Intervertebral disc degeneration (IDD) is a primary cause of low back pain, characterized by cell loss, extracellular matrix (ECM) degradation, and a harsh microenvironment with excessive oxidative stress, creating an urgent need for regenerative therapies. This study aimed to develop and evaluate a multifunctional injectable hydrogel (Gel@MnO2/GDF6) co-delivering growth differentiation factor 6 (GDF6) for anabolic stimulation and manganese dioxide (MnO2) nanozymes for reactive oxygen species (ROS) scavenging to treat IDD. The MnO2 nanorods and the chitosan-arginine/oxidized dextran-based hydrogel were synthesized and characterized, demonstrating sustained GDF6 release and pH-responsive degradation. In vitro, Gel@MnO2/GDF6 protected nucleus pulposus (NP) cells from H2O2-induced oxidative stress by reducing ROS, upregulating antioxidant enzymes, promoting anabolic ECM metabolism (increasing Aggrecan and Collagen II while decreasing ADAMTS-4 and MMP-13), reducing key inflammatory cytokine expression (TNF-α and IL-6), and activating the Smad pathway. In vivo, intra-discal injection of Gel@MnO2/GDF6 into a rat IDD model significantly attenuated disc degeneration over 12 weeks, as evidenced by improved histological scores, preserved disc height and hydration on radiological and MRI assessments, restoration of ECM protein homeostasis, reduced cellular apoptosis, mitigated inflammatory marker expression, and activated Smad pathway, with these therapeutic effects being superior to those achieved with hydrogels containing only MnO2 or GDF6. Importantly, all tested hydrogel formulations, including Gel@MnO2/GDF6, demonstrated good systemic biocompatibility. These findings collectively demonstrate that the multifunctional Gel@MnO2/GDF6 hydrogel effectively promotes intervertebral disc regeneration by concurrently mitigating oxidative stress and fostering an anabolic, anti-inflammatory microenvironment, validating its potential as a promising therapeutic method for IDD.

Intervertebral disc degeneration (IDD), a condition characterized by extracellular matrix (ECM) degradation and nucleus pulposus (NP) cell senescence, compromises disc function. The Akt signaling pathway is essential for NP cell viability and ECM homeostasis. This study identifies the serine/threonine kinase PIM3 as significantly downregulated in degenerated human NP tissues. Notably, PIM3 mRNA expression levels were found to be negatively correlated with the clinical severity of IDD (Pfirrmann grade). The functional role of PIM3 was investigated through knockdown and overexpression experiments in human NP cells. PIM3 overexpression restored cell viability, suppressed senescence, and enhanced ECM protein levels in degenerated NP cells, while its knockdown in normal cells produced the opposite results. These protective effects were critically dependent on the kinase activity of PIM3. Mechanistically, PIM3 was found to physically interact with and activate the Akt signaling pathway, thereby regulating downstream molecules, including mTOR and FoxO1, to modulate cell viability and senescence. In an IDD rat model, AAV-mediated PIM3 overexpression improved ECM integrity, reduced senescence and activated the Akt pathway, mitigating disc degeneration. In conclusion, this study establishes PIM3 as a key regulator of NP cell homeostasis that acts through direct engagement with the Akt/mTOR/FoxO1 axis. Its correlation with disease severity and the kinase-dependent nature of its function highlight PIM3 as a promising, mechanistically-defined therapeutic target for treating IDD.

In the original publication [...].

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.

Background: 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. Methods: 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 inter -vertebral 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-18 treated NPC cell proliferation, cell senescence, reactive oxygen species (ROS) accumulation and cell apoptosis were determined by CCK-8, SA-8-gal staining, flowcy-tometry and western blot assays.Results: 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 enrich-ment 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 cas-cades, ERK1/2 cascades, PI3K/Akt signaling pathway, skeletal system, etc. Disease association analysis by Dis-GeNET 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 Cyto-scape, and kaempferol was identified as the hub active compound of DZ. In the IL-18-induced IDD in vitro model, kaempferol treatment significantly improved IL-18-induced NPC cell viability suppression and senescence. In addition, kaempferol treatment significantly attenuated IL-18-induced ROS accumulation and apoptosis. Furthermore, kaempferol treatment partially eliminated IL-18-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-18 stimulation upon p38, JNK, and ERK1/2 phosphorylation. ERK1/2 inhibitor PD0325901 further enhanced the effect of kaempferol on the inhibition of ERK1/2 phos-phorylation, downregulation of MMP3 and ADAMTS-4 expression, and upregulation of aggrecan and collagen II expressions.Conclusion: Kaempferol has been regarded as the major active compound of DZ, protecting NPCs from IL-18 -induced damages through promoting cell viability, inhibiting cell senescence and apoptosis, increasing ECM production, and decreasing ECM degradation. MAPK signaling pathway may be involved.The translational poteintial of this article: This study provides in vitro experimental data support for the pharma-cological effects of kaempferol in treating IDD, and lays a solid experimental foundation for its future clinical application in IDD treatment.

(1) Background: intervertebral disc degeneration (IVDD) defined as the degenerative changes in intervertebral disc is characterized by extracellular matrix (ECM) degradation and death in nucleus pulposus (NP) cells. (2) Methods: The model of IVDD was established in male Sprague Dawley rats using a puncture of a 21-gauge needle at the endplates located in the L4/5 intervertebral disc. Primary NP cells were stimulated by 10 ng/mL IL-1β for 24 h to mimic IVDD impairment in vitro. (3) Results: circFGFBP1 was downregulated in the IVDD samples. circFGFBP1 upregulation inhibited apoptosis and extracellular matrix (ECM) degradation and promoted proliferation in IL-1β-stimulated NP cells. Additionally, circFGFBP1 upregulation mitigated the loss of NP tissue and the destruction of the intervertebral disc structure in vivo during IVDD. FOXO3 could bind to the circFGFBP1 promoter to enhance its expression. circFGFBP1 upregulated BMP2 expression in NP via sponging miR-9-5p. FOXO3 enhanced the protection of circFGFBP1 in IL-1β-stimulated NP cells, whereas a miR-9-5p increase partly reversed the protection. miR-9-5p downregulation contributed to the survival of IL-1β-stimulated NP cells, which was partially reversed by BMP2 silence. (4) Conclusions: FOXO3 could activate the transcription of circFGFBP1 via binding to its promoter, which resulted in the enhancement of BMP2 via sponging miR-9-5p and then inhibited apoptosis and ECM degradation in NP cells during IVDD.

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.

Abstract Background: We analyzed the maximum standardized uptake value (SUVmax) of 18F-fluorodeoxyglucose positron emission tomography with integrated computed tomography (18F-FDG PET/CT) and expressions of glucose metabolism regulatory proteins in epithelial ovarian cancer (EOC)，and aimed to confirm the quantitative relationship between SUVmax and glucose metabolism.Methods: From November 2017 to November 2019, 30 patients with EOC in the study group and 30 women without ovary disease in the control group underwent PET / CT examination. SUVmax of primary and metastatic lesions of each patient before initial treatment, and that of normal ovaries of each woman were measured. The SUVmax of primary EOC lesions, metastatic EOC lesions and normal ovaries were compared. The expressions of glucose metabolism regulatory proteins, containing glucose transporter 1 (Glut1), c-Myc, p53, Ki-67 and hypoxia-inhibitory factor-1α (HIF-1α) were tested by immunohistochemistry in primary and metastatic tissues of the study group. The correlation between SUVmax and the expression levels of glucose metabolism regulatory proteins was analyzed.Results: The SUVmax of primary EOC lesions was the highest (16.61±7.70), followed by metastatic EOC lesions (9.13±5.43), and that of normal ovaries was the lowest (19.40±2.14) among three different tissues (P < 0.0001). SUVmax of primary EOC lesions showed no correlation with age, tumor differentiation, clinical stage and histopathological subtype in the study group (p>0.05). The expressions of Glut1, p53 and c-Myc in primary lesions were higher than those in metastatic lesions (P=0.002,0.23,0.022, respectively). SUVmax was only correlated with expression of Glut1 in primary and metastatic EOC lesions (correlation coefficients 0.474 and 0.469, respectively; both p<0.05).Conclusion: High levels of SUVmax can reflect the active glucose metabolism of primary and metastatic lesions in EOC. Glut1 is a glucose metabolism regulatory protein closely related to SUVmax in EOC.

Lung cancer has the highest mortality rate among all malignancies. Non-micro pulmonary nodules are the primary manifestation of early-stage lung cancer. If patients can be detected with nodules in the early stage and receive timely treatment, their survival rate can be improved. Due to the large number of patients and limited medical resources, doctors take a longer time to make a diagnosis, which reduces efficiency and accuracy. Besides, there are no suitable approaches for developing countries. Therefore, we propose a 2.5D-based cascaded multi-stage framework for automatic detection and segmentation (DS-CMSF) of pulmonary nodules. The first three stages of the framework are used to discover lesions, and the latter stage is used to segment them. The first locating stage introduces the classical 2D-based Yolov5 model to locate the nodules roughly on axial slices. The second aggregation stage proposes a candidate nodule selection (CNS) algorithm to locate further and reduce redundant candidate nodules. The third classification stage uses a multi-size 3D-based fusion model to accommodate nodules of varying sizes and shapes for false-positive reducing. The last segmentation stage introduces multi-scale and attention modules into 3D-based UNet autoencoder to segment the nodular regions finely. Our proposed framework achieves 95.95% sensitivity and 89.50% CPM for nodules detection on the LUNA16 dataset, and 86.75% DSC for nodules segmentation on the LIDC-IDRI dataset. Moreover, our approach also achieves the accuracy-complexity trade-off, which can effectively realize the auxiliary diagnosis of pulmonary nodules in developing countries.

Objective: Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual procedures might lead to inaccurate measurement results. Therefore, we designed a new automatic multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning in this study. PET image pre-screening can reduce the time cost of noise reduction, segmentation and lesion partitioning methods, and denoising can enhance both quantitative metrics and visual quality for better segmentation accuracy. For pre-screening, we propose a new differential activation filter (DAF) to screen the lesion images from whole-body scanning. For noise reduction, neural network inverse (NN inverse) as the inverse transformation of generalized Anscombe transformation (GAT), which does not depend on the distribution of residual noise, was presented to improve the SNR of images. For segmentation and lesion partitioning, definition density peak clustering (DDPC) was proposed to realize instance segmentation of lesion and normal tissue with unsupervised images, which helped reduce the cost of density calculation and completely deleted the cluster halo. The experimental results of clinical data demonstrate that our proposed methods have good results and better performance in noise reduction, segmentation and lesion partitioning compared with state-of-the-art methods.

Due to the complexity of the tumor and a large amount of patient information, an intelligent system is used to filter and extract hidden information, which will be beneficial to make accurate diagnostic decisions. The treatment and prognosis of breast cancer depend on the tumor stage. The PET-CT image can clearly show the lesion area and lesion range, especially for advanced-stage patients. Images and blood tests are crucial for accurate staging, tumor monitoring, and providing guided treatment plans. Multi-source data collaborative analysis can mine hidden attributes to provide a more intelligent treatment plan. This paper proposes a framework for predicting the diagnosis of the patient's disease by combining images and labeling parameters. The blood test data and image data of patients are filtered based on the establishment of the medical decision-making module. The module selects detection indicators that correlate with tumor staging for analysis and trains a prediction model to assist doctors in providing a second diagnosis. The proposed framework for breast cancer diagnosis was tested on 5470 patient data from three well-known hospitals in China. The test results indicate that it performs well in diagnosing cancer staging with a prediction accuracy of 0.88.

In many developing countries and regions, there are medical problems such as dense populations, lack of medical resources, and shortage of doctors, making it impossible to provide patients with more convenient full-cycle services. Non-small cell lung cancer is a malignant tumor with the highest morbidity and mortality in the world. Research on the auxiliary treatment of intelligent systems is helpful in understanding and evaluate the disease. The system can help doctors provide patients with effective drug treatments and personalized medical services by actively learning the experience of outstanding experts. According to expert knowledge, through quantitative efficacy scores and specific analysis of evaluation indicators, based on the efficacy evaluation matrix and the extraction of key features of patients and drugs, a predictive model of drug efficacy evaluation for adjuvant therapy is established. The model divides into latent feature extraction and curative effect collaborative prediction modules. In the feature extraction module, adding noise to the original data in model training process helps reduce the impact of the sparseness of the patient's medication data. By considering the uncertainty of experts in drug efficacy evaluation modeling, based on probability analysis and efficacy prediction, the proposed method demonstrates the potential options in the face of hesitating choices. According to the predicted efficacy score, candidate drugs are selected to assist doctors in disease analysis and secondary diagnosis. Experiments have shown that drug efficacy prediction methods can provide adjuvant treatments for diseases and quantify the therapeutic effects of targeted drugs. The efficacy information and detection information of patient-drug pairs are helpful to improve decision-making ability, and the proposed medical decision support system framework is superior to other deep learning methods. By adding data, the performance can be significantly improved.

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.

Prostate cancer (PCa) is one of the main diseases that endanger men’s health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance.

To observe thoracolumbar segmental mobility using kinetic magnetic resonance imaging (kMRI) in patients with minimal thoracolumbar spondylosis and establish normal values for translational and angular segmental motion as well as the relative contribution of each segment to total thoracolumbar segmental motion in order to obtain a more complete understanding of this segmental motion in healthy and pathological conditions. Mid-sagittal images obtained by weight-bearing, multi-position kMRI in patients with symptomatic low back pain or radiculopathy were reviewed. The translational motion and angular variation of each segment from T10-L2 were calculated using MRAnalyzer Automated software. Only patients with a Pfirrmann grade of I or II, indicating minimal disc disease, for all thoracolumbar discs from T10-T11 to L1-L2 were included for further analysis. The mean translational motion measurements for each level of the lumbar spine were 1.15 mm at T10-T11, 1.20 mm at T11-T12, 1.23 mm at T12-L1, and 1.34 mm at L1-L2 (P < .05 for L1-L2 vs T10-T11). The mean angular motion measurements at each level were 3.26 degrees at T10-T11, 3.92 degrees at T11-T12, 4.95 degrees at T12-L1, and 6.85 degrees at L1-L2. The L1-L2 segment had significantly more angular motion than all other levels (P < .05). The mean percentage contribution of each level to the total angular mobility of the thoracolumbar spine was highest at L1-L2 (36.1%) and least at T10-T11 (17.1%; P < .01). Segmental motion was greatest in the proximal lumbar levels, and angular motion showed a gradually increasing trend from T10 to L2.

There are many factors affecting the survival of people in developing countries, such as the tremendous number of population, nonuniform medical resources, and the threatening of malignant diseases. The improvements in medical information system in developing countries may lead to a bright future. By using effect medical resources and utilizing the information coming from the medical system, the doctors could come to a diagnosis with analysis. The probability of getting sick is very useful information which assists doctors to improve the accuracy of disease diagnosis, shortening treatment time, and reducing the incidence of misdiagnosis. This paper aims to build a model, considering not only probability analysis but also decision making, which can play a crucial role to figure out the probability of non-small lung cancer transitions in four different stages. In each process of the model, selecting effective parameters with big data are adopted for finding maximum effect with the top three high relevancy diagnose and decision data. With effective treatment methods that improve the relevancy diagnose data, the probability of malignant disease development will decrease. It is proved by the statistical analysis of clinical data that the model provides clinical data fast with enough accuracy.

Hamartoma of mature cardiac myocytes is an extremely rare type of benign cardiac tumor with a slow growth rate and generally occurs in adults. We report a case with hamartoma of mature cardiac myocytes of the right auricle demonstrating intense F-FDG uptake and a large amount of pericardial effusion on PET/CT mimicking malignancy in a 41-year-old man. Hamartoma of mature cardiac myocytes should be considered among the differential diagnoses when an F-FDG-avid primary focal cardiac mass is found in patients with malignant features on PET/CT imaging.

The uneven distribution of medical resources is a serious problem in developing countries. Those seeking timely treatment have difficulty choosing the right hospital. To found sustainable development with medical system, this paper establishes a model of the hospital confidence evaluation index by combining national evaluation and a third-party evaluation. The model is applied to a social network. Users from any region can use the model through APP in IoT, a hospital analysis index query, which selects the best hospital for diagnosis and treatment. The model can locate different personnel characteristics by modifying the control variables. Establishing a medical system with big data provides good model characteristics. Effective data analysis through large data users in the sample is established to provide the most effective hospital recommendation, which is a good solution to the selectivity problem. The contributions in this works are: (1) Models of initial trust and hospital evaluations are established by combining national and third-party assessments; (2) the initial trust evaluation model is modified and optimized by establishing control variables; (3) the trust evaluation mechanism of users in social networks is obtained through big data sampling and model analysis, and the balanced distribution of the medical staff is realized.

Rationale: 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). Patient concerns: A 30-year-old male was admitted to our hospital with a complaint of abdominal distension, fatigue, and anorexia for 2 months. Diagnoses: 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. Interventions: The patient underwent LTx and splenectomy. Outcomes: 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. Lessons: 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.

In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped countries. In this study, we built a system model for the drug therapy, relevance parameter analysis, and data decision making in non-small cell lung cancer. Based on the probability analysis and status decision, the optimized therapeutic schedule can be calculated and selected, and then effective drug therapy methods can be determined to improve relevance parameters. Statistical analysis of clinical data proves that the model of the probability analysis and decision making can provide fast and accurate clinical data.