The lactate threshold (LT1), which is defined as the first rise in lactate concentration during incremental exercise, has not been non-invasively and conveniently determined in a clinical setting. We aimed to visualize changes in lactate concentration in sweat during exercise using our wearable lactate sensor and investigate the relationship between the lactate threshold (LT1) and ventilatory threshold (VT1). Twenty-three healthy subjects and 42 patients with cardiovascular diseases (CVDs) were enrolled. During exercise, the dynamic changes in lactate values in sweat were visualized in real-time with a sharp continuous increase up to volitional exhaustion and a gradual decrease during the recovery period. The LT1 in sweat was well correlated with the LT1 in blood and the VT1 (r = 0.92 and 0.71, respectively). In addition, the Bland–Altman plot described no bias between the mean values (mean differences: − 4.5 and 2.5 W, respectively). Continuous monitoring of lactate concentrations during exercise can provide additional information for detecting the VT1.
Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.
Doxorubicin (DOX) is the most widely used anthracycline anticancer agent; however, its cardiotoxicity limits its clinical efficacy. Numerous studies have elucidated the mechanisms underlying DOX-induced cardiotoxicity, wherein apoptosis has been reported as the most common final step leading to cardiomyocyte death. However, in the past two years, the involvement of ferroptosis, a novel programmed cell death, has been proposed. The purpose of this review is to summarize the historical background that led to each form of cell death, focusing on DOX-induced cardiotoxicity and the molecular mechanisms that trigger each form of cell death. Furthermore, based on this understanding, possible therapeutic strategies to prevent DOX cardiotoxicity are outlined. DNA damage, oxidative stress, intracellular signaling, transcription factors, epigenetic regulators, autophagy, and metabolic inflammation are important factors in the molecular mechanisms of DOX-induced cardiomyocyte apoptosis. Conversely, the accumulation of lipid peroxides, iron ion accumulation, and decreased expression of glutathione and glutathione peroxidase 4 are important in ferroptosis. In both cascades, the mitochondria are an important site of DOX cardiotoxicity. The last part of this review focuses on the significance of the disruption of mitochondrial homeostasis in DOX cardiotoxicity.
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