A predictive model for patent ductus arteriosus seven days postpartum in preterm infants: an ultrasound-based assessment of ductus arteriosus intimal thickness within 24†h after birth.

Objectives: To develop a predictive model for patent ductus arteriosus (PDA) in preterm infants at seven days postpartum. The model employs ultrasound measurements of the ductus arteriosus (DA) intimal thickness (IT) obtained within 24 h after birth. Methods: One hundred and five preterm infants with gestational ages ranging from 27.0 to 36.7 weeks admitted within 24 h following birth were prospectively enrolled. Echocardiographic assessments were performed to measure DA IT within 24 h after birth, and DA status was evaluated through echocardiography on the seventh day postpartum. Potential predictors were considered, including traditional clinical risk factors, M-mode ultrasound parameters, lumen diameter of the DA (LD), and DA flow metrics. A final prediction model was formulated through bidirectional stepwise regression analysis and subsequently subjected to internal validation. The model's discriminative ability, calibration, and clinical applicability were also assessed. Results: The final predictive model included birth weight, application of mechanical ventilation, left ventricular end-diastolic diameter (LVEDd), LD, and the logarithm of IT (logIT). The receiver operating characteristic (ROC) curve for the model, predicated on logIT, exhibited excellent discriminative power with an area under the curve (AUC) of 0.985 (95% CI: 0.966-1.000), sensitivity of 1.000, and specificity of 0.909. Moreover, the model demonstrated robust calibration and goodness-of-fit (χ2 value = 0.560, p > 0.05), as well as strong reproducibility (accuracy: 0.935, Kappa: 0.773), as evidenced by 10-fold cross-validation. A decision curve analysis confirmed the model's broad clinical utility. Conclusions: Our study successfully establishes a predictive model for PDA in preterm infants at seven days postpartum, leveraging the measurement of DA IT. This model enables identifying, within the first 24 h of life, infants who are likely to benefit from timely DA closure, thereby informing treatment decisions.

Correlation between epicardial adipose tissue and myocardial injury in patients with COVID-19.

Background: Many people infected with COVID-19 develop myocardial injury. Epicardial adipose tissue (EAT) is among the various risk factors contributing to coronary artery disease. However, its correlation with myocardial injury in patients diagnosed with COVID-19 remains uncertain. Methods: We examined myocardial biomarkers in population affected by COVID-19 during the period from December 2022 to January 2023. The patients without myocardial injury were referred to as group A (n = 152) and those with myocardial injury were referred to as group B (n = 212). Results: 1) The A group and the B group exhibitedstatistically significant differences in terms of age, TC, CRP, Cr, BUN, LDL-C, IL-6, BNP, LVEF and EAT (p < 0.05). 2) EAT volumehad a close relationship with IL-6, LDL-C, cTnI, and CRP (p < 0.05); the corresponding correlation coefficient values were 0.24, 0.21, 0.24, and 0.16. In contrast to those with lower EAT volume, more subjects with a higher volume of EAT had myocardial injury (p < 0.05). Regression analysis showed that EAT, LDL-C, Age and Cr were established as independent risk variables for myocardial injury in subjects affected by COVID-19. 3) In COVID-19 patients, the likelihood of myocardial injury rised notably as EAT levels increase (p < 0.001). Addition of EAT to the basic risk model for myocardial injury resulted in improved reclassification. (Net reclassification index: 58.17%, 95% CI: 38.35%, 77.99%, p < 0.001). Conclusion: Patients suffering from COVID-19 with higher volume EAT was prone to follow myocardial injury and EAT was an independent predictor of heart damage in these individuals.

Human umbilical cord mesenchymal stem cells promote steroid-induced osteonecrosis of the femoral head repair by improving microvascular endothelial cell function.

Recently, there has been growing interest in using cell therapy through core decompression (CD) to treat osteonecrosis of the femoral head (ONFH). Our study aimed to investigate the effectiveness and mechanism of human umbilical cord mesenchymal stem cells (hUCMSCs) in treating steroid-induced ONFH. We constructed a steroid-induced ONFH rabbit model as well as dexamethasone (Dex)-treated bone microvascular endothelial cells (BMECs) model of human femoral head. We injected hUCMSCs into the rabbit femoral head via CD. The effects of hUCMSCs on steroid-induced ONFH rabbit model and Dex-treated BMECs were evaluated via micro-CT, microangiography, histology, immunohistochemistry, wound healing, tube formation, and western blotting assay. Furthermore, we conducted single-cell RNA sequencing (scRNA-seq) to examine the characteristics of endothelial cells, the activation of signaling pathways, and inter-cellular communication in ONFH. Our data reveal that hUCMSCs improved the femoral head microstructure and bone repair and promoted angiogenesis in the steroid-induced ONFH rabbit model. Importantly, hUCMSCs improved the migration ability and angioplasty of Dex-treated BMECs by secreting COL6A2 to activate FAK/PI3K/AKT signaling pathway via integrin α1ß1.

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

Study on the Effect of Additives on the Performance of Cement-Based Composite Anti-Corrosion Coatings for Steel Bars in Prefabricated Construction.

The influence of polymer emulsion, pigment filler, and dispersant on the corrosion resistance of polymer cement-based composite anti-corrosion coatings were investigated in this study. Adhesion loss rate tests and electrochemical tests were conducted on samples. The research results show that optimal corrosion resistance can be achieved with a 45 wt% dosage of emulsion, a 6 wt% dosage of pigment filler, and a 0.30 wt% dosage of dispersant. The bonding properties of bare steel bars, epoxy-coated steel bars, and polymer cement-based composite anti-corrosion coated steel bars with grout were compared. The results show that the polymer cement-based composite anti-corrosion coating can enhance the bonding properties of the samples. Furthermore, the microscopic analysis was conducted on the samples. The results demonstrate that the appropriate addition of emulsion can fill internal pores of the coating, tightly bonding hydration products with unhydrated cement particles. Moreover, incorporating a suitable dosage of functional additives enhances the stability of the coating system and leads to a denser microstructure.

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model.

Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively capturing the nuanced relationships between samples and labels in the feature space, thereby mitigating this limitation. Simultaneously, we propose the U-like Hierarchical Residual Fusion Network (BioUMixer), a bespoke biomass prediction network tailored for image data. BioUMixer enhances feature extraction from biomass image data, facilitating information exchange and fusion while considering both global and local features within the images. The efficacy of the proposed method is validated on the Pepper_Biomass dataset, which encompasses over 600 original images paired with corresponding biomass labels. The results demonstrate a noteworthy enhancement in deep regression tasks, as evidenced by performance metrics on the Pepper_Biomass dataset, including RMSE = 252.18, MAE = 201.98, and MAPE = 0.107. Additionally, assessment on the publicly accessible GrassClover dataset yields metrics of RMSE = 47.92, MAE = 31.74, and MAPE = 0.192. This study not only introduces a novel approach but also provides compelling empirical evidence supporting the digitization and precision improvement of agricultural technology. The research outcomes align closely with the identified problem and research statement, underscoring the significance of the proposed methodologies in advancing the field of biomass prediction through state-of-the-art deep learning techniques.

Alien flora are accumulating steadily in China over the last 80 years.

New alien species are increasingly introduced and established outside their native range. The knowledge of the spatiotemporal dynamics of their accumulation and the factors determining their residence time in the introduced range is critical for proactive management, especially in emerging economies. Based on a comprehensive time series dataset of 721 alien angiosperms in China, we show that new alien flora has been accumulating steadily in China, particularly in the coastal regions, for the last 80 years without saturation. The ability to occupy a large number of habitats facilitates the early introduction of alien flora, whereas a large naturalized range, greater number of uses, and multiple introduction pathways directly contribute to their naturalization and invasion. The temporal pattern is predicted to remain consistent in the foreseeable future. We propose upgrading the country's biosecurity infrastructure based on a standardized risk assessment framework to safeguard the country from ongoing and future invasions.

Rethinking the potential role of dose painting in personalized ultra-fractionated stereotactic adaptive radiotherapy.

Fractionated radiotherapy was established in the 1920s based upon two principles: (1) delivering daily treatments of equal quantity, unless the clinical situation requires adjustment, and (2) defining a specific treatment period to deliver a total dosage. Modern fractionated radiotherapy continues to adhere to these century-old principles, despite significant advancements in our understanding of radiobiology. At UT Southwestern, we are exploring a novel treatment approach called PULSAR (Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy). This method involves administering tumoricidal doses in a pulse mode with extended intervals, typically spanning weeks or even a month. Extended intervals permit substantial recovery of normal tissues and afford the tumor and tumor microenvironment ample time to undergo significant changes, enabling more meaningful adaptation in response to the evolving characteristics of the tumor. The notion of dose painting in the realm of radiation therapy has long been a subject of contention. The debate primarily revolves around its clinical effectiveness and optimal methods of implementation. In this perspective, we discuss two facets concerning the potential integration of dose painting with PULSAR, along with several practical considerations. If successful, the combination of the two may not only provide another level of personal adaptation ("adaptive dose painting"), but also contribute to the establishment of a timely feedback loop throughout the treatment process. To substantiate our perspective, we conducted a fundamental modeling study focusing on PET-guided dose painting, incorporating tumor heterogeneity and tumor control probability (TCP).

Efficient reduction of vanadium (V) with biochar and experimental parameters optimized by response surface methodology.

Water pollution deteriorates ecosystems and has a great threaten to the environment. The environmental benefits of wastewater treatment are extremely important to minimize pollutants. Here, the biochar purchased from the related industry was used to treat the wastewater which contained high concentration of vanadium (V). The concentration of vanadium was measured by the IC-OES and the results showed that 96.1% vanadium (V) was reduced at selected reaction conditions: the mass ratio of biochar to vanadium of 5.4, reaction temperature of 90 °C, reaction time at 60 min and concentration of H2SO4 of 10 g/L, respectively. Response surface methodology confirmed that all the experimental parameters had positive effect on the reduction of vanadium (V), which could improve the reduction efficiency of vanadium (V) as increased. The influence of each parameter on the reduction process followed the order: A (Concentration of H2SO4) > C (mass ratio of biochar to vanadium) > B (mass ratio of biochar to vanadium). Especially, the mass ratio of biochar to vanadium and concentration of H2SO4 had the greatest influence on the reduction process. This paper provides a versatile strategy for the treatment of wastewater containing vanadium (V) and shows a bright tomorrow for wastewater treatment.

DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation.

Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.

Remnant cholesterol and risk of major adverse cardiovascular events: a systematic review and dose-response meta-analysis of cohort studies.

Emerging evidence indicates a significant role of remnant cholesterol in contributing to the residual risk associated with major adverse cardiovascular events (MACE). This study aims to evaluate the dose-response relationship between remnant cholesterol and the risk of MACE. PubMed, Embase and Cochrane databases were reviewed to identify cohort studies published in English up to 1 August 2023. Twenty-eight articles were selected. Pooled hazard ratios (HR) and their 95% confidence intervals (CIs) were calculated using fixed or random-effects models to evaluate the association between remnant cholesterol and the risk of MACE. The dose-response relationship between remnant cholesterol levels and the risk of MACE was analyzed using the linear model and restricted cubic spline regression models. For calculated remnant cholesterol levels, the pooled HR (95% CI) of MACE for per 1-SD increase was 1.13 (1.08, 1.17); HR (95% CI) for the second quartile (Q2), the third quartile (Q3) and the highest quartile (Q4) of remnant cholesterol levels were 1.14 (1.03, 1.25), 1.43 (1.23, 1.68) and 1.68 (1.44, 1.97), respectively, compared with the lowest quartile (Q1). For measured remnant cholesterol levels, the pooled HR (95% CI) of MACE per 1-SD increase was 1.67 (1.39, 2.01). The dose-response meta-analysis showed a dose-response relationship between remnant cholesterol levels and the risk of MACE, both on a linear trend (P < 0.0001) and a nonlinear trend (P < 0.0001). The risk of MACE is associated with increased levels of remnant cholesterol, and the dose-response relationship between remnant cholesterol levels and the risk of MACE showed both linear and nonlinear trends.

An AI-based approach for modeling the synergy between radiotherapy and immunotherapy.

Personalized, ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is designed to administer tumoricidal doses in a pulsed mode with extended intervals, spanning weeks or months. This approach leverages longer intervals to adapt the treatment plan based on tumor changes and enhance immune-modulated effects. In this investigation, we seek to elucidate the potential synergy between combined PULSAR and PD-L1 blockade immunotherapy using experimental data from a Lewis Lung Carcinoma (LLC) syngeneic murine cancer model. Employing a long short-term memory (LSTM) recurrent neural network (RNN) model, we simulated the treatment response by treating irradiation and anti-PD-L1 as external stimuli occurring in a temporal sequence. Our findings demonstrate that: (1) The model can simulate tumor growth by integrating various parameters such as timing and dose, and (2) The model provides mechanistic interpretations of a "causal relationship" in combined treatment, offering a completely novel perspective. The model can be utilized for in-silico modeling, facilitating exploration of innovative treatment combinations to optimize therapeutic outcomes. Advanced modeling techniques, coupled with additional efforts in biomarker identification, may deepen our understanding of the biological mechanisms underlying the combined treatment.

Twenty-four-hour blood pressure trajectories and clinical outcomes in patients who had an acute ischaemic stroke.

OBJECTIVE: The management of blood pressure (BP) in acute ischaemic stroke remains a subject of controversy. This investigation aimed to explore the relationship between 24-hour BP patterns following ischaemic stroke and clinical outcomes. METHODS: A cohort of 4069 patients who had an acute ischaemic stroke from 26 hospitals was examined. Five systolic BP trajectories were identified by using latent mixture modelling: trajectory category 5 (190-170 mm Hg), trajectory category 4 (180-140 mm Hg), trajectory category 3 (170-160 mm Hg), trajectory category 2 (155-145 mm Hg) and trajectory category 1 (150-130 mm Hg). The primary outcome was a composite outcome of death and major disability at 3 months poststroke. RESULTS: Patients with trajectory category 5 exhibited the highest risk, while those with trajectory category 1 had the lowest risk of adverse outcomes at 3-month follow-up. Compared with the patients in the trajectory category 5, adjusted ORs (95% CIs) for the primary outcome were 0.79 (0.58 to 1.10), 0.70 (0.53 to 0.93), 0.64 (0.47 to 0.86) and 0.47 (0.33 to 0.66) among patients in trajectory category 4, trajectory category 3, trajectory category 2 and trajectory category 1, respectively. Similar trends were observed for death, vascular events and the composite outcome of death and vascular events. CONCLUSION: Patients with persistently high BP at 180 mm Hg within 24 hours of ischaemic stroke onset had the highest risk, while those maintaining stable BP at a moderate-low level (150 mm Hg) or even a low level (137 mm Hg) had more favourable outcomes.

Reinforced GNNs for Multiple Instance Learning.

Multiple instance learning (MIL) trains models from bags of instances, where each bag contains multiple instances, and only bag-level labels are available for supervision. The application of graph neural networks (GNNs) in capturing intrabag topology effectively improves MIL. Existing GNNs usually require filtering low-confidence edges among instances and adapting graph neural architectures to new bag structures. However, such asynchronous adjustments to structure and architecture are tedious and ignore their correlations. To tackle these issues, we propose a reinforced GNN framework for MIL (RGMIL), pioneering the exploitation of multiagent deep reinforcement learning (MADRL) in MIL tasks. MADRL enables the flexible definition or extension of factors that influence bag graphs or GNNs and provides synchronous control over them. Moreover, MADRL explores structure-to-architecture correlations while automating adjustments. Experimental results on multiple MIL datasets demonstrate that RGMIL achieves the best performance with excellent explainability. The code and data are available at https://github.com/RingBDStack/RGMIL.

Protocol for quantitative evaluation of misfolded protein degradation using Agrobacterium-mediated expression system in Nicotiana benthamiana.

Cellular protein homeostasis is maintained by the disposal of aggregated misfolded proteins. Here, we present a protocol for investigating the involvement of the proteins of interest in misfolded protein degradation via Agrobacterium-mediated transient expression in Nicotiana benthamiana. We describe in detail the steps of misfolded protein design, transient protein expression in N. benthamiana, subsequent total protein extraction, and quantification of misfolded proteins through western blotting. This generalizable system can be used for misfolded proteins derived from various plants or microbes. For complete details on the use and execution of this protocol, please refer to Ai et al.1.

Fatty acid-binding protein 5 is a functional biomarker and indicator of ferroptosis in cerebral hypoxia.

The progression of human degenerative and hypoxic/ischemic diseases is accompanied by widespread cell death. One death process linking iron-catalyzed reactive species with lipid peroxidation is ferroptosis, which shows hallmarks of both programmed and necrotic death in vitro. While evidence of ferroptosis in neurodegenerative disease is indicated by iron accumulation and involvement of lipids, a stable marker for ferroptosis has not been identified. Its prevalence is thus undetermined in human pathophysiology, impeding recognition of disease areas and clinical investigations with candidate drugs. Here, we identified ferroptosis marker antigens by analyzing surface protein dynamics and discovered a single protein, Fatty Acid-Binding Protein 5 (FABP5), which was stabilized at the cell surface and specifically elevated in ferroptotic cell death. Ectopic expression and lipidomics assays demonstrated that FABP5 drives redistribution of redox-sensitive lipids and ferroptosis sensitivity in a positive-feedback loop, indicating a role as a functional biomarker. Notably, immunodetection of FABP5 in mouse stroke penumbra and in hypoxic postmortem patients was distinctly associated with hypoxically damaged neurons. Retrospective cell death characterized here by the novel ferroptosis biomarker FABP5 thus provides first evidence for a long-hypothesized intrinsic ferroptosis in hypoxia and inaugurates a means for pathological detection of ferroptosis in tissue.

Clinical efficiency of three-port inflatable robot-assisted thoracoscopic surgery in mediastinal tumor resection.

BACKGROUND: Aimed to assess clinical effect of three-port inflatable robot-assisted thoracoscopic surgery in mediastinal tumor resection by comparing results of the robot group with the video group. METHODS: Retrospectively analyze 179 patients diagnosed with anterior mediastinal tumor from May 2017 to August 2021. Two groups were divided according to the surgical approach, including 92 cases in the RATS group and 87 cases in the VATS group. The results were analyzed between two groups with variables of age, sex, BMI, tumor size, and diagnosis. Perioperative clinical data was gathered to compare. RESULT: There were no significant differences between the 2 groups with regards to demographic data and clinical features. There were no significant differences inoperative time and duration of chest tube via RATS vs. VATS. The intraoperative blood loss was statistically significantly different among the RATS and VATS groups (75.9 ± 39.6 vs. 97.4 ± 35.8 ml p = 0.042). The postoperative stay of patients in RATS group were significantly shorter than that in VATS group (2.3 ± 1.0 vs. 3.4 ± 1.4 day p = 0.035), CONCLUSION: Three-port inflatable robot-assisted thoracoscopic surgery for mediastinal tumor is feasible and reliable it is more advantageous, and it provides the surgeon with advice on treatment choice.

Early and mid-term outcomes of open thoracoabdominal aortic aneurysm repair after thoracic endovascular aortic repair.

OBJECTIVE: To evaluate the early and mid-term outcomes of open repair in patients with thoracoabdominal aortic aneurysm (TAAA) after thoracic endovascular aortic repair (TEVAR). METHODS: This was a retrospective single center study. Data were retrospectively collected and analyzed for consecutive patients undergoing open TAAA repair (TAAAR) after TEVAR from November 2016 to June 2021. Indications for TAAAR included aneurysm progression due to endoleak, persisted false lumen perfusion, proximal/distal disease progression, and aorta rupture. The risk factor of operative mortality was analyzed by multivariable logistic regression model and the survival was evaluated by Kaplan-Meier. RESULTS: Sixty-three patients who met the inclusion criteria for the study were identified. The mean age at TAAAR was 41 ± 12 years and 43 (68.3%) were male. Marfan syndrome (MFS) was presented in 39 patients (61.9%). 60 (95.2%) patients presented with post-dissection aneurysm and 3 (4.8%) patients with degenerative aneurysm. The extent of TAAA was Crawford I in 9 (14.3%), II in 22 (34.9%), III in 23 (36.5%), and IV in 9 (14.3%). Emergent TAAAR was done in 10 (15.9%) patients, and deep hypothermic circulatory arrest was used in 22 (34.6%). Endograft was explanted in 31 (49.2%). Operative mortality was 11 (17.5%). Stroke, paraplegia, and acute kidney failure occurred in 5 (7.9%), 7 (11.1%), and 6 (9.5%) patients, respectively. Pulmonary complications occurred in 19 (30.2%) patients. The estimated survival was 74.8 ± 4.9% at 5 years. Late reoperations were performed in 2 patients at 2.5 years and 1.3 years, respectively. CONCLUSIONS: In this series of TAAA after TEVAR, TAAAR was related with a high risk of operative mortality and morbidity and the midterm outcomes represented a durable treatment and were respectable.

DeepKa Web Server: High-Throughput Protein pKa Prediction.

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Flexible microfluidic colorimetric detection chip integrated with ABTS·+ and Co@MnO2 nanozyme catalyzed TMB reaction systems for bio-enzyme free detection of sweat uric acid.

BACKGROUND: The development of wearable detection devices that can achieve noninvasive, on-site and real-time monitoring of sweat metabolites is of great demand and practical significance for point-of-care testing and healthcare monitoring. Monitoring uric acid (UA) content in sweat provides a simple and promising way to reduce the risk of gout and hyperuricemia. Traditional bioenzyme based UA assays suffer from high cost, poor stability, inconvenience for storage and easy deactivation of bioenzymes. Wearable microfluidic colorimetric detection device for sweat UA detection has not been reported. The development of novel wearable microfluidic colorimetric detection chip with no requirement of bioenzymes for sweat UA detection is of great importance for health care monitoring. RESULTS: Firstly, Co@MnO2 nanozyme with high oxidase-like activity was synthesized and characterized. Co@MnO2 can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) directly to generate blue-green colored ox-TMB. Green colored 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) radical (ABTS·+) was produced by the oxidation of ABTS by potassium persulfate. UA exhibits distinct quenching effect on Co@MnO2 catalyzed TMB colorimetric reaction system and ABTS·+ based colorimetric system, leading to obvious color fading of the two colorimetric systems. Then, a flexible microfluidic colorimetric detection chip for UA detection was fabricated by assembling Co@MnO2/TMB modified paper chips and ABTS·+ modified paper chips into a polydimethylsiloxane (PDMS) microfluidic chip. The fabricated microfluidic colorimetric detection chip exhibits good linear relationship for sweat UA detection. The linear range is from 20 to 200 µmol/L with detection limit as low as 6.6 µmol/L. Good results were obtained for the detection of UA in actual sweat from three volunteers. SIGNIFICANCE: This work provides two bio-enzyme free colorimetric detection systems for UA detection. Furthermore, a simple, low-cost and selective flexible wearable microfluidic colorimetric detection chip was fabricated for noninvasive and on-site detection of sweat UA, which holds great application potential for personal health monitoring and point-of-care testing.