Circ_0039960 regulates growth and Warburg effect of breast cancer cells via modulating miR-1178/PRMT7 axis.

BACKGROUND: Breast cancer (BC) is a serious threat to women's life and healthy. Increasing evidence indicated that blocking Warburg effect could attenuate the development of BC. Circular RNAs (circRNAs) has been found to be dysregulated in various carcinomas, including BC. Our study aims to illustrate the role and regulatory mechanism of circ_0039960 in BC development. METHODS: RT-qPCR and western blotting were utilized to evaluate the expression of circ_0039960 in tissues recruited from 32 cases of BC patients and also BC cell lines. Circ_0039960 shRNA was transfected into cells to explore its function on cell processes. CCK-8, flow cytometry and ELISA were used to measure cell viability, cell cycle and apoptosis. Warburg effect was detected by using commercial kits. Besides, bioinformatic prediction, RIP and luciferase reporter assays were performed to validate the interactions between circ_0039960, miR-1178 and PRMT7. RESULTS: The results showed that circ_0039960 and PRMT7 were both up-regulated, while miR-1178 was down-regulated, in BC tissues and cells. Silencing circ_0039960 effectively inhibited cell viability and Warburg effect of BC cells, also, induced cell cycle arrest and apoptosis. Moreover, we validated that circ_0039960 positively mediated PRMT7 expression via directly targeting to miR-1178. The inhibition of miR-1178 and overexpression of PRMT7 reversed the effect of circ_0039960 knockdown on BC cell growth and Warburg effect. CONCLUSION: In general, our research demonstrated that circ_0039960 regulates cell growth and Warburg effect in BC cells via miR-1178/PRMT7 axis. This may provide new evidence for the exploration of BC diagnostic and therapeutic targets.

Organochlorine pesticides (OCPs) in corals and plankton from a coastal coral reef ecosystem, south China sea.

Recent studies have indicated that coral mucus plays an important role in the bioaccumulation of a few organic pollutants by corals, but no relevant studies have been conducted on organochlorine pesticides (OCPs). Previous studies have also indicated that OCPs widely occur in a few coral reef ecosystems and have a negative effect on coral health. Therefore, this study focused on the occurrence and bioaccumulation of a few OCPs, such as dichlorodiphenyltrichloroethanes (DDTs), hexachlorobenzene (HCB) and p,p'-methoxychlor (MXC), in the coral tissues and mucus as well as in plankton and seawater from a coastal reef ecosystem (Weizhou Island) in the South China Sea. The results indicated that DDTs were the predominant OCPs in seawater and marine biota. Higher concentrations of OCPs in plankton may contribute to the enrichment of OCPs by corals. The significantly higher total OCP concentration (∑8OCPs) found in coral mucus than in coral tissues suggested that coral mucus played an essential role in resisting enrichment of OCPs by coral tissues. This study explored the different functions of coral tissues and mucus in OCP enrichment and biodegradation for the first time, highlighting the need for OCP toxicity experiments from both tissue and mucus perspectives.

IL-27 Promotes Human Placenta-Derived Mesenchymal Stromal Cell Ability To Induce the Generation of CD4+IL-10+IFN-γ+ T Cells via the JAK/STAT Pathway in the Treatment of Experimental Graft-versus-Host Disease.

Human mesenchymal stromal cells (MSCs) harbor immunomodulatory properties to induce the generation of suppressive T cells. MSCs have been successfully used in treating graft-versus-host disease (GVHD) accompanied by abundant inflammatory cytokines such as IL-27. This study investigated the effects of IL-27 on the human placenta-derived MSCs (hPMSCs) to induce generation of CD4+IL-10+IFN-γ+ T cells in vitro and in the humanized xenogenic GVHD NOD/SCID model. The results showed that the percentages of CD4+IL-10+IFN-γ+ T cells were significantly increased in activated human PBMC from both healthy donors and GVHD patients with hPMSCs and in the liver and spleen of hPMSC-treated GVHD mice, and the level of CD4+IL-10+IFN-γ+ T cells in the liver was greater than that in the spleen in hPMSC-treated GVHD mice. The serum level of IL-27 decreased and the symptoms abated in hPMSC-treated GVHD. Further, in vitro results showed that IL-27 promoted the regulatory effects of hPMSCs by enhancing the generation of CD4+IL-10+IFN-γ+ T cells from activated PBMC. Activation occurred through increases in the expression of programmed death ligand 2 (PDL2) in hPMSCs via the JAK/STAT signaling pathway. These findings indicated that hPMSCs could alleviate GVHD mice symptoms by upregulating the production of CD4+IL-10+IFN-γ+ T cells in the spleen and liver and downregulating serum levels of IL-27. In turn, the ability of hPMSCs to induce the generation of CD4+IL-10+IFN-γ+ T cells could be promoted by IL-27 through increases in PDL2 expression in hPMSCs. The results of this study will be of benefit for the application of hPMSCs in clinical trials.

Wavelength detection of model-sharing fiber Bragg grating sensor networks using long short-term memory neural network.

In this paper, an effective wavelength detection approach based on long short-term memory (LSTM) network is proposed for fiber Bragg grating (FBG) sensor networks. The FBG sensor network utilizes a model-sharing mechanism, where the whole spectral wavelength is divided into several shareable regions and spectral overlap is allowed in each region. LSTM, a representative recurrent neural network in deep learning, is applied to learn the features directly from the spectra of FBGs and build the wavelength detection model. By feeding the spectra sequentially into the well-trained model, the Bragg wavelengths of FBGs can be quickly determined under overlap. The obtained LSTM model can be repeatedly used without re-training to improve the multiplexing capability. The results demonstrate that the LSTM-based method can realize high-accuracy and high-speed wavelength detection in the spectral overlapping situations. The proposed approach offers a flexible tool to enhance the sensing capacity of FBG sensor networks.

Interferon-γ suppresses the proliferation and migration of human placenta-derived mesenchmal stromal cells and enhances their ability to induce the generation of CD4+CXCR5+Foxp3+Treg subset.

We investigate the effects of interferon (IFN)-γ on human placenta-derived mesenchymal stromal cells (hPMSCs), in particular, their adhesion, proliferation and migration and modulatory effects on the CD4+CXCR5+Foxp3+Treg subset. And we compared hPMSCs ability to induce the generation of different Treg subsets in response to treatment with IFN-γ. We found that IFN-γ suppressed the proliferation and migration for hPMSCs. The ability of hPMSCs to induce the generation of CD4+CXCR5+Foxp3+Treg subset was enhanced by IFN-γ. And maximal effectiveness of IFN-γ treated hPMSCs upon inducing the generation of Treg subsets was for CD4+CXCR5+Foxp3+Treg subset as compared with that of CD4+CD25+Foxp3+, CD8+CD25+Foxp3+, CD4+IL-10+ and CD8+IL-10+Treg subsets. These results have important implications for the development and application of hPMSCs in clinical use.

Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization.

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification.

Unsupervised domain adaptation (UDA) is becoming a prominent solution for the domain-shift problem in many time-series classification tasks. With sequence properties, time-series data contain both local and sequential features, and the domain shift exists in both features. However, conventional UDA methods usually cannot distinguish those two features but mix them into one variable for direct alignment, which harms the performance. To address this problem, we propose a novel virtual-label-based hierarchical domain adaptation (VLH-DA) approach for time-series classification. Specifically, we first slice the original time-series data and introduce virtual labels to represent the type of each slice (called local patterns). With the help of virtual labels, we decompose the end-to-end (i.e., signal to time-series label) time-series task into two parts, i.e., signal sequence to local pattern sequence and local pattern sequence to time-series label. By decomposing the complex time-series UDA task into two simpler subtasks, the local features and sequential features can be aligned separately, making it easier to mitigate distribution discrepancies. Experiments on four public time-series datasets demonstrate that our VLH-DA outperforms all state-of-the-art (SOTA) methods.

Graph Convolutional Network With Connectivity Uncertainty for EEG-Based Emotion Recognition.

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.

SEA++: Multi-Graph-based Higher-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation.

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between labeled source domains and unlabeled target domains. However, these methods face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically originates from multiple sensors, each with its unique distribution. This property poses difficulties in adapting existing UDA techniques, which mainly focus on aligning global features while overlooking the distribution discrepancies at the sensor level, thus limiting their effectiveness for MTS data. To address this issue, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to address domain discrepancy at both local and global sensor levels. At the local sensor level, we design endo-feature alignment, which aligns sensor features and their correlations across domains. To reduce domain discrepancy at the global sensor level, we design exo-feature alignment that enforces restrictions on global sensor features. We further extend SEA to SEA++ by enhancing the endo-feature alignment. Particularly, we incorporate multi-graph-based higher-order alignment for both sensor features and their correlations. Extensive empirical results have demonstrated the state-of-the-art performance of our SEA and SEA++ on six public MTS datasets for MTS-UDA.

Human placental mesenchymal stromal cells promote the formation of CD8+CD122+PD-1+Tregs via CD73/Foxo1 to alleviate liver injury in graft-versus-host disease mice.

BACKGROUND: Human placental mesenchymal stromal cells (hPMSCs) are known to limit graft-versus-host disease (GVHD). CD8+CD122+PD-1+Tregs have been shown to improve the survival of GVHD mice. However, the regulatory roles of hPMSCs in this subgroup remain unclear. Here, the regulatory mechanism of hPMSCs in reducing liver fibrosis in GVHD mice by promoting CD8+CD122+PD-1+Tregs formation and controlling the balance of IL-6 and IL-10 were explored. METHODS: A GVHD mouse model was constructed using C57BL/6J and BALB/c mice and treated with hPMSCs. LX-2 cells were explored to study the effects of IL-6 and IL-10 on the activation of hepatic stellate cells (HSCs). The percentage of CD8+CD122+PD-1+Tregs and IL-10 secretion were determined using FCM. Changes in hepatic tissue were analysed by HE, Masson, multiple immunohistochemical staining and ELISA, and the effects of IL-6 and IL-10 on LX-2 cells were detected using western blotting. RESULTS: hPMSCs enhanced CD8+CD122+PD-1+Treg formation via the CD73/Foxo1 and promoted IL-10, p53, and MMP-8 levels, but inhibited IL-6, HLF, α-SMA, Col1α1, and Fn levels in the liver of GVHD mice through CD73. Positive and negative correlations of IL-6 and IL-10 between HLF were found in liver tissue, respectively. IL-6 upregulated HLF, α-SMA, and Col1α1 expression via JAK2/STAT3 pathway, whereas IL-10 upregulated p53 and inhibited α-SMA and Col1α1 expression in LX-2 cells by activating STAT3. CONCLUSIONS: hPMSCs promoted CD8+CD122+PD-1+Treg formation and IL-10 secretion but inhibited HSCs activation and α-SMA and Col1α1 expression by CD73, thus controlling the balance of IL-6 and IL-10, and alleviating liver injury in GVHD mice.

From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks.

Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory, energy, and computation. To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning. Recently, there has been a surge in research on compression methods to achieve model efficiency while retaining performance. Furthermore, more and more works focus on customizing the DNN hardware accelerators to better leverage the model compression techniques. In addition to efficiency, preserving security and privacy is critical for deploying DNNs. However, the vast and diverse body of related works can be overwhelming. This inspires us to conduct a comprehensive survey on recent research toward the goal of high-performance, cost-efficient, and safe deployment of DNNs. Our survey first covers the mainstream model compression techniques, such as model quantization, model pruning, knowledge distillation, and optimizations of nonlinear operations. We then introduce recent advances in designing hardware accelerators that can adapt to efficient model compression approaches. In addition, we discuss how homomorphic encryption can be integrated to secure DNN deployment. Finally, we discuss several issues, such as hardware evaluation, generalization, and integration of various compression approaches. Overall, we aim to provide a big picture of efficient DNNs from algorithm to hardware accelerators and security perspectives.

Concentration, distribution and sources of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from Lijiang River, South China.

Concentrations, distributions and sources of PAHs were investigated in surface sediments from Lijiang River, South China. The total PAHs concentrations ranged from 160 to 602 ng g(-1) dry weight.The total PAHs concentrations from different area descended in the order: middle reach > upper reach > down reach. Based on the PAHs indicators and the surrounding along Lijiang River, PAHs were mainly derived from the burning of coal. The ecological risk assessment suggested that the probability of negative toxic effective caused by PAHs in Lijiang River was lower than 25 %.

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning.

The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.

Local-Global Correlation Fusion-Based Graph Neural Network for Remaining Useful Life Prediction.

Remaining useful life (RUL) prediction is an essential component for prognostics and health management of a system. Due to the powerful ability of nonlinear modeling, deep learning (DL) models have emerged as leading solutions by capturing temporal dependencies within time series sensory data. However, in RUL prediction tasks, data are typically collected from multiple sensors, introducing spatial dependencies in the form of sensor correlations. Existing methods are limited in effectively modeling and capturing the spatial dependencies, restricting their performance to learn representative features for RUL prediction. To overcome the limitations, we propose a novel LOcal-GlObal correlation fusion-based framework (LOGO). Our approach combines both local and global information to model sensor correlations effectively. From a local perspective, we account for local correlations that represent dynamic changes of sensor relationships in local ranges. Simultaneously, from a global perspective, we capture global correlations that depict relatively stable relations between sensors. An adaptive fusion mechanism is proposed to automatically fuse the correlations from different perspectives. Subsequently, we define sequential micrographs for each sample to effectively capture the fused correlations. Graph neural network (GNN) is introduced to capture the spatial dependencies within each micrograph, and the temporal dependencies between these sequential micrographs are then captured. This approach allows us to effectively model and capture the dependency information within the data for accurate RUL prediction. Extensive experiments have been conducted, verifying the effectiveness of our method.

Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification.

Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this work, we propose a novel Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC) that learns representations from unlabeled data with contrastive learning. Specifically, we propose time-series-specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module. Additionally, we conduct a systematic study of time-series data augmentation selection, which is a key part of contrastive learning. We also extend TS-TCC to the semi-supervised learning settings and propose a Class-Aware TS-TCC (CA-TCC) that benefits from the available few labeled data to further improve representations learned by TS-TCC. Specifically, we leverage the robust pseudo labels produced by TS-TCC to realize a class-aware contrastive loss. Extensive experiments show that the linear evaluation of the features learned by our proposed framework performs comparably with the fully supervised training. Additionally, our framework shows high efficiency in few labeled data and transfer learning scenarios.

Privacy-Preserving Cross-Environment Human Activity Recognition.

Recent studies have demonstrated the success of using the channel state information (CSI) from the WiFi signal to analyze human activities in a fixed and well-controlled environment. Those systems usually degrade when being deployed in new environments. A straightforward solution to solve this limitation is to collect and annotate data samples from different environments with advanced learning strategies. Although workable as reported, those methods are often privacy sensitive because the training algorithms need to access the data from different environments, which may be owned by different organizations. We present a practical method for the WiFi-based privacy-preserving cross-environment human activity recognition (HAR). It collects and shares information from different environments, while maintaining the privacy of individual person being involved. At the core of our approach is the utilization of the Johnson-Lindenstrauss transform, which is theoretically shown to be differentially private. Based on that, we further design an adversarial learning strategy to generate environment-invariant representations for HAR. We demonstrate the effectiveness of the proposed method with different data modalities from two real-life environments. More specifically, on the raw CSI dataset, it shows 2.18% and 1.24% improvements over challenging baselines for two environments, respectively. Moreover, with the discrete wavelet transform features, it further yields 5.71% and 1.55% improvements, respectively.

Diagnostic Significance of Combined Calcitoninogen, Platelet, and D-Dimer Assay in Severe Heatstroke: with Clinical Data Analysis of 70 Patients with Severe Heatstroke.

The significance of calcitoninogen detection among inpatients was discussed by analyzing the clinical characteristics of severe heatstroke (HS). HS patients who were admitted to the Second Hospital of Nantong University, Jiangsu Province, China, between July 1, 2015, and October 30, 2020, were reviewed. Patients' clinical characteristics and laboratory data were recorded, and they were divided into three groups, that is, a control group (heat cramps and heat exhaustion), an exertional HS (EHS) group, and a classical HS (CHS) group to compare the differences among them. Receiver operating characteristic (ROC) curves were plotted to evaluate patients' clinical utility. (1) The body temperatures in the EHS and CHS groups were significantly higher than in the control group (all p < 0.05). (2) The D-dimer (DD), procalcitonin (PCT), and Acute Physiology and Chronic Health Evaluation (APACHE) II score of the EHS group were significantly higher compared with the control and CHS groups (all p < 0.05); the platelets (PLT), C-reactive protein (CRP), blood sodium (Na), and intravenous glucose (GLU) of the EHS group were lower than in the control and CHS groups (all p < 0.05). (3) The ROC curve analysis showed the performance results for DD (area under the curve [AUC] 0.670, 95% confidence interval [CI] 0.547-0.777), PCT (AUC 0.705, 95% CI 0.584-0.808), and PLT (AUC 0.791, 95% CI 0.677-0.879). The sensitivity was 40.48%, 100%, and 73.81%, and the specificity was 96.43%, 32.14%, and 78.57%, respectively. Using three combined analyses, an elevated AUC of 0.838, 95% CI 0.731-0.916, with a sensitivity of 71.43% and a specificity of 85.71%, respectively, was revealed. Patients in the EHS group had higher DD, PCT, and APACHE II values, whereas PLT, CRP, Na, and GLU were reduced. The apparent decrease in the PLT, as well as the increase in PCT and DD values, could be considered as early sensitivity indicators of severe HS. A combined test of these three indicators presented significant diagnostic value for detecting severe cases of HS.

SSA-ICL: Multi-domain adaptive attention with intra-dataset continual learning for Facial expression recognition.

Facial expression recognition (FER) is a kind of affective computing that identifies the emotional state represented in facial photographs. Various methods have been developed for completing this critical task. In spite of this progress, three significant obstacles, the interaction between spatial action units, the inadequacy of semantic information about spectral expressions and the unbalanced data distribution, are not well addressed. In this work, we propose SSA-ICL, a novel approach for FER, and solve these three difficulties inside a coherent framework. To address the first two challenges, we develop a Spectral and Spatial Attention (SSA) module that integrates spectral semantics with spatial locations to improve the performance of the model. We provide an Intra-dataset Continual Learning (ICL) module to combat the issue of long-tail distribution in FER datasets. By subdividing a single long-tail dataset into multiple sub-datasets, ICL repeatedly trains well-balanced representations from each subset and finally develop a independent classifier. We performed extensive experiments on two publicly available datasets, AffectNet and RAFDB. In comparison to existing attention modules, our SSA achieves an accuracy improvement of 3.8%∼6.7%, as evidenced by testing results. In the meanwhile, our proposed SSA-ICL can achieve superior or comparable performance to state-of-the-art FER methods (65.78% on AffectNet and 89.44% on RAFDB).

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation.

The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling can be expensive and time-consuming. Recently, the self-supervised learning (SSL) paradigm has emerged as one of the most successful techniques to overcome labels' scarcity. In this paper, we evaluate the efficacy of SSL to boost the performance of existing SSC models in the few-labels regime. We conduct a thorough study on three SSC datasets, and we find that fine-tuning the pretrained SSC models with only 5% of labeled data can achieve competitive performance to the supervised training with full labels. Moreover, self-supervised pretraining helps SSC models to be more robust to data imbalance and domain shift problems.

A systematic review of image-guided, surgical robot-assisted percutaneous puncture: Challenges and benefits.

Percutaneous puncture is a common medical procedure that involves accessing an internal organ or tissue through the skin. Image guidance and surgical robots have been increasingly used to assist with percutaneous procedures, but the challenges and benefits of these technologies have not been thoroughly explored. The aims of this systematic review are to furnish an overview of the challenges and benefits of image-guided, surgical robot-assisted percutaneous puncture and to provide evidence on this approach. We searched several electronic databases for studies on image-guided, surgical robot-assisted percutaneous punctures published between January 2018 and December 2022. The final analysis refers to 53 studies in total. The results of this review suggest that image guidance and surgical robots can improve the accuracy and precision of percutaneous procedures, decrease radiation exposure to patients and medical personnel and lower the risk of complications. However, there are many challenges related to the use of these technologies, such as the integration of the robot and operating room, immature robotic perception, and deviation of needle insertion. In conclusion, image-guided, surgical robot-assisted percutaneous puncture offers many potential benefits, but further research is needed to fully understand the challenges and optimize the utilization of these technologies in clinical practice.