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Additional file 1. Concludes the results of the characterization of multifunctional nanoliposomes and DOX-CB and the results of cellular distribution and the cell viability of DOX-CB. The results of additional animal experiments such as evaluation of the GL261-orthotopicglioma in the C57BL/6 mouse model and HE staining of different organs.

Glioblastoma is the most common brain primary malignant tumor with the highest mortality. Boron neutron capture therapy (BNCT) can efficiently kill cancer cells on the cellular scale, with high accuracy, short course and low side-effects, which is regarded as the most promising therapy for malignant brain tumors like glioma. As the keypoint of BNCT, all boron delivery agents currently in clinical use are beset by insufficient tumor uptake, especially in the tumor nucleus, which limits the clinical application of BNCT. In this study, nuclear targeting of boron is achieved by DOX-CB, consisting of doxorubicin (DOX) and carborane (CB) utilizing the nuclear translocation property of DOX. The nucleus of GL261 cells takes up almost three times the concentration of boron required for BNCT. To further kill glioma and inhibit recurrence, a new multifunctional nanoliposome delivery system DOX-CB@lipo-pDNA-iRGD is constructed. It combines DOX-CB with immunotherapy strategy of blocking macrophage immune checkpoint pathway CD47-SIRPα by CRISPR-Cas9 system, coupling BNCT with immunotherapy simultaneously. Compared with clinical drug Borocaptate Sodium (BSH), DOX-CB@lipo-pDNA-iRGD significantly enhances the survival rate of tumor-bearing mice, reduces tumor stemness, and improves the prognosis. The excellent curative effect of this nanoliposome delivery system provides an insight into the combined treatment of BNCT. Graphical Abstract

In various types of malignant tumors, metastasis is responsible for most of the tumor-induced death. Though emerging technologies provided early detection of tumor metastasis or even warning of high metastatic risk before the actual occurrence of metastasis, clinical treatment on metastasis prevention lags far behind. Evidences have illustrated that primary tumor induced pre-metastatic niche (PMN) formation in distal organs by producing pro-metastasis factors, spreading the spark to ignite the distal microenvironment. Given the fundamental role of PMN in the development of metastases, interruption of PMN formation would be a promising strategy to take early actions against tumor metastasis. Here we report an enzyme-activatable assembled peptide FR17 that can serve as a “flame-retarding blanket” at PMN site specifically to extinguish the “fire” of tumor-supportive microenvironment adaption. Our experiment demonstrated that the assembled peptide successfully reversed extracellular matrix deposition, vascular leakage and angiogenesis through inhibition on fibroblasts activation in PMN, which suppressed the remodeling of metastasis-supportive host stromal, and further prevented the recruitment of myeloid cells to PMN and then recovered the immunosuppressive microenvironment. Cell transcriptomic analysis of the pulmonary recruited MDSC suggested that FR17 intervention could regulate immune response activation, immune cells chemotaxis and migration pathways. Consequently, FR17 administration effectively inhibited pulmonary PMN formation and postoperative metastasis of melanoma, with only 30% lung-metastasis occurrence was observed for FR17 treated group at the time point when 100% occurrence was observed for the control group and 80% occurrence for anti-PD1 treated group, offering a robust therapeutic strategy against PMN establishing to prevent metastasis.

To solve the problem that fault diagnosis accuracy of complex equipment bearings is not high due to the complexity of its structure and the environment, a cooperative algorithm for fault diagnosis of complex equipment bearings based on ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) is proposed. First, the vibration signal of the bearings is decomposed by EEMD. Second, the correlation coefficient and kurtosis value are selected as the evaluation indexes for the intrinsic mode function (IMF) components after decomposition, and the weights of the parameters are set dynamically by the mean-guided weight method. Then, the IMF components are filtered by an improved genetic algorithm to obtain the optimal IMF component combination, which can effectively eliminate redundant components and retain as much fault information as possible. Next, using the orthogonality of IMF components, the energy distribution of the selected IMF components is calculated as the Eigenvector. Finally, using the advantage of accurate classification of SVM in small samples, the fault status of complex equipment bearings can be identified. The effectiveness of the algorithm model is proven by example simulation data, and the model has certain scalability and applicability in engineering.

Since the characteristics of the incipient faults for the rolling bearing under the sectional jumping speed are difficult to be extracted, the conventional fault diagnostic approaches usually with poor monitoring performance and low diagnostic accuracy (i.e., the ratio of samples correctly classified to total samples). This paper proposes a novel cooperative diagnostic approach for the incipient faults of the rolling bearing based on the optimized local mean decomposition (LMD) and support vector machine (SVM). First, to resolve the problem of selecting the appropriate number of product function (PF) components, an optimally weighted fusion model of PF components is established by introducing a genetic algorithm, aiming to maximize the correlation with the original incipient fault signal. Besides, in this model, a novel rule is set to calculate the weight coefficients of fusion. Second, considering the sparsity of the incipient fault characteristics caused by the sectional jumping speed, from the perspective of energy distribution, a novel characteristic extraction model is constructed based on the equal interval energy projection. This characteristic extraction model can correctly extract the fault characteristics and effectively eliminate redundant information. Moreover, the SVM has collaborated with the above-mentioned characteristic extraction model to diagnosis incipient faults. Finally, the effectiveness and correctness of the proposed approach are verified by the experimental simulation results. The comparison and analysis show that the proposed algorithm cannot only correctly extract fault characteristics but also have a high accuracy of fault characteristics recognition with good operability and scalability.