted. IMPORTANCE This study reported a new type of COVID-19 patients and explored the underlying molecular mechanism by quantitative proteomic analyses. DEPs were significantly enriched in "coagulation" and "immune response". Importantly, we identified 7 "coagulation system"- and 9 "immune response"-related DEPs, the expression levels of which were consistent with those previously reported for patients in the acute phase of COVID-19, which appeared to play a role in avoiding the complete elimination of SARS-CoV-2 in LTPPs. On the contrary, 6 "coagulation system"- and 5 "immune response"-related DEPs showed the opposite trend in expression. The 11 inconsistent serum proteins seem to play a key role in the fight against long-term persistent SARS-CoV-2 infection, further retaining control of COVID-19 clinical symptom of LTPPs. The 26 proteins can serve as potential therapeutic targets and are thus valuable for the treatment of LTPPs; further studies on them are warranted.

Effect of Green Plants on Individuals' Mental Stress during the COVID-19 Pandemic: A Preliminary Study.

The COVID-19 pandemic has not only jeopardized people's physical health, but also put additional strain on their mental health. This study explored the role of indoor natural elements (i.e., green plants) in relieving individuals' mental stress during a prolonged stressful period. A pilot and three formal studies examined the effect of indoor green plants placed in living and working environments on people's perceived stress during the pandemic and further uncovered its underlying mechanism emphasizing a mediating role of emotion. The pilot study confirmed that the severity of the pandemic positively correlated with individuals' level of stress. Study 1 then demonstrated that indoor green plants in people's living environments might reduce their perceived stress during the pandemic, which is referred to as the "plant effect". Study 2 repeated the plant effect in a field experiment conducted in a working environment and Study 3 revealed a mediating role of positive emotion. This study provides preliminary evidence for the mitigating effect of indoor green plants on individuals' mental stress during the COVID-19 pandemic period. The indoor green plants placed in living and working environments may elicit positive emotion, which in turn reduce people's mental stress. In addition, our results reveal that growth status of the indoor green plants affected the plant effect as well.

Density-based detection of cell transition states to construct disparate and bifurcating trajectories.

Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

Scalable Printing of Prussian Blue Analogue@Au Edge-Rich Microcubes as Flexible Biosensing Microchips Performing Ultrasensitive Sucrose Fermentation Monitoring.

Sucrose is one of the most applied carbon sources in the fermentation process, and it directly determines the microbial metabolism with its concentration fluctuation. Meanwhile, sucrose also plays a key role of a protective agent in the production of biological vaccines, especially in the new mRNA vaccines for curing COVID-19. However, rapid and precise detection of sucrose is always desired but unrealized in industrial fermentation and synthetic biology research. In order to address the above issue, we proposed an ultrasensitive biosensor microchip achieving accurate sucrose recognition within only 12 s, relying on the construction of a Prussian blue analogue@Au edge-rich (PBA@AuER) microarchitecture. This special geometric structure was formed through exactly inducing the oriented PBA crystallization toward a certain plane to create more regular and continuous edge features. This composite was further transformed to a screen-printed ink to directly and large-scale fabricate an enzymatic biosensor microchip showing ultrahigh sensitivity, a wide detection range, and a low detection limit to the accurate sucrose recognition. As confirmed in a real alcohol fermentation reaction, the as-prepared microchip enabled us to accurately detect the sucrose and glucose concentrations with outstanding reusability (more than 300 times) during the whole process through proposing a novel analytical strategy for the binary mixture substrate system.

Unequal impact of the COVID-19 pandemic on mental health: Role of the neighborhood environment.

The COVID-19 pandemic has taken a significant toll on people's mental wellbeing. Few studies have investigated how the neighborhood environment might help to moderate the mental health impact in a natural disaster context. We aim to investigate the unequal impact of the pandemic on mental health between different population groups, and the role of the neighborhood environment in alleviating this impact. We collected survey data (n=2,741) on mental health, neighborhood environment, and pandemic-related behaviors in Beijing metropolitan region between July 10 and 28, 2020, and then applied the partial proportional odds model. Overall, we found that the pandemic has disproportionately affected the lower-income people. The lower-income residents experienced a greater psychological impact than the higher-income residents. We further found that distance to an urban park was a key built environment variable that moderates mental health impact. Residents who lived near urban parks were 4.2 to 4.6% less likely to report an increase in negative emotions, and therefore are more resilient to the mental health impact. In addition to the built environment, a cohesive neighborhood environment may have also helped to mitigate the negative mental health impacts. These findings can inform planning policies that aim to promote healthy and resilient communities.

Frailty is an independent determinant of COVID-19 vaccine hesitancy in the elderly: a cross-sectional study.

COVID-19 vaccines have become an important hope for slowing down or stopping the pandemic. As the population ages, older adults will comprise a greater proportion of the vaccinated population. We aimed to assess influencing factors of COVID-19 vaccine hesitancy in older adults. For this aim, We conducted a cross-sectional study on a questionnaire survey of the elderly over 65 years living in the community of Haikou City from August 1st to September 30th, 2021. Univariate and multivariate Logistic regression analyses were performed to identify factors related to vaccine hesitancy. We analyzed completed questionnaires from 225 respondents (42.2% women, mean age 73.4±6.2 years). There were 99 people in the vaccine hesitation group and 126 people in the vaccine acceptance group, the incidence of vaccine hesitation in the elderly population is about44%（99/225）. The incidence of frailty in the vaccine hesitation group was much higher than that in the vaccine trust group (62.63 vs. 30.95%, P＜0.001). The risk factors of vaccine hesitancy in the elderly aged 70-75years and over 75 years were 2.987 times and 3.587 times higher than that in the population aged 65-70 years (OR=2.987,95%CI: 1.424-6.265, P＜0.001; OR=3.587,95% CI:1.804-7.131, P＜0.001). Frailty is also an independent risk factor of vaccine hesitancy in the elderly population (OR=2.624,95%CI: 1.447-4.757, P＜0.001). Then the vaccination rate of the COVID-19 vaccine is far from reaching the requirements of herd immunity, and more flexible and comprehensive efforts are needed to increase the vaccination willingness of the frail elderly.

Association Between COVID-19 Vaccination Coverage and Case Fatality Ratio: a Comparative Study - Hong Kong SAR, China and Singapore, December 2021-March 2022.

What is already known about this topic?: Hong Kong Special Administrative Region (SAR), China and Singapore are both facing considerable Omicron variant epidemic. However, the overwhelmed medical system and high case fatality ratio (CFR) just occurred in Hong Kong SAR, China but not in Singapore. What is added by this report?: The low vaccination coverage in Hong Kong SAR, China, especially among the older adults, is shown to be a primary reason of its recent high CFR. What are the implications for public health practice?: Facing the potential epidemic risk, non-vaccinated, non-fully-vaccinated, and non-booster-vaccinated people in China, especially the elderly, should get any type of accessible vaccine, which could save lives when the infection unfortunately befalls.

Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.

Motivation: Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission. Methods: In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness. Results: The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 (p < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort (n = 566, AUC = 0.84). Conclusion: A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.