Drug resistance revealed by in silico deep mutational scanning and mutation tracker (preprint)

As COVID-19 enters its fifth year, it continues to pose a significant global health threat, with the constantly mutating SARS-CoV-2 virus challenging drug effectiveness. A comprehensive understanding of virus-drug interactions is essential for predicting and improving drug effectiveness, especially in combating drug resistance during the pandemic. In response, the Path Laplacian Transformer-based Prospective Analysis Framework (PLFormer-PAF) has been proposed, integrating historical data analysis and predictive modeling strategies. This dual-strategy approach utilizes path topology to transform protein-ligand complexes into topological sequences, enabling the use of advanced large language models for analyzing protein-ligand interactions and enhancing its reliability with factual insights garnered from historical data. It has shown unparalleled performance in predicting binding affinity tasks across various benchmarks, including specific evaluations related to SARS-CoV-2, and assesses the impact of virus mutations on drug efficacy, offering crucial insights into potential drug resistance. The predictions align with observed mutation patterns in SARS-CoV-2, indicating that the widespread use of the Pfizer drug has lead to viral evolution and reduced drug efficacy. PLFormer-PAF's capabilities extend beyond identifying drug-resistant strains, positioning it as a key tool in drug discovery research and the development of new therapeutic strategies against fast-mutating viruses like COVID-19.

Persistent Topological Laplacian Analysis of SARS-CoV-2 Variants

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 inability to handle heterogeneous information, such as multiple types of geometric objects;being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe nontopological changes, such as homotopic changes in protein–protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations 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). 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 a topological deep learning paradigm 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. [ FROM AUTHOR] Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Study on prescription medication mode and mechanism of traditional Chinese medicine in the treatment of noncritical COVID-19 based on data mining

Background: As of 2023, coronavirus disease 2019 (COVID-19) is still spreading globally. Therefore, we aim to integrate non-critical COVID-19 high-frequency and high-targeting Chinese medicines to provide a reference for clinical prescriptions to improve COVID-19-related symptoms. Materials and methods: The information on non-critical COVID-19 high-frequency Chinese medicines in the diagnosis and treatment of COVID-19 was obtained by the TCM inheritance support platform. Using network pharmacology and molecular docking technology, high-targeting Chinese medicines with good docking activity with COVID-19 receptors angiotensin-converting enzyme-II (ACE2), 3CLpro and tyrosine-protein kinase receptor UFO (AXL) were obtained. A new prescription for non-critical COVID-19 was established by integrating high-frequency and high-targeting Chinese medicines. Rats with acute lung injury induced by lipopolysaccharide were used as the experimental model. The histopathological changes in the lungs of rats in each group were observed by hematoxylin-eosin staining. The lung coefficient of rats was measured. The levels of IL-6, TNF-a, and IL-1ß in serum were detected by enzyme-linked immunosorbent assay. The mRNA and protein levels of ACE2 and AXL in lung tissue were detected by real-time quantitative polymerase chain reaction and western blot. Results: Through data mining, it was found that there were 39 high-frequency traditional Chinese medicines for non-critical COVID-19 in the diagnosis and treatment guidelines. According to network pharmacology and molecular docking, 30 highly targeted traditional Chinese drugs for COVID-19 were found. The new prescriptions for non-critical COVID-19 were comprehensively obtained, including Glycyrrhizae Radix, Ephedra Herba, Amygdalus Communis Vas, Gypsum Fibrosum, Descurainiae Semen, Atractylodes Lancea, Scutellariae Radix, Amomum Tsao-Ko Crevostet, Forsythiae Fructus, Pogostemon cablin, Magnolia Officinalis. Compared with the LPS-induced lung injury model group, the medium dose of the new prescription group had significantly alleviated pathological changes in lung tissue, decreased lung coefficient, decreased contents of IL-6, TNF-a and IL-1ß, and increased mRNA and protein expression of ACE2 and AXL (P < 0.05). Conclusion: Based on data mining, network pharmacology and molecular docking technology, the new prescription for non-critical COVID-19 established by this method has an anti-inflammatory effect on rats with acute lung injury induced by lipopolysaccharide and can provide a reference for clinicians to alleviate the symptoms related to non-critical COVID-19. [ FROM AUTHOR] Copyright of Traditional Medicine Research is the property of TMR Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells.

Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.

Clinical application of SARS-CoV-2 antibody detection and monoclonal antibody therapies against COVID-19.

The purpose of this study was to investigate the clinical application of severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) specific antibody detection and anti-SARS-CoV-2 specific monoclonal antibodies (mAbs) in the treatment of coronavirus infectious disease 2019 (COVID-19). The dynamic changes of SARS-CoV-2 specific antibodies during COVID-19 were studied. Immunoglobulin M (IgM) appeared earlier and lasted for a short time, while immunoglobulin G (IgG) appeared later and lasted longer. IgM tests can be used for early diagnosis of COVID-19, and IgG tests can be used for late diagnosis of COVID-19 and identification of asymptomatic infected persons. The combination of antibody testing and nucleic acid testing, which complement each other, can improve the diagnosis rate of COVID-19. Monoclonal anti-SARS-CoV-2 specific antibodies can be used to treat hospitalized severe and critically ill patients and non-hospitalized mild to moderate COVID-19 patients. COVID-19 convalescent plasma, highly concentrated immunoglobulin, and anti-SARS-CoV-2 specific mAbs are examples of anti-SARS-CoV-2 antibody products. Due to the continuous emergence of mutated strains of the novel coronavirus, especially omicron, its immune escape ability and infectivity are enhanced, making the effects of authorized products reduced or invalid. Therefore, the optimal application of anti-SARS-CoV-2 antibody products (especially anti-SARS-CoV-2 specific mAbs) is more effective in the treatment of COVID-19 and more conducive to patient recovery.

The Impact of mNGS Technology in the Etiological Diagnosis of Severe Pneumonia in Children During the Epidemic of COVID-19.

Purpose: Metagenomic next-generation sequencing (mNGS) is an emerging technique for pathogen detection. However, most literature on the clinical application of pediatrics generally comprises case reports or small-scale cohort studies. Patients and Methods: A total of 101 children with community-acquired severe pneumonia admitted to Tianjin Children's Hospital from November 2021 to February 2022 were included. Pathogens in bronchoalveolar lavage fluid (BALF) specimens were detected using mNGS. The performances of mNGS and conventional tests on pulmonary infection diagnosis and pathogen identification were compared. Results: According to our data, mNGS had a broader spectrum for pathogen detection. The mNGS results of BALF showed that the number of children with severe pneumonia hospitalized for mycoplasma pneumoniae infection was more than that for other bacterial infections during the COVID-19 epidemic. In addition, 43 cases (42.6%) had been identified with mixed infection, including 36 cases (35.6%) of Mycoplasma pneumoniae mixed with other pathogenic bacteria. Analytically, the mNGS exhibited significantly enhanced detection in the BALF as compared with the conventional laboratory pathogenic detection approaches (P < 0.05). The Pearson correlation analysis revealed positive correlation between the time of fever during hospitalization and the number of mycoplasma sequences (P < 0.05). Conclusion: Compared with traditional methods, mNGS has a higher etiological detection rate and can comprehensively detect various pathogens of severe pneumonia. Therefore, mNGS of bronchoalveolar lavage fluid should be performed in children with severe pneumonia, which is of great significance for guiding treatment.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1–BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1–BNT and volunteers vaccinated with homologous ChAd–ChAd and BNT–BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1–BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human-Machine Interfaces.

Recent advances in flexible wearable devices have boosted the remarkable development of devices for human-machine interfaces, which are of great value to emerging cybernetics, robotics, and Metaverse systems. However, the effectiveness of existing approaches is limited by the quality of sensor data and classification models with high computational costs. Here, a novel gesture recognition system with triboelectric smart wristbands and an adaptive accelerated learning (AAL) model is proposed. The sensor array is well deployed according to the wrist anatomy and retrieves hand motions from a distance, exhibiting highly sensitive and high-quality sensing capabilities beyond existing methods. Importantly, the anatomical design leads to the close correspondence between the actions of dominant muscle/tendon groups and gestures, and the resulting distinctive features in sensor signals are very valuable for differentiating gestures with data from 7 sensors. The AAL model realizes a 97.56% identification accuracy in training 21 classes with only one-third operands of the original neural network. The applications of the system are further exploited in real-time somatosensory teleoperations with a low latency of <1 s, revealing a new possibility for endowing cyber-human interactions with disruptive innovation and immersive experience.

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body's immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2-4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1-4, and days 7-10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1-BNT and volunteers vaccinated with homologous ChAd-ChAd and BNT-BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1-BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.

Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.

Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods.

The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20-85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications.

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.

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.

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.

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.

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.