The Effect of Ceruloplasmin Against Ischemia-Reperfusion Injury in Epigastric Island Flap in Rats

Propionyl-l-carnitine mitigates ischemia-reperfusion injury in rat epigastric island flaps

Background

Ischemia-reperfusion injury presents a substantial concern in various medical scenarios, notably in reconstructive surgery involving tissue flaps. Despite reports on the protective benefits of Propionyl-l-carnitine against ischemia-reperfusion injury, a thorough assessment of its efficacy in epigastric island flap models is currently lacking.

Methods

Sixteen male Sprague-Dawley rats underwent epigastric island flap surgery and were divided into two groups: a Propionyl-l-carnitine group that received intraperitoneal Propionyl-l-carnitine prior to ischemia induction and a sham group that received saline treatment. A comprehensive evaluation was performed including macroscopic, biochemical and histological assessments encompassing measurements of flap survival areas, lipid peroxidation (malondialdehyde), glutathione, myeloperoxidase, nitric oxide and peripheral neutrophil counts.

Results

The Propionyl-l-carnitine group demonstrated significantly increased flap survival areas when compared to the sham group. Administration of Propionyl-l-carnitine led to reduced malondialdehyde levels and elevated glutathione levels indicating a reduction in oxidative stress. Furthermore, the Propionyl-l-carnitine group exhibited lower myeloperoxidase levels, higher nitric oxide levels and reduced peripheral neutrophil counts, suggesting a decrease in the inflammatory response. Histopathological analysis revealed decreased levels of inflammation, necrosis, polymorphonuclear leukocyte infiltration and edema in the Propionyl-l-carnitine group. Additionally, vascularity was enhanced in the Propionyl-l-carnitine group.

Conclusion

This study provides compelling evidence that Propionyl-l-carnitine administration effectively mitigates the deleterious effects of ischemia-reperfusion injury in epigastric island flaps. This is substantiated by the improved flap survival, diminished oxidative stress and inflammation, as well as the enhanced vascularity observed. Propionyl-l-carnitine emerges as a promising therapeutic intervention to enhance tissue flap survival in reconstructive surgery, warranting further exploration through larger-scale investigations.

Nanomedicine-Based Therapeutics for Myocardial Ischemic/Reperfusion Injury

Myocardial ischemic/reperfusion (IR) injury is a global cardiovascular disease with high mortality and morbidity. Therapeutic interventions for myocardial ischemia involve restoring the occluded coronary artery. However, reactive oxygen species (ROS) inevitably impair the cardiomyocytes during the ischemic and reperfusion phases. Antioxidant therapy holds great promise against myocardial IR injury. The current therapeutic methodologies for ROS scavenging depend predominantly on administering antioxidants. Nevertheless, the intrinsic drawbacks of antioxidants limit their further clinical transformation. The use of nanoplatforms with versatile characteristics greatly benefits drug delivery in myocardial ischemic therapy. Nanoplatform-mediated drug delivery significantly improves drug bioavailability, increases therapeutic index, and reduces systemic toxicity. Nanoplatforms can be specifically and reasonably designed to enhance molecule accumulation at the myocardial site. The present review initially summarizes the mechanism of ROS generation during the process of myocardial ischemia. The understanding of this phenomenon will facilitate the advancement of innovative therapeutic strategies against myocardial IR injury. The latest developments in nanomedicine for treating myocardial ischemic injury are then discussed. Finally, the current challenges and perspectives in antioxidant therapy for myocardial IR injury are addressed.

Improving the ischemia-reperfusion injury in vascularized composite allotransplantation: Clinical experience and experimental implications

Long-time ischemia worsening transplant outcomes in vascularized composite allotransplantation (VCA) is often neglected. Ischemia-reperfusion injury (IRI) is an inevitable event that follows reperfusion after a period of cold static storage. The pathophysiological mechanism activates local inflammation, which is a barrier to allograft long-term immune tolerance. The previous publications have not clearly described the relationship between the tissue damage and ischemia time, nor the rejection grade. In this review, we found that the rejection episodes and rejection grade are usually related to the ischemia time, both in clinical and experimental aspects. Moreover, we summarized the potential therapeutic measures to mitigate the ischemia-reperfusion injury. Compare to static preservation, machine perfusion is a promising method that can keep VCA tissue viability and extend preservation time, which is especially beneficial for the expansion of the donor pool and better MHC-matching.

Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms

BACKGROUND

Microvascular tissue reconstruction is a well-established, commonly used technique for a wide variety of the tissue defects. However, flap failure is associated with an additional hospital stay, medical cost burden, and mental stress. Therefore, understanding of the risk factors associated with this event is of utmost importance.

AIM

To develop machine learning-based predictive models for flap failure to identify the potential factors and screen out high-risk patients.

METHODS

Using the data set of 946 consecutive patients, who underwent microvascular tissue reconstruction of free flap reconstruction for head and neck, breast, back, and extremity, we established three machine learning models including random forest classifier, support vector machine, and gradient boosting. Model performances were evaluated by the indicators such as area under the curve of receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was performed for the most critical variables in the random forest model.

RESULTS

Post-surgery, the flap failure event occurred in 34 patients (3.6%). The machine learning models based on various preoperative and intraoperative variables were successfully developed. Among them, the random forest classifier reached the best performance in receiver operating characteristic curve, with an area under the curve score of 0.770 in the test set. The top 10 variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity. Interestingly, only age, body mass index, and ischemic time were statistically associated with the outcomes.

CONCLUSION

Machine learning-based algorithms, especially the random forest classifier, were very important in categorizing patients at high risk of flap failure. The occurrence of flap failure was a multifactor-driven event and was identified with numerous factors that warrant further investigation. Importantly, the successful application of machine learning models may help the clinician in decision-making, understanding the underlying pathologic mechanisms of the disease, and improving the long-term outcome of patients.