A Novel TSC2 c.2489T>C Missense Variant Associated With Tuberous Sclerosis Complex: Case Report.

Objectives: Tuberous sclerosis complex (TSC) is a genetic disorder caused by a TSC1 or TSC2 gene variation characterized by widespread hamartomas in organs such as the skin, brain, heart, lungs, liver, and kidneys. Methods: We report a case of a patient with TSC who presented with broad clinical manifestations, including epilepsy. Results: An 18-year-old man was diagnosed with recurrent drug-resistant epilepsy. Neuroimaging revealed bilateral cortical and subcortical tubers with multiple calcified subependymal nodules. His skin involvement and psychomotor retardation raised the suspicion of TSC. Genetic testing confirmed the diagnosis, and a combined treatment including mTOR inhibitors was initiated. Discussion: TSC, although considered rare, needs to be considered when evaluating patients with broad clinical manifestations. Our report has significant implications for understanding the impact of genotype on the prognosis of TSC and the selection of treatment strategies for TSC-related refractory epilepsy.

Adaptive Neural Consensus Observer Networks Design for a Class of Semilinear Parabolic PDE Systems.

This article concerns the investigation on the consensus problem for the joint state-uncertainty estimation of a class of parabolic partial differential equation (PDE) systems with parametric and nonparametric uncertainties. We propose a two-layer network consisting of informed and uninformed boundary observers where novel adaptation laws are developed for the identification of uncertainties. Particularly, all observer agents in the network transmit their information with each other across the entire network. The proposed adaptation laws include a penalty term of the mismatch between the parameter estimates generated by the other observer agents. Moreover, for the nonparametric uncertainties, radial basis function (RBF) neural networks are employed for the universal approximation of unknown nonlinear functions. Given the persistently exciting condition, it is shown that the proposed network of adaptive observers can achieve exponential joint state-uncertainty estimation in the presence of parametric uncertainties and ultimate bounded estimation in the presence of nonparametric uncertainties based on the Lyapunov stability theory. The effects of the proposed consensus method are demonstrated through a typical reaction-diffusion system example, which implies convincing numerical findings.

Locoregional therapies for hepatocellular carcinoma: The current status and future perspectives.

Hepatocellular carcinoma (HCC) is one of the most common cancers and a leading cause of cancer-related mortality. Locoregional therapies (LRTs) play a crucial role in HCC management and are selectively adopted in real-world practice across various stages. Choosing the best form of LRTs depends on technical aspects, patient clinical status and tumour characteristics. Previous studies have consistently highlighted the efficacy of combining LRTs with molecular targeted agents in HCC treatment. Recent studies propose that integrating LRTs with immune checkpoint inhibitors and molecular targeted agents could provide substantial therapeutic benefits, a notion underpinned by both basic and clinical evidence. This review summarised the current landscape of LRTs in HCC and discussed the anticipated outcomes of combinations with immunotherapy regimens.

Efficacy and safety of tongxinluo capsule for angina pectoris of coronary heart disease: an overview of systematic reviews and meta-analysis.

Background: Tongxinluo capsule (TXLC) is a common drug for treating angina pectoris of coronary heart disease (CHD). In recent years, many systematic reviews (SRs) and meta-analyses (MAs) have reported the efficacy and safety of TXLC for improving angina symptoms in patients with CHD. We aimed to comprehensively evaluate the existing SRs and MAs of TXLC in treating angina pectoris of CHD, summarize the evidence quality, and provide scientific evidence and recommendations. Methods: We searched seven databases for relevant SRs/MAs published up to 1 June 2023. Two reviewers independently completed the literature retrieval, screening, and data extraction. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) to evaluate the methodological quality, the Risk of Bias in Systematic Reviews (ROBIS) to assess the risk of bias, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to determine the strength of the evidence. RevMan 5.3 was used to synthesize data. Results: We identified 15 SRs/MAs, including 329 RCTs and 33,417 patients. According to the evaluation results of AMSTAR-2, only one SR was of high methodological quality, the others were very low. ROBIS assessment showed that one SR (6.67%) had a low risk, 3 SRs (20%) had an unclear risk, and 11 SRs (73.33%) had a high risk. We assessed 42 outcomes by the GRADE, 10 (23.81%) for moderate-quality evidence, 17 (40.48%) for low-quality evidence, and 15 (35.71%) for very-low-quality evidence. Mate-analysis showed that TXLC combined with conventional western medications improved electrocardiogram efficacy (RR = 1.38, 95% CI: 1.23-1.43, P < 0.001) and angina efficacy (OR = 3.58, 95% CI: 3.02-4.24, P < 0.001), reduced angina attack frequency (SMD = -0.54, 95% CI: -0.64 to -0.44, P < 0.001) and angina duration (SMD = -0.42, 95% CI: -0.57 to -0.28, P < 0.001), with general heterogeneity. The pooled results showed that TXLC appears to have some efficacy in improving cardiac function and relieving angina symptoms, but there is limited evidence that it improves cardiovascular event rates, hemorheology, lipids, or hs-CRP. In the assessment of drug safety, TXLC was associated with different degrees of adverse drug reactions. Conclusion: Based on the evidence, TXLC may be effective as an adjuvant treatment for angina pectoris of CHD. However, the quality of the evidence is low, and the drug's safety must be carefully interpreted. In future studies, high-quality randomized controlled trials are needed to confirm the effectiveness and safety of TXLC. Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO/, identifier (CRD42022365372).

New First-line Immunotherapy-based Therapies for Unresectable Hepatocellular Carcinoma: A Living Network Meta-analysis.

Background and Aims: Several first-line immune checkpoint inhibitor (ICI)-based combination therapies have been identified for unresectable hepatocellular carcinoma (uHCC). This network meta-analysis (NMA) aimed to provide the most updated evidence about the preferred first-line ICI-based regimens for uHCC. Methods: A comprehensive literature search was performed in various databases from database inception to May 2022. The phase 3 trials evaluating first-line single-agent ICIs, molecular-target agents (MTAs), or their combinations in uHCC were included. The main endpoints were overall survival (OS) and progression-free survival (PFS). Pooled effect estimates were calculated using a random effects model within the frequentist framework. Subgroup analyses based on etiology were also conducted. Results: Twelve trials at low risk of bias with 8,275 patients comparing 13 treatments were included. OS with atezolizumab plus bevacizumab was comparable to sintilimab plus IBI305 [hazard ratio (HR): 1.16; 95% confidence interval (CI): 0.80-1.68] and camrelizumab plus apatinib (HR: 1.06; 95% CI: 0.75-1.51). The combination therapies, apart from atezolizumab plus cabozantinib in OS and durvalumab plus tremelimumab in PFS, had higher P-score than single-agent MTAs or ICIs. The survival benefits were associated with a high risk of adverse events leading to treatment discontinuation. The proportion of patients with hepatitis B virus-related HCC receiving ICIs combinations might positively correlate with survival advantages (R2=0.8039, p=0.0155). Conclusion: This NMA demonstrated that atezolizumab plus bevacizumab remains the stand of care and confers comparable survival benefits to sintilimab plus IBI305 and camrelizumab plus apatinib in first-line therapy for uHCC. The optimal treatment algorithms should consider efficacy, safety, and etiology.

Cerebral furin deficiency causes hydrocephalus in mice.

Furin is a pro-protein convertase that moves between the trans-Golgi network and cell surface in the secretory pathway. We have previously reported that cerebral overexpression of furin promotes cognitive functions in mice. Here, by generating the brain-specific furin conditional knockout (cKO) mice, we investigated the role of furin in brain development. We found that furin deficiency caused early death and growth retardation. Magnetic resonance imaging showed severe hydrocephalus. In the brain of furin cKO mice, impaired ciliogenesis and the derangement of microtubule structures appeared along with the down-regulated expression of RAB28, a ciliary vesicle protein. In line with the widespread neuronal loss, ependymal cell layers were damaged. Further proteomics analysis revealed that cell adhesion molecules including astrocyte-enriched ITGB8 and BCAR1 were altered in furin cKO mice; and astrocyte overgrowth was accompanied by the reduced expression of SOX9, indicating a disrupted differentiation into ependymal cells. Together, whereas alteration of RAB28 expression correlated with the role of vesicle trafficking in ciliogenesis, dysfunctional astrocytes might be involved in ependymal damage contributing to hydrocephalus in furin cKO mice. The structural and molecular alterations provided a clue for further studying the potential mechanisms of furin.

SHMT2 Mediates Small-Molecule-Induced Alleviation of Alzheimer Pathology Via the 5'UTR-dependent ADAM10 Translation Initiation.

It is long been suggested that one-carbon metabolism (OCM) is associated with Alzheimer's disease (AD), whereas the potential mechanisms remain poorly understood. Taking advantage of chemical biology, that mitochondrial serine hydroxymethyltransferase (SHMT2) directly regulated the translation of ADAM metallopeptidase domain 10 (ADAM10), a therapeutic target for AD is reported. That the small-molecule kenpaullone (KEN) promoted ADAM10 translation via the 5' untranslated region (5'UTR) and improved cognitive functions in APP/PS1 mice is found. SHMT2, which is identified as a target gene of KEN and the 5'UTR-interacting RNA binding protein (RBP), mediated KEN-induced ADAM10 translation in vitro and in vivo. SHMT2 controls AD signaling pathways through binding to a large number of RNAs and enhances the 5'UTR activity of ADAM10 by direct interaction with GAGGG motif, whereas this motif affected ribosomal scanning of eukaryotic initiation factor 2 (eIF2) in the 5'UTR. Together, KEN exhibits therapeutic potential for AD by linking OCM with RNA processing, in which the metabolic enzyme SHMT2 "moonlighted" as RBP by binding to GAGGG motif and promoting the 5'UTR-dependent ADAM10 translation initiation.

ADP-Based Event-Triggered Constrained Optimal Control on Spatiotemporal Process: Application to Temperature Field in Roller Kiln.

The precise control of the spatiotemporal process in a roller kiln is crucial in the production of Ni-Co-Mn layered cathode material of lithium-ion batteries. Since the product is extremely sensitive to temperature distribution, temperature field control is of great significance. In this article, an event-triggered optimal control (ETOC) method with input constraints for the temperature field is proposed, which takes up an important position in reducing the communication and computation costs. A nonquadratic cost function is adopted to describe the system performance with input constraints. First, we present the problem description of the temperature field event-triggered control, where this field is described by a partial differential equation (PDE). Then, the event-triggered condition is designed according to the information of system states and control inputs. On this basis, a framework of the event-triggered adaptive dynamic programming (ETADP) method that is based on the model reduction technology is proposed for the PDE system. A critic network is used to approach the optimal performance index by a neural network (NN) together with that an actor network is used to optimize the control strategy. Furthermore, an upper bound of the performance index and a lower bound of interexecution times, as well as the stabilities of the impulsive dynamic system and the closed-loop PDE system, are also proved. Simulation verification demonstrates the effectiveness of the proposed method.

Multi-scale feature selection network for lightweight image super-resolution.

Recently, many super-resolution (SR) methods based on convolutional neural networks (CNNs) have achieved superior performance by utilizing deep and heavy models, which may not be suitable for real-world low-budget devices. To address this issue, we propose a novel lightweight SR network called a multi-scale feature selection network (MFSN). As the basic building block of MFSN, the multi-scale feature selection block (MFSB) is presented to extract the rich multi-scale features from a coarse-to-fine receptive field level. For a better representation ability, a wide-activated residual unit is adopted in each branch of MFSB except the last one. In MFSB, the scale selection module (SSM) is designed to effectively fuse the features from two adjacent branches by adjusting receptive field sizes adaptively. Further, a comprehensive channel attention mechanism (CCAM) is integrated into SSM to learn the dynamic selection weight by considering the local and global inter-channel dependencies. Extensive experimental results illustrate that the proposed MFSN is superior to other lightweight methods.

Severe photochemical pollution formation associated with strong HONO emissions from dew and guttation evaporation.

The unknown daytime source of HONO has been extensively investigated due to unexplained atmospheric oxidation capacity and current modelling bias, especially during cold seasons. In this study, abrupt morning increases in atmospheric HONO at a rural site in the North China Plain (NCP) were observed almost on daily basis, which were closely linked to simultaneous rises in atmospheric water vapor content and NH3 concentrations. Dew and guttation water formation was frequently observed on wheat leaves, from which water samples were taken and chemically analyzed for the first time. Results confirmed that such natural processes likely governed the daily nighttime deposition and daytime release of HONO and NH3, which have not been considered in the numerous HONO budget studies investigating its large missing daytime source in the NCP. The dissolved HONO and NH3 in leaf surface water droplets reached 1.4 and 23 mg L-1 during the morning on average, resulting in averaged atmospheric HONO and NH3 increases of 0.89 ± 0.61 and 43.7 ± 29.3 ppb during morning hours, with relative increases of 186 ± 212 % and 233 ± 252 %, respectively. The high atmospheric oxidation capacity contained within HONO was stored in near surface liquid water (such as dew, guttation and soil surface water) during nighttime, which prevented its atmospheric dispersion after sunset and protected it from photodissociation during early morning hours. HONO was released in a blast during later hours with stronger solar radiation, which triggered and then accelerated daytime photochemistry through the rapid photolysis of HONO and subsequent OH production, especially under high RH conditions, forming severe secondary gaseous and particulate pollution. Results of this study demonstrate that global ecosystems might play significant roles in atmospheric photochemistry through nighttime dew formation and guttation processes.

Modular hierarchical reinforcement learning for multi-destination navigation in hybrid crowds.

Real-world robot applications usually require navigating agents to face multiple destinations. Besides, the real-world crowded environments usually contain dynamic and static crowds that implicitly interact with each other during navigation. To address this challenging task, a novel modular hierarchical reinforcement learning (MHRL) method is developed in this paper. MHRL is composed of three modules, i.e., destination evaluation, policy switch, and motion network, which are designed exactly according to the three phases of solving the original navigation problem. First, the destination evaluation module rates all destinations and selects the one with the lowest cost. Subsequently, the policy switch module decides which motion network to be used according to the selected destination and the obstacle state. Finally, the selected motion network outputs the robot action. Owing to the complementary strengths of a variety of motion networks and the cooperation of modules in each layer, MHRL is able to deal with hybrid crowds effectively. Extensive simulation experiments demonstrate that MHRL achieves better performance than state-of-the-art methods.

iTRAQ-based proteomics reveals potential markers and treatment pathways for acute Achilles tendon rupture.

BACKGROUND: Due to its limited blood supply and irregular mechanical loading, the Achilles tendon is the most frequently ruptured tendon. Despite the rising incidence of acute Achilles tendon rupture (AATR), the optimal treatment remains controversial. Missed diagnoses and delayed treatments lead to poor outcomes and limited treatment options. This study aimed to identify potential biomarkers for diagnosing and developing therapies for AATR. METHODS: We employed the coupled isobaric tag for relative and absolute quantitation-liquid chromatography-electrospray ionization-tandem mass spectrometry approach to investigate protein expression in tissues from AATR patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify differentially expressed proteins (DEPs) between AATR patients and healthy individuals. A protein-protein interaction (PPI) network of DEPs was constructed using the Search Tool for the Retrieval of Interacting Genes. The screened hub genes were selectively verified by immunohistochemical staining. RESULTS: We identified 410 DEPs between AATR patients and controls. The DEPs were significantly enriched in GO terms such as the extracellular region, extracellular region part, and defense response, as well as KEGG pathways, including complement and coagulation cascades, focal adhesion, and regulation of actin cytoskeleton. The main hub nodes in the PPI network comprised fibronectin 1 (FN1), major histocompatibility complex, class I, B (HLA-B), filamin A (FLNA), heat shock 27-kDa protein 1 (HSPB1), heat shock protein family A member 5 (HSPA5), apolipoprotein A4 (APOA4), and myosin IC (MYO1C). Although APOA4 and collagens I, II, and III were detectable in healthy tendons, immunohistochemical staining confirmed higher expression of these proteins in the acutely ruptured Achilles tendon. CONCLUSIONS: Our findings lay a foundation for further molecular studies of AATR. Inflammation and age-related degeneration may contribute to the pathogenesis of AATR. Moreover, the identified DEPs could be potential biomarkers for AATR diagnosis and treatment.

SMONAC: Supervised Multiobjective Negative Actor-Critic for Sequential Recommendation.

Recent research shows that the sole accuracy metric may lead to the homogeneous and repetitive recommendations for users and affect the long-term user engagement. Multiobjective reinforcement learning (RL) is a promising method to achieve a good balance in multiple objectives, including accuracy, diversity, and novelty. However, it has two deficiencies: neglecting the updating of negative action Q values and limited regulation from the RL Q-networks to the (self-)supervised learning recommendation network. To address these disadvantages, we develop the supervised multiobjective negative actor-critic (SMONAC) algorithm, which includes a negative action update mechanism and multiobjective actor-critic mechanism. For the negative action update mechanism, several negative actions are randomly sampled during each time updating, and then, the offline RL approach is utilized to learn their Q values. For the multiobjective actor-critic mechanism, accuracy, diversity, and novelty Q values are integrated into the scalarized Q value, which is used to criticize the supervised learning recommendation network. The comparative experiments are conducted on two real-world datasets, and the results demonstrate that the developed SMONAC achieves tremendous performance promotion, especially for the metrics of diversity and novelty.

Single-arm, phase II study of intra-arterial chemotherapy plus total neoadjuvant therapy to optimise complete response in distal rectal cancer: a study protocol.

INTRODUCTION: Organ preservation is now considered an acceptable alternative option in distal rectal cancer patients with clinical complete response (cCR) after neoadjuvant chemoradiation (CRT). But the cCR rate is low and about one-third of tumour will regrow, which requires more effective local treatment. CRT combined with intra-arterial chemotherapy (IAC) might be a promising approach. Additionally, total neoadjuvant therapy using FOLFIRINOX induction chemotherapy improved survival while consolidation chemotherapy improved organ preservation. We assess whether IAC plus CRT and FOLFIRINOX consolidation chemotherapy can improve the chance of organ preservation and survival in distal rectal cancer. METHODS AND ANALYSIS: This prospective, monocentric, open-label, single-arm phase II study will include 32 patients with cT3-4NanyM0 distal rectal adenocarcinoma. All patients will receive one cycle of IAC (irinotecan, raltitrexed and oxaliplatin), followed by CRT (50 Gy/25 fractions with concomitant capecitabine) and then with six cycles of FOLFIRINOX (leucovorin, 5-fluorouracil, oxaliplatin and irinotecan). After final evaluation, patients with cCR will receive non-operative management or surgery at their own discretion and others are mandatorily referred to surgery. Adjuvant chemotherapy with six cycles of mFOLFOX6 (leucovorin, 5-fluorouracil and oxaliplatin) will be used for patients with adverse pathological features. The primary endpoint is the rate of complete response (CR; pathological CR or sustained cCR≥2 years). The main secondary endpoints are toxicity, compliance, short-term and long-term oncological outcomes, surgical morbidity and quality of life. This protocol has been designed in accordance with the Standard Protocol Items: Recommendations for Interventional Trials 2013 guidelines. ETHICS AND DISSEMINATION: This study was approved by the Academic and Ethics Committee of The Affiliated Hospital of Youjiang Medical University for Nationalities in March 2023. Trial results will be published in peer-reviewed international journals and on the ChiCTR website. PROTOCOL VERSION: Registered on 18 April 2023; version #1. TRIAL REGISTRATION NUMBER: ChiCTR2300070620.

LJIR: Learning Joint-Action Intrinsic Reward in cooperative multi-agent reinforcement learning.

Effective exploration is the key to achieving high returns for reinforcement learning. Agents must explore jointly in multi-agent systems to find the optimal joint policy. Due to the exploration problem and the shared reward, the policy-based multi-agent reinforcement learning algorithms face policy overfitting, which may lead to the joint policy falling into a local optimum. This paper introduces a novel general framework called Learning Joint-Action Intrinsic Reward (LJIR) for improving multi-agent reinforcement learners' joint exploration ability and performance. LJIR observes agents' state and joint actions to learn to construct an intrinsic reward online that can guide effective joint exploration. With the novel combination of Transformer and random network distillation, LJIR selects the novel states to give more intrinsic rewards, which help agents find the best joint actions. LJIR can dynamically adjust the weight of exploration and exploitation during training and keep the policy invariance finally. To ensure LJIR seamlessly adopts existing MARL algorithms, we also provide a flexible combination method for intrinsic and external rewards. Empirical results on the SMAC benchmark show that the proposed method achieves state-of-the-art performance in challenging tasks.

Human-in-the-Loop Reinforcement Learning in Continuous-Action Space.

Human-in-the-loop for reinforcement learning (RL) is usually employed to overcome the challenge of sample inefficiency, in which the human expert provides advice for the agent when necessary. The current human-in-the-loop RL (HRL) results mainly focus on discrete action space. In this article, we propose a Q value-dependent policy (QDP)-based HRL (QDP-HRL) algorithm for continuous action space. Considering the cognitive costs of human monitoring, the human expert only selectively gives advice in the early stage of agent learning, where the agent implements human-advised action instead. The QDP framework is adapted to the twin delayed deep deterministic policy gradient algorithm (TD3) in this article for the convenience of comparison with the state-of-the-art TD3. Specifically, the human expert in the QDP-HRL considers giving advice in the case that the difference between the twin Q -networks' output exceeds the maximum difference in the current queue. Moreover, to guide the update of the critic network, the advantage loss function is developed using expert experience and agent policy, which provides the learning direction for the QDP-HRL algorithm to some extent. To verify the effectiveness of QDP-HRL, the experiments are conducted on several continuous action space tasks in the OpenAI gym environment, and the results demonstrate that QDP-HRL greatly improves learning speed and performance.

Transcriptomic and Metabolomic Investigation on Leaf Necrosis Induced by ZmWus2 Transient Overexpression in Nicotiana benthamiana.

WUSCHEL (WUS) is a crucial transcription factor in regulating plant stem cell development, and its expression can also improve genetic transformation. However, the ectopic expression of WUS always causes pleiotropic effects during genetic transformation, making it important to understand the regulatory mechanisms underlying these phenomena. In our study, we found that the transient expression of the maize WUS ortholog ZmWus2 caused severe leaf necrosis in Nicotiana benthamiana. We performed transcriptomic and non-target metabolomic analyses on tobacco leaves during healthy to wilted states after ZmWus2 transient overexpression. Transcriptomic analysis revealed that ZmWus2 transformation caused active metabolism of inositol trisphosphate and glycerol-3-phosphate, while also upregulating plant hormone signaling and downregulating photosystem and protein folding pathways. Metabolomic analysis mainly identified changes in the synthesis of phenylpropanoid compounds and various lipid classes, including steroid synthesis. In addition, transcription factors such as ethylene-responsive factors (ERFs), the basic helix-loop-helix (bHLH) factors, and MYBs were found to be regulated by ZmWus2. By integrating these findings, we developed a WUS regulatory model that includes plant hormone accumulation, stress responses, lipid remodeling, and leaf necrosis. Our study sheds light on the mechanisms underlying WUS ectopic expression causing leaf necrosis and may inform the development of future genetic transformation strategies.

MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning.

Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems. In many real-world scenarios, tasks often have several conflicting objectives and may require multiple agents to cooperate, which are the multi-objective multi-agent decision-making problems. However, only few works have been conducted on this intersection. Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective, or multi-objective decision-making with a single agent. In this paper, we propose MO-MIX to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem. Our approach is based on the centralized training with decentralized execution (CTDE) framework. A weight vector representing preference over the objectives is fed into the decentralized agent network as a condition for local action-value function estimation, while a mixing network with parallel architecture is used to estimate the joint action-value function. In addition, an exploration guide approach is applied to improve the uniformity of the final non-dominated solutions. Experiments demonstrate that the proposed method can effectively solve the multi-objective multi-agent cooperative decision-making problem and generate an approximation of the Pareto set. Our approach not only significantly outperforms the baseline method in all four kinds of evaluation metrics, but also requires less computational cost.

Real-world efficacy and safety of TACE plus camrelizumab and apatinib in patients with HCC (CHANCE2211): a propensity score matching study.

OBJECTIVES: This study aimed to investigate the efficacy and safety of transarterial chemoembolization (TACE) plus camrelizumab, a monoclonal antibody targeting programmed death-1, and apatinib for patients with intermediate and advanced hepatocellular carcinoma (HCC) in a real-world setting. METHODS: A total of 586 HCC patients treated with either TACE plus camrelizumab and apatinib (combination group, n = 107) or TACE monotherapy (monotherapy group, n = 479) were included retrospectively. Propensity score matching analysis was used to match patients. The overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and safety in the combination group were described in comparison to monotherapy. RESULTS: After propensity score matching (1:2), 84 patients in the combination group were matched to 147 patients in the monotherapy group. The median age was 57 years and 71/84 (84.5%) patients were male in the combination group, while the median age was 57 years with 127/147 (86.4%) male in the monotherapy group. The median OS, PFS, and ORR in the combination group were significantly higher than those in the monotherapy group (median OS, 24.1 vs. 15.7 months, p = 0.008; median PFS, 13.5 vs. 7.7 months, p = 0.003; ORR, 59.5% [50/84] vs. 37.4% [55/147], p = 0.002). On multivariable Cox regression, combination therapy was associated with significantly better OS (adjusted hazard ratio [HR], 0.41; 95% confidence interval [CI], 0.26-0.64; p < 0.001) and PFS (adjusted HR, 0.52; 95% CI, 0.37-0.74; p < 0.001). Grade 3 or 4 adverse events occurred in 14/84 (16.7%) and 12/147 (8.2%) in the combination and monotherapy groups, respectively. CONCLUSIONS: TACE plus camrelizumab and apatinib showed significantly better OS, PFS, and ORR versus TACE monotherapy for predominantly advanced HCC. CLINICAL RELEVANCE STATEMENT: Compared with TACE monotherapy, TACE plus immunotherapy and molecular targeted therapy showed better clinical efficacy for predominantly advanced HCC patients, with a higher incidence of adverse events. KEY POINTS: • This propensity score-matched study demonstrates that TACE plus immunotherapy and molecular targeted therapy have a longer OS, PFS, and ORR compared with TACE monotherapy in HCC. • Grade 3 or 4 adverse events occurred in 14/84 (16.7%) patients treated with TACE plus immunotherapy and molecular targeted therapy compared with 12/147 (8.2%) patients in the monotherapy group, while no grade 5 adverse events were observed in all cohorts.