Osteolytic bone disorders are characterized by an overall reduction in bone mineral density which enhances bone ductility and vulnerability to fractures. This disorder is primarily associated with superabundant osteoclast formation and bone resorption activity. Nicorandil (NIC) is a vasodilatory anti-anginal drug with ATP-dependent potassium (KATP) channel openings. However, NIC is adopted to manage adverse cardiovascular and coronary events. Recent research has demonstrated that NIC also possesses anti-inflammatory peculiarity through the regulation of p38 MAPK and NF-κB signaling pathways. Both MAPK and NF-κB signaling pathways play pivotal roles in RANKL-induced osteoclast formation and bone resorption function. Herein, we hypothesized that NIC may exert potential biological effects against osteoclasts, and revealed that NIC dose-dependently suppressed bone marrow macrophage (BMM) precursors to differentiate into TRAP + multinucleated osteoclasts in vitro. Furthermore, osteoclast resorption assays demonstrated anti-resorptive effects exhibited by NIC. NIC had no impact on osteoblast differentiation or mineralization function. Based on Biochemical analyses, NIC relieved RANKL-induced ERK, NF-κB and p38 MAPK signaling without noticeable effects on JNK MAPK activation. However, the attenuation of NF-κB and p38 MAPK activation was sufficient to hamper the downstream induction of c-Fos and NFATc1 expression. Meanwhile, NIC administration markedly protected mice from ovariectomy (OVX)-induced bone loss through in vivo inhibition of osteoclast formation and bone resorption activity. Collectively, this work demonstrated the potential of NIC in the management of osteolytic bone disorders mediated by osteoclasts.

The study focused on the clinical application value of artificial intelligence-based computed tomography angiography (CTA) in the diagnosis of orthotopic liver transplantation (OLT) after ischemic type biliary lesions (ITBL). A total of 66 patients receiving OLT in hospital were selected. Convolutional neural network (CNN) algorithm was used to denoise and detect the edges of CTA images of patients. At the same time, the quality of the processed image was subjectively evaluated and quantified by Hmax, Ur, Cr, and other indicators. Then, the digital subtraction angiography (DSA) diagnosis and CTA diagnosis based on CNN were compared for the sensitivity, specificity, positive predictive value, negative predictive value, and patient classification results. It was found that CTA can clearly reflect the information of hepatic aorta lesions and thrombosis in patients with ischemic single-duct injury after liver transplantation. After neural network algorithm processing, the image quality is obviously improved, the lesions are more prominent, and the details of lesion parts are also well displayed. ITBL occurred in 40 (71%) of 56 patients with abnormal CTA at early stage. ITBL occurred in only 8 (12.3%) of 65 patients with normal CTA at early stage. Early CTA manifestations had high sensitivity (72.22%), specificity (87.44%), positive predictive value (60.94%), and negative predictive value (92.06%) for the diagnosis of ITBL. It was concluded that artificial intelligence-based CTA had high clinical application value in the diagnosis of ITBL after OLT.