Experimental Study on Damage Effect of Mid-Infrared Pulsed Laser on Charge Coupled Device (CCD) and HgCgTe Detectors.

As the weak link in electro-optical imaging systems, photodetectors have always faced the threat of laser damage. In this paper, we experimentally investigated the damage mechanism of the photodetector induced by the out-of-band laser. The damage thresholds of the mid-infrared pulsed laser for Charge Coupled Device (CCD) and HgCdTe detectors were determined through damage experiments. The analysis of the damage phenomena and data for both CCD and HgCdTe detectors clearly demonstrated that out-of-band mid-infrared pulsed lasers could entirely incapacitate CCD and HgCdTe detectors. Our analysis of the damage process and data revealed that the primary mechanism of damage to CCD and HgCdTe detectors by mid-infrared pulsed lasers was primarily thermal. This study serves as a reference for further research on the mid-infrared pulsed laser damage mechanisms of CCD and HgCdTe detectors, as well as for laser protection and performance optimization in imaging systems.

Dazzling Evaluation of the Impact of a High-Repetition-Rate CO2 Pulsed Laser on Infrared Imaging Systems.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of a high-repetition-rate CO2 pulsed laser on the target extraction and tracking performance of an infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between the target and the dazzling images is performed to quantitatively describe the laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that the laser power and target position are crucial factors affecting the detection performance of infrared imaging detector systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of the infrared imaging detector at different distances. And laser dazzling produces a considerable quantity of false edge information, which seriously affects the performance of the pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for the functional damage of the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to the evaluation of the damage results. Finally, the proposed evaluation formula aligns with the experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and the tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging detectors.

Diabetic kidney disease as an independent predictor of long-term adverse outcomes in patients with coronary artery disease and diabetic mellitus.

Background: Diabetic kidney disease (DKD) had been proposed as a contributor in the pathogenesis of coronary artery disease (CAD). However, the relationship of DKD and the long-term adverse outcomes in patients with CAD after percutaneous coronary intervention (PCI) was still undiscovered. Methods: Approximately 892 patients with CAD enrolled from January 2012 to December 2016. The patients were divided into two groups, the DKD group (n = 341) and the None DKD group (n = 551). The primary outcome was major adverse cardiac events (MACE) after PCI. The average follow-up time was 1,897 ± 1,276 days. Results: Baseline data showed that some factors were significantly different between the two groups, including age, body mass index, gender (female), hypertension, smoking, stroke history, heart failure, duration of diabetic mellitus (DM), low-density lipoprotein cholesterol, urinary protein/creatinine ratio, serum creatinine, hemoglobin, platelet, antiplatelet, beta blocker, statin, antihypertensive drugs, and insulin (all p < 0.005). There were significant differences between the two groups in MACE, 40.3% vs. 52.2% (p = 0.001), and in cardiovascular death events and all-cause death events (5.6% vs. 20.5%, p < 0.001 and 4.4% vs. 13.5%, p < 0.001, respectively). In the DKD group, the risk of MACE was elevated to 141.9% [hazard ratio (HR) = 1.419, 95% confidence interval (CI): 1.164-1.730, p = 0.001] in the Cox univariable regression analyses; after adjusting co-variables, the Cox multivariable regression analyses demonstrated that DKD was an independent predictor for MACE (HR = 1.291, 95% CI: 1.027-1.624, p = 0.029) in patients with CAD after PCI, as well as in cardiovascular death events (HR = 2.148, 95% CI: 1.292-3.572, p = 0.003) and all-cause death events (HR = 2.229, 95% CI: 1.325-3.749, p = 0.003). Conclusion: This study suggests that DKD is an independent and novel predictor of long-term adverse outcomes in patients with CAD and DM who underwent PCI.

Human-robot facial coexpression.

Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that the robot appears natural, timely, and genuine. Here, we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial coexpression feels more genuine because it requires correct inference of the human's emotional state for timely execution. We found that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles and, using a learned inverse kinematic facial self-model, coexpress the smile simultaneously with the human. We demonstrated this ability using a robot face comprising 26 degrees of freedom. We believe that the ability to coexpress simultaneous facial expressions could improve human-robot interaction.

LoGo-GR: A Local to Global Graphical Reasoning Framework for Extracting Structured Information From Biomedical Literature.

In the biomedical literature, entities are often distributed within multiple sentences and exhibit complex interactions. As the volume of literature has increased dramatically, it has become impractical to manually extract and maintain biomedical knowledge, which would entail enormous costs. Fortunately, document-level relation extraction can capture associations between entities from complex text, helping researchers efficiently mine structured knowledge from the vast medical literature. However, how to effectively synthesize rich global information from context and accurately capture local dependencies between entities is still a great challenge. In this paper, we propose a Local to Global Graphical Reasoning framework (LoGo-GR) based on a novel Biased Graph Attention mechanism (B-GAT). It learns global context feature and information of local relation path dependencies from mention-level interaction graph and entity-level path graph respectively, and collaborates with global and local reasoning to capture complex interactions between entities from document-level text. In particular, B-GAT integrates structural dependencies into the standard graph attention mechanism (GAT) as attention biases to adaptively guide information aggregation in graphical reasoning. We evaluate our method on three publicly biomedical document-level datasets: Drug-Mutation Interaction (DV), Chemical-induced Disease (CDR), and Gene-Disease Association (GDA). LoGo-GR has advanced and stable performance compared to other state-of-the-art methods (it achieves state-of-the-art performance with 96.14%-97.39% F1 on DV dataset, advanced performance with 68.89% F1 and 84.22% F1 on CDR and GDA datasets, respectively). In addition, LoGo-GR also shows advanced performance on general-domain document-level relation extraction dataset, DocRED, which proves that it is an effective and robust document-level relation extraction framework.

ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study.

PURPOSE: To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progression in children. METHODS: The school-based prospective cohort study enrolled 1,997 individuals aged 7 to 9 years in 11 elementary schools in Mojiang, China. Cycloplegic refraction and biometry were performed at baseline and 4-year visits. The baseline fundus photographs were taken, and TD, defined as the percentage of exposed choroidal vessel area in the photographs, was quantified using an artificial intelligence-assisted semiautomatic labeling approach. After the exclusion of 330 ineligible participants because of loss to follow-up or ineligible fundus photographs, logistic models were used to assess the association of TD with rapid AL elongation (>0.36 mm/year) and SE progression (>1.00 D/year). RESULTS: The prevalence of tessellation was 477 of 1,667 (28.6%) and mean TD was 0.008 ± 0.019. The mean AL elongation and SE progression in 4 years were 0.90 ± 0.58 mm and -1.09 ± 1.25 D. Higher TD was associated with longer baseline AL (ß, 0.030; 95% confidence interval: 0.015-0.046; P < 0.001) and more myopic baseline SE (ß, -0.017; 95% confidence interval: -0.032 to -0.002; P = 0.029). Higher TD was associated with rapid AL elongation (odds ratio, 1.128; 95% confidence interval: 1.055-1.207; P < 0.001) and SE progression (odds ratio, 1.123; 95% confidence interval: 1.020-1.237; P = 0.018). CONCLUSION: Tessellation density is a potential indicator of rapid AL elongation and refractive progression in children. TD measurement could be a routine to monitor AL elongation.

Development and validation of a deep learning model to predict axial length from ultra-wide field images.

BACKGROUND: To validate the feasibility of building a deep learning model to predict axial length (AL) for moderate to high myopic patients from ultra-wide field (UWF) images. METHODS: This study included 6174 UWF images from 3134 myopic patients during 2014 to 2020 in Eye and ENT Hospital of Fudan University. Of 6174 images, 4939 were used for training, 617 for validation, and 618 for testing. The coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were used for model performance evaluation. RESULTS: The model predicted AL with high accuracy. Evaluating performance of R2, MSE and MAE were 0.579, 1.419 and 0.9043, respectively. Prediction bias of 64.88% of the tests was under 1-mm error, 76.90% of tests was within the range of 5% error and 97.57% within 10% error. The prediction bias had a strong negative correlation with true AL values and showed significant difference between male and female (P < 0.001). Generated heatmaps demonstrated that the model focused on posterior atrophy changes in pathological fundus and peri-optic zone in normal fundus. In sex-specific models, R2, MSE, and MAE results of the female AL model were 0.411, 1.357, and 0.911 in female dataset and 0.343, 2.428, and 1.264 in male dataset. The corresponding metrics of male AL models were 0.216, 2.900, and 1.352 in male dataset and 0.083, 2.112, and 1.154 in female dataset. CONCLUSIONS: It is feasible to utilize deep learning models to predict AL for moderate to high myopic patients with UWF images.

Risk assessment for commercial crime and legal protection based on modularity-optimized LM and SCA-optimized CNN.

With the Internet of Things (IoT) making significant strides in recent years, the challenges associated with data collection and analysis have emerged as a pressing concern in public security. When employed to tackle extensive criminal networks, the conventional deep learning model encounters issues such as heightened computational complexity, sluggish operational efficiency, and even system failures. Consequently, this research article introduces an intricately devised framework for detecting commercial offenses, employing a modularity-optimized Louvain-Method (LM) algorithm. Additionally, a convolutional neural networks (CNN)-based model is formulated to determine the feasibility of extending legal aid, wherein feature transformation is facilitated by utilizing TFIDF and Word2vec algorithms aligned with diverse legal text corpora. Furthermore, the hyper-parameter optimization is accomplished using the sine cosine algorithm (SCA), ultimately enabling the classification of relevant legal guidance. The experimental outcomes comprehensively affirm the exceptional training effectiveness of this model. The commercial crime identification model, grounded in modular optimization as proposed in this article, adeptly discerns criminal syndicates within the commercial trading network, achieving an accuracy rate exceeding 90%. This empowers the identification of such syndicates and bestows the judicial sphere with pertinent legal insights.

Damage Mechanism of HgCdTe Focal Plane Array Detector Irradiated Using Mid-Infrared Pulse Laser.

To investigate the damage threshold and mechanism of a mid-infrared HgCdTe focal plane array (FPA) detector, relevant experimental and theoretical studies were conducted. The line damage threshold of a HgCdTe FPA detector may be within the range of 0.59 Jcm-2 to 0.71 Jcm-2. The full frame damage threshold of the detector may be in the range of 0.86 Jcm-2 to 1.17 Jcm-2. Experimental results showed that when the energy density reaches 1.17 Jcm-2, the detector exhibits irreversible full frame damage and is completely unable to image. Based on the finite element method, a three-dimensional model of HgCdTe FPAs detector was established to study the heat transfer mechanism, internal stress, and damage sequence. When HgCdTe melts, we think that the detector is damaged. Under these conditions, the theoretical damage threshold calculated using the detector model is 0.55 Jcm-2. The difference between theoretical and experimental values was analyzed. The relationship between damage threshold and pulse width was also studied. It was found that when the pulse width is less than 1000 ns, the damage threshold characterized by peak power density is inversely proportional to pulse width. This relationship can help us predict the experimental damage threshold of an FPA detector. This model is reasonable and convenient for studying the damage of FPA detectors with a mid-infrared pulse laser. The research content in this article has important reference significance for the damage and protection of HgCdTe FPA detectors.

Beraprost sodium attenuates the development of myocardial fibrosis after myocardial infarction by regulating GSK-3ß expression in rats.

OBJECTIVE: The aim of this study was to elucidate the mechanism of beraprost sodium (BPS) in the intervention of myocardial fibrosis after myocardial infarction (MI) through glycogen synthase kinase-3ß (GSK-3ß) and to provide new ideas for intervention in myocardial fibrosis. MATERIALS AND METHODS: MI model rats given BPS and cardiac fibroblasts (CFs) treated with BPS and TGF-ß. HE staining and Masson staining were used to detect the pathological changes of myocardial tissue. Fibrotic markers were detected by immunohistochemical staining. The expressions of GSK-3ß, cAMP response element binding protein (CREB), and p-CREB were analyzed by qPCR and western blot analysis. EDU staining was used to detect the proliferation of CFs. The promoter activity of GSK-3ß was detected by luciferase assay. Chromatin immunoprecipitation assay was used to detect the binding levels of GSK-3ß promoter and Y-box binding protein 1 (YBX1). The levels of intracellular cyclic adenosine monophosphate (cAMP) were analyzed by enzyme-linked immunosorbent assay (ELISA). RESULTS: After operation, BPS improved myocardial fibrosis and upregulated GSK-3ß protein expression in male SD rats. BPS can down-regulate α-smooth muscle actin (α-SMA) level and up-regulate GSK-3ß protein expression in CFs after TGF-ß stimulation. Furthermore, GSK-3ß knockdown can reverse the effect of BPS on TGF-ß-activated CFs, enhance α-SMA expression, and promote the proliferation of CFs. BPS could regulate GSK-3ß expression by promoting the binding of GSK-3ß promoter to YBX1. BPS induced upregulation of p-CREB and cAMP, resulting in reduced fibrosis, which was reversed by the knockdown of GSK-3ß or prostaglandin receptor (IPR) antagonists. CONCLUSION: BPS treatment increased the binding of YBX1 to the GSK-3ß promoter, and GSK-3ß protein expression was upregulated, which further caused the upregulation of p-CREB and cAMP, and finally inhibited myocardial fibrosis.

Extracting biomedical relation from cross-sentence text using syntactic dependency graph attention network.

In biomedical literature, cross-sentence texts can usually express rich knowledge, and extracting the interaction relation between entities from cross-sentence texts is of great significance to biomedical research. However, compared with single sentence, cross-sentence text has a longer sequence length, so the research on cross-sentence text information extraction should focus more on learning the context dependency structural information. Nowadays, it is still a challenge to handle global dependencies and structural information of long sequences effectively, and graph-oriented modeling methods have received more and more attention recently. In this paper, we propose a new graph attention network guided by syntactic dependency relationship (SR-GAT) for extracting biomedical relation from the cross-sentence text. It allows each node to pay attention to other nodes in its neighborhood, regardless of the sequence length. The attention weight between nodes is given by a syntactic relation graph probability network (SR-GPR), which encodes the syntactic dependency between nodes and guides the graph attention mechanism to learn information about the dependency structure. The learned feature representation retains information about the node-to-node syntactic dependency, and can further discover global dependencies effectively. The experimental results demonstrate on a publicly available biomedical dataset that, our method achieves state-of-the-art performance while requiring significantly less computational resources. Specifically, in the "drug-mutation" relation extraction task, our method achieves an advanced accuracy of 93.78% for binary classification and 92.14% for multi-classification. In the "drug-gene-mutation" relation extraction task, our method achieves an advanced accuracy of 93.22% for binary classification and 92.28% for multi-classification. Across all relation extraction tasks, our method improves accuracy by an average of 0.49% compared to the existing best model. Furthermore, our method achieved an accuracy of 69.5% in text classification, surpassing most existing models, demonstrating its robustness in generalization across different domains without additional fine-tuning.

Recent Advances in Phase-Engineered Photocatalysts: Classification and Diversified Applications.

Phase engineering is an emerging strategy for tuning the electronic states and catalytic functions of nanomaterials. Great interest has recently been captured by phase-engineered photocatalysts, including the unconventional phase, amorphous phase, and heterophase. Phase engineering of photocatalytic materials (including semiconductors and cocatalysts) can effectively affect the light absorption range, charge separation efficiency, or surface redox reactivity, resulting in different catalytic behavior. The applications for phase-engineered photocatalysts are widely reported, for example, hydrogen evolution, oxygen evolution, CO2 reduction, and organic pollutant removal. This review will firstly provide a critical insight into the classification of phase engineering for photocatalysis. Then, the state-of-the-art development of phase engineering toward photocatalytic reactions will be presented, focusing on the synthesis and characterization methodologies for unique phase structure and the correlation between phase structure and photocatalytic performance. Finally, personal understanding of the current opportunities and challenges of phase engineering for photocatalysis will also be provided.

Targeting choroidal vasculopathy via up-regulation of tRNA-derived fragment tRF-22 expression for controlling progression of myopia.

BACKGROUND: Myopia has emerged as a major public health concern globally, which is tightly associated with scleral extracellular matrix (ECM) remodeling and choroidal vasculopathy. Choroidal vasculopathy has gradually been recognized as a critical trigger of myopic pathology. However, the precise mechanism controlling choroidal vasculopathy remains unclear. Transfer RNA-derived fragments (tRFs) are known as a novel class of small non-coding RNAs that plays important roles in several biological and pathological processes. In this study, we investigated the role of tRF-22-8BWS72092 (tRF-22) in choroidal vasculopathy and myopia progression. METHODS: The tRF-22 expression pattern under myopia-related stresses was detected by qRT-PCR. MTT assays, EdU incorporation assays, Transwell migration assays, and Matrigel assays were conducted to detect the role of tRF-22 in choroidal endothelial cell function in vitro. Isolectin B4 staining and choroidal sprouting assay ex vivo were conducted to detect the role of tRF-22 in choroidal vascular dysfunction in vivo. Immunofluorescent staining, western blot assays and ocular biometric parameters measurement were performed to examine whether altering tRF-22 expression in choroid affects scleral hypoxia and ECM remodeling and myopia progression in vivo. Bioinformatics analysis and luciferase activity assays were conducted to identify the downstream targets of tRF-22. RNA-sequencing combined with m6A-qPCR assays were used to identify the m6A modified targets of METTL3. Gain-of-function and Loss-of-function analysis were performed to reveal the mechanism of tRF-22/METTL3-mediated choroidal vascular dysfunction. RESULTS: The results revealed that tRF-22 expression was significantly down-regulated in myopic choroid. tRF-22 overexpression alleviated choroidal vasculopathy and retarded the progression of myopia in vivo. tRF-22 regulated choroidal endothelial cell viability, proliferation, migration, and tube formation ability in vitro. Mechanistically, tRF-22 interacted with METTL3 and blocked m6A methylation of Axin1 and Arid1b mRNA transcripts, which led to increased expression of Axin1 and Arid1b. CONCLUSIONS: Our study reveals that the intervention of choroidal vasculopathy via tRF-22-METTL3- Axin1/Arid1b axis is a promising strategy for the treatment of patients with myopic pathology.

Cationic proteins from eosinophils bind bone morphogenetic protein receptors promoting vascular calcification and atherogenesis.

AIMS: Blood eosinophil count and eosinophil cationic protein (ECP) concentration are risk factors of cardiovascular diseases. This study tested whether and how eosinophils and ECP contribute to vascular calcification and atherogenesis. METHODS AND RESULTS: Immunostaining revealed eosinophil accumulation in human and mouse atherosclerotic lesions. Eosinophil deficiency in ΔdblGATA mice slowed atherogenesis with increased lesion smooth muscle cell (SMC) content and reduced calcification. This protection in ΔdblGATA mice was muted when mice received donor eosinophils from wild-type (WT), Il4-/-, and Il13-/- mice or mouse eosinophil-associated-ribonuclease-1 (mEar1), a murine homologue of ECP. Eosinophils or mEar1 but not interleukin (IL) 4 or IL13 increased the calcification of SMC from WT mice but not those from Runt-related transcription factor-2 (Runx2) knockout mice. Immunoblot analyses showed that eosinophils and mEar1 activated Smad-1/5/8 but did not affect Smad-2/3 activation or expression of bone morphogenetic protein receptors (BMPR-1A/1B/2) or transforming growth factor (TGF)-ß receptors (TGFBR1/2) in SMC from WT and Runx2 knockout mice. Immunoprecipitation showed that mEar1 formed immune complexes with BMPR-1A/1B but not TGFBR1/2. Immunofluorescence double-staining, ligand binding, and Scatchard plot analysis demonstrated that mEar1 bound to BMPR-1A and BMPR-1B with similar affinity. Likewise, human ECP and eosinophil-derived neurotoxin (EDN) also bound to BMPR-1A/1B on human vascular SMC and promoted SMC osteogenic differentiation. In a cohort of 5864 men from the Danish Cardiovascular Screening trial and its subpopulation of 394 participants, blood eosinophil counts and ECP levels correlated with the calcification scores of different arterial segments from coronary arteries to iliac arteries. CONCLUSION: Eosinophils release cationic proteins that can promote SMC calcification and atherogenesis using the BMPR-1A/1B-Smad-1/5/8-Runx2 signalling pathway.

A database of low-energy atomically precise nanoclusters.

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies.

A "T.E.S.T." hydrogel bioadhesive assisted by corneal cross-linking for in situ sutureless corneal repair.

Corneal transplantation is an effective clinical treatment for corneal diseases, which, however, is limited by donor corneas. It is of great clinical value to develop bioadhesive corneal patches with functions of "Transparency" and "Epithelium & Stroma generation", as well as "Suturelessness" and "Toughness". To simultaneously meet the "T.E.S.T." requirements, a light-curable hydrogel is designed based on methacryloylated gelatin (GelMA), Pluronic F127 diacrylate (F127DA) & Aldehyded Pluronic F127 (AF127) co-assembled bi-functional micelles and collagen type I (COL I), combined with clinically applied corneal cross-linking (CXL) technology for repairing damaged cornea. The patch formed after 5 min of ultraviolet irradiation possesses transparent, highly tough, and strongly bio-adhesive performance. Multiple cross-linking makes the patch withstand deformation near 600% and exhibit a burst pressure larger than 400 mmHg, significantly higher than normal intraocular pressure (10-21 mmHg). Besides, the slower degradation than GelMA-F127DA&AF127 hydrogel without COL I makes hydrogel patch stable on stromal beds in vivo, supporting the regrowth of corneal epithelium and stroma. The hydrogel patch can replace deep corneal stromal defects and well bio-integrate into the corneal tissue in rabbit models within 4 weeks, showing great potential in surgeries for keratoconus and other corneal diseases by combining with CXL.

Idebenone attenuates ferroptosis by inhibiting excessive autophagy via the ROS-AMPK-mTOR pathway to preserve cardiac function after myocardial infarction.

Cardiovascular diseases (CVDs) are the leading causes of mortality worldwide. As a type of CVDs, myocardial infarction (MI) induces ischemia hypoxia, which leads to excessive reactive oxygen species (ROS), resulting in multiple cell deaths and contributing to the subsequent development of heart failure or premature death. Recent evidence indicates that ROS-induced lipid peroxidation promotes autophagy and ferroptosis, leading to the loss of healthy myocardium and resulting in the dysfunction of cardiac tissue. Theoretically, cardiac function would be preserved after MI by inhibiting autophagy and ferroptosis. As an analog of coenzyme Q10 (CoQ10) and a clinically approved drug, idebenone would be used to inhibit ferroptosis and preserve cardiac function due to its capacity to improve mitochondrial physiology with antioxidant and anti-inflammatory properties. Here, we confirmed that the addition of idebenone inhibited H2O2-induced and RSL3-induced ferroptosis. Furthermore, the ROS-AMPK-mTOR pathway axis was identified as the signaling pathway that idebenone stimulated to prevent excessive autophagy and consequent ferroptosis. In the MI animal model, idebenone demonstrated a cardioprotective role by regulating ROS-dependent autophagy and inhibiting ferroptosis, which paves the way for the future clinical translation of idebenone in MI management.

A Bioequivalence Study With Pharmacokinetic Endpoints for Azithromycin Eye Drops.

Azithromycin eye drops with a bioadhesive ocular drug-delivery system can offer a simplified dosing regimen. In this study, we compared the pharmacokinetic properties and assessed the bioequivalence of a newly developed generic azithromycin eye drop with a branded formulation. This open-label, single-dose, randomized, crossover, sparse-sampling ocular bioequivalence study was conducted on 48 healthy Chinese volunteers. Tear samples were collected for up to 36 hours, and each participant was randomly allocated to one of the prespecified sampling times. Tear drug concentrations were determined using a validated liquid chromatography-tandem mass spectrometry method. The pharmacokinetic parameters were calculated via noncompartmental analysis. A nonparametric bootstrap method was used to obtain 90% confidence intervals (CIs) for the ratios of the test and reference drugs. Tolerability was evaluated for adverse events (AEs). After bootstrapping (1000 iterations), the 90%CIs for the log-transformed ratios of Cmax , AUC0-t , and AUC0-∞ were within the acceptable bioequivalence range (80%-125%). No moderate-to-severe AEs were reported for either formulation. Bioequivalence was demonstrated between the two formulations. The sparse-sampling design with the bootstrapping technique is promising for bioequivalence studies of topical ophthalmic drugs.

The Spatial Spillover Effect of Clean Energy Development on Economic Development: A Case of Theoretical and Empirical Analyses from China.

Does clean energy development (CED) have a spatial spillover effect on economic growth (EG)? Using the panel data of 30 provincial administrative units from 2000 to 2019 in China, this study empirically investigates the spatial spillover effect of CED on EG. From the perspective of the supply side rather than the consumption side, using the spatial Durbin model (SDM), the study finds that CED does not have a significant impact on EG, while there is an apparent positive spillover effect of CED on EG in China, meaning that CED in one province can boost EG in the surrounding provinces. Theoretically, this paper provides a new perspective for studying the relationship between CED and EG. In practice, it provides a reference for further improving the government's future energy policy.

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes.

BACKGROUND: Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis. METHODS: The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded. RESULTS: We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1-C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment. CONCLUSIONS: This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management.