Senescence and apoptosis of the nucleus pulposus cells (NPCs) are essential components of the intervertebral disc degeneration (IDD) process. Senescence and anti-apoptosis treatments could be effective ways to delay or even stop disc degeneration. IDD has been treated with Eucommia ulmoides Oliver (Du Zhong, DZ) and its active ingredients. However, the roles and mechanisms of DZ in NPC apoptosis and senescence remain unclear. Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to select the main active ingredients of DZ with the threshold of oral bioavailability (OB) ​≥ ​30% and drug-likeness (DL) ​≥ ​0.2. GSE34095 contained expression profile of degenerative intervertebral disc tissues and non-degenerative intervertebral disc tissues were downloaded for different expression genes analysis. The disease targets genes of IDD were retrieved from GeneCards. The online tool Metascape was used for functional enrichment annotation analysis. The specific effects of the ingredient on IL-1β treated NPC cell proliferation, cell senescence, reactive oxygen species (ROS) accumulation and cell apoptosis were determined by CCK-8, SA-β-gal staining, flowcytometry and western blot assays. A total of 8 active compounds of DZ were found to meet the threshold of OB ​≥ ​30% and DL ​≥ ​0.2 with 4151 drug targets. After the intersection of 879 IDD disease targets obtained from GeneCards and 230 DEGs obtained from the IDD-related GSE dataset, a total of 13 hub genes overlapped. According to functional enrichment annotation analysis by Metascape, these genes showed to be dramatically enriched in AGE-RAGE signaling, proteoglycans in cancer, wound healing, transmembrane receptor protein tyrosine kinase signaling, MAPK cascades, ERK1/2 cascades, PI3K/Akt signaling pathway, skeletal system, etc. Disease association analysis by DisGeNET indicated that these genes were significantly associated with IDD, intervertebral disc disease, skeletal dysplasia, and other diseases. Active ingredients-targets-signaling pathway networks were constructed by Cytoscape, and kaempferol was identified as the hub active compound of DZ. In the IL-1β-induced IDD in vitro model, kaempferol treatment significantly improved IL-1β-induced NPC cell viability suppression and senescence. In addition, kaempferol treatment significantly attenuated IL-1β-induced ROS accumulation and apoptosis. Furthermore, kaempferol treatment partially eliminated IL-1β-induced decreases in aggrecan, collagen II, SOX9, and FN1 levels and increases in MMP3, MMP13, ADAMTS-4, and ADAMTS-5. Moreover, kaempferol treatment significantly relieved the promotive effects of IL-1β stimulation upon p38, JNK, and ERK1/2 phosphorylation. ERK1/2 inhibitor PD0325901 further enhanced the effect of kaempferol on the inhibition of ERK1/2 phosphorylation, downregulation of MMP3 and ADAMTS-4 expression, and upregulation of aggrecan and collagen II expressions. Kaempferol has been regarded as the major active compound of DZ, protecting NPCs from IL-1β-induced damages through promoting cell viability, inhibiting cell senescence and apoptosis, increasing ECM production, and decreasing ECM degradation. MAPK signaling pathway may be involved. This study provides in vitro experimental data support for the pharmacological effects of kaempferol in treating IDD, and lays a solid experimental foundation for its future clinical application in IDD treatment.

As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristics of PET images, the framework uses a differential activation filter to select whole-body images containing lesion tissue. Second, a new neural network containing residual connections which has powerful generalization performance compared with normal FCN network is proposed for PET image reconstruction and denoising. Finally, in the segmentation of lesion tissues, a custom clustering algorithm based on the density is used to distinguishe the lesion tissue part from the normal tissue. Tests on real medical PET images show that the whole automated framework has good performance and time cost in PET lesion image screening, image denoising and lesion tissue segmentation compared with other algorithms. The framework shows promising scientific study and application prospects.

The present study aimed to explore the underlying mechanisms of bone morphogenetic protein 2 (BMP2) in alleviating intervertebral disc degeneration (Idd). A rat puncture Idd model was constructed, and the rats were randomly divided into six groups: control; Idd (model); Idd+PBS [containing 10 10 adeno-associated virus serotype 2 (AAV)]; and Idd + AAV2-BMP2 (10 6 , 10 8 and 10 10). IL-1β was used to treat primary nucleus pulposus (NP) cells to mimic Idd in vitro. The effects of BMP2 in Idd were determined by magnetic resonance imaging (MRI), hematoxylin and eosin staining and Alcian Blue staining in vivo. The levels of collagen II, aggrecan, transcription factor SOX9 (SOX9) and matrix metalloproteinase 13 (MMP-13) were examined using western blot analysis and reverse transcription quantitative polymerase chain reaction (RT-qPcR) in NP tissues and cells. The expression of c-telopeptide of type II collagen (cTX-II) in the sera or cell supernatants was determined by ELISA. In addition, the levels of phosphorylation of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt), and the levels of apoptosis-associated proteins and apoptosis ratio of NP cells were also determined by western blot analysis and flow cytometry, respectively. LY29400, an inhibitor of PI3K, was used to additionally confirm the signal pathway mechanism of BMP2 treatment in IDD. BMP2 significantly extended the interval between discs and alleviated the fibrous ring rupture and the decrease in the levels of glycoproteins in Idd rats, as determined by MRI and histological staining. Additionally, BMP2 treatment significantly upregulated the levels of collagen II, aggrecan and SOX9, but downregulated the levels of MMP-13 and cTX-II in Idd rats and NP cells in a dose-dependent manner. concurrently, recombinant human (rh)BMP2 pretreatment also significantly decreased the apoptosis ratio of interleukin (IL)-1β-treated NP cells via downregulating the level of cleaved caspase-3 and upregulating the level of uncleaved poly (adenosine 5'-diphosphate-ribose) polymerase. It was demonstrated that rhBMP2 also significantly decreased the inflammatory response in NP tissues and cells, based on levels of IL-6, TNF-α and IL-10. In addition, rhBMP2 inhibited cell apoptosis via upregulating the phosphorylation levels of the PI3K/Akt signaling pathway, and LY29400 pretreatment inhibited the effects of BMP2 in IL-1β treated NP cells. BMP2 alleviated Idd via the PI3K/Akt signaling pathway by inhibiting NP cell apoptosis and decreasing the levels of matrix proteins.

At present, human health is threatened by many diseases, and lung cancer is one of the most dangerous tumors that threaten human life. In most developing countries, due to the large population and lack of medical resources, it is difficult for doctors to meet patients’ needs for medical treatment only by relying on the manual diagnosis. Based on massive medical information, the intelligent decision-making system has played a great role in assisting doctors in analyzing patients’ conditions, improving the accuracy of clinical diagnosis, and reducing the workload of medical staff. This article is based on the data of 8,920 nonsmall cell lung cancer patients collected by different medical systems in three hospitals in China. Based on the intelligent medical system, on the basis of the intelligent medical system, this paper constructs a nonsmall cell lung cancer staging auxiliary diagnosis model based on convolutional neural network (CNNSAD). CNNSAD converts patient medical records into word sequences, uses convolutional neural networks to extract semantic features from patient medical records, and combines dynamic sampling and transfer learning technology to construct a balanced data set. The experimental results show that the model is superior to other methods in terms of accuracy, recall, and precision. When the number of samples reaches 3000, the accuracy of the system will reach over 80%, which can effectively realize the auxiliary diagnosis of nonsmall cell lung cancer and combine dynamic sampling and migration learning techniques to train nonsmall cell lung cancer staging auxiliary diagnosis models, which can effectively achieve the auxiliary diagnosis of nonsmall cell lung cancer. The simulation results show that the model is better than the other methods in the experiment in terms of accuracy, recall, and precision.

Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child’s rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS.

Malignant hepatic epithelioid hemangioendotheliom (HEH) is a rare vascular tumor of endothelial origin, with multiple metastases to the spleen. This report describes a diffuse HEH with splenic metastasis on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images and delayed mutifocal bone metastasis after liver transplantation (LTx). A 30-year-old male was admitted to our hospital with a complaint of abdominal distension, fatigue, and anorexia for 2 months. Mild to moderate FDG uptake in the whole liver, and multifocal FDG uptake in the spleen were observed on 18F-FDG PET/CT scan. Ultrasound guided liver biopsy was performed, and a diagnosis of HEH was confirmed. The patient underwent LTx and splenectomy. The patient developed low back pain due to unknown etiology, 3 months after surgery. A follow-up 18F-FDG PET/CT scan demonstrated multifocal bone destruction. Unfortunately, the patient died 12 months after surgery. It is noteworthy that despite liver transplantation for the treatment of HEH, there may be a risk of recurrence. For these patients with extrahepatic lesions, adjuvant chemotherapy may be a useful alternative treatment method for the prevention of recurrence.