The mechanisms underlying acute myocardial infarction in chronic kidney disease patients undergoing hemodialysis.

Cardiovascular disease (CVD) is a leading cause of death in chronic kidney disease (CKD). Hemodialysis is one of the main treatments for patients with end-stage kidney disease. Epidemiological data has shown that acute myocardial infarction (AMI) accounts for the main reason for death in patients with CKD under hemodialysis therapy. Immune dysfunction and changes in metabolism (including a high level of inflammatory cytokines, a disorder of lipid and mineral ion homeostasis, accumulation of uremic toxins et al.) during CKD can deteriorate stability of atherosclerotic plaque and promote vascular calcification, which are exactly the pathophysiological mechanisms underlying the occurrence of AMI. Meanwhile, the hemodialysis itself also has adverse effects on lipoprotein, the immune system and hemodynamics, which contribute to the high incidence of AMI in these patients. This review aims to summarize the mechanisms and further promising methods of prevention and treatment of AMI in CKD patients undergoing hemodialysis, which can provide an excellent paradigm for exploring the crosstalk between the kidney and cardiovascular system.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

New Strategies for Responding to SARS-CoV-2: The Present and Future of Dual-Target Drugs.

The 2019 coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in millions of deaths, posing a serious threat to public health and safety. Rapid mutations of SARS-CoV-2 and complex interactions among multiple targets during infection pose a risk of expiry for small molecule inhibitors. This suggests that the traditional concept of "one bug, one drug" could be ineffective in dealing with the coronavirus. The dual-target drug strategy is expected to be the key to ending coronavirus infections. However, the lack of design method and improper combination of dual-targets poses obstacle to the discovery of new dual-target drugs. In this Perspective, we summarized the profiles concerning drug design methods, structure-activity relationships, and pharmacological parameters of dual-target drugs for the treatment of COVID-19. Importantly, we underscored how target combination and rational drug design illuminate the development of dual-target drugs for SARS-CoV-2.

Cell-free DNA: a promising biomarker in infectious diseases.

Infectious diseases pose serious threats to public health worldwide. Conventional diagnostic methods for infectious diseases often exhibit low sensitivity, invasiveness, and long turnaround times. User-friendly point-of-care tests are urgently needed for early diagnosis, treatment monitoring, and prognostic prediction of infectious diseases. Cell-free DNA (cfDNA), a promising non-invasive biomarker widely used in oncology and pregnancy, has shown great potential in clinical applications for diagnosing infectious diseases. Here, we discuss the most recent cfDNA research on infectious diseases from both the pathogen and host perspectives. We also discuss the technical challenges in this field and propose solutions to overcome them. Additionally, we provide an outlook on the potential of cfDNA as a diagnostic, treatment, and prognostic marker for infectious diseases.

Leveraging Long-Distance Singlet-Oxygen Transfer for Bienzyme-Locked Afterglow Imaging of Intratumoral Granule Enzymes.

Dual-locked activatable optical probes, leveraging the orthogonal effects of two biomarkers, hold great promise for the specific imaging of biological processes. However, their design approaches are limited to a short-distance energy or charge transfer mechanism, while the signal readout relies on fluorescence, which inevitably suffers from tissue autofluorescence. Herein, we report a long-distance singlet oxygen transfer approach to develop a bienzyme-locked activatable afterglow probe (BAAP) that emits long-lasting self-luminescence without real-time light excitation for the dynamic imaging of an intratumoral granule enzyme. Composed of an immuno-biomarker-activatable singlet oxygen (1O2) donor and a cancer-biomarker-activatable 1O2 acceptor, BAAP is initially nonafterglow. Only in the presence of both immune and cancer biomarkers can 1O2 be generated by the activated donor and subsequently diffuse toward the activated acceptor, resulting in bright near-infrared afterglow with a high signal-to-background ratio and specificity toward an intratumoral granule enzyme. Thus, BAAP allows for real-time tracking of tumor-infiltrating cytotoxic T lymphocytes, enabling the evaluation of cancer immunotherapy and the differentiation of tumor from local inflammation with superb sensitivity and specificity, which are unachievable by single-locked probes. Thus, this study not only presents the first dual-locked afterglow probe but also proposes a new design way toward dual-locked probes via reactive oxygen species transfer processes.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients with Nasopharyngeal Carcinoma based on TabNet.

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models. This model is specifically designed to predict the survival outcomes of patients with nasopharyngeal carcinoma. The model utilizes TabNet's sequential attention mechanism to extract more interpretable features, providing an interpretable method for identifying disease risk factors. Consequently, the model ensures accurate survival prediction while also making the results more comprehensible for both patients and doctors. The paper tested the efficacy of the model by conducting experiments on various diverse datasets in comparison with other commonly used survival models. The results showed that the proposed model delivered the highest or second-highest accuracy across all datasets. Furthermore, the paper conducted a comparative interpretability analysis against the classical Cox model. In addition and compare the interpretability of the Tab-Cox model with the classical Cox model and discuss the advantages and disadvantages of its interpretability. This demonstrates that Tab-Cox can assist doctors in identifying risk factors that are challenging to capture using artificial methods.

[Application of three-dimensional ultrasound technique in repairing dorsal foot wounds with medial sural artery perforator flaps].

Objective: To investigate the accuracy of positioning perforator of medial sural artery with three-dimensional ultrasound technique guided by a wide band linear matrix array volume transducer probe before operation, and the effectiveness of the flap design based on this in repairing the dorsal foot wounds. Methods: Between January 2019 and December 2022, 30 patients with skin and soft tissue defects of the dorsal foot were treated. There were 19 males and 11 females, with an average age of 43.9 years (range, 22-63 years). There were 12 cases of traffic accident injury, 15 cases of heavy crushing injury, and 3 cases of machine injury. The time from injury to hospitalization was 1-8 hours (mean, 3.5 hours). The wounds in size of 5 cm×3 cm to 17 cm×5 cm were thorough debrided and covered with vacuum sealing drainage dressing. Then the wounds were repaired with the medial sural artery perforator flaps after no obvious infection observed. To obtain the complete three-dimensional image, the number and position of the medial sural artery perforator branches and the position of the main blood vessels in the muscle were detected and recorded by wide band linear matrix array volume transducer probe before operation. Suitable perforating branches were selected to design the flap and guide the flap incision on this basis. The size of the perforating flap ranged from 6 cm×4 cm to 18 cm×6 cm. The sensitivity and positive predictive value were calculated by comparing preoperative exploration with intraoperative observation of perforating branches, so as to evaluate the positioning accuracy of three-dimensional ultrasound technique. The donor sites were sutured directly in 25 cases and repaired with free skin grafting in 5 cases. Results: The 60 perforating branches of medial sural artery were found before operation and 58 during operation in 30 patients. Among them, pre- and intra-operative perforations were consistent with 56. The sensitivity was 93.3% and positive predictive value was 96.6%. The intramuscular position and route of the main blood vessels were basically consistent with the pre- and intra-operative observation. All flaps survived and wounds healed by first intention. All incisions at the donor sites healed by first intention, and all skin grafts survived. All patients were follow up 9-24 months (mean, 14.7 months). The appearance, color, and texture of the flaps were good, and no obvious effect on wearing shoes and walking. At last follow-up, the American Orthopaedic Foot and Ankle Society (AOFAS) ankle-hind score ranged from 80 to 92, with an average of 87.5. The patient satisfaction was excellent in 29 cases and good in 1 case. Conclusion: The three-dimensional ultrasound technique guided by the wide band linear matrix array volume transducer probe can accurately locate the perforating branch of the medial sural artery, and the three-dimensional imaging is more intuitive, which can be used to guide the design and incision of the medial sural artery perforator flap.

One-Dimensional Crystal-Structure Te-Se Alloy for Flexible Shortwave Infrared Photodetector and Imaging.

Flexible shortwave infrared detectors play a crucial role in wearable devices, bioimaging, automatic control, etc. Commercial shortwave infrared detectors face challenges in achieving flexibility due to the high fabrication temperature and rigid material properties. Herein, we develop a high-performance flexible Te0.7Se0.3 photodetector, resulting from the unique 1D crystal structure and small elastic modulus of Te-Se alloying. The flexible photodetector exhibits a broad-spectrum response ranging from 365 to 1650 nm, a fast response time of 6 µs, a broad linear dynamic range of 76 dB, and a specific detectivity of 4.8 × 1010 Jones at room temperature. The responsivity of the flexible detector remains at 93% of its initial value after bending with a small curvature of 3 mm. Based on the optimized flexible detector, we demonstrate its application in shortwave infrared imaging. These results showcase the great potential of Te0.7Se0.3 photodetectors for flexible electronics.

Bienzyme-Locked Activatable Fluorescent Probes for Specific Imaging of Tumor-Associated Mast Cells.

Tumor-associated mast cells (TAMCs) have been recently revealed to play a multifaceted role in the tumor microenvironment. Noninvasive optical imaging of TAMCs is thus highly desired to gain insights into their functions in cancer immunotherapy. However, due to the lack of a single enzyme that is specific to mast cells, a common probe design approach based on single-enzyme activation is not applicable. Herein, we reported a bienzyme-locked molecular probe (THCMC) based on a photoinduced electron transfer-intramolecular charge-transfer hybrid strategy for in vivo imaging of TAMCs. The bienzyme-locked activation mechanism ensures that THCMC exclusively turns on near-infrared (NIR) fluorescence only in the presence of both tryptase and chymase specifically coexpressed by mast cells. Thus, THCMC effectively distinguishes mast cells from other leukocytes, including T cells, neutrophils, and macrophages, a capability lacking in single-locked probes. Such a high specificity of THCMC allows noninvasive tracking of the fluctuation of TAMCs in the tumor of living mice during cancer immunotherapy. The results reveal that the decreased intratumoral signal of THCMC after combination immunotherapy correlates well with the reduced population of TAMCs, accurately predicting the inhibition of tumor growth. Thus, this study not only presents the first NIR fluorescent probe specific for TAMCs but also proposes a generic bienzyme-locked probe design approach for in vivo cell imaging.

Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU). METHODS AND RESULTS: We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH. CONCLUSION: The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

Ingestible Artificial Urinary Biomarker Probes for Urine Test of Gastrointestinal Cancer.

Although colorectal cancer diagnosed at an early stage shows high curability, methods simultaneously possessing point-of-care testing ability and high sensitivity are limited. Here, an orally deliverable biomarker-activatable probe (termed as HATS) for early detection of orthotopic tumors via remote urinalysis is presented. To enable its oral delivery to the colon, HATS is designed to have remarkable resistance to acidity and digestive enzymes in the stomach and small intestine and negligible intestinal absorption. Upon reaction with a cancer biomarker in the colon segment, HATS releases a small fragment of tetrazine that can transverse the intestinal barrier, enter blood circulation, and ultimately undergo renal clearance to urine. Subsequently, the urinary tetrazine fragment is detected by bioorthogonal reaction with trans-cyclooctene-caged resorufin (TCO-Reso) to afford a rapid and specific fluorescence enhancement of TCO-Reso. Such signal readout is correlated with the urinary tetrazine concentration and thus measures the level of cancer biomarkers in the colon. HATS-based optical urinalysis detects orthotopic colon tumors two weeks earlier than clinical serological tests and can be developed to a point-of-care paper test. Thereby, HATS-based urinalysis provides a non-invasive and sensitive approach to cancer screening at low-resource settings.

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

Defining and identifying cell sub-crosstalk pairs for characterizing cell-cell communication patterns.

Current cell-cell communication analysis focuses on quantifying intercellular interactions at cell type level. In the tissue microenvironment, one type of cells could be divided into multiple cell subgroups that function differently and communicate with other cell types or subgroups via different ligand-receptor-mediated signaling pathways. Given two cell types, we define a cell sub-crosstalk pair (CSCP) as a combination of two cell subgroups with strong and similar intercellular crosstalk signals and identify CSCPs based on coupled non-negative matrix factorization. Using single-cell spatial transcriptomics data of mouse olfactory bulb and visual cortex, we find that cells of different types within CSCPs are significantly spatially closer with each other than those in the whole single-cell spatial map. To demonstrate the utility of CSCPs, we apply 13 cell-cell communication analysis methods to sampled single-cell transcriptomics datasets at CSCP level and reveal ligand-receptor interactions masked at cell type level. Furthermore, by analyzing single-cell transcriptomics data from 29 breast cancer patients with different immunotherapy responses, we find that CSCPs are useful predictive features to discriminate patients responding to anti-PD-1 therapy from non-responders. Taken together, partitioning a cell type pair into CSCPs enables fine-grained characterization of cell-cell communication in tissue and tumor microenvironments.

Narrowband Fluorescent Emitters Based on BN-Doped Polycyclic Aromatic Hydrocarbons for Efficient and Stable Organic Light-Emitting Diodes.

Organic light-emitting diodes (OLEDs) using conventional fluorescent emitters are currently attracting considerable interests due to outstanding stability and abundant raw materials. To construct high-performance narrowband fluorophores to satisfy requirements of ultra-high-definition displays, a strategy fusing multi-resonance BN-doped moieties to naphthalene is proposed to construct two novel narrowband fluorophores. Green Na-sBN and red Na-dBN, manifest narrow full-width at half-maxima of 31 nm, near-unity photoluminescence quantum yields and molecular horizontal dipole ratios above 90 %. Their OLEDs exhibit the state-of-the-art performances including high external quantum efficiencies (EQE), ultra-low efficiency roll-off and long operational lifetimes. The Na-sBN-based device achieves EQE as high as 28.8 % and remains 19.8 % even at luminance of 100,000 cd m-2 , and Na-dBN-based device acquires a record-high EQE of 25.2 % among all red OLEDs using pure fluorescent emitters.

Pharmacological Effects of Botanical Drugs on Myocardial Metabolism in Chronic Heart Failure.

Although there have been significant advances in the treatment of heart failure in recent years, chronic heart failure remains a leading cause of cardiovascular disease-related death. Many studies have found that targeted cardiac metabolic remodeling has good potential for the treatment of heart failure. However, most of the drugs that increase cardiac energy are still in the theoretical or testing stage. Some research has found that botanical drugs not only increase myocardial energy metabolism through multiple targets but also have the potential to restore the balance of myocardial substrate metabolism. In this review, we summarized the mechanisms by which botanical drugs (the active ingredients/formulas/Chinese patent medicines) improve substrate utilization and promote myocardial energy metabolism by activating AMP-activated protein kinase (AMPK), peroxisome proliferator-activated receptors (PPARs) and other related targets. At the same time, some potential protective effects of botanical drugs on myocardium, such as alleviating oxidative stress and dysbiosis signaling, caused by metabolic disorders, were briefly discussed.

Nitrogen vacancy-induced spin polarization of ultrathin zinc porphyrin nanosheets for efficient photocatalytic CO2 reduction.

Metalloporphyrin compounds have excellent electron transfer and visible light absorption ability, demonstrating broad application prospects in the field of photocatalysis. In this work, the nitrogen vacancies (NVs) were successfully introduced into zinc porphyrin (ZnTCPP) ultrathin nanosheets through surface N2 plasma treatment, which is environmentally friendly and can react in low temperatures. Furthermore, the prepared nitrogen vacancies-zinc porphyrin (NVs-ZnTCPP) materials exhibited excellent photocatalytic CO2 reduction activity and selectivity, specifically, the CO production rate of ZnTCPP-1 (N2 plasma treatment, 1 min) achieved as high as 12.5 µmol g-1h-1, which is about 2.7 times greater than that of untreated ZnTCPP. Based on the experimental and density functional theory calculation (DFT) results, it is found that the promoted photocatalytic performance of NVs-ZnTCPP could be mainly attributed to nitrogen vacancy-induced spin polarization by reducing the reaction barriers and inhibiting the recombination of photoexcited carriers. This work provides a new perspective for the construction of vacancy-based metalloporphyrin, and further explores the intrinsic mechanism between the electron spin property and the performance of the photocatalyst.

RobustCCC: a robustness evaluation tool for cell-cell communication methods.

Cell-cell communication (CCC) inference has become a routine task in single-cell data analysis. Many computational tools are developed for this purpose. However, the robustness of existing CCC methods remains underexplored. We develop a user-friendly tool, RobustCCC, to facilitate the robustness evaluation of CCC methods with respect to three perspectives, including replicated data, transcriptomic data noise and prior knowledge noise. RobustCCC currently integrates 14 state-of-the-art CCC methods and 6 simulated single-cell transcriptomics datasets to generate robustness evaluation reports in tabular form for easy interpretation. We find that these methods exhibit substantially different robustness performances using different simulation datasets, implying a strong impact of the input data on resulting CCC patterns. In summary, RobustCCC represents a scalable tool that can easily integrate more CCC methods, more single-cell datasets from different species (e.g., mouse and human) to provide guidance in selecting methods for identification of consistent and stable CCC patterns in tissue microenvironments. RobustCCC is freely available at https://github.com/GaoLabXDU/RobustCCC.

Carbon dots-based fluorescence enhanced probe for the determination of glucose.

In this work, a novel "turn-on" fluorescence sensor for the detection of H2O2 and glucose was developed based on green fluorescent carbon dots (CDs). The CDs was newly prepared by a facile one-pot hydrothermal method with Eosin Y and branched polyethylenimine as precursors. Interestingly, in the presence of H2O2 and HRP, the fluorescence of the CDs enhanced significantly with a red-shift emission due to their "aggregation". Meanwhile, the oxidation of glucose catalyzed by glucose oxidase could generate H2O2. Thus, a simple sensing system based on the CDs as fluorescent probes was constructed for H2O2 and glucose determination, avoiding the fluorescence quenching and subsequent recovery process in conventional turn-on strategy. The method showed good selectivity and sensitivity for glucose sensing with the detection limit of 0.12 µM. The method was further applied to glucose detection in real samples. The obtained results demonstrated the simplicity, selectivity and practicality of the method. This work expands the carbon nanomaterials with fluorescence emission enhancement properties. It provides a new and direct "turn-on" strategy for H2O2 and glucose detection, which could be a simple and effective tool for screening biological substances involved in H2O2-generation reaction.