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Abstract This study aimed to explore the effects of peroxisome proliferator-activated receptor α (PPAR-α), a known inhibitor of ferroptosis, in Myocardial ischemia/reperfusion injury (MIRI) and its related mechanisms. In vivo and in vitro MIRI models were established. Our results showed that activation of PPAR-α decreased the size of the myocardial infarct, maintained cardiac function, and decreased the serum contents of creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), and Fe2+ in ischemia/reperfusion (I/R)-treated mice. Additionally, the results of H&E staining, DHE staining, TUNEL staining, and transmission electron microscopy demonstrated that activation of PPAR-α inhibited MIRI-induced heart tissue and mitochondrial damage. It was also found that activation of PPAR-α attenuated MIRI-induced ferroptosis as shown by a reduction in malondialdehyde, total iron, and reactive oxygen species (ROS). In vitro experiments showed that intracellular contents of malondialdehyde, total iron, LDH, reactive oxygen species (ROS), lipid ROS, oxidized glutathione disulphide (GSSG), and Fe2+ were reduced by the activation of PPAR-α in H9c2 cells treated with anoxia/reoxygenation (A/R), while the cell viability and GSH were increased after PPAR-α activation. Additionally, changes in protein levels of the ferroptosis marker further confirmed the beneficial effects of PPAR-α activation on MIRI-induced ferroptosis. Moreover, the results of immunofluorescence and dual-luciferase reporter assay revealed that PPAR-α achieved its activity via binding to the 14-3-3η promoter, promoting its expression level. Moreover, the cardioprotective effects of PPAR-α could be canceled by pAd/14-3-3η-shRNA or Compound C11 (14-3-3η inhibitor). In conclusion, our results indicated that ferroptosis plays a key role in aggravating MIRI, and PPAR-α/14-3-3η pathway-mediated ferroptosis and mitochondrial injury might be an effective therapeutic target against MIRI.

Previous studies have demonstrated that the underlying mechanisms of myocardial ischemia/reperfusion injury (MIRI) are complex and involve multiple types of regulatory cell death, including ferroptosis, apoptosis, and autophagy. Thus, we aimed to identify the mechanisms underlying MIRI and validate the protective role of epigallocatechin-3-gallate (EGCG) and its related mechanisms in MIRI. An in vivo and in vitro models of MIRI were constructed. The results showed that pretreatment with EGCG could attenuate MIRI, as indicated by increased cell viability, reduced lactate dehydrogenase (LDH) activity and apoptosis, inhibited iron overload, abnormal lipid metabolism, preserved mitochondrial function, decreased infarct size, maintained cardiac function, decreased reactive oxygen species (ROS) level, and reduced TUNEL-positive cells. Additionally, EGCG pretreatment could attenuate ferroptosis, apoptosis, and autophagy induced by MIRI via upregulating 14–3–3η protein levels. Furthermore, the protective effects of EGCG could be abolished with pAd/14–3–3η-shRNA or Compound C11 (a 14–3–3η inhibitor) but not pAd/NC-shRNA. In conclusion, EGCG pretreatment attenuated ferroptosis, apoptosis, and autophagy by mediating 14–3–3η and protected cardiomyocytes against MIRI.

Background and objectiveMacrophages play a crucial and dichotomous role cardiac repair following myocardial ischemia-reperfusion, as they can both facilitate tissue healing and contribute to injury. This duality is intricately linked to environmental factors, and the identification of macrophage subtypes within the context of myocardial ischemia-reperfusion injury (MIRI) may offer insights for the development of more precise intervention strategies.MethodsSpecific marker genes were used to identify macrophage subtypes in GSE227088 (mouse single-cell RNA sequencing dataset). Genome Set Enrichment Analysis (GSEA) was further employed to validate the identified LAM subtypes. Trajectory analysis and single-cell regulatory network inference were executed using the R packages Monocle2 and SCENIC, respectively. The conservation of LAM was verified using human ischemic cardiomyopathy heart failure samples from the GSE145154 (human single-cell RNA sequencing dataset). Fluorescent homologous double-labeling experiments were performed to determine the spatial localization of LAM-tagged gene expression in the MIRI mouse model.ResultsIn this study, single-cell RNA sequencing (scRNA-seq) was employed to investigate the cellular landscape in ischemia-reperfusion injury (IRI). Macrophage subtypes, including a novel Lipid-Associated Macrophage (LAM) subtype characterized by high expression of Spp1, Trem2, and other genes, were identified. Enrichment and Progeny pathway analyses highlighted the distinctive functional role of the SPP1+ LAM subtype, particularly in lipid metabolism and the regulation of the MAPK pathway. Pseudotime analysis revealed the dynamic differentiation of macrophage subtypes during IRI, with the activation of pro-inflammatory pathways in specific clusters. Transcription factor analysis using SCENIC identified key regulators associated with macrophage differentiation. Furthermore, validation in human samples confirmed the presence of SPP1+ LAM. Co-staining experiments provided definitive evidence of LAM marker expression in the infarct zone. These findings shed light on the role of LAM in IRI and its potential as a therapeutic target.ConclusionIn conclusion, the study identifies SPP1+ LAM macrophages in ischemia-reperfusion injury and highlights their potential in cardiac remodeling.

Abstract Large-scale trapped-ion quantum computers hold great promise to outperform classical computers and are crucially desirable for finance, pharmaceutical industry, fundamental chemistry and other fields. Currently, a big challenge for trapped-ion quantum computers is the poor scalability mainly brought by the optical elements that are used for optical addressing. Metasurfaces provide a promising solution due to their excellent flexibility and integration ability. Here, we propose and numerically demonstrate a scalable off-axis metalens array for optical addressing working at the wavelength of 350 nm. Metalens arrays designed for x linearly polarized and left circularly polarized light respectively can focus the collimated addressing beam array into a compact focused spot array with spot spacing of 5 μm, featuring crosstalk below 0.82%. Graphical Abstract

Septic cardiomyopathy (SCM) is severe organ dysfunction caused by sepsis that is associated with poor prognosis, and its pathobiological mechanisms remain unclear. Autophagy is a biological process that has recently been focused on SCM, yet the current understanding of the role of dysregulated autophagy in the pathogenesis of SCM remains limited and uncertain. Exploring the molecular mechanisms of disease based on the transcriptomes of human pathological samples may bring the closest insights. In this study, we analyzed the differential expression of autophagy-related genes in SCM based on the transcriptomes of human septic hearts, and further explored their potential crosstalk and functional pathways. Key functional module and hub genes were identified by constructing a protein–protein interaction network. Eight key genes (CCL2, MYC, TP53, SOD2, HIF1A, CTNNB1, CAT, and ADIPOQ) that regulate autophagy in SCM were identified after validation in a lipopolysaccharide (LPS)-induced H9c2 cardiomyoblast injury model, as well as the autophagic characteristic features. Furthermore, we found that key genes were associated with abnormal immune infiltration in septic hearts and have the potential to serve as biomarkers. Finally, we predicted drugs that may play a protective role in SCM by regulating autophagy based on our results. Our study provides evidence and new insights into the role of autophagy in SCM based on human septic heart transcriptomes, which would be of great benefit to reveal the molecular pathological mechanisms and explore the diagnostic and therapeutic targets for SCM.

Septic cardiomyopathy (SCM) is a cardiac dysfunction caused by severe sepsis and septic shock that increases the risk of heart failure and death and its molecular mechanism remains unclear. Ferroptosis, a novel form of programmed cell death, has been reported to be present in the heart tissue of patients with sepsis, which demonstrated that ferroptosis may be a potential mechanism of myocardial injury in SCM. Therefore, we explored the role of ferroptosis-related genes (FRGs) in SCM and aimed to identify pivotal ferroptosis-related targets in SCM and potential therapeutic targets involved in the pathological process of SCM. To explore the regulatory mechanisms of ferroptosis in SCM, we identified differentially expressed genes (DEGs) in SCM and FRGs by bioinformatics analysis, and further identified hub genes. And the crucial microRNAs (miRNAs)-FRGs regulatory network was subsequently constructed. Finally, several candidate drugs associated with the hub genes were predicted, and Real-time quantitative reverse Transcription PCR (qRT-PCR) and western blotting analysis were performed to confirm the abnormal expression of hub genes. In this study, we identified several FRGs that may be involved in the pathogenesis of SCM, which helps us further clarify the role of ferroptosis in SCM and deeply understand the molecular mechanisms and potential therapeutic targets of SCM.

This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.

Acute myocardial infarction is a life-threatening condition with high mortality and complication rates. Although myocardial reperfusion can preserve ischemic myocardial tissue, it frequently exacerbates tissue injury, a phenomenon known as ischemia-reperfusion injury (IRI). However, the underlying pathological mechanisms of IRI remain to be completely understood. Ferroptosis is a novel type of regulated cell death that is associated with various pathological conditions, including angiocardiopathy. The purpose of this article was to elucidate the possible mechanistic role of ferroptosis in IRI through bioinformatics analysis and experimental validation. Healthy and IRI heart samples were screened for differentially expressed ferroptosis-related genes and functional enrichment analysis was performed to determine the potential crosstalk and pathways involved. A protein-protein interaction network was established for IRI, and 10 hub genes that regulate ferroptosis, including HIF1A, EGFR, HMOX1, and ATF3 were identified. In vitro, an anoxia/reoxygenation (A/R) injury model was established using H9c2 cardiomyoblasts to validate the bioinformatics analysis results, and extensive ferroptosis was detected. A total of 4 key hub genes and 3 key miRNAs were also validated. It was found that IRI was related to the aberrant infiltration of immune cells and the small-molecule drugs that may protect against IRI by preventing ferroptosis were identified. These results provide novel insights into the role of ferroptosis in IRI, which can help identify novel therapeutic targets. [ABSTRACT FROM AUTHOR]

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field.

Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al., which could obviously improve recognition speed based on lightweight skeleton node calculation, and improve recognition accuracy based on occluded skeleton node prediction analysis in order to effectively solve the behavior occlusion problem. The experiments show that our proposed All-MSFCF model has a video action recognition average accuracy rate of 92.7% for eight kinds of abnormal behavior recognition. Although our proposed lightweight L-MSFCF model has an 87.3% average accuracy rate, its average recognition speed is 62.7% higher than the full-skeleton recognition model, which is more suitable for solving real-time tracing problems. Moreover, our proposed Trajectory Prediction Tracking (TPT) model could real-time predict the moving positions based on the dynamically selected core skeleton node calculation, especially for the short-term prediction within 15 frames and 30 frames that have lower average loss errors.