Radical-triggered translocation of C-C double bond and functional group.

Multi-site functionalization of molecules provides a potent approach to accessing intricate compounds. However, simultaneous functionalization of the reactive site and the inert remote C(sp3)-H poses a formidable challenge, as chemical reactions conventionally occur at the most active site. In addition, achieving precise control over site selectivity for remote C(sp3)-H activation presents an additional hurdle. Here we report an alternative modular method for alkene difunctionalization, encompassing radical-triggered translocation of functional groups and remote C(sp3)-H desaturation via photo/cobalt dual catalysis. By systematically combining radical addition, functional group migration and cobalt-promoted hydrogen atom transfer, we successfully effectuate the translocation of the carbon-carbon double bond and another functional group with precise site selectivity and remarkable E/Z selectivity. This redox-neutral approach shows good compatibility with diverse fluoroalkyl and sulfonyl radical precursors, enabling the migration of benzoyloxy, acetoxy, formyl, cyano and heteroaryl groups. This protocol offers a resolution for the simultaneous transformation of manifold sites.

Formation Characteristics of Pt-Ni Alloy Nanoparticles Fabricated by Nanolamination of Atomic Layer Deposition in Hydrogen.

Forced-flow atomic layer deposition nanolamination is employed to fabricate Pt-Ni nanoparticles on XC-72, with the compositions ranging from Pt94Ni6 to Pt67Ni33. Hydrogen is used as a co-reactant for depositing Pt and Ni. The growth rate of Pt is slower than that using oxygen reactant, and the growth exhibits preferred orientation along the (111) plane. Ni shows much slower growth rate than Pt, and it is only selectively deposited on Pt, not on the substrate. Higher ratios of Ni would hinder subsequent stacking of Pt atoms, resulting in lower overall growth rate and smaller particles (1.3-2.1 nm). Alloying of Pt with Ni causes shifted lattice that leads to larger lattice parameter and d-spacing as Ni fraction increases. From the electronic state analysis, Pt 4f peaks are shifted to lower binding energies with increasing the Ni content, suggesting charge transfer from Ni to Pt. Schematic of the growth behavior is proposed. Most of the alloy nanoparticles exhibit higher electrochemical surface area and oxygen reduction reaction activity than those of commercial Pt. Especially, Pt83Ni17 and Pt87Ni13 show excellent mass activities of 0.76 and 0.59 A mgPt -1, respectively, higher than the DOE target of 2025, 0.44 A mgPt -1.

Research progress and application strategies of sugar transport mechanisms in rice.

In plants, carbohydrates are central products of photosynthesis. Rice is a staple that contributes to the daily calorie intake for over half of the world's population. Hence, the primary objective of rice cultivation is to maximize carbohydrate production. The "source-sink" theory is proposed as a valuable principle for guiding crop breeding. However, the "flow" research lag, especially in sugar transport, has hindered high-yield rice breeding progress. This review concentrates on the genetic and molecular foundations of sugar transport and its regulation, enhancing the fundamental understanding of sugar transport processes in plants. We illustrate that the apoplastic pathway is predominant over the symplastic pathway during phloem loading in rice. Sugar transport proteins, such as SUTs and SWEETs, are essential carriers for sugar transportation in the apoplastic pathway. Additionally, we have summarized a regulatory pathway for sugar transport genes in rice, highlighting the roles of transcription factors (OsDOF11, OsNF-YB1, OsNF-YC12, OsbZIP72, Nhd1), OsRRM (RNA Recognition Motif containing protein), and GFD1 (Grain Filling Duration 1). Recognizing that the research shortfall in this area stems from a lack of advanced research methods, we discuss cutting-edge analytical techniques such as Mass Spectrometry Imaging and single-cell RNA sequencing, which could provide profound insights into the dynamics of sugar distribution and the associated regulatory mechanisms. In summary, this comprehensive review serves as a valuable guide, directing researchers toward a deep understanding and future study of the intricate mechanisms governing sugar transport.

Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography.

Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings. Categorizing these narrowings can provide insight into whether patients should receive vascular revascularization treatment. The majority of current deep learning methods for analyzing coronary angiography are mostly confined to the theoretical research domain, with limited studies offering direct practical support to clinical practitioners. This paper proposes an integrated deep-learning model for the localization and classification of narrowings in coronary angiography images. The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection. Furthermore, we developed the "Hemadostenosis" web platform (http://bioinfor.imu.edu.cn/hemadostenosis) using Django, a highly mature HTTP framework. Users are able to submit coronary angiography image data for assessment via a visual interface. Subsequently, the system sends the images to a trained convolutional neural network model to localize and categorize the narrowings. Finally, the visualized outcomes are displayed to users and are downloadable. Our proposed approach pioneers the recognition and categorization of arterial narrowings in vascular angiography, offering practical support to clinical practitioners in their learning and diagnostic processes.

Bioprinted, spatially defined breast tumor microenvironment models of intratumoral heterogeneity and drug resistance.

Cellular, extracellular matrix (ECM), and spatial heterogeneity of tumor microenvironments (TMEs) regulate disease progression and treatment efficacy. Developing in vitro models that recapitulate the TME promises to accelerate studies of tumor biology and identify new targets for therapy. Here, we used extrusion-based, multi-nozzle 3D bioprinting to spatially pattern triple-negative MDA-MB-231 breast cancer cells, endothelial cells (ECs), and human mammary cancer-associated fibroblasts (HMCAFs) with biomimetic ECM inks. Bioprinted models captured key features of the spatial architecture of human breast tumors, including varying-sized dense regions of cancer cells and surrounding microvessel-rich stroma. Angiogenesis and ECM stiffening occurred in the stromal area but not the cancer cell-rich (CCR) regions, mimicking pathological changes in patient samples. Transcriptomic analyses revealed upregulation of angiogenesis-related and ECM remodeling-related signatures in the stroma region and identified potential ligand-receptor (LR) mediators of these processes. Breast cancer cells in distinct parts of the bioprinted TME showed differing sensitivities to chemotherapy, highlighting environmentally mediated drug resistance. In summary, our 3D-bioprinted tumor model will act as a platform to discover integrated functions of the TME in cancer biology and therapy.

Integrated analysis of patients with bladder cancer from prospective transcription factor activity: Implications for personalized treatment approaches.

Transcription factors are a specialized group of proteins that play important roles in regulating gene expression in human. These proteins control the transcription and translation of genes by binding to specific sites on DNA, thereby regulating key biological processes such as cell differentiation, proliferation, immune response, and neural development. Moreover, transcription factors are also involved in apoptosis and the pathogenesis of various diseases. By investigating transcription factors, researchers can uncover the mechanisms of gene regulation in organisms and develop more effective methods for preventing and treating human diseases. In the present study, the Virtual Inference of Protein-activity by Enriched Regulon algorithm was utilized to calculate the protein activity of transcription factors, and the metabolic-related protein activity were used for classifying bladder cancer patients into different subtype. To identify chemotherapy drugs with clinical benefits, the differences in prognosis and drug sensitivity between two distinct subtypes of bladder cancer patients were investigated. Simultaneously, the master regulators that display varying levels of transcription factor activity between two different bladder cancer subtypes were explored. Additionally, the potential transcriptional regulatory mechanisms and targets of these factors were investigated, thereby generating novel insights into bladder cancer research at the transcriptional regulation level.

Machine learning-based prediction of diabetic patients using blood routine data.

Diabetes stands as one of the most prevalent chronic diseases globally. The conventional methods for diagnosing diabetes are frequently overlooked until individuals manifest noticeable symptoms of the condition. This study aimed to address this gap by collecting comprehensive datasets, including 1000 instances of blood routine data from diabetes patients and an equivalent dataset from healthy individuals. To differentiate diabetes patients from their healthy counterparts, a computational framework was established, encompassing eXtreme Gradient Boosting (XGBoost), random forest, support vector machine, and elastic net algorithms. Notably, the XGBoost model emerged as the most effective, exhibiting superior predictive results with an area under the receiver operating characteristic curve (AUC) of 99.90% in the training set and 98.51% in the testing set. Moreover, the model showcased commendable performance during external validation, achieving an overall accuracy of 81.54%. The probability generated by the model serves as a risk score for diabetes susceptibility. Further interpretability was achieved through the utilization of the Shapley additive explanations (SHAP) algorithm, identifying pivotal indicators such as mean corpuscular hemoglobin concentration (MCHC), lymphocyte ratio (LY%), standard deviation of red blood cell distribution width (RDW-SD), and mean corpuscular hemoglobin (MCH). This enhances our understanding of the predictive mechanisms underlying diabetes. To facilitate the application in clinical and real-life settings, a nomogram was created based on the logistic regression algorithm, which can provide a preliminary assessment of the likelihood of an individual having diabetes. Overall, this research contributes valuable insights into the predictive modeling of diabetes, offering potential applications in clinical practice for more effective and timely diagnoses.

Optimal scheduling model using the IGDT method for park integrated energy systems considering P2G-CCS and cloud energy storage.

To enhance the energy efficiency and financial gains of the park integrated energy system (PIES). This paper constructs a bi-level optimization model of PIES-cloud energy storage (CES) based on source-load uncertainty. Firstly, the scheduling framework of PIES with refined power-to-gas (P2G), carbon capture and storage (CCS) and CES coupling is constructed. Moreover, a bi-level optimization model with the upper tier subject being the PIES operator and the lower tier subject being the CES operator is established under the ladder-type carbon price mechanism with reward and punishment (LCPMRP). Then a proposed entropy weight adaptive information gap decision theory method (EAIGDT) is proposed to eliminate the subjectivity factor and retain its non-probabilistic features while dealing with multiple source-load uncertainties, and according to the operator's risk preference to build risk-averse (RA) and risk-seeking (RS) strategies, respectively. Finally, the measured data in a certain area of Xinjiang verifies the proposed optimal scheduling method. The results show that the method can effectively take into account the interests of various subjects and realise PIES low-carbon economic operation.

Hybridization of short-range and long-range charge transfer boosts room-temperature phosphorescence performance.

At present, mainstream room-temperature phosphorescence (RTP) emission relies on organic materials with long-range charge-transfer effects; therefore, exploring new forms of charge transfer to generate RTP is worth studying. In this work, indole-carbazole was used as the core to ensure the narrowband fluorescence emission of the material based on its characteristic short-range charge-transfer effect. In addition, halogenated carbazoles were introduced into the periphery to construct long-range charge transfer, resulting in VTCzNL-Cl and VTCzNL-Br. By encapsulating these phosphors into a robust host (TPP), two host-guest crystalline systems were further developed, achieving efficient RTP performance with phosphorescence quantum yields of 26% and phosphorescence lifetimes of 3.2 and 39.2 ms, respectively.

The effect of a hybrid structured pulmonary rehabilitation education program for patients with lung cancer with a high risk of postoperative pulmonary complications: A quasi-experimental study.

PURPOSE: The absence of standardized protocols and education are the main obstacles to perioperative pulmonary rehabilitation (PR), especially for patients with high-risk factors of postoperative pulmonary complications (PPCs). We aimed to explore the effect of a hybrid structured pulmonary rehabilitation education program (SPREP) on patients with lung cancer at high risk of PPCs. METHODS: A quasi-experimental trial with a pre-post test design was conducted. The control group (n = 53) adopted routine perioperative pulmonary rehabilitation, while the intervention group (n = 53) received SPREP. Respiratory function, 6-min walk distance, Borg dyspnea scale, quality of life, anxiety-depression scores at admission, discharge, 2 weeks and 3 months post-discharge, and incidence of PPCs were compared between the two groups. RESULTS: There were no significant differences on the 6-min walk distance and Borg Dyspnoea Scale at discharge between the two groups (P > 0.05), whereas the intervention group showed improved performance at the remaining time points (P < 0.05). In addition, the intervention group had improved exercise capacity, pulmonary function and quality of life, reduced levels of anxiety and depression at discharge, 2 weeks post-discharge and 3 months post-discharge (P < 0.05). In addition, incidence of PPCs was significantly reduced in the intervention group, especially postoperative pneumonia. CONCLUSIONS: The SPREP could show significant benefits in enhancing exercise capacity, lung function, and quality of life, while diminishing the occurrence of PPCs and mitigating the levels of anxiety and depression, future large RCT need to further explore the efficacy. TRIAL REGISTRATION: This study was registered with the China Clinical Trial Registration Center (ChiCTR) under the Clinical Trial Registration Number [ChiCTR2200066698].

Trophic Niche Differentiation in Two Sympatric Nuthatch Species (Sitta yunnanensis and Sitta nagaensis).

Sympatric closely related species may experience interspecific trophic competition due to ecological similarity; they may isolate in terms of diet or habitat use as a strategy to avoid competition. The body tissues of consumers contain stable isotope signatures information that can be applied to infer their dietary information. In this study, δ13C and δ15N stable isotopes were analyzed to determine the dietary information and trophic niches of sympatric coexisting Sitta yunnanensis and Sitta nagaensis. The results showed that the food sources of S. yunnanensis and S. nagaensis were from six orders, including Orthoptera, and the cumulative contribution rate was 99.97%, with the two species eating similar diets but at different rates. The larger δ13C of S. yunnanensis indicates that it had a wider range of habitats for feeding, while the difference in δ15N values was not significant (p > 0.05), indicating that both species feed on similar nutrient levels. As determined by Bayesian ellipses, the isotopic niches of S. yunnanensis and S. nagaensis were differentiated; the isotopic niche width of S. yunnanensis is 2.69‱2, which was larger than that of S. nagaensis (0.73‱2), indicates that differentiation between the two species in diet or habitat use reduced competition. Trophic niche differentiation and differences in foraging proportions may be the principal resource allocation mechanisms behind S. yunnanensis and S. nagaensis coexistence.

Impact of prior SARS-CoV-2 infection on college students' hesitancy to receive additional COVID-19 vaccine booster doses: A study from Taizhou, China.

Purpose: This study aimed to examine the impact of a history of SARS-CoV-2 infection on the hesitancy of college students to receive additional COVID-19 vaccine booster doses. Methods: A population-based self-administered online survey was conducted in July 2024 in Taizhou, China. A total of 792 respondents were included in this study. Logistic regression was conducted to identify factors associated with college students' hesitation to receive booster doses of the COVID-19 vaccine. Results: Of 792 respondents, 32.2 % hesitated to receive additional doses of the COVID-19 vaccine booster. Furthermore, 23.5 % of the respondents reported an increase in hesitancy to receiving additional COVID-19 vaccine booster doses compared to before they were infected with SARS-CoV-2. In the regression analyses, college students who had a secondary infection were more hesitant to receive additional COVID-19 vaccine booster doses (OR = 0.481, 95 % CI: (0.299-0.774), P = 0.003). Moreover, students with secondary infections who were male (OR = 0.417, 95 % CI: 0.221-0.784, P = 0.007), with lower than a bachelor's degree (OR = 0.471, 95 % CI: 0.272-0.815, P = 0.007), in non-medical majors (OR = 0.460, 95 % CI: 0.248-0.856, P = 0.014), and sophomores or below (OR = 0.483, 95 % CI: 0.286-0.817, P = 0.007) were more hesitant to receive additional COVID-19 vaccine booster doses. Conclusion: A history of SARS-CoV-2 infection affects college students' hesitation to receive additional COVID-19 vaccine booster doses, which was higher in those who experienced secondary infections.

Implicit weight bias: shared neural substrates for overweight and angry facial expressions revealed by cross-adaptation.

The perception of facial expression plays a crucial role in social communication, and it is known to be influenced by various facial cues. Previous studies have reported both positive and negative biases toward overweight individuals. It is unclear whether facial cues, such as facial weight, bias facial expression perception. Combining psychophysics and event-related potential technology, the current study adopted a cross-adaptation paradigm to examine this issue. The psychophysical results of Experiments 1A and 1B revealed a bidirectional cross-adaptation effect between overweight and angry faces. Adapting to overweight faces decreased the likelihood of perceiving ambiguous emotional expressions as angry compared to adapting to normal-weight faces. Likewise, exposure to angry faces subsequently caused normal-weight faces to appear thinner. These findings were corroborated by bidirectional event-related potential results, showing that adaptation to overweight faces relative to normal-weight faces modulated the event-related potential responses of emotionally ambiguous facial expression (Experiment 2A); vice versa, adaptation to angry faces relative to neutral faces modulated the event-related potential responses of ambiguous faces in facial weight (Experiment 2B). Our study provides direct evidence associating overweight faces with facial expression, suggesting at least partly common neural substrates for the perception of overweight and angry faces.

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023.

Background: Epilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization. Methods: Relevant articles and reviews were searched for 2004-2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio). Results: A total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper "Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals" was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments. Conclusions: This study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.

[Cadmium Phytoremediation Effect of Sweet Sorghum Assisted with Citric Acid on Typical Parent Soil in Southern China].

Sweet sorghum has a large biomass and strong cadmium (Cd) absorption capacity, which has the potential for phytoremediation of Cd-contaminated soil. In order to study the Cd phytoremediation effect of sweet sorghum assisted with citric acid on the typical parent materials in southern China, a field experiment was carried out in two typical parent material farmland areas (neutral purple mud field and jute sand mud field) with Cd pollution in Hunan Province. The results showed that:① Citric acid had no inhibitory effect on the growth of sweet sorghum. After the application of citric acid, the aboveground biomass of sweet sorghum at the maturity stage increased by 10.1%-24.7%. ② Both sweet sorghum planting and citric acid application reduced the soil pH value, and the application of citric acid further reduced the soil pH value at each growth stage of sweet sorghum; this decrease was greater in the neutral purple mud field, which decreased by 0.24-0.72 units. ③ Both sweet sorghum planting and citric acid application reduced the total amount of soil Cd, and the decreases in the neutral purple mud field and jute sand mud field were 23.8%-52.2% and 17.1%-31.8%, respectively. The acid-extractable percentage of soil Cd in both places increased by 38.6%-147.7% and 4.8%-22.7%, respectively. ④ The application of citric acid could significantly increase the Cd content in various tissues of sweet sorghum. The Cd content in the aboveground part of the plant in the neutral purple mud field was higher than that in the jute sand mud field, and the Cd content in stems and leaves was 0.25-1.90 mg·kg-1 and 0.21-0.64 mg·kg-1, respectively. ⑤ After applying citric acid, the Cd extraction amount of sweet sorghum in neutral purple mud soil in the mature stage reached 47.56 g·hm-2. In summary, citric acid could enhance the efficiency of sweet sorghum in the phytoremediation of Cd-contaminated soil, and the effect was better in neutral purple mud fields. This technology has the potential for remediation coupled with agro-production for heavy metal-contaminated farmland.

A bionic self-driven retinomorphic eye with ionogel photosynaptic retina.

Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.

Dysregulation of iron transport-related biomarkers in blood leukocytes is associated with poor prognosis of early trauma.

Objective: The early targeted and effective diagnosis and treatment of severe trauma are crucial for patients' outcomes. Blood leukocytes act as significant effectors during the initial inflammation and activation of innate immunity in trauma. This study aims to identify hub genes related to patients' prognosis in blood leukocytes at the early stages of trauma. Methods: The expression profiles of Gene Expression Omnibus (GEO) Series (GSE) 36809 and GSE11375 were downloaded from the GEO database. R software, GraphPad Prism 9.3.1 software, STRING database, and Cytoscape software were used to process the data and identify hub genes in blood leukocytes of early trauma. Results: Gene Ontology (GO) analysis showed that the differentially expressed genes (DEGs) of blood leukocytes at the early stages of trauma (0-4 h, 4-8 h, and 8-12 h) were mainly involved in neutrophil activation and neutrophil degranulation, neutrophil activation involved in immune response, neutrophil mediated immunity, lymphocyte differentiation, and cell killing. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly involved in Osteoclast differentiation and Hematopoietic cell lineage. Sixty-six down-regulated DEGs and 148 up-regulated DEGs were identified and 37 hub genes were confirmed by Molecular Complex Detection (MCODE) of Cytoscape. Among the hub genes, Lipocalin 2 (LCN2), Lactotransferrin (LTF), Olfactomedin 4 (OLFM4), Resistin (RETN), and Transcobalamin 1 (TCN1) were related to prognosis and connected with iron transport closely. LCN2 and LTF were involved in iron transport and had a moderate predictive value for the poor prognosis of trauma patients, and the AUC of LCN2 and LTF was 0.7777 and 0.7843, respectively. Conclusion: As iron transport-related hub genes in blood leukocytes, LCN2 and LTF can be used for prognostic prediction of early trauma.

Syringin alleviates bisphenol A-induced spermatogenic defects and testicular injury by suppressing oxidative stress and inflammation in male zebrafish.

Syringin (SRG) is a bioactive principle possessing extensive activities including scavenging of free radicals, inhibition of apoptosis, and anti-inflammatory properties. However, its effects on spermatogenic defects and testicular injury as well as the underlying mechanisms are still unclear. This study aims to investigate the protective effect of SRG on testis damage in zebrafish and explore its potential molecular events. Zebrafish testicular injury was induced by exposure to bisphenol A (BPA) (3000 µg/L) for two weeks. Fish were treated with intraperitoneal injection of SRG at different doses (5 and 50 mg/kg bodyweight) for two more weeks under BPA induction. Subsequently, the testis and sperm were collected for morphological, histological, biochemical and gene expression examination. It was found that the administration of SRG resulted in a significant protection from BPA-caused impact on sperm concentration, morphology, motility, fertility rate, testosterone level, spermatogenic dysfunction and resulted in increased apoptotic and reactive oxygen species' levels. Furthermore, testicular transcriptional profiling alterations revealed that the regulation of inflammatory response and oxidative stress were generally enriched in differentially expressed genes (DEGs) after SRG treatment. Additionally, it was identified that SRG prevented BPA-induced zebrafish testis injury through upregulation of fn1a, krt17, fabp10a, serpina1l and ctss2. These results indicate that SRG alleviated spermatogenic defects and testicular injury by suppressing oxidative stress and inflammation in male zebrafish.