Failure Model Research Articles

Leptin drives glucose metabolism to promote cardiac protection via OPA1-mediated HDAC5 translocation and Glut4 transcription

Metabolic reprogramming, the shifting from fatty acid oxidation to glucose utilization, improves cardiac function as heart failure (HF) progresses. Leptin plays an essential role in regulating glucose metabolism. However, the crosstalk between leptin and metabolic reprogramming is poorly understood. We tested the hypothesis that leptin improves cardiac function after myocardial infarction via enhancing glucose metabolism. In the isoproterenol (ISO)-induced heart failure model in vitro, H9c2 cell apoptosis was assessed by the TUNEL and Annexin V/PI staining assay. Leptin-mediated mitochondrial fusion was performed via TEM, and glucose oxidation was explored, as well as the ECAR, OCR, and protein expression of the vital metabolic enzymes. By blocking OPA1 expression or HDAC5 inhibition, the mitochondrial dynamic and glucose metabolic were detected to evaluate the role of OPA1 and HDAC5 in leptin-stimulated glucose metabolism. In the mouse model of HF in vivo, intraperitoneal leptin administration appreciably increased glucose oxidation and preserved cardiac function 56 days after coronary artery ligation. In vitro, we identified the OPA1-dependent HDAC5 nucleus export as a crucial process in boosting glucose utilization by activating MEF2 to upregulate Glut4 expression using the RNA interference technique in H9c2 cells. In vivo, leptin promotes glucose utilization and confers heart functional and survival benefits in chronic ischemic HF. The current study provided a novel insight into the role of leptin in metabolic reprogramming and revealed potential therapeutic targets for chronic HF.

Impact of Race on Transplantation in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of end-stage kidney disease (ESKD) and occurs without racial predilection. In general, non-White ESKD patients have less access to transplantation, especially living donor transplantation. We examined long-term outcomes of ADPKD-ESKD patients by self-reported race, with attention to the trajectory of Estimated Post-Transplant Survival (EPTS) scores over time. United Network for Organ Sharing Standard Transplant Analysis and Research files were used to identify 32,611 ADPKD transplant recipients between 1/2000-12/2022. EPTS scores were calculated from the date of waitlisting until transplantation occurred. Cumulative incidences of living and deceased transplantation were calculated and plotted. Cox models were made for graft failure and death, and a sub-distribution hazards model for graft failure accounted for death as a competing outcome, with adjustment for patient, donor, and transplant factors. Compared to White ADPKD patients, all other groups had more dialysis years, more delayed graft function, and fewer living and pre-emptive transplants; mean EPTS scores were lower in African American (AA) and Hispanic patients at each timepoint on the waitlist. However, EPTS scores at the time of transplant was less likely to be <20% in AA and Hispanic patients, due to longer waiting time. AA patients had a significantly higher risk of graft failure with death as competing risk compared to White patients. Asian and Hispanic patients had similar graft survivals but better patient survival compared to White patients. Waitlist experience, allograft quality, and post-transplant outcomes of patients with ADPKD are influenced by patient race.

The Random RFPA Method for Modelling Rock Failure

Abstract The random rock failure process analysis (RRFPA) method was developed in this research to characterize the material spatial variability and uncertainty in rock failure modelling. The random field theory (RFT) was integrated with the traditional rock failure process analysis (RFPA) to model rock heterogeneity. In this approach, the variation of rock properties is represented as a function of relative distance, such that the influence of material intrinsic correlation on its fracturing behaviour can be appropriately captured. To validate the theory, 300 RRFPA simulations were conducted to investigate the failure characteristics of rock samples under compressive loading. The results showed that by incorporating a spectrum of material properties, the numerical outcomes exhibited distinct upper and lower bounds of stress across all testing scenarios, closely aligning with the experimental relationships. The histograms for uniaxial compressive strength and elastic modulus showed that both properties followed normal distributions, with the average values of 10.099 MPa and 1.818 GPa, respectively. The corresponding coefficients of variation were 0.450 and 0.038. The localized failure tended to result in a more rapid release of acoustic emission energy, but generated smaller cumulative energy compared to the overall failure pattern. In general, the maximum relative error of the RRFPA model was only 0.66% for uniaxial compressive strength, elastic modulus, and critical axial strain.

Shenmai injection revives cardiac function in rats with hypertensive heart failure: involvement of microbial-host co-metabolism

Heart failure (HF) is a complex syndrome marked by considerable expenditures and elevated mortality and morbidity rates globally. Shenmai injection (SMI), a form of Traditional Chinese Medicine-based therapy, has demonstrated effectiveness in treating HF. Recent research suggests that Traditional Chinese Medicine (TCM) may induce beneficial changes in microbial-host co-metabolism, potentially providing cardiovascular protection. This study used a rat model of hypertensive heart failure (H-HF) to explore the mechanism of SMI. The possible compounds and key targets of SMI against H-HF were investigated using network pharmacology. The pharmacodynamics of SMI were validated using the H-HF animal model, with analysis of fecal gut microbiota integrating metabolomics and 16S rRNA sequencing. Metorigin metabolite traceability analysis and the MetaboAnalyst platform were utilized to explore the action mechanism. To evaluate changes in serum TMAO levels, targeted metabolomics was performed. Finally, the study looked at the intrinsic relationships among modifications in the intestinal flora, metabolite profile changes, and the targets of SMI compounds to clarify how they might be used to treat H-HF. According to metabolomics and 16S rRNA sequencing, by reestablishing homeostasis in the gut microbiota, SMI affects vital metabolic pathways, such as energy metabolism, amino acid metabolism, and bile acid metabolism. Increased serum TMAO levels were identified to be a risk factor for H-HF, and SMI was able to downregulate the levels of TMAO-related metabolites. Network pharmacology analysis identified 13 active components of SMI targeting 46 proteins, resulting in differential expression changes in 8 metabolites and 24 gut microbes. In conclusion, this study highlights the effectiveness of SMI in alleviating H-HF and its potential to modulate microbial-host co-metabolism. Through a comprehensive discussion of the interconnected relationships among the components, targets, metabolites, and gut microbiota, it provided fresh light on the therapeutic mechanism of SMI on H-HF.

Machine learning-driven discovery of structurally related natural products as activators of the cardiac calcium pump SERCA2a.

A key molecular dysfunction in heart failure is the reduced activity of the cardiac sarcoplasmic reticulum Ca2+-ATPase (SERCA2a) in cardiac muscle cells. Reactivating SERCA2a improves cardiac function in heart failure models, making it a validated target and an attractive therapeutic approach for heart failure therapy. However, finding small-molecule SERCA2a activators is challenging. In this study, we used a machine learning-based virtual screening to identify SERCA2a activators among 57,423 natural products. The machine learning model identified ten structurally related natural products fromZingiber officinale,Aframomum melegueta,Alpinia officinarum, Alpinia oxyphylla,andCapsicum(chili peppers) as SERCA2a activators.Initial ATPase assays showed seven of these activate SERCA at low micromolar concentrations. Notably, two natural products, Yakuchinone A and Alpinoid D displayed robust concentration-dependent responses in primary ATPase activity assays, efficient lipid bilayer binding and permeation in atomistic simulations, and enhanced intracellular Ca2+transport in adult mouse cardiac cells. While these natural products exert off-target effects on Ca2+signaling, these compounds offer promising avenues for the design and optimization of lead compounds. In conclusion,this study increases the array of calcium pump effectors and provides new scaffolds for the development of novel SERCA2a activators as new therapies for heart failure.

Anticipating impending peripheral intravenous catheter failure: A diagnostic accuracy observational study combining ultrasound and artificial intelligence to improve clinical care.

Peripheral intravenous catheter (PIVC) failure occurs in approximately 50% of insertions. Unexpected PIVC failure leads to treatment delays, longer hospitalizations, and increased risk of patient harm. In current practice there is no method to predict if PIVC failure will occur until it is too late and a grossly obvious complication has occurred. The aim of this study is to demonstrate the diagnostic accuracy of a predictive model for PIVC failure based on artificial intelligence (AI). This study evaluated the capabilities of a novel machine learning algorithm. The algorithm was trained using real-world ultrasound videos of PIVC sites with a goal of predicting which PIVCs would fail within the following day. After training, AI models were validated using another, unseen, collection of real-world ultrasound videos of PIVC sites. 2133 ultrasound videos (361 failure and 1772 non-failure) were used for algorithm development. When the algorithm was tasked with predicting failure in the unseen collection of videos, the best achieved results were an accuracy of 0.93, sensitivity of 0.77, specificity of 0.98, positive predictive value of 0.91, negative predictive value of 0.93, and area under the curve of 0.87. This proprietary and novel machine learning algorithm can accurately and reliably predict PIVC failure 1 day prior to clinically evident failure. Implementation of this technology in the patient care setting would provide timely information for clinicians to plan and manage impending device failure. Future research on the use of AI technology and PIVCs should focus on improving catheter function and longevity, while limiting complication rates.

Placement of an elastic, biohybrid patch in a model of right heart failure with pulmonary artery banding

Placement of an elastic, biohybrid patch in a model of right heart failure with pulmonary artery banding

Quercetin improves heart failure by inhibiting cardiomyocyte apoptosis via suppressing the MAPK signaling pathway.

To explore the mechanism that mediate the therapeutic effect of quercetin on heart failure. We searched the TCMSP and Swiss ADME databases for the therapeutic targets of quercetin and retrieved heart failure targets from the Genecards and OMIM databases. The intersecting targets were analyzed with GO and KEGG pathway analysis using DAVID database, and the key genes were identified via PPI analysis. Molecular docking between the core targets and quercetin was performed using PyMOL and AutoDock Tools. In a heart failure model established in H9C2 cardiomyocytes by treatment with isoproterenol, the effect of quercetin on the expressions of the MAPK signaling pathway was tested. A total of 60 intersecting targets were identified. Enrichment analysis revealed that quercetin may inhibit heart failure through the MAPK signaling pathway. The core genes, including AMPK3 and BCL-2, were identified as potential key regulators in quercetin-mediated improvement of heart failure. Cellular experiments demonstrated that quercetin significantly reduced isoproterenol-induced apoptosis of cardiomyocytes in a dose-dependent manner and obviously decreased the Bax/Bcl-2 ratio and the expression levels of caspase-3, ERK and p38 in the cells. Quercetin improves heart failure possibly by inhibiting cardiomyocyte apoptosis through the MAPK signaling pathway.

Baicalin Mitigates Cardiac Hypertrophy and Fibrosis by Inhibiting the p85a Subunit of PI3K.

Background: Heart failure (HF) is a serious public health concern. Baicalin is one of the major active ingredients of a traditional Chinese herbal medicine, Huang Qin, which is used to treat patients with chest pain or cardiac discomfort. However, the underlying mechanism(s) of the cardioprotective effect of baicalin are still not fully understood. Methods: Isoprenaline injection or transverse aortic constriction-induced animal models and isoprenaline or angiotensin 2 administration-induced cell models of heart failure were established. Baicalin (15 mg/kg/day or 25 mg/kg/day) was administered in vivo, and 10 μM baicalin was administered in vitro. Potential pharmacological targets of baicalin and genes related to heart failure were identified via different databases, which suggested that PI3K-Akt may be involved in the effects of baicalin. Molecular docking was carried out to reveal the effect of baicalin on p85a. Results: We observed significant antihypertrophic and antifibrotic effects of baicalin both in vivo and in vitro. The mean cross-sectional area of cardiomyocytes recovered from 390 μm2 in the HF group to 195 μm2 in the baicalin-treated group. The area of fibrosis was reduced from 2.8-fold in the HF group to 1.62-fold in the baicalin-treated group. Baicalin displayed a significant cardioprotective effect via the inhibition of the PI3K signaling pathway by binding with five amino acid residues of the p85a regulatory subunit of PI3K. The combination treatment of baicalin and an inhibitor of PI3K p110 demonstrated a stronger cardioprotective effect. The mean ejection fraction increased from 54% in the baicalin-treated group to 67% in the combination treatment group. Conclusions: Our work identified baicalin as a new active herbal ingredient that is able to treat isoprenaline-induced heart dysfunction and suggests that p85a is a pharmacological target. These findings reveal the significant potential of baicalin combined with an inhibitor of PI3K p110 for the treatment of heart failure and support more clinical trials in the future.

Identifying risk factors and constructing a predictive model for heart failure combined with intracardiac thrombus in non-compaction cardiomyopathy patients

This study aims to develop a nomogram prediction model for assessing the cardiogenic composite endpoint, which includes intracardiac thrombosis (ICT) combined with heart failure (HF) in patients with non-compaction cardiomyopathy (NCM) patients. We retrospectively analyzed clinical data from NCM patients (January 2018 to May 2024), who were randomly assigned to training and validation cohorts. Independent predictors were identified using logistic regression, and a nomogram model was developed. The model’s discriminative ability, accuracy, and clinical applicability were subsequently validated. A total of 976 patients were included, of whom 54 had ICT and 191 had HF. Diabetes mellitus (DM), left ventricular end-systolic diameter (LVESD), and ejection fraction (EF) were identified as independent predictors for the composite endpoint. The nomogram demonstrated good performance, with an area under the curve (AUC) of 0.747 (95% CI: 0.707–0.787) in the training group and 0.803 (95% CI: 0.752–0.854) in the validation group. The calibration curve for the training group showed an average absolute error of 0.028, with a Hosmer-Lemeshow test P-value of 0.076. Decision curve analysis and clinical impact curves further indicated that the clinical net benefit was maximized at a threshold probability of 0.05–0.61. This study establishes and validates a nomogram for predicting cardiogenic composite endpoint in NVM patients, demonstrating robust clinical predictive value.

Application of failure model and effect analysis in nursing care for patients who have undergone endoscopic sub-mucosal dissection

ObjectiveThe objective of this study is to investigate the effect after the application of Failure Model and Effect Analysis (FMEA) in nursing care for patients who have undergone endoscopic submucosal dissection (ESD).MethodsA cohort of 40 patients who underwent ESD between July and September 2023 were selected as the control group, while 42 patients who underwent ESD between October 2023 and June 2024 after implementing FMEA were selected as the observation group. A multidisciplinary team was established based on the FMEA model to analyze and create a nursing flowchart. The 3 primary processes and 13 sub-processes were thoroughly analyzed and assessed to identify potential failure models, possible causes of failure, and consequences for each sub-process. Risk Priority Numbers (RPNs) were calculated to determine priority failure models, including medication and item preparation, specimen collection, equipment/instrument/accessory preparation, and nursing coordination. Corresponding improvement measures were formulated and implemented followed by a subsequent analysis of the effects.ResultsAfter implementing the improvement measures, there was a significant decrease in RPNs in the observation group when compared with the control group. A statistical significance was observed in context of medication and item preparation (P < 0.001), specimen collection (P < 0.001), equipment/instrument/accessory preparation (P < 0.001), and nursing coordination (P < 0.001).ConclusionThe application of the FMEA model can effectively facilitate early nursing interventions for identified risks in patient who have undergone ESD. By instituting suitable corrective measures for aspects deemed high-risk, this approach significantly diminishes surgical nursing hazards, enhances the quality of nursing care, and guarantees patient safety.

Supply risk propagation in international trade networks of the tungsten industry chain

Tungsten plays an irreplaceable role in industrial development and high technology and is vital for national security and economic development. Supply and demand imbalances, and geopolitical and social health events, among other factors, lead to supply risks for tungsten resources in different economies. The existing studies have focused on modeling supply risk in the upstream mineral trade and lack a quantitative analysis of supply risk propagation in tungsten product trade networks from an industry chain perspective. Therefore, this paper uses trade data from 2009 to 2020 to construct an international trade network for tungsten products and applies a cascading failure model to simulate the risk propagation process in the trade network of the industry chain. The results show that China, Germany, and the US have the greatest impact ranges. The influence of economies is related to the trade export value, number of trade partners, and location. The influence of economies on the trade of midstream and downstream products is significantly related to intermediary capacity, the level of economic development, and trade closeness, with technological factors becoming increasingly important as the chain extends downward. Supply risk in upstream and midstream products is propagated mainly through indirect routes, whereas risk in downstream products is propagated mainly through direct trade relationships. The number of affected economies increases substantially when there is a small decline in the export value of tungsten products from core economies. The results can provide reference and decision support for different economies to secure the supply security and sustainability of tungsten resources.

CURRENT ISSUES OF MODELING HEART FAILURE AND MYOCARDIAL FIBROSIS IN RATS

HighlightsMethods for modeling heart failure in rats are classified into models requiring surgical intervention, toxic, genetic and autoimmune models, models based on the effects of physical factors, the use of special dietary regimens.It is impossible to single out a single ideal model, since the development of heart failure is a multifactorial process, it is preferable to use a combination of several methods.The correctness of the choice of the model and its compliance with the tasks of future research must be confirmed by combining several diagnostic methods for verifying myocardial fibrosis - laboratory, histological, instrumental. AnnotationDetailed study of the mechanisms of heart failure and its main pathogenetic factor, myocardial fibrosis, is required for developing new effective treatment strategies. The choice of an appropriate model is key for a reliable experimental study. The aim of the review is to systematize current data on methods for modeling heart failure in rats. The main advantages of these animals are high genetic, biochemical and physiological similarity with humans, ease of breeding, availability of maintenance, small size, while allowing for surgery. This article classifies the currently available rat models of heart failure, discusses their pathophysiological basis, timing of the formation of heart failure, clinical features, advantages and disadvantages of each experimental model. The authors have paid particular attention to methods developed by domestic scientists. Methods for assessing the developed myocardial fibrosis and the influence of drugs on its formation are considered. The study of heart failure requires reliable animal models to assess biochemical, functional, morphological changes in damaged myocardium, and controlled testing of new drugs. Should the necessity arise, specialists should use several methods simultaneously, whereas the choice of treatment strategy depends on the aim of the study.

Service evaluation of 'GP at Door' of accident and emergency services in Eastern England.

We describe activity, outcomes, and benefits after streaming low urgency attenders to General practice services at Door of Accident and Emergency departments (GDAE). Many attendances to A&Es are for non-urgent health problems that could be better met by primary care rather than urgent care clinicians. It is valuable to monitor service activity, outcomes, service user demographics, and potential benefits when primary care is co-located with A&E departments. As a service evaluation, we describe and analyse GDAE users, reasons for presentation, wait times, outcomes, and co-located A&E wait times at two hospitals in eastern England. Distributions of outcomes, wait times, reasons for attendance, deprivation, and age groups were compared for GDAE and usual A&E attenders at each site using Pearson chi-square tests and accelerated time failure modelling. Performance in a four-hour key performance indicator was descriptively compared for co-located and similar emergency departments. Each GDAE saw about 1025 patients per month. Wait times for usual accident and emergency (A&E) care are relatively short at only one site. Reattendances were common (about 11% of unique patients), 75% of GDAE attenders were seen within 1 hour of arrival, 7% of patients initially allocated to GDAE were referred back to A&E for further investigations, and 59% of GDAE patients were treated and discharged with no further treatment or referral required. Pain, injury, infection, or feeling generally unwell each comprised > 10% of primary reasons for attendance. At James Paget University Hospital, 4.3%, and at Queen Elizabeth Hospital, 16.1% of GDAE attendances led to referral to specialist health services. GDAE attenders were younger and more socially deprived than attenders to co-located A&Es. Patients were seen quickly at both GDAE sites, but there were differences in counts of specialist referrals and wait times. Process evaluation could illuminate reasons for differences between study sites.

Cardiovascular Disease and Cancer: A Dangerous Liaison.

The field of cardio-oncology has traditionally focused on the impact of cancer and its therapies on cardiovascular health. Mounting clinical and preclinical evidence, however, indicates that the reverse may also be true: cardiovascular disease can itself influence tumor growth and metastasis. Numerous epidemiological studies have reported that individuals with prevalent cardiovascular disease have an increased incidence of cancer. In parallel, studies using preclinical mouse models of myocardial infarction, heart failure, and cardiac remodeling support the notion that cardiovascular disorders accelerate the growth of solid tumors and metastases. These findings have ushered in a new and burgeoning field termed reverse cardio-oncology that investigates the impact of cardiovascular disease cause and pathophysiology on cancer emergence and progression. Recent studies have begun to illuminate the mechanisms driving this relationship, including shared risk factors, reprogramming of immune responses, changes in gene expression, and the release of cardiac factors that result in selective advantages for tumor cells or their local milieu, thus exacerbating cancer pathology. Here, we review the evidence supporting the relationship between cardiovascular disease and cancer, the mechanistic pathways enabling this connection, and the implications of these findings for patient care.

Tricuspid valve leaflet remodeling in sheep with biventricular heart failure: A comparison between leaflets.

Our study is significant as it advances our understanding of tricuspid valve remodeling by providing a comprehensive analysis of all three leaflets in a sheep model of biventricular heart failure. Unlike prior works that focused primarily on the anterior leaflet or generalized leaflet changes, we integrated morphological, histological, immunohistochemistry, biaxial mechanical testing, and two-photon microscopy to quantify differences between all three tricuspid valve leaflets (anterior, posterior, and septal) across multiple functional scales. This comprehensive approach highlights the unique remodeling response of each leaflet. Our findings offer critical insights for developing targeted therapeutic strategies and improving computational models of disease progression.

Enhanced fibrotic potential of COL1A1hiNR4A1low fibroblasts in ischemic heart revealed by transcriptional dynamics heterogeneity analysis at both bulk and single-cell levels.

Fibroblasts in the fibrotic heart exhibit a heterogeneous biological behavior. The specific subsets of fibroblasts that contribute to progressive cardiac fibrosis remain unrevealed. Our aim is to identify the heart fibroblast (FB) subsets that most significantly promote fibrosis and the related critical genes as biomarkers for ischemic heart disease. The single nuclei RNA sequencing (snRNA-seq) and bulk RNA sequencing datasets used in this study were obtained from the Gene Expression Omnibus (GEO). The activity of gene sets related to progressive fibrosis was quantified for each FB cluster using the AddmoleculeScore function. Differentially expressed genes (DEGs) for the specific cell cluster with the highest fibrotic transcription dynamics were identified and integrated with bulk RNA sequencing data for analysis. Multiple machine learning models were employed to identify the optimal gene panel for diagnosing ischemic heart disease (IHD) based on the intersected DEGs. The effectiveness and robustness of the gene-derived diagnostic tool were validated using two independent IHD cohorts.Subsequently, we validated the signature genes using a rat post-myocardial infarction heart failure model. We conducted an analysis on high-quality snRNA-seq data obtained from 3 IHD and 4 cardiac sarcoidosis heart samples, resulting in the identification of 16 FB clusters. Cluster2 exhibited the highest gene activity in terms of fibrosis-related transcriptome dynamics. The characteristic gene expression profile of this FB subset indicated a specific upregulation of COL1A1 and several pro-fibrotic factors, including CCDC102B, GUCY1A3, TEX41, NREP, TCAP, and WISP, while showing a downregulation of NR4A1, an endogenous inhibitor of the TGF-β pathway. Consequently, we designated this subgroup as COL1A1hiNR4A1low FB. Gene set enrichment analysis (GSEA) shows that the gene expression pattern of COL1A1hiNR4A1low FB was closer to pathways associated with cardiac fibrosis. Through machine learning, ten feature genes from COL1A1hiNR4A1low FB were selected to construct a diagnostic tool for IHD. The robustness of this new tool was validated using an independent cohort and heart failure rats. COL1A1hiNR4A1low FB possess heightened capability in promoting cardiac fibrosis. Additionally, it offers molecular insights into the mechanisms underlying the regulation of the TGF-β pathway. Furthermore, the characteristic genes of COL1A1hiNR4A1 FB could serve as valuable tools for diagnosing of IHD.

Dapagliflozin inhibits ferroptosis to improve chronic heart failure by regulating Nrf2/HO-1/GPX4 signaling pathway.

To study the effect of Dapagliflozin on ferroptosis in rabbits with chronic heart failure and to reveal its possible mechanism. Nine healthy adult male New Zealand white rabbits were randomly divided into Sham group (only thorax opening was performed in Sham group, no ascending aorta circumferential ligation was performed), Heart failure group (HF group, ascending aorta circumferential ligation was performed in HF group to establish the animal model of heart failure), and Dapagliflozin group (DAPA group, after the rabbit chronic heart failure model was successfully made in DAPA group). Dapagliflozin was given by force-feeding method. Echocardiography was used to assess cardiac function, HE staining to evaluate pathological changes in the heart, Prussia blue staining to observe iron ions in myocardial tissue, and enzyme-linked immunosorbent assay (ELISA) to determine serum levels of the inflammatory factors interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) at the end of week 12 and/or the end of week 16. The oxidative stress related indexes of malondialdehyde (MDA), superoxide dismutase (SOD) and superoxide dismutase (GSH-Px) in serum were quantitatively analyzed by colorimetry. Protein expression levels of nuclear factor E2-related factor 2(Nrf2), heme oxygenase-1(HO-1), glutathione peroxidase 4(Gpx4) were detected by Western blot. In animals with chronic heart failure, Dapagliflozin improved cardiomyocyte hypertrophy, degeneration and necrosis. Dapagliflozin increased serum GSH-Px and SOD levels and decreased IL-1β, IL-6, TNF-α and MDA levels (P < 0.05) in a rabbit model of heart failure. Dapagliflozin also decreased cardiac iron ion levels and increased Nrf2, HO-1 and GPX4 protein expression. Dapagliflozin can improve heart failure by inhibiting oxidative stress and ferroptosis, and its mechanism may be related to the regulation of Nrf2/HO-1/GPX4 signaling pathway.

An Ovine Model of Chronic Heart Failure Induced by Repeated Coronary Microembolizations.

The use of large animals in research provides a unique bridge between preclinical findings and clinical relevance, offering a valuable perspective for advancing our understanding of the complexities of heart failure. Multiple models of heart failure have been established with advantages and limitations of each model. Many insights have been gained from these models for understanding both pathophysiological mechanisms and therapeutic interventions for heart failure. We have developed a chronic model of heart failure with reduced ejection fraction in sheep using the coronary microembolization technique. These animals gradually develop heart failure over a period of 3months. In this chapter, we document how this model can be established. The left main coronary artery is cannulated under fluoroscopic guidance, and polystyrene latex microspheres are injected into the artery. Up to three sequential selective microembolizations are undertaken over a period of 3weeks. Changes in ECG are monitored, and echocardiography is used to check on the progression of left ventricular dysfunction. All animals were followed for 3months after the first embolization, and heart tissue was examined to check for changes in cardiac myocyte size. All animals developed stable signs of HF. We propose that multiple sequential intracoronary microembolizations can induce stable myocardial dysfunction with hemodynamic signs of chronic heart failure.

Development and Validation of a Predictive Model for Liver Failure After Transarterial Chemoembolization Using Gadoxetic Acid-Enhanced MRI and Functional Liver Imaging Score.

Post-transarterial chemoembolization liver failure (PTLF) is a potentially fatal complication of transarterial chemoembolization (TACE). Accurate preoperative prediction of PTLF is crucial for improving patient outcomes. This study aimed to develop and validate a prediction model based on the functional liver imaging score (FLIS) to assess the risk of PTLF. A total of 156 patients underwent Gadoxetic acid-enhanced MRI within four weeks before TACE. Two radiologists, unaware of the clinical data, independently assessed FLIS on hepatobiliary phase images to quantitatively assess liver function. Univariate and multivariate logistic regression analyses identified independent predictors of PTLF. A nomogram was developed and subjected to internal validation through bootstrap resampling of 1000 samples. The model's performance was conducted through the area under the curve (AUC), Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). P<0.05 was considered statistically significant. PTLF occurred in 37.2% of patients (58/156). Significant differences were observed in factors such as portal vein thrombosis, albumin, aspartate transaminase, international normalized ratio (INR), model for end-stage liver diseasescoring, albumin-bilirubinscore, and FLIS. Multivariate analysis showed FLIS, portal vein thrombosis, and INR as independent predictors. The model achieved an AUC of 0.759, with 87.8% specificity and 56.9% sensitivity, and demonstrated good calibration (χ² = 7.101, P=0.526). Calibration curves and DCA confirmed its clinical utility. This FLIS-based prediction model performs well in predicting PTLF, potentially serving as a practical clinical tool.