Glioma accounts for a large proportion of cancer, and an effective treatment for this disease is still lacking because of the absence of specific driver molecules. Current challenges in the treatment ...of glioma are the accurate and timely diagnosis of brain glioma and targeted treatment plans. To investigate the diagnostic biomarkers and prospective role of miRNAs in the tumorigenesis and progression of glioma, we analyzed the expression of miRNAs and key genes in glioma based on The Cancer Genome Atlas database.
Of the 701 cases that were downloaded, five were normal and 696 were glioma. Then, 1626 differentially expressed genes were identified, and 173 aberrantly expressed miRNAs were calculated by edgeR. GO and KEGG pathway enrichment analyses were performed using Cytoscape software. A coexpression network was built by weighted correlation network analysis (WGCNA). A cell scratch test and transwell, cell apoptosis and cell cycle assays were performed to validate the function of hsa-let-7b-5p.
Based on crosstalk genes in the KEGG, PPI network, and WGCNA analyses, PLK1, CCNA2, cyclin B2 (CCNB2), and AURKA were screened as candidate diagnostic marker genes. The survival analysis revealed that high mRNA expression of PLK1, CCNA2, and AURKA was significantly associated with poor overall survival. Furthermore, hsa-let-7b-5p was identified as a core miRNA in the regulation of candidate genes involved in glioma development. We confirmed that hsa-let-7b-5p could inhibit the migration, invasion, and cell cycle of glioma cells.
This study provides four potential biomarkers for the diagnosis of glioma, offers a potential explanation of its pathogenesis, and proposes hsa-let-7b-5p as a therapeutic target.
Background
The authors aimed to create a novel model to predict lymphatic metastasis in thymic epithelial tumors.
Methods
Data of 1018 patients were collected from the Surveillance, Epidemiology, and ...End Results database from 2004 to 2015. To construct a nomogram, the least absolute shrinkage and selection operator (LASSO) regression model was used to select candidate features of the training cohort from 2004 to 2013. A simple model called the Lymphatic Node Metastasis Risk Scoring System (LNMRS) was constructed to predict lymphatic metastasis. Using patients from 2014 to 2015 as the validation cohort, the predictive performance of the model was determined by receiver operating characteristic (ROC) curves.
Results
The LASSO regression model showed that age, extension, and histology type were significantly associated with lymph node metastasis, which were used to construct the nomogram. Through analysis of the area under the curve (AUC), the nomogram achieved a AUC value of 0.80 (95 % confidence interval Cl 0.75–0.85) in the training cohort and 0.82 (95 % Cl 0.70–0.93) in the validation cohort, and had closed calibration curves. Based on the nomogram, the authors constructed the LNMRS model, which had an AUC of 0.80 (95 % Cl 0.75–0.85) in the training cohort and 0.82 (95% Cl 0.70–0.93) in the validation cohort. The ROC curves indicated that the LNMRS had excellent predictive performance for lymph node metastasis.
Conclusion
This study established a nomogram for predicting lymph node metastasis. The LNMRS model, constructed to predict lymphatic involvement of patients, was more convenient than the nomogram.
Convolutional neural networks (CNNs) are popularly-used in various AI fields, yet the design of CNN architectures heavily depends on domain expertise. Evolutionary neural architecture search (ENAS) ...methods can search for neural architectures automatically using evolutionary computation algorithms, e.g. genetic algorithm. However, most existing ENAS methods solely focus on the network accuracy, which leads to large-sized networks to be evolved and huge cost in computation resources and search time. Even though there are ENAS works using multi-objective techniques to optimize both the accuracy and size of CNNs, they are complex and time/resource-consuming. In this work, two new ENAS methods are designed, which aim to evolve both accurate and lightweight CNN architectures efficiently using genetic algorithm (GA). They are termed as GACNN_WS (GA CNN Weighted Sum) and GACNN_LE (GA CNN Local Elitism) respectively. Specifically, GACNN_WS designs a weighted-sum fitness of two items (i.e. accuracy and size) to evaluate candidate networks. GACNN_LE sets the accuracy as its fitness like most other ENAS methods, and designs a local elitism strategy to consider the network size. Thus, GACNN_WS and GACNN_LE can search for both accurate and lightweight CNNs without using multi-objective techniques. Results show that the proposed methods have better search ability than state-of-the-art NAS methods, which consume less time and generate better CNNs with lower error rates and parameter numbers for classification on CIFAR-10. Moreover, the evolved CNNs of the proposed methods generally perform better than eleven hand-designed CNNs.
Air pollution prediction is a process of predicting the levels of air pollutants in a specific area over a given period. Since LSTM (Long Short-Term Memory) networks are particularly effective in ...capturing long-term dependencies and patterns in sequential data, they are widely-used for air pollution prediction. However, designing appropriate LSTM architectures and hyperparameters for given tasks can be challenging, which are normally determined by users in existing LSTM-based methods. Note that Genetic Algorithm (GA) is an effective optimization technique, and local search in augmenting the global search ability of GA has been proved, which is rarely considered by existing GA-optimzied LSTM methods. In this work, simultaneous LSTM architecture and hyperparameter search based on GA and local search techniques is investigated for air pollution prediction. Specifically, a new LSTM model search method is designed, termed as HGA-LSTM. HGA is a hybrid GA, which is proposed by integrating GA with local search adaptively. Based on HGA, HGA-LSTM is developed to search for LSTM models with simultaneous LSTM architecture and hyperparameter optimization. In HGA-LSTM, a new crossover is designed to be adaptive to the variable-length representation of LSTM models. The proposed HGA-LSTM is compared with widely-used LSTM-based and nonLSTM-based prediction methods on UCI (University of California Irvine) datasets for air pollution prediction. Results show that HGA-LSTM is generally better than both types of reference methods with its evolved LSTM models achieving lower mean square/absolute errors. Moreover, compared with a baseline method (a GA without local search), HGA-LSTM converges to lower error values, which reflects that HGA has better search ability than GA.
Introduction
Neurofilament light chains (NfL) have been reported as potential markers for neuronal-axonal injury in neuroinflammatory diseases. In the current study, we describe serum NfL levels as a ...prognostic marker for anti-N-methyl-D-aspartate receptor encephalitis (NMDARE).
Methods
Serum levels of NfL of 64 patients with anti-NMDARE and 84 healthy controls were measured by Simoa. The anti-NMDAR Encephalitis One-Year Functional Status (NEOS) score, Modified Rankin Scale (mRS) scores, and clinical and cerebrospinal fluid parameters were evaluated in patients with anti-NMDARE. Meanwhile, we performed a receiver-operator characteristic analysis to assess the power of the serum NFL in predicting the 1-year functional status.
Results
Serum NfL levels were significantly elevated in patients with anti-NMDARE compared to healthy controls (
p
< 0.001,
p
adjusted
< 0.001), especially in patients with severe impairments (mRS > 3 vs ≤ 3,
p
= 0.035) or with limited response to treatment (vs. favorable outcome,
p
= 0.011). Serum NFL was positively associated with the initial admission mRS (
r
= 0.23,
p
= 0.072) and 1-year mRS (
r
= 0.29,
p
= 0.018). The AUC of serum NfL and NEOS score for 1-year poor functional status was 0.697 (95% CI 0.527–0.866,
p
= 0.011), 0.753 (95% CI 0.616–0.890,
p
= 0.001), respectively. Furthermore, AUC of the combination of serum NfL and NEOS was 0.815 (95% CI 0.680–0.950,
p
< 0.001).
Conclusion
Our findings show that serum NfL levels evaluated in anti-NMDAR encephalitis may be a good predictor of the risk of 1-year poor functional status.
Predicting postoperative pain risk in patients with impacted mandibular third molar extractions is helpful in guiding clinical decision-making, enhancing perioperative pain management, and improving ...the patients’ medical experience. This study aims to develop a prediction model based on machine learning algorithms to identify patients at high risk of postoperative pain after tooth extraction.
We conducted a prospective cohort study. Outpatients with impacted mandibular third molars were recruited and the outcome was defined as the NRS (Numerical Rating Scale) score of peak postoperative pain within 24 h after the operation ≥7, which is considered a high risk of postoperative pain. We compared the models built using nine different machine learning algorithms and conducted internal and time-series external validations to evaluate the model's predictive performances in terms of the area under the curve (AUC), accuracy, sensitivity, specificity, and F1-value.
A total of 185 patients and 202 cases of impacted mandibular third molar data were included in this study. Five modeling variables were screened out using least absolute selection and shrinkage operator regression, including physician qualification, patient self-reported maximum pain sensitivity, OHI–S–CI, BMI, and systolic blood pressure. The overall performance of the random forest model was evaluated. The AUC, sensitivity, and specificity of the prediction model built using the random forest method were 0.879 (0.861–0.891), 0.857, and 0.846, respectively, for the training set and 0.724 (0.673–0.732), 0.667, and 0.600, respectively, for the time series validation set.
This study developed a machine learning-based postoperative pain risk prediction model for impacted mandibular third molar extraction, which is promising for providing a theoretical basis for better pain management to reduce postoperative pain after third molar extraction.
This paper takes the surrounding rock of deep tunnel as the research object and considers the action mechanism under the influence of seepage. Based on the Mohr-Coulomb criterion, the stress ...mechanism of surrounding rock of deep buried tunnel is analyzed by a convergence constraint method. Based on the elastic-plastic solution, the nonlinear elastic-plastic solution of the interaction between surrounding rock and lining structure considering the effect of seepage force is proposed, and the radius of surrounding rock plastic zone is obtained. The relationship between surrounding rock stress and displacement, radial deformation of lining, and support reaction force was observed. At the same time, considering the effects of seepage, strain softening, and intermediate principal stress, the surrounding rock is divided into a plastic residual zone, plastic softening zone, and elastic zone, and the stress distribution expressions of the plastic zone and each zone of surrounding rock of circular tunnel are derived. The results show that with the change of nonuniform permeability coefficient, the seepage shows anisotropy in different directions, and the closer to the horizontal or vertical direction, the more obvious the influence of nonuniform permeability coefficient on pore water pressure distribution. Seepage and material softening have different effects on the distribution of surrounding rock stress field and the size of plastic zone. Material softening is more unfavorable to the stability of surrounding rock than seepage. The intermediate principal stress coefficient has a significant impact on the tangential stress and plastic zone of surrounding rock. When the intermediate principal stress effect is not considered, the calculation results are relatively conservative and cannot give full play to the strength of surrounding rock effectively. The research conclusion can provide a theoretical reference for studying the stability of surrounding rock in tunnel excavation under water-bearing rock.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
There is no highly effective chemotherapy for malignant gliomas to date. We found that dimethylaminomicheliolide (DMAMCL), a selective inhibitor of acute myeloid leukemia (AML) stem/progenitor cells, ...inhibited the growth of glioma cells.
The distribution of DMAMCL in brain was analyzed by an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS/MS) system. The anti-tumor evaluations of DMAMCL in vitro were performed by MTT, FACS and RT-PCR. In vivo, the mixture of C6 cells and matrigel was injected into caudatum, and the anti-tumor activity of DMAMCL was evaluated by tumor growth and rat survival. The toxicity of DMAMCL was evaluated by body weight, daily food intake, hematological or serum biochemical analyses, and histological appearance of tissues.
The IC50 values of DMAMCL against the C6 and U-87MG cell lines in vitro were 27.18 ± 1.89 μM and 20.58 ± 1.61 μM, respectively. DAMMCL down-regulated the anti-apoptosis gene Bcl-2 and increased apoptosis in C6 and U-87MG cells in a dose-dependent manner. In a C6 rat tumor model, daily administration of DMAMCL for 21 days reduced the burden of C6 tumors by 60% to 88% compared to controls, and more than doubled the mean lifespan of tumor-bearing rats. Distribution analysis showed that the DMAMCL concentration was higher in the brain than in plasma. Evaluations for toxicity revealed that oral administration of DMAMCL at 200 or 300 mg/kg once a day for 21 days did not result in toxicity.
These results suggest that DMAMCL is highly promising for the treatment of glioma.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To explore the outcomes of NMOSD attacks and investigate serum biomarkers for prognosis and severity.
Patients with NMOSD attacks were prospectively and observationally enrolled from January 2019 to ...December 2020 at four hospitals in Guangzhou, southern China. Data were collected at attack, discharge and 1/3/6 months after acute treatment. Serum cytokine/chemokine and neurofilament light chain (NfL) levels were examined at the onset stage.
One hundred patients with NMOSD attacks were included. The treatment comprised intravenous methylprednisolone pulse therapy alone (IVMP, 71%), IVMP combined with apheresis (8%), IVMP combined with intravenous immunoglobulin (18%) and other therapies (3%). EDSS scores decreased significantly from a medium of 4 (interquartile range 3.0-5.5) at attack to 3.5 (3.0-4.5) at discharge, 3.5 (2.0-4.0) at the 1-month visit and 3.0 (2.0-4.0) at the 3-month visit (p<0.01 in all comparisons). The remission rate was 38.0% at discharge and 63.3% at the 1-month visit. Notably, relapse occurred in 12.2% of 74 patients by the 6-month follow-up. Higher levels of T helper cell 2 (Th2)-related cytokines, including interleukin (IL)-4, IL-10, IL-13, and IL-1 receptor antagonist, predicted remission at the 1-month visit (OR=9.33, p=0.04). Serum NfL levels correlated positively with onset EDSS scores in acute-phase NMOSD (p<0.001, R
= 0.487).
Outcomes of NMOSD attacks were generally moderate. A high level of serum Th2-related cytokines predicted remission at the 1-month visit, and serum NfL may serve as a biomarker of disease severity at attack.
https://clinicaltrials.gov/ct2/show/NCT04101058, identifier NCT04101058.
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