Attribution classification method of APT malware based on multi-feature fusion.

In recent years, with the development of the Internet, the attribution classification of APT malware remains an important issue in society. Existing methods have yet to consider the DLL link library and hidden file address during the execution process, and there are shortcomings in capturing the local and global correlation of event behaviors. Compared to the structural features of binary code, opcode features reflect the runtime instructions and do not consider the issue of multiple reuse of local operation behaviors within the same APT organization. Obfuscation techniques more easily influence attribution classification based on single features. To address the above issues, (1) an event behavior graph based on API instructions and related operations is constructed to capture the execution traces on the host using the GNNs model. (2) ImageCNTM captures the local spatial correlation and continuous long-term dependency of opcode images. (3) The word frequency and behavior features are concatenated and fused, proposing a multi-feature, multi-input deep learning model. We collected a publicly available dataset of APT malware to evaluate our method. The attribution classification results of the model based on a single feature reached 89.24% and 91.91%. Finally, compared to single-feature classifiers, the multi-feature fusion model achieves better classification performance.

Transfer learning improves predictions in lignin content of Chinese fir based on Raman spectra.

Lignin in biomass plays significant role in substitution of synthetic polymer and reduction of energy expenditure, and the lignin content was usually determined by wet chemical methods. However, the methods' heavy workload, low efficiency, huge consumption of chemicals and use of toxic reagents render them unsuitable for sustainable development and environmental protection. Chinese fir, a prevalent angiosperm tree, holds immense importance for various industries. Since our previous work found that Raman spectroscopy could accurately predict the lignin content in poplar, we propose that the lignin content of Chinese fir can be estimated by similar strategy. The results suggested that the peak at 2895 cm-1 is the optimal choice of internal standard peak and algorithm of XGBoost demonstrates the highest accuracy among all algorithms. Furthermore, transfer learning was successfully introduced to enhance the accuracy and robustness of the model. Ultimately, we report that a machine learning algorithm, combining transfer learning with XGBoost or LightGBM, offers an accurate, high-efficiency and environmental friendly method for predicting the lignin content of Chinese fir using Raman spectra.

[Reversal Effect of NVP-BEZ235 on Doxorubicin-Resistance in Burkitt Lymphoma RAJI Cell Line].

OBJECTIVE: To study the reversal effect of NVP-BEZ235 on doxorubicin resistance in Burkitt lymphoma RAJI cell line. METHODS: The doxorubicin-resistant cell line was induced by treating RAJI cells with a concentration gradient of doxorubicin. The levels of Pgp, p-AKT, and p-mTOR in cells were detected by Western blot. Cell viability was detected by MTT assay. IC50 was computed by SPSS. RESULTS: The doxorubicin-resistant Burkitt lymphoma cell line, RAJI/DOX, was established successfully. The expression of Pgp and the phosphorylation levels of AKT and mTOR in RAJI/DOX cell line were both higher than those in RAJI cell line. NVP-BEZ235 downregulated the phosphorylation levels of AKT and mTOR in RAJI/DOX cell line. NVP-BEZ235 inhibited the proliferation of RAJI/DOX cell line, and the effect was obvious when it was cooperated with doxorubicin. CONCLUSION: The constitutive activation of PI3K/AKT/mTOR pathway of RAJI/DOX cell line was more serious than RAJI cell line. NVP-BEZ235 reversed doxorubicin resistance of RAJI/DOX cell line by inhibiting the PI3K/AKT/mTOR signal pathway.

The interactions and biological pathways among metabolomics products of patients with coronary heart disease.

BACKGROUND: Through bioinformatics analysis, this study explores the interactions and biological pathways involving metabolomic products in patients diagnosed with coronary heart disease (CHD). METHODS: A comprehensive search for relevant studies focusing on metabolomics analysis in CHD patients was conducted across databases including CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Nature, Web of Science, Springer, and Science Direct. Metabolites reported in the literature underwent statistical analysis and summarization, with the identification of differential metabolites. The pathways associated with these metabolites were examined using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Molecular annotation of metabolites and their relationships with enzymes or transporters were elucidated through analysis with the Human Metabolome Database (HMDB). Visual representation of the properties related to these metabolites was achieved using Metabolomics Pathway Analysis (metPA). RESULTS: A total of 13 literatures satisfying the criteria for enrollment were included. A total of 91 metabolites related to CHD were preliminarily screened, and 87 effective metabolites were obtained after the unrecognized metabolites were excluded. A total of 45 pathways were involved. Through the topology analysis (TPA) of pathways, their influence values were calculated, and 13 major metabolic pathways were selected. The pathways such as Phenylalanine, tyrosine, and tryptophan biosynthesis, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism, and Glycine, serine, and threonine metabolism primarily involved the regulation of processes and metabolites related to inflammation, oxidative stress, one-carbon metabolism, energy metabolism, lipid metabolism, immune regulation, and nitric oxide expression. CONCLUSION: Multiple pathways, including Phenylalanine, tyrosine, and tryptophan biosynthesis, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism, and Glycine, serine, and threonine metabolism, were involved in the occurrence of CHD. The occurrence of CHD is primarily associated with the regulation of processes and metabolites related to inflammation, oxidative stress, one-carbon metabolism, energy metabolism, lipid metabolism, immune regulation, and nitric oxide expression.

Effects of gas signaling molecule SO2 in cardiac functions of hyperthyroid rats.

Sulfur dioxide (SO2), a novel endogenous gas signaling molecule, is involved in the regulation of cardiac function. Exerting a key role in progression of hyperthyroidism-induced cardiomyopathy (HTC), myocardial fibrosis is mainly caused by myocardial apoptosis, leading to poor treatment outcomes and prognoses. This study aimed to investigate the effect of SO2 on the hyperthyroidism-induced myocardial fibrosis and the underlying regulatory mechanisms. Elisa, Masson staining, Western-Blot, transmission electron microscope, and immunofluorescence were employed to evaluate the myocardial interstitial collagen deposition, endoplasmic reticulum stress (ERS), apoptosis, changes in endogenous SO2, and Hippo pathways from in vitro and in vivo experiments. The study results indicated that the hyperthyroidism-induced myocardial fibrosis was accompanied by decreased cardiac function, and down-regulated ERS, apoptosis, and endogenous SO2-producing enzyme aspartate aminotransferase (AAT)1/2 in cardiac myocytes. In contrast, exogenous SO2 donors improved cardiac function, reduced myocardial interstitial collagen deposition, up-regulated AAT1/2, antagonized ERS and apoptosis, and inhibited excessive activation of Hippo pathway in hyperthyroid rats. In conclusion, the results herein suggested that SO2 inhibited the overactivation of the Hippo pathway, antagonized ERS and apoptosis, and alleviated myocardial fibrosis in hyperthyroid rats. Therefore, this study was expected to identify intervention targets and new strategies for prevention and treatment of HTC.

How does ultrasound contribute to the migration of extractives inside Ailanthus altissima wood?

Extractives have an impact on the processing and commercial value of wood. Ultrasound is an environmentally friendly technology commonly employed to reduce the extractive content and thus enhance the permeability of wood. This study aimed to understand the migration mechanism of extractives inside wood during ultrasonic treatment, which may help to obtain the desired wood properties. The extractive distribution of Ailanthus altissima was observed by using stereo microscopy, optical microscopy, and scanning electron microscopy, the extractive content was determined, and the relationship between the concentration of water-soluble extractives and absorbance was measured using a UV/Vis spectrophotometer, and the migration model of extractives was studied using layered extraction by innovatively combining the weight and the absorbance methods. The results revealed that the extractives were predominantly distributed in the vessels and diminished after ultrasonic treatment. The extractive content gradually decreased over time (0 ∼ 5 h), with a rapid decline observed within the first 2 h. The concentration of the water-soluble extractives exhibited a proportional relationship with the absorbance. Through the comparison of the layered-extractive concentration, accumulating evidence suggested that the migration of the extractives was a dynamic process, which included the extractives migrating towards easy-extracted area, moving along the direction of ultrasound propagation inside the wood, and leaching out of wood during ultrasonic treatment.

AP39 inhibits ferroptosis by inhibiting mitochondrial autophagy through the PINK1/parkin pathway to improve myocardial fibrosis with myocardial infarction.

BACKGROUND AND PURPOSE: Research has revealed the involvement of mitochondrial autophagy and iron death in the pathogenesis of myocardial fibrosis. The objective of this study is to investigate whether the mitochondrial-targeted H2S donor AP39 inhibits mitochondrial autophagy and antagonizes myocardial cell iron death through the PINK1/Parkin pathway, thereby improving myocardial fibrosis in rats with myocardial infarction. EXPERIMENTAL APPROACH: A rat model of myocardial infarction was created by intraperitoneal injection of a high dose of isoproterenol, and H9c2 myocardial cells were subjected to hypoxic injury induced by CoCl2. Western blot, RT-PCR, transmission electron microscopy, immunohistochemistry, as well as echocardiography, and studies on isolated hearts were employed. KEY RESULTS: In the hearts of rats with myocardial infarction, there was a significant accumulation of interstitial collagen fibers, accompanied by downregulation of CSE protein expression, activation of the PINK1/Parkin signaling pathway, and activation of mitochondrial autophagy. Intervention with AP39 resulted in a significant improvement of the aforementioned changes, which could be reversed by the addition of PAG. Similar results were observed in vitro experiments. Furthermore, the addition of CCCP reversed the antagonistic effect of AP39 on myocardial cell iron death, while the addition of RSL3 reversed the inhibitory effect of AP39 on collagen production in myocardial cells. CONCLUSION AND IMPLICATIONS: The mitochondrial-targeted H2S donor AP39 can inhibit mitochondrial autophagy through the PINK1/Parkin pathway, antagonize myocardial cell iron death, and improve myocardial fibrosis in rats with myocardial infarction.

Information Extraction Network Based on Multi-Granularity Attention and Multi-Scale Self-Learning.

Transforming the task of information extraction into a machine reading comprehension (MRC) framework has shown promising results. The MRC model takes the context and query as the inputs to the encoder, and the decoder extracts one or more text spans as answers (entities and relationships) from the text. Existing approaches typically use multi-layer encoders, such as Transformers, to generate hidden features of the source sequence. However, increasing the number of encoder layers can lead to the granularity of the representation becoming coarser and the hidden features of different words becoming more similar, potentially leading to the model's misjudgment. To address this issue, a new method called the multi-granularity attention multi-scale self-learning network (MAML-NET) is proposed, which enhances the model's understanding ability by utilizing different granularity representations of the source sequence. Additionally, MAML-NET can independently learn task-related information from both global and local dimensions based on the learned multi-granularity features through the proposed multi-scale self-learning attention mechanism. The experimental results on two information extraction tasks, named entity recognition and entity relationship extraction, demonstrated that the method was superior to the method based on machine reading comprehension and achieved the best performance on the five benchmark tests.

Hydrogen sulfide alleviates hypothyroidism-induced myocardial fibrosis in rats through stimulating autophagy and inhibiting TGF-ß1/Smad2 pathway.

Hypothyroidism alone can lead to myocardial fibrosis and result in heart failure, but traditional hormone replacement therapy does not improve the fibrotic situation. Hydrogen sulfide (H2S), a new gas signaling molecule, possesses anti-inflammatory, antioxidant, and anti-fibrotic capabilities. Whether H2S could improve hypothyroidism-induced myocardial fibrosis are not yet studied. In our study, H2S could decrease collagen deposition in the myocardial tissue of rats caused by hypothyroidism. Furthermore, in hypothyroidism-induced rats, we found that H2S could enhance cystathionine-gamma-lyase (CSE), not cystathionine ß-synthase (CBS), protein expressions. Finally, we noticed that H2S could elevate autophagy levels and inhibit the transforming growth factor-ß1 (TGF-ß1) signal transduction pathway. In conclusion, our experiments not only suggest that H2S could alleviate hypothyroidism-induced myocardial fibrosis by activating autophagy and suppressing TGF-ß1/SMAD family member 2 (Smad 2) signal transduction pathway, but also show that it can be used as a complementary treatment to conventional hormone therapy.

Gaseous signal molecule SO2 regulates autophagy through PI3K/AKT pathway inhibits cardiomyocyte apoptosis and improves myocardial fibrosis in rats with type II diabetes.

Myocardial fibrosis is a key link in the occurrence and development of diabetic cardiomyopathy. Its etiology is complex, and the effect of drugs is not good. Cardiomyocyte apoptosis is an important cause of myocardial fibrosis. The purpose of this study was to investigate the effect of gaseous signal molecule sulfur dioxide (SO2) on diabetic myocardial fibrosis and its internal regulatory mechanism. Masson and TUNEL staining, Western-blot, transmission electron microscopy, RT-qPCR, immunofluorescence staining, and flow cytometry were used in the study, and the interstitial collagen deposition, autophagy, apoptosis, and changes in phosphatidylinositol 3-kinase (PI3K)/AKT pathways were evaluated from in vivo and in vitro experiments. The results showed that diabetic myocardial fibrosis was accompanied by cardiomyocyte apoptosis and down-regulation of endogenous SO2-producing enzyme aspartate aminotransferase (AAT)1/2. However, exogenous SO2 donors could up-regulate AAT1/2, reduce apoptosis of cardiomyocytes induced by diabetic rats or high glucose, inhibit phosphorylation of PI3K/AKT protein, up-regulate autophagy, and reduce interstitial collagen deposition. In conclusion, the results of this study suggest that the gaseous signal molecule SO2 can inhibit the PI3K/AKT pathway to promote cytoprotective autophagy and inhibit cardiomyocyte apoptosis to improve myocardial fibrosis in diabetic rats. The results of this study are expected to provide new targets and intervention strategies for the prevention and treatment of diabetic cardiomyopathy.

Four new diphenyl ether derivatives from a mangrove endophytic fungus Epicoccum sorghinum.

Four new diphenyl ethers, named epicoccethers K-N (1-4), were purified from the fermentation medium of a fungus Epicoccum sorghinum derived from Myoporum bontioides, and identified through HR-ESI-MS and NMR spectral analysis. Except that compound 1 showed moderate antifungal activity against Penicillium italicum and Fusarium graminearum, the other three compounds showed stronger activity against them than triadimefon. All of them showed moderate or weak antibacterial activity towards Staphylococcus aureus and Escherichia coli with O6 and O78 serotypes except that 3 was inactive to E. coli O6.

The Metabolomic Characterization of Different Types of Coronary Atherosclerotic Heart Disease in Male.

Background: In clinical practice, many patients with coronary atherosclerotic heart disease (CAD) have atypical clinical symptoms. It is difficult to accurately identify stable CAD or unstable CAD early through clinical symptoms and coronary angiography. This study aimed to screen the potential metabolite biomarkers in male patients with stable CAD and unstable CAD. Methods: In this work, the metabolomic characterization of the male patients with healthy control (n = 42), stable coronary artery disease (n = 60), non-ST-elevation acute coronary syndrome (n = 45), including prepercutaneous corona intervention (n = 14), and postpercutaneous coronary intervention (n = 31) were performed by using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). The serum samples of patients were analyzed by multivariate statistics. Results: Results showed that 17 altered metabolites were identified to have a clear distinction between the stable CAD group and the healthy subjects. Compared with the stable coronary artery disease group, 15 specific metabolite markers were found in the acute coronary syndrome group. The percutaneous coronary intervention also affected the metabolic behavior of patients with CAD. Conclusions: In summary, CAD is closely related to energy metabolism, lipid metabolism, and amino acid metabolism disorders. The different metabolic pattern characteristics of healthy, stable coronary artery disease and acute coronary syndrome are constructed, which brings a novel theoretical basis for the early diagnosis of patients with stable and unstable CAD.

Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy.

In this study, regularization algorithms (RR, LR, and ENR), classical ML algorithms (SVR, DT, and RF), and advanced GBM algorithms (LightGBM, CatBoost, and XGBoost) were applied to build the holocellulose content predictive models of poplar based on features extracted from Raman spectra. Evaluation results of models indicate that classical ML algorithms show higher predictive accuracy than regularization algorithms, and the advanced GBM algorithms better than the classical ML algorithms. Furthermore, models built by CatBoost and XGBoost can estimate holocellulose content with high predictive accuracy of test R2 above 0.93 and test RMSE less than 0.29%. It provides the heretofore best precision of holocellulose content predictive model based on Raman spectroscopy so far for our knowledge. Therefore, it is suggested that Raman spectroscopy coupled with ML algorithms is a promising tool for predicting the holocellulose content in poplar which can be applied in large-scale tree genetic and breeding programs.

Machine learning prediction of lignin content in poplar with Raman spectroscopy.

Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF, LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin content in poplar. Firstly, Raman features extracted from FT-Raman spectra after data processing were used as input of models and determined lignin contents were output. Secondly, grid-search combined with cross-validation was used to adjust the hyper-parameters of models. Finally, the predictive models were built by aforementioned algorithms. The results indicated regularization algorithms, SVR, DT held test R2 were >0.80 which means the predictive values from model still deviate from measured ones. Meanwhile, RF, LightGBM, CatBoost, and XGBoost were better than above algorithms, and their test R2 were >0.91 which suggesting the predictive values was nearly close to measured ones. Therefore, fast and accurate methods for predicting lignin content were obtained and will be useful for screening suitable lignocellulosic resource with expected lignin content.

Novel strategy for establishment of an FT-Raman spectroscopy based quantitative model for poplar holocellulose content determination.

Raman spectroscopy is effective for studying the ultrastructure, lignin content, and cellulose crystallinity of lignocellulosic materials. However, the quantitative analysis of holocellulose in lignocellulosic materials by this technique is challenging. In this study, based on Fourier-transform Raman (FT-Raman) spectroscopy, a novel strategy for building poplar holocellulose content quantitative model was proposed. Different algorithms were applied, including Principal component regression (PCR), partial least square regression (PLSR), ridge regression (RR), lasso regression (LR), and elastic net regression (ENR). Combined with different algorithms, twelve candidates of internal standard were selected. Sixty models combined by five regression algorithms and twelve internal standards were performed by five-fold cross validation. Consequently, the models constructed through RR, LR, and ENR combined with the internal standard of peak intensity of 2945 cm-1 were credible (Rp > 0.9, RMSEp < 1.0, and MAEp < 0.9). Credible models were obtained, indicating the high potential of FT-Raman spectroscopy for predicting the holocellulose content of lignocellulosic materials.

New diphenyl ethers from a fungus Epicoccum sorghinum L28 and their antifungal activity against phytopathogens.

The strategy "IEMAHC" (Induction of Endophyte Metabolism by Adding Host Components) was applied to the fermentation of the endophytic fungus Epicoccum sorghinum L28 from Myoporum bontioides by introducing guaiol, an ingredient of M. bontioides, into the cultivation medium, which resulted in the purification of nine new diphenyl ethers, epicoccethers A-I (1-9). Their structures were determined by overall spectroscopic analysis. HPLC-MS analysis revealed that compounds 5-7 were products generated by induction of guaiol. Compounds 6 and 7 are the first members containing an ester moiety formed by the natural long-chain fatty acid and the hydroxyl group in the phenylmethanol unit of the diphenyl ether class. The antifungal activities of compounds 1, 2, and 4-7 against Fusarium oxysporum were 1, 1, 2, 1, 2 and 4 times as high as those of the positive control triadimefon, respectively. Compounds 4 and 5 showed 1.6 times the antifungal activities of triadimefon towards Colletotrichum musae.

Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform-Raman Spectrometry.

The quick and non-invasive evaluation of lignin from biomass has been the focus of much attention. Several types of spectroscopies, for example, near-infrared (NIR) and Fourier transform-Raman (FT-Raman), have been successfully applied to build quantitative predictive lignin models based on chemometrics. However, due to the effect of sample moisture content and ambient humidity on its signals, NIR spectroscopy requires sophisticated pre-testing preparation. In addition, the current FT-Raman predictive models require large variations in the independent value inputs as restrictions in the corresponding mathematical algorithms prevent the effective biomass screening of suitable genotypes for lignin contents within a narrow range. In order to overcome the limitations associated with the current methods, in this paper, we employed Raman spectra excited using a 1064 nm laser, thus avoiding the impact of water and auto-fluorescence on NIR signals. The optimal baseline correction method, data type, mathematical algorithm, and internal reference were selected in order to build quantitative lignin models based on the data with limited variation. The resulting two predictive models, constructed through lasso and ridge regressions, respectively, proved to be effective in assessing the lignin content of poplar in large-scale breeding and genetic engineering programs.

APDL1-CART cells exhibit strong PD-L1-specific activity against leukemia cells.

Chimeric antigen receptor (CAR) T cells target specific tumor antigens and lyse tumor cells in an MHC-independent manner. However, the efficacy of CAR-T cell and other cancer immunotherapies is limited by the expression of immune-checkpoint molecules such as programmed death-ligand 1 (PD-L1) on tumor cells, which binds to PD-1 receptors on T cells leading to T cell inactivation and immune escape. Here, we incorporated a PD-L1-targeted single-chain variable fragment (scFv) fusion protein sequence into a CAR vector to generate human anti-PD-L1-CAR-T cells (aPDL1-CART cells) targeting the PD-L1 antigen. Unlike control T cells, aPDL1-CART cells significantly halted the expansion and reduced the viability of co-cultured leukemia cells (Raji, CD46, and K562) overexpressing PD-L1, and this effect was paralleled by increased secretion of IL-2 and IFN-γ. The antitumor efficacy of aPDL1-CART cells was also evaluated in vivo by co-injecting control T cells or aPDL1-CART cells along with PDL1-CA46 cells to generate subcutaneous xenografts in NCG mice. Whereas large tumors developed in mice inoculated with PDL1-CA46 cells alone or together with control T cells, no tumor formation was detected in xenografts containing aPDL1-CART cells. Our data suggest that immune checkpoint-targeted CAR-T cells may be useful for controlling and eradicating immune-refractory hematological malignancies.

Calreticulin increases growth and progression of natural killer/T-cell lymphoma.

In this study, we investigated the role of calreticulin (CALR) in the pathogenesis of natural killer/T-cell lymphoma (NKTCL). CALR expression was significantly higher in the NKTCL tissues than normal control tissues in the GSE80632 dataset. High CALR expression correlated with poorer overall survival of NKTCL patients (P = 0.0248). CALR mRNA and protein levels were significantly higher in NKTCL cell lines (NK92, SNK6, and SNT8) than normal NK cells. CALR-silenced SNK6 cells generated significantly smaller xenograft tumors in immunodeficient NCG mice than control SNK6 cells. CALR-knockdown NKTCL cells showed significantly less in vitro proliferation and Transwell migration than the controls. CALR knockdown inhibited G1-to-S phase cell cycle progression by increasing the levels of p27 cell cycle inhibitor and reducing the levels of cyclin E2 and cyclin-dependent kinase 2 (CDK2). CALR knockdown inhibited epithelial-to-mesenchymal transition (EMT) by decreasing the levels of ß-catenin and TCF/ZEB1 and upregulating E-cadherin. These data demonstrate that CALR regulates the growth and progression of NKTCL cells by modulating G1-to-S cell cycle progression and EMT.

Low Water Absorption, High-Strength Polyamide 6 Composites Blended with Sustainable Bamboo-Based Biochar.

To promote the application of polyamide 6 (PA6) in wood-plastic composites, the negative effects associated with the thermal degradation of plant fibers must be overcome. In this study, waste bamboo fibers were subjected to pyrolysis and ball milling to afford nano bamboo-based biochar (BC), which was subsequently used as reinforcement to prepare PA6/BC nano composites by injection molding. In addition, the processing fluidity, water absorption, mechanical properties, and interface compatibility of PA6/BC composites were discussed. Results revealed that a BC content of less than 30 wt% is beneficial to improve the processing fluidity of the composites. With the increase in the BC content, the density of the PA6/BC composites gradually increased, while the water absorption of the PA6/BC composites gradually decreased, and the maximum decrease was 46%. Compared to that of pure PA6, the mechanical strength of PA6/BC composites was improved by the addition of BC, and the maximum tensile/flexural strength and modulus of PA6/BC composites increased by 41%/72% and 195%/244%, respectively. However, the impact strength decreased by 27%. After immersion treatment, the dimensional stability and mechanical strength of the composites decreased, while toughness improved. At a BC content of less than 40 wt%, BC particles exhibited good dispersibility and wettability in the PA6 matrix, and the rough surface and rich pore structure of BC rendered strong mechanical interlocking effects and good interface compatibility, thereby enhancing the mechanical properties of the composites.