Multidimensional Encryption by Chip-Integrated Metasurfaces.

Facing the challenge of information security in the current era of information technology, optical encryption based on metasurfaces presents a promising solution to this issue. However, most metasurface-based encryption techniques rely on limited decoding keys and struggle to achieve multidimensional complex encryption. It hinders the progress of optical storage capacity and puts encryption security at a disclosing risk. Here, we propose and experimentally demonstrate a multidimensional encryption system based on chip-integrated metasurfaces that successfully incorporates the simultaneous manipulation of three-dimensional optical parameters, including wavelength, direction, and polarization. Hence, up to eight-channel augmented reality (AR) holograms are concealed by near- and far-field fused encryption, which can only be extracted by correctly providing the three-dimensional decoding keys and then vividly exhibit to the authorizer with low crosstalk, high definition, and no zero-order speckle noise. We envision that the miniature chip-integrated metasurface strategy for multidimensional encryption functionalities promises a feasible route toward the encryption capacity and information security enhancement of the anticounterfeiting performance and optically cryptographic storage.

A novel anti-inflammatory strategy for myocardial ischemia-reperfusion in rats with cinnamamide derivative compound 7.

Reperfusion after myocardial ischemia would aggravate myocardial structural and functional damage, known as myocardial ischemia-reperfusion (MI/R) injury. Cinnamamide derivatives have been reported to exert cardioprotective effects, and we have previously reported that compound 7 played a role in cardioprotection against MI/R via anti-inflammatory effect. However, exact mechanism underlying such beneficial action of compound 7 is still unclear. The protective effect of compound 7 was determined in H9c2 cells under H2O2 stimulation with or without nigerin (NLRP3 activator). Electrocardiogram, echocardiography, myocardial infarction size, histopathology and serum biochemical assay were performed in MI/R rats. Metabolomics in vivo and mRNA or protein levels of NLRP3, ASC, cleaved caspase-1 and its downstream IL-18 and IL-1ß were detected both in vitro and in vivo. Compound 7 significantly ameliorate H2O2-induced cardiomyocyte damage, which was supported by in vivo data determined by improved left ventricular systolic function and histopathological changes, reduced myocardial infarction area and cellular apoptosis in heart tissue. Cardiac differential metabolites demonstrated that compound 7 indeed altered the cardiac reprogramming of inflammation-related metabolites, which was evidenced by down-regulated cardiac inflammation by compound 7. Additionally, compound 7 alleviated myocardial injury by inhibiting the NLRP3 pathway rather than other members of the inflammasome both in vitro and in vivo, which was further evidenced by CETSA assay. Whereas, nigerin blocked the inhibitory activity of compound 7 against NLRP3. Cinnamamide derivative compound 7 ameliorated MI/R injury by inhibiting inflammation via NLRP3.

Points of interest linear attention network for real-time non-rigid liver volume to surface registration.

BACKGROUND: In laparoscopic liver surgery, accurately predicting the displacement of key intrahepatic anatomical structures is crucial for informing the doctor's intraoperative decision-making. However, due to the constrained surgical perspective, only a partial surface of the liver is typically visible. Consequently, the utilization of non-rigid volume to surface registration methods becomes essential. But traditional registration methods lack the necessary accuracy and cannot meet real-time requirements. PURPOSE: To achieve high-precision liver registration with only partial surface information and estimate the displacement of internal liver tissues in real-time. METHODS: We propose a novel neural network architecture tailored for real-time non-rigid liver volume to surface registration. The network utilizes a voxel-based method, integrating sparse convolution with the newly proposed points of interest (POI) linear attention module. POI linear attention module specifically calculates attention on the previously extracted POI. Additionally, we identified the most suitable normalization method RMSINorm. RESULTS: We evaluated our proposed network and other networks on a dataset generated from real liver models and two real datasets. Our method achieves an average error of 4.23 mm and a mean frame rate of 65.4 fps in the generation dataset. It also achieves an average error of 8.29 mm in the human breathing motion dataset. CONCLUSIONS: Our network outperforms CNN-based networks and other attention networks in terms of accuracy and inference speed.

Catalytic Nanodots-Driven Pyroptosis Suppression in Nucleus Pulposus for Antioxidant Intervention of Intervertebral Disc Degeneration.

Low back pain resulting from intervertebral disc degeneration (IVDD) is a prevalent global concern; however, its underlying mechanism remains elusive. Single-cell sequencing analyses revealed the critical involvement of pyroptosis in IVDD. Considering the involvement of reactive oxygen species (ROS) as the primary instigator of pyroptosis and the lack of an efficient intervention approach, this study developed carbonized Mn-containing nanodots (MCDs) as ROS-scavenging catalytic biomaterials to suppress pyroptosis of nucleus pulposus (NP) cells to efficiently alleviate IVDD. Catalytic MCDs have superior efficacy in scavenging intracellular ROS and rescuing homeostasis in the NP microenvironment compared with N-acetylcysteine, a classical antioxidant. The data validates that pyroptosis plays a vital role in mediating the protective effects of catalytic MCDs against oxidative stress. Systematic in vivo assessments substantiate the effectiveness of MCDs in rescuing a puncture-induced IVDD rat model, further demonstrating their ability to suppress pyroptosis. This study highlights the potential of antioxidant catalytic nanomedicine as a pyroptosis inhibitor and mechanistically unveils an efficient strategy for the treatment of IVDD.

Secretory IgA reduced the ergosterol contents of Candida albicans to repress its hyphal growth and virulence.

Candida albicans, one of the most prevalent conditional pathogenic fungi, can cause local superficial infections and lethal systemic infections, especially in the immunocompromised population. Secretory immunoglobulin A (sIgA) is an important immune protein regulating the pathogenicity of C. albicans. However, the actions and mechanisms that sIgA exerts directly against C. albicans are still unclear. Here, we investigated that sIgA directs against C. albicans hyphal growth and virulence to oral epithelial cells. Our results indicated that sIgA significantly inhibited C. albicans hyphal growth, adhesion, and damage to oral epithelial cells compared with IgG. According to the transcriptome and RT-PCR analysis, sIgA significantly affected the ergosterol biosynthesis pathway. Furthermore, sIgA significantly reduced the ergosterol levels, while the addition of exogenous ergosterol restored C. albicans hyphal growth and adhesion to oral epithelial cells, indicating that sIgA suppressed the growth of hyphae and the pathogenicity of C. albicans by reducing its ergosterol levels. By employing the key genes mutants (erg11Δ/Δ, erg3Δ/Δ, and erg3Δ/Δ erg11Δ/Δ) from the ergosterol pathway, sIgA lost the hyphal inhibition on these mutants, while sIgA also reduced the inhibitory effects of erg11Δ/Δ and erg3Δ/Δ and lost the inhibition of erg3Δ/Δ erg11Δ/Δ on the adhesion to oral epithelial cells, further proving the hyphal repression of sIgA through the ergosterol pathway. We demonstrated for the first time that sIgA inhibited C. albicans hyphal development and virulence by affecting ergosterol biosynthesis and suggest that ergosterol is a crucial regulator of C. albicans-host cell interactions. KEY POINTS: • sIgA repressed C. albicans hyphal growth • sIgA inhibited C. albicans virulence to host cells • sIgA affected C. albicans hyphae and virulence by reducing its ergosterol levels.

The mechanism by which SIRT1 regulates autophagy and EMT in drug-resistant oesophageal cancer cells.

Cancer cell resistance presents a significant clinical challenge. The mechanisms underlying drug resistance in cancer cells are intricate and remain incompletely understood. Notably, tumor cell resistance often coincides with the epithelial-mesenchymal transition (EMT). In this study, we observed an elevation in autophagy levels following the development of drug resistance in oesophageal cancer cells. Inhibition of autophagy led to a reduction in drug-resistant cell migration and the inhibition of EMT. Furthermore, we identified an upregulation of SIRT1 expression in drug-resistant oesophageal cancer cells. Subsequent inhibition of SIRT1 expression in drug-resistant cells resulted in the suppression of autophagy levels, migration ability, and the EMT process. Our additional investigations revealed that a SIRT1 inhibitor effectively curbed tumor growth in human oesophageal cancer xenograft model mice (TE-1, TE-1/PTX) without evident toxic effects. This mechanism appears to be associated with the autophagy levels within the tumor tissue.

Thermoelectric Performance Improvement in the ZrNiSn-Based Composite via Modulating Si Addition.

To ensure optimal performance, ZrNiSn is required to possess a low thermal conductivity and exhibit minimal bipolar effects under high-temperature conditions. This study demonstrates the integration of silicon (Si) at different doping levels into ZrNiSn. The composites consist of secondary phases of in situ ZrNiSi and Si. At a temperature of 873 K, the Seebeck coefficient experiences a 16% increase, despite the charge carrier concentration increasing three times as a result of the electron injection from ZrNiSi. The phenomenon can be elucidated by the introduction of Si, which causes energy filtering and inhibits the flow of minority charge carriers. When the doping levels in n- or p-type Si reach high levels (1019 to 1020 cm-3), the mixed interfaces ZrNiSn/ZrNiSi and ZrNiSn/Si reduce the thermal conductivity by 15%, resulting in a 50% increase in zT. These findings indicate that electron transfer in ZrNiSn can be regulated by precise doping in Si. They also demonstrate that incorporating an optimal p-type semiconductor can enhance the thermoelectric performance of n-type ZrNiSn. Additionally, a novel approach is proposed to separate electrical conductivity and the Seebeck coefficient by designing unique secondary phase interfaces.

On-Chip Meta-Optics for Engineering Arbitrary Trajectories with Longitudinal Polarization Variation.

On-chip integrated meta-optics promise to achieve high-performance and compact integrated photonic devices. To arbitrarily engineer the optical trajectory along the propagation path in an on-chip integrated scheme is of significance in fundamental physics and various emerging applications. Here, we experimentally demonstrate an on-chip metasurface integrated on a waveguide to enable predefined arbitrary optical trajectories in the visible regime. By transformation of the transverse phase to generate longitudinal mapping, the guided waves are extracted and molded into any different optical trajectories (parabola, hyperbola, and cosine). More intriguingly, predefined polarization states with longitudinal variation are also successfully imparted along the trajectory. Owing to the on-chip propagation scheme, the trajectories are uniquely free from zero-order diffraction interference, naturally having a higher signal-to-noise ratio beyond conventional free-space forms. Overall, such on-chip optical trajectory engineering allows for miniaturized integration and can find paths in potential applications of complex optical manipulation, advanced laser fabrication, and microscopic imaging.

Switchable unidirectional emissions from hydrogel gratings with integrated carbon quantum dots.

Directional emission of photoluminescence despite its incoherence is an attractive technique for light-emitting fields and nanophotonics. Optical metasurfaces provide a promising route for wavefront engineering at the subwavelength scale, enabling the feasibility of unidirectional emission. However, current directional emission strategies are mostly based on static metasurfaces, and it remains a challenge to achieve unidirectional emissions tuning with high performance. Here, we demonstrate quantum dots-hydrogel integrated gratings for actively switchable unidirectional emission with simultaneously a narrow divergence angle less than 1.5° and a large diffraction angle greater than 45°. We further demonstrate that the grating efficiency alteration leads to a more than 7-fold tuning of emission intensity at diffraction order due to the variation of hydrogel morphology subject to change in ambient humidity. Our proposed switchable emission strategy can promote technologies of active light-emitting devices for radiation control and optical imaging.

Multi-Dimensional Light-Emitting Meta-Display: Photoluminescence and Pumping Light Multiplexing.

The advent of intelligent display devices has given rise to diverse and complex demands for miniature light-emitting devices. Light-emitting metasurfaces have emerged as a practical and efficient means of achieving precise light modulation. However, their practicality is limited by certain constraints. First, there is a need for further exploration of the ability to manipulate both pumping and emitting light simultaneously. Second, there is currently no encoding freedom in multi-dimensional emitting light. To address these concerns, using meta-atoms is proposed to encode both fluorescence and pumping light independently, and expand the encoding freedom with different incident wavevector directions. A light-emitting metasurface with quad-fold multiplex encoding meta-displays, including dual scattering images and dual fluorescence images, is further demonstrated. This design strategy not only manipulates both pumping and fluorescence light but also broadens encoding freedom for comprehensive multi-functionality. This can pave the way for multiplexing optical displays, information storage, and next-generation wearable displays.

Prognostic value of oxygen saturation index trajectory phenotypes on ICU mortality in mechanically ventilated patients: a multi-database retrospective cohort study.

BACKGROUND: Heterogeneity among critically ill patients undergoing invasive mechanical ventilation (IMV) treatment could result in high mortality rates. Currently, there are no well-established indicators to help identify patients with a poor prognosis in advance, which limits physicians' ability to provide personalized treatment. This study aimed to investigate the association of oxygen saturation index (OSI) trajectory phenotypes with intensive care unit (ICU) mortality and ventilation-free days (VFDs) from a dynamic and longitudinal perspective. METHODS: A group-based trajectory model was used to identify the OSI-trajectory phenotypes. Associations between the OSI-trajectory phenotypes and ICU mortality were analyzed using doubly robust analyses. Then, a predictive model was constructed to distinguish patients with poor prognosis phenotypes. RESULTS: Four OSI-trajectory phenotypes were identified in 3378 patients: low-level stable, ascending, descending, and high-level stable. Patients with the high-level stable phenotype had the highest mortality and fewest VFDs. The doubly robust estimation, after adjusting for unbalanced covariates in a model using the XGBoost method for generating propensity scores, revealed that both high-level stable and ascending phenotypes were associated with higher mortality rates (odds ratio [OR]: 1.422, 95% confidence interval [CI] 1.246-1.623; OR: 1.097, 95% CI 1.027-1.172, respectively), while the descending phenotype showed similar ICU mortality rates to the low-level stable phenotype (odds ratio [OR] 0.986, 95% confidence interval [CI] 0.940-1.035). The predictive model could help identify patients with ascending or high-level stable phenotypes at an early stage (area under the curve [AUC] in the training dataset: 0.851 [0.827-0.875]; AUC in the validation dataset: 0.743 [0.709-0.777]). CONCLUSIONS: Dynamic OSI-trajectory phenotypes were closely related to the mortality of ICU patients requiring IMV treatment and might be a useful prognostic indicator in critically ill patients.

Automatic Localization of Key Structures for Subthalamic Nucleus-Deep Brain Stimulation Surgery via Prior-Enhanced Multi-Object Magnetic Resonance Imaging Segmentation.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established and effective neurosurgical treatment for relieving motor symptoms in Parkinson disease. The localization of key brain structures is critical to the success of DBS surgery. However, in clinical practice, this process is heavily dependent on the radiologist's experience. METHODS: In this study, we propose an automatic localization method of key structures for STN-DBS surgery via prior-enhanced multi-object magnetic resonance imaging segmentation. We use the U-Net architecture for the multi-object segmentation, including STN, red nucleus, brain sulci, gyri, and ventricles. To address the challenge that only half of the brain sulci and gyri locate in the upper area, potentially causing interference in the lower area, we perform region of interest detection and ensemble joint processing to enhance the segmentation performance of brain sulci and gyri. RESULTS: We evaluate the segmentation accuracy by comparing our method with other state-of-the-art machine learning segmentation methods. The experimental results show that our approach outperforms state-of-the-art methods in terms of segmentation performance. Moreover, our method provides effective visualization of key brain structures from a clinical application perspective and can reduce the segmentation time compared with manual delineation. CONCLUSIONS: Our proposed method uses deep learning to achieve accurate segmentation of the key structures more quickly than and with comparable accuracy to human manual segmentation. Our method has the potential to improve the efficiency of surgical planning for STN-DBS.

Integrated 16 S rRNA gene sequencing and serum metabolomics approaches to decipher the mechanism of Qingre Lidan decoction in the treatment of cholestatic liver injury.

BACKGROUND: Cholestasis is a commonly occurring disorder induced by impaired bile flow, for which there is no effective treatment so far. Qingre Lidan decoction (QRLD) is a clinically used herbal compound for the long-term treatment of bile circulation disorders arising from inflammation and obstruction in the gallbladder and bile ducts. The objective of this study was to investigate the protective effect of QRLD on cholestatic liver injury and its possible mechanism. METHODS: α-Naphthyl isothiocyanate (ANIT) was used to induce cholestatic liver injury in rats. Liver histopathology and serum biochemical markers were used to assess QRLD's protective impact. The possible biomarkers and mechanism of the therapeutic benefits of QRLD were investigated using a UHPLC-based Q-Exactive Orbitrap MS / MS untargeted serum metabolomics technique together with 16 S rRNA microbiota profiling. Afterwards, using RT-qPCR as well as Western Blot techniques, the expression of pertinent indicators was determined. RESULTS: The intervention effect of QRLD was stronger at medium and high dosages than at low doses, and it dramatically decreased the levels of serum biochemical markers in cholestatic rats reflecting alterations in liver function and relieving ANIT-induced abnormalities in the liver's histopathology. Serum metabolomics showed that QRLD could affect the metabolic profile of cholestatic rats, mainly related to glycerophospholipid metabolism, taurine and hypotaurine metabolism, alanine, aspartate and glutamate metabolism, and histidine metabolic pathway. Additionally, analysis of 16 S rRNA gene sequencing indicated that QRLD could moderate ANIT-induced microbiota disorders, particularly Romboutsia, Bifidobacterium, Fusicatenibacter, Prevotella_9, Prevotellaceae_NK3B31_group and Prevotella_1. Other experimental results showed that QRLD significantly upregulated the mRNA and protein expression of PPARα, CYP7A1 and NTCP in the liver, inhibited the expression of p-IκBα, p-p65 and TNFα while increasing the anti-inflammatory factor IL-10, and downregulated the expression of MDA (a peroxidation product) and D-lactic acid (an intestinal barrier indicator) while increasing the expression of SOD and GSH. CONCLUSIONS: QRLD can effectively regulate endogenous metabolites and microbiota disorders in cholestatic rats that are correlated with the attenuation of inflammation and oxidative stress.

Ni-Activated and Ni-P-Activated Porous Titanium Carbide Ceramic Electrodes as Efficient Electrocatalysts for Overall Water Splitting.

Developing electrocatalysts based on transition-metal carbides that can be utilized for commercial water splitting is a challenging endeavor. To address this challenge, we employed a novel approach that merged phase-inversion tape-casting and sintering, subsequently implementing a simple and efficient electrodeposition process, to synthesize Ni-activated and Ni-P-activated titanium carbide (Ni/TiC, Ni-P/TiC) ceramic electrodes as water splitting catalytic cathodes and anodes. These self-supported Ni/TiC and Ni-P/TiC electrodes are binder-free with abundant finger-like pores, which contribute to the formation of highly conductive skeleton and more exposed active sites for direct water splitting. These catalysts exhibit outstanding performance, superior to many reported bifunctional nickel-based catalysts supported on other substrates. Moreover, the exceptional electrocatalytic performance of the Ni/TiC and Ni-P/TiC catalysts is attributed to the synergistic effect between Ni oxides (phosphides) and TiC, as revealed by density functional theory (DFT) calculations. This self-template strategy paves the way for fabricating industrially applicable electrodes to generate hydrogen and oxygen through water splitting.

Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte signature in SKCM.

Background: Global patterns of immune cell communications in the immune microenvironment of skin cutaneous melanoma (SKCM) haven't been well understood. Here we recognized signaling roles of immune cell populations and main contributive signals. We explored how multiple immune cells and signal paths coordinate with each other and established a prognosis signature based on the key specific biomarkers with cellular communication. Methods: The single-cell RNA sequencing (scRNA-seq) dataset was downloaded from the Gene Expression Omnibus (GEO) database, in which various immune cells were extracted and re-annotated according to cell markers defined in the original study to identify their specific signs. We computed immune-cell communication networks by calculating the linking number or summarizing the communication probability to visualize the cross-talk tendency in different immune cells. Combining abundant analyses of communication networks and identifications of communication modes, all networks were quantitatively characterized and compared. Based on the bulk RNA sequencing data, we trained specific markers of hub communication cells through integration programs of machine learning to develop new immune-related prognostic combinations. Results: An eight-gene monocyte-related signature (MRS) has been built, confirmed as an independent risk factor for disease-specific survival (DSS). MRS has great predictive values in progression free survival (PFS) and possesses better accuracy than traditional clinical variables and molecular features. The low-risk group has better immune functions, infiltrated with more lymphocytes and M1 macrophages, with higher expressions of HLA, immune checkpoints, chemokines and costimulatory molecules. The pathway analysis based on seven databases confirms the biological uniqueness of the two risk groups. Additionally, the regulon activity profiles of 18 transcription factors highlight possible differential regulatory patterns between the two risk groups, suggesting epigenetic event-driven transcriptional networks may be an important distinction. MRS has been identified as a powerful tool to benefit SKCM patients. Moreover, the IFITM3 gene has been identified as the key gene, validated to express highly at the protein level via the immunohistochemical assay in SKCM. Conclusion: MRS is accurate and specific in evaluating SKCM patients' clinical outcomes. IFITM3 is a potential biomarker. Moreover, they are promising to improve the prognosis of SKCM patients.

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics.

PURPOSE: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. METHODS: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients' PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. RESULTS: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849-0.851). CONCLUSIONS: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.

The role of histone deacetylases in cardiac energy metabolism in heart diseases.

Heart diseases are associated with substantial morbidity and mortality worldwide. The underlying mechanisms and pathological changes associated with cardiac diseases are exceptionally complex. Highly active cardiomyocytes require sufficient energy metabolism to maintain their function. Under physiological conditions, the choice of fuel is a delicate process that depends on the whole body and organs to support the normal function of heart tissues. However, disordered cardiac metabolism has been discovered to play a key role in many forms of heart diseases, including ischemic heart disease, cardiac hypertrophy, heart failure, and cardiac injury induced by diabetes or sepsis. Regulation of cardiac metabolism has recently emerged as a novel approach to treat heart diseases. However, little is known about cardiac energy metabolic regulators. Histone deacetylases (HDACs), a class of epigenetic regulatory enzymes, are involved in the pathogenesis of heart diseases, as reported in previous studies. Notably, the effects of HDACs on cardiac energy metabolism are gradually being explored. Our knowledge in this respect would facilitate the development of novel therapeutic strategies for heart diseases. The present review is based on the synthesis of our current knowledge concerning the role of HDAC regulation in cardiac energy metabolism in heart diseases. In addition, the role of HDACs in different models is discussed through the examples of myocardial ischemia, ischemia/reperfusion, cardiac hypertrophy, heart failure, diabetic cardiomyopathy, and diabetes- or sepsis-induced cardiac injury. Finally, we discuss the application of HDAC inhibitors in heart diseases and further prospects, thus providing insights into new treatment possibilities for different heart diseases.

Coumarin-derived imino sulfonate 5h ameliorates cardiac injury induced by myocardial infarction via activating the Sirt1/Nrf2 signaling pathway.

Myocardial infarction (MI) is irreversible damage caused by ischemia and hypoxia in coronary arteries accompanied by elevated catecholamine levels, leading to the accumulation of free radicals. Our previous study discovered coumarin-derived imino sulfonates as a novel class of potential cardioprotective agents possessing strong anti-oxidative effects in cardiomyocytes. Therefore, identifying the compound with the highest cardioprotective activity, 5h, and the mechanism involved was necessary. As a kind of catecholamine, isoproterenol can clinically induce myocardial infarction injury similar to the symptoms of myocardial infarction patients. Our experiments explored the underlying mechanism of this effect of compound 5h by assessing cardiac function, infarct size, histopathological changes, and downregulation of Sirt1 by transfection of adenovirus in vitro and by administering Ex527, a specific inhibitor of Sirt1, in vivo. Compound 5h exhibited strong cardioprotective actions in vivo and in vitro via improving cell survival and cardiac function and decreasing the cellular oxidative stress and cardiac infarct size against MI. Furthermore, compound 5h significantly enhanced cardiac expression of Sirt1, subsequently activating the Nrf2/NQO1 signaling pathway. However, adenovirus-induced Sirt1 downregulation or Sirt1-specific inhibitor largely blocked such beneficial effects of 5h in vitro and in vivo, respectively. Taken together, our results demonstrated, for the first time, that the cardioprotective action of 5h against MI was mediated by reducing oxidative stress and apoptosis through the Sirt1/Nrf2 signaling pathway. Our findings proposed novel insights in developing and evaluating coumarin-derived imino sulfonate compounds as epigenetics-targeted drug therapy for MI.