Antibodies targeting programmed cell death protein-1 (PD-1) or its ligand PD-L1 rescue T cells from exhausted status and revive immune response against cancer cells. Based on the immense success in ...clinical trials, ten α-PD-1 (nivolumab, pembrolizumab, cemiplimab, sintilimab, camrelizumab, toripalimab, tislelizumab, zimberelimab, prolgolimab, and dostarlimab) and three α-PD-L1 antibodies (atezolizumab, durvalumab, and avelumab) have been approved for various types of cancers. Nevertheless, the low response rate of α-PD-1/PD-L1 therapy remains to be resolved. For most cancer patients, PD-1/PD-L1 pathway is not the sole speed-limiting factor of antitumor immunity, and it is insufficient to motivate effective antitumor immune response by blocking PD-1/PD-L1 axis. It has been validated that some combination therapies, including α-PD-1/PD-L1 plus chemotherapy, radiotherapy, angiogenesis inhibitors, targeted therapy, other immune checkpoint inhibitors, agonists of the co-stimulatory molecule, stimulator of interferon genes agonists, fecal microbiota transplantation, epigenetic modulators, or metabolic modulators, have superior antitumor efficacies and higher response rates. Moreover, bifunctional or bispecific antibodies containing α-PD-1/PD-L1 moiety also elicited more potent antitumor activity. These combination strategies simultaneously boost multiple processes in cancer-immunity cycle, remove immunosuppressive brakes, and orchestrate an immunosupportive tumor microenvironment. In this review, we summarized the synergistic antitumor efficacies and mechanisms of α-PD-1/PD-L1 in combination with other therapies. Moreover, we focused on the advances of α-PD-1/PD-L1-based immunomodulatory strategies in clinical studies. Given the heterogeneity across patients and cancer types, individualized combination selection could improve the effects of α-PD-1/PD-L1-based immunomodulatory strategies and relieve treatment resistance.
Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is ...harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration.
A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research ...aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R2 of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.
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•Six XGBoost-based hybrid models for predicting TBM penetration rate are proposed.•GWO, PSO, SSO, SCA, MVO and MFO can assist the hyper-parameters tuning of XGBoost.•The prediction performance from high to low is PSO-XGB, MFO-XGB, GWO-XGB, MVO-XGB, SCA-XGB, SSO-XGB.•The Mutual information method is applied to demonstrate the relative importance of each input indicator.
The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy ...of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
Our previous work showed that the anti-TGF-β/PD-L1 bispecific antibody YM101 effectively overcame anti-PD-L1 resistance in immune-excluded tumor models. However, in immune-desert models, the efficacy ...of YM101 was limited. Bivalent manganese (Mn
) is identified as a natural stimulator of interferon genes (STING) agonist, which might enhance cancer antigen presentation and improve the therapeutic effect of YM101.
The effect of Mn
on STING pathway was validated by western blotting and enzyme-linked immunosorbent assay. Dendritic cell (DC) maturation was measured by flow cytometry. The synergistic effect between Mn
and YM101 in vitro was determined by one-way mixed lymphocyte reaction, CFSE dilution assay, and cytokine detection. The in vivo antitumor effect of Mn
plus YM101 therapy was assessed in CT26, EMT-6, H22, and B16 tumor models. Flow cytometry, RNA-seq, and immunofluorescent staining were adopted to investigate the alterations in the tumor microenvironment.
Mn
could activate STING pathway and promote the maturation of human and murine DC. The results of one-way mixed lymphocyte reaction showed that Mn
synergized YM101 in T cell activation. Moreover, in multiple syngeneic murine tumor models, Mn
plus YM101 therapy exhibited a durable antitumor effect and prolonged the survival of tumor-bearing mice. Relative to YM101 monotherapy and Mn
plus anti-PD-L1 therapy, Mn
plus YM101 treatment had a more powerful antitumor effect and a broader antitumor spectrum. Mechanistically, Mn
plus YM101 strategy simultaneously regulated multiple components in the antitumor immunity and drove the shift from immune-excluded or immune-desert to immune-inflamed tumors. The investigation in the TME indicated Mn
plus YM101 strategy activated innate and adaptive immunity, enhanced cancer antigen presentation, and upregulated the density and function of tumor-infiltrating lymphocytes. This normalized TME and reinvigorated antitumor immunity contributed to the superior antitumor effect of the combination therapy.
Combining Mn
with YM101 has a synergistic antitumor effect, effectively controlling tumor growth and prolonging the survival of tumor-bearing mice. This novel cocktail strategy has the potential to be a universal regimen for inflamed and non-inflamed tumors.
The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such as weak rock masses ...and high in situ stress. Several problems such as budget increase and construction period extension can be caused by squeezing in rock mass. It is significant to propose a model for accurate prediction of rock squeezing. In this research, the support vector machine (SVM) as a machine learning model was optimized by the whale optimization algorithm (WOA), WOA-SVM, to classify the tunnel squeezing based on 114 real cases. The role of WOA in this system is to optimize the hyper-parameters of SVM model for receiving a higher level of accuracy. In the established database, five input parameters, i.e., buried depth, support stiffness, rock tunneling quality index, diameter and the percentage strain, were used. In the process of model classification, different effective parameters of SVM and WOA were considered, and the optimum parameters were designed. To examine the accuracy of the WOA-SVM, the base SVM, ANN (refers to the multilayer perceptron) and GP (refers to the Gaussian process classification) were also constructed. Evaluation of these models showed that the optimized WOA-SVM is the best model among all proposed models in classifying the tunnel squeezing. It has the highest accuracy (approximately 0.9565) than other un-optimized individual classifiers (SVM, ANN, and GP). This was obtained based on results of different performance indexes. In addition, according to sensitivity analysis, the percentage strain is highly sensitive to the model, followed by buried depth and support stiffness. That means, ɛ, H and K are the best combination of parameters for the WOA–SVM model.
Cancer immunotherapy has made remarkable progress in the past decade. Bispecific antibodies (BsAbs) have acquired much attention as the next generation strategy of antibody-target cancer ...immunotherapy, which overwhelmingly focus on T cell recruitment and dual receptors blockade. So far, BsAb drugs have been proved clinically effective and approved for the treatment of hematologic malignancies, but no BsAb have been approved in solid tumors. Numerous designed BsAb drugs for solid tumors are now undergoing evaluation in clinical trials. In this review, we will introduce the formats of bispecific antibodies, and then update the latest preclinical studies and clinical trials in solid tumors of BsAbs targeting EpCAM, CEA, PMSA, ErbB family, and so on. Finally, we discuss the BsAb-related adverse effects and the alternative strategy for future study.
BackgroundAgents blocking programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) have been approved for triple-negative breast cancer (TNBC). However, the response rate of ...anti-PD-1/PD-L1 is still unsatisfactory, partly due to immunosuppressive factors such as transforming growth factor-beta (TGF-β). In our previous pilot study, the bispecific antibody targeting TGF-β and murine PD-L1 (termed YM101) showed potent antitumor effect. In this work, we constructed a bispecific antibody targeting TGF-β and human PD-L1 (termed BiTP) and explored the antitumor effect of BiTP in TNBC.MethodsBiTP was developed using Check-BODYTM bispecific platform. The binding affinity of BiTP was measured by surface plasmon resonance, ELISA, and flow cytometry. The bioactivity was assessed by Smad and NFAT luciferase reporter assays, immunofluorescence, western blotting, and superantigen stimulation assays. The antitumor activity of BiTP was explored in humanized epithelial-mesenchymal transition-6-hPDL1 and 4T1-hPDL1 murine TNBC models. Immunohistochemical staining, flow cytometry, and bulk RNA-seq were used to investigate the effect of BiTP on immune cell infiltration.ResultsBiTP exhibited high binding affinity to dual targets. In vitro experiments verified that BiTP effectively counteracted TGF-β-Smad and PD-L1-PD-1-NFAT signaling. In vivo animal experiments demonstrated that BiTP had superior antitumor activity relative to anti-PD-L1 and anti-TGF-β monotherapy. Mechanistically, BiTP decreased collagen deposition, enhanced CD8+ T cell penetration, and increased tumor-infiltrating lymphocytes. This improved tumor microenvironment contributed to the potent antitumor activity of BiTP.ConclusionBiTP retains parent antibodies’ binding affinity and bioactivity, with superior antitumor activity to parent antibodies in TNBC. Our data suggest that BiTP might be a promising agent for TNBC treatment.
Bacterial infection, excessive inflammation and damaging blood vessels network are the major factors to delay the healing of diabetic ulcer. At present, most of wound repair materials are passive and ...can't response to the wound microenvironment, resulting in a low utilization of bioactive substances and hence a poor therapeutic effect. Therefore, it's essential to design an intelligent wound dressing responsive to the wound microenvironment to achieve the release of drugs on-demand on the basis of multifunctionality. In this work, metformin-laden CuPDA NPs composite hydrogel (Met@ CuPDA NPs/HG) was fabricated by dynamic phenylborate bonding of gelatin modified by dopamine (Gel-DA), Cu-loaded polydopamine nanoparticles (CuPDA NPs) with hyaluronic acid modified by phenyl boronate acid (HA-PBA), which possessed good injectability, self-healing, adhesive and DPPH scavenging performance. The slow release of metformin was achieved by the interaction with CuPDA NPs, boric groups (B–N coordination) and the constraint of hydrogel network. Metformin had a pH and glucose responsive release behavior to treat different wound microenvironment intelligently. Moreover, CuPDA NPs endowed the hydrogel excellent photothermal responsiveness to kill bacteria of >95% within 10 min and also the slow release of Cu2+ to protect wound from infection for a long time. Met@ CuPDA NPs/HG also recruited cells to a certain direction and promoted vascularization by releasing Cu2+. More importantly, Met@CuPDA NPs/HG effectively decreased the inflammation by eliminating ROS and inhibiting the activation of NF-κB pathway. Animal experiments demonstrated that Met@CuPDA NPs/HG significantly promoted wound healing of diabetic SD rats by killing bacteria, inhibiting inflammation, improving angiogenesis and accelerating the deposition of ECM and collagen. Therefore, Met@CuPDA NPs/HG had a great application potential for diabetic wound healing.
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•A metformin-laden CuPDA NPs composite hydrogel (Met@ CuPDA NPs/HG) was fabricated.•The hydrogel had a microenvironment response to release metformin intelligently.•The hydrogel eradicated bacteria by photothermal responsiveness and the slow release of Cu2+.•The hydrogel promoted vascularization and decreased the inflammation.•Met@ CuPDA NPs/HG had a significant promotion on diabetic infected wound healing.
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