RLBind: a deep learning method to predict RNA-ligand binding sites.

Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics compared with regular therapeutics targeting proteins. RNAs can provide many potential drug targets with diverse structures and functions. However, up to now, only a few methods have been proposed. Predicting RNA-small molecule binding sites still remains a big challenge. New computational model is required to better extract the features and predict RNA-small molecule binding sites more accurately. In this paper, a deep learning model, RLBind, was proposed to predict RNA-small molecule binding sites from sequence-dependent and structure-dependent properties by combining global RNA sequence channel and local neighbor nucleotides channel. To our best knowledge, this research was the first to develop a convolutional neural network for RNA-small molecule binding sites prediction. Furthermore, RLBind also can be used as a potential tool when the RNA experimental tertiary structure is not available. The experimental results show that RLBind outperforms other state-of-the-art methods in predicting binding sites. Therefore, our study demonstrates that the combination of global information for full-length sequences and local information for limited local neighbor nucleotides in RNAs can improve the model's predictive performance for binding sites prediction. All datasets and resource codes are available at https://github.com/KailiWang1/RLBind.

GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

MOTIVATION: Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity and achieve significant performance improvement. However, protein-ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins. RESULTS: To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein-ligand binding affinity, which takes the combination of graph neural network, bitransport information mechanism and physics-based distance terms into account for the first time. Unlike other methods, GraphscoreDTA can not only effectively capture the protein-ligand pairs' mutual information but also highlight the important atoms of the ligands and residues of the proteins. The results show that GraphscoreDTA significantly outperforms existing methods on multiple test sets. Furthermore, the tests of drug-target selectivity on the cyclin-dependent kinase and the homologous protein families demonstrate that GraphscoreDTA is a reliable tool for protein-ligand binding affinity prediction. AVAILABILITY AND IMPLEMENTATION: The resource codes are available at https://github.com/CSUBioGroup/GraphscoreDTA.

A nomogram and risk stratification to predict subsequent pregnancy loss in patients with recurrent pregnancy loss.

STUDY QUESTION: Could the risk of subsequent pregnancy loss be predicted based on the risk factors of recurrent pregnancy loss (RPL) patients? SUMMARY ANSWER: A nomogram, constructed from independent risk factors identified through multivariate logistic regression, serves as a reliable tool for predicting the likelihood of subsequent pregnancy loss in RPL patients. WHAT IS KNOWN ALREADY: Approximately 1-3% of fertile couples experience RPL, with over half lacking a clear etiological factor. Assessing the subsequent pregnancy loss rate in RPL patients and identifying high-risk groups for early intervention is essential for pregnancy counseling. Previous prediction models have mainly focused on unexplained RPL, incorporating baseline characteristics such as age and the number of previous pregnancy losses, with limited inclusion of laboratory and ultrasound indicators. STUDY DESIGN, SIZE, DURATION: The retrospective study involved 3387 RPL patients who initially sought treatment at the Reproductive Immunology Clinic of Renji Hospital, Shanghai Jiao Tong University School of Medicine, between 1 January 2020 and 31 December 2022. Of these, 1153 RPL patients met the inclusion criteria and were included in the analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS: RPL was defined as two or more pregnancy losses (including biochemical pregnancy loss) with the same partner before 28 weeks of gestation. Data encompassing basic demographics, laboratory indicators (autoantibodies, peripheral immunity coagulation, and endocrine factors), uterine and endometrial ultrasound results, and subsequent pregnancy outcomes were collected from enrolled patients through initial questionnaires, post-pregnancy visits fortnightly, medical data retrieval, and telephone follow-up for lost patients. R software was utilized for data cleaning, dividing the data into a training cohort (n = 808) and a validation cohort (n = 345) in a 7:3 ratio according to pregnancy success and pregnancy loss. Independent predictors were identified through multivariate logistic regression. A nomogram was developed, evaluated by 10-fold cross-validation, and compared with the model incorporating solely age and the number of previous pregnancy losses. The constructed nomogram was evaluated using the AUC, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA). Patients were then categorized into low- and high-risk subgroups. MAIN RESULTS AND THE ROLE OF CHANCE: We included age, number of previous pregnancy losses, lupus anticoagulant, anticardiolipin IgM, anti-phosphatidylserine/prothrombin complex IgM, anti-double-stranded DNA antibody, arachidonic acid-induced platelet aggregation, thrombin time and the sum of bilateral uterine artery systolic/diastolic ratios in the nomogram. The AUCs of the nomogram were 0.808 (95% CI: 0.770-0.846) in the training cohort and 0.731 (95% CI: 0.660-0.802) in the validation cohort, respectively. The 10-fold cross-validated AUC ranged from 0.714 to 0.925, with a mean AUC of 0.795 (95% CI: 0.750-0.839). The AUC of the nomogram was superior compared to the model incorporating solely age and the number of previous pregnancy losses. Calibration curves, DCAs, and CICAs showed good concordance and clinical applicability. Significant differences in pregnancy loss rates were observed between the low- and high-risk groups (P < 0.001). LIMITATIONS, REASONS FOR CAUTION: This study was retrospective and focused on patients from a single reproductive immunology clinic, lacking external validation data. The potential impact of embryonic chromosomal abnormalities on pregnancy loss could not be excluded, and the administration of medication to all cases impacted the investigation of risk factors for pregnancy loss and the model's predictive efficacy. WIDER IMPLICATIONS OF THE FINDINGS: This study signifies a pioneering effort in developing and validating a risk prediction nomogram for subsequent pregnancy loss in RPL patients to effectively stratify their risk. We have integrated the nomogram into an online web tool for clinical applications. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by the National Natural Science Foundation of China (82071725). All authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

Advancing osteoarthritis therapy with GMOCS hydrogel-loaded BMSCs-exos.

This study investigated the mechanism of the extracellular matrix-mimicking hydrogel-mediated TGFB1/Nrf2 signaling pathway in osteoarthritis using bone marrow mesenchymal stem cell-derived exosomes (BMSCs-Exos). A GMOCS-Exos hydrogel was synthesized and evaluated for its impact on chondrocyte viability and neutrophil extracellular traps (NETs) formation. In an OA rat model, GMOCS-Exos promoted cartilage regeneration and inhibited NETs formation. Transcriptome sequencing identified TGFB1 as a key gene, with GMOCS-Exos activating Nrf2 signaling through TGFB1. Depletion of TGFB1 hindered the cartilage-protective effect of GMOCS-Exos. This study sheds light on a promising therapeutic strategy for osteoarthritis through GMOCS-Exos-mediated TGFB1/Nrf2 pathway modulation.

DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein-ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein-ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein-ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: //github.com/KailiWang1/DeepDTAF.

Histone methyltransferase EZH2 in proliferation, invasion, and migration of fibroblast-like synoviocytes in rheumatoid arthritis.

OBJECTIVE: Rheumatoid arthritis (RA) may lead to irreversible joint damage. The role of histone modifications in RA has been emphasized. This study investigated the effect of histone methyltransferase EZH2 on fibroblast-like synoviocytes (FLSs) in RA. MATERIALS AND METHODS: Synovial tissues were collected from RA patients and non-RA patients (NC). RA-FLSs and NC-FLSs were isolated and identified using flow cytometry. EZH2 expression in synovial tissues and FLSs was detected using RT-qPCR and Western blot. The proliferation, migration, and invasion of RA-FLSs and NC-FLSs were measured using MTT, EdU, and Transwell assays. The binding of EZH2, H3K27me3, and miR-22-3p was analyzed using ChIP assay. The targeting relationship between miR-22-3p and CYR61 was verified using dual-luciferase assay. miR-22-3p and CYR61 expressions were detected using RT-qPCR. CYR61 and H3K27me3 levels were detected using Western blot. Functional rescue experiments were performed to verify the effect of miR-22-3p or CYR61 on RA-FLSs. RESULTS: EZH2 was highly expressed in synovial tissues and FLSs from RA patients. The proliferation, migration, and invasion ability of RA-FLSs was stronger than that of NC-FLSs. Downregulation of EZH2 repressed proliferation, migration, and invasion of RA-FLSs. EZH2 inhibited miR-22-3p expression by binding to the miR-22-3p promoter and increasing H3K27me3 methylation level, and thereby upregulated CYR61 expression. Downregulation of miR-22-3p or overexpression of CYR61 annulled the inhibitory effect of EZH2 silencing on RA-FLS proliferation, migration, and invasion. CONCLUSION: EZH2 bound to the miR-22-3p promoter and inhibited miR-22-3p expression by upregulating H3K27me3 level, thereby promoting CYR61 expression and inducing the proliferation, migration, and invasion of RA-FLSs.

NEDD: a network embedding based method for predicting drug-disease associations.

BACKGROUND: Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. RESULTS: In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. CONCLUSIONS: The experiments on a gold standard dataset which contains 1933 validated drug-disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

A simultaneous bilateral quadriceps and patellar tendons rupture in patients with chronic kidney disease undergoing long-term hemodialysis: a case report.

BACKGROUND: The incidence of rupture of the quadriceps or patellar tendon s is low, especially that of bilateral quadriceps tendon rupture, and it is generally considered a complication secondary to chronic systemic disorders. We report two rare cases of simultaneous bilateral tendon rupture affecting the extensor function of the knee in patients with chronic kidney disease who have been treated with long-term haemodialysis. CASE PRESENTATION: Two young males with a history of chronic kidney disease who were being treated with long-term haemodialysis presented to our hospital with clinical signs of disruption of the extensor mechanism of the knee. One patient was diagnosed with bilateral quadriceps tendon rupture, and the other patient had bilateral patellar tendon rupture. They underwent surgical repair of the tendons, and their knees were actively mobilized during physiotherapy. CONCLUSION: Bilateral quadriceps or patellar tendons rupture is a rare occurrence in patients with chronic kidney disease who are being treated with long-term haemodialysis. Timely surgical treatment and scientific physiotherapy can lead to good recovery of knee joint function.

KIFC3 promotes the proliferation, migration and invasion of non-small cell lung cancer through the PI3K/AKT signaling pathway.

KIFC3 is a member of the Kinesin superfamily proteins (KIFs). The role of KIFC3 in non-small cell lung cancer (NSCLC) is unknown. This study aimed to elucidate the function of KIFC3 in NSCLC and the underlying mechanism. Immunohistochemistry indicated that KIFC3 was highly expressed in NSCLC tissues and correlated with the degree of differentiation, tumor size, lymph node metastasis and TNM stage. MTT, colony formation and Transwell assays demonstrated that KIFC3 overexpression promoted the proliferation, migration and invasion of NSCLC cells in vitro, while KIFC3 knockdown led to the opposite results. The protein expression levels of PI3Kp85α and p-Akt were increased after KIFC3 overexpression, meanwhile the downstream protein expression levels such as cyclin D1, CDK4, CDK6, RhoA, RhoC and MMP2 were increased. This promotion effect could be inhibited by a specific inhibitor of the PI3K/Akt pathway, LY294002. Co-immunoprecipitation assays confirmed the interaction between endogenous/exogenous KIFC3 and PI3Kp85α. Tumor formation experiments in nude mice confirmed that KIFC3 overexpression promoted the proliferation, migration and invasion of NSCLC cells in vivo and performed its biological function through the PI3K/Akt signaling pathway.In conclusion, KIFC3 promotes the malignant behavior of NSCLC cells through the PI3K/Akt signaling pathway.

A pharmacophore-guided deep learning approach for bioactive molecular generation.

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.

How heavy is the medical expense burden among the older adults and what are the contributing factors? A literature review and problem-based analysis.

In recent years, the aging population and increasing medical expenses among the older adults have emerged as significant public health concerns. National governments must conduct medical expense accounting and implement measures to reduce the burden of medical costs on the older population. However, limited studies have focused on total medical expenditure from a macro perspective, with many researches exploring individual medical expenses from different perspectives. This review introduces the trend of population aging and its impact on health cost change, reviews research on the medical expense burden of the older population and contributing factors, and points out underlying problems and limitations of current studies. Based on the present studies, the review emphasizes the necessity of medical expense accounting and analyzes the medical expense burden of the older population. Future studies should explore the impacts of medical insurance funds and health service system reforms on reducing medical expenses and developing a supporting medical insurance reform plan.

Osteoporosis in Patients With Respiratory Diseases.

Climate change, environmental pollution, and virus epidemics have sharply increased the number of patients suffering from respiratory diseases in recent years. Prolonged periods of illness and drug use increase the occurrence of complications in these patients. Osteoporosis is the common bone metabolism disease with respiratory disturbance, which affects prognosis and increases mortality of patients. The problem of osteoporosis in patients with respiratory diseases needs more attention. In this review, we concluded the characteristics of osteoporosis in some respiratory diseases including COPD, asthma, COVID-19, tuberculosis, and lung cancer. We revealed that hypoxia was the common pathogenesis of osteoporosis secondary to respiratory diseases, with malnutrition and corticosteroid abuse driving the progression of osteoporosis. Hypoxia-induced ROS accumulation and activated HIF-1α lead to attenuated osteogenesis and enhanced osteoclastogenesis in patients with respiratory diseases. Tuberculosis and cancer also invaded bone tissue and reduced bone strength by direct infiltration. For the treatment of osteoporosis in respiratory patients, oral-optimized bisphosphonates were the best treatment modality. Vitamin D was a necessary supplement, both for calcium absorption in osteogenesis and for improvement of respiratory lesions. Reasonable adjustment of the dose and course of corticosteroids according to the etiology and condition of patients is beneficial to prevent the occurrence and development of osteoporosis. Additionally, HIF-1α was a potential target for the treatment of osteoporosis in respiratory patients, which could be activated under hypoxia condition and involved in the process of bone remodeling.

In Silico Prediction of New Mutations That Can Improve the Binding Abilities Between 2019-nCoV Coronavirus and Human ACE2.

The Coronavirus Disease 2019 (COVID-19) has become an international public health emergency, posing a serious threat to human health and safety around the world. The 2019-nCoV coronavirus spike protein was confirmed to be highly susceptible to various mutations, which can trigger apparent changes of virus transmission capacity and the pathogenic mechanism. In this article, the binding interface was obtained by analyzing the interaction modes between 2019-nCoV coronavirus and the human ACE2. Based on the "SIFT server" and the "bubble" identification mechanism, 9 amino acid sites were selected as potential mutation-sites from the 2019-nCoV-S1-ACE2 binding interface. Subsequently, a total number of 171 mutant systems for 9 mutation-sites were optimized for binding-pattern comparsion analysis, and 14 mutations that may improve the binding capacity of 2019-nCoV-S1 to ACE2 were selected. The Molecular Dynamic Simulations were conducted to calculate the binding free energies of all the 14 mutant systems. Finally, we found that most of the 14 mutations on the 2019-nCoV-S1 protein could enhance the binding ability between 2019-nCoV coronavirus and human ACE2. Among which, the binding capacities for G446R, Y449R and F486Y mutations could be increased by 20 percent, and that for S494R mutant increased even by 38.98 percent. We hope this research could provide significant help for the future epidemic detection, drug and vaccine development.

Global research hotspots and research trends on idiopathic pulmonary fibrosis: a bibliometric and visualization analysis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease, the progression of which current drug therapy cannot reverse. This study analyzed current research hotspots and future research trends in IPF through bibliometric methods, with the aim of providing a reference for new therapeutic strategies. METHODS: Publications on IPF obtained from the Web of Science Core Collection database, The Literature Metrology Online Analysis Platform, and CiteSpace were used to analyze publication characteristics. VOSviewer was used to conduct keywords co-occurrence analysis and analyze research hotspots. RESULTS: A total of 7,016 publications related to IPF were identified from 2011 to 2020. The most contributions were from the USA and the five research institutions with the largest number of publications were all from that country. The American Journal of Respiratory and Critical Care Medicine was the most cited journal and had an incontrovertible academic impact with five of the top 10 high-cited references published in this journal. G Raghu was the academic authority in this domain in terms of both the number of publications and the most citations. By analyzing keywords, we identified three IPF research hotspot clusters, which are "clinical research", "pathogenesis research" and "diagnosis research" respectively. CONCLUSIONS: We evaluated all publications concerning IPF research in the past decade through bibliometric analysis. The current research hotspot in this field is drug therapy for the condition using nintedanib and pirfenidone. Future research will focus on conducting multi-center randomized controlled trials to explore and evaluate new therapeutic drugs for IPF. It is hoped that this study can provide information and data support for further research and the development of new therapeutic drugs.

Analysis of Cells Proliferation and MicroRNAs Expression Profile in Human Chondrosarcoma SW1353 Cells Exposed to Iodine-125 Seeds Irradiation.

Chondrosarcoma is the second most common bone malignancy in adults, and it is often resistant to traditional chemotherapy and radiation therapy. Permanent implantation of iodine-125 (125I) seeds has been explored for the treatment of many types of cancer. In this study, the aim was to investigate the proliferative and microRNA (miRNA) effects of 125I seeds irradiation on human chondrosarcoma SW1353 cells. First, a new in vitro 125I seed irradiation model was established, and cell viability and miRNA microarray assays were performed before and after exposure to the 125I seeds. Cell proliferation was inhibited, and miRNA expression was substantially altered by irradiation exposure. The inhibition of cell proliferation was positively correlated with increased radiation doses, with cells showing the highest total radiation dose 7 days after irradiation. A total of 2549 miRNAs were detected in the SW1353 cells after exposure to 6 Gy of radiation, which included 189 differentially expressed miRNAs (98 upregulated and 91 downregulated). Four miRNAs were found to play important roles in the inhibition of cell proliferation after irradiation exposure, including miR-1224-5p, miR-492, miR-135b-5p, and miR-6839-5p. The target genes of the associated miRNAs mentioned were vascular endothelial growth factor A (VEGFA), C-X-C motif chemokine 12 (CXCL12), mitogen-activated protein kinase kinase kinase kinase 3 (MAP4K3), and apoptosis facilitator Bcl-2-like protein 14 (BCL2L14). Hence, the mitogen-activated protein kinase signaling pathway may be involved in how chondrosarcoma cells respond to 125I seed irradiation.

Metformin promotes cell proliferation and osteogenesis under high glucose condition by regulating the ROS­AKT­mTOR axis.

Metformin, a cost­effective and safe orally administered antidiabetic drug used by millions of patients, has exhibited great interest for its potential osteogenic­promoting properties in different types of cells, including mesenchymal stem cells (MSCs). Diabetic osteopathy is a common comorbidity of diabetes mellitus; however, the underlying molecular mechanisms of metformin on the physiological processes of MSCs, under high glucose condition, remain unknown. To determine the effects of metformin on the regulatory roles of proliferation and differentiation in MSCs, under high glucose conditions, osteogenesis after metformin treatment was detected with Alizarin Red S and ALP staining. The results demonstrated that high glucose levels significantly inhibited cell proliferation and osteogenic differentiation under high glucose conditions. Notably, addition of metformin reversed the inhibitory effects induced by high glucose levels on cell proliferation and osteogenesis. Furthermore, high glucose levels significantly decreased mitochondrial membrane potential (MMP), whereas treatment with metformin helped maintain MMP. Further analysis of mitochondrial function revealed that metformin significantly promoted ATP synthesis, mitochondrial DNA mass and mitochondrial transcriptional activity, which were inhibited by high glucose culture. Furthermore, metformin significantly scavenged reactive oxygen species (ROS) induced by high glucose levels, and regulated the ROS­AKT­mTOR axis inhibited by high glucose levels, suggesting the protective effects of metformin against high glucose levels via regulation of the ROS­AKT­mTOR axis. Taken together, the results of the present study demonstrated the protective role of metformin on the physiological processes of MSCs, under high glucose condition and highlighted the potential molecular mechanism underlying the effect of metformin in promoting cell proliferation and osteogenesis under high glucose condition.

Melatonin Inhibits Glucose-Induced Apoptosis in Osteoblastic Cell Line Through PERK-eIF2α-ATF4 Pathway.

Osteoporosis is a common disease resulting in deteriorated microarchitecture and decreased bone mass. In type 2 diabetes patients, the incidence of osteoporosis is significantly higher accompanied by increased apoptosis of osteoblasts. In this study, using the osteoblastic cell line MC3T3-E1, we show that high glucose reduces cell viability and induces apoptosis. Also, high glucose leads to endoplasmic reticulum (ER) stress (ERS) via an increase in calcium flux and upregulation of the ER chaperone binding immunoglobulin protein (BiP). Moreover, it induces post-translational activation of eukaryotic initiation factor 2 alpha (eIF2α) which functions downstream of PKR-like ER kinase (PERK). This subsequently leads to post-translational activation of the transcription factor 4 (ATF4) and upregulation of C/EBP-homologous protein (CHOP) which is an ER stress-induced regulator of apoptosis, as well as downstream effectors DNAJC3, HYOU1, and CALR. Interestingly, melatonin treatment significantly alleviates the high-glucose induced changes in cell growth, apoptosis, and calcium influx by inhibiting the PERK-eIF2α-ATF4-CHOP signaling pathway. Additionally, the MC3T3-E1 cells engineered to express a phosphodead eIF2α mutant did not show high glucose induced ER stress, confirming that melatonin protects osteoblasts against high-glucose induced changes by decreasing ER-stress induced apoptosis by impacting the PERK-eIF2α-ATF4-CHOP signaling pathway. The protective of melatonin against high glucose-induced ER stress and apoptosis was attenuated when the cells were pre-treated with a melatonin receptor antagonist, indicating that the effect of melatonin was mediated via the melatonin receptors in this context. These findings lay the provide mechanistic insights of melatonin's protective action on osteoblasts and will be potentially be useful in ongoing pre-clinical and clinical studies to evaluate melatonin as a therapeutic option for diabetic osteoporosis.

Diosmetin reduces bone loss and osteoclastogenesis by regulating the expression of TRPV1 in osteoporosis rats.

BACKGROUND: Osteoporosis is a systemic skeletal disorder and occurs frequently in postmenopausal women and older men. This study aimed to examine whether diosmetin (DIO) can relieve estrogen deficiency-induced osteoporosis and to explore the underlying mechanisms of this potential effect. METHODS: Forty-nine Sprague-Dawley (SD) rats were divided into seven groups. Six groups underwent bilateral ovariectomy (OVX), while the sham group underwent ovarian exposure surgery. DIO and evodiamine were administered 3 days before surgery, and then subcutaneously every 3 days for 3 months in the following fashion: group I, DIO (100 mg/kg); group II, OVX; group III, OVX + DIO (50 mg/kg); group IV, OVX + DIO (100 mg/kg); group V, OVX + evodiamine (10 mg/kg) group; group VI, OVX + DIO (100 mg/kg) + evodiamine (10 mg/kg) group. Bone histopathological damage, bone loss, osteoclast production, and the expression level of transient receptor potential vanilloid 1 (TRPV1) were detected. RESULTS: Compared with the sham group, the expression of bone resorption-related genes, osteoclast-associated receptor (OSCAR) (1.00%±0.16% versus 4.5%±0.28%, **, P<0.01) and tartrate-resistant acid phosphatase (TRAP) (2.0%±0.6% versus 18.00±1.2%, ***, P<0.001), was increased significantly. The protein level of osteogenic marker proteins, osterix (Osx) (1.0%±0.1% versus 0.03%±0.01%, **, P<0.01) and type 1 collagen (COL1A1) (1.0%±0.13% versus 0.13%±0.05%, **, P<0.01) was decreased significantly with the increase of TRPV1 (1.0%±0.15% versus 2.89%±0.28%, **, P<0.01) protein level. Notably, DIO can alleviate some abnormal symptoms related to osteoporosis. CONCLUSIONS: DIO can relieve typical osteoporosis symptoms in an OVX osteoporosis rat model. The underlying mechanism may be associated with the downregulation of TRPV1.

Postoperative three-dimensional cervical range of motion and neurological outcomes in patients with cervical ossification of the posterior longitudinal ligament: Cervical laminoplasty versus laminectomy with fusion.

OBJECTIVE: Laminoplasty (LP) and laminectomy with fusion (LCF) are acceptable surgical options for cervical myelopathy caused by ossification of the posterior longitudinal ligament (OPLL). This study focused on evaluating cervical range of motion (ROM) on a three-dimensional basis as well as neurological outcomes after LP and LCF. METHODS: This prospective cohort study consisted of 38 patients undergoing LP (n=20) or LCF (n=18) from December 2010 to December 2012. Before surgery and at the 3rd, 6th, 12th month follow-up, patients were assessed with three-dimensional cervical ROM, Japanese Orthopaedic Association (JOA) scores, Visual Analogue Scale (VAS) and complications. RESULTS: The patients in both groups had significant ROM loss after surgery in six directions of motion. At the 12th month follow-up, the LP group preserved more ROM than LCF in all directions except bilateral rotations. Major reduction was observed in extension, as with only 59.8% and 54.3% ROM preserved in LP and LCF groups. However, the most preserved ROM was witnessed in rotation, especially in the LP group (90.8%). For JOA and VAS, both groups showed significant improvements postoperatively, and the difference between the two groups was not statistically significant. CONCLUSIONS: Patients with OPLL had an obvious reduction in active cervical ROM following LP and LCF. Major reduction was observed in extension, and less impact was detected on rotation. Compared with LCF, LP had better ROM preserved. Both LP and LCF provided patients with significant neurological improvement.