TMPRSS2 and glycan receptors synergistically facilitate coronavirus entry.

The entry of coronaviruses is initiated by spike recognition of host cellular receptors, involving proteinaceous and/or glycan receptors. Recently, TMPRSS2 was identified as the proteinaceous receptor for HCoV-HKU1 alongside sialoglycan as a glycan receptor. However, the underlying mechanisms for viral entry remain unknown. Here, we investigated the HCoV-HKU1C spike in the inactive, glycan-activated, and functionally anchored states, revealing that sialoglycan binding induces a conformational change of the NTD and promotes the neighboring RBD of the spike to open for TMPRSS2 recognition, exhibiting a synergistic mechanism for the entry of HCoV-HKU1. The RBD of HCoV-HKU1 features an insertion subdomain that recognizes TMPRSS2 through three previously undiscovered interfaces. Furthermore, structural investigation of HCoV-HKU1A in combination with mutagenesis and binding assays confirms a conserved receptor recognition pattern adopted by HCoV-HKU1. These studies advance our understanding of the complex viral-host interactions during entry, laying the groundwork for developing new therapeutics against coronavirus-associated diseases.

Antivirals with common targets against highly pathogenic viruses.

Historically, emerging viruses appear constantly and have cost millions of human lives. Currently, climate change and intense globalization have created favorable conditions for viral transmission. Therefore, effective antivirals, especially those targeting the conserved protein in multiple unrelated viruses, such as the compounds targeting RNA-dependent RNA polymerase, are urgently needed to combat more emerging and re-emerging viruses in the future. Here we reviewed the development of antivirals with common targets, including those against the same protein across viruses, or the same viral function, to provide clues for development of antivirals for future epidemics.

Structural Basis for RNA Replication by the SARS-CoV-2 Polymerase.

Nucleotide analog inhibitors, including broad-spectrum remdesivir and favipiravir, have shown promise in in vitro assays and some clinical studies for COVID-19 treatment, this despite an incomplete mechanistic understanding of the viral RNA-dependent RNA polymerase nsp12 drug interactions. Here, we examine the molecular basis of SARS-CoV-2 RNA replication by determining the cryo-EM structures of the stalled pre- and post- translocated polymerase complexes. Compared with the apo complex, the structures show notable structural rearrangements happening to nsp12 and its co-factors nsp7 and nsp8 to accommodate the nucleic acid, whereas there are highly conserved residues in nsp12, positioning the template and primer for an in-line attack on the incoming nucleotide. Furthermore, we investigate the inhibition mechanism of the triphosphate metabolite of remdesivir through structural and kinetic analyses. A transition model from the nsp7-nsp8 hexadecameric primase complex to the nsp12-nsp7-nsp8 polymerase complex is also proposed to provide clues for the understanding of the coronavirus transcription and replication machinery.

Crystal Structures of Membrane Transporter MmpL3, an Anti-TB Drug Target.

Despite intensive efforts to discover highly effective treatments to eradicate tuberculosis (TB), it remains as a major threat to global human health. For this reason, new TB drugs directed toward new targets are highly coveted. MmpLs (Mycobacterial membrane proteins Large), which play crucial roles in transporting lipids, polymers and immunomodulators and which also extrude therapeutic drugs, are among the most important therapeutic drug targets to emerge in recent times. Here, crystal structures of mycobacterial MmpL3 alone and in complex with four TB drug candidates, including SQ109 (in Phase 2b-3 clinical trials), are reported. MmpL3 consists of a periplasmic pore domain and a twelve-helix transmembrane domain. Two Asp-Tyr pairs centrally located in this domain appear to be key facilitators of proton-translocation. SQ109, AU1235, ICA38, and rimonabant bind inside the transmembrane region and disrupt these Asp-Tyr pairs. This structural data will greatly advance the development of MmpL3 inhibitors as new TB drugs.

Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir.

Nirmatrelvir, an oral antiviral targeting the 3CL protease of SARS-CoV-2, has been demonstrated to be clinically useful against COVID-19 (refs. 1,2). However, because SARS-CoV-2 has evolved to become resistant to other therapeutic modalities3-9, there is a concern that the same could occur for nirmatrelvir. Here we examined this possibility by in vitro passaging of SARS-CoV-2 in nirmatrelvir using two independent approaches, including one on a large scale. Indeed, highly resistant viruses emerged from both and their sequences showed a multitude of 3CL protease mutations. In the experiment peformed with many replicates, 53 independent viral lineages were selected with mutations observed at 23 different residues of the enzyme. Nevertheless, several common mutational pathways to nirmatrelvir resistance were preferred, with a majority of the viruses descending from T21I, P252L or T304I as precursor mutations. Construction and analysis of 13 recombinant SARS-CoV-2 clones showed that these mutations mediated only low-level resistance, whereas greater resistance required accumulation of additional mutations. E166V mutation conferred the strongest resistance (around 100-fold), but this mutation resulted in a loss of viral replicative fitness that was restored by compensatory changes such as L50F and T21I. Our findings indicate that SARS-CoV-2 resistance to nirmatrelvir does readily arise via multiple pathways in vitro, and the specific mutations observed herein form a strong foundation from which to study the mechanism of resistance in detail and to inform the design of next-generation protease inhibitors.

Molecular mechanisms of SARS-CoV-2 resistance to nirmatrelvir.

Nirmatrelvir is a specific antiviral drug that targets the main protease (Mpro) of SARS-CoV-2 and has been approved to treat COVID-191,2. As an RNA virus characterized by high mutation rates, whether SARS-CoV-2 will develop resistance to nirmatrelvir is a question of concern. Our previous studies have shown that several mutational pathways confer resistance to nirmatrelvir, but some result in a loss of viral replicative fitness, which is then compensated for by additional alterations3. The molecular mechanisms for this observed resistance are unknown. Here we combined biochemical and structural methods to demonstrate that alterations at the substrate-binding pocket of Mpro can allow SARS-CoV-2 to develop resistance to nirmatrelvir in two distinct ways. Comprehensive studies of the structures of 14 Mpro mutants in complex with drugs or substrate revealed that alterations at the S1 and S4 subsites substantially decreased the level of inhibitor binding, whereas alterations at the S2 and S4' subsites unexpectedly increased protease activity. Both mechanisms contributed to nirmatrelvir resistance, with the latter compensating for the loss in enzymatic activity of the former, which in turn accounted for the restoration of viral replicative fitness, as observed previously3. Such a profile was also observed for ensitrelvir, another clinically relevant Mpro inhibitor. These results shed light on the mechanisms by which SARS-CoV-2 evolves to develop resistance to the current generation of protease inhibitors and provide the basis for the design of next-generation Mpro inhibitors.

Roton pair density wave in a strong-coupling kagome superconductor.

The transition metal kagome lattice materials host frustrated, correlated and topological quantum states of matter1-9. Recently, a new family of vanadium-based kagome metals, AV3Sb5 (A = K, Rb or Cs), with topological band structures has been discovered10,11. These layered compounds are nonmagnetic and undergo charge density wave transitions before developing superconductivity at low temperatures11-19. Here we report the observation of unconventional superconductivity and a pair density wave (PDW) in CsV3Sb5 using scanning tunnelling microscope/spectroscopy and Josephson scanning tunnelling spectroscopy. We find that CsV3Sb5 exhibits a V-shaped pairing gap Δ ~ 0.5 meV and is a strong-coupling superconductor (2Δ/kBTc ~ 5) that coexists with 4a0 unidirectional and 2a0 × 2a0 charge order. Remarkably, we discover a 3Q PDW accompanied by bidirectional 4a0/3 spatial modulations of the superconducting gap, coherence peak and gap depth in the tunnelling conductance. We term this novel quantum state a roton PDW associated with an underlying vortex-antivortex lattice that can account for the observed conductance modulations. Probing the electronic states in the vortex halo in an applied magnetic field, in strong field that suppresses superconductivity and in zero field above Tc, reveals that the PDW is a primary state responsible for an emergent pseudogap and intertwined electronic order. Our findings show striking analogies and distinctions to the phenomenology of high-Tc cuprate superconductors, and provide groundwork for understanding the microscopic origin of correlated electronic states and superconductivity in vanadium-based kagome metals.

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors.

A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019-2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)1-4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (Mpro) of SARS-CoV-2: Mpro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-25,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of Mpro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds-including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds-as inhibitors of Mpro. Six of these compounds inhibited Mpro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 µM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Conversion of chirality to twisting via sequential one-dimensional and two-dimensional growth of graphene spirals.

The properties of two-dimensional (2D) van der Waals materials can be tuned through nanostructuring or controlled layer stacking, where interlayer hybridization induces exotic electronic states and transport phenomena. Here we describe a viable approach and underlying mechanism for the assisted self-assembly of twisted layer graphene. The process, which can be implemented in standard chemical vapour deposition growth, is best described by analogy to origami and kirigami with paper. It involves the controlled induction of wrinkle formation in single-layer graphene with subsequent wrinkle folding, tearing and re-growth. Inherent to the process is the formation of intertwined graphene spirals and conversion of the chiral angle of 1D wrinkles into a 2D twist angle of a 3D superlattice. The approach can be extended to other foldable 2D materials and facilitates the production of miniaturized electronic components, including capacitors, resistors, inductors and superconductors.

In vivo mRNA expression of a multi-mechanistic mAb combination protects against Staphylococcus aureus infection.

Single monoclonal antibodies (mAbs) can be expressed in vivo through gene delivery of their mRNA formulated with lipid nanoparticles (LNPs). However, delivery of a mAb combination could be challenging due to the risk of heavy and light variable chain mispairing. We evaluated the pharmacokinetics of a three mAb combination against Staphylococcus aureus first in single chain variable fragment scFv-Fc and then in immunoglobulin G 1 (IgG1) format in mice. Intravenous delivery of each mRNA/LNP or the trio (1 mg/kg each) induced functional antibody expression after 24 h (10-100 µg/mL) with 64%-78% cognate-chain paired IgG expression after 3 days, and an absence of non-cognate chain pairing for scFv-Fc. We did not observe reduced neutralizing activity for each mAb compared with the level of expression of chain-paired mAbs. Delivery of the trio mRNA protected mice in an S. aureus-induced dermonecrosis model. Intravenous administration of the three mRNA in non-human primates achieved peak serum IgG levels ranging between 2.9 and 13.7 µg/mL with a half-life of 11.8-15.4 days. These results suggest nucleic acid delivery of mAb combinations holds promise and may be a viable option to streamline the development of therapeutic antibodies.

Structural basis for replicase polyprotein cleavage and substrate specificity of main protease from SARS-CoV-2.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key enzyme, which extensively digests CoV replicase polyproteins essential for viral replication and transcription, making it an attractive target for antiviral drug development. However, the molecular mechanism of how Mpro of SARS-CoV-2 digests replicase polyproteins, releasing the nonstructural proteins (nsps), and its substrate specificity remain largely unknown. Here, we determine the high-resolution structures of SARS-CoV-2 Mpro in its resting state, precleavage state, and postcleavage state, constituting a full cycle of substrate cleavage. The structures show the delicate conformational changes that occur during polyprotein processing. Further, we solve the structures of the SARS-CoV-2 Mpro mutant (H41A) in complex with six native cleavage substrates from replicase polyproteins, and demonstrate that SARS-CoV-2 Mpro can recognize sequences as long as 10 residues but only have special selectivity for four subsites. These structural data provide a basis to develop potent new inhibitors against SARS-CoV-2.

Formation and Manipulation of Diatomic Rotors at the Symmetry-Breaking Surfaces of a Kagome Superconductor.

Construction of diatomic rotors, which is crucial for artificial nanomachines, remains challenging due to surface constraints and limited chemical design. Here we report the construction of diatomic Cr-Cs and Fe-Cs rotors where a Cr or Fe atom switches around a Cs atom at the Sb surface of the newly discovered kagome superconductor CsV3Sb5. The switching rate is controlled by the bias voltage between the rotor and scanning tunneling microscope (STM) tip. The spatial distribution of rates exhibits C2 symmetry, possibly linked to the symmetry-breaking charge orders of CsV3Sb5. We have expanded the rotor construction to include different transition metals (Cr, Fe, V) and alkali metals (Cs, K). Remarkably, designed configurations of rotors are achieved through STM manipulation. Rotor orbits and quantum states are precisely controlled by tuning the inter-rotor distance. Our findings establish a novel platform for the controlled fabrication of atomic motors on symmetry-breaking quantum materials, paving the way for advanced nanoscale devices.

Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning.

Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study, we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) of molybdenum disulfide (MoS2) and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of MoS2 flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a data set comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayers.

Temperature-Driven Rotation Symmetry-Breaking States in an Atomic Kagome Metal KV3Sb5.

Kagome lattice AV3Sb5 has attracted tremendous interest because it hosts correlated and topological physics. However, an in-depth understanding of the temperature-driven electronic states in AV3Sb5 is elusive. Here we use scanning tunneling microscopy to directly capture the rotational symmetry-breaking effect in KV3Sb5. Through both topography and spectroscopic imaging of defect-free KV3Sb5, we observe a charge density wave (CDW) phase transition from an a0 × a0 atomic lattice to a robust 2a0 × 2a0 superlattice upon cooling the sample to 60 K. An individual Sb-atom vacancy in KV3Sb5 further gives rise to the local Friedel oscillation (FO), visible as periodic charge modulations in spectroscopic maps. The rotational symmetry of the FO tends to break at the temperature lower than 40 K. Moreover, the FO intensity shows an obvious competition against the intensity of the CDW. Our results reveal a tantalizing electronic nematicity in KV3Sb5, highlighting the multiorbital correlation in the kagome lattice framework.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

BACKGROUND: Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. METHODS: We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. RESULTS: wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. CONCLUSIONS: The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Evaluation of the antitumor effect of neoantigen peptide vaccines derived from the translatome of lung cancer.

Emerging evidence suggests that tumor-specific neoantigens are ideal targets for cancer immunotherapy. However, how to predict tumor neoantigens based on translatome data remains obscure. Through the extraction of ribosome-nascent chain complexes (RNCs) from LLC cells, followed by RNC-mRNA extraction, RNC-mRNA sequencing, and comprehensive bioinformatic analysis, we successfully identified proteins undergoing translatome and exhibiting mutations in the cells. Subsequently, novel antigens identification was analyzed by the interaction between their high affinity and the Major Histocompatibility Complex (MHC). Neoantigens immunogenicity was analyzed by enzyme-linked immunospot assay (ELISpot). Finally, in vivo experiments in mice were conducted to evaluate the antitumor effects of translatome-derived neoantigen peptides on lung cancer. The results showed that ten neoantigen peptides were identified and synthesized by translatome data from LLC cells; 8 out of the 10 neoantigens had strong immunogenicity. The neoantigen peptide vaccine group exhibited significant tumor growth inhibition effect. In conclusion, neoantigen peptide vaccine derived from the translatome of lung cancer exhibited significant tumor growth inhibition effect.

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views' raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views' common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.

Composition and phase engineering of metal chalcogenides and phosphorous chalcogenides.

Two-dimensional (2D) materials with multiphase, multielement crystals such as transition metal chalcogenides (TMCs) (based on V, Cr, Mn, Fe, Cd, Pt and Pd) and transition metal phosphorous chalcogenides (TMPCs) offer a unique platform to explore novel physical phenomena. However, the synthesis of a single-phase/single-composition crystal of these 2D materials via chemical vapour deposition is still challenging. Here we unravel a competitive-chemical-reaction-based growth mechanism to manipulate the nucleation and growth rate. Based on the growth mechanism, 67 types of TMCs and TMPCs with a defined phase, controllable structure and tunable component can be realized. The ferromagnetism and superconductivity in FeXy can be tuned by the y value, such as superconductivity observed in FeX and ferromagnetism in FeS2 monolayers, demonstrating the high quality of as-grown 2D materials. This work paves the way for the multidisciplinary exploration of 2D TMPCs and TMCs with unique properties.

PD-1+ T lymphocyte proportions and hospitalized exacerbation of COPD: a prospective cohort study.

OBJECTIVE: To evaluate the predictive value of PD-1 expression in T lymphocytes for rehospitalization due to acute exacerbations of COPD (AECOPD) in discharged patients. METHODS: 115 participants hospitalized with COPD (average age 71.8 ± 6.0 years) were recruited at Fujian Provincial Hospital. PD1+T lymphocytes proportions (PD1+T%), baseline demographics and clinical data were recorded at hospital discharge. AECOPD re-admission were collected at 1-year follow-up. Kaplan-Meier analysis compared the time to AECOPD readmissions among groups stratified by PD1+T%. Multivariable Cox proportional hazards regression and stratified analysis determined the correlation between PD1+T%, potential confounders, and AECOPD re-admission. ROC and DCA evaluated PD1+T% in enhancing the clinical predictive values of Cox models, BODE and CODEX. RESULTS: 68 participants (59.1%) were AECOPD readmitted, those with AECOPD readmission exhibited significantly elevated baseline PD-1+CD4+T/CD4+T% and PD-1+CD8 + T/CD8 + T% compared to non-readmitted counterparts. PD1+ T lymphocyte levels statistically correlated with BODE and CODEX indices. Kaplan-Meier analysis demonstrated that those in Higher PD1+ T lymphocyte proportions had reduced time to AECOPD readmission (logRank p < 0.05). Cox analysis identified high PD1+CD4+T and PD1+CD8+T ratios as risk factors of AECOPD readmission, with hazard ratios of 1.384(95%CI [1.043-1.725]) and 1.401(95%CI [1.013-1.789]), respectively. Notably, in patients aged < 70 years and with fewer than twice AECOPD episodes in the previous year, high PD1+T lymphocyte counts significantly increased risk for AECOPD readmission(p < 0.05). The AECOPD readmission predictive model, incorporating PD1+T% exhibited superior discrimination to the Cox model, BODE index and CODEX index, AUC of ROC were 0.763(95%CI [0.633-0.893]) and 0.734(95%CI [0.570-0.899]) (DeLong's test p < 0.05).The DCA illustrates that integrating PD1+T% into models significantly enhances the utility in aiding clinical decision-making. CONCLUSION: Evaluation of PD1+ lymphocyte proportions offer a novel perspective for identifying high-risk COPD patients, potentially providing insights for COPD management. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR, URL: www.chictr.org.cn/ ), Registration number: ChiCTR2200055611 Date of Registration: 2022-01-14.