Prognostic Value of Combined Neutrophil-to-Lymphocyte Ratio and Imaging Tumor Capsule in Solitary Hepatocellular Carcinoma Patients after Narrow-Margin Hepatectomy.

BACKGROUND: The study aimed to investigate the clinical value and prognostic patterns of the neutrophil-to-lymphocyte ratio (NLR) and imaging tumor capsule (ITC) in solitary hepatocellular carcinoma (HCC) patients undergoing narrow-margin hepatectomy. METHODS: Data for solitary HCC patients treated with narrow-margin surgery were extracted from Shanghai General Hospital. Clinical features of recurrence-free survival (RFS), overall survival (OS), and early recurrence were investigated by Cox/logistic regression. The significant variables were subsequently incorporated into the nomogram pattern. Survival analysis stratified by NLR and ITC was also performed. RESULTS: The study included a cohort of 222 patients, with median RFS and OS of 24.083 and 32.283 months, respectively. Both an NLR ≥ 2.80 and incomplete ITC had a significant impact on prognosis. NLR and ITC independently affected RFS and OS, whereas alpha-fetoprotein (AFP) and ITC were identified as independent factors for early relapse. The RFS and OS nomogram, generated based on the Cox model, demonstrated good performance in validation. The combination of NLR and ITC showed greater predictive accuracy for 5-year RFS and OS. Subgroups with an NLR ≥ 2.80 and incomplete ITC had the worst prognosis. CONCLUSIONS: Both NLR and ITC significantly affected RFS, OS, and early recurrence among solitary HCC patients who underwent narrow-margin hepatectomy. The combination of NLR and ITC has the potential to guide rational clinical treatment and determine the prognosis.

Minimally Invasive Surgery for Spontaneous Intracerebral Hemorrhage With or Without Intraventricular Hemorrhage.

Objective: The treatment effect of minimally invasive surgery (MIS) for spontaneous intracerebral hemorrhage (sICH) remains controversial. Intracerebral hemorrhage patients with intraventricular hemorrhage (IVH) seemingly have a worse prognosis. So we aim to verify the efficacy of MIS for small and medium cerebral hemorrhage (15-30ml) using the propensity score matching (PSM)method which could reduce the heterogeneity, and further analyze the different treatment effects of MIS for sICH with or without IVH. Methods: We collected the data of patients with sICH from January 2016 to March 2021 retrospectively. The propensity score matching method was used to compare the clinical outcomes of surgery and conservative treatments. The primary outcome was neurological prognosis. The second outcomes were the rate of complications, length of stay, and hospitalization expenses. Furthermore, we use the binary logistic regression analysis to explore the influence of MIS on patients' prognosis. Results: For all sICH patients, the Modified Rankin Scale (MRS) and Glasgow Outcome Scale (GOS) of the surgery group were worse than those of the conservative group. The length of stay (P = .001), hospitalization expenses (P < .01), pneumonia incidence (P < 0.01), and history of tracheotomy (P = .002) of the surgery group were higher than those of the conservative group. For sICH patients without IVH, the GOS and MRS of surgery patients were statistically better than those of conservative patients at 3 months. The length of stay (P = .046), hospitalization expenses (P < .001), and pneumonia incidence (P < .001) of the surgery group were also higher than the conservative group. Binary logistic analysis showed that MIS is the protective factor for patients' neurological function, especially for intracerebral hemorrhage patients without IVH (OR = 66.636). Conclusions: For small and medium cerebral hemorrhage, stereotactic puncture drainage minimally invasive surgery could result in better functional outcomes, especially for the sICH patients without IVH.Nevertheless, surgery cannot reduce the occurrence of complications, hospitalization length, and expenses.

A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth.

OBJECTIVES: To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer. METHODS: A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images. Two feature selection methods were performed and compared, the key of which are the Least Absolute Shrinkage and Selection Operator (LASSO) and the Max Relevance Min Redundancy (mRMR). The classifier of the prediction model was selected from six advanced machine-learning techniques. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were applied to assess the efficiency of the models. RESULTS: The models with the best performance for pathological invasion prediction and muscular invasion prediction consisted of LASSO as the feature selection method and random forest as the classifier. In the training set, the AUC of the pathological invasion model and muscular invasion model were 0.808 and 0.828. Furthermore, with the mRMR as the feature selection method, the external invasion model based on random forest achieved excellent discrimination (AUC, 0.857). CONCLUSIONS: The full-sequence models demonstrated excellent accuracy for preoperatively predicting the bladder cancer invasion status. CLINICAL RELEVANCE STATEMENT: This study introduces a full-sequence MRI model for preoperative prediction of the depth of bladder cancer infiltration, which could help clinicians to recognise pathological features associated with tumour infiltration prior to invasive procedures. KEY POINTS: • Full-sequence MRI prediction model performed better than Vesicle Imaging-Reporting and Data System (VI-RADS) for preoperatively evaluating the invasion status of bladder cancer. • Machine learning methods can extract information from T1-weighted image (T1WI) sequences and benefit bladder cancer invasion prediction.

Identification and validation of cancer-associated fibroblast-related subtypes and the prognosis model of biochemical recurrence in prostate cancer based on single-cell and bulk RNA sequencing.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are an essential component of the tumor immune microenvironment that are involved in extracellular matrix (ECM) remodeling. We aim to investigate the characteristics of CAFs in prostate cancer and develop a biochemical recurrence (BCR)-related CAF signature for predicting the prognosis of PCa patients. METHODS: The bulk RNA-seq and relevant clinical information were obtained from the TCGA and GEO databases, respectively. The infiltration scores of CAFs in prostate cancer patients were calculated using the MCP counter and EPIC algorithms. The single-cell RNA sequencing (scRNA-seq) was downloaded from the GEO database. Subsequently, univariate Cox regression analysis was employed to identify prognostic genes associated with CAFs. We identified two subtypes (C1 and C2) of prostate cancer that were associated with CAFs via non-negative matrix factorization (NMF) clustering. In addition, the BCR-related CAF signatures were constructed using Lasso regression analysis. Finally, a nomogram model was established based on the risk score and clinical characteristics of the patients. RESULTS: Initially, we found that patients with high CAF infiltration scores had shorter biochemical recurrence-free survival (BCRFS) times. Subsequently, CAFs in four pairs of tumors and paracancerous tissues were identified. We discovered 253 significantly differentially expressed genes, of which 13 had prognostic significance. Using NMF clustering, we divided PCa patients into C1 and C2 subgroups, with the C1 subgroup having a worse prognosis and substantially enriched cell cycle, homologous recombination, and mismatch repair pathways. Furthermore, a BCR-related CAFs signature was established. Multivariate COX regression analysis confirmed that the BCR-related CAFs signature was an independent prognostic factor for BCR in PCa. In addition, the nomogram was based on the clinical characteristics and risk scores of the patient and demonstrated high accuracy and reliability for predicting BCR. Lastly, our findings indicate that the risk score may be a useful tool for predicting PCa patients' sensitivity to immunotherapy and drug treatment. CONCLUSION: NMF clustering based on CAF-related genes revealed distinct TME immune characteristics between groups. The BCR-related CAF signature accurately predicted prognosis and immunotherapy response in prostate cancer patients, offering a promising new approach to cancer treatment.

Characterization of the Clinical Significance and Immunological Landscapes of a Novel TMEMs Signature in Hepatocellular Carcinoma and the Contribution of TMEM201 to Hepatocarcinogenesis.

Aberrant transmembrane protein (TMEM) expression is implicated in tumor progression, but its functional role in hepatocellular carcinoma (HCC) is unclear. Thus, we aim to characterize the functional contributions of TMEM in HCC. In this study, four novel TMEM-family genes (TMEMs), TMEM106C, TMEM201, TMEM164, and TMEM45A, were screened to create a TMEMs signature. These candidate genes are distinguished between patients with varying survival statuses. High-risk HCC patients had a significantly worse prognosis and more advanced clinicopathological characteristics in both the training and validation groups. The GO and KEGG analyses unveiled that the TMEMs signature might play a crucial role in cell-cycle-relevant and immune-related pathways. We found that the high-risk patients had lower stromal scores and a more immunosuppressive tumor microenvironment with massive infiltration of macrophages and Treg cells, whereas the low-risk group had higher stromal scores and gamma delta T-cell infiltration. Moreover, the expression level of suppressive immune checkpoints increased as the TMEM-signature scores increased. Furthermore, the in vitro experiments validated TMEM201, one feature of the TMEMs signature, and facilitated HCC proliferation, survival, and migration. The TMEMs signature provided a more precise prognostic evaluation of HCC and reflected the immunological status of HCC. Of the TMEMs signature studied, TMEM201 was found to significantly promote HCC progression.

Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer.

OBJECTIVES: Bladder cancer is among the most prevalent urothelial malignancies. Radiomics-based preoperative prediction of Ki67 and histological grade will facilitate clinical decision-making. METHODS: This retrospective study recruited 283 bladder cancer patients between 2012 and 2021. Multiparameter MRI sequences included: T1WI, T2WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The radiomics features of intratumoral and peritumoral regions were extracted simultaneously. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were employed to select the features. Six machine learning-based classifiers were adopted to construct the radiomics models, and the best was chosen for the model construction. RESULTS: The mRMR and LASSO algorithms were more suitable for Ki67 and histological grade, respectively. Additionally, Ki67 had a higher proportion of intratumoral features, while peritumoral features accounted for a greater proportion of the histological grade. Random forests performed the best in predicting both pathological outcomes. Consequently, the multiparameter MRI (MP-MRI) models achieved area under the curve (AUC) values of 0.977 and 0.852 for Ki67 in training and test sets, respectively, and 0.972 and 0.710 for the histological grade. CONCLUSION: Radiomics holds the potential to predict multiple pathological outcomes of bladder cancer preoperatively and are expected to provide clinical decision-making guidance. Furthermore, our work inspired the process of radiomics research. ADVANCES IN KNOWLEDGE: This study demonstrated that different feature selection techniques, segmentation regions, classifiers, and MRI sequences will affect the performance of the model. We systematically demonstrated that radiomics can predict histological grade and Ki67.

KLF4 loss in hepatocellular carcinoma: Improving prognostic prediction and correlating immune infiltrates.

Introduction: Although the molecular mechanisms of Krüpple-like factor 4 (KLF4) as a tumor suppressor in HCC tumorigenesis have been thoroughly examined, its clinical application in terms of precise prognostication and its influence on tumor immune microenvironment in patients with HCC require further investigation. Methods: Bioinformatics and immunohistochemistry (IHC) were used to validate KLF4 expressions in a tissue microarray (TMA) containing HCC samples. Using Cox regression models, independent prognostic factors were identified and employed in the development of nomograms. Decision curve analysis (DCA) demonstrated the superiority of the nomograms. GO and KEGG pathway analyses were applied to the functional study of KLF4. The GSVA program explored the link between KLF4 expression and tumor-infiltrating immune cells, and CAMOIP was used to construct KLF4 expression immune scores. Changes in immune-related gene markers were also investigated in relation to KLF4 expression. The association between immune cell infiltration and KLF4 expression was validated by IHC in TMA. Results: HCC was reported to have a notable depletion of KLF4. The absence of KLF4 was associated with advanced clinicopathological characteristics of HCC and predicted a bad prognosis for patients. Nomograms constructed using KLF4 expression, tumor differentiation, and TNM stage provided a more accurate prognostic assessment of HCC patients than TNM stage alone. KLF4 expression was associated with immunological-related functions, infiltration of macrophages, CD8+ T cells, and other immune cells, and elevation of immune checkpoints. Higher levels of CD8+ T cells and macrophage infiltration are associated with increased KLF4 expression in HCC TMA. Conclusion: KLF4 loss in HCC is a prognostic biomarker that influences the tumor immune microenvironment (TIME).

Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling.

Continuous-time interaction data is usually generated under time-evolving environment. Hawkes processes (HP) are commonly used mechanisms for the analysis of such data. However, typical model implementations (such as e.g., stochastic block models) assume that the exogenous (background) interaction rate is constant, and so they are limited in their ability to adequately describe any complex time-evolution in the background rate of a process. In this paper, we introduce a stochastic exogenous rate Hawkes process (SE-HP) which is able to learn time variations in the exogenous rate. The model affiliates each node with a piecewise-constant membership distribution with an unknown number of changepoint locations, and allows these distributions to be related to the membership distributions of interacting nodes. The time-varying background rate function is derived through combinations of these membership functions. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods.

Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer.

BACKGROUND: Radiomics-based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision-making for pancreatic cancer and further efforts are needed to develop effective models. PURPOSE: To develop multiparametric MRI (MP-MRI)-based radiomics models to evaluate LNM and HG. STUDY TYPE: Retrospective. POPULATION: The pancreatic cancer patients from the main center (n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center (n = 40) served as external test sets. FIELD STRENGTH/SEQUENCE: A 3.0 T and 1.5 T/T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhancement T1-weighted imaging. ASSESSMENT: A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first-order, shape-based, and texture features. The following three-step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit-learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE-RF). Six classifiers (random forest, logistic regression, support vector machine, K-nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models. STATISTICAL TESTS: Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant. RESULTS: Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM. DATA CONCLUSION: Radiomics holds the potential to evaluate LNM and HG of pancreatic cancer. The combination of peritumoral and intratumoral features will make models more accurate. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model.

RATIONALE AND OBJECTIVES: Pancreatic cancer is a common malignant tumor with a dismal prognosis. Preoperative differentiation of extrapancreatic extension (EPE) based on radiomics will facilitate treatment decision-making. MATERIALS AND METHODS: This research retrospectively recruited 156 patients from two medical centers. 122 patients from the center A were randomly divided into the training set and the internal test set in a 4:1 ratio. Additionally, 34 patients from the center B served as the external test set. Radiomics features were extracted from multiparametric MRI (MP-MRI), containing axial T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic contrast enhancement (DCE) sequences. The three-step method was used for feature extraction: SelecteKBest, least absolute shrinkage and selection operator (LASSO) algorithm, and recursive feature elimination based on random forest (RFE-RF). The model was constructed using six classifiers based on machine learning, and the classifier with the best performance was chosen. Finally, clinical factors associated with EPE were incorporated into the combined model. RESULTS: The classifier with the best performance was XGBoost, which obtained area under curve (AUC) values of 0.853 and 0.848 in the internal and external test sets, respectively. Through SelectKBest, the most relevant clinical factor for EPE was determined to be platelet, which was then added to the combined model, yielding AUC values of 0.880 and 0.848 in the internal and external test sets, respectively. CONCLUSION: Radiomics models had the potential to noninvasively and accurately predict EPE before surgery. Additionally, it would add value to personalized precision treatment.

Mid-term efficacy grading evaluation and predictive factors of magnetic resonance-guided focused ultrasound surgery for painful bone metastases: a multi-center study.

OBJECTIVES: MR imaging-guided focused ultrasound surgery (MRgFUS) is an emerging non-invasive treatment. It is helpful in investigating the mid-term grading efficacy and safety of MRgFUS, and possible risk factors in participants with painful bone metastases. METHODS: This four-center prospective study enrolled 96 participants between June 2016 and May 2019 with painful bone metastases. The Numerical Rating Scale (NRS), Brief Pain Inventory-Quality of Life (BPI-QoL) score, morphine equivalent daily dose (MEDD), and the adverse events (AEs) were recorded before and at 1 week, 1 month, 2 months, and 3 months after MRgFUS. The repeated ANOVA tests were used to analyze the change in NRS and BPI-QoL, and logistic regression analysis was used to analyze the possible risk factors. RESULTS: A total of 82 participants completed the 3-month follow-up period. And 16 (19.5%) participants were complete responders (CR), 46 (56.1%) participants were effective responders (ER), and the other 20 (24.4%) participants were non-responders (NR). The NRS (2.67 ± 2.47 at 3 months compared to 6.38 ± 1.70 before treatment) and BPI-QoL score (3.11 ± 2.51 at 3 months compared to 5.40 ± 1.85 before treatment) significantly decreased after the treatment at all time points (p < 0.001). Eleven adverse events were recorded and they were all cured within 1 to 52 days after treatment. The non-perfused volume (NPV) ratio (p = 0.001) and the bone metastases lesion type (p = 0.025) were the key risk factors. CONCLUSIONS: MRgFUS can be used as a non-invasive, effective, and safe modality to treat painful bone metastases. NPV ratio and the lesion type may be used as affecting factors to predict the mid-term efficacy of MRgFUS. KEY POINTS: • MRgFUS can be considered a non-invasive, effective, and safe modality to treat painful bone metastases. • The NRS and BPI-QoL score at 1 week, 1 month, 2 months, and 3 months all decreased significantly (p < 0.001) after receiving MRgFUS. Among 82 participants, 16 (19.5%) were complete responders, 46 (56.1%) were effective responders, and the other 20 (24.4%) were non-responders. • According to logistic regression analysis, non-perfused volume ratio and the bone metastases lesion type were the affecting factors to predict the mid-term efficacy of MRgFUS. The adjusted OR of non-perfused volume ratio was 0.86 (p = 0.001), and osteoblastic lesion type was 0.06 (p = 0.025).

Dual-dynamic-bond cross-linked injectable hydrogel of multifunction for intervertebral disc degeneration therapy.

Developing smart hydrogels with integrated and suitable properties to treat intervertebral disc degeneration (IVDD) by minimally invasive injection is of high desire in clinical application and still an ongoing challenge. In this work, an extraordinary injectable hydrogel PBNPs@OBG (Prussian blue nanoparticles@oxidized hyaluronic acid/borax/gelatin) with promising antibacterial, antioxidation, rapid gelation, and self-healing characteristics was designed via dual-dynamic-bond cross-linking among the oxidized hyaluronic acid (OHA), borax, and gelatin. The mechanical performance of the hydrogel was studied by dynamic mechanical analysis. Meanwhile, the swelling ratio and degradation level of the hydrogel was explored. Benefiting from its remarkable mechanical properties, sufficient tissue adhesiveness, and ideal shape-adaptability, the injectable PBNPs containing hydrogel was explored for IVDD therapy. Astoundingly, the as-fabricated hydrogel was able to alleviate H2O2-induced excessive ROS against oxidative stress trauma of nucleus pulposus, which was further revealed by theoretical calculations. Rat IVDD model was next established to estimate therapeutic effect of this PBNPs@OBG hydrogel for IVDD treatment in vivo. On the whole, combination of the smart multifunctional hydrogel and nanotechnology-mediated antioxidant therapy can serve as a fire-new general type of therapeutic strategy for IVDD and other oxidative stress-related diseases.

Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer.

OBJECTIVES: This study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer. METHODS: A total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC. RESULTS: RF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong's tests. CONCLUSIONS: Radiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.

Synergic Enhancement of Energy Storage Density and Efficiency in MnO2-Doped AgNbO3@SiO2 Ceramics via A/B-Site Substitutions.

Dielectric energy storage devices with high power density show great potential in applications of smart grids, electrical vehicles, pulsed power weapons, and so on. However, their limited recoverable energy density badly restricts their utilization and harms the miniaturization, portability, and integration of electronics. Herein, equivalent amounts of Bi2O3 and Sc2O3 were introduced to improve the energy storage property of 0.10 wt % MnO2-doped AgNbO3@SiO2 ceramics by simultaneously enhancing the maximum polarization, breakdown strength, and relaxation feature. It is particularly interesting that the AgNbO3-based ceramics with 4 mol % Bi2O3 and Sc2O3 demonstrate the recoverable energy storage density of 5.9 J/cm3 with the energy storage efficiency of 71%, exhibiting 1.9 and 1.4 times enhancement compared to 0.10 wt % MnO2-doped AgNbO3@SiO2 ceramics. In addition, the benign energy storage performance can be maintained at elevated temperatures and frequencies and up to 105 cycling, indicating great potential in advanced high-power applications.

Supervised Categorical Metric Learning With Schatten p-Norms.

Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development of categorical data still needs further exploration. In this article, we propose a method, called CPML for categorical projected metric learning, which tries to efficiently (i.e., less computational time and better prediction accuracy) address the problem of metric learning in categorical data. We make use of the value distance metric to represent our data and propose new distances based on this representation. We then show how to efficiently learn new metrics. We also generalize several previous regularizers through the Schatten p -norm and provide a generalization bound for it that complements the standard generalization bound for metric learning. The experimental results show that our method provides state-of-the-art results while being faster.

CircFOXM1 promotes the proliferation, migration, invasion, and glutaminolysis of glioblastoma by regulating the miR-577/E2F5 axis.

Circular RNA (circRNA) is a key regulator of tumor progression. However, the role of circFOXM1 in glioblastoma (GBM) progression is unclear. The aim of this study was to investigate the role of circFOXM1 in GBM progression. The expression levels of circFOXM1, miR-577, and E2F transcription factor 5 (E2F5) were examined by real-time quantitative polymerase chain reaction. Cell counting kit 8 assay, EdU staining, and transwell assay were used to detect cell proliferation, migration, and invasion. The levels of glutamine, glutamate, and α-ketoglutarate were determined to evaluate the glutaminolysis ability of cells. Protein expression was tested by Western blot analysis. Dual-luciferase reporter assay, RNA pull-down assay, and RNA immunoprecipitation assay were employed to verify the interaction between miR-577 and circFOXM1 or E2F5. Mice xenograft model for GBM was constructed to perform in vivo experiments. Our results showed that circFOXM1 was highly expressed in GBM tumor tissues and cells. Silencing of cir FOXM1 inhibited GBM cell proliferation, migration, invasion, glutaminolysis, as well as tumor growth. MiR-577 could be sponged by circFOXM1, and its inhibitor could reverse the suppressive effect of circFOXM1 downregulation on GBM progression. E2F5 was a target of miR-577, and the effect of its knockdown on GBM progression was consistent with that of circFOXM1 silencing. CircFOXM1 positively regulated E2F5 expression, while miR-577 negatively regulated E2F5 expression. In conclusion, our data confirmed that circFOXM1 could serve as a sponge of miR-577 to enhance the progression of GBM by targeting E2F5, which revealed that circFOXM1 might be a biomarker for GBM treatment.

Relationship between serum miR-199a-5p and miR-378a-3p levels and recurrence of infants with proliferative facial hemangioma after propranolol withdrawal / 口腔疾病防治

Objective@# To investigate the relationship between serum miR-199a-5p and miR-378a-3p levels and the recurrence of infants with proliferative facial hemangioma relapsed after propranolol withdrawal in infants.@*Methods@#Ninety-three infants with proliferative facial hemangioma were selected, all of whom received propranolol treatment. The recurrence of the hemangioma after treatment was followed up, and the children were divided into a recurrence group (23 patients) and a nonrecurrence group (70 patients). Venous blood was collected before and after treatment, and the serum levels of miR-199a-5p and miR-378a-3p were detected by qRT-PCR, and the relationship between the serum levels of miR-199a-5p and miR-378a-3p and the recurrence of proliferative facial hemangioma relapsed after propranolol withdrawal in infants was analyzed.@*Results @# The serum expressions levels of miR-199a-5p and miR-378a-3p in non-relapsed group were increased after treatment compared with before treatment (P < 0.05), and there was no significant difference in the levels of miR-199a-5p and miR-378a-3p in the serum of the recurrence group after treatment compared with before treatment (P > 0.05). After treatment, the levels of miR-199a-5p and miR-378a-3p in the serum of the recurrence group were lower than those of the nonrecurrence group (P < 0.05). After treatment, the serum levels of miR-199a-5p and miR-378a-3p in patients with Ⅲ ~Ⅳ Norm grade hemangioma were higher than those in patients with Ⅰ~Ⅱ Norm grade hemangioma (P < 0.05). Logistic regression analysis showed that the low expression of miR-199a-5p, low expression of miR-378a-3p and tumor grade Ⅰ~Ⅱ after treatment were risk factors for the recurrence of proliferative facial hemangioma after propranolol withdrawal in infants (P < 0.05).@*Conclusion@# Low serum levels of miR-199a-5p and miR-378a-3p are associated with the recurrence of proliferative facial hemangioma after propranolol withdrawal in infants.

Inhibition of JAK2/STAT3 signaling pathway by panaxadiol limits the progression of pancreatic cancer.

Pancreatic cancer is the fourth leading cause of cancer-related death with the characteristics of chemoresistance and early metastasis. Panaxadiol, a triterpenoid saponin extracted from the roots of American ginseng, has been proved to display anti-tumor activity in colon cancer. In this study, we found panaxadiol significantly inhibited proliferation, and induced apoptosis in human pancreatic cancer cell lines PANC-1 and Patu8988 in a dose-dependent manner. Furthermore, the expression of apoptosis-related proteins (Bax, Bcl2, Cleaved-caspase3) was detected via western blot and immunofluorescence staining. In addition, panaxadiol was also found to inhibit the migration of pancreatic cancer cells by wound healing and transwell assays. In vivo, the growth of xenograft pancreatic cancer models was also notably suppressed by panaxadiol compared to the control group. Moreover, the down-regulation of JAK2-STAT3 signaling pathway was responsible for the underlying pro-apoptosis mechanism of panaxadiol, and this result was in good agreement with molecular docking analysis between panaxadiol and STAT3. In conclusion, our work comprehensively explored the anti-tumor ability in PANC-1 and Patu8988 cells of panaxadiol and provided a potential choice for the clinical treatment of pancreatic cancer patients.

The Harvest of a Lateral Segment of Costal Cartilage for Treating Nasal Deformities.

BACKGROUND: Although costal cartilage has many uses and is a reliable source of cartilage for rhinoplasty procedures, donor-site complications may arise with conventional harvesting techniques. The present report reports a novel technique of harvesting costal cartilage using a specially designed scalpel and studies the use of the harvested cartilage in the reconstruction of secondary nasal deformities in patients with cleft lips. METHODS: Ten patients (7 females and 3 males) with nasal deformities secondary to cleft lip underwent rhinoplasty using this new technique at the Department of Oral and Maxillofacial Surgery, Second Hospital of Hebei Medical University, China, between May 2011 and December 2013. Clinical outcomes were evaluated with a follow-up period of 6 to 30 months. RESULTS: The new technique successfully corrected primary nasal deformities, including flat nasal tip, short columella, flaring alae, and asymmetrical nostrils. Surgeons and patients assessed the outcome to be either good or satisfactory. Patients experienced transient discomfort at the wound site but there were no major complications (such as wound infection, dehiscence, exposure, graft extrusion, and pulmonary involvement). CONCLUSIONS: The novel technique can harvest a lateral segment of costal cartilage for use in the reconstruction of nasal deformities secondary to cleft lip in a one-stage procedure, with minimal donor-site morbidity.