A Deep Learning Model Combining Multimodal Factors to Predict the Overall Survival of Transarterial Chemoembolization.

Background: To develop and validate an overall survival (OS) prediction model for transarterial chemoembolization (TACE). Methods: In this retrospective study, 301 patients with hepatocellular carcinoma (HCC) who received TACE from 2012 to 2015 were collected. The residual network was used to extract prognostic information from CT images, which was then combined with the clinical factors adjusted by COX regression to predict survival using a modified deep learning model (DLOPCombin). The DLOPCombin model was compared with the residual network model (DLOPCTR), multiple COX regression model (DLOPCox), Radiomic model (Radiomic), and clinical model. Results: In the validation cohort, DLOPCombin shows the highest TD AUC of all cohorts, which compared with Radiomic (TD AUC: 0.96vs 0.63) and clinical model (TD AUC: 0.96 vs 0.62) model. DLOPCombin showed significant difference in C index compared with DLOPCTR and DLOPCox models (P < 0.05). Moreover, the DLOPCombin showed good calibration and overall net benefit. Patients with DLOPCombin model score ≤ 0.902 had better OS (33 months vs 15.5 months, P < 0.0001). Conclusion: The deep learning model can effectively predict the patients' overall survival of TACE.

Reinforcement Learning-Based Model Predictive Control for Discrete-Time Systems.

This article proposes a novel reinforcement learning-based model predictive control (RLMPC) scheme for discrete-time systems. The scheme integrates model predictive control (MPC) and reinforcement learning (RL) through policy iteration (PI), where MPC is a policy generator and the RL technique is employed to evaluate the policy. Then the obtained value function is taken as the terminal cost of MPC, thus improving the generated policy. The advantage of doing so is that it rules out the need for the offline design paradigm of the terminal cost, the auxiliary controller, and the terminal constraint in traditional MPC. Moreover, RLMPC proposed in this article enables a more flexible choice of prediction horizon due to the elimination of the terminal constraint, which has great potential in reducing the computational burden. We provide a rigorous analysis of the convergence, feasibility, and stability properties of RLMPC. Simulation results show that RLMPC achieves nearly the same performance as traditional MPC in the control of linear systems and exhibits superiority over traditional MPC for nonlinear ones.

Cucurbitacin B protects against myocardial ischemia-reperfusion injury through activating JAK2/STAT3 signaling pathway.

Cucurbitacin B, a tetracyclic triterpenoid compound extracted from various plants, has been proven to exert a vital role in various diseases. However, the effect of cucurbitacin B on myocardial infarction (MI) and ischemia-reperfusion (I/R) injury is still relatively unclear. The main purpose of the present study was to investigate the effect of cucurbitacin B on cell apoptosis and oxidative damage after myocardial I/R injury in vitro and in vivo and elucidate the molecular mechanisms underlying its role. The 56-day-old adult mice and 1-day-old neonatal mice cardiomyocytes were used to construct I/R or oxygen-glucose deprivation/reoxygenation (OGD/R) injury models. The oxidative injury, western blot and TUNEL assay were performed to evaluate cardiomyocyte damage in the present study. In vitro， we confirmed that cucurbitacin B could attenuate LDH release, oxidative stress and cell apoptosis in cardiomyocytes exposed to OGD/R. Besides, we confirmed in an adult I/R mouse model that cucurbitacin B can improve cardiac repair and block cell apoptosis in the acute phase (24 h) post-myocardial I/R injury, as well as promote long-term cardiac function and fiber scar area after 28 days of I/R. Mechanically, we clarify that cucurbitacin B exerts cardiomyocyte protective effects through activating the JAK2/STAT3 signaling pathway. In conclusion, our study elucidates for the first time the protective role of cucurbitacin B in cardiac I/R injury, which provides a novel perspective for better prevention of I/R injury through the JAK2/STAT3 signaling pathway.

A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma.

PURPOSE: Identifying robust prognosis and treatment efficiency predictive biomarkers of hepatocellular carcinoma (HCC) is challenging. The purpose of this study is to develop a radiomics approach for predicting the overall survival (OS) based on pretreatment CT images and to explore the radiomic-associated key genes. METHODS: Patients with pathologically or clinically proven HCC from three data sets were retrospectively included in this study. The institute internal data that received transarterial chemoembolization (TACE) treatment was used as the training set to construct the radiomics signature to predict OS by the least absolute shrinkage and selection operator COX (LASSO-COX) regression algorithms. The model was externally tested in 41 patients from The Cancer Genome Atlas (TCGA) with available CT images. Area under the receiver operating characteristics curve (AUC) and the log-rank test were used for survival analysis based on high versus low radiomics score. RNA sequencing data of TCGA and Gene Expression Omnibus (GEO) public database were used for gene expression analysis. RESULTS: A total of 752 patients were divided into the Radiomics cohort (n = 267), the TCGA cohort (n = 338) and GEO cohort (n = 147). The rad-score divided patients into high and low risk groups, with significant survival differences (P < 0.0001 and P = 0.0055) in the training and external test set. The AUC for 5 years' OS were 0.730 and 0.695, respectively. Seven OS-related genes (SPP1, GJA5, GJA4, INMT, PDZD4, ALDOA and MAFG) were identified, all of which were related with TACE efficiency, except for MAFG (P greater than 0.05). CONCLUSIONS: CT-radiomics signature could effectively predict the prognosis and treatment response of HCC, which were also associated with the tumor microenvironment heterogeneity.

Plasmonic nanobipyramids with photo-enhanced catalytic activity under near-infrared II window for effective treatment of breast cancer.

Nanozyme-based catalytic therapy is an effective method for cancer treatment, but insufficient catalytic activity presents a challenge in achieving optimal therapeutic outcomes. External light can provide an innovative approach to modulate nanozyme catalytic activity. Herein, we report on plasmonic gold nanobipyramid@cuprous oxide (Au NBP@Cu2O) nanozyme for the effective phototherapy of breast cancer. In the tumor microenvironment, Cu+-mediated Fenton-like reaction catalyzes the generation of toxic hydroxyl radicals (•OH) from endogenous hydrogen peroxide to induce apoptosis. Additionally, the Au NBP@Cu2O nanostructure improves the absorption performance of Au NBPs in the near-infrared II region through near-field enhancement of equipartite exciters and achieves a high photothermal conversion efficiency value of 58%. Remarkably, the Au NBP@Cu2O nanoheterostructure can capture hot electrons induced by equipartition excitations and promote electron-hole separation under 1064 nm laser irradiation, facilitating the production of more reactive oxygen species (ROS). The mechanism behind this enhanced catalytic activity was unraveled using femtosecond transient absorption spectroscopy. Both in vitro and in vivo investigations have demonstrated the efficacious tumor therapeutic potential of Au NBP@Cu2O nanozyme, particularly under 1064 nm laser irradiation. Furthermore, the proposed therapeutic approach has been proved to effectively block tumor metastasis, providing a promising strategy for the development of multifunctional nanotherapeutics to tackle metastatic tumors. STATEMENT OF SIGNIFICANCE: A highly effective plasmonic nanozyme has been developed to improve catalytic therapy for breast cancer. When exposed to 1064 nm laser irradiation, Au NBP@Cu2O nanozyme can promote the separation of hot electrons and holes thereby facilitating the production of reactive oxygen species. Hot electrons transfer behavior is unveiled by femtosecond transient absorption spectroscopy technique. This enhanced catalytic activity, along with the intrinsic photothermal effect, effectively kills tumor cells.

A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer.

RATIONALE AND OBJECTIVES: To establish a prediction model for the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), using pretreatment magnetic resonance imaging (MRI) multisequence image features and clinical parameters. MATERIALS AND METHODS: Patients with clinicopathologically confirmed LARC were included (training and validation datasets, n = 100 and 27, respectively). Clinical data of patients were collected retrospectively. We analyzed MRI multisequence imaging features. The tumor regression grading (TRG) system proposed by Mandard et al was adopted. Grade 1-2 of TRG was a good response group, and grade 3-5 of TRG was a poor response group. In this study, a clinical model, a single sequence imaging model, and a comprehensive model combined with clinical imaging were constructed, respectively. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive efficacy of clinical, imaging, and comprehensive models. The decision curve analysis method evaluated the clinical benefit of several models, and the nomogram of efficacy prediction was constructed. RESULTS: The AUC value of the comprehensive prediction model is 0.99 in the training data set and 0.94 in the test data set, which is significantly higher than other models. Radiomic Nomo charts were developed using Rad scores obtained from the integrated image omics model, circumferential resection margin(CRM), DoTD, and carcinoembryonic antigen(CEA). Nomo charts showed good resolution. The calibrating and discriminating ability of the synthetic prediction model is better than that of the single clinical model and the single sequence clinical image omics fusion model. CONCLUSION: Nomograph, based on pretreatment MRI characteristics and clinical risk factors, has the potential to be used as a noninvasive tool to predict outcomes in patients with LARC after nCRT.

Nickel-Atom Doping as a Potential Means to Enhance the Photoluminescence Performance of Carbon Dots.

Heteroatom doping, particularly with nonmetallic atoms such as N, P, and S, has proven to be an effective strategy for modulating the fluorescent properties of carbon dots (CDs). However, there are few reports on the regulation of the photoluminescence of CDs by transition-metal doping. In this work, nickel-doped CDs (Ni-CDs) were fabricated using the hydrothermal approach. Ni atoms were incorporated into the sp2 domains of the CDs through Ni-N bonds, resulting in an increased degree of graphitization of the Ni-CDs. Additionally, Ni-atom doping served to shorten the electron transition and recombination lifetimes, and suppress the nonradiative recombination process, resulting in an absolute fluorescence quantum yield of 54.7% for the Ni-CDs. Meanwhile, the as-prepared Ni-CDs exhibited excellent biocompatibility and were utilized for fluorescent bioimaging of HeLa cells. Subsequently, the Ni-CDs were employed as fluorescent anticounterfeiting inks for the successful encryption of two-dimensional barcodes. Our work demonstrates a novel heteroatom doping strategy for the synthesis of highly fluorescence-emitting CDs.

Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma.

BACKGROUND: According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. METHODS: A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. RESULTS: Of all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260-0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002-1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001-1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200-4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780-0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416-8.552, P = 0.007). CONCLUSIONS: The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.

Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Accurate prediction of treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) is critical for precision treatment. This study aimed to develop a comprehensive model (DLRC) that incorporates contrast-enhanced computed tomography (CECT) images and clinical factors to predict the response to TACE in patients with HCC. MATERIALS AND METHODS: A total of 399 patients with intermediate-stage HCC were included in this retrospective study. Deep learning and radiomic signatures were established based on arterial phase CECT images, Correlation analysis and the least absolute shrinkage and selection (LASSO) regression analysis were applied for features selection. The DLRC model incorporating deep learning radiomic signatures and clinical factors was developed using multivariate logistic regression. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the models. Kaplan-Meier survival curves based on the DLRC were plotted to assess overall survival in the follow-up cohort (n = 261). RESULTS: The DLRC model was developed using 19 quantitative radiomic features, 10 deep learning features, and 3 clinical factors. The AUC of the DLRC model was 0.937 (95% confidence interval [CI], 0.912-0.962) and 0.909 (95% CI, 0.850-0.968) in the training and validation cohorts, respectively, outperforming models established with two signatures or a single signature (p < 0.05). Stratified analysis showed that the DLRC was not statistically different between subgroups (p > 0.05), and the DCA confirmed the greater net clinical benefit. In addition, multivariable cox regression revealed that DLRC model outputs were independent risk factors for the overall survival (hazard ratios: 1.20, 95% CI: 1.03-1.40; p = 0.019). CONCLUSION: The DLRC model exhibited a remarkable accuracy in predicting response to TACE, and it can be utilized as a potent tool for precision treatment.

Radioimmunotherapy study of 131I-labeled Atezolizumab in preclinical models of colorectal cancer.

BACKGROUND: Programmed cell death 1 ligand 1(PD-L1) is overexpressed in many tumors. The radionuclide-labeled anti-PD-L1 monoclonal antibody can be used for imaging and therapy of PD-L1 overexpressing cancer. Here, we described 131I-labeled Atezolizumab (131I-Atezolizumab, targeting PD-L1) as a therapeutic agent for colorectal cancer with PD-L1 overexpression. METHODS: 131I-Atezolizumab was prepared by the Iodogen method. The expression levels of PD-L1 in different human colorectal cells were determined by flow cytometry, western blot and cell binding assay. The immunoreactivity of 131I-Atezolizumab to PD-L1 high-expressing cells was determined by immunoreactive fraction. The killing abilities of different concentrations of 131I-Atezolizumab on cells with high and low expression of PD-L1 were detected by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Cerenkov luminescence imaging (CLI) and radioimmunotherapy (RIT) of 131I-Atezolizumab were performed on two human colorectal cancer models. The distribution and tumor targeting of 131I-Atezolizumab were evaluated by imaging. Tumor volume and survival time were used as indicators to evaluate the anti-tumor effect of 131I-Atezolizumab. RESULTS: The expression level of PD-L1 in vitro determined by the cell binding assay was related to the data of flow cytometry and western blot. 131I-Atezolizumab can specifically bind to PD-L1 high-expressing cells in vitro to reflect the expression level of PD-L1. Immunoreactive fraction of PD-L1 high-expressing RKO cells with 131I-Atezolizumab was 52.2%. The killing ability of 131I-Atezolizumab on PD-L1 high-expressing cells was higher than that of low-expressing cells. CLI proved that the specific uptake level of tumors depends on the expression level of PD-L1. Effect of 131I-Atezolizumab RIT showed an activity-dependent tumor suppressor effect on RKO tumor-bearing mice with high PD-L1 expression. 131I-Atezolizumab (37 MBq) can improve the median survival time of mice (34 days), compared to untreated mice (27 days) (P = 0.027). Although a single activity(37 MBq) of 131I-Atezolizumab also inhibited the tumors of HCT8 tumor-bearing mice with low PD-L1 expression (P < 0.05), it could not prolong the survival of mice(P = 0.29). CONCLUSION: 131I-Atezolizumab can be used as a CLI agent for screening PD-L1 expression levels. It may be used as a radioimmunotherapy drug target for PD- L1 overexpressing tumors.

Study on nucleotide, myofibrillar protein biochemical properties and microstructure of freeze-dried scallop striated muscle during storage and rehydration.

The biochemical properties and microstructural changes of freeze-dried Japanese scallop (Patinopecten yessoensis) striated muscle during room temperature storage and rehydration were investigated. The results showed that the content of ATP in freeze-dried scallop muscle remained stable with no significant difference (p > 0.05). However, ATP was rapidly decomposed and AMP accumulated within 1.5 min of rehydration, and HxR and Hx were gradually produced from AMP decomposition with the extension of rehydration time. Besides, the results of chymotryptic digestion patterns demonstrated that the rod of myosin was unstable after dehydration, reflecting lower salt solubility than that of frozen-thawed scallop. In contrast, the myosin subfragment-1 (S-1) was stable, as indicated by the constant of Ca2+-ATPase activity of freeze-dried scallops throughout the storage and rehydration (p > 0.05). Furthermore, the microstructural analysis revealed that the Z line of the freeze-dried scallop was broken after dehydration process. This study might be useful for developing high-quality dehydrated scallops in the future.

A Multilayer Graph for Multiagent Formation and Trajectory Tracking Control Based on MPC Algorithm.

This article studies the formation and trajectory tracking control of multiagent systems. We present a novel multilayer graph for the multiagent system to enable extensibility of the interaction network. Based on the multilayer graph, a formation control law by using the potential function approach is developed for autonomous formation, formation maintenance, collision, and obstacle avoidance. When the desired formation is achieved, the barycentric of the formation shape is viewed as a virtual leader, and a model predictive control (MPC) scheme is applied to the virtual leader for tracking a reference trajectory; meanwhile, the agents will maintain the desired angles and distances via the formation control law. By applying the proposed schemes, the tasks of formation maintenance and trajectory tracking in a constrained space are fulfilled. Comprehensive simulation studies under different environmental constraints and trajectories confirm the effectiveness of the proposed approaches in addressing the formation and trajectory tracking problems.

Dynamic Event-Triggered MPC With Shrinking Prediction Horizon and Without Terminal Constraint.

This article develops a dynamic version of event-triggered model predictive control (MPC) without utilizing any terminal constraint. Such a dynamic event-triggering mechanism takes the advantages of both event- and self-triggering approaches by dealing explicitly with conservatism in the triggering rate and measurement frequency. The prediction horizon shrinks as the system states converge; we prove that the proposed strategy is able to stabilize the system even without any stability-related terminal constraint. Recursive feasibility of the optimization control problem (OCP) is also guaranteed. The simulation results illustrate the effectiveness of the scheme.

Fixed-Time Cooperative Behavioral Control for Networked Autonomous Agents With Second-Order Nonlinear Dynamics.

In this article, we investigate the fixed-time behavioral control problem for a team of second-order nonlinear agents, aiming to achieve a desired formation with collision/obstacle avoidance. In the proposed approach, the two behaviors(tasks) for each agent are prioritized and integrated via the framework of the null-space-based behavioral projection, leading to a desired merged velocity that guarantees the fixed-time convergence of task errors. To track this desired velocity, we design a fixed-time sliding-mode controller for each agent with state-independent adaptive gains, which provides a fixed-time convergence of the tracking error. The control scheme is implemented in a distributed manner, where each agent only acquires information from its neighbors in the network. Moreover, we adopt an online learning algorithm to improve the robustness of the closed system with respect to uncertainties/disturbances. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Distributed Economic MPC for Dynamically Coupled Linear Systems With Uncertainties.

In this article, we propose a novel economic model-predictive control (MPC) algorithm for a group of disturbed linear systems and implement it in a distributed manner. The system consists of multiple subsystems interacting with each other via dynamics and aims to optimize an economic objective. Each subsystem is subject to constraints both on states and inputs as well as unknown but bounded disturbances. First, we divide the computation of control inputs into several local optimization problems based on each subsystem's local information. This is done by introducing compatibility constraints to confine the difference between the actual information and the previously published reference information of each subsystem, which is the key feature of the proposed distributed algorithm. Then, to ensure the satisfaction of both state and input constraints under disturbances, constraints are tightened on the state and the input of nominal systems by considering explicitly the effect of uncertainties. Moreover, based on an overall optimal steady state, a dissipativity constraint and a terminal constraint are designed and incorporated in the local optimization problems to establish recursive feasibility and guarantee stability for the resulting closed-loop system. Finally, the efficiency of the distributed economic MPC algorithm is demonstrated in a building temperature control case study.

Magnetic Microdimer as Mobile Meter for Measuring Plasma Glucose and Lipids.

With the development of designed materials and structures, a wide array of micro/nanomachines with versatile functionalities are employed for specific sensing applications. Here, we demonstrated a magnetic propelled microdimer-based point-of-care testing system, which can be used to provide the real-time data of plasma glucose and lipids relying on the motion feedback of mechanical properties. On-demand and programmable speed and direction of the microdimers can be achieved with the judicious adjustment of the external magnetic field, while their velocity and instantaneous postures provide estimation of glucose, cholesterol, and triglycerides concentrations with high temporal accuracy. Numerical simulations reveal the relationship between motility performance and surrounding liquid properties. Such technology presents a point-of-care testing (POCT) approach to adapt to biofluid measurement, which advances the development of microrobotic system in biomedical fields.

Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) ranks the second most lethal tumor globally and is the fourth leading cause of cancer-related death worldwide. Unfortunately, HCC is commonly at intermediate tumor stage or advanced tumor stage, in which only some palliative treatment can be used to offer a limited overall survival. Due to the high heterogeneity of the genetic, molecular, and histological levels, HCC makes the prediction of preoperative transarterial chemoembolization (TACE) efficacy and the development of personalized regimens challenging. In this study, a new multi-modal point-of-care system is employed to predict the response of TACE in HCC by a concept of integrating multi-modal large-scale data of clinical index and computed tomography (CT) images. This multi-modal point-of-care predicting system opens new possibilities for predicting the response of TACE treatment and can help clinicians select the optimal patients with HCC who can benefit from the interventional therapy.

Study on the prognosis predictive model of COVID-19 patients based on CT radiomics.

Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong's test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.

Reference-frame-independent measurement-device-independent quantum key distribution with mismatched-basis statistics.

The source flaw associated with the basis vector in the reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) has not been systematically studied. As a result, it is often assumed that bit error is equal to phase error, which is not theoretically rigorous. Here, we propose a postprocessing method to estimate the phase error rate from the discarded mismatched-basis statistics, where the qubit source does not need to be characterized in detail. The source flaw in the basis vector of the RFI-MDI-QKD protocol can thus be corrected using this method. The numerical simulation results clearly demonstrate that the RFI-MDI-QKD protocol with uncharacterized sources is also insensitive to the misalignment of the reference frame.