Ischemia reperfusion injury is partly responsible for the high mortality associated with induced myocardial injury and the reduction in the full benefit of myocardial reperfusion. Remote ischemic preconditioning, perconditioning, and postconditioning have all been shown to be cardioprotective. However, it is still unknown which one is the most beneficial. To examine this issue, we used adult male Wistar rat ischemia reperfusion models to compare the cardioprotective effect of these three approaches applied on double-sided hind limbs. The rats were randomly distributed to the following five groups: sham, ischemia reperfusion, remote preconditioning, remote perconditioning, and remote post-conditioning. The ischemia/reperfusion model was established by sternotomy followed by a 30-min ligation of the left coronary artery and a subsequent 3-h reperfusion. Remote conditioning was induced with three 5-min ischemia/5-min reperfusion cycles of the double-sided hind limbs using a tourniquet. A lower early reperfusion arrhythmia score (1.50 + 0.97) was found in the rats treated with remote perconditioning compared to those in the ischemia reperfusion group (2.33 + 0.71). Meanwhile, reduced infarct size was also observed (15.27 + 5.19% in remote perconditioning, 14.53 + 3.45% in remote preconditioning, and 19.84+5.85% in remote post-conditioning vs. 34.47 + 7.13% in ischemia reperfusion, p<0.05), as well as higher expression levels of the apoptosis-relevant protein Bcl-2/Bax following global (ischemia/reperfusion) injury in in vivo rat heart models (1.255 + 0.053 in remote perconditioning, 1.463 + 0.290 in remote preconditioning, and 1.461 +0.541 in remote post-conditioning vs. 1.003 + 0.159 in ischemia reperfusion, p<0.05). Three remote conditioning strategies implemented with episodes of double-sided hind limb ischemia/reperfusion have similar therapeutic potential for cardiac ischemia/reperfusion injury, and remote perconditioning has a greater ability to prevent reperfusion arrhythmia.

Artificial intelligence (AI) adopting deep learning technology has been widely used in the med-ical imaging domain in recent years. It realized the automatic judgment of benign and malig-nant solitary pulmonary nodules (SPNs) and even replaced the work of doctors to some extent. However, misdiagnoses can occur in certain cases. Only by determining the causes can AI play a larger role. A total of 21 Coronavirus disease 2019 (COVID-19) patients were diagnosed with SPN by CT imaging. Their Clinical data, including general condition, imaging features, AI re-ports, and outcomes were included in this retrospective study. Although they were confirmed COVID-19 by testing reverse transcription-polymerase chain reaction (RT-PCR) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), their CT imaging data were misjudged by AI to be high-risk nodules for lung cancer. Imaging characteristics included burr sign (76.2%), lobulated sign (61.9%), pleural indentation (42.9%), smooth edges (23.8%), and cavity (14.3%). The accuracy of AI was different from that of radiologists in judging the nature of be-nign SPNs (p < 0.001, κ = 0.036 < 0.4, means the two diagnosis methods poor fit). COVID-19 patients with SPN might have been misdiagnosed using the AI system, suggesting that the AI system needs to be further optimized, especially in the event of a new disease outbreak.