Rhein exerts anti-multidrug resistance in acute myeloid leukemia via targeting FTO to inhibit AKT/mTOR.

Chemotherapy failure and resistance are the leading causes of mortality in patients with acute myeloid leukemia (AML). However, the role of m6A demethylase FTO and its inhibitor rhein in AML and AML drug resistance is unclear. Therefore, this study aimed to investigate the antileukemic effect of rhein on AML and explore its potential mechanisms underlying drug resistance. Bone marrow fluid was collected to assess FTO expression in AML. The Cell Counting Kit 8 reagent was used to assess cell viability. Migration assays were conducted to assess the cell migration capacity. Flow cytometry was used to determine the apoptotic effects of rhein and western blot analysis was used to detect protein expression. Online SynergyFinder software was used to calculate the drug synergy scores. The in-vivo antileukemic effect of rhein was assessed in an AML xenograft mouse model. We analyzed different types of AML bone marrow specimens to confirm that FTO is overexpressed in AML, particularly in cases of multidrug resistance. Subsequently, we conducted in-vivo and in-vitro investigations to explore the pharmacological activity and mechanism of rhein in AML and AML with multidrug resistance. The findings demonstrated that rhein effectively suppressed the proliferation and migration of AML cells in a time- and dose-dependent manner and induced apoptosis. Rhein targets FTO, inhibits the AKT/mTOR pathway, and exhibits synergistic antitumor effects when combined with azacitidine. This study elucidates the significant role of FTO and its inhibitor rhein in AML and AML with multidrug resistance, providing new insights for overcoming multidrug resistance in AML.

A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Modeling.

High-quality protein-ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However, experimentally determined complex structures are scarce compared with the vast chemical space. In this study, we addressed this issue by constructing the BindingNet data set via comparative complex structure modeling, which contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogues' structure-activity relationships. It can also be used for evaluating machine-learning-based scoring functions. Our results indicate that machine learning models trained on BindingNet could reduce the bias caused by buried solvent-accessible surface area, as we previously found for models trained on the PDBbind data set. We also discussed strategies to improve BindingNet and its potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches. The BindingNet complements PDBbind in constructing a sufficient and unbiased protein-ligand binding data set and is freely available at http://bindingnet.huanglab.org.cn.

Flower-shaped Ni(OH)2decorated with biomass-derived carbon TPB-1 for asymmetric supercapacitors.

In recent years, notable headway has been made in augmenting supercapacitor functioning through employment of pioneering components, exceptional nanostructures and additional investigation of electrolytes. Nonetheless, achieving superior performance with straightforward techniques remains a significant hurdle. In order to surmount this, an experimental three-dimensional nanospherical pore structure (TPB-20@Ni(OH)2) was designed and prepared. TPB-1 was obtained through carbonisation and activation. TPB-20@Ni(OH)2nanoparticles were synthesized using TPB-1 as the carbon source and nickel chloride hexahydrate as the nickel source. Furthermore, the TPB-20@Ni(OH)2//AC supercapacitor displayed an impressive energy density of 22.1 Wh kg-1. The TPB-20@Ni(OH)2composites exhibited a specific capacity of 978 F-1, which is noteworthy. The exceptional output exhibited by the TPB-20@Ni(OH)2composite derives from its innovative structure, presenting an extensive specific surface area of 237.4 m2g-1and porosity of roughly 4.0 nm. Following 20 000 cycles (at a current density of 1 A g-1), asymmetric supercapacitors assembled from TPB-20@Ni(OH)2//AC retained 80.0% of its initial specific electrostatic capacity, indicating superior electrochemical stability and high electrochemical reversibility.

Percutaneous endoscopic transforaminal discectomy and unilateral biportal endoscopic discectomy for lumbar disc herniation: a comparative analysis of learning curves.

OBJECTIVE: The purpose of this study was to investigate the learning curve of percutaneous endoscopic transforaminal discectomy (PETD) and interlaminar unilateral biportal endoscopic discectomy (UBED) in the treatment of lumbar disc herniation (LDH). METHODS: Between 2018 and 2023, 120 consecutive patients with lumbar disc herniation (LDH) treated by endoscopic lumbar discectomy were retrospectively included. The PETD group comprised 87 cases, and the UBED group comprised 33 cases. Cumulative sum analysis was used to evaluate the learning curve, with the occurrence of complications or unresolved symptoms defined as surgical failure, and variables of different phases of the learning curve being compared. RESULTS: The learning curve analysis identified the cutoff point at 40 cases in the PETD group and 15 cases in the UBED group. In the mastery phase, both PETD and UBED demonstrated a significant reduction in operation times (approximately 38 min for PTED and 49 min for UBED). In both PETD and UBED groups, the surgical failure rates during the learning and mastery phases showed no statistically significant differences. The visual analogue scale at the last follow-up was significantly lower than before surgery in both the PETD and UBED groups. CONCLUSION: PETD and UBED surgery are effective in the treatment of LDH with a low incidence of complications. However, achieving mastery in PETD necessitates a learning curve of 40 cases, while UBED requires a minimum of 15 cases to reach proficiency.

Acute myeloid leukemia (AML)-derived mesenchymal stem cells induce chemoresistance and epithelial-mesenchymal transition-like program in AML through IL-6/JAK2/STAT3 signaling.

Acute myeloid leukemia (AML) has a high rate of treatment failure due to increased prevalence of therapy resistance. Mesenchymal stem cells (MSCs) in the leukemia microenvironment contribute to chemoresistance in AML, but the specific mechanism remains unclear. The critical role of the epithelial-mesenchymal transition (EMT)-like profile in AML chemoresistance has been gradually recognized. However, there is no research to suggest that the AML-derived bone marrow mesenchymal stem cells (AML-MSCs) induce the EMT program in AML thus far. We isolated AML-MSCs and cocultured them with AML cells. We found that AML-MSCs induced a significant mesenchymal-like morphology in drug-resistant AML cells, but it was scarce in parental AML cells. The AML-MSCs promoted growth of AML cells in the presence or absence of chemotherapeutics in vitro and in vivo. Acute myeloid leukemia MSCs also induced EMT marker expression in AML cells, especially in chemoresistant AML cells. Mechanistically, AML-MSCs secreted abundant interleukin-6 (IL-6) and upregulated IL-6 expression in AML cells. Acute myeloid leukemia cells upregulated IL-6 expression in AML-MSCs in turn. Meanwhile, AML-MSCs activated the JAK2/STAT3 pathway in AML cells. Two JAK/STAT pathway inhibitors counteracted the AML-MSCs induced morphology change and EMT marker expression in AML cells. In conclusion, AML-MSCs not only promote the emergence of chemoresistance but also enhance it once AML acquires chemoresistance. AML-MSCs induce EMT-like features in AML cells; this phenotypic change could be related to chemoresistance progression. AML-MSCs induce the EMT-like program in AML cells through IL-6/JAK2/STAT3 signaling, which provides a therapeutic target to reverse chemoresistance in AML.

Immune scoring model based on immune cell infiltration to predict prognosis in diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is genetically heterogeneous in both pathogenesis and clinical symptoms. Most studies on tumor prognosis have not fully considered the role of tumor-infiltrating immune cells. This study focused on the role of tumor-infiltrating immune cells in the prognosis of DLBCL. METHODS: The GSE10846 data set from the National Center for Biotechnology Information's Gene Expression Omnibus was used as the training set, and the GSE53786 data set was used as the validation set. The proportion of immune cells in each sample was calculated with the CIBERSORT algorithm using R software. After 10 immune cells were screened out (activated memory CD4 positive T cells, follicular helper T cells, regulatory T cells, gamma-delta T cells, activated natural killer cells, M0 macrophages, M2 macrophages, resting dendritic cells, and eosinophils) by univariate Cox analysis, Lasso regression and random forest sampling analyses were performed, the intersecting immune cells were selected for multifactor Cox analysis, and a predictive model was constructed combined with clinical information. Predictive performance was assessed using survival analysis and time-dependent receiver operating characteristic curve analysis. RESULTS: In total, 539 samples were included in this study, and samples with p < .05 were retained using CIBERSORT. Univariate Cox analysis yielded 10 cell types that were associated with overall survival. Two kinds of immune cells were obtained by Lasso regression combined with the random forest method and were used to construct a prognostic model combined with clinical information. The reliability of the model was validated in two data sets. CONCLUSIONS: The immune cell-based prediction model constructed by the authors can effectively predict the prognostic outcome of patients with DLBCL, whereas nomogram plots can help clinicians assess the probability of long-term survival.

OLIF versus ALIF: Which is the better surgical approach for degenerative lumbar disease? A systematic review.

PURPOSE: The aim of this study was to compare the clinical and radiographical outcomes between OLIF and ALIF in treating lumbar degenerative diseases. METHODS: We searched PubMed, Embase, Web of Science, and Cochrane Library for relevant studies. Changes in disc height (DH), segmental lordosis angle (SLA), lumbar lordosis (LL), visual analogue scale (VAS) score, and Oswestry disability index (ODI) between baseline and final follow-up, along with other important surgical outcomes, were assessed and analysed. Data on the global fusion rate and main complications were collected and compared. RESULTS: Approximately, 2041 patients from 36 studies were included, consisting of 1057 patients who underwent OLIF and 984 patients who underwent ALIF. The results reveal no significant difference in DH, SLA, VAS score, and ODI between the two groups (all P > 0.05). The operation time, estimated blood loss, and length of hospital stay were also comparable between the two groups. Over 90% of the fusion rate was achieved in both groups. The OLIF group showed a higher complication rate than the ALIF group (OLIF 18.83% vs ALIF 7.32%). CONCLUSIONS: OLIF leads to a higher complication rate, with the most notable complication being cage subsidence. Both OLIF and ALIF are effective treatments for degenerative lumbar diseases and have similar therapeutic effects. ALIF was expected to be more expensive for patients because of the necessity of involving vascular surgeons.

Implications of frailty in acute ischemic stroke receiving endovascular treatment: systematic review and meta-analysis.

BACKGROUND: Frailty is a state of cumulative degeneration of bodily functions that is consistently associated with poor outcomes in older people following illness. Combined stroke intervention and frailty may yield additive and synergistic effects adults with stroke. OBJECTIVE: To evaluate the safety and efficacy of endovascular therapy (EVT) in frail patients. METHODS: We conducted a systematic review of the relationship between debilitation and acute ischemic stroke (AIS) after EVT. Until August 2022, researchers have searched three databases (Pubmed, EMBASE and Cochrane). Random-effects meta-analysis, combined ratio (OR) and 95% confidence interval (95%CI) were used to assess efficacy values. The I2 statistic was used to assess heterogeneity. Comprehensive meta-analysis software was used for meta-analysis. RESULTS: We ultimately included eight studies including 3662 non-overlapping participants. Four studies used the Clinical Frailty Scale (CFS), two studies used the Hospital Frailty Risk Score (HFRS), a study used frailty index and a study used the comprehensive geriatric assessment (CGA). Frailty prevalence: 35%; 95% CI, 0.27-0.43; low quality evidence, downgraded due to heterogeneity, bias. Random effects showed that poor functional outcome (5 studies, OR 1.956, 95% CI 1.256-3.048) and mortality (9 studies, OR 2.320, 95% CI 1.680-3.205) was significantly associated with frailty. In adjusted analyses, poor functional outcome (4 studies, ORadj 1.189, 95% CI 1.043-1.357), and mortality (3 studies, ORadj 1.036, 95% CI 1.008-1.065) were significantly associated with frailty. CONCLUSION: Pre-stroke frailty is an important predictor of poor prognosis assessed by EVT and can be added to the classical predictors of stroke outcome. Routine assessment of pre-stroke frailty can help patients to make decisions about the efficacy of their choice of EVT.

Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening.

In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein-ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a standardized pocket Pfam-based clustering (Pfam-cluster) approach was developed to assess the cross-target generalization ability of machine-learning scoring functions (MLSFs). Subsequently, 12 typical MLSFs were evaluated using random cross-validation (Random-CV), protein sequence similarity-based cross-validation (Seq-CV), and pocket Pfam-based cross-validation (Pfam-CV) methods. Surprisingly, all of the tested models showed decreased performances from Random-CV to Seq-CV to Pfam-CV experiments, not showing satisfactory generalization capacity. Our interpretable analysis suggested that the predictions on novel targets by MLSFs were dependent on buried solvent-accessible surface area (SASA)-related features of complex structures, with greater predicted binding affinities on complexes owning larger protein-ligand interfaces. By combining buried SASA-related features with target-specific patterns that were only shared among structurally similar compounds in the same cluster, the random forest (RF)-Score attained a good performance in the Random-CV test. Based on these findings, we strongly advise assessing the generalization ability of MLSFs with the Pfam-cluster approach and being cautious with the features learned by MLSFs.

The feasibility and efficacy of computer-assisted screw inserting planning in the surgical treatment for severe spinal deformity: a prospective study.

BACKGROUND: The objective of the study was to explore the feasibility and efficacy of computer-assisted screw inserting planning (CASIP) in the surgical treatment for severe spinal deformity. METHODS: A total of 50 patients participated in this prospective cohort study. 25 patients were allocated into CASIP group and 25 patients were in Non-CASIP group. The demographic data, radiological spinal parameters were documented and analyzed. Each pedicle screw insertion was classified as satisfactory insertion or unsatisfactory insertion based on Gertzbein-Robbins classification. The primary outcome was the accuracy of pedicle screw placement. The secondary outcomes were the rate of puncturing screws, estimated blood loss, surgical time, correction rate and other radiological parameters. RESULTS: A total of 45 eligible patients completed the study. 20 patients were in CASIP group and 25 patients were in Non- CASIP group. The accuracy of pedicle screw placement in CASIP Group and Non-CASIP Group were (92.0 ± 5.5) % and (82.6 ± 8.3) % (P < 0.05), and the rate of puncturing screws were (0 (0-0)) % and (0 (0-6.25)) % (P < 0.05). The median surgical time were 280.0 (IQR: 260.0-300.0) min and 310 (IQR: 267.5-390.0) min in two group and showed significant statistic difference (P < 0.05). CONCLUSIONS: CASIP has good feasibility and can gain a more accurate and reliable instruments fixation, with which spine surgeons can make a detailed and personalized screw planning preoperatively to achieve satisfying screw placement.

Clinical effect evaluation and correlation between preoperative imaging parameters and clinical effect of endoscopic Transforaminal decompression for lumbar spinal stenosis.

BACKGROUND: The objective of this study was to evaluate the clinical effect and correlation between preoperative imaging parameters and the clinical effect of endoscopic transforaminal decompression for lumbar spinal stenosis. METHODS: In this prospective study, 87 patients from Shanxi Province People's Hospital met the criteria for lumbar spinal stenosis and were recruited from June 2014 to January 2016. These patients underwent endoscopic transforaminal decompression. The clinical symptoms were evaluated by VAS, ODI, and claudication at 3 and 6 months after surgery. The overall clinical efficacy was evaluated using the MacNab score. Yellow ligament thickness and area of the dural sac were examined by MRI. Bony vertebral canal area, real spinal canal area, nerve root canal bony area, nerve root canal real area, distance between the articular joints, and vertebral canal sagittal diameter were examined by CT. The soft tissue invasion ratio of the vertebral canal and the invasion ratio of the nerve root canal were calculated. Correlations between imaging parameters and age, sex, and clinical efficacy were examined. RESULTS: The MacNab scores were excellent in 47% of cases, good in 34%, generally good in 8%, and poor in 11%. VAS, ODI, and claudication were significantly improved compared with the preoperative values (P < 0.01). A significant difference was observed between the 71-81 year age group and the other age groups (P < 0.05). There were good correlations between clinical efficacy and vertebral canal sagittal diameter, distance between the articular joints, soft tissue invasion ratio of the vertebral canal, and invasion ratio of the nerve root canal. CONCLUSION: Treatment of lumbar spinal stenosis by endoscopic transforaminal decompression can achieve good clinical results. This operation is less effective in patients older than 71 years of age. There were positive correlations between clinical efficacy and the vertebral canal sagittal diameter, the articular joints, soft tissue invasion ratio of the vertebral canal, and invasion ratio of the nerve root canal.

Prioritizing disease-causing microbes based on random walking on the heterogeneous network.

As we all know, the microbiota show remarkable variability within individuals. At the same time, those microorganisms living in the human body play a very important role in our health and disease, so the identification of the relationships between microbes and diseases will contribute to better understanding of microbes interactions, mechanism of functions. However, the microbial data which are obtained through the related technical sequencing is too much, but the known associations between the diseases and microbes are very less. In bioinformatics, many researchers choose the network topology analysis to solve these problems. Inspired by this idea, we proposed a new method for prioritization of candidate microbes to predict potential disease-microbe association. First of all, we connected the disease network and microbe network based on the known disease-microbe relationships information to construct a heterogeneous network, then we extended the random walk to the heterogeneous network, and used leave-one-out cross-validation and ROC curve to evaluate the method. In conclusion, the algorithm could be effective to disclose some potential associations between diseases and microbes that cannot be found by microbe network or disease network only. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and finally presented the potential microbes associated with these diseases by ranking candidate disease-causing microbes, respectively. We confirmed that the discovery of the new associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.

Identifying protein complexes based on brainstorming strategy.

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance.

Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network.

Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein's neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.

Role of Runx2 polymorphisms in risk and prognosis of ossification of posterior longitudinal ligament.

BACKGROUND: Our study was aimed at finding out if Runx2 SNPs (single-nucleotide polymorphisms) are related to susceptibility to and prognosis of ossification of posterior longitudinal ligament (OPLL). METHODS: We selected 80 OPLL patients and another 80 independent patients without OPLL from September 2013 to November 2014. Serum was collected to detect the genotypes of rs1321075, rs12333172, and rs1406846 on Runx2 with direct sequencing analysis. RESULTS: Differences in clinical characteristics, including age, weight, height, sex ratio, as well as smoking and drinking history, between OPLL and control groups appeared to be insignificant (all P-value >.05). The allele of rs1406846 (A) emerged as a key element in raising OPLL risk with the biggest statistical significance (P<.001). Conversely, alleles of rs967588 (T) and rs16873379 (C) were associated with reduced predisposition to OPLL less remarkably (both P=.033). Regarding rs16873379, the case group exhibited a smaller frequency of homozygote CC in comparison with TT genotype than the control group (P=.016). Furthermore, the improvement rate based on calculation of JOA score suggested that genotype AA of rs6908650 was beneficial for OPLL patients' recovery from posterior laminoplasty surgery (P<.05), while genotypes of rs16873379 (CC), rs1406846 (AA), and rs2677108 (CC) significantly restrained this process (P<.05). Besides, rs16873379, rs1406846, and rs2677108 were significantly associated with number of ossification segments (P<.05). CONCLUSIONS: Runx2 SNPs (e.g., rs16873379, rs1406846, and rs2677108) were strongly correlated with onset and treatment efficacy of OPLL, and they might regulate severity of OPLL.

Selective hemivertebrae resection in a congenital scoliosis patient with multiple hemivertebrae deformities.

PURPOSE: To report the surgical experience of selective hemivertebrae resection for a case of congenital scoliosis with multiple hemivertebrae deformities. METHODS: A 14-year-old male presented with progressive rib hump, tilted torso and spine deformity was admitted in our department. No abnormalities were detected in neurological examination and the comprehensive imaging study demonstrated congenital scoliosis of multiple hemivertebrae in T5, T10, L1 and L3. Treatment of the patient commenced with a 10-day skin traction therapy prior to the surgery. Selective resection of hemivertebrae in T5 and L1 was performed with segmental fusion from T3 to L2. RESULTS: After surgical procedure, the patient achieved a good coronal and sagittal balance along with a good correction of the curve. 18-month postoperative follow-up showed no evidence of significant loss of correction. No device-related complication such as implant loosening or failure or neurologic complication occurred during the follow-up. Besides, patient's shoulder balance was further improved and coronal balance was maintained in a normal range. CONCLUSION: Many factors have to be considered in the clinical decision-making of congenital scoliosis with multiple hemivertebrae deformities patients. Much emphasis in this regard is laid on the type and location of the hemivertebrae as well as the patient's age. Selective hemivertebrae resection may be more suitable for such patients.

DOOR 2.0: presenting operons and their functions through dynamic and integrated views.

We have recently developed a new version of the DOOR operon database, DOOR 2.0, which is available online at http://csbl.bmb.uga.edu/DOOR/ and will be updated on a regular basis. DOOR 2.0 contains genome-scale operons for 2072 prokaryotes with complete genomes, three times the number of genomes covered in the previous version published in 2009. DOOR 2.0 has a number of new features, compared with its previous version, including (i) more than 250,000 transcription units, experimentally validated or computationally predicted based on RNA-seq data, providing a dynamic functional view of the underlying operons; (ii) an integrated operon-centric data resource that provides not only operons for each covered genome but also their functional and regulatory information such as their cis-regulatory binding sites for transcription initiation and termination, gene expression levels estimated based on RNA-seq data and conservation information across multiple genomes; (iii) a high-performance web service for online operon prediction on user-provided genomic sequences; (iv) an intuitive genome browser to support visualization of user-selected data; and (v) a keyword-based Google-like search engine for finding the needed information intuitively and rapidly in this database.

Adjacent segment degeneration after lumbar spinal fusion compared with motion-preservation procedures: a meta-analysis.

PURPOSE: This meta-analysis aimed to evaluate the efficacy of motion-preservation procedures to prevent the adjacent segment degeneration (ASDeg) or adjacent segment disease (ASDis) compared with fusion in lumbar spine. METHODS: PubMed, Embase and the Cochrane Library were comprehensively searched and a meta-analysis was performed of all randomized controlled trials and well designed prospective or retrospective comparative cohort studies assessing the lumbar fusion and motion-preservation procedures. We compared the ASDeg and ASDis rate, reoperation rate, operation time, blood loss, length of hospital stay, visual analogue scale (VAS) and oswestry disability index (ODI) improvement of the two procedures. RESULTS: A total of 15 studies consisting of 1474 patients were included in this study. The meta-analysis indicated that the prevalence of ASDeg, ASDis and reoperation rate on the adjacent level were lower in motion-preservation procedures group than in the fusion group (P = 0.001; P = 0.0004; P < 0.0001). Moreover, shorter length of hospital stay was found in motion-preservation procedures group (P < 0.0001). No difference was found in terms of operation time (P = 0.57), blood loss (P = 0.27), VAS (P = 0.76) and ODI improvement (P = 0.71) between the two groups. CONCLUSIONS: The present evidences indicated that the motion-preservation procedures had an advantage on reducing the prevalence of ASDeg, ASDis and the reoperation rate due to the adjacent segment degeneration compared with the lumbar fusion. And the clinical outcomes of the two procedures are similar.

Interspinous dynamic stabilization adjacent to fusion versus double-segment fusion for treatment of lumbar degenerative disease with a minimum follow-up of three years.

PURPOSE: The aim of this study was to assess the outcome of symptomatic lumbar degenerative disease treated with topping-off technique (Coflex(™) combined with fusion) and compare two-segment fusion at mid-long term follow-up; and find out whether the topping-off technique can reduce the rate of adjacent segment degeneration (ASD) after fusion. METHODS: One hundred and fifty-four consecutive patients who received topping-off surgery (76 patients) and two-segment fusion surgery (88 patients) from March 2009 to March 2012 were studied. All patients included in the analysis had a minimum of three years of follow-up. Radiographic and clinical outcomes between the two groups were compared. A logistic regression analysis was used to analyze risk factors for developing radiographic ASD. RESULTS: Significant differences in clinical outcomes were observed between these two groups at three post-operative years (all, p < 0.05). Compared with the fusion group, the topping-off group showed preserved mobility at the Coflex(™) level (p = 0.000), which is associated with less blood loss (p = 0.000), shorter duration of surgery (p = 0.000) and lower incidence of ASD (Chi-square test, rate topping-off vs fusion = 13.2 vs 26.1 %, p = 0.039). There were no differences in complications between the two groups. CONCLUSION: Mid-long term follow-up efficacy and safety between topping-off and fusion were similar, while topping-off reduced the rate of ASD. Under strict indications, topping-off surgery is an acceptable alternative to fusion surgery for the treatment of two-segment lumbar disease.

Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks.

BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. RESULTS: In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. CONCLUSIONS: The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization.