Effects of a prior failed attempt on the outcomes of subsequent chronic total occlusion-percutaneous coronary intervention.

Objectives: Patients undergoing a prior failed attempt of chronic total occlusion-percutaneous coronary intervention (CTO-PCI) represent a challenging subgroup across all patients undergoing CTO-PCI. There are limited data on the effects of a prior failed attempt on the outcomes of subsequent CTO-PCI. We aimed to compare the procedural results and 24-month outcomes of prior-failed-attempt CTO-PCI with those of initial-attempt CTO-PCI. Methods: Patients who underwent attempted CTO-PCI between January 2017 and December 2019 were prospectively enrolled. We analyzed the procedural results and 24-month major adverse cardiac events (MACE) between patients who underwent prior-failed-attempt and initial-attempt CTO-PCI. MACE was defined as a composite of cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization (TVR) during follow-up. Results: In total, 484 patients who underwent CTO-PCI (prior-failed-attempt, n = 49; initial-attempt, n = 435) were enrolled during the study period. After propensity score matching (1:3), 147 patients were included in the initial-attempt group. The proportion of the Japanese-CTO (J-CTO) score ≥2 was higher in the patients who underwent prior failed attempt than in those who underwent initial attempt (77.5% vs. 38.8%, p < 0.001). The retrograde approach was more often adopted in the prior-failed-attempt group than in the initial-attempt group (32.7% vs. 3.4%, [P< 0.001). Successful CTO revascularization rates were significantly lower in the prior-failed attempt-group than in the initial attempt group (53.1% vs. 83.3%, P < 0.001). The multivariate analysis revealed that J-CTO score ≥2 [odds ratio (OR), 0.359; 95% confidence interval (CI), 0.159-0.812; P = 0.014], intravascular ultrasound procedure (OR, 4.640; 95% CI, 1.380-15.603; P = 0.013), and prior failed attempt (OR, 0.285; 95% CI, 0.125-0.648; P = 0.003) were the independent predictors for successful CTO revascularization. There were no significant differences in major procedural complications (2.0% vs. 0.7%, p = 0.438) and MACE rates (4.1% vs. 8.8%, p = 0.438) between the groups, mainly due to the TVR rate (4.1% vs. 8.2%, P = 0.522). Conclusions: Compared with initial-attempt CTO-PCI, prior-failed-attempt CTO-PCI deserves more attention, since it is associated with a lower successful CTO revascularization rate. Prior failed attempt, J-CTO score ≥2, and IVUS procedure are the determining factors for predicting successful CTO revascularization. There are no significantly different unfavorable outcomes between patients who undergo prior-failed-attempt and initial-attempt CTO-PCI.

Measuring and Predicting Multisensory Reaction Latency: A Probabilistic Model for Visual-Auditory Integration.

Virtual/augmented reality (VR/AR) devices offer both immersive imagery and sound. With those wide-field cues, we can simultaneously acquire and process visual and auditory signals to quickly identify objects, make decisions, and take action. While vision often takes precedence in perception, our visual sensitivity degrades in the periphery. In contrast, auditory sensitivity can exhibit an opposite trend due to the elevated interaural time difference. What occurs when these senses are simultaneously integrated, as is common in VR applications such as 360° video watching and immersive gaming? We present a computational and probabilistic model to predict VR users' reaction latency to visual-auditory multisensory targets. To this aim, we first conducted a psychophysical experiment in VR to measure the reaction latency by tracking the onset of eye movements. Experiments with numerical metrics and user studies with naturalistic scenarios showcase the model's accuracy and generalizability. Lastly, we discuss the potential applications, such as measuring the sufficiency of target appearance duration in immersive video playback, and suggesting the optimal spatial layouts for AR interface design.

Metabolic interdependencies in thermophilic communities are revealed using co-occurrence and complementarity networks.

Microbial communities exhibit intricate interactions underpinned by metabolic dependencies. To elucidate these dependencies, we present a workflow utilizing random matrix theory on metagenome-assembled genomes to construct co-occurrence and metabolic complementarity networks. We apply this approach to a temperature gradient hot spring, unraveling the interplay between thermal stress and metabolic cooperation. Our analysis reveals an increase in the frequency of metabolic interactions with rising temperatures. Amino acids, coenzyme A derivatives, and carbohydrates emerge as key exchange metabolites, forming the foundation for syntrophic dependencies, in which commensalistic interactions take a greater proportion than mutualistic ones. These metabolic exchanges are most prevalent between phylogenetically distant species, especially archaea-bacteria collaborations, as a crucial adaptation to harsh environments. Furthermore, we identify a significant positive correlation between basal metabolite exchange and genome size disparity, potentially signifying a means for streamlined genomes to leverage cooperation with metabolically richer partners. This phenomenon is also confirmed by another composting system which has a similar wide range of temperature fluctuations. Our workflow provides a feasible way to decipher the metabolic complementarity mechanisms underlying microbial interactions, and our findings suggested environmental stress regulates the cooperative strategies of thermophiles, while these dependencies have been potentially hardwired into their genomes during co-evolutions.

Noise-robust Vision-language Pre-training with Positive-negative Learning.

Vision-Language Pre-training (VLP) has shown promising performance in various tasks by learning a generic image-text representation space. However, most existing VLP methods encounter the Noisy Correspondence (NC) problem which refers to wrongly matched image-text pairs harvested from the wild. In this paper, we empirically study the influence of NC on the VLP model and obtain the following two observations. First, the NC will largely degrade the performance in downstream tasks even via fine-tuning, indicating the necessity of handling NC in the pre-training period. Second, the influence of NC varies in different pre-training objectives, suggesting the objective-customized solution for achieving NC robustness. Based on the above observations, we propose a novel NoisE-robust Vision-languagE pRe-training method (NEVER) to endow the VLP model with robustness against NC. In brief, NEVER first divides the training data into clean and noisy subsets in a progressive and adaptive manner. Then NEVER employs the positive learning (PL) and negative learning (NL) on the splits to enjoy model convergence and noise robustness, respectively. To further handle the false negative in PL and NL, NEVER proposes to smoothen and sharpen the training targets with the predictions from a twin momentum model. Extensive experiments on the various V+L tasks verify the effectiveness of the proposed method. The code will be released upon acceptance.

Unveiling the deterministic dynamics of microbial meta-metabolism: a multi-omics investigation of anaerobic biodegradation.

BACKGROUND: Microbial anaerobic metabolism is a key driver of biogeochemical cycles, influencing ecosystem function and health of both natural and engineered environments. However, the temporal dynamics of the intricate interactions between microorganisms and the organic metabolites are still poorly understood. Leveraging metagenomic and metabolomic approaches, we unveiled the principles governing microbial metabolism during a 96-day anaerobic bioreactor experiment. RESULTS: During the turnover and assembly of metabolites, homogeneous selection was predominant, peaking at 84.05% on day 12. Consistent dynamic coordination between microbes and metabolites was observed regarding their composition and assembly processes. Our findings suggested that microbes drove deterministic metabolite turnover, leading to consistent molecular conversions across parallel reactors. Moreover, due to the more favorable thermodynamics of N-containing organic biotransformations, microbes preferentially carried out sequential degradations from N-containing to S-containing compounds. Similarly, the metabolic strategy of C18 lipid-like molecules could switch from synthesis to degradation due to nutrient exhaustion and thermodynamical disadvantage. This indicated that community biotransformation thermodynamics emerged as a key regulator of both catabolic and synthetic metabolisms, shaping metabolic strategy shifts at the community level. Furthermore, the co-occurrence network of microbes-metabolites was structured around microbial metabolic functions centered on methanogenesis, with CH4 as a network hub, connecting with 62.15% of total nodes as 1st and 2nd neighbors. Microbes aggregate molecules with different molecular traits and are modularized depending on their metabolic abilities. They established increasingly positive relationships with high-molecular-weight molecules, facilitating resource acquisition and energy utilization. This metabolic complementarity and substance exchange further underscored the cooperative nature of microbial interactions. CONCLUSIONS: All results revealed three key rules governing microbial anaerobic degradation. These rules indicate that microbes adapt to environmental conditions according to their community-level metabolic trade-offs and synergistic metabolic functions, further driving the deterministic dynamics of molecular composition. This research offers valuable insights for enhancing the prediction and regulation of microbial activities and carbon flow in anaerobic environments. Video Abstract.

Artemisinin inhibits neuronal ferroptosis in Alzheimer's disease models by targeting KEAP1.

Ferroptosis, a form of cell death characterized by lipid peroxidation, is involved in neurodegenerative diseases such as Alzheimer´s disease (AD). Recent studies have shown that a first-line antimalarial drug artemisinin is effective to counteract AD pathology. In this study, we investigated the protective effect of artemisinin against neuronal ferroptosis and the underlying mechanisms. In hippocampal HT22 cells, pretreatment with artemisinin dose-dependently protected against Erastin-induced cell death with an EC50 value of 5.032 µM, comparable to the ferroptosis inhibitor ferrostatin-1 (EC50 = 4.39 µM). We demonstrated that artemisinin (10 µM) significantly increased the nuclear translocation of Nrf2 and upregulated SLC7A11 and GPX4 in HT22 cells. Knockdown of Nrf2, SLC7A11 or GPX4 prevented the protective action of artemisinin, indicating that its anti-ferroptosis effect is mediated by the Nrf2-SLC7A11-GPX4 pathway. Molecular docking and Co-Immunoprecipitation (Co-IP) analysis revealed that artemisinin competitively binds with KEAP1, promoting the dissociation of KEAP1-Nrf2 complex and inhibiting the ubiquitination of Nrf2. Intrahippocampal injection of imidazole-ketone-Erastin (IKE) induced ferroptosis in mice accompanied by cognitive deficits evidenced by lower preference for exploration of new objects and new object locations in the NOR and NOL tests. Artemisinin (5, 10 mg/kg, i.p.) dose-dependently inhibited IKE-induced ferroptosis in hippocampal CA1 region and ameliorated learning and memory impairments. Moreover, we demonstrated that artemisinin reversed Aß1-42-induced ferroptosis, lipid peroxidation and glutathione depletion in HT22 cells, primary hippocampal neurons, and 3×Tg mice via the KEAP1-Nrf2 pathway. Our results demonstrate that artemisinin is a novel neuronal ferroptosis inhibitor that targets KEAP1 to activate the Nrf2-SLC7A11-GPX4 pathway.

Salvianolic acid B in fibrosis treatment: a comprehensive review.

Fibrosis is a public health issue of great concern characterized by the excessive deposition of extracellular matrix, leading to the destruction of parenchymal tissue and organ dysfunction that places a heavy burden on the global healthcare system due to its high incidence, disability, and mortality. Salvianolic acid B (SalB) has positively affected various human diseases, including fibrosis. In this review, we concentrate on the anti-fibrotic effects of SalB from a molecular perspective while providing information on the safety, adverse effects, and drug interactions of SalB. Additionally, we discuss the innovative SalB formulations, which give some references for further investigation and therapeutic use of SalB's anti-fibrotic qualities. Even with the encouraging preclinical data, additional research is required before relevant clinical trials can be conducted. Therefore, we conclude with recommendations for future studies. It is hoped that this review will provide comprehensive new perspectives on future research and product development related to SalB treatment of fibrosis and promote the efficient development of this field.

RoMo: Robust Unsupervised Multimodal Learning With Noisy Pseudo Labels.

The rise of the metaverse and the increasing volume of heterogeneous 2D and 3D data have created a growing demand for cross-modal retrieval, enabling users to query semantically relevant data across different modalities. Existing methods heavily rely on class labels to bridge semantic correlations; however, collecting large-scale, well-labeled data is expensive and often impractical, making unsupervised learning more attractive and feasible. Nonetheless, unsupervised cross-modal learning faces challenges in bridging semantic correlations due to the lack of label information, leading to unreliable discrimination. In this paper, we reveal and study a novel problem: unsupervised cross-modal learning with noisy pseudo-labels. To address this issue, we propose a 2D-3D unsupervised multimodal learning framework that leverages multimodal data. Our framework consists of three key components: 1) Self-matching Supervision Mechanism (SSM) warms up the model to encapsulate discrimination into the representations in a self-supervised learning manner. 2) Robust Discriminative Learning (RDL) further mines the discrimination from the learned imperfect predictions after warming up. To tackle the noise in the predicted pseudo labels, RDL leverages a novel Robust Concentrating Learning Loss (RCLL) to alleviate the influence of the uncertain samples, thus embracing robustness against noisy pseudo labels. 3) Modality-invariance Learning Mechanism (MLM) minimizes the cross-modal discrepancy to enforce SSM and RDL to produce common representations. We conduct comprehensive experiments on four 2D-3D multimodal datasets, comparing our method against 14 state-of-the-art approaches, thereby demonstrating its effectiveness and superiority.

Effects of Castanopsis echinocarpa on Sensorineural Hearing Loss via Neuronal Gene Regulation.

Sensorineural hearing loss (SNHL), characterized by damage to the inner ear or auditory nerve, is a prevalent auditory disorder. This study explores the potential of Castanopsis echinocarpa (CAE) as a therapeutic agent for SNHL. In vivo experiments were conducted using zebrafish and mouse models. Zebrafish with neomycin-induced ototoxicity were treated with CAE, resulting in otic hair cell protection with an EC50 of 0.49 µg/mL and a therapeutic index of 1020. CAE treatment improved auditory function and protected cochlear sensory cells in a mouse model after noise-induced hearing loss (NIHL). RNA sequencing of NIHL mouse cochleae revealed that CAE up-regulates genes involved in neurotransmitter synthesis, secretion, transport, and neuronal survival. Real-time qPCR validation showed that NIHL decreased the mRNA expression of genes related to neuronal function, such as Gabra1, Gad1, Slc32a1, CaMK2b, CaMKIV, and Slc17a7, while the CAE treatment significantly elevated these levels. In conclusion, our findings provide strong evidence that CAE protects against hearing loss by promoting sensory cell protection and enhancing the expression of genes critical for neuronal function and survival.

Spectral Embedding Fusion for Incomplete Multiview Clustering.

Incomplete multiview clustering (IMVC) aims to reveal the underlying structure of incomplete multiview data by partitioning data samples into clusters. Several graph-based methods exhibit a strong ability to explore high-order information among multiple views using low-rank tensor learning. However, spectral embedding fusion of multiple views is ignored in low-rank tensor learning. In addition, addressing missing instances or features is still an intractable problem for most existing IMVC methods. In this paper, we present a unified spectral embedding tensor learning (USETL) framework that integrates the spectral embedding fusion of multiple similarity graphs and spectral embedding tensor learning for IMVC. To remove redundant information from the original incomplete multiview data, spectral embedding fusion is performed by introducing spectral rotations at two different data levels, i.e., the spectral embedding feature level and the clustering indicator level. The aim of introducing spectral embedding tensor learning is to capture consistent and complementary information by seeking high-order correlations among multiple views. The strategy of removing missing instances is adopted to construct multiple similarity graphs for incomplete multiple views. Consequently, this strategy provides an intuitive and feasible way to construct multiple similarity graphs. Extensive experimental results on multiview datasets demonstrate the effectiveness of the two spectral embedding fusion methods within the USETL framework.

Beyond water and soil: Air emerges as a major reservoir of human pathogens.

Assessing the risk of human pathogens in the environment is crucial for controlling the spread of diseases and safeguarding human health. However, conducting a thorough assessment of low-abundance pathogens in highly complex environmental microbial communities remains challenging. This study compiled a comprehensive catalog of 247 human-pathogenic bacterial taxa from global biosafety agencies and identified more than 78 million genome-specific markers (GSMs) from their 17,470 sequenced genomes. Subsequently, we analyzed these pathogens' types, abundance, and diversity within 474 shotgun metagenomic sequences obtained from diverse environmental sources. The results revealed that among the four habitats studied (air, water, soil, and sediment), the detection rate, diversity, and abundance of detectable pathogens in the air all exceeded those in the other three habitats. Air, sediment, and water environments exhibited identical dominant taxa, indicating that these human pathogens may have unique environmental vectors for their transmission or survival. Furthermore, we observed the impact of human activities on the environmental risk posed by these pathogens, where greater amounts of human activities significantly increased the abundance of human pathogenic bacteria, especially in water and air. These findings have remarkable implications for the environmental risk assessment of human pathogens, providing valuable insights into their presence and distribution across different habitats.

Difluorocarbene-Promoted O-O Bond Activation of Peroxy Acids for Electrophilic Carboxylation of Boronic Acids.

In this study, a difluorocarbene-promoted O-O bond activation of peroxy acids is developed through the insertion of difluorocarbene into O-H bond. This activation strategy in synergy with O-B coordination with boronic acids/ester greatly polarizes the O-O bond for in-situ generation of carboxylium species that reacts with the nucleophilic part of boronic acids in a concerted way to produce esters. Good efficiency and functional group tolerance are demonstrated. Application of this method to the functionalization of a boronic acid drug used as HSL enzyme inhibitor produces smoothly the ester derivative. This difluorocarbene-mediated O-O bond activation strategy is conceptually different from traditional radical type methods, and is also complementary to conventional esterification methods with a distinct retro-synthetic disconnection.

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.

INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI. METHODS: Six ML algorithms were included to perform model fitting. Feature selection was based on the feature importance evaluated by the SHapley Additive exPlanations (SHAP) values. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminatory ability of the prediction model. Calibration curve and Brier score were employed to assess the calibrated ability. Our ML-based prediction models were validated both internally and externally. RESULTS: A total of 12,750 patients with SA-AKI and 55 features were included to build the prediction models. We identified the top 10 predictors including age, ICU stay and GCS score based on the feature importance. Among the six ML algorithms, the CatBoost showed the best prediction performance with an AUROC of 0.813 and Brier score of 0.119. In the external validation set, the predictive value remained favorable (AUROC = 0.784). CONCLUSION: In this study, we developed and validated a ML-based prediction model based on 10 commonly used clinical features which could accurately and early identify the individuals at high-risk of long-term mortality in patients with SA-AKI.

Sex Differences in the Epidemiology, Risk Factors, and Prognosis of Malignant Ventricular Arrhythmias in Sepsis Patients.

Background: Women are frequently underrepresented in clinical trials and databases focusing on ventricular arrhythmias (VAs). However, understanding sex-based differences in risk factors and the prognosis of VAs is essential for tailoring personalized prevention and treatment strategies. This study aimed to investigate sex differences in the epidemiology, risk factors, and prognosis of VAs in patients with sepsis. Methods: We conducted a comprehensive analysis of 27,139 sepsis patients (mean [SD] age, 66.6 [16.2] years; 15,626 [57.6%] male), among whom 1136 (4.2%) developed VAs during their hospitalization. We evaluated VAs incidence and potential risk elements in both male and female patients, along with in-hospital mortality. Results: Men had a significantly higher likelihood of developing VAs compared to women (odds ratio [OR]: 1.70, 95% confidence interval [CI]: 1.50-1.94, p < 0.001). In the case of non-ischemic cardiomyopathy (NICM), the association with VAs was stronger in men than in women (relative risk ratio [RRR] = 1.63, 95% CI: 1.10-2.40, interaction p = 0.014). Furthermore, we observed significant sex-specific interactions in the relationship between incident VAs, congestive heart failure (CHF) (RRR = 1.35, 95% CI: 1.03-1.76, interaction p = 0.031), and pneumonia (RRR = 1.33, 95% CI: 1.02-1.74, interaction p = 0.036) when considering the adjusted model. The presence of VAs was associated with a nearly twofold increase in the risk of in-hospital mortality, a result that was observed in both sexes. Conclusions: In sepsis patients, the emergence of VAs independently escalates the risk of in-hospital mortality, with a notable correlation between male sex and an increased VAs risk. The impacts of CHF, NICM and pneumonia on incident VAs were significantly influenced by sex.

Coronary Microvascular Function Assessment using the Coronary Angiography-Derived Index of Microcirculatory Resistance in Patients with ST-segment Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention.

Background: Studies reporting the status of coronary microvascular function in the infarct-related artery (IRA) after primary percutaneous coronary intervention (PCI) remain limited. This study utilized the coronary angiography-derived index of microcirculatory resistance (caIMR) to assess coronary microvascular function in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary PCI. Methods: We used the FlashAngio system to measure the caIMR after primary PCI in 157 patients with STEMI. The primary endpoint was the occurrence of a major adverse cardiovascular event (MACE), defined as a composite endpoint encompassing cardiac mortality, target vessel revascularization, and rehospitalization due to congestive heart failure (CHF), myocardial infarction (MI), or angina. Results: Approximately 30% of patients diagnosed with STEMI and who experienced successful primary PCI during the study period had a caIMR in the IRA of > 40. The caIMR in the IRA was significantly higher than in the reference vessel (32.9 ± 15.8 vs. 27.4 ± 11.1, p < 0.001). The caIMR in the reference vessel of the caIMR > 40 group was greater than in the caIMR ≤ 40 group (30.9 ± 11.3 vs. 25.9 ± 10.7, p = 0.009). Moreover, the caIMR > 40 group had higher incidence rates of MACEs at 3 months (25.5% vs. 8.3%, p = 0.009) and 1 year (29.8% vs. 13.9%, p = 0.04), than in the caIMR ≤ 40 group, which were mainly driven by a higher rate of rehospitalization due to CHF, MI, or angina. A caIMR in the IRA of > 40 was an independent predictor of a MACE at 3 months (hazard ratio (HR): 3.459, 95% confidence interval (CI): 1.363-8.779, p = 0.009) and 1 year (HR: 2.384, 95% CI: 1.100-5.166, p = 0.03) in patients with STEMI after primary PCI. Conclusions: Patients with STEMI after primary PCI often have coronary microvascular dysfunction, which is indicated by an increased caIMR in the IRA. An elevated caIMR of > 40 in the IRA was associated with an increased risk of adverse outcomes in STEMI patients undergoing primary PCI.

Inoculum source determines the stress resistance of electroactive functional taxa in biofilms: A metagenomic perspective.

The inoculum has a crucial impact on bioreactor initialization and performance. However, there is currently a lack of guidance on selecting appropriate inocula for applications in environmental biotechnology. In this study, we applied microbial electrolysis cells (MECs) as models to investigate the differences in the functional potential of electroactive microorganisms (EAMs) within anodic biofilms developed from four different inocula (natural or artificial), using shotgun metagenomic techniques. We specifically focused on extracellular electron transfer (EET) function and stress resistance, which affect the performance and stability of MECs. Community profiling revealed that the family Geobacteraceae was the key EAM taxon in all biofilms, with Geobacter as the dominant genus. The c-type cytochrome gene imcH showed universal importance for Geobacteraceae EET and was utilized as a marker gene to evaluate the EET potential of EAMs. Additionally, stress response functional genes were used to assess the stress resistance potential of Geobacter species. Comparative analysis of imcH gene abundance revealed that EAMs with comparable overall EET potential could be enriched from artificial and natural inocula (P > 0.05). However, quantification of stress response gene copy numbers in the genomes demonstrated that EAMs originating from natural inocula possessed superior stress resistance potential (196 vs. 163). Overall, this study provides novel perspectives on the inoculum effect in bioreactors and offers theoretical guidance for selecting inoculum in environmental engineering applications.

Zinc and Inflammatory Bowel Disease: From Clinical Study to Animal Experiment.

Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gastrointestinal tract (GI) with a high incidence rate globally, and IBD patients are often accompanied by zinc deficiency. This review aims to summarize the potential therapeutic value of zinc supplementation in IBD clinical patients and animal models. Zinc supplementation can relieve the severity of IBD especially in patients with zinc deficiency. The clinical severity of IBD were mainly evaluated through some scoring methods involving clinical performance, endoscopic observation, blood biochemistry, and pathologic biopsy. Through conducting animal experiments, it has been found that zinc plays an important role in alleviating clinical symptoms and improving pathological lesions. In both clinical observation and animal experiment of IBD, the therapeutic mechanisms of zinc interventions have been found to be related to immunomodulation, intestinal epithelial repair, and gut microbiota's balance. Furthermore, the antioxidant activity of zinc was clarified in animal experiment. Appropriate zinc supplementation is beneficial for IBD therapy, and the present evidence highlights that alleviating zinc-deficient status can effectively improve the severity of clinical symptoms in IBD patients and animal models.

[Modified QuEChERS method combined with ultra performance liquid chromatography-tandem mass spectrometry for detection of cyclopiazonic acid in feeds].

Mycotoxins are toxic secondary metabolites produced by fungal species that can cause acute, subacute, and chronic toxicity in humans and animals. Thus, these toxins pose a significant threat to health and safety. Owing to the lack of effective antimold measures in the agricultural industry, feed ingredients such as corn, peanuts, wheat, barley, millet, nuts, oily feed, forage, and their byproducts are prone to mold and mycotoxin contamination, which can affect animal production, product quality, and safety. Cyclopiazonic acid (CPA), which is mainly biosynthesized from mevalonate, tryptophan, and diacetate units, is a myotoxic secondary metabolite produced by Penicillium and Aspergillus fungi. CPA is widely present as a copollutant with aflatoxins in various crops. Compared with some common mycotoxins such as aflatoxins, fumonisins, ochratoxins, zearalenones, and their metabolites, CPA has not been well investigated. In the United States, a survey showed that 51% of corn and 90% of peanut samples contained CPA, with a maximum level of 2.9 mg/kg. In Europe, CPA was found in Penicillium-contaminated cheeses as high as 4.0 mg/kg. Some studies have shown that CPA can cause irreversible damage to organs such as the liver and spleen in mice. Therefore, the establishment of a rapid and efficient analytical method for CPA is of great significance for the risk assessment of CPA in feeds, the development of standard limits, and the protection of feed product quality and safety. The QuEChERS method, a sample pretreatment method that is fast, simple, cheap, effective, and safe, is widely used in the analysis of pesticide residues in food. In this study, a modified QuEChERS method combined with ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to determine CPA levels in feeds. The chromatographic separation and MS detection of CPA as well as the key factors affecting the extraction efficiency of CPA, including the type of extraction solvent, type of inorganic salt, and type and dosage of adsorbent, were optimized in detail. During the optimization of the chromatographic-separation step, the acid and salt concentrations of the mobile phase affected the separation and detection of CPA. During the optimization of the QuEChERS method, the addition of a certain amount of acetic acid improved the extraction efficiency of CPA because of its acidic nature; in addition, GCB and PSA significantly adsorbed CPA from the feed extract. Under optimal conditions, the CPA in the feed sample (1.0 g) was extracted with 2 mL of water and 4 mL of acetonitrile (ACN) containing 0.5% acetic acid. After salting out with 0.4 g of NaCl and 1.6 g of MgSO4, 1 mL of the ACN supernatant was purified by dispersive solid-phase extraction using 150 mg of MgSO4 and 50 mg of C18 and analyzed by UPLC-MS/MS. The sample was separated on a Waters HSS T3 column (100 mm×2.1 mm, 1.8 µm) using 2 mmol/L ammonium acetate aqueous solution with 0.5% formic acid and ACN as the mobile phases and then analyzed by positive electrospray ionization in multiple reaction monitoring mode. CPA exhibited good linearity in the range of 2-200 ng/mL, with a high correlation coefficient (r=0.9995). The limits of detection and quantification of CPA, which were calculated as 3 and 10 times the signal-to-noise ratio, respectively, were 0.6 and 2.0 µg/kg, respectively. The average recoveries in feed samples spiked with 10, 100, and 500 µg/kg CPA ranged from 70.1% to 78.5%, with an intra-day precision of less than 5.8% and an inter-day precision of less than 7.2%, indicating the good accuracy and precision of the proposed method. Finally, the modified QuEChERS-UPLC-MS/MS method was applied to the analysis of CPA in 10 feed samples obtained from Wuhan market. The analysis results indicated that the developed method has good applicability for CPA analysis in feed samples. In summary, an improved QuEChERS method was applied to the extraction and purification of CPA from feeds for the first time; this method provides a suitable analytical method for the risk monitoring, assessment, and standard-limit setting of CPA in feed samples.

Bridging metabolic syndrome and cognitive dysfunction: role of astrocytes.

Metabolic syndrome (MetS) and cognitive dysfunction pose significant challenges to global health and the economy. Systemic inflammation, endocrine disruption, and autoregulatory impairment drive neurodegeneration and microcirculatory damage in MetS. Due to their unique anatomy and function, astrocytes sense and integrate multiple metabolic signals, including peripheral endocrine hormones and nutrients. Astrocytes and synapses engage in a complex dialogue of energetic and immunological interactions. Astrocytes act as a bridge between MetS and cognitive dysfunction, undergoing diverse activation in response to metabolic dysfunction. This article summarizes the alterations in astrocyte phenotypic characteristics across multiple pathological factors in MetS. It also discusses the clinical value of astrocytes as a critical pathologic diagnostic marker and potential therapeutic target for MetS-associated cognitive dysfunction.