Ferroptosis, a newly discovered iron-dependent cell death pathway, is characterized by lipid peroxidation and GSH depletion mediated by iron metabolism and is morphologically, biologically and genetically different from other programmed cell deaths. Besides, ferroptosis is usually found accompanied by inflammatory reactions. So far, it has been found participating in the development of many kinds of diseases. Macrophages are a group of immune cells that widely exist in our body for host defense and play an important role in tissue homeostasis by mediating inflammation and regulating iron, lipid and amino acid metabolisms through their unique functions like phagocytosis and efferocytosis, cytokines secretion and ROS production under different polarization. According to these common points in ferroptosis characteristics and macrophages functions, it’s obvious that there must be relationship between macrophages and ferroptosis. Therefore, our review aims at revealing the interaction between macrophages and ferroptosis concerning three metabolisms and integrating the application of certain relationship in curing diseases, mostly cancer. Finally, we also provide inspirations for further studies in therapy for some diseases by targeting certain resident macrophages in distinct tissues to regulate ferroptosis. Facts: Ferroptosis is considered as a newly discovered form characterized by its nonapoptotic and iron-dependent lipid hydroperoxide, concerning iron, lipid and amino acid metabolisms. Ferroptosis has been widely found playing a crucial part in various diseases, including hepatic diseases, neurological diseases, cancer, etc. Macrophages are phagocytic immune cells, widely existing and owning various functions such as phagocytosis and efferocytosis, cytokines secretion and ROS production. Macrophages are proved to participate in mediating metabolisms and initiating immune reactions to maintain balance in our body. Recent studies try to treat cancer by altering macrophages’ polarization which damages tumor microenvironment and induces ferroptosis of cancer cells. Open questions: How do macrophages regulate ferroptosis of other tissue cells specifically? Can we use the interaction between macrophages and ferroptosis in treating diseases other than cancer? What can we do to treat diseases related to ferroptosis by targeting macrophages? Is the use of the relationship between macrophages and ferroptosis more effective than other therapies when treating diseases?

Nrf2 and NF-κB are important regulators of the response to oxidative stress and inflammation in the body. Previous pharmacological and genetic studies have confirmed crosstalk between the two. The deficiency of Nrf2 elevates the expression of NF-κB, leading to increased production of inflammatory factors, while NF-κB can affect the expression of downstream target genes by regulating the transcription and activity of Nrf2. At the same time, many therapeutic drug-induced organ toxicities, including hepatotoxicity, nephrotoxicity, cardiotoxicity, pulmonary toxicity, dermal toxicity, and neurotoxicity, have received increasing attention from researchers in clinical practice. Drug-induced organ injury can destroy body function, reduce the patients’ quality of life, and even threaten the lives of patients. Therefore, it is urgent to find protective drugs to ameliorate drug-induced injury. There is substantial evidence that protective medications can alleviate drug-induced organ toxicity by modulating both Nrf2 and NF-κB signaling pathways. Thus, it has become increasingly important to explore the crosstalk mechanism between Nrf2 and NF-κB in drug-induced toxicity. In this review, we summarize the potential molecular mechanisms of Nrf2 and NF-κB pathways and the important effects on adverse effects including toxic reactions and look forward to finding protective drugs that can target the crosstalk between the two.

Aims: The aims of the present study were to characterize the pharmacokinetics of voriconazole in renal transplant recipients and to identify factors significantly affecting pharmacokinetic parameters. We also aimed to explore the optimal dosing regimens for patients who developed invasive fungal infections.Methods: A total of 105 patients (342 concentrations) were included prospectively in a population pharmacokinetic analysis. Nonlinear mixed‐effects models were developed using Phoenix NLME software. Dosing simulations were performed based on the final model.Results: A one‐compartment model with first‐order absorption and elimination was used to characterize voriconazole pharmacokinetics. Population estimates of clearance, volume of distribution and oral bioavailability were 2.88 l·h⁻¹, 169.3 l and 58%, respectively. The allele frequencies of cytochrome P450 gene (CYP) 2C19*2, *3 and *17 variants were 29.2%, 5.2% and 0.5%, respectively. CYP2C19 genotype had a significant effect on the clearance. Voriconazole trough concentrations in poor metabolizers were significantly higher than in intermediate metabolizers and extensive metabolizers alike. The volume of distribution increased with increased body weight. The oral bioavailability was substantially lower within 1 month after transplantation but increased with postoperative time. Dosing simulations indicated that during the early postoperative period, poor metabolizers could be treated with 150 mg intravenously or 250 mg orally twice daily; intermediate metabolizers with 200 mg intravenously or 350 mg orally twice daily; and extensive metabolizers with 300 mg intravenously twice daily.Conclusions: Using a combination of CYP2C19 genotype and postoperative time to determine the initial voriconazole dosing regimens followed by therapeutic drug monitoring could help to advance individualized treatment in renal transplantation patients with invasive fungal infections.

Background: The triglyceride-glucose (TyG) index is a validated surrogate for insulin resistance, while frailty reflects cumulative physiological decline. The combined impact of TyG-Frailty Index (TyGFI) has not been adequately explored. This study aimed to investigate the association between TyGFI and the risk of cardiovascular disease (CVD) and stroke. Methods: A total of 5448 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 1139 participants from the U.S. National Health and Nutrition Examination Survey (NHANES) were included. Multivariable logistic regression models were used to estimate associations with CVD and stroke, adjusting for demographic, clinical, and lifestyle covariates. Restricted cubic spline (RCS) and subgroup analyses were employed to examine dose–response relationships and interaction effects. Results: Higher TyGFI levels were associated with older age, adverse metabolic parameters, and increased prevalence of hypertension, diabetes, and dyslipidemia. In fully adjusted models, the highest TyGFI quartile was significantly associated with increased risks of CVD (CHARLS: OR 15.09, 95% CI 9.65–23.60; NHANES: OR 4.98, 95% CI 2.04–12.19) and stroke (CHARLS: OR 21.12, 95% CI 6.44–69.23; NHANES: OR 12.98, 95% CI 2.58–65.17), with consistent dose–response trends confirmed by RCS analyses. Subgroup analyses further demonstrated the robustness of these associations across diverse demographic and clinical strata. Conclusions: TyGFI is a strong and independent predictor of CVD and stroke in two nationally representative cohorts. By integrating metabolic and functional risk dimensions, TyGFI provides a more comprehensive risk stratification tool, with significant implications for early identification and prevention of cardiovascular events in aging populations.