Nonswellable Hydrogel Patch with Tissue-Mimetic Mechanical Characteristics Remodeling In Vivo Microenvironment for Effective Adhesion Prevention.

Postoperative adhesion is a common complication after abdominal surgery, but current clinical products have unsatisfactory therapeutic effects. Here, we present a hydrogel patch formed in a single step through dialysis. The exchange of DMSO into water facilitates hydrophobic aggregate in situ formation and the formation of hydrogen bonds within the hydrogel. Thanks to the optimized component ratio and precise structural design. The hydrogel patch has soft-tissue-like mechanical characteristics, including high strength, high toughness, low modulus similar to the abdominal wall, good fatigue resistance, and fast self-recovery properties. The nonswellable hydrogel patch retains over 80% of its original mechanical properties after 7 days of immersion in physiological saline, with a maximum swelling ratio of 5.6%. Moreover, the hydrophobic biomultifunctionality of benzyl isothiocyanate can self-assemble onto the hydrogel patch during the sol-gel transition process, enabling it to remodel the inflammatory microenvironment through synergistic antibacterial, antioxidant, and anti-inflammatory effects. The hydrogel patch prevents postsurgical adhesion in a rat sidewall defect-cecum abrasion model and outperforms the leading commercial Interceed. It holds promising potential for clinical translation, considering that FDA-approved raw materials (PVA and gelatin) form the backbone of this effective hydrogel patch.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Lactobacillus rhamnosus GG ameliorates triptolide-induced liver injury through modulation of the bile acid-FXR axis.

Triptolide (TP) is the principal bioactive compound of Tripterygium wilfordii with significant anti-tumor, anti-inflammatory and immunosuppressive activities. However, its severe hepatotoxicity greatly limits its clinical use. The underlying mechanism of TP-induced liver damage is still poorly understood. Here, we estimate the role of the gut microbiota in TP hepatotoxicity and investigate the bile acid metabolism mechanisms involved. The results of the antibiotic cocktail (ABX) and fecal microbiota transplantation (FMT) experiment demonstrate the involvement of intestinal flora in TP hepatotoxicity. Moreover, TP treatment significantly perturbed gut microbial composition and reduced the relative abundances of Lactobacillus rhamnosus GG (LGG). Supplementation with LGG reversed TP-induced hepatotoxicity by increasing bile salt hydrolase (BSH) activity and reducing the increased conjugated bile acids (BA). LGG supplementation upregulates hepatic FXR expression and inhibits NLRP3 inflammasome activation in TP-treated mice. In summary, this study found that gut microbiota is involved in TP hepatotoxicity. LGG supplementation protects mice against TP-induced liver damage. The underlying mechanism was associated with the gut microbiota-BA-FXR axis. Therefore, LGG holds the potential to prevent and treat TP hepatotoxicity in the clinic.

Cooperative Rh/Achiral Phosphoric Acid-Enabled [3+3] Cycloannulation of Carbonyl Ylides with Quinone Monoimines: Synthesis of Benzofused Dioxabicyclo[3.2.1]octane Scaffolds.

A cooperative Rh/achiral phosphoric acid-enabled [3+3] cycloaddition of in situ-generated carbonyl ylides with quinone monoimines has been developed. With the ability to build up the molecular complexity rapidly and efficiently, this method furnishes highly functionalized oxa-bridged benzofused dioxabicyclo[3.2.1]octane scaffolds bearing two quaternary centers in good to excellent yields under mild conditions. Moreover, the utility of the current method was demonstrated by gram-scale synthesis and elaboration of the products into various functionalized oxa-bridged heterocycles.

Nickel-catalyzed hydrodefluorination/deuterodefluorination of CF3-alkenes with formic acid.

The synthesis of deuterated gem-difluoroalkenes via selective deuterodefluorination of ß-CF3-cinnamates using a nickel catalyst has been reported for the first time. Commercially available deuterated formic acid is a cheap and convenient deuterium source. The nickel-catalyst showed high selectivity for monodefluorination and avoided competitive reactions such as multiple defluorination or hydrogenation.

Sources and applications of endothelial seed cells: a review.

Endothelial cells (ECs) are widely used as donor cells in tissue engineering, organoid vascularization, and in vitro microvascular model development. ECs are invaluable tools for disease modeling and drug screening in fundamental research. When treating ischemic diseases, EC engraftment facilitates the restoration of damaged blood vessels, enhancing therapeutic outcomes. This article presents a comprehensive overview of the current sources of ECs, which encompass stem/progenitor cells, primary ECs, cell lineage conversion, and ECs derived from other cellular sources, provides insights into their characteristics, potential applications, discusses challenges, and explores strategies to mitigate these issues. The primary aim is to serve as a reference for selecting suitable EC sources for preclinical research and promote the translation of basic research into clinical applications.

Time-dependent effects of high-fat diet on cognition and cerebral insulin signaling: Window for recovery and potential therapeutic target.

While high-fat diet (HFD)-induced obesity is a major threat to global public health, the effect of HFD on cognition and insulin signaling during ageing remains controversial. The aim of this study was to characterize the dynamic alterations in cognition and cerebral insulin signaling during 6-month HFD consumption, and to investigate the potential therapeutic target and optimal timing to rescue obesity-related cognitive deficits. In the present study, impaired memory retention induced by 2-month HFD was recovered after 4 months on HFD. Prolonged (6-month) HFD did not further enhance tau hyperphosphorylation and ß-amyloid deposition, which was consistent with the alleviation of memory retention. In brain insulin signaling, 2-month HFD increased IRS-1 and p-IRS-1(Ser307)/IRS-1, while decreasing pAKT(Ser473)/AKT, PI3K and mTOR; 4-month HFD decreased IRS-1 and pAKT(Ser473)/AKT, while increasing AKT; 6-month HFD increased IRS-1, pAKT(Ser473)/AKT, and mTOR, while decreasing p-IRS-1(Ser307)/IRS-1, PI3K and AKT. Notably, bioinformatic analysis revealed a rhythmic process presented only in 4-month HFD group, with Srebf1 emerging as a link between circadian rhythms and insulin signaling pathway. These results suggest that prolonged HFD prevents further cognitive decline and the progression of Alzheimer's disease (AD)-related pathologies during ageing. Moreover, there may be a window for recovery, in which Srebf1 acts as a self-recovery switch to address obesity-related cognitive disorders in elders.

Optimal use of ß-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for ß-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections. METHODS: Five ß-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses. FINDINGS: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses. INTERPRETATION: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal ß-lactam antibiotic doses. FUNDING: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

DNAzyme-RCA-based colorimetric and lateral flow dipstick assays for the point-of-care testing of exosomal m5C-miRNA-21.

Methylation of microRNAs (miRNAs) is a post-transcriptional modification that affects miRNA activity by altering the specificity of miRNAs to target mRNAs. Abnormal methylation of miRNAs in cancer suggests their potential as a tumor marker. However, the traditional methylated miRNA detection mainly includes mass spectrometry, sequencing and others; complex procedures and reliance on large instruments greatly limit their application in point-of-care testing (POCT). Based on this, we developed DNAzyme-RCA-based gold nanoparticle (AuNP) colorimetric and lateral flow dipstick (LFD) assays to achieve convenient detection of exosomal 5-methylcytosine miRNA-21 (m5C-miRNA-21) for the first time. The two assays achieved specific recognition and linear amplification of m5C-miRNA-21 through the DNAzyme triggered RCA reaction and color output with low background interference through AuNP aggregation induced by base complementary pairing. The lowest concentration of m5C-miRNA-21 visible to the naked eye of the two assays can reach 1 pM and 0.1 pM, respectively. Detection of exosomal m5C-miRNA-21 in clinical blood samples showed that the expression level of m5C-miRNA-21 in colorectal cancer patients was significantly higher than that in healthy individuals. This approach not only demonstrates a new strategy for the detection of colorectal cancer but also provides a reference for the development of novel diagnostic tools for other miRNA methylation-related diseases.

Transcriptome Analysis Reveals Antioxidant Defense Mechanisms in the Silkworm Bombyx mori after Exposure to Lead.

Lead (Pb) is a major source of heavy metal contamination, and poses a threat to biodiversity and human health. Elevated levels of Pb can hinder insect growth and development, leading to apoptosis via mechanisms like oxidative damage. The midgut of silkworms is the main organ exposed to heavy metals. As an economically important lepidopteran model insect in China, heavy metal-induced stress on silkworms causes considerable losses in sericulture, thereby causing substantial economic damage. This study aimed to investigate Pb-induced detoxification-related genes in the midgut of silkworms using high-throughput sequencing methods to achieve a deeper comprehension of the genes' reactions to lead exposure. This study identified 11,567 unigenes and 14,978 transcripts. A total of 1265 differentially expressed genes (DEGs) were screened, comprising 907 upregulated and 358 downregulated genes. Subsequently, Gene Ontology (GO) classification analysis revealed that the 1265 DEGs were distributed across biological processes, cellular components, and molecular functions. This suggests that the silkworm midgut may affect various organelle functions and biological processes, providing crucial clues for further exploration of DEG function. Additionally, the expression levels of 12 selected detoxification-related DEGs were validated using qRT-PCR, which confirmed the reliability of the RNA-seq results. This study not only provides new insights into the detoxification defense mechanisms of silkworms after Pb exposure, but also establishes a valuable foundation for further investigation into the molecular detoxification mechanisms in silkworms.

Research on short-term photovoltaic power generation forecasting model based on multi-strategy improved squirrel search algorithm and support vector machine.

Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm.

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %-26.3 %, and increases Charging pile revenue.

Human Serum Albumin Mediated Controllable Synthesis of Defect-Rich Copper Hydroxide Nanowire for Cuproptosis-Based Anti-Tumor Therapy.

Cuproptosis, as a newly identified form of programmed cell death, shows great promise in cancer treatment. Efficient Cu+ delivery while avoiding systemic toxicity and elimination of the resistance from over-expressed intracellular copper chelator glutathione (GSH) are critical for cuproptosis. Herein, this work innovatively constructs a biocompatible and defect-rich copper hydroxide nanowire (HCu nanowire) through a human serum albumin (HSA) mediated biomineralization method. This work finds that the morphology and size of HCu nanowires can be controlled adjusted by the feed ratio of HSA and Cu2+. Remarkably, except for outstanding biocompatibility, HSA coordination endows HCu nanowires abundant oxygen vacancies (OVs), and the defect-rich HCu nanowire possesses excellent GSH consumption efficiency. Density functional theory studies indicate that OVs change GSH absorption energy on defective HCu nanowires. In cancer cells, HCu nanowires deplete GSH and simultaneously produce sufficient free Cu+ for enhanced cuproptosis. Meanwhile, Cu+ can catalyze endogenous H2O2 into hydroxyl radicals (·OH) via a Fenton-like reaction. Thus, synergetic cuproptosis and ROS mediated apoptosis against tumor are achieved. The experimental results show that HCu nanowires have a better performance in both antitumor efficiency and safety compared with chemotherapeutic drug Dox at the same dose, demonstrating its great potential in clinical applications.

Synthesis of ß,ß-Dithioketones by Merging C-C and C-S Bond Cleavage in [1 + 1 + 1 + 1 + 1 + 1] Annulation.

An unconventional [1 + 1 + 1 + 1 + 1 + 1] annulation process was developed for the construction of ß,ß-dithioketones by merging C-C and C-S bond cleavage. In this reaction, rongalite concurrently served as triple C1 units, dual sulfur(II) synthons, and a reductant for the first time. Mechanism investigation indicated that the reaction involved the self-mediated valence state change of rongalite. By performing this step-economical method, the challenging construction of C5-substituted 1,3-dithiane can be achieved under mild and simple conditions.

A comparative study on the dose-effect of low-dose radiation based on microdosimetric analysis and single-cell sequencing technology.

The biological mechanisms triggered by low-dose exposure still need to be explored in depth. In this study, the potential mechanisms of low-dose radiation when irradiating the BEAS-2B cell lines with a Cs-137 gamma-ray source were investigated through simulations and experiments. Monolayer cell population models were constructed for simulating and analyzing distributions of nucleus-specific energy within cell populations combined with the Monte Carlo method and microdosimetric analysis. Furthermore, the 10 × Genomics single-cell sequencing technology was employed to capture the heterogeneity of individual cell responses to low-dose radiation in the same irradiated sample. The numerical uncertainties can be found both in the specific energy distribution in microdosimetry and in differential gene expressions in radiation cytogenetics. Subsequently, the distribution of nucleus-specific energy was compared with the distribution of differential gene expressions to guide the selection of differential genes bioinformatics analysis. Dose inhomogeneity is pronounced at low doses, where an increase in dose corresponds to a decrease in the dispersion of cellular-specific energy distribution. Multiple screening of differential genes by microdosimetric features and statistical analysis indicate a number of potential pathways induced by low-dose exposure. It also provides a novel perspective on the selection of sensitive biomarkers that respond to low-dose radiation.

KDM5B promotes SMAD4 loss-driven drug resistance through activating DLG1/YAP to induce lipid accumulation in pancreatic ductal adenocarcinoma.

Inactivated suppressor of mothers against decapentaplegic homolog (SMAD) 4 significantly affects cancer development in pancreatic ductal adenocarcinoma (PDAC). However, the contribution of smad4 loss to drug resistance in PDAC is largely undetermined. In the present study, we reported that the loss of SMAD4 endows PDAC cells the ability to drug resistance through upregulating histone lysine demethylase, Lysine-Specific Demethylase 5B (KDM5B, also known as JARID1B or PLU1). Upregulated KDM5B was found in PDAC, associated with poor prognosis and recurrence of PDAC patients. Upregulated KDM5B promotes PDAC tumor malignancy, i.e. cancer cells stemness and drug resistance in vitro and in vivo, while KDM5B knockout exerts opposite effects. Mechanistically, loss of Smad4-mediated upregulation of KDM5B promotes drug resistance through inhibiting the discs-large homolog 1 (DLG1), thereby facilitating nuclear translocation of YAP to induce de novo lipogenesis. Moreover, m6A demethylase FTO is involved in the upregulation of KDM5B by maintaining KDM5B mRNA stability. Collectively, the present study suggested FTO-mediated KDM5B stabilization in the context of loss of Smad4 activate DLG1/YAP1 pathway to promote tumorigenesis by reprogramming lipid accumulation in PDAC. Our study confirmed that the KDM5B-DLG1-YAP1 pathway axis plays a crucial role in the genesis and progression of PDAC, and KDM5B was expected to become a target for the treatment of PDAC. The schematic diagram of KDM5B-DLG1-YAP pathway axis in regulating drug resistance of PDAC to gemcitabine (GEM). In the context of SMAD4 loss PDAC cells, FTO-mediated stabilization and upregulation of KDM5B promotes drug resistance through directly targeting DLG1 to promote YAP1 translocation to nucleus to induce de novo lipogenesis (DNL).

The impact of comorbidities on prolonged mechanical ventilation in patients with chronic obstructive pulmonary disease.

BACKGROUND: In patients with chronic obstructive pulmonary disease (COPD) and acute respiratory failure, approximately 10% of them are considered to be at high risk for prolonged mechanical ventilation (PMV, > 21 days). PMV have been identified as independent predictors of unfavorable outcomes. Our previous study revealed that patients aged 70 years older and COPD severity were at a significantly higher risk for PMV. We aimed to analyze the impact of comorbidities and their associated risks in patients with COPD who require PMV. METHODS: The data used in this study was collected from Kaohsiung Medical University Hospital Research Database. The COPD subjects were the patients first diagnosed COPD (index date) between January 1, 2012 and December 31, 2020. The exclusion criteria were the patients with age less than 40 years, PMV before the index date or incomplete records. COPD and non-COPD patients, matched controls were used by applying the propensity score matching method. RESULTS: There are 3,744 eligible patients with COPD in the study group. The study group had a rate of 1.6% (60 cases) patients with PMV. The adjusted HR of PMV was 2.21 (95% CI 1.44-3.40; P < 0.001) in the COPD patients than in non-COPD patients. Increased risks of PMV were found significantly for patients with diabetes mellitus (aHR 4.66; P < 0.001), hypertension (aHR 3.20; P = 0.004), dyslipidemia (aHR 3.02; P = 0.015), congestive heart failure (aHR 6.44; P < 0.001), coronary artery disease (aHR 3.11; P = 0.014), stroke (aHR 6.37; P < 0.001), chronic kidney disease (aHR 5.81 P < 0.001) and Dementia (aHR 5.78; P < 0.001). CONCLUSIONS: Age, gender, and comorbidities were identified as significantly higher risk factors for PMV occurrence in the COPD patients compared to the non-COPD patients. Beyond age, comorbidities also play a crucial role in PMV in COPD.

A Multimode Optical Sensor for Selective and Sensitive Detection of Harmful Heavy Metal Cr(VI) in Fresh Water and Sea Water.

Water pollution originating from heavy metals has shown great impacts on the ecological environment and human health due to their extremely low biodegradability. Hexavalent chromium Cr(VI), as one harmful heavy metal with strong oxidation, high biological permeability, and high carcinogenicity, is becoming an increasingly serious threat to human health. Therefore, conveniently but accurately, monitoring the Cr(VI) level in water to maintain its normal level and ensuring the stability of the ecosystem and human health become very valuable. However, most of these heavy metal sensors reported are turn-off type single-emission sensors. In this work, a ratiometric fluorescence/colorimetry/smartphone triple-mode turn-on optical sensor for Cr(VI) was developed based on a multifunctional metal-organic framework platform. The detection limits for these three mutual verification modes were only 1.28, 4.89, and 68.4 nM, respectively. Additionally, the color changes of the detection system under sunlight can also be observed directly by the naked eye. The accuracy and practicability of this multimode sensor were further proved by the detection of Cr(VI) in actual water and seawater samples, and the recovery rate ranged from 97.308 to 104.041%.

Fluorescence imaging sheds light on the immune evasion mechanisms of hepatic stellate cells mediated by superoxide anion.

Whether and how the reactive oxygen species generated by hepatic stellate cells (HSCs) promote immune evasion of hepatocellular carcinoma (HCC) remains mysterious. Therefore, investigating the function of superoxide anion (O2•-), the firstly generated reactive oxygen species, during the immune evasion become necessary. In this work, we establish a novel in situ imaging method for visualization of O2•- changes in HSCs based on a new two-photon fluorescence probe TPH. TPH comprises recognition group for O2•- and HSCs targeting peptides. We observe that O2•- in HSCs gradually rose, impairing the infiltration of CD8+ T cells in HCC mice. Further studies reveal that the cyclin-dependent kinase 4 is deactivated by O2•-, and then cause the up-regulation of PD-L1. Our work provides molecular insights into HSC-mediated immune evasion of HCC, which may represent potential targets for HCC immunotherapy.