Early Detection of Parkinson's Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings.

With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early detection of Parkinson's disease (PD) and finds that walking recording empowered by deep learning is a valid digital biomarker for early-recognizing PD patients. Specifically, the inertial sensor data is preprocessed, including normalization, scaling, and rotation, and then the processed data is fed into the proposed deep NeuroEnhanceNet. Finally, determine the individual prediction score using the PD-prone strategy and generate the detection results. The proposed deep NeuroEnhanceNet, specifically designed for inertial sensor data, can focus on both the long-term data characteristics within a single channel and the inter-channel correlations. Our method obtains a low false negative rate of 0.053 for the early detection of PD. We further analyze and compare the effectiveness of digital biomarkers captured from the walking and resting processes for early detection of PD. All the code for this work is available at: https://github.com/heyiyia/NeuroEnhanceNet.

High-performance electrolyte-gated amorphous InGaZnO field-effect transistor for label-free DNA sensing.

Accurate, convenient, label-free, and cost-effective biomolecules detection platforms are currently in high demand. In this study, we showcased the utilization of electrolyte-gated InGaZnO field-effect transistors (IGZO FETs) featuring a large on-off current ratio of over 106 and a low subthreshold slope of 78.5 mV/dec. In the DNA biosensor, the modification of target DNA changed the effective gate voltage of IGZO FETs, enabling an impressive low detection limit of 0.1 pM and a wide linear detection range from 0.1 pM to 1 µM. This label-free detection method also exhibits high selectivity, allowing for the discrimination of single-base mismatch. Furthermore, the reuse of gate electrodes and channel films offers cost-saving benefits and simplifies device fabrication processes. The electrolyte-gated IGZO FET biosensor presented in this study shows great promise for achieving low-cost and highly sensitive detection of various biomolecules.

Sleep Stage Classification With Multi-Modal Fusion and Denoising Diffusion Model.

Sleep stage classification plays a crucial role in sleep quality assessment and sleep disorder prevention. Nowadays, many studies have developed algorithms for this purpose, but they still face two challenges. The first is noise in physiological signals from various devices. The second challenge is that most studies simply concatenate multi-modal features without considering their correlations. To this end, we propose a framework, namely Diff-SleepNet, to efficiently classify sleep stages from multi-modal input. This framework begins with a diffusion model with peak signal-to-noise ratio (PNSR) loss function that adaptively filters noise. The filtered signals are then transformed into a multi-view spectrum through data pre-processing. These spectra are processed by a transformer-based backbone to extract multi-modal features. The production is fed into the following multi-scale attention module for robust feature fusion. The sleep stage category is finally determined by a fully connected layer. Our framework is trained and validated on three typical datasets, i.e., SHHS, Sleep-EDF-SC, and Sleep-EDF-X. Experimental results demonstrate that it is effective and has advantages over other peer methods.

Lefamulin Overcomes Acquired Drug Resistance via Regulating Mitochondrial Homeostasis by Targeting ILF3 in Hepatocellular Carcinoma.

Acquired resistance represents a critical clinical challenge to molecular targeted therapies such as tyrosine kinase inhibitors (TKIs) treatment in hepatocellular carcinoma (HCC). Therefore, it is urgent to explore new mechanisms and therapeutics that can overcome or delay resistance. Here, a US Food and Drug Administration (FDA)-approved pleuromutilin antibiotic is identified that overcomes sorafenib resistance in HCC cell lines, cell line-derived xenograft (CDX) and hydrodynamic injection mouse models. It is demonstrated that lefamulin targets interleukin enhancer-binding factor 3 (ILF3) to increase the sorafenib susceptibility of HCC via impairing mitochondrial function. Mechanistically, lefamulin directly binds to the Alanine-99 site of ILF3 protein and interferes with acetyltransferase general control non-depressible 5 (GCN5) and CREB binding protein (CBP) mediated acetylation of Lysine-100 site, which disrupts the ILF3-mediated transcription of mitochondrial ribosomal protein L12 (MRPL12) and subsequent mitochondrial biogenesis. Clinical data further confirm that high ILF3 or MRPL12 expression is associated with poor survival and targeted therapy efficacy in HCC. Conclusively, this findings suggest that ILF3 is a potential therapeutic target for overcoming resistance to TKIs, and lefamulin may be a novel combination therapy strategy for HCC treatment with sorafenib and regorafenib.

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS: This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS: A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS: In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

Toward the Internet of Medical Things: Architecture, trends and challenges.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

Overexpression of FHL1 suppresses papillary thyroid cancer proliferation and progression via inhibiting Wnt/ß-catenin pathway.

PURPOSE: The four and a half LIM domain protein 1 (FHL1) has been found to act as a tumor suppressor in several cancers. However, the clinical and functional significance, as well as underlying molecular mechanisms of FHL1 in papillary thyroid cancer (PTC) are largely unknown. METHODS: Bioinformatics analyses, qRT-PCR and Western blotting were used to investigate the expression of FHL1 in PTC. Cell proliferation was measured using CCK8, Edu, colony formation, and flow cytometry assays. Cell migration and invasion were examined by wound healing and Transwell assays. qRT-PCR, Western blot, immunofluorescence and Top/Fop reporter assays were performed to assess the underlying mechanisms. RESULTS: FHL1 expression was significantly downregulated in PTC. FHL1 downregulation negatively correlated with stage, T classification, and N classification of the patients. The downregulation of FHL1 is associated with poor prognosis. Overexpression of FHL1 inhibited PTC cells' proliferation, invasion, migration and Wnt/ß-catenin pathway activity. LiCl partially restored the inhibitory effects of FHL1 on aggressive phenotypes and Wnt/ß-catenin pathway activity of PTC cells. CONCLUSION: FHL1 is downregulated in PTC and its expression is associated with better clinical outcomes for patients with the disease. FHL1 acts as a tumor suppressor via, at least partially, suppressing Wnt/ß-catenin pathway.

Loss of STARD13 contributes to aggressive phenotype transformation and poor prognosis in papillary thyroid carcinoma.

PURPOSE: StAR Related Lipid Transfer Domain Containing 13 (STARD13) serves as a tumor suppressor and has been characterized in several types of malignancies. However, the role and the molecular mechanism of STARD13 in regulating the progression of papillary thyroid carcinoma (PTC) remain underexplored. METHODS: The gene expression and clinical information of thyroid cancer were downloaded using "TCGAbiolinks" R package. Quantitative PCR and immunohistochemical staining were conducted to detect the expression of STARD13 in clinical tumor and adjacent non-tumor samples. Wound-healing assay, Transwell assay and 3D spheroid invasion assay were performed to evaluate the migratory and invasive capacities of PTC cells. Cell proliferation ability was determined by CCK-8 assay, colony formation assay and 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay. The alterations of indicated proteins were detected by Western blotting. RESULTS: In the present study, we found that STARD13 was significantly underexpressed in PTC, which was correlated with poor prognosis. Downregulation of STARD13 might be due to methylation of promoter region. Loss-and gain-of-function experiments demonstrated that STARD13 impeded migratory and invasive capacities of PTC cells in vitro and in vivo. In addition, we found that STARD13 regulated the morphology of PTC cells and inhibited epithelial-mesenchymal transition (EMT). CONCLUSION: Our results suggest that STARD13 acts as a metastasis suppressor and might be a potential therapeutic target in PTC.

Aldol Condensation for the Construction of Organic Functional Materials.

Aldol condensation is a cost-effective and sustainable synthetic method, offering the advantages of low complexity, substrate universality, and high efficiency. Over the past decade, it has become popular for creating next-generation organic functional materials, particularly rigid-rod conjugated (semi)conductors. This review focuses on conjugated small molecules, oligomers, and polymeric (semi)conductors synthesized through aldol condensation, with emphasis on their remarkable features in advancing n-type organic field-effect transistors (OFETs), organic electrochemical transistors (OECTs), organic photovoltaics (OPVs), and organic thermoelectrics (OTEs) as well as NIR-II photothermal conversion. Coherence character, optical properties, microstructure, and chain conformation are investigated to understand material-property relationships. Future applications and challenges in this area are also discussed.

Versatile Neuromorphic Modulation and Biosensing based on N-type Small-molecule Organic Mixed Ionic-Electronic Conductors.

The ion/chemical-based modulation feature of organic mixed ionic-electronic conductors (OMIECs) are critical to advancing next generation bio-integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n-type fused all-acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR-Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state-of-art n-type OMIECs based OENS. These superior artificial synapse characteristics of gNR-Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n-type small-molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub-µM level in aqueous electrolyte. This work may open a new path of small-molecule ion-electron conductors for next-generation ANN and bioelectronics.

Regulation of the integrin αVß3- actin filaments axis in early osteogenic differentiation of human mesenchymal stem cells under cyclic tensile stress.

BACKGROUND: Integrins are closely related to mechanical conduction and play a crucial role in the osteogenesis of human mesenchymal stem cells. Here we wondered whether tensile stress could influence cell differentiation through integrin αVß3. METHODS: We inhibited the function of integrin αVß3 of human mesenchymal stem cells by treating with c(RGDyk). Using cytochalasin D and verteporfin to inhibit polymerization of microfilament and function of nuclear Yes-associated protein (YAP), respectively. For each application, mesenchymal stem cells were loaded by cyclic tensile stress of 10% at 0.5 Hz for 2 h daily. Mesenchymal stem cells were harvested on day 7 post-treatment. Western blotting and quantitative RT-PCR were used to detect the expression of alkaline phosphatase (ALP), RUNX2, ß-actin, integrin αVß3, talin-1, vinculin, FAK, and nuclear YAP. Immunofluorescence staining detected vinculin, actin filaments, and YAP nuclear localization. RESULTS: Cyclic tensile stress could increase the expression of ALP and RUNX2. Inhibition of integrin αVß3 activation led to rearrangement of actin filaments and downregulated the expression of ALP, RUNX2 and promoted YAP nuclear localization. When microfilament polymerization was inhibited, ALP, RUNX2, and nuclear YAP nuclear localization decreased. Inhibition of YAP nuclear localization could reduce the expression of ALP and RUNX2. CONCLUSIONS: Cyclic tensile stress promotes early osteogenesis of human mesenchymal stem cells via the integrin αVß3-actin filaments axis. YAP nuclear localization participates in this process of human mesenchymal stem cells. Video Abstract.

Comparative proteomics unveils the bacteriostatic mechanisms of Ga(III) on the regulation of metabolic pathways in Pseudomonas aeruginosa.

Gallium has a long history as a chemotherapeutic agent. The mechanisms of action of Ga(III)-based anti-infectives are different from conventional antibiotics, which primarily result from the chemical similarities of Ga(III) with Fe(III) and substitution of gallium into iron-dependent biological pathways. However, more aspects of the molecular mechanisms of Ga(III) against human pathogens, especially the effects on bacterial metabolic processes, remain to be understood. Herein, by using conventional quantitative proteomics, we identified the protein changes of Pseudomonas aeruginosa (P. aeruginosa) in response to Ga(NO3)3 treatment. We show that Ga(III) exhibits bacteriostatic mode of action against P. aeruginosa through affecting the expressions of a number of key enzymes in the main metabolic pathways, including glycolysis, TCA cycle, amino acid metabolism, and protein and nucleic acid biosynthesis. In addition, decreased expressions of proteins associated with pathogenesis and virulence of P. aeruginosa were also identified. Moreover, the correlations between protein expressions and metabolome changes in P. aeruginosa upon Ga(III) treatment were identified and discussed. Our findings thus expand the understanding on the antimicrobial mechanisms of Ga(III) that shed light on enhanced therapeutic strategies. BIOLOGICAL SIGNIFICANCE: Mounting evidence suggest that the efficacy and resistance of clinical antibiotics are closely related to the metabolic homeostasis in bacterial pathogens. Ga(III)-based compounds have been repurposed as antibacterial therapeutic candidates against antibiotics resistant pathogens, and represent a safe and promising treatment for clinical human infections, while more thorough understandings of how bacteria respond to Ga(III) treatment are needed. In the present study, we provide evidences at the proteome level that indicate Ga(III)-induced metabolic perturbations in P. aeruginosa. We identified and discussed the interference of Ga(III) on the expressions and activities of enzymes in the main metabolic pathways in P. aeruginosa. In view of our previous report that the antimicrobial efficacy of Ga(III) could be modulated according to Ga(III)-induced metabolome changes in P. aeruginosa, our current analyses may provide theoretical basis at the proteome level for the development of efficient gallium-based therapies by exploiting bacterial metabolic mechanisms.

Continuously tunable modulation scheme for homodyne detection and state tomography.

Homodyne detection is a common self-referenced technique to extract optical quadratures. Due to ubiquitous fluctuations, experiments measuring optical quadratures require homodyne angle control. Current homodyne angle locking techniques only provide high quality error signals in a span significantly smaller than π radians, the span required for full state tomography, leading to inevitable discontinuities during full tomography. Here, we present and demonstrate a locking technique using a universally tunable modulator which produces high quality error signals at an arbitrary homodyne angle. Our work enables continuous full-state tomography and paves the way to backaction evasion protocols based on a time-varying homodyne angle.

CircRREB1 mediates lipid metabolism related senescent phenotypes in chondrocytes through FASN post-translational modifications.

Osteoarthritis is a prevalent age-related disease characterized by dysregulation of extracellular matrix metabolism, lipid metabolism, and upregulation of senescence-associated secretory phenotypes. Herein, we clarify that CircRREB1 is highly expressed in secondary generation chondrocytes and its deficiency can alleviate FASN related senescent phenotypes and osteoarthritis progression. CircRREB1 impedes proteasome-mediated degradation of FASN by inhibiting acetylation-mediated ubiquitination. Meanwhile, CircRREB1 induces RanBP2-mediated SUMOylation of FASN and enhances its protein stability. CircRREB1-FASN axis inhibits FGF18 and FGFR3 mediated PI3K-AKT signal transduction, then increased p21 expression. Intra-articular injection of adenovirus-CircRreb1 reverses the protective effects in CircRreb1 deficiency mice. Further therapeutic interventions could have beneficial effects in identifying CircRREB1 as a potential prognostic and therapeutic target for age-related OA.

Tunable control of the performance of aqueous-based electrochemical devices by post-polymerization functionalization.

Functionalized polymeric mixed ionic-electronic conductors (PMIECs) are highly desired for the development of electrochemical applications, yet are hindered by the limited conventional synthesis techniques. Here, we propose a "graft-onto-polymer" synthesis strategy by post-polymerization functionalization (GOP-PPF) to prepare a family of PMIECs sharing the same backbone while functionalized with varying ethylene glycol (EG) compositions (two, four, and six EG repeating units). Unlike the typical procedure, GOP-PPF uses a nucleophilic aromatic substitution reaction for the facile and versatile attachment of functional units to a pre-synthesized conjugated-polymer precursor. Importantly, these redox-active PMIECs are investigated as a platform for energy storage devices and organic electrochemical transistors (OECTs) in aqueous media. The ion diffusivity, charge mobility and charge-storage capacity can be significantly improved by optimizing the EG composition. Specifically, g2T2-gBT6 containing the highest EG density gives the highest charge-storage capacity exceeding 180 F g-1 among the polymer series, resulting from the improved ion diffusivity. Moreover, g2T2-gBT4 with four EG repeating units exhibits a superior performance compared to its two analogues in OECTs, associated with a high µC* up to 359 F V-1 cm-1 s-1, owing to the optimal balance between ionic-electronic coupling and charge mobility. Through the GOP-PPF, PMIECs can be tailored to access desirable performance metrics at the molecular level.

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

An Ensemble Classification Model for Depression Based on Wearable Device Sleep Data.

Depression is one of the most common mental disorders, with sleep disturbances as typical symptoms. With the popularity of wearable devices increasing in recent years, more and more people wear portable devices to track sleep quality. Based on this, we believe that depression detection through wearable sleep data is more intelligent and economical. However, the majority of wearable devices face the problem of missing data during the data collection process. Otherwise, most existing studies of depression identification focus on the utilization of complex data, making it difficult to generalize and susceptible to noise interference. To address these issues, we propose a systematic ensemble classification model for depression (ECD). For the missing data problem of wearable devices, we design an improved GAIN method to further control the generation range of interpolated values, which can achieve a more reasonable treatment of missing values. Compared with the original GAIN approach, the improved method shows a 28.56% improvement when using MAE as the metric. For depression recognition, we use ensemble learning to construct a depression classification model which combines five classification models, including SVM, KNN, LR, CBR, and DT. Ensemble learning can improve the model's robustness and generalization. The voting mechanism is used in several places to improve noise immunity. The final classification model performed great on the dataset, with a precision of 92.55% and a recall of 91.89%. These results illustrate how efficient this method is in automatically detecting depression.

Highly Efficient Mixed Conduction in a Fused Oligomer n-Type Organic Semiconductor Enabled by 3D Transport Pathways.

Tailoring organic semiconductors to facilitate mixed conduction of ionic and electronic charges when interfaced with an aqueous media has spurred many recent advances in organic bioelectronics. The field is still restricted, however, by very few n-type (electron-transporting) organic semiconductors with adequate performance metrics. Here, a new electron-deficient, fused polycyclic aromatic system, TNR, is reported with excellent n-type mixed conduction properties including a µC* figure-of-merit value exceeding 30 F cm-1 V-1 s-1 for the best performing derivative. Comprising three naphthalene bis-isatin moieties, this new molecular design builds on successful small-molecule mixed conductors; by extending the molecular scaffold into the oligomer domain, good film-forming properties, strong π-π interactions, and consequently excellent charge-transport properties are obtained. Through judicious optimization of the side chains, the linear oligoether and branched alkyl chain derivative bgTNR is obtained which shows superior mixed conduction in an organic electrochemical transistor configuration including an electron mobility around 0.3 cm2 V-1 s-1 . By optimizing the side chains, the dominant molecular packing can be changed from a preferential edge-on orientation (with high charge-transport anisotropy) to an oblique orientation that can support 3D transport pathways which in turn ensure highly efficient mixed conduction properties across the bulk semiconductor film.

Metabolite asymmetric dimethylarginine (ADMA) functions as a destabilization enhancer of SOX9 mediated by DDAH1 in osteoarthritis.

Osteoarthritis (OA) is a degenerative disease with a series of metabolic changes accompanied by many altered enzymes. Here, we report that the down-regulated dimethylarginine dimethylaminohydrolase-1 (DDAH1) is accompanied by increased asymmetric dimethylarginine (ADMA) in degenerated chondrocytes and in OA samples. Global or chondrocyte-conditional knockout of ADMA hydrolase DDAH1 accelerated OA development in mice. ADMA induces the degeneration and senescence of chondrocytes and reduces the extracellular matrix deposition, thereby accelerating OA progression. ADMA simultaneously binds to SOX9 and its deubiquitinating enzyme USP7, blocking the deubiquitination effects of USP7 on SOX9 and therefore leads to SOX9 degradation. The ADMA level in synovial fluids of patients with OA is increased and has predictive value for OA diagnosis with good sensitivity and specificity. Therefore, activating DDAH1 to reduce ADMA level might be a potential therapeutic strategy for OA treatment.

CircZSWIM6 mediates dysregulation of ECM and energy homeostasis in ageing chondrocytes through RPS14 post-translational modification.

BACKGROUND: Circular RNAs (CircRNAs) are important and have different roles in disease progression. Herein, we aim to elucidate the roles of a novel CircRNA (CircZSWIM6) which is upregulated in ageing chondrocytes. METHODS: We verified the roles of CircZSWIM6 in senescent and osteoarthritis (OA) development in vitro through CircZSWIM6 knockdown and overexpression. RNA pulldown assay and RNA binding protein immunoprecipitation were performed to identify the interaction between CircZSWIM6 and Ribosomal protein S14 (RPS14). The roles of CircZSWIM6 in ageing-related OA were also confirmed in non-traumatic and traumatic model respectively. RESULTS: CircZSWIM6 regulates extracellular matrix (ECM) and energy metabolism in ageing chondrocyte. Mechanistically, CircZSWIM6 competitively bound to the E3 ligase STUB1 binding site on RPS14 (K125) to inhibit proteasomal degradation of RPS14 to maintain RPS14 function. CircZSWIM6-RPS14 axis is highly associated with AMPK signaling transduction, which keeps energy metabolism in chondrocyte. Furthermore, CircZSWIM6 AAV infection leads to senescent and OA phenotypes in a non-traumatic model and accelerates OA progression in a traumatic model. CONCLUSION: Our results revealed a significant role of CircZSWIM6 in age-related OA by regulating ECM metabolism and AMPK-associated energy metabolism. We highlight the CircZSWIM6-RPS14-PCK1-AMPK axis is a potential biomarker for OA.