Ionic Polymerization-Based Synthesis of Bioinspired Adhesive Hydrogel Microparticles with Tunable Morphologies from Microfluidics.

Shape-anisotropic hydrogel microparticles have attracted considerable attention for drug-delivery applications. Particularly, nonspherical hydrogel microcarriers with enhanced adhesive and circulatory abilities have demonstrated value in gastrointestinal drug administration. Herein, inspired by the structures of natural suckers, we demonstrate an ionic polymerization-based production of calcium (Ca)-alginate microparticles with tunable shapes from Janus emulsion for the first time. Monodispersed Janus droplets composed of sodium alginate and nongelable segments were generated using a coflow droplet generator. The interfacial curvatures, sizes, and production frequencies of Janus droplets can be flexibly controlled by varying the flow conditions and surfactant concentrations in the multiphase system. Janus droplets were ionically solidified on a chip, and hydrogel beads of different shapes were obtained. The in vitro and in vivo adhesion abilities of the hydrogel beads to the mouse colon were investigated. The anisotropic beads showed prominent adhesive properties compared with the spherical particles owing to their sticky hydrogel components and unique shapes. Finally, a novel computational fluid dynamics and discrete element method (CFD-DEM) coupling simulation was used to evaluate particle migration and contact forces theoretically. This review presents a simple strategy to synthesize Ca-alginate particles with tunable structures that could be ideal materials for constructing gastrointestinal drug delivery systems.

MEIS2 suppresses breast cancer development by downregulating IL10.

BACKGROUND: Breast cancer (BC) is the most commonly diagnosed female cancer. Homeobox protein MEIS2, a key transcription factor, is involved in the regulation of many developmental and cellular processes. However, the role of MEIS2 in the development of breast cancer is still unclear. AIMS: We aimed to examine the role of myeloid ecotropic insertion site (MEIS2) in breast cancer and the association of MEIS2 with breast cancer clinical stages and pathological grades. We revealed the underlying mechanism by which MEIS2 affected breast cancer cell growth and tumor development. METHODS AND RESULTS: Using human BC cell lines, clinical samples and animal xenograft model, we reveal that MEIS2 functions as a tumor suppressor in breast cancer. The expression of MEIS2 is inversely correlated with BC clinical stages and pathological grades. MEIS2 knockdown (MEIS2-KD) promotes while MEIS2 overexpression suppresses breast cancer cell proliferation and tumor development in vitro and in animal xenograft models, respectively. To determine the biological function of MEIS2, we screen the expression of a group of MEIS2 potential targeting genes in stable-established cell lines. Results show that the knockdown of MEIS2 in breast cancer cells up-regulates the IL10 expression, but MEIS2 overexpression opposed the effect on IL10 expression. Furthermore, the suppressive role of MEIS2 in breast cancer cell proliferation is associated with the IL10 expression and myeloid cells infiltration. CONCLUSION: Our study demonstrates that the tumor suppressor of MEIS2 in breast cancer progression is partially via down regulating the expression of IL10 and promoting myeloid cells infiltration. Targeting MEIS2 would be a potentially therapeutic avenue for BC.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study.

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

Clinical outcomes of hypomethylating agents plus Venetoclax as frontline treatment in patients 75 years and older with acute myeloid leukemia: Real-world data from eight US academic centers.

Venetoclax (VEN) combined with hypomethylating agents (HMAs) is the standard of care for the treatment of patients with newly diagnosed acute myeloid leukemia (AML) unfit for intensive chemotherapy. To date, real-world data published on HMAs plus VEN have been either single-center studies or using community-based electronic databases with limited details on mutational landscape, tolerability, and treatment patterns in elderly patients. Therefore, we conducted a multicenter retrospective study to assess the real-world experience of 204 elderly patients (≥75 years) with newly diagnosed AML treated with HMAs plus VEN from eight academic centers in the United States. Overall, 64 patients achieved complete remission (CR; 38%) and 43 CR with incomplete count recovery (CRi; 26%) for a CR/CRi rate of 64%, with a median duration of response of 14.2 months (95% CI: 9.43, 22.1). Among responders, 63 patients relapsed (59%) with median overall survival (OS) after relapse of 3.4 months (95% CI, 2.4, 6.7). Median OS for the entire population was 9.5 months (95% CI, 7.85-13.5), with OS significantly worse among patients with TP53-mutated AML (2.5 months) and improved in patients harboring NPM1, IDH1, and IDH2 mutations (13.5, 18.3, and 21.1 months, respectively). The 30-day and 60-day mortality rates were 9% and 19%, respectively. In conclusion, HMAs plus VEN yielded high response rates in elderly patients with newly diagnosed AML. The median OS was inferior to that reported in the VIALE-A trial. Outcomes are dismal after failure of HMAs plus VEN, representing an area of urgent unmet clinical need.

Finite temperature string by K-means clustering sampling with order parameters as collective variables for molecular crystals: application to polymorphic transformation between ß-CL-20 and ε-CL-20.

Polymorphic transformation of molecular crystals is a fundamental phase transition process, and it is important practically in the chemical, material, biopharmaceutical, and energy storage industries. However, understanding of the transformation mechanism at the molecular level is poor due to the extreme simulating challenges in enhanced sampling and formulating order parameters (OPs) as the collective variables that can distinguish polymorphs with quite similar and complicated structures so as to describe the reaction coordinate. In this work, two kinds of OPs for CL-20 were constructed by the bond distances, bond orientations and relative orientations. A K-means clustering algorithm based on the Euclidean distance and sample weight was used to smooth the initial finite temperature string (FTS), and the minimum free energy path connecting ß-CL-20 and ε-CL-20 was sketched by the string method in collective variables, and the free energy profile along the path and the nucleation kinetics were obtained by Markovian milestoning with Voronoi tessellations. In comparison with the average-based sampling, the K-means clustering algorithm provided an improved convergence rate of FTS. The simulation of transformation was independent of OP types but was affected greatly by finite-size effects. A surface-mediated local nucleation mechanism was confirmed and the configuration located at the shoulder of potential of mean force, rather than overall maximum, was confirmed to be the critical nucleus formed by the cooperative effect of the intermolecular interactions. This work provides an effective way to explore the polymorphic transformation of caged molecular crystals at the molecular level.

A Novel Bubble-based Microreactor for Enhanced Mass Transfer Dynamics toward Efficient Electrocatalytic Nitrogen Reduction.

Electrocatalytic nitrogen reduction reaction (eNRR) is a promising method for sustainable ammonia production. Although the majority of studies on the eNRR are devoted to developing efficient electrocatalysts, it is critical to study the influence of mass transfer because of the poor N2 transfer efficiency. Herein, a novel bubble-based microreactor (BBMR) is proposed that efficiently promotes the mass transfer behavior during the eNRR using microfluidic strategies. The BBMR possesses abundant triphasic interfaces and provides spatial confinement and accurate potential control, ensuring rapid mass transfer dynamics and improved eNRR performance, as confirmed by experimental and simulation studies. The ammonia yield of the reaction over Ag nanoparticles can be enhanced to 31.35 µg h-1 mgcat. -1, which is twice that of the H-cell. Excellent improvements are also achieved using Ru/C and Fe/g-CN catalysts, with 5.0 and 8.5 times increase in ammonia yield, respectively. This work further demonstrates the significant effect of mass transfer on the eNRR performance and provides an effective strategy for process enhancement through electrode design.

Grammar of Impact Sensitivity: An Incremental Theory.

In this paper, we report the first attempt to quantify impact sensitivity using the second-order incremental approach based on the structural features of explosives. It has been found that impact height (h50) can be expressed via a multiplicative incremental exponential form, in which the exponents are characteristic coefficients of structural increments multiplied by their numbers in the molecule. The method was developed on a large array of experimental data (450 molecules and salts) of different energetic materials, namely, nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc., while testing of the model was performed for 170 compounds. The results demonstrate a noticeable correlation with the experimental h50 values. Thus, the corresponding R2 and RMSE for the training and test sets are 0.56 (12.5 J) and 0.63 (18.8 J), respectively. In this work, we use 53 individual structural increments, but their number can be extended, and the corresponding coefficients can be refined; this allows for increasing the prediction accuracy on-the-fly. The calculation algorithm is discussed, and the corresponding examples are presented. The performed machine-based regression analysis using genetic function approximation, multiple linear regression, and artificial neural network has proven the reasonability and informativity of the proposed incremental theory. Thus, the developed approach significantly extends our understanding of the impact sensitivity phenomenon and translates it into the category of one that can be calculated by a pocket calculator.

Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data.

In the ZINC20 database, with the aid of maximum substructure searches, common substructures were obtained from molecules with high-strain-energy and combustion heat values, and further provided domain knowledge on how to design high-energy-density hydrocarbon (HEDH) fuels. Notably, quadricyclane and syntin could be topologically assembled through these substructures, and the corresponding assembled schemes guided the design of 20 fuel molecules (ZD-1 to ZD-20). The fuel properties of the molecules were evaluated by using group-contribution methods and density functional theory (DFT) calculations, where ZD-6 stood out due to the high volumetric net heat of combustion, high specific impulse, low melting point, and acceptable flash point. Based on the neural network model for evaluating the synthetic complexity (SCScore), the estimated value of ZD-6 was close to that of syntin, indicating that the synthetic complexity of ZD-6 was comparable to that of syntin. This work not only provides ZD-6 as a potential HEDH fuel, but also illustrates the superiority of learning design strategies from the data in increasing the understanding of structure and performance relationships and accelerating the development of novel HEDH fuels.

Force field-inspired transformer network assisted crystal density prediction for energetic materials.

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.

Bio-Inspired Low-Cost Fabrication of Stretchable, Adhesive, Transparent, and Multi-Functionalized Joint Wound Dressings.

Ideal joint wound dressings should not only promote wound healing and have good mechanical properties including stretchability and adhesion but also possess functions such as sterilization or motion monitoring. The multiple characteristic requirements have greatly limited the material's alternative, resulting in research on functional joint wound dressings falling far short of market demand. Therefore, low-cost, comprehensive designs need to be developed. Herein, inspired by the spiral arteries in the endometrium, alginate-based helical fibers were introduced into polyacrylamide/gelatin (PAM-Gel) to obtain composite polymer membranes, realizing a combination of both mechanical and functional properties. Large scale (100 m) and high-throughput (10 times higher than literature) fabrication of helical microfibers were first achieved, ensuring the low cost of fiber preparation. The composite film had adequate stretchability (>300% strain), adhesion strength (14 kPa), high transparency, and good biocompatibility. The helical fibers could be easily functionalized without affecting the mechanical properties of the dressings, thus broadening the range of materials available for joint dressings. After different treatments of the helical fibers, controlled drug release and joint motion monitoring were realized. Therefore, this helical microfiber composite membrane design achieved low-cost preparation, good mechanical properties, and functionalities including healing promotion, drug release, and motion monitoring ability, demonstrating application potential.

Strong External Electric Fields Reduce Explosive Sensitivity: A Theoretical Investigation into the Reaction Selectivity in NH2NO2∙∙∙NH3.

Controlling the selectivity of a detonation initiation reaction of explosive is essential to reduce sensitivity, and it seems impossible to reduce it by strengthening the external electric field. To verify this, the effects of external electric fields on the initiation reactions in NH2NO2∙∙∙NH3, a model system of the nitroamine explosive with alkaline additive, were investigated at the MP2/6-311++G(2d,p) and CCSD(T)/6-311++G(2d,p) levels. The concerted effect in the intermolecular hydrogen exchange is characterized by an index of the imaginary vibrations. Due to the weakened concerted effects by the electric field along the -x-direction opposite to the "reaction axis", the dominant reaction changes from the intermolecular hydrogen exchange to 1,3-intramolecular hydrogen transference with the increase in the field strengths. Furthermore, the stronger the field strengths, the higher the barrier heights become, indicating the lower sensitivities. Therefore, by increasing the field strength and adjusting the orientation between the field and "reaction axis", not only can the reaction selectivity be controlled, but the sensitivity can also be reduced, in particular under a super-strong field. Thus, a traditional concept, in which the explosive is dangerous under the super-strong external electric field, is theoretically broken. Compared to the neutral medium, a low sensitivity of the explosive with alkaline can be achieved under the stronger field. Employing atoms in molecules, reduced density gradient, and surface electrostatic potentials, the origin of the reaction selectivity and sensitivity change is revealed. This work provides a new idea for the technical improvement regarding adding the external electric field into the explosive system.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials.

Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.

Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure-Property Relationship for Density Prediction of Energetic Materials.

With the further development of the concept of green chemistry, the new generation of energetic materials tends to exhibit detonation properties such as higher insensitivity, higher density, and higher energy. Therefore, the precise molecular design and green and efficient synthesis of energetic materials will be one of the serious challenges. For the purpose of accurate prediction of detonation performance of energetic materials, an ensemble modeling strategy based on the combination of Monte Carlo (MC) and variable importance measurement (VIM) improved random forest (RF) and quantitative structure-property relationship (QSPR) is proposed, which was successfully used for density prediction of energetic materials. First, the structure of 162 energetic compounds was optimized by Gaussian software, and the molecular descriptor data were calculated by CODESSA software based on the optimized molecular structure. Then, the MCVIMRF_Med ensemble model was constructed on the basis of the above molecular descriptor data and the corresponding energetic compound density index. The joint X-Y distance algorithm (SPXY) is used to partition the data set. And then, MC is used to further divide the calibration set data into multiple subsets for the construction of the ensemble model. The subset size and the number of iterations of the MCVIMRF_Med ensemble model were optimized through MC cross validation. The final output strategy of the ensemble model is optimized based on the optimized parameters, and an output optimization method based on median screening is proposed and successfully applied for the prediction performance optimization of the MCVIMRF_Med ensemble model. To further investigate the performance of the MCVIMRF_Med ensemble model, the performance of it was compared with partial least squares, RF, VIMRF, and MCVIMRF calibration models. It shows that the MCVIMRF_Med ensemble model can achieve a better prediction result for the density of energetic materials, with R 2 CV of 0.9596, RMSECV of 0.0437 g/cm3, R 2 P of 0.9768, RMSEP of 0.0578 g/cm3, and relative analysis deviation of prediction set of 3.951. Therefore, the MCVIMRF_Med ensemble modeling strategy combined with QSPR is an effective approach for the density prediction of energetic materials. This work is expected to provide new research ideas and technical support for accurate prediction of detonation performance of energetic materials.

Rational design of covalent heptazine framework photocatalysts with high oxidation ability through reaction-dependent strategy.

Due to structural tunability, high surface area, abundant pore structures, and abundant active sites, covalent heptazine frameworks (CHFs) constructed from heptazine and other molecular blocks are especially prominent. Here, we proposed a reaction-dependent strategy for designing two dimensional CHFs including high-throughput precursors screening, structure generation, and performance evaluation. Assuming that oxamide-like precursors can undergo the same thermal polymerization reaction as producing C6N7, seven precursors were screened from more than 109 molecules in the ZINC20 database in terms of molecular weight, number of substructures, shape index, and symmetry. Furthermore, CHF-L1 to CHF-L7 were constructed from urea and the seven precursors according to the topologically assembling scheme in thermal polymerization. The designed CHFs had band gaps ranging from 1.89 to 3.10 eV. Among them, CHF-L3 assembled structurally by urea and 1,2,4,5-tetrazine-3,6-dicarboxamide with the smallest bandgap and an oxidative potential bias of 1.38 V for oxygen evolution reaction was screened as the candidate with high oxidative ability. The negative formation energy based on the synthesis route indicated the synthetic feasibility of CHF-L3, and negative cohesive energy as well as the stable structure under ab initio molecular dynamics simulations confirmed the stability of CHF-L3. The present work is expected to provide a powerful design strategy for two-dimensional CHFs design and is broadly applicable to various computational covalent organic framework design systems and experimental studies.

Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM.

In this study, fault diagnosis method of bearing utilizing gray level co-occurrence matrix (GLCM) and multi-beetles antennae search algorithm (MBASA)-based kernel extreme learning machine (KELM) is presented. In the proposed method, feature extraction of time-frequency image based on GLCM is proposed to extract the features of the bearing vibration signal, and multi-beetles antennae search algorithm-based KELM (MBASA-KELM) is presented to recognize the states of bearing. KELM employs the kernel-based framework, which has better generalization than traditional extreme learning machine, and it is necessary to look for an excellent optimization algorithm to select appropriate regularization parameter and kernel parameter of the KELM model because these parameters of the KELM model can affect its performance. As traditional beetle antennae search algorithm only employs one beetle, which is difficult to find the optimal parameters when the ranges of the parameters to be optimized are wide, multi-beetles antennae search algorithm (MBASA) employing multi-beetles is presented to select the regularization parameter and kernel parameter of KELM. The experimental results demonstrate that MBASA-KELM has stronger fault diagnosis ability for bearing than LSSVM, and KNN.

Microfluidic generation of monodispersed Janus alginate hydrogel microparticles using water-in-oil emulsion reactant.

Microparticles with uniform anisotropic structures are widely used in physical, chemical, and biological fields owing to their ability to combine multiple functions on a micro-scale. Here, a microfluidic emulsion-based external gelation method was demonstrated for the first time to produce monodisperse Janus calcium alginate (Ca-alginate) hydrogel microparticles consisting of two compartments. This approach provided a fast reaction condition under which we could prepare magnetic Janus Ca-alginate microparticles with diameters ranging from 148 to 179 µm and a coefficient of variation (CV) less than 4%. Moreover, the boundaries between the two compartments were clear. In addition, the volume fraction of each compartment could be adjusted by varying the flow rate ratio between two dispersed phases. Next, we produced fluorescent Janus beads and magnetic-fluorescent Janus beads with an average diameter of ∼150 µm (CV < 4.0%). The magnetic Janus hydrogel microparticles we produced could be manipulated by applying a magnetic field to achieve self-assembly, rotation, and accumulation. Magnetic Janus hydrogel microparticles are also capable of mammalian cell encapsulation with good cell viability. This article presents a simple and stable approach for producing monodisperse bi-compartmental Janus hydrogel microparticles that could have great potential for application in physical, biochemical, and biomedical fields.

Comparative Studies on Thermal Decompositions of Dinitropyrazole-Based Energetic Materials.

Dinitropyrazole is an important structure for the design and synthesis of energetic materials. In this work, we reported the first comparative thermal studies of two representative dinitropyrazole-based energetic materials, 4-amino-3,5-dinitropyrazole (LLM-116) and its novel trimer derivative (LLM-226). Both the experimental and theoretical results proved the active aromatic N-H moiety would cause incredible variations in the physicochemical characteristics of the obtained energetic materials. Thermal behaviors and kinetic studies of the two related dinitropyrazole-based energetic structures showed that impressive thermal stabilization could be achieved after the trimerization, but also would result in a less concentrated heat-release process. Detailed analysis of condensed-phase systems and the gaseous products during the thermal decomposition processes, and simulation studies based on ReaxFF force field, indicated that the ring opening of LLM-116 was triggered by hydrogen transfer of the active aromatic N-H moiety. In contrast, the initial decomposition of LLM-226 was caused by the rupture of carbon-nitrogen bonds at the diazo moiety.

Accelerating Molecular Design of Cage Energetic Materials with Zero Oxygen Balance through Large-Scale Database Search.

Domain-related knowledge promoted high-throughput cage scaffold screening from the ZINC15 database containing over 130 000 scaffolds and cooperated with combinatorial design to alleviate the lack of cage energetic materials. A dozen candidates were discovered that show excellent energy and safety performance, confirming the effectiveness of our strategy.

Fear in the Chinese Population: Influential Patterns in the Early Stage of the COVID-19 Pandemic.

Major global public health emergencies challenge public mental health. Negative emotions, and especially fear, may endanger social stability. To better cope with epidemics and pandemics, early emotional guidance should be provided based on an understanding of the status of public emotions in the given circumstances. From January 27 to February 11, 2020 (during which the cases of COVID-19 were increasing), a national online survey of the Chinese public was conducted. A total of 132,482 respondents completed a bespoke questionnaire, the Emotion Regulation Questionnaire, and the Berkeley Expressivity Questionnaire (BEQ). Results showed that at the early stage of the COVID-19 epidemic, 53.0% of the Chinese population reported varying degrees of fear, mostly mild. As seen from regression analysis, for individuals who were unmarried and with a relatively higher educational level, living in city or area with fewer confirmed cases, cognitive reappraisal, positive expressivity and negative inhibition were the protective factors of fear. For participants being of older age, female, a patient or medical staff member, risk perception, negative expressivity, positive impulse strength and negative impulse strength were the risk factors for fear. The levels of fear and avoidant behavior tendencies were risk factors for disturbed physical function. Structural equation modeling suggested that fear emotion had a mediation between risk perception and escape behavior and physical function disturbance. The findings help to reveal the public emotional status at the early stage of the pandemic based on a large Chinese sample, allowing targeting of the groups that most need emotional guidance under crisis. Findings also provide evidence of the need for psychological assistance in future major public health emergencies.

Negative Emotions in Chinese Frontline Medical Staff During the Early Stage of the COVID-19 Epidemic: Status, Trend, and Influential Pathways Based on a National Investigation.

Objective: The outbreak of coronavirus disease 2019 (COVID-19), declared as a major public health emergency, has had profound effects on public mental health especially emotional status. Due to professional requirements, medical staff are at a higher risk of infection, which might induce stronger negative emotions. This study aims to reveal the emotional status of Chinese frontline medical staff in the early epidemic period to better maintain their mental health, and provide adequate psychological support for them. Methods: A national online survey was carried out in China at the early stage of the COVID-19 epidemic. In total, 3025 Chinese frontline medical staff took part in this investigation which utilized a general information questionnaire, the Emotion Regulation Questionnaire (ERQ), and the Berkeley Expressivity Questionnaire (BEQ). Results: At the early stage of COVID-19, anxiety was the most common negative emotion of Chinese medical staff, followed by sadness, fear, and anger, mainly at a mild degree, which declined gradually over time. Nurses had the highest level of negative emotions compared with doctors and other healthcare workers. Women experienced more fear than men, younger and unmarried medical staff had more anxiety and fear compared with elders and married ones. Risk perception and emotional expressivity increased negative emotions, cognitive reappraisal reduced negative emotions, while negative emotions led to more avoidant behavior and more physical health disturbances, in which negative emotions mediated the effect of risk perception on avoidant behavior tendency in the model test. Conclusion: Chinese frontline medical staff experienced a mild level of negative emotions at the early stage of COVID-19, which decreased gradually over time. The findings suggest that during the epidemic, nurses' mental health should be extensively attended to, as well as women, younger, and unmarried medical staff. To better ensure their mental health, reducing risk perception and improving cognitive reappraisal might be important, which are potentially valuable to form targeted psychological interventions and emotional guidance under crisis in the future.