Investigation of γ-polyglutamic acid production via asynchronous saccharification and fermentation of raw corn starch.

Starch, a crucial raw material, has been extensively investigated for biotechnological applications. However, its application in γ-polyglutamic acid (γ-PGA) production remains unexplored. Based on γ-PGA output of Bacillus subtilis SCP010-1, a novel asynchronous saccharification and fermentation process for γ-PGA synthesis was implemented. The results revealed that a starch concentration of 20%, α-amylase dosage of 75 U/g, liquefaction temperature of 72℃, and γ-PGA yield of 36.31 g/L was achieved. At a glucoamylase dosage of 100 U/g, saccharification 38 h at 60℃, the yield of γ-PGA increased to 48.88 g/L. The contents of total sugar, glucose, maltose and oligosaccharide in saccharified liquid were determined. Through batch fermentation of saccharified liquid in fermentor, the γ-PGA output was elevated to 116.08 g/L. This study can offer a potential cost reduction of 40%, which can be a promising advancement in industrial γ-PGA production. Moreover, our approach can be applied in other starch-based fermentation industries.

Dendrite Growth and Dead Lithium Formation in Lithium Metal Batteries and Mitigation Using a Protective Layer: A Phase-Field Study.

Lithium metal batteries (LMBs) are considered one of the most promising next-generation rechargeable batteries due to their high specific capacity. However, severe dendrite growth and subsequent formation of dead lithium (Li) during the battery cycling process impede its practical application. Although extensive experimental studies have been conducted to investigate the cycling process, and several theoretical models were developed to simulate the Li dendrite growth, there are limited theoretical studies on the dead Li formation, as well as the entire cycling process. Herein, we developed a phase-field model to simulate both electroplating and stripping process in a bare Li anode and Li anode covered with a protective layer. A step function is introduced in the stripping model to capture the dynamics of dead Li. Our simulation clearly shows the growth of dendrites from a bare Li anode during charging. These dendrites detach from the bulk anode during discharging, forming dead Li. Dendrite growth becomes more severe in subsequent cycles due to enhanced surface roughness of the Li anode, resulting in an increasing amount of dead Li. In addition, it is revealed that dendrites with smaller base diameters detach faster at the base and produce more dead lithium. Meanwhile, the Li anode covered with a protective layer cycles smoothly without forming Li dendrite and dead Li. However, if the protective layer is fractured, Li metal preferentially grows into the crack due to enhanced Li-ion (Li+) flux and forms a dendrite structure after penetration through the protective layer, which accelerates the dead Li formation in the subsequent stripping process. Our work thus provides a fundamental understanding of the mechanism of dead Li formation during the charging/discharging process and sheds light on the importance of the protective layer in the prevention of dead Li in LMBs.

Multiple analyses of main flavor components in reconstituted tobacco and transfer behavior of their key substances during heating.

Reconstituted tobacco (RT) is a product made by reprocessing tobacco waste, experiencing a growing demand for heat-not-burn products. The purpose of this study is to analyze the main flavor ingredients in RT aerosol, as well as the transfer behavior of key flavor substances from substrates to aerosol and the concentrations of these compounds in the substrate after heating. First, we demonstrated that the odor of four RT aerosol samples could be distinguished using an electronic nose. Through non-targeted analysis, 93 volatile compounds were detected by gas chromatography-mass spectrometry, and 286 non/semi-volatile compounds were identified by ultra-high-performance liquid electrophoresis chromatography-mass spectrometry in aerosol. Furthermore, we found that the formation of RT aerosol involves primarily evaporation and distillation, however, the total content delivered from unheated RT samples to aerosol remains relatively low due to compound volatility and cigarette filtration. Thermal reactions during heating indicated the pyrolysis of chlorogenic acid to generate catechol and resorcinol, while Maillard reactions involving glucose and proline produced 2,3-dihydro-3,5-dihydroxy-6-methyl-4h-pyran-4-one. The study highlighted that heating RT at approximately 300°C could mitigate the production of harmful substances while still providing a familiar sensory experience with combusted tobacco.

Multiplexing Imaging of Closely Located Single-Nucleotide Mutations in Single Cells via Encoded in situ PCR.

Mutation accumulation in RNAs results in closely located single-nucleotide mutations (SNMs), which is highly associated with the drug resistance of pathogens. Imaging of SNMs in single cells has significance for understanding the heterogeneity of RNAs that are related to drug resistance, but the direct "see" closely located SNMs remains challenging. Herein, we designed an encoded ligation-mediated in situ polymerase chain reaction method (termed enPCR), which enabled the visualization of multiple closely located SNMs in bacterial RNAs. Unlike conventional ligation-based probes that can only discriminate a single SNM, this method can simultaneously image different SNMs at closely located sites with single-cell resolution using modular anchoring probes and encoded PCR primers. We tested the capacity of the method to detect closely located SNMs related to quinolone resistance in the gyrA gene of Salmonella enterica (S. enterica), and found that the simultaneous detection of the closely located SNMs can more precisely indicate the resistance of the S. enterica to quinolone compared to the detection of one SNM. The multiplexing imaging assay for SNMs can serve to reveal the relationship between complex cellular genotypes and phenotypes.

Generalization of cut-in pre-crash scenarios for autonomous vehicles based on accident data.

The utilization of high-risk test cases constitutes an effective approach to enhance the safety testing of autonomous vehicles (AVs) and enhance their efficiency. This research paper presents a derivation of 2052 high-hazard pre-crash scenarios for testing autonomous driving, which were based on 23 high-hazard cut-in accident scenarios from the National Automobile Accident In-Depth Investigation System (NAIS) through combining importance sampling and combined testing methods. Compared to the direct combination of the original distribution after sampling, the proposed method has a 2.92 times higher crash rate of 69.32% for the test case set in this paper. It also has a 5.8 times higher rate of triggering Automatic Emergency Braking (AEB), improving hazardous scenario coverage. Using the proposed method, the generated parameters of the cut-in accident scenario test set were compared with those of the cut-in test scenarios included in existing Chinese autonomous driving test protocols and standards. The velocity of the ego-vehicle obtained using the proposed method matched those in the existing protocols, whereas the velocity, time gap, and time to collision of the target vehicle were significantly lower than those existing protocols indicating scenarios obtained from accident data can enrich the selection of testing scenarios for autonomous driving.

Food-derived peptides unleashed: emerging roles as food additives beyond bioactivities.

Innovating food additives stands as a cornerstone for the sustainable evolution of future food systems. Peptides derived from food proteins exhibit a rich array of physicochemical and biological attributes crucial for preserving the appearance, flavor, texture, and nutritional integrity of foods. Leveraging these peptides as raw materials holds great promise for the development of novel food additives. While numerous studies underscore the potential of peptides as food additives, existing reviews predominantly focus on their biotic applications, leaving a notable gap in the discourse around their abiotic functionalities, such as their physicochemical properties. Addressing this gap, this review offers a comprehensive survey of peptide-derived food additives in food systems, accentuating the application of peptides' abiotic properties. It furnishes a thorough exploration of the underlying mechanisms and diverse applications of peptide-derived food additives, while also delineating the challenges encountered and prospects for future applications. This well-time review will set the stage for a deeper understanding of peptide-derived food additives.

Potential anti-liver cancer targets and mechanisms of kaempferitrin based on network pharmacology, molecular docking and experimental verification.

AIM: Kaempferitrin is an active component in Chenopodium ambrosioides, showing medicinal functions against liver cancer. This study aimed to identify the potential targets and pathways of kaempferitrin against liver cancer using network pharmacology and molecular docking, and verify the essential hub targets and pathway in mice model of SMMC-7721 cells xenografted tumors and SMMC-7721 cells. METHODS: Kaempferitrin therapeutical targets were obtained by searching SwissTargetPrediction, PharmMapper, STITCH, DrugBank, and TTD databases. Liver cancer specific genes were obtained by searching GeneCards, DrugBank, TTD, OMIM, and DisGeNET databases. PPI network of "kaempferitrin-targets-liver cancer" was constructed to screen the hub targets. GO, KEGG pathway and MCODE clustering analyses were performed to identify possible enrichment of genes with specific biological subjects. Molecular docking and molecular dynamics simulation were employed to determine the docking pose, potential and stability of kaempferitrin with hub targets. The potential anti-liver cancer mechanisms of kaempferitrin, as predicted by network pharmacology analyses, were verified by in vitro and in vivo experiments. RESULTS: 228 kaempferitrin targets and 2186 liver cancer specific targets were identified, of which 50 targets were overlapped. 8 hub targets were identified through network topology analysis, and only SIRT1 and TP53 had a potent binding activity with kaempferitrin as indicated by molecular docking and molecular dynamics simulation. MCODE clustering analysis revealed the most significant functional module of PPI network including SIRT1 and TP53 was mainly related to cell apoptosis. GO and KEGG enrichment analyses suggested that kaempferitrin exerted therapeutic effects on liver cancer possibly by promoting apoptosis via p21/Bcl-2/Caspase 3 signaling pathway, which were confirmed by in vivo and in vitro experiments, such as HE staining of tumor tissues, CCK-8, qRT-PCR and Western blot. CONCLUSION: This study provided not only insight into how kaempferitrin could act against liver cancer by identifying hub targets and their associated signaling pathways, but also experimental evidence for the clinical use of kaempferitrin in liver cancer treatment.

[Evaluation of chemical constituents of Draconis Sanguis and its protective effect on renal tubular injury in diabetic kidney disease].

The chemical constituents of Draconis Sanguis were preliminarily studied by macroporous resin, silica gel, dextran gel, and high-performance liquid chromatography. One retro-dihydrochalcone, four flavonoids, and one stilbene were isolated. Their chemical structures were identified as 4-hydroxy-2,6-dimethoxy-3-methyldihydrochalcone(1), 4'-hydroxy-5,7-dimethoxy-8-methylflavan(2), 7-hydroxy-4',5-dimethoxyflavan(3),(2S)-7-hydroxy-5-methoxy-6-methylflavan(4),(2S)-7-hydroxy-5-methoxyflavan(5), and pterostilbene(6) by modern spectroscopy, physicochemical properties, and literature comparison. Compound 1 was a new compound. Compounds 2 and 6 were first found in the Arecaceae family. Compound 5 had the potential to prevent and treat diabetic kidney disease.

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients.

Purpose: This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). Methods: We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. Results: The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. Discussions: The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

Phase-Field Simulation of a Dynamic Protective Layer for the Inhibition of Dendrite Growth in Zinc Metal Batteries.

Metallic zinc (Zn) has been considered one of the most promising anode materials for next-generation aqueous Zn batteries due to its low redox potential and high storage capacity. However, excessive dendrite formation in Zn metal, corrosion, the evolution of hydrogen gas during the cycling process, and the poor Zn-ion (Zn2+) transport from the electrolyte to the electrode limit its practical application. One of the most effective strategies to suppress Zn dendrite growth and promote Zn2+ transport is to introduce suitable protective layers between the Zn metal electrode and the electrolyte. Herein, we mathematically simulated the dynamic interactions between the Zn deposition on the anode and the resulting displacement of a protective layer that covers the anode, the latter of which can simultaneously inhibit Zn dendrite growth and enhance the Zn2+ transport through the interface between the Zn anode and the protective layer. Our simulation results indicate that a protective layer of high Zn2+ diffusivity not only improves the deposition rate of the Zn metal but also prevents dendrite growth by homogenizing the Zn2+ concentration at the anode surface. In addition, it is revealed that the anisotropic Zn2+ diffusivity in the protective layer influences the 2D diffusion of Zn2+. Higher Zn2+ diffusivity perpendicular to the Zn metal surface inhibits dendrite growth, while higher diffusivity parallel to the Zn metal surface promotes dendrite growth. Our work thus provides a fundamental understanding and a design principle for controlling anisotropic Zn2+ diffusion in the protective layer for better suppression of dendrite growth in Zn metal batteries.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis.

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

CRISPR-based biosensors for pathogenic biosafety.

Pathogenic biosafety is a worldwide concern. Tools for analyzing pathogenic biosafety, that are precise, rapid and field-deployable, are highly demanded. Recently developed biotechnological tools, especially those utilizing CRISPR/Cas systems which can couple with nanotechnologies, have enormous potential to achieve point-of-care (POC) testing for pathogen infection. In this review, we first introduce the working principle of class II CRISPR/Cas system for detecting nucleic acid and non-nucleic acid biomarkers, and highlight the molecular assays that leverage CRISPR technologies for POC detection. We summarize the application of CRISPR tools in detecting pathogens, including pathogenic bacteria, viruses, fungi and parasites and their variants, and highlight the profiling of pathogens' genotypes or phenotypes, such as the viability, and drug-resistance. In addition, we discuss the challenges and opportunities of CRISPR-based biosensors in pathogenic biosafety analysis.

A cyclic organosulfide cathode with ultrastable cycling performance in lithium batteries.

We report a cyclic organosulfide synthesized via a condensation reaction. It can be cycled for 1000 times in half cells. Impressively, it can work with lithiated carbon paper as the anode in ether electrolyte in a full cell. This work shows the promising property of the organosulfide cathode in lithium batteries.

Decreasing Water Activity Using the Tetrahydrofuran Electrolyte Additive for Highly Reversible Aqueous Zinc Metal Batteries.

Aqueous zinc metal batteries show great promise in large-scale energy storage. However, the decomposition of water molecules leads to severe side reactions, resulting in the limited lifespan of Zn batteries. Here, the tetrahydrofuran (THF) additive was introduced into the zinc sulfate (ZnSO4) electrolyte to reduce water activity by modulating the solvation structure of the Zn hydration layer. The THF molecule can play as a proton acceptor to form hydrogen bonds with water molecules, which can prevent water-induced undesired reactions. Thus, in an optimal 2 M ZnSO4/THF (5% by volume) electrolyte, the hydrogen evolution reaction and byproduct precipitation can be suppressed, which greatly improves the cycling stability and Coulombic efficiency of reversible Zn plating/stripping. The Zn symmetrical cells exhibit ultralong working cycles with a wide range of current density and capacity. The THF additive also enables a high Coulombic efficiency in the Zn||Cu cell with an average value of 99.59% over 400 cycles and a high reversible capacity with a capacity retention of 97.56% after 250 cycles in the Zn||MnO2 full cells. This work offers an effective strategy with high scalability and low cost for the protection of the Zn metal electrodes in aqueous rechargeable batteries.

Rationally designed Mn2O3@ZnMn2O4/C core-shell hollow microspheres for aqueous zinc-ion batteries.

Manganese-based oxides are common cathode materials for aqueous zinc ion batteries (AZIBs) because of their great capacity and high working voltage. However, the sharp decline of capacity caused by the dissolution of manganese-based cathode materials and the low-rate performance restrict their development. To address these problems, unique core-shell structured Mn2O3@ZnMn2O4/C hollow microspheres are reported as an ideal cathode material for AZIBs. Benefiting from the hollow structure, the zeolitic imidazolate framework (ZIF)-derived carbon and ZnMn2O4. Its application in AZIBs as the cathode demonstrates its satisfactory rate performance, high cycle stability, and excellent reversibility. Its high reversible capacity is remarkable, which reaches its maximum of 289.9 mA h g-1 at 200 mA g-1 and maintains a capacity of 203.5 mA h g-1 after cycling for 700 times at 1000 mA g-1. These excellent performances indicate that this material is a potential cathode material of AZIBs.

Primer-Engineered Transferase Enzyme for One-Pot and Amplified Detection of Cobalt Pollution and Peptide Remover Screening.

Extensive consumption of cobalt in the chemical field such as for battery materials, alloy, pigments, and dyes has aggravated the pollution of cobalt both in food and the environment, and assays for its on-site monitoring are urgently demanded. Herein, we utilized enzyme dependence on metal cofactors to develop terminal transferase (TdT) as a recognition element, achieving a one-pot sensitive and specific assay for detecting cobalt pollution. We engineered a 3'-OH terminus primer to improve the discrimination capacity of TdT for Co2+ from other bivalent cations. The TdT extension reaction amplified the recognition of Co2+ and yielded a limit of detection of 0.99 µM for Co2+ detection. Then, the TdT-based assay was designed to precisely detect cobalt in food and agricultural soil samples. By end-measurement of fluorescence using a microplate reader, the multiplexing assay enabled the rapid screening of the peptide remover for cobalt pollution. The TdT-based assay can be a promising tool for cobalt pollution monitoring and control.

Transcriptomic and enzymatic analysis reveals the roles of glutamate dehydrogenase in Corynebacterium glutamicum.

Glutamate dehydrogenase (Gdh), catalyzing the reversible conversion between 2-oxoglutarate and glutamate, plays an important role in the connection of nitrogen and carbon metabolism. Yet little is known about these enzymes in the amino acid-manufacturing Corynebacterium glutamicum. In the present study, we firstly identified the enzymatic characteristics of two Gdhs (GdhA and GdhB). The results showed that both GdhA and GdhB prefers NADPH as a coenzyme and have higher affinity for 2-OG than glutamate. The growth characteristics of gdhAΔ mutant and gdhBΔ mutant, gdhABΔ mutant showed GdhA serves as the main conduit for ammonium assimilation, and GdhB is the main glutamate- metabolizing enzyme in C. glutamicum. The full-genome transcriptomic analysis was used to investigate physiological response of C. glutamicum to the glutamate as nitrogen source, and gdh deletion. The results showed that the nitrogen starvation response was elicited when glutamine served as the sole nitrogen source. gdhAΔBΔ double deletion trigger a partially deregulated nitrogen starvation response, in which genes involved in nitrogen assimilation showed obviously upregulated in a certain extent. On the other hand, the genes of phosphotransferase system (PTS) and glycolysis pathway, most genes in pentose phosphate pathway were significantly upregulated, indicating that gdh deficiency initiated the enhancement of the absorption and metabolism of carbon sources. We believed that our results in this study will give new insights on the molecular mechanism of Gdh activity cross-talks with carbon and nitrogen metabolism, also setting a new background for further flux redistribution applied research of biotechnological interest.

Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study.

Purpose: Radiation-induced dermatitis is one of the most common side effects for breast cancer patients treated with radiation therapy (RT). Acute complications can have a considerable impact on tumor control and quality of life for breast cancer patients. In this study, we aimed to develop a novel quantitative high-accuracy machine learning tool for prediction of radiation-induced dermatitis (grade ≥ 2) (RD 2+) before RT by using data encapsulation screening and multi-region dose-gradient-based radiomics techniques, based on the pre-treatment planning computed tomography (CT) images, clinical and dosimetric information of breast cancer patients. Methods and Materials: 214 patients with breast cancer who underwent RT between 2018 and 2021 were retrospectively collected from 3 cancer centers in China. The CT images, as well as the clinical and dosimetric information of patients were retrieved from the medical records. 3 PTV dose related ROIs, including irradiation volume covered by 100%, 105%, and 108% of prescribed dose, combined with 3 skin dose-related ROIs, including irradiation volume covered by 20-Gy, 30-Gy, 40-Gy isodose lines within skin, were contoured for radiomics feature extraction. A total of 4280 radiomics features were extracted from all 6 ROIs. Meanwhile, 29 clinical and dosimetric characteristics were included in the data analysis. A data encapsulation screening algorithm was applied for data cleaning. Multiple-variable logistic regression and 5-fold-cross-validation gradient boosting decision tree (GBDT) were employed for modeling training and validation, which was evaluated by using receiver operating characteristic analysis. Results: The best predictors for symptomatic RD 2+ were the combination of 20 radiomics features, 8 clinical and dosimetric variables, achieving an area under the curve (AUC) of 0.998 [95% CI: 0.996-1.0] and an AUC of 0.911 [95% CI: 0.838-0.983] in the training and validation dataset, respectively, in the 5-fold-cross-validation GBDT model. Meanwhile, the top 12 most important characteristics as well as their corresponding importance measures for RD 2+ prediction in the GBDT machine learning process were identified and calculated. Conclusions: A novel multi-region dose-gradient-based GBDT machine learning framework with a random forest based data encapsulation screening method integrated can achieve a high-accuracy prediction of acute RD 2+ in breast cancer patients.

[Therapeutic effects of alkaloids in Tibetan medicine Bangna (Aconiti Penduli et Aconiti Flavi Radix) on osteoarthritis rats and mechanisms].

This study aims to investigate the therapeutic effects of alkaloids in Tibetan medicine Bangna(Aconiti Penduli et Aconiti Flavi Radix) on osteoarthritis(OA) rats in vitro and in vivo and the underlying mechanisms. Chondrocytes were isolated from 2-3 week-old male SD rats and lipopolysaccharide(LPS) was used to induce OA in chondrocytes in vitro. Methyl thiazolyl tetrazolium(MTT) assay was used to investigate the toxicity of seven alkaloids(12-epi-napelline, songorine, benzoylaconine, aconitine, 3-acetylaconitine, mesaconitine, and benzoylmesaconine) to chondrocytes. Chondrocytes were classified into the control group, model group(induced by LPS 5 µg·mL~(-1) for 12 h), and administration groups(induced by LPS 5 µg·mL~(-1) for 12 h and incubated for 24 h). The protein expression of inflammatory factors cyclooxygenase-2(COX-2), inducible nitric oxide synthetase(iNOS), tumor necrosis factor-α(TNF-α), and interleukin-1ß(IL-1ß) in each group were detected by Western blot, and the protein expression of matrix metalloprotease-13(MMP-13), aggrecan, collagen Ⅱ, fibroblast growth factor 2(FGF2) by immunofluorescence staining. For the in vivo experiment, sodium iodoacetate was used to induce OA in rats, and the expression of MMP-13, TNF-α, and FGF2 in cartilage tissues of rats in each group was detected by immunohistochemistry. The results showed that the viability of chondrocytes could reach more than 90% under the treatment of the seven alkaloids in a certain dose range. Aconitine, 12-epi-napelline, songorine, 3-acetylaconitine, and mesaconitine could decrease the protein expression of inflammatory factors COX-2, iNOS, TNF-α and IL-1ß compared with the model group. Moreover, 12-epi-napelline, aconitine, and mesaconitine could down-regulate the expression of MMP-13 and up-regulate the expression of aggrecan and collagen Ⅱ. In addition, compared with the model group and other Bangna alkaloids, 12-epi-napelline significantly up-regulated the expression of FGF2. Therefore, 12-epi-napelline was selected for the animal experiment in vivo. Immunohistochemistry results showed that 12-epi-napelline could significantly reduce the expression of MMP-13 and TNF-α in cartilage tissues, and up-regulate the expression of FGF2 compared with the model group. In conclusion, among the seven Bangna alkaloids, 12-epi-napelline can promote the repair of OA in rats by down-regulating the expression of MMP-13 and TNF-α and up-regulating the expression of FGF2.

Phase-Field Simulation and Machine Learning Study of the Effects of Elastic and Plastic Properties of Electrodes and Solid Polymer Electrolytes on the Suppression of Li Dendrite Growth.

Lithium (Li) dendrite growth in Li batteries is a long-standing problem, which causes critical safety concerns and severely limits the advancement of rechargeable Li batteries. Replacing a conventional liquid electrolyte with a solid electrolyte of high mechanical strength and rigidity has become a potential approach to inhibiting the Li dendrite growth. However, there still lacks an accurate understanding of the role of the mechanical properties of the metal electrode and the solid electrolyte in the Li dendrite growth. In this work, we develop a phase-field model coupled with the elastoplastic deformation to investigate the Li dendrite growth and its inhibition in the cell. Different mechanical properties, including the elastic modulus and the initial yield strength of both the metal electrode and the solid electrolyte, are explored to understand their independent roles in the inhibition of Li dendrite growth. High-throughput phase-field simulations are performed to establish a database of relationships between the aforementioned mechanical properties and the Li dendrite morphology, based on which a compressed-sensing machine learning model is trained to derive interpretable analytical correlations between the key material parameters and the dendrite morphology, as described by the dendrite length and area ratio. It is revealed that the Li dendrite can be effectively inhibited by electrolytes of high elastic moduli and initial yield strengths. Meanwhile, the role of the yield strength of the Li metal is also critical when the yield strength of the electrolyte becomes low. This work provides a fundamental understanding of the dendrite inhibition by mechanical suppression and demonstrates a computational data-driven methodology to potentially guide the electrode and electrolyte material selection for better inhibition of the dendrite growth.