The Neurovascular Unit Dysfunction in the Molecular Mechanisms of Epileptogenesis and Targeted Therapy.

Epilepsy is a multifaceted neurological syndrome characterized by recurrent, spontaneous, and synchronous seizures. The pathogenesis of epilepsy, known as epileptogenesis, involves intricate changes in neurons, neuroglia, and endothelium, leading to structural and functional disorders within neurovascular units and culminating in the development of spontaneous epilepsy. Although current research on epilepsy treatments primarily centers around anti-seizure drugs, it is imperative to seek effective interventions capable of disrupting epileptogenesis. To this end, a comprehensive exploration of the changes and the molecular mechanisms underlying epileptogenesis holds the promise of identifying vital biomarkers for accurate diagnosis and potential therapeutic targets. Emphasizing early diagnosis and timely intervention is paramount, as it stands to significantly improve patient prognosis and alleviate the socioeconomic burden. In this review, we highlight the changes and molecular mechanisms of the neurovascular unit in epileptogenesis and provide a theoretical basis for identifying biomarkers and drug targets.

Pan-cancer analysis reveals IL32 is a potential prognostic and immunotherapeutic biomarker in cancer.

Interleukin 32 (IL32) is a pro-inflammatory cytokine that plays a key role in promoting sterile inflammation by modulating immune responses. However, the role of IL32 in various cancers remains unclear. This research aimed to investigate the correlation between IL32 expression and immunity and visualize its prognostic landscape in pan-cancer. We investigated gene expression, genomic alterations, and survival analysis of IL32 in pan-cancer in numerous databases including TCGA, GTEx, cBioPortal, and GDC databases. Tumor immune cell infiltration was assessed using the CIBERSORT computational method as well as the ESTIMATE method to analyze the correlation of IL32 expression with stromal and immune components. Protein-protein interaction analysis was performed in the STRING and GeneMANIA databases, and gene function enrichment was performed by GO set enrichment analysis. Tumor tissues had higher IL32 expression levels than normal tissues. Elevated IL32 expression was associated with poor OS and prognosis. In addition, tumor stemness, TMB, MSI, and immune checkpoint genes were also associated with IL32 expression. Correlations were observed between IL32 expression and B cell, CD4T cell, CD8T cell, neutrophil, macrophage, and DC infiltration in multiple cancers. GO enrichment analysis showed that IL32 expression was associated with cancer pathways, cytokine-receptor interactions, and NOD-like receptor signaling pathways. These findings suggest that IL32 may serve as a biomarker of cancer immune infiltration and poor prognosis, providing new therapeutic targets for cancer treatment.

16S rRNA seq-identified Corynebacterium promotes pyroptosis to aggravate diabetic foot ulcer.

BACKGROUND: Diabetic foot ulcer (DFU) is one of the main chronic complications caused by diabetes, leading to amputation in severe cases. Bacterial infection affects the wound healing in DFU. METHODS: DFU patients who met the criteria were selected, and the clinical data were recorded in detail. The pus exudate from the patient's foot wound and venous blood were collected for biochemical analysis. The distribution of bacterial flora in pus exudates of patients was analyzed by 16S rRNA sequencing, and the correlation between DFU and pathogenic variables, pyroptosis and immunity was analyzed by statistical analysis. Then, the effects of key bacteria on the inflammation, proliferation, apoptosis, and pyroptosis of polymorphonuclear leukocytes were investigated by ELISA, CCK-8, flow cytometry, RT-qPCR and western blot. RESULTS: Clinical data analysis showed that Wagner score was positively correlated with the level of inflammatory factors, and there was high CD3+, CD4+, and low CD8+ levels in DFU patients with high Wagner score. Through alpha, beta diversity analysis and species composition analysis, Corynebacterium accounted for a large proportion in DFU. Logistics regression model and Person correlation analysis demonstrated that mixed bacterial infections could aggravate foot ulcer, and the number of bacteria was closely related to inflammatory factors PCT, PRT, immune cells CD8+, and pyroptosis-related proteins GSDMD and NLRP3. Through in vitro experiments, Corynebacterium inhibited cell proliferation, promoted inflammation (TNF-α, PCT, CRP), apoptosis and pyroptosis (IL-1ß, LDH, IL-18, GSDMD, NLRP3, and caspase-3). CONCLUSION: Mixed bacterial infections exacerbate DFU progression with a high predominance of Corynebacterium, and Corynebacterium promotes inflammation, apoptosis and pyroptosis to inhibit DFU healing.

Pharmacogenomic profiling of intra-tumor heterogeneity using a large organoid biobank of liver cancer.

Inter- and intra-tumor heterogeneity is a major hurdle in primary liver cancer (PLC) precision therapy. Here, we establish a PLC biobank, consisting of 399 tumor organoids derived from 144 patients, which recapitulates histopathology and genomic landscape of parental tumors, and is reliable for drug sensitivity screening, as evidenced by both in vivo models and patient response. Integrative analysis dissects PLC heterogeneity, regarding genomic/transcriptomic characteristics and sensitivity to seven clinically relevant drugs, as well as clinical associations. Pharmacogenomic analysis identifies and validates multi-gene expression signatures predicting drug response for better patient stratification. Furthermore, we reveal c-Jun as a major mediator of lenvatinib resistance through JNK and ß-catenin signaling. A compound (PKUF-01) comprising moieties of lenvatinib and veratramine (c-Jun inhibitor) is synthesized and screened, exhibiting a marked synergistic effect. Together, our study characterizes the landscape of PLC heterogeneity, develops predictive biomarker panels, and identifies a lenvatinib-resistant mechanism for combination therapy.

Associations between type 2 diabetes mellitus and chronic liver diseases: evidence from a Mendelian ranldomization study in Europeans and East Asians.

Objective: Multiple observational studies have demonstrated an association between type 2 diabetes mellitus (T2DM) and chronic liver diseases (CLDs). However, the causality of T2DM on CLDs remained unknown in various ethnic groups. Methods: We obtained instrumental variables for T2DM and conducted a two-sample mendelian randomization (MR) study to examine the causal effect on nonalcoholic fatty liver disease (NAFLD), hepatocellular carcinoma (HCC), viral hepatitis, hepatitis B virus (HBV) infection, and hepatitis C virus (HCV) infection risk in Europeans and East Asians. The primary analysis utilized the inverse variance weighting (IVW) technique to evaluate the causal relationship between T2DM and CLDs. In addition, we conducted a series of rigorous analyses to bolster the reliability of our MR results. Results: In Europeans, we found that genetic liability to T2DM has been linked with increased risk of NAFLD (IVW : OR =1.3654, 95% confidence interval [CI], 1.2250-1.5219, p=1.85e-8), viral hepatitis (IVW : OR =1.1173, 95%CI, 1.0271-1.2154, p=0.0098), and a suggestive positive association between T2DM and HCC (IVW : OR=1.2671, 95%CI, 1.0471-1.5333, p=0.0150), HBV (IVW : OR=1.1908, 95% CI, 1.0368-1.3677, p=0.0134). No causal association between T2DM and HCV was discovered. Among East Asians, however, there was a significant inverse association between T2DM and the proxies of NAFLD (ALT: IVW OR=0.9752, 95%CI 0.9597-0.9909, p=0.0021; AST: IVW OR=0.9673, 95%CI, 0.9528-0.9821, p=1.67e-5), and HCV (IVW: OR=0.9289, 95%CI, 0.8852-0.9747, p=0.0027). Notably, no causal association was found between T2DM and HCC, viral hepatitis, or HBV. Conclusion: Our MR analysis revealed varying causal associations between T2DM and CLDs in East Asians and Europeans. Further research is required to investigate the potential mechanisms in various ethnic groups, which could yield new insights into early screening and prevention strategies for CLDs in T2DM patients.

Correction: Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.

[This corrects the article DOI: 10.2196/37881.].

Bioactive materials from berberine-treated human bone marrow mesenchymal stem cells promote alveolar bone regeneration by regulating macrophage polarization.

Alveolar bone regeneration has been strongly linked to macrophage polarization. M1 macrophages aggravate alveolar bone loss, whereas M2 macrophages reverse this process. Berberine (BBR), a natural alkaloid isolated and refined from Chinese medicinal plants, has shown therapeutic effects in treating metabolic disorders. In this study, we first discovered that culture supernatant (CS) collected from BBR-treated human bone marrow mesenchymal stem cells (HBMSCs) ameliorated periodontal alveolar bone loss. CS from the BBR-treated HBMSCs contained bioactive materials that suppressed the M1 polarization and induced the M2 polarization of macrophages in vivo and in vitro. To clarify the underlying mechanism, the bioactive materials were applied to different animal models. We discovered macrophage colony-stimulating factor (M-CSF), which regulates macrophage polarization and promotes bone formation, a key macromolecule in the CS. Injection of pure M-CSF attenuated experimental periodontal alveolar bone loss in rats. Colony-stimulating factor 1 receptor (CSF1R) inhibitor or anti-human M-CSF (M-CSF neutralizing antibody, Nab) abolished the therapeutic effects of the CS of BBR-treated HBMSCs. Moreover, AKT phosphorylation in macrophages was activated by the CS, and the AKT activator reversed the negative effect of the CSF1R inhibitor or Nab. These results suggest that the CS of BBR-treated HBMSCs modulates macrophage polarization via the M-CSF/AKT axis. Further studies also showed that CS of BBR-treated HBMSCs accelerated bone formation and M2 polarization in rat teeth extraction sockets. Overall, our findings established an essential role of BBR-treated HBMSCs CS and this might be the first report to show that the products of BBR-treated HBMSCs have active effects on alveolar bone regeneration.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model.

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.

Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer.

BACKGROUND: Deep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated. METHODS: A combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve. RESULTS: DL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation. CONCLUSIONS: DL-based model was established to accurately predict the survival rate of postoperative patients with GC.

Microfluidic-based preparation of artificial antigen-presenting gel droplets for integrated and minimalistic adoptive cell therapy strategies.

Adoptive T-cell transfer for cancer therapy is limited by the inefficiency ofin vitroT-cell expansion and the ability ofin vivoT-cells to infiltrate tumors. The construction of multifunctional artificial antigen-presenting cells is a promising but challenging approach to achieve this goal. In this study, a multifunctional artificial antigen-presenting gel droplet (AAPGD) was designed. Its surface provides regulated T-cell receptor (TCR) stimulation and co-stimulation signals and is capable of slow release of mitogenic cytokines and collagen mimetic peptide. The highly uniform AAPGD are generated by a facile method based on standard droplet microfluidic devices. The results of the study indicate that, T-cell proliferatedin vitroutilizing AAPGD have a fast rate and high activity. AAPGD increased the proportion ofin vitroproliferating T cells low differentiation and specificity. The starting number of AAPGDs and the quality ratio of TCR-stimulated and co-stimulated signals on the surface have a large impact on the rapid proliferation of low-differentiated T cellsin vitro. During reinfusion therapy, AAPGD also enhanced T-cell infiltration into the tumor site. In experiments using AAPGD for adoptive T cell therapy in melanoma mice, tumor growth was inhibited, eliciting a potent cytotoxic T-lymphocyte immune response and improving mouse survival. In conclusion, AAPGD promotes rapid low-differentiation proliferation of T cellsin vitroand enhances T cell infiltration of tumorsin vivo. It simplifies the preparation steps of adoptive cell therapy, improves the therapeutic effect, and provides a new pathway for overdosing T cells to treat solid tumors.

The hydrophobic cluster on the surface of protein is the key structural basis for the SDS-resistance of chondroitinase VhChlABC.

The application of chondroitinase requires consideration of the complex microenvironment of the target. Our previous research reported a marine-derived sodium dodecyl sulfate (SDS)-resistant chondroitinase VhChlABC. This study further investigated the mechanism of VhChlABC resistance to SDS. Focusing on the hydrophobic cluster on its strong hydrophilic surface, it was found that the reduction of hydrophobicity of surface residues Ala181, Met182, Met183, Ala184, Val185, and Ile305 significantly reduced the SDS resistance and stability. Molecular dynamics (MD) simulation and molecular docking analysis showed that I305G had more conformational flexibility around residue 305 than wild type (WT), which was more conducive to SDS insertion and binding. The affinity of A181G, M182A, M183A, V185A and I305G to SDS was significantly higher than that of WT. In conclusion, the surface hydrophobic microenvironment composed of six residues was the structural basis for SDS resistance. This feature could prevent the binding of SDS and the destruction of hydrophobic packaging by increasing the rigid conformation of protein and reducing the binding force of SDS-protein. The study provides a new idea for the rational design of SDS-resistant proteins and may further promote chondroitinase research in the targeted therapy of lung diseases under the pressure of pulmonary surfactant. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-023-00201-1.

[Effect of high selenium on insulin signaling pathway PI3K-AKT-mTOR in L02 cells].

OBJECTIVE: To observe the effect of high selenium on insulin signaling pathway PI3K-AKT-mTOR in L02 cells. METHODS: One group of L02 cell was treated with different concentrations of selenomethionine(SeMet, 0.001, 0.0025, 0.005, 0.0075, 0.01, 0.025, 0.05, 0.075 and 0.1µmol/L) for 48 h, then cultured with serum-free medium for 4 h and stimulated with 1 µmol/L insulin for 15 min. The insulin signaling pathway(PI3K-AKT-mTOR) was detected by WB. Another group of L02 cell was treated with the same concentrations of SeMet as above for 48 h. The cell supernatant and lysates were collected for the analysis of SELENOP and GPX1, respectively by WB. RESULTS: The expressions of P-AKT-(Ser-473), P-AKT-(Thr-308), PI3K and mTOR in L02 cells under high-Se were decreased with the increase of SeMet concentration. The expressions of GPX1 and SELENOP were enhanced with the increase of SeMet. CONCLUSION: The insulin signaling pathway, PI3K-AKT-mTOR, was damaged in L02 cell under high-Se stress.

Enzymatic Synthesis of TNA Protects DNA Nanostructures.

Xeno-nucleic acids (XNAs) are synthetic genetic polymers with improved biological stabilities and offer powerful molecular tools such as aptamers and catalysts. However, XNA application has been hindered by a very limited repertoire of tool enzymes, particularly those that enable de novo XNA synthesis. Here we report that terminal deoxynucleotide transferase (TdT) catalyzes untemplated threose nucleic acid (TNA) synthesis at the 3' terminus of DNA oligonucleotide, resulting in DNA-TNA chimera resistant to exonuclease digestion. Moreover, TdT-catalyzed TNA extension supports one-pot batch preparation of biostable chimeric oligonucleotides, which can be used directly as staple strands during self-assembly of DNA origami nanostructures (DONs). Such TNA-protected DONs show enhanced biological stability in the presence of exonuclease I, DNase I and fetal bovine serum. This work not only expands the available enzyme toolbox for XNA synthesis and manipulation, but also provides a promising approach to fabricate DONs with improved stability under the physiological condition.

Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits.

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure sensors. However, existing methods for enhancing stress tolerance employ dome-shaped, wrinkle-shaped, and pyramidal-shaped microstructures in intricate molding and demolding processes, which introduce significant fabrication challenges and limit the sensing performance. To address these shortcomings, this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa-1. The periodic microslits permit extensive deformation under high pressure (e.g., 400 kPa) to widen the detection range. Moreover, the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing cells. The proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection, robot movement sensing, and human health monitoring. In these experiments, vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%. The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

Research on optimization method of railway construction scheme based on multidimensional combination weighting and improved grey theory.

The optimization of railway construction schemes is a complexity system engineering task with multiple dimensions, diverse conditional constraints, and multifaceted objective assessments. The decision-making and scheme evaluation entail subjectivity, randomness, and fuzziness. To address the comprehensive optimization challenge in construction schemes effectively and efficiently, we investigate an optimization method for railway construction schemes. This method is based on multi-dimensional combination weighting and improved grey theory. After analyzing the primary influencing factors, we established a railway construction plan optimization index system comprising 4 dimensions and 18 factors. The weight combination coefficient is determined using the pros and cons solution distance method, and the optimal weight set for the index is determined through the multi-dimensional combination weighting approach. Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar. The verification results indicate that the gray relative closeness values for schemes A, B, and C are 0.7089, 0.4813, and 0.4463, respectively. Scheme A has the highest gray relative closeness value, thus making it the optimal route scheme. The optimal results obtained through this method align with the outcomes of expert validation and existing research, thereby validating the effectiveness and practicality of the model. By employing a multidimensional combination weighting method, the deficiencies of traditional indicator weight calculations are mitigated, resulting in indicator weights that are more reflective of the actual circumstances. At the same time, the application of improvements in the grey theory comprehensive evaluation method enables the integration and computation of indicator data for each construction plan. Through the intuitive representation of grey relative closeness, the advantages and disadvantages of each plan are effectively characterized. This enhances the scientific rigor and applicability of the railway construction plan optimization process. The research findings can serve as a reference for similar railway construction scheme selection problems in the future.

High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit.

Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.

Towards Inductive and Efficient Explanations for Graph Neural Networks.

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging and nascent problem. The leading method mainly considers the local explanations, i.e., important subgraph structure and node features, to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph. As a result, the explanation generated is painstakingly customized at the instance level. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to the lack of generalizability and hindering it from being used in the inductive setting. Besides, training the explanation model explaining for each instance is time-consuming for large-scale real-life datasets. In this study, we address these key challenges and propose PGExplainer, a parameterized explainer for GNNs. PGExplainer adopts a deep neural network to parameterize the generation process of explanations, which renders PGExplainer a natural approach to multi-instance explanations. Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting without training the model for new instances. Thus, PGExplainer is much more efficient than the leading method with significant speed-up. In addition, the explanation networks can also be utilized as a regularizer to improve the generalization power of existing GNNs when jointly trained with downstream tasks. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification over the leading baseline.

Computed tomography-guided percutaneous cryoablation and microwave ablation in the treatment of perivascular hepatocellular carcinoma: A comparative study with propensity score matching.

OBJECTIVE: To evaluate the safety and efficacy of cryoablation (CYA) and microwave ablation (MWA) in the treatment of patients with perivascular hepatocellular carcinoma (HCC). METHODS: Patients with perivascular HCC who underwent computed tomography (CT)-guided percutaneous CYA or MVA treatment in our hospital from August 2009 to March 2019 were included. Propensity score matching (PSM) was performed to adjust for potential baseline differences in the two groups. The technical success rate (TS), complications, and visual analog scale (VAS) were analyzed. The overall survival (OS) was evaluated using Kaplan-Meier curves and Cox proportional hazards models. RESULTS: After PSM, 32 patients from each group were selected. The technical success rate was 94 % for CYA and 91 % for MWA, and 13 patients developed recurrence (CYA, n = 5, 2 local, 3 distant; MWA, n = 8, 6 local, 2 distant). There were no significant differences in OS (36-months OS: CYA 53.1 % vs, MWA 40.6 %; P = 0.191). No intraoperative deaths or complication-related deaths were observed, and 19 patients (CYA, n = 8; MWA, n = 11) experienced complications (P = 0.435). The VAS in the MWA group (5.38 ± 1.21) was significantly higher than that in the CYA group (2.22 ± 0.87; P < 0.001). CONCLUSIONS: While CYA has equal safety and high primary efficacy as MWA in the treatment of perivascular HCC, it is associated with less periprocedural pain.

Development of an activatable far-red fluorescent probe for rapid visualization of hypochlorous acid in live cells and mice with neuroinflammation.

Recent investigations have suggested that abnormally elevated levels of HOCl may be tightly related to the severity of neuroinflammation. Although some successes have been achieved, fluorescent probes with far-red fluorescence emission and capable of detecting HOCl with high specificity in pure aqueous solution are still urgently needed. Herein, a responsive far-red fluorescent probe, DCI-H, has been constructed to monitor HOCl activity in vivo and in vitro. DCI-H could rapidly respond to HOCl within 120 s and had a low detection limit for HOCl of 1.5 nM. Importantly, physiologically common interfering species, except for HOCl, did not cause a change in the fluorescence intensity of DCI-HOCl at 655 nm. The results of confocal imaging demonstrated the ability of DCI-H to visualize endogenous HOCl produced by MPO-catalyzed H2O2/Cl- and LPS stimulation. With the assistance of DCI-H, upregulation of HOCl levels was observed in the mice model of LPS-induced neuroinflammation. Thus, we believed that DCI-H provided a valuable tool for HOCl detection and diagnosis of inflammation-related diseases.