Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset.

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis.

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics.

In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.

GDNF triggers proliferation of rat C6 glioma cells via the NF-κB/CXCL1 signaling pathway.

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor that is characterized by its high proliferative and migratory potential, leading to a high invasiveness of this tumor type. However, the underlying mechanism of GBM proliferation and migration has not been fully elucidated. In this study, at first, we used RNA-seq together with bioinformatics technology to screen for C-X-C motif ligand 1 (CXCL1) as a proliferation-related gene. And exogenous glial cell line-derived neurotrophic factor (GDNF) induced proliferation and up-regulated the level of CXCL1 in rat C6 glioma cells determined by sqPCR and ELISA. Then, we manipulated the CXCL1 expression by using a lentiviral vector (CXCL1-RNAi) approach. By this, the proliferation of C6 cells was decreased, suggesting that CXCL1 plays a key role in proliferation in these cells. We hypothesized that exogenous GDNF promoted NF-κB nuclear translocation and therefore, analyzed the interaction of CXCL1 with NF-κB by Western Blot and immunofluorescence. Additionally, we used BAY 11-7082, a phosphorylation inhibitor of NF-κB, to elucidate NF-κB mediated the effect of GDNF on CXCL1. These results demonstrated that GDNF enhanced the proliferation of rat C6 glioma cells through activating the NF-κB/CXCL1 signaling pathway. In summary, these studies not only revealed the mechanism of action of exogenous GDNF in promoting the proliferation of C6 glioma cells but may also provide a new biological target for the treatment of malignant glioma.

Moderating effects of cognitive reserve on the relationship between brain structure and cognitive abilities in middle-aged and older adults.

The cognitive reserve (CR) hypothesis is reinforced by negative moderating effects, suggesting that those with higher CR are less reliant on brain structure for cognitive function. Previous research on CR's moderating effects yielded inconsistent results, motivating our 3 studies using UK Biobank data. Study I examined five CR proxies' moderating effects on global, lobar, and regional brain-cognition models; study II extended study I by using a larger sample size; and study III investigated age-related moderating effects on the hippocampal regions. In study I, most moderating effects were negative and none survived the multiple comparison correction, but study II identified 13 global-level models with significant negative moderating effects that survived correction. Study III showed age influenced CR proxies' moderating effects in hippocampal regions. Our findings suggest that the effects of CR proxies on brain integrity and cognition varied depending on the proxy used, brain integrity indicators, cognitive domain, and age group. This study offers significant insights regarding the importance of CR for brain integrity and cognitive outcomes.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer's disease staging.

Accurately diagnosing of Alzheimer's disease (AD) and its early stages is critical for prompt treatment or potential intervention to delay the the disease's progression. Convolutional neural networks (CNNs) models have shown promising results in structural MRI (sMRI)-based diagnosis, but their performance, particularly for 3D models, is constrained by the lack of labeled training samples. To address the overfitting problem brought on by the insufficient training sample size, we propose a three-round learning strategy that combines transfer learning with generative adversarial learning. In the first round, a 3D Deep Convolutional Generative Adversarial Networks (DCGAN) model was trained with all available sMRI data to learn the common feature of sMRI through unsupervised generative adversarial learning. The second round involved transferring and fine-tuning, and the pre-trained discriminator (D) of the DCGAN learned more specific features for the classification task between AD and cognitively normal (CN). In the final round, the weights learned in the AD versus CN classification task were transferred to the MCI diagnosis. By highlighting brain regions with high prediction weights using 3D Grad-CAM, we further enhanced the model's interpretability. The proposed model achieved accuracies of 92.8%, 78.1%, and 76.4% in the classifications of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results show that our proposed model avoids overfitting brought on by a paucity of sMRI data and enables the early detection of AD.

[Professor SUN Shen-tian's experience in acupuncture treatment of depression based on "psychosomatic medicine"].

This paper introduces professor SUN Shen-tian's clinical thoughts and his characteristics of acupuncture techniques for the treatment of depression based on "psychosomatic medicine". Professor SUN, the master of traditional Chinese medicine, believes that depression refers to comorbidity of "heart mind" and "body", resulting from the "body-mind" disharmony, specially dominated by the emotional disorder. This disease is located in the brain, with the injury of mind and closely related to the heart and liver dysfunction. In pathogenesis, the dysfunction of brain mind and the unhealthy conditions of body and mind are involved. The treatment should focus on "regulating the mind, improving the intelligence, co-modulating the abdominal and brain functions and treating the physical and mental disorders". Baihui (GV 20), Ningshen (Extra) and emotional area on the head are selected as the main points to benefit the intelligence and calming down the mind; the abdominal region 1 and region 8 of "Sun's abdominal acupuncture" are used as the main points of the abdomen to regulate the brain functions. The point prescription is modified according to the symptoms and etiologies. The repeated transcranial acupuncture stimulation and electroacupuncture at low frequency (2 Hz) are crucial to the therapeutic effect. Reliving anxious emotions is specially considered before acupuncture, and the mind is protected and deqi is consolidated during acupuncture.

[SUN Shen-tian's clinical experience in treating Tourette's syndrome with acupuncture].

To introduce the clinical experience of professor SUN Shen-tian in treatment of Tourette's syndrome (TS) with acupuncture. TS is a psychosomatic disease and the core pathogenesis refers to blood deficiency producing internal wind. The disease is located in the heart and liver. Acupoints are selected according to the functional orientation of the cerebral cortex. The extrapyramidal system area is preferred for tic disorder, and the emotional area is for behavioral disorder. The treatment focuses on regulating the mind by multiple methods, including manual needling technique used the transcranial repeated acupuncture, and regulating the mind by taking multiple acupoints, Baihui (GV 20), Neiguan (PC 6), Shenmen (HT 7) and Dazhong (KI 4) are stimulated. For abdominal twitching and mental symptoms of TS children, the first and third abdominal areas are selected. The target symptoms (biao) are treated specially by local acupoints, the combination of the starting and ending acupoints of the affected meridian, or the acupoints of the meridians with same name. The modified chaihu longgu muli decoction and siwu decoction are prescribed to sooth liver, nourish blood and soothe wind. In association with the characteristics and target symptoms of TS, the sequential therapy is used with filiform needling, intradermal needling, Chinese herbal medication and psychotherapy.

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification.

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects' features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs' input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph's imaging features and edge-assigning functions can both significantly affect classification accuracy.

A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer's disease using neuroimaging.

Alzheimer's disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.

Non-prompt surgery for patients with acute type A aortic dissection without pre-operative shock and malperfusion.

Background: Acute type A aortic dissection (ATAAD) requires urgent surgical treatment. However, during daily practice, there were some patients with ATAAD sought for medical attention several days after symptoms occurred and some other patients hesitated to receive aortic surgery after the diagnosis of ATAAD was made. This study aims to investigate the surgical outcomes of non-prompt aortic surgery (delayed diagnosis caused by the patient or delayed surgery despite immediate diagnosis) for ATAAD patients. Methods: From November 2004 to June 2020, of more than 200 patients with ATAAD patients who underwent aortic surgery at our hospital, there were 30 patients without pre-operative shock and malperfusion who sought for medical attention with symptoms for several days or delayed aortic surgery several days later despite ATAAD was diagnosed. Of the 30 patients (median age 60.9, range 33.4~82.5 years) in the study group, there were 18 patients undergoing surgery when they arrived at our hospital (delayed diagnosis by the patient) and 12 patients receiving surgery days later (delayed surgery despite immediate diagnosis). Patients with prompt surgery after symptom onset (control group) were matched from our database by propensity score matching. The surgical mortality rate and post-operative morbidities were compared between the study group and control group. Results: The in-hospital mortality was 3.3% for the study group and 6.7% for the control group (p = non-significant). The incidence of post-operative cerebral permanent neurological defect was 0% for the study group and 13.3% for the control group (p = 0.112). There were three patients receiving aortic re-intervention or re-do aortic surgery during follow-up for the study group and two patients for the control group. Conclusion: Prompt surgery for ATAAD is usually a good choice if everything is well prepared. Besides, urgent but non-prompt aortic surgery could also provide acceptable surgical results for ATAAD patients without pre-operative shock and malperfusion who did not seek medical attention or who could not make their minds to undergo surgery immediately after symptom onset. Hospitalization with intensive care is very important for pre-operative preparation and monitoring for the patients who decline prompt aortic surgery.

Ropivacaine with intraspinal administration alleviates preeclampsia-induced kidney injury via glycocalyx /alpha 7 nicotinic acetylcholine receptor pathway.

Preeclampsia is characterized by hypertension and proteinuria, which is associated with kidney injury. Glycocalyx (GCX) degradation mediated endothelial injury can result in proteinuria and kidney damage. alpha 7 nicotinic acetylcholine receptor (α7nAChR) connects nervous and immune systems to respond to stress or injury. We aimed to explore the protective effect and mechanism of intraspinal analgesia on maternal kidney injury in preeclampsia. Endotoxin-induced preeclampsia rats treated with ropivacaine via intraspinal administration. Renal histopathological examination was performed, cell apoptosis in the kidney, the levels of Glycocalyx markers of Syndecan-1 and heparin sulfate (HS) in maternal serum, Syndecan-1 along with α7nAChR in the kidney were measured. Our results showed that kidney injury was obviously in preeclampsia rats with proteinuria, endothelial damage, higher apoptosis rate, increasing levels of Syndecan-1 and HS in serum, upregulated Syndecan-1 expression but downregulated α7nAChR expression in kidney. Preeclampsia rats treated with intraspinal injected ropivacaine attenuated preeclampsia-induced kidney injury as Syndecan-1 and HS were decreased in serum, Syndecan-1 expression was suppressed as well as α7nAChR was activated in the kidney. Our results suggested that Ropivacaine administered through the spinal canal may protect preeclampsia-induced renal injury by decreasing GCX and α7nAChR activation.

Effects of nalbuphine on the cardiotoxicity of ropivacaine in rats.

When combined with nalbuphine, local anesthetics show a longer duration of nerve block without increasing complications. However, no evidence is available concerning the effect of nalbuphine on the cardiotoxicity of local anesthetics. The objective of this work is to investigate whether nalbuphine pretreatment can increase the lethal dose threshold of ropivacaine in rats. Anesthetized Sprague Dawley rats were pretreated with different doses of nalbuphine (0.4, 0.8, 1.5, 3.0, 5.0 mg/kg) or NS (normal saline, negative control) or 30% LE (lipid emulsion, positive control) 2 ml/kg/min for 5 min (n = 6). Then 0.5% ropivacaine was infused at a rate of 2.5 mg/kg/min until asystole occurs. Time of arrhythmia, 50% mean arterial pressure- and 50% heart rate-reduction, and asystole were recorded, and ropivacaine doses were calculated. Nalbuphine (0.4-5.0 mg/kg) did not affect ropivacaine-induced arrhythmia, 50% mean arterial pressure-reduction and 50% heart rate-reduction, and asystole in rats compared with NS pre-treatment. The asystole dose threshold (in milligrams per kilogram) of group LE was higher than that of group NS (NS 28.25(6.32) vs. LE, 41.58(10.65); P = 0.04; 95% confidence interval 0.23 to 26.45), while thresholds of arrhythmia, 50% mean arterial pressure-reduction, and 50% heart rate-reduction were not affected by LE. Nalbuphine doses of 0.4-5.0 mg/kg pretreatment did not increase the threshold of ropivacaine cardiotoxicity compared with NS control; 30% LE increases the lethal dose threshold of ropivacaine in rats.

A genome-wide association study for varicose veins.

BACKGROUND: The aim was to compare the genetic information of varicose vein patients with that of a healthy population attempting to identify certain significant genetic associations. METHOD: Patients' clinical characteristics and demographics were collected, and their genetic samples were examined. The results were compared to the genetic information of one thousand sex-matched healthy controls from Taiwan Biobank database. The Clinical-Etiology-Anatomy-Pathophysiology classification was applied for further subgroup analysis. RESULTS: After comparison of genetic information of ninety-six patients to that of healthy controls, two significant single nucleotide polymorphisms (SNPs) were identified. One was in DPYSL2 gene, and the other was in VSTM2L gene. A further comparison between C2-3 patient subgroup and C4-6 subgroup identified another four significant SNPs, which were located in ZNF664-FAM101A, PHF2, ACOT11, and TOM1L1 genes. CONCLUSION: Our preliminary result identified six significant SNPs located in six different genes. All of them and their genetic products may warrant further investigations.

[Essence of SUN Shen-tian's clinical features on acupuncture and moxibustion].

To summarize SUN Shen-tian's treatment ideas and clinical features. SUN applies meridian syndrome differentiation to the diagnosis and treatment of diseases; advocates that prevention and treatment of diseases should be regulated mind firstly; applies transcranial repetitive acupuncture combined modern cerebral cortex function positioning; emphasizes the application of multiple acupuncture methods and manipulation, and includes the meridian penetrating needling method, the flat needling and penetrating needling method, and the stagnant needle lifting method, pays attention to the importance of achieving qi and manipulation for the effect.

Effects of delta-opioid receptor agonist pretreatment on the cardiotoxicity of bupivacaine in rats.

BACKGROUND: Delta-opioid receptor is widely expressed in human and rodent hearts, and has been proved to protect cardiomyocytes against ischemia/reperfusion and heart failure. The antagonist of delta-opioid receptor could block the rescue effect of lipid emulsion against local anesthetic cardiotoxicity. However, no evidence is available for the direct effect of delta-opioid-receptor agonists on the cardiotoxicity of local anesthetics. METHODS: Anesthetized Sprague Dawley rats were divided into five groups. Group NS received 2 ml·kg-1·min-1 normal saline, group LE received 2 ml·kg-1·min-1 30% lipid emulsion and group BW received 0.1, 1.0, or 5.0 mg/kg BW373U86, a delta-opioid-receptor agonist, for 5 min. Then 0.5% bupivacaine was infused intravenously at a rate of 3.0 mg·kg-1·min-1 until asystole. The time of arrhythmia, 50% mean arterial pressure-, 50% heart rate-reduction and asystole were recorded, and the dose of bupivacaine at each time point was calculated. RESULTS: All three different doses of BW373U86 did not affect the arrhythmia, 50% mean arterial pressure-reduction, 50% heart rate-reduction and asystole dose of bupivacaine compared with group NS. 30% LE significantly increased the bupivacaine threshold of 50% mean arterial pressure-reduction (17.9 [15.4-20.7] versus 7.2 [5.9-8.7], p = 0.018), 50% heart rate-reduction (18.7 ± 4.2 versus 8.8 ± 1.7, p < 0.001) and asystole (26.5 [21.0-29.1] versus 11.3 [10.7-13.4], p = 0.008) compared with group NS. There was no difference between group LE and group NS in the arrhythmia dose of bupivacaine (9.9 [8.9-11.7] versus 5.6 [4.5-7.0], p = 0.060). CONCLUSIONS: Our data show that BW373U86 does not affect the cardiotoxicity of bupivacaine compared with NS control in rats. 30% LE pretreatment protects the myocardium against bupivacaine-induced cardiotoxicity.