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Myocardial ischemia/reperfusion (MI/R) injury is a pathological process that seriously affects the health of patients with coronary artery disease. Long non-coding RNAs (lncRNAs) represents a new class of regulators of diverse biological processes and disease conditions, the study aims to discover the pivotal lncRNA in MI/R injury. The microarray screening identifies a down-regulated heart-enriched lncRNA-CIRPIL (Cardiac ischemia reperfusion associated p53 interacting lncRNA, lncCIRPIL) from the hearts of I/R mice. LncCIRPIL inhibits apoptosis of cultured cardiomyocytes exposed to anoxia/reoxygenation (A/R). Cardiac-specific transgenic overexpression of lncCIRPIL alleviates I/R injury in mice, while knockout of lncCIRPIL exacerbates cardiac I/R injury. LncCIRPIL locates in the cytoplasm and physically interacts with p53, which leads to the cytoplasmic sequestration and the acceleration of ubiquitin-mediated degradation of p53 triggered by E3 ligases CHIP, COP1 and MDM2. p53 overexpression abrogates the protective effects of lncCIRPIL. Notably, the human fragment of conserved lncCIRPIL mimics the protective effects of the full-length lncCIRPIL on cultured human AC16 cells. Collectively, lncCIRPIL exerts its cardioprotective action via sequestering p53 in the cytoplasm and facilitating its ubiquitin-mediated degradation. The study highlights a unique mechanism in p53 signal pathway and broadens our understanding of the molecular mechanisms of MI/R injury.

The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis. The clinical manifestations of patients with tooth-marked tongue include loss of appetite, borborygmus, gastric distention, and loose stool. Traditional clinical tooth-marked tongue recognition is conducted subjectively based on the doctor’s visual observation, and its performance is affected by the doctor’s subjectivity, experience, and environmental lighting changes. In addition, the tooth marks typically have various shapes and colors on the tongue, which make it very challenging for doctors to identify tooth marks. The existing methods based on deep learning have made great progress for tooth-marked tongue recognition, but there are still shortcomings such as requiring a large amount of manual labeling of tooth marks, inability to detect and locate the tooth marks, and not conducive to clinical diagnosis and interpretation. In this study, we propose an end-to-end deep neural network for tooth-marked tongue recognition based on weakly supervised learning. Note that the deep neural network only requires image-level annotations of tooth-marked or non-tooth marked tongues. In this method, a deep neural network is trained to classify tooth-marked tongues with the image-level annotations. Then, a weakly supervised tooth-mark detection network (WSTDN) as an architecture variant of the pre-trained deep neural network is proposed for the tooth-marked region detection. Finally, the WSTDN is re-trained and fine-tuned using only the image-level annotations to simultaneously realize the classification of the tooth-marked tongue and the positioning of the tooth-marked region. Experimental results of clinical tongue images demonstrate the superiority of the proposed method compared with previously reported deep learning methods for tooth-marked tongue recognition. The proposed tooth-marked tongue recognition model may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms.

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