Emerging Dominant SARS-CoV-2 Variants.

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants, Omicron (BA.1), BA.2, and BA.4/BA.5, were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. On the basis of newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BP.1, BL*, BA.2.75*, BQ.1*, and particularly BN.1* have a high potential to become the new dominant variants to drive the next surge. Our key projection about these variants dominance made on Oct. 18, 2022 (see arXiv:2210.09485) became reality in late November 2022.

Humoral immune response to inactivated COVID-19 vaccination at the 3rd month among people living with HIV.

BACKGROUND: Research on the immune response to inactivated COVID-19 vaccination among people living with HIV (PLWH) is limited, especially among those with low CD4+ T lymphocyte (CD4 cell) count. This prospective cohort study aimed to assess the humoral immune response to inactivated COVID-19 vaccination among PLWH compared to HIV negative controls (HNCs) and to determine the impact of CD4 cell count on vaccine response among PLWH. METHODS: The neutralizing antibodies (nAbs) and the specific IgM and IgG-binding antibody responses to the inactivated COVID-19 vaccine at the third month after the second dose of inactivated COVID-19 vaccination were measured among 138 PLWH and 35 HNCs. Multivariable logistic regression and multiple linear regression models were conducted to identify factors associated with the seroconversion rate of antibodies and the magnitude of anti-SARS-CoV-2 antibody titers, respectively. RESULTS: At the end of the third month after two doses of vaccination, the seroconversion rates of IgG were comparable between PLWH (44.9%; 95% CI 36.5-53.3%) and HNCs (60.0%; 95% CI 42.9-77.1%), respectively. The median titers and seroconversion rate of nAbs among PLWH were 0.57 (IQR: 0.30-1.11) log10 BAU/mL and 29.0% (95% CI 21.3-36.8%), respectively, both lower than those in HNCs (P < 0.05). After adjusting for age, sex, comorbidities, and CD4 cell count, the titers and seroconversion rate of nAbs were comparable between PLWH and HNCs (P > 0.05). Multivariable regression analyses showed that CD4 cell count < 200/µL was independently associated with lower titers and seroconversion rate of nAbs among PLWH (P < 0.05). A positive correlation was observed between the CD4 cell count and nAbs titers in PLWH (Spearman's ρ = 0.25, P = 0.0034). CONCLUSION: Our study concluded that the immune response to inactivated COVID-19 vaccination among PLWH was independently associated with CD4 cell count, PLWH with lower CD4 cell count showed a weaker humoral immune response, especially those with CD4 cell count < 200/µL. This finding suggests that expanding COVID-19 vaccination coverage among PLWH is impendency. In addition, aggressive ART should be carried out for PLWH, especially for those with low CD4 cell count, to improve the immune response to vaccines.

Non-clinical safety evaluation of prophylactic mRNA vaccine:general principles and concerns

Messenger RNA（mRNA） vaccine, with antigen-encoded mRNA packaged in delivery vehicles, performs its functions via antigen translation and specific immune response. mRNA vaccines have proven their protective effects and safety in the ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2（SARS-CoV-2）. The World Health Organization issued guidelines specifically for prophylactic mRNA vaccines in 2021, which provide important guidance for non-clinical research on mRNA vaccines. Furthermore, some unusual adverse reactions, such as cerebrovascular disease, embolic stroke, transient cerebral ischemia, deep vein thrombosis, myocarditis（pericarditis） and allergic reactions, have been also found in clinical trials and applications of mRNA vaccines, which deserves attention in non-clinical studies.

Synergistic relationship or not? Understanding the resilience and efficiency of the tourism economy: evidence from Hainan Province, China.

The COVID-19 pandemic has dealt a serious blow to the global tourism industry, causing a fracturing of and decline in tourism development efficiency and even a stagnation of tourism development in some regions. To solve the contradiction between efficiency and quality, it is necessary to ensure the endogenous power of tourism resilience while pursuing the efficiency of tourism development. This study assumes that Hainan Province follows a tourism development path led by resilience. The improved weighting method, EBM model and Haken model are used to evaluate the level of resilience, the level of efficiency and their co-evolution. The findings indicate that the core tourism cities represented by Sanya and Haikou have a high level in the individual fields of tourism development efficiency and tourism economic resilience but have limited performance in the synergistic relationship between tourism development efficiency and tourism economic resilience. In contrast, the marginal tourism cities represented by Tunchang County and Ledong County have low tourism development efficiency and resilience, but their synergistic development level is high. This result proves that co-evolution plays a dual forward and reverse driving role. Based on the identification of the order parameters, it is concluded that Hainan Province is characterized by a synergistic evolutionary synergy dominated by resilience, which is in line with the trend of social development and the sustainable development of tourism. While reasonably pursuing the tourism economy and development efficiency, we should pay attention to strengthening resilience construction based on multiple aspects, such as tourists, enterprises, organizations, governments and destinations.

Persistent topological Laplacian analysis of SARS-CoV-2 variants (preprint)

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its incapability of describing the homotopic shape evolution of data during filtration. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the drawback of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD) and its variants, i.e., Alpha, Beta, Gamma, BA.1, and BA.2. First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on PTL-based machine learning, including deep learning, and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science.

Amyloid Protein Cross-Seeding Provides a New Perspective on Multiple Diseases In Vivo.

Amyloid protein cross-seeding is a peculiar phenomenon of cross-spreading among different diseases. Unlike traditional infectious ones, diseases caused by amyloid protein cross-seeding are spread by misfolded proteins instead of pathogens. As a consequence of the interactions among misfolded heterologous proteins or polypeptides, amyloid protein cross-seeding is considered to be the crucial cause of overlapping pathological transmission between various protein misfolding disorders (PMDs) in multiple tissues and cells. Here, we briefly review the phenomenon of cross-seeding among amyloid proteins. As an interesting example worth mentioning, the potential links between the novel coronavirus pneumonia (COVID-19) and some neurodegenerative diseases might be related to the amyloid protein cross-seeding, thus may cause an undesirable trend in the incidence of PMDs around the world. We then summarize the theoretical models as well as the experimental techniques for studying amyloid protein cross-seeding. Finally, we conclude with an outlook on the challenges and opportunities for basic research in this field. Cross-seeding of amyloid opens up a new perspective in our understanding of the process of amyloidogenesis, which is crucial for the development of new treatments for diseases. It is therefore valuable but still challenging to explore the cross-seeding system of amyloid protein as well as to reveal the structural basis and the intricate processes.

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.

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.

Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

A simplified economic model and case study for recovery ventilation based on SPECO method

Covid-19 further revealed the significance of ventilation by air conditioning systems. Most common split heaters and resistance heaters recirculate the indoor air without ventilation process. Ventilation wastes energy consumption by the building. However, adding an air-to-air heat recovery unit seems a quick solution to reduce the wasted heat of the ventilation process. Nonetheless, recovery unit means further pumping power (pressure drop through the air-to-air heat exchanger), capital cost, additional fans and their electricity consumption, exergy costs and so on. Hence, the profitability of the recovery unit depends on outdoor temperature, desired indoor temperature, electricity price of the region, exergy loss and also the aforementioned factors. In this research the general standard Specific Exergy Costing theory is employed and simplified as an economic strategy for recovery ventilation. The model not only is able to predict the profitability of the ventilation process using air-to-air heat exchanger, but also it is an optimization tool for air-to-air heat recovery units as provided as a case study in this paper.

Six-month humoral immune response to inactivated COVID-19 vaccine among people living with HIV.

Longitudinal humoral immune response to inactivated COVID-19 vaccines among people living with HIV (PLWH) have not yet been systematically investigated. We conducted a 6-month longitudinal study among vaccinated PLWH and HIV-Negative Controls (HNC) to determine whether the humoral immune response effects of the inactivated COVID-19 vaccine are different between the two groups of people. Totally, 46 PLWH and 38 HNC who received the inactivated COVID-19 vaccine on days 0 and 28 were enrolled. The SARS-CoV-2 neutralizing antibodies (nAbs) and total specific IgM and IgG antibodies were examined on Day 0-Day190. The level and positive seroconversion rate of nAbs peaked on Day 42 in HNC while peaked on Day 70 in PLWH, then decreased gradually with the extension of the vaccination period after the peaks. The peak level of nAbs in PLWH on Day 70, (GMC 8.07 BAU/mL, 95% CI 5.67-11.48) was significantly lower than in HNC on Day 42 (GMC 18.28 BAU/mL, 95% CI 10.33-32.33, P =0.03). The decrease in the geometric mean concentrations (GMCs) of nAbs was observed as 42.9% in PLWH after peak level, which decreased from 8.07 BAU/mL [95% CI: 5.67-11.48] on Day 70 to 4.61 BAU/mL [95% CI: 3.35-6.34] on Day 190 (p = 0.02). On Day 190, only seven (18%, [95% CI: 6-40]) HNC and five (11%, [95% CI: 4-25]) PLWH maintained positive nAbs response respectively. The geometric mean ELISA units (GMEUs) and positive seroconversion rate of IgG in PLWH dropped significantly from Day 70 (GMEUs, 0.20 EU/mL, [95% CI: 0.13-0.34]; seroconversion, 52%, [95% CI: 34-69]) to Day 190 (GMEUs, 0.05 EU/mL, [95% CI: 0.03-0.08], P<0.001; seroconversion, 18%, [95% CI: 8-33], P<0.001). There was no significant difference in levels and seroconversion rates of nAbs and IgG between the two groups on Day 190. The peak immunogenicity of the inactivated COVID-19 vaccine was delayed and inferior in PLWH compared to HNC, while no significant difference was found in six-month immunogenicity between the two groups.

Plague and "Anti-Quarantine" Writing in Traditional Chinese Biography

Quarantine remains important in treating infectious diseases in the world, but its legitimacy has frequently been questioned, not only during the COVID-19 pandemic but throughout world history - from Ancient Greece to Ancient China, from the past to the present. Anti-quarantine writings in traditional Chinese biography serve as a good example in this regard. They tend to "copy" from each other, since similar and repetitive narrative structures abound in these writings, usually to the neglect of the important dimensions, such as the medical knowledge and the psychological activities of the protagonists. The popularity of "anti-quarantine" writing in traditional Chinese biographies does not come from its literariness, and the biographies are mostly boring and full of cliches. What makes this writing popular is how it appeals to social realities and external factors such as politics, medicine, and ethics.

Sooner, later, or never: Changing fertility intentions due to Covid-19 in China's Covid-19 epicentre.

Using survey data collected from Hubei province, China's Covid-19 epicentre, in August 2020, this study examines how fertility intentions of Chinese citizens changed during the Covid-19 pandemic. We consider not only whether people changed their fertility plans due to Covid-19 but also distinguish three types of change: bringing forward ('sooner'), postponing ('later'), and abandoning ('never') planned fertility. Over half of those who planned to have a child intended to change their fertility plans due to Covid-19. Younger individuals, those of non-Han ethnicities, urban residents, those with one child already, and those with ever-infected family members were more likely to change their fertility plans. While the effects of some characteristics seem to be short term, other characteristics such as age and number of children show more consequential influences. Older individuals and those planning their second child were particularly prone to abandoning their childbearing plans due to Covid-19. The pandemic may thus complicate China's latest efforts to boost its low fertility.

Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants.

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the past. However, topology is insensitive to homotopic shape evolution in virus-human protein-protein binding, which is crucial to viral evolution and transmission. This challenge is tackled with persistent Laplacian, which is able to capture both the topological change and homotopic shape evolution of data. Persistent Laplacian-based deep learning models are developed to systematically evaluate variant infectivity. Our comparative analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1, BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular, BA.4 and BA.5 are about 36% more infectious than BA.2 and are projected to become new dominant variants by natural selection. Moreover, the proposed models outperform the state-of-the-art methods on three major benchmark datasets for mutation-induced protein-protein binding free energy changes. Our key projection about BA4 and BA.5's dominance made on May 1, 2022 (see arXiv:2205.00532) became a reality in late June 2022.

Six-month humoral immune response to inactivated COVID-19 vaccine among people living with HIV

Longitudinal humoral immune response to inactivated COVID-19 vaccines among people living with HIV (PLWH) have not yet been systematically investigated. We conducted a 6-month longitudinal study among vaccinated PLWH and HIV-Negative Controls (HNC) to determine whether the humoral immune response effects of the inactivated COVID-19 vaccine are different between the two groups of people. Totally, 46 PLWH and 38 HNC who received the inactivated COVID-19 vaccine on days 0 and 28 were enrolled. The SARS-CoV-2 neutralizing antibodies (nAbs) and total specific IgM and IgG antibodies were examined on Day 0-Day190. The level and positive seroconversion rate of nAbs peaked on Day 42 in HNC while peaked on Day 70 in PLWH, then decreased gradually with the extension of the vaccination period after the peaks. The peak level of nAbs in PLWH on Day 70, (GMC 8.07 BAU/mL, 95% CI 5.67-11.48) was significantly lower than in HNC on Day 42 (GMC 18.28 BAU/mL, 95% CI 10.33-32.33, P =0.03). The decrease in the geometric mean concentrations (GMCs) of nAbs was observed as 42.9% in PLWH after peak level, which decreased from 8.07 BAU/mL [95% CI: 5.67-11.48] on Day 70 to 4.61 BAU/mL [95% CI: 3.35-6.34] on Day 190 (p = 0.02). On Day 190, only seven (18%, [95% CI: 6-40]) HNC and five (11%, [95% CI: 4-25]) PLWH maintained positive nAbs response respectively. The geometric mean ELISA units (GMEUs) and positive seroconversion rate of IgG in PLWH dropped significantly from Day 70 (GMEUs, 0.20 EU/mL, [95% CI: 0.13-0.34];seroconversion, 52%, [95% CI: 34-69]) to Day 190 (GMEUs, 0.05 EU/mL, [95% CI: 0.03-0.08], P<0.001;seroconversion, 18%, [95% CI: 8-33], P<0.001). There was no significant difference in levels and seroconversion rates of nAbs and IgG between the two groups on Day 190. The peak immunogenicity of the inactivated COVID-19 vaccine was delayed and inferior in PLWH compared to HNC, while no significant difference was found in six-month immunogenicity between the two groups.

Identification and high-throughput quantification of baicalein and its metabolites in plasma and urine

Ethnopharmacological relevance Scutellaria baicalensis Georgi. contains varieties of function compounds, and it has been used as traditional drug for centuries. Baicalein is the highest amount of flavonoid found in Scutellaria baicalensis Georgi., which exerts various pharmacological activities and might be a promising drug to treat COVID-19. Aim of the study The present work aims to investigate the metabolism of baicalein in humans after oral administrations, and study the pharmacokinetics of BA and its seven metabolites in plasma and urine. Materials and methods The metabolism profiling and the identification of baicalein metabolites were performed on HPLC-Q-TOF. Then a column-switching method named MPX™-2 system was applied for the high-throughput determination of BA and seven metabolites. Results Seven metabolites were identified using HPLC-Q-TOF, including sulfate, glucuronide, glucoside, and methyl-conjugated metabolites. Pharmacokinetic study found that BA was extensively metabolized in vivo, and only 5.65% of the drug remained intact in the circulatory system after single dosing. Baicalein-7-O-sulfate and baicalein-6-O-glucuronide-7-O-glucuronide were the most abundant metabolites. About 7.2% of the drug was excreted through urine and mostly was metabolites. Conclusion Seven conjugated metabolites were identified in our assay. A high-throughput HPLC-MS/MS method using column switch was established for quantifying BA and its metabolites. The method has good sensitivity and reproducibility, and successfully applied for the clinical pharmacokinetic study of baicalein and identified metabolites. We expect that our results will provide a metabolic and pharmacokinetic foundation for the potential application of baicalein in medicine.

Development of a duplex real-time PCR assay for simultaneous detection and differentiation of Theileria equi and Babesia caballi.

Equine Piroplasmosis (EP) is a tick-borne disease caused by three apicomplexan protozoan parasites, Theileria equi (T. equi), Babesia caballi (B. caballi) and T. haneyi, which can cause similar clinical symptoms. There are five known 18S rRNA genotypes of T. equi group (including T. haneyi) and three of B. caballi. Real-time PCR methods for detecting EP based on 18S rRNA analysis have been developed, but these methods cannot detect all genotypes of EP in China, especially genotype A of T. equi. In this study, a duplex real-time PCR detection method was developed for the simultaneous detection and differentiation of T. equi and B. caballi. The primers and probes for this duplex real-time PCR assay were designed based on the conserved 18S rRNA gene sequences of all genotypes of T. equi and B. caballi including Chinese strain. Double-quenched probes were used in this method, which provide less background and more signal to decrease the number of false positives relative to single-quenched probes. The newly developed real-time PCR assays exhibited good specificity, sensitivity, repeatability and reproducibility. The real-time PCR assays were further validated by comparison with a nested PCR assay and a previous developed real-time PCR for EP and sequencing results in the analysis of 506 clinical samples collected from 2019 to 2020 in eleven provinces and regions of China. Based on clinical performance, the agreements between the duplex real-time PCR assay and the nPCR assay or the previous developed real-time PCR assay were 92.5% (T. equi) and 99.4% (B. caballi) or 87.4% (T. equi) and 97.2% (B. caballi). The detection results showed that the positivity rate of T. equi was 43.87% (222/506) (10 genotype A, 1 genotype B, 4 genotype C, 207 genotype E), while that of B. caballi was 5.10% (26/506) (26 genotype A), and the rate of T. equi and B. caballi co-infection was 2.40% (12/506). The established method could contribute to the accurate diagnosis, pathogenic surveillance and epidemiological investigation of T. equi and B. caballi infections in horses.

Emerging dominant SARS-CoV-2 variants (preprint)

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge.

Data science in the intensive care unit.

In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.

Safety monitoring in inactivated COVID-19 vaccines by clinical pharmacists from a single center in China.

Given that vaccine-induced adverse effects were mostly based on previous laboratory research and clinical trials, real-world data on the safety of coronavirus disease 2019 (COVID-19) vaccination were lacking. This study reported the adverse events (AEs) among inactivated COVID-19 vaccine recipients. Data were collected from a total of 2,808 hospital employees and their family members in Wuhan, China, with all of them receiving the first dose of inactivated COVID-19 vaccines from two pharmaceutical companies. The first dose was given between 29th April and 13th May 2021. A total of 2,732 vaccinees received the second dose between 27th May and 8th July 2021. The whole process of receiving the vaccine was monitored by clinical pharmacists, and the information on AEs including demographics, occurrence, types, and severity was recorded through an online questionnaire and telephone follow-up. Most of the common AEs were mild and tolerable, and the overall incidence of AEs was lower than the data from the safety profile in clinical trials. Moreover, the incidence of AEs in the first dose (21.30%, 598) was higher than that in the second dose (16.07%, 439). Furthermore, the first injection had more severe AEs (4, 0.14%) than the second injection (2, 0.07%). The AEs involved the skin, muscle, respiratory tract, gastrointestinal tract, cardiovascular system, and other tissues and systems. The most common AE was pain at the injection site (first dose: 10.19%, second dose: 12.55%). All the vaccinees with AEs for both doses recovered fully in the end. It was noted that some AEs might cause blood coagulation disorder and bleeding risk. Therefore, ongoing monitoring of AEs after COVID-19 vaccination is essential in evaluating the benefits and risks of each vaccine.

Topological AI forecasting of future dominating viral variants (preprint)

The understanding of the mechanisms of SARS-CoV-2 evolution and transmission is one of the greatest challenges of our time. By integrating artificial intelligence (AI), viral genomes isolated from patients, tens of thousands of mutational data, biophysics, bioinformatics, and algebraic topology, the SARS-CoV-2 evolution was revealed to be governed by infectivity-based natural selection. Two key mutation sites, L452 and N501 on the viral spike protein receptor-binding domain (RBD), were predicted in summer 2020, long before they occur in prevailing variants Alpha, Beta, Gamma, Delta, Kappa, Theta, Lambda, Mu, and Omicron. Recent studies identified a new mechanism of natural selection: antibody resistance. AI-based forecasting of Omicron's infectivity, vaccine breakthrough, and antibody resistance was later nearly perfectly confirmed by experiments. The replacement of dominant BA.1 by BA.2 in later March was predicted in early February. On May 1, 2022, persistent Laplacian-based AI projected Omicron BA.4 and BA.5 to become the new dominating COVID-19 variants. This prediction became reality in late June. Topological AI models offer accurate prediction of mutational impacts on the efficacy of monoclonal antibodies (mAbs).