LncRNA MALAT1 knockdown alleviates myocardial apoptosis in rats with myocardial ischemia-reperfusion through activating PI3K/AKT signaling pathway.

OBJECTIVE: To observe the effect of long non-coding ribonucleic acid metastasis-associated lung adenocarcinoma transcript 1 (lncRNA MALAT1) on the myocardial ischemia-reperfusion (I/R) injury in rats and to explore its potential mechanism, to provide certain references for clinical prevention and treatment of myocardial I/R injury. MATERIALS AND METHODS: A total of 60 male Wistar rats were randomly divided into the Control group (n=20), I/R group (n=20) and I/R + MALAT1 small-interfering RNA (siRNA) group (n=20) using a random number table. The I/R model was established through recanalization after ligation of left anterior descending coronary artery (LAD), and the MALAT1 knockdown model was established via tail intravenous injection of MALAT1 siRNA in the I/R + MALAT1 siRNA group. The ejection fraction (EF%) and fractional shortening (FS%) of rats in each group were detected via echocardiography and the infarction area in each group was detected using 2,3,5-triphenyl tetrazolium chloride (TTC) assay. Moreover, the morphological changes in myocardial cells in each group were detected via hematoxylin-eosin (H&E) staining, and the myocardial apoptosis level was detected via terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining. At the same time, the expression levels of the anti-apoptotic protein B-cell lymphoma-2 (Bcl-2) and pro-apoptotic protein Bcl-2 associated X protein (Bax) in myocardial tissues in each group were determined via Western blotting. Finally, the effect of MALAT1 knockdown on the phosphatidylinositol 3-hydroxy kinase/protein kinase B (PI3K/AKT) protein expression was detected via Western blotting. RESULTS: The expression level of lncRNA MALAT1 in myocardial tissues was significantly higher in the I/R group than that in the Control group (p<0.05). The MALAT1 knockdown could significantly improve the cardiac insufficiency caused by I/R injury, and increase both EF% and FS% in rats (p<0.05). In addition, the MALAT1 knockdown could markedly inhibit myocardial infarction caused by I/R injury and reduce the infarction area from (62.12 ± 1.29) to (27.66 ± 3.58; p<0.05). The results of the H&E staining showed that the myofilaments were arranged more orderly, the degrees of degradation and necrosis were lower and the cellular edema was significantly alleviated in the I/R + MALAT1 siRNA group compared with those in the I/R group. According to the results of TUNEL staining, the rats in I/R + MALAT1 siRNA group had a markedly lower level of myocardial apoptosis than the I/R group (p<0.05), and the Bax/Bcl-2 ratio also remarkably declined in the I/R + MALAT1 siRNA group (p<0.05). Furthermore, the results of Western blotting revealed that MALAT1 siRNA could significantly reverse the I/R injury-induced inhibition on the AKT phosphorylation (p<0.05). CONCLUSIONS: The MALAT1 knockdown can markedly improve the I/R-induced myocardial injury and promote the cardiac function of rats, whose mechanism may be related to the activation of the AKT signaling pathway by MALAT1 siRNA. Therefore, lncRNA MALAT1 is expected to be a new therapeutic target for myocardial I/R injury.

The rise of artificial intelligence and the uncertain future for physicians.

Physicians in everyday clinical practice are under pressure to innovate faster than ever because of the rapid, exponential growth in healthcare data. "Big data" refers to extremely large data sets that cannot be analyzed or interpreted using traditional data processing methods. In fact, big data itself is meaningless, but processing it offers the promise of unlocking novel insights and accelerating breakthroughs in medicine-which in turn has the potential to transform current clinical practice. Physicians can analyze big data, but at present it requires a large amount of time and sophisticated analytic tools such as supercomputers. However, the rise of artificial intelligence (AI) in the era of big data could assist physicians in shortening processing times and improving the quality of patient care in clinical practice. This editorial provides a glimpse at the potential uses of AI technology in clinical practice and considers the possibility of AI replacing physicians, perhaps altogether. Physicians diagnose diseases based on personal medical histories, individual biomarkers, simple scores (e.g., CURB-65, MELD), and their physical examinations of individual patients. In contrast, AI can diagnose diseases based on a complex algorithm using hundreds of biomarkers, imaging results from millions of patients, aggregated published clinical research from PubMed, and thousands of physician's notes from electronic health records (EHRs). While AI could assist physicians in many ways, it is unlikely to replace physicians in the foreseeable future. Let us look at the emerging uses of AI in medicine.