Early matrix softening contributes to vascular smooth muscle cell phenotype switching and aortic dissection through down-regulation of microRNA-143/145.

Thoracic aortic dissection (TAD) is characterized by extracellular matrix (ECM) dysregulation. Aberrations in the ECM stiffness can lead to changes in cellular functions. However, the mechanism by which ECM softening regulates vascular smooth muscle cell (VSMCs) phenotype switching remains unclear. To understand this mechanism, we cultured VSMCs in a soft extracellular matrix and discovered that the expression of microRNA (miR)-143/145, mediated by activation of the AKT signalling pathway, decreased significantly. Furthermore, overexpression of miR-143/145 reduced BAPN-induced aortic softening, switching the VSMC synthetic phenotype and the incidence of TAD in mice. Additionally, high-throughput sequencing of immunoprecipitated RNA indicated that the TEA domain transcription factor 1 (TEAD1) is a common target gene of miR-143/145, which was subsequently verified using a luciferase reporter assay. TEAD1 is upregulated in soft ECM hydrogels in vitro, whereas the switch to a synthetic phenotype in VSMCs decreases after TEAD1 knockdown. Finally, we verified that miR-143/145 levels are associated with disease severity and prognosis in patients with thoracic aortic dissection. ECM softening, as a result of promoting the VSMCs switch to a synthetic phenotype by downregulating miR-143/145, is an early trigger of TAD and provides a therapeutic target for this fatal disease. miR-143/145 plays a role in the early detection of aortic dissection and its severity and prognosis, which can offer information for future risk stratification of patients with dissection.

Surface-Pore-Modified N-Doped Amorphous Carbon Nanospheres Tailored with Toluene as Anode Materials for Lithium-Ion Batteries.

The surface modification of amorphous carbon nanospheres (ACNs) through templates has attracted great attention due to its great success in improving the electrochemical properties of lithium storage materials. Herein, a safe methodology with toluene as a soft template is employed to tailor the nanostructure, resulting in ACNs with tunable surface pores. Extensive characterizations through transmission electron microscopy (TEM), scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption/desorption isotherms elucidate the impact of surface pore modifications on the external structure, morphology, and surface area. Electrochemical assessments reveal the enhanced performance of the surface-pore-modified carbon nanospheres, particularly ACNs-100 synthesized with the addition of 100 µL toluene, in terms of the initial discharge capacity, rate performance, and cycling stability. The interesting phenomenon of persistent capacity increase is ascribed to lithium ion movement within the graphite-like interlayer, resulting in ACNs-100 experiencing a capacity upswing from an initial 320 mAh g-1 to a zenith of 655 mAh g-1 over a thousand cycles at a rate of 2 C. The findings in this study highlight the pivotal role of tailored nanostructure engineering in optimizing energy storage materials.

Corrigendum: The role of microtubules in pancreatic cancer: therapeutic progress.

[This corrects the article DOI: 10.3389/fonc.2021.640863.].

Connectional-style-guided contextual representation learning for brain disease diagnosis.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Multimodal fall detection for solitary individuals based on audio-video decision fusion processing.

Falls often pose significant safety risks to solitary individuals, especially the elderly. Implementing a fast and efficient fall detection system is an effective strategy to address this hidden danger. We propose a multimodal method based on audio and video. On the basis of using non-intrusive equipment, it reduces to a certain extent the false negative situation that the most commonly used video-based methods may face due to insufficient lighting conditions, exceeding the monitoring range, etc. Therefore, in the foreseeable future, methods based on audio and video fusion are expected to become the best solution for fall detection. Specifically, this article outlines the following methodology: the video-based model utilizes YOLOv7-Pose to extract key skeleton joints, which are then fed into a two stream Spatial Temporal Graph Convolutional Network (ST-GCN) for classification. Meanwhile, the audio-based model employs log-scaled mel spectrograms to capture different features, which are processed through the MobileNetV2 architecture for detection. The final decision fusion of the two results is achieved through linear weighting and Dempster-Shafer (D-S) theory. After evaluation, our multimodal fall detection method significantly outperforms the single modality method, especially the evaluation metric sensitivity increased from 81.67% in single video modality to 96.67% (linear weighting) and 97.50% (D-S theory), which emphasizing the effectiveness of integrating video and audio data to achieve more powerful and reliable fall detection in complex and diverse daily life environments.

Multi-scale full spike pattern for semantic segmentation.

Spiking neural networks (SNNs), as the brain-inspired neural networks, encode information in spatio-temporal dynamics. They have the potential to serve as low-power alternatives to artificial neural networks (ANNs) due to their sparse and event-driven nature. However, existing SNN-based models for pixel-level semantic segmentation tasks suffer from poor performance and high memory overhead, failing to fully exploit the computational effectiveness and efficiency of SNNs. To address these challenges, we propose the multi-scale and full spike segmentation network (MFS-Seg), which is based on the deep direct trained SNN and represents the first attempt to train a deep SNN with surrogate gradients for semantic segmentation. Specifically, we design an efficient fully-spike residual block (EFS-Res) to alleviate representation issues caused by spiking noise on different channels. EFS-Res utilizes depthwise separable convolution to improve the distributions of spiking feature maps. The visualization shows that our model can effectively extract the edge features of segmented objects. Furthermore, it can significantly reduce the memory overhead and energy consumption of the network. In addition, we theoretically analyze and prove that EFS-Res can avoid the degradation problem based on block dynamical isometry theory. Experimental results on the Camvid dataset, the DDD17 dataset, and the DSEC-Semantic dataset show that our model achieves comparable performance to the mainstream UNet network with up to 31× fewer parameters, while significantly reducing power consumption by over 13×. Overall, our MFS-Seg model demonstrates promising results in terms of performance, memory efficiency, and energy consumption, showcasing the potential of deep SNNs for semantic segmentation tasks. Our code is available in https://github.com/BICLab/MFS-Seg.

One Endothelium-Targeted Combined Nucleic Acid Delivery System for Myocardial Infarction Therapy.

Acute myocardial infarction (MI) and ischemic heart disease are the leading causes of heart failure and mortality. Currently, research on MI treatment is focused on angiogenic and anti-inflammatory therapies. Although endothelial cells (ECs) are critical for triggering inflammation and angiogenesis, no approach has targeted them for the treatment of MI. In this study, we proposed a nonviral combined nucleic acid delivery system consisting of an EC-specific polycation (CRPPR-grafted ethanolamine-modified poly(glycidyl methacrylate), CPC) that can efficiently codeliver siR-ICAM1 and pCXCL12 for the treatment of MI. Animals treated with the combination therapy exhibited better cardiac function than those treated with each nucleic acid alone. In particular, the combination therapy of CPC/siR-ICAM1 and CPC/pCXCL12 significantly improved cardiac systolic function, anti-inflammatory responses, and angiogenesis compared to the control group. In conclusion, CPC-based combined gene delivery systems show impressive performance in the treatment of MI and provide a programmed strategy for the development of codelivery systems for various EC-related diseases.

Macrophage-Myofibroblast Transition as a Potential Origin for Skeletal Muscle Fibrosis After Injury via Complement System Activation.

Background: Acute skeletal muscle injury is common in sports. The injured muscle cannot fully recover due to fibrosis resulting from myofibroblasts. Understanding the origin of fibroblasts is, therefore, important for the development of anti-fibrotic therapies. Accumulating evidence shows that a mechanism called macrophage-myofibroblast transition (MMT) can lead to tissue or organ fibrosis, yet it is still unclear whether MMT exists in skeletal muscle and the exact mechanisms. Methods: Single-cell transcriptome of mice skeletal muscle after acute injury was analyzed with a specific attention on the process of MMT. Cell-cell interaction network, pseudotime trajectory analysis, Gene Ontology (GO), and Kyoto Genome Encyclopedia (KEGG) were conducted. A series of experiments in vivo and in vitro were launched for verification. Results: Single cell transcriptomic analysis indicated that, following acute injury, there were much interactions between macrophages and myofibroblasts. A detailed analysis on macrophages indicated that, CD68+α-SMA+ cells, which represented the status of MMT, mainly appeared at five days post-injury. KEGG/GO analysis underlined the involvement of complement system, within which C3ar1, C1qa, C1qb, and C1qc were up-regulated. Trajectory analysis also confirmed a potential shift from macrophages to myofibroblasts. These findings were verified by histological study in mice skeletal muscle, that there were much MMT cells at five days, declined gradually, and vanished 14 days after trauma, when there was remarkable fibrosis formation within the injured muscle. Moreover, C3a stimulation could directly induce MMT in BMDMs. Conclusion: Fibrosis following acute injury is disastrous to skeletal muscle, but the origin of myofibroblasts remains unclear. We proved that, following acute injury, macrophage-myofibroblast transition happened in skeletal muscle, which may contribute to fibrosis formation. This phenomenon mainly occurred at five days post-injury. The complement system can activate MMT. More evidence is needed to directly support the pro-fibrotic role of MMT in skeletal muscle fibrosis after acute injury.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC.

BACKGROUND: Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC). METHODS: HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types. RESULTS: With this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients. CONCLUSION: We built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

Advancing Spiking Neural Networks Toward Deep Residual Learning.

Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks, but rarely did previous work assessed their applicability to the specifics of SNNs. In this article, we first identify that this negligence leads to impeded information flow and the accompanying degradation problem in a spiking version of vanilla ResNet. To address this issue, we propose a novel SNN-oriented residual architecture termed MS-ResNet, which establishes membrane-based shortcut pathways, and further proves that the gradient norm equality can be achieved in MS-ResNet by introducing block dynamical isometry theory, which ensures the network can be well-behaved in a depth-insensitive way. Thus, we are able to significantly extend the depth of directly trained SNNs, e.g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem. To validate the effectiveness of MS-ResNet, experiments on both frame-based and neuromorphic datasets are conducted. MS-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, which is the highest to the best of our knowledge in the domain of directly trained SNNs. Great energy efficiency is also observed, with an average of only one spike per neuron needed to classify an input sample. We believe our powerful and scalable models will provide strong support for further exploration of SNNs.

Impact of Lysine Succinylation on the Biology of Fungi.

Post-translational modifications (PTMs) play a crucial role in protein functionality and the control of various cellular processes and secondary metabolites (SMs) in fungi. Lysine succinylation (Ksuc) is an emerging protein PTM characterized by the addition of a succinyl group to a lysine residue, which induces substantial alteration in the chemical and structural properties of the affected protein. This chemical alteration is reversible, dynamic in nature, and evolutionarily conserved. Recent investigations of numerous proteins that undergo significant succinylation have underscored the potential significance of Ksuc in various biological processes, encompassing normal physiological functions and the development of certain pathological processes and metabolites. This review aims to elucidate the molecular mechanisms underlying Ksuc and its diverse functions in fungi. Both conventional investigation techniques and predictive tools for identifying Ksuc sites were also considered. A more profound comprehension of Ksuc and its impact on the biology of fungi have the potential to unveil new insights into post-translational modification and may pave the way for innovative approaches that can be applied across various clinical contexts in the management of mycotoxins.

Non-Equilibrium Assembly of Atomically-Precise Copper Nanoclusters.

Accurate structure control in dissipative assemblies (DSAs) is vital for precise biological functions. However, accuracy and functionality of artificial DSAs are far from this objective. Herein, a novel approach is introduced by harnessing complex chemical reaction networks rooted in coordination chemistry to create atomically-precise copper nanoclusters (CuNCs), specifically Cu11(µ9-Cl)(µ3-Cl)3L6Cl (L = 4-methyl-piperazine-1-carbodithioate). Cu(I)-ligand ratio change and dynamic Cu(I)-Cu(I) metallophilic/coordination interactions enable the reorganization of CuNCs into metastable CuL2, finally converting into equilibrium [CuL·Y]Cl (Y = MeCN/H2O) via Cu(I) oxidation/reorganization and ligand exchange process. Upon adding ascorbic acid (AA), the system goes further dissipative cycles. It is observed that the encapsulated/bridging halide ions exert subtle influence on the optical properties of CuNCs and topological changes of polymeric networks when integrating CuNCs as crosslink sites. CuNCs duration/switch period could be controlled by varying the ions, AA concentration, O2 pressure and pH. Cu(I)-Cu(I) metallophilic and coordination interactions provide a versatile toolbox for designing delicate life-like materials, paving the way for DSAs with precise structures and functionalities. Furthermore, CuNCs can be employed as modular units within polymers for materials mechanics or functionalization studies.

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Dynamic changes in cardiac morphology, function, and diffuse myocardial fibrosis duration of diabetes in type 1 and type 2 diabetic mice models using 7.0 T CMR and echocardiography.

Background: Diabetes mellitus (DM) is associated with an increased risk of cardiovascular disease (CVD). Hence, early detection of cardiac changes by imaging is crucial to reducing cardiovascular complications. Purpose: Early detection of cardiac changes is crucial to reducing cardiovascular complications. The study aimed to detect the dynamic change in cardiac morphology, function, and diffuse myocardial fibrosis(DMF) associated with T1DM and T2DM mice models. Materials and methods: 4-week-old C57Bl/6J male mice were randomly divided into control (n=30), T1DM (n=30), and T2DM (n=30) groups. A longitudinal study was conducted every 4 weeks using serial 7.0T CMR and echocardiography imaging. Left ventricular ejection fraction (LV EF), tissue tracking parameters, and DMF were measured by cine CMR and extracellular volume fraction (ECV). Global peak circumferential strain (GCPS), peak systolic strain rate (GCPSSR) values were acquired by CMR feature tracking. LV diastolic function parameter (E/E') was acquired by echocardiography. The correlations between the ECV and cardiac function parameters were assessed by Pearson's test. Results: A total of 6 mice were included every 4 weeks in control, T1DM, and T2DM groups for analysis. Compared to control group, an increase was detected in the LV mass and E/E' ratio, while the values of GCPS, GCPSSR decreased mildly in DM. Compared to T2DM group, GCPS and GCPSSR decreased earlier in T1DM(GCPS 12W,P=0.004; GCPSSR 12W,P=0.04). ECV values showed a significant correlation with GCPS and GCPSSR in DM groups. Moreover, ECV values showed a strong positive correlation with E/E'(T1DM,r=0.757,P<0.001;T2DM, r=0.811,P<0.001). Conclusion: The combination of ECV and cardiac mechanical parameters provide imaging biomakers for pathophysiology, early diagnosis of cardiac morphology, function and early intervention in diabetic cardiomyopathy in the future.

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

Spike Attention Coding for Spiking Neural Networks.

Spiking neural networks (SNNs), an important family of neuroscience-oriented intelligent models, play an essential role in the neuromorphic computing community. Spike rate coding and temporal coding are the mainstream coding schemes in the current modeling of SNNs. However, rate coding usually suffers from limited representation resolution and long latency, while temporal coding usually suffers from under-utilization of spike activities. To this end, we propose spike attention coding (SAC) for SNNs. By introducing learnable attention coefficients for each time step, our coding scheme can naturally unify rate coding and temporal coding, and then flexibly learn optimal coefficients for better performance. Several normalization and regularization techniques are further incorporated to control the range and distribution of the learned attention coefficients. Extensive experiments on classification, generation, and regression tasks are conducted and demonstrate the superiority of the proposed coding scheme. This work provides a flexible coding scheme to enhance the representation power of SNNs and extends their application scope beyond the mainstream classification scenario.

Boosting Zero-Shot Learning via Contrastive Optimization of Attribute Representations.

Zero-shot learning (ZSL) aims to recognize classes that do not have samples in the training set. One representative solution is to directly learn an embedding function associating visual features with corresponding class semantics for recognizing new classes. Many methods extend upon this solution, and recent ones are especially keen on extracting rich features from images, e.g., attribute features. These attribute features are normally extracted within each individual image; however, the common traits for features across images yet belonging to the same attribute are not emphasized. In this article, we propose a new framework to boost ZSL by explicitly learning attribute prototypes beyond images and contrastively optimizing them with attribute-level features within images. Besides the novel architecture, two elements are highlighted for attribute representations: a new prototype generation module (PM) is designed to generate attribute prototypes from attribute semantics; a hard-example-based contrastive optimization scheme is introduced to reinforce attribute-level features in the embedding space. We explore two alternative backbones, CNN-based and transformer-based, to build our framework and conduct experiments on three standard benchmarks, Caltech-UCSD Birds-200-2011 (CUB), SUN attribute database (SUN), and animals with attributes 2 (AwA2). Results on these benchmarks demonstrate that our method improves the state of the art by a considerable margin. Our codes will be available at https://github.com/dyabel/CoAR-ZSL.git.

Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition.

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and µs level temporal resolution, has prominent advantages in many specific visual scenarios and gained much research interest. Spiking neural network (SNN) is naturally suitable for dealing with event streams due to its temporal information processing capability and event-driven nature. However, existing works SNN neglect the fact that the input event streams are spatially sparse and temporally non-uniform, and just treat these variant inputs equally. This situation interferes with the effectiveness and efficiency of existing SNNs. In this paper, we propose the feature Refine-and-Mask SNN (RM-SNN), which has the ability of self-adaption to regulate the spiking response in a data-dependent way. We use the Refine-and-Mask (RM) module to refine all features and mask the unimportant features to optimize the membrane potential of spiking neurons, which in turn drops the spiking activity. Inspired by the fact that not all events in spatio-temporal streams are task-relevant, we execute the RM module in both temporal and channel dimensions. Extensive experiments on seven event-based benchmarks, DVS128 Gesture, DVS128 Gait, CIFAR10-DVS, N-Caltech101, DailyAction-DVS, UCF101-DVS, and HMDB51-DVS demonstrate that under the multi-scale constraints of input time window, RM-SNN can significantly reduce the network average spiking activity rate while improving the task performance. In addition, by visualizing spiking responses, we analyze why sparser spiking activity can be better. Code.

Effects of Microstate Dynamic Brain Network Disruption in Different Stages of Schizophrenia.

Schizophrenia is a heterogeneous mental disorder with unknown etiology or pathological characteristics. Microstate analysis of the electroencephalogram (EEG) signal has shown significant potential value for clinical research. Importantly, significant changes in microstate-specific parameters have been extensively reported; however, these studies have ignored the information interactions within the microstate network in different stages of schizophrenia. Based on recent findings, since rich information about the functional organization of the brain can be revealed by functional connectivity dynamics, we use the first-order autoregressive model to construct the functional connectivity of intra- and intermicrostate networks to identify information interactions among microstate networks. We demonstrate that, beyond abnormal parameters, disrupted organization of the microstate networks plays a crucial role in different stages of the disease by 128-channel EEG data collected from individuals with first-episode schizophrenia, ultrahigh-risk, familial high-risk, and healthy controls. According to the characteristics of the microstates of patients at different stages, the parameters of microstate class A are reduced, those of class C are increased, and the transitions from intra- to intermicrostate functional connectivity are gradually disrupted. Furthermore, decreased integration of intermicrostate information might lead to cognitive deficits in individuals with schizophrenia and those in high-risk states. Taken together, these findings illustrate that the dynamic functional connectivity of intra- and intermicrostate networks captures more components of disease pathophysiology. Our work sheds new light on the characterization of dynamic functional brain networks based on EEG signals and provides a new interpretation of aberrant brain function in different stages of schizophrenia from the perspective of microstates.

Arthroscopic Repair for the Anterior Horn of the Lateral Meniscus With Suture Anchor.

The anterior horn tear of the lateral meniscus, often accompanied with local parameniscal cysts, is usually managed by cysts debridement and meniscus repair with the outside-in technique (OIT). However, a big gap between the meniscus and anterior capsule would be produced after cysts debridement and be difficult to be closed by the OIT. Or, the OIT would result in knee pain because of the overly tight knots. Therefore, we devised an anchor repair technique. Following the cysts resection, the anterior horn of the lateral meniscus (AHLM) is fixed at the anterolateral edge of the tibial plateau with 1 suture anchor, and then followed by suturing the AHLM with the surrounding synovium to promote healing. We recommend this technique as an alternative method for repairing an AHLM tear accompanied with local parameniscal cysts.