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To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network.This study retrospectively recruited 621 CKD patients and 625 healthy controls in Jinling hospital and 57 CKD patients in Hainan hospital. These CKD patients were divided into cognitive function impairment (CFI) group and non-CFI group based on diagnostic criteria. All patients underwent brain MRI scan, neuropsychological test and laboratory exam. A deep learning model (Attention MLP) based on structural and functional sub-network (determined by the comparison between the patients and healthy controls) topological properties was developed to generate the MRI signature for the discrimination of CFI. Finally, a clinical-topological logistic regression model was built by combining MRI signature and clinical features. The area under curve (AUC), sensitivity and specificity were calculated to evaluate the model performance. Delong test was used to examine the difference of AUCs between models. The integrated discrimination improvement (IDI) and net reclassification index (NRI) between models were calculated.Attention MLP model performed well in both internal test set and external test set (AUC = 0.744 and 0.763, respectively). After combining with the clinical features, the model performance was further improved both in the internal (AUC: 0.748) and external test sets (AUC: 0.774), while both IDI and NRI were significant (all p 0.05) in the external test set. According to the comprehensive comparison, the AUC of the Attention MLP model was significantly or marginal significantly higher than that of traditional machine learning models (logistic regression: AUC = 0.634; support vector machine: AUC = 0.613; decision tree: AUC = 0.539; XGBoost: AUC = 0.639) in internal test set. The results showed that the model built on the combining of structural and functional networks data outperformed those on the single network, as well as the connection matrix.The result indicated that the integration of the clinical information and the MRI signature generated by artificial intelligence model based on structural and functional network topological properties could help to predict the CFI of CKD patients effectively. Our results provided a set of quantifiable imaging biomarkers for CFI which may be beneficial to CKD patients.

To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets.Patients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods.The training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p 0.05). The AUCs of ML models using random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM) were 0.871, 0.851, and 0.863, respectively. There were no difference among AUCs of conventional LR, RF, and SVM (all p 0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055).Hemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters.• The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.

This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs).A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort.The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment.Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning.

Lipidomics has been applied in the identification and quantification of molecular lipids within an organism, and to provide insights into mechanisms in clinical medicine. Sepsis is a major systemic inflammatory syndrome and the liver here is a potential target organ for dysfunctional response. However, the study of alterations in global lipid profiles associated with sepsis-induced liver injury is still limited. In this work, we set out to determine alterations of lipidomics profiles in a rat model of sepsis-induced liver injury using an untargeted lipidomics strategy. Liquid chromatography coupled with mass spectrometry in conjunction with multivariate data analysis and pathway analysis were used to acquire a global lipid metabolite profile. Meanwhile, biochemistry index and histopathological examinations of the liver were performed to obtain auxiliary measurements for determining the pathological changes associated with sepsis-induced liver injury. Eleven lipid metabolites and two metabolic pathways were discovered and associated with sepsis-induced liver injury. The results indicated that various biomarkers and pathways may provide evidence for and insight into lipid profile alterations associated with sepsis-induced liver injury, and hence pointed to potential strategic targets for clinical diagnosis and therapy in the future.

Background: Identification of the Aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists is lack of sensitivity and suboptimal at present. Methods: A total of 452 patients underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form an internal cohort (341 patients, 139 AD, 202 non-AD) and an external testing cohort (111 patients, 46 AD, 65 non-AD). The internal cohort was divided into the training cohort (n=238), validation cohort (n=35) and internal testing cohort (n=68). The morphologic characteristics were extracted from the aortic segmentation. A Gaussian Naive Bayes model integrating the deep-learning model score and the morphologic characteristics were built to differentiate AD from non-AD. The areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists. Findings: After the combination of all morphologic characteristics, our proposed model significantly outperformed the deep-learning model (AUC: 0. 948 vs. 0.803 on the internal testing cohort, 0.969 vs. 0.814 on the external testing cohort, both p 0.05). Interpretation: The proposed model presented good performance on the AD detection on non-contrast CT scans, thus early diagnosis as well as prompt treatment would be available. Funding Information: CAMS Innovation Fund for Medical Sciences (CIFMS); the Major International (Regional) Joint Research Project of National Natural Science Foundation of China; the National Natural Science Foundation of China; the National Key Research and Development Program of China; the China Postdoctoral Science Foundation. Declaration of Interests: All the other authors have no conflict of interest to disclose. Ethics Approval Statement: This study was reviewed and approved by the local clinical institutional ethics committees of the two centers involved, and informed consent was waived because of the retrospective nature of this study.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients’ care in comparison to clinicians’ assessment., Interpretation of Computed Tomography Angiography for intracranial aneurysm diagnosis can be time-consuming and challenging. Here, the authors present a deep-learning-based framework achieving improved performance compared to that of radiologists and expert neurosurgeons.

Background The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Methods Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist’s assessment were also compared. Results Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P Conclusions Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Hyperglycemia is a major factor in vascular endothelial injury that finally leads to a cardiovascular event. Steroid receptor coactivators (SRCs) are a group of non-DNA binding proteins that induce structural changes in steroid receptors (nuclear receptors) critical for transcriptional activation. SRCs, namely, SRC-1, SRC-2, and SRC-3, are implicated in the regulation of vascular homeostasis. In this study we investigate the role of SRCs in hyperglycemia-induced endothelial injury. Aortic endothelial cells were prepared from normal and diabetic rats, respectively. Diabetic rats were prepared by injection of streptozotocin (50 mg/kg, i.p.). The expression levels of SRC-1 and SRC-3 were significantly decreased in endothelial cells from the diabetic rats. Similar phenomenon was also observed in aortic endothelial cells from the normal rats treated with a high glucose (25 mM) for 4 h or 8 h. The expression levels of SRC-2 were little affected by hyperglycemia. Overexpression of SRC-1 and SRC-3 in high glucose-treated endothelial cells significantly increased the cell viability, suspended cell senescence, and inhibited cell apoptosis compared with the control cells. We further showed that overexpression of SRC-1 and SRC-3 markedly suppressed endothelial injury through restoring nitric oxide production, upregulating the expression of antioxidant enzymes (SOD, GPX, and CAT), and activating the PI3K/Akt pathway. The beneficial effects of SRC-1 and SRC-3 overexpression were blocked by treatment with the PI3K inhibitor LY294002 (10 mM) or with the Akt inhibitor MK-2206 (100 nM). In conclusion, hyperglycemia decreased SRC-1 and SRC-3 expression levels in rat aortic endothelial cells. SRC-1 and SRC-3 overexpression might protect against endothelial injury via inhibition of oxidative stress and activation of PI3K/Akt pathway.

Patent foramen ovale (PFO) is a common congenital heart disease that can cause cryptogenic stroke through paradoxical embolization. For patients with PFO combined with cryptogenic stroke, whether anticoagulant therapy is superior to antiplatelet therapy in the prevention of recurrent stroke? And whether PFO closure can significantly reduce the risk of stroke recurrence compared with medical therapy alone? All those raised the clinical problems to be solved urgently. The advances in treatment of cryptogenic stroke associated with PFO are herewith summarized in present paper by reviewing randomized trials, meta-analyses and the guidelines or expert consensus about PFO and cryptogenic stroke. DOI: 10.11855/j.issn.0577-7402.2018.04.17

Metabolism of lipids is essential for the regulation of a variety of key cellular functions. Recent advances in high performance liquid chromatography coupled with high resolution mass spectrometry have expanded our knowledge of lipid metabolism in diseases. Currently, sepsis is one of the most important public health problems all over the world, which is a serious systemic inflammatory syndrome leading to infection by various agents or trauma and subsequently to a multiple organ dysfunction response. However, little is known about the lipids affected by sepsis and their roles in kidney injuries. In this study, we present targeted and non-targeted lipidomics strategies to discover the lipid metabolism variation in serum in rats with sepsis-induced kidney injuries. Liquid chromatography (LC) coupled with mass spectrometry (MS) and multivariate data analysis were used to obtain the global lipid metabolic profiles. In addition, biochemical parameters and histopathological examination results for the kidney were analyzed to support the pathological changes during sepsis-induced kidney injury. The identification of ten proposed lipids and five relevant pathways will promote a better understanding of lipid profile alterations in kidney injury. The results suggested that lipid metabolism in sepsis-induced kidney injury had changed significantly and contribute by offering potential targets for clinical diagnosis and therapy in the future, which would be worth further studies to broaden the applications of high performance liquid chromatography coupled with high resolution mass spectrometry in the study of lipid metabolism.

Pearling is an effective method for evaluating the distribution of chemical components in wheat grain. Five pearling fractions (representing approximately 20% of the original sample weight) of wheat grain were obtained using the JNMJ3 rice polisher for two cultivars with different methods of Zinc (Zn) application; the residual portion (approximately 80%) was ground as flour. Results showed that foliar or soil+foliar Zn application methods effectively increased Zn concentrations and bioavailability in whole grain and pearling fractions, but soil Zn application was ineffective in field conditions. In addition, the concentrations of Zn, Fe, Mn and Cu within wheat grain showed a diminishing trend from the outer layer to the inner portions of the wheat grain as the pearling level increased. These results clearly showed the distribution of minerals in wheat grain, especially in the outer part of the grain (bran). The results also suggest that precise milling techniques combined with foliar Zn application could improve the Zn and Fe nutritional qualities of consumed flour and mitigate human Zn and Fe deficiencies.

High-throughput metabolomics can be used to investigate the therapeutic targets and metabolic mechanisms of traditional Chinese medicine (TCM) formulae, which have multiple targets in disease therapy, but it is a great challenge to explore their mechanism of action. Huaxian capsule (HXC) is a classical formula in TCM that has therapeutic effects on a sepsis-associated Qi deficiency and blood stasis syndrome (SQBS). However, its targets and metabolic mechanisms need more investigation. To investigate the therapeutic effects of HXC in the treatment of SQBS and elucidate the potential mechanism, we used a high-throughput metabolomics strategy based on the ultraperformance liquid chromatography/mass spectrometry combined with chemometrics to analyze and identify differential metabolites and pathways. The pathological examination of organs and biochemical indices was also performed to verify the successful establishment of the rat model and protective effects of HXC. Pathological symptoms and biochemical indicators of SQBS rats were reversed by the HXC treatment. A total of 24 potential biomarkers were identified to indicate the difference between the control and model groups; they were closely associated with ten metabolic pathways and regulated by the HXC administration. From the pathway analysis, we further understood the protective activity of HXC against SQBS, which affected amino acid metabolism, molecular transport, small molecule biochemistry and cell signaling as well as vitamin and mineral metabolism. In conclusion, HXC protects against SQBS by modulating the metabolic biomarkers and functional pathways.

To explore the effect and mechanism of surround needling combined with acupoint injection on acute herpetic neuralgia (AHN).Ninety-nine patients with TThe blistering, crusting and dislocation time in the combined group were earlier than the other two groups (allBoth surround needling and acupoint injection have an adjuvant effect on AHN. The combination of the two is better, the skin is healed quickly, the analgesia is significant, and the contents of local NSE, SP and CGRP are significantly decreased. The mechanism of action is to exert neuroprotective effects.

Objective: The objective of this study is to explore the clinical effects of structured skin care plan of integrated Chinese and Western medicine in intervening elderly patients with incontinence-associated dermatitis (IAD). Materials and Methods: Totally, 66 elderly patients with IAD were randomly divided into the experiment group (32 cases) and control group (34 cases). The control group was given routine nursing care, while the experiment group was given a structured skin care plan. The observational course was 2 weeks. The treatment efficiency and healing time were compared between the two groups. Results: After 2-week intervention, the total effective rate of the experiment group was higher than that of the control group (97.1% vs. 78.1%, X2 = 3.913, P = 0.048). The skin assessment tool score of the experiment group was lower than that of the control group (0.56 ± 1.58 vs. 1.75 ± 2.46, Z = −−2.401, P = 0.016). The healing time of the experiment group was shorter than that of the control group (7.29 ± 4.76 days vs. 10.69 ± 6.36 days, Z = −2.280, P = 0.026). Conclusion: The structured skin care plan of integrated Chinese and Western medicine showed a good effect in elderly IAD patients, and provided a reference for clinical treatment and care of elderly patients with IAD.

High-throughput metabolic profiling technology has been used for biomarker discovery and to reveal underlying metabolic mechanisms. Sepsis-induced myocardial dysfunction (SMD) is a common complication in sepsis patients, and severely affects their quality of life. However, the pathogenesis of SMD is currently unclear, and there has been inadequate basic research. In this study, metabolic profiling was explored by liquid chromatography/mass spectrometry (LC/MS) combined with chemometrics and bioinformatic analysis. The global metabolome data were analyzed using chemometrics analysis including principal component analysis and partial least squares discriminant analysis for significant metabolites. Variable importance for projection values obtained utilizing a pattern recognition method were used to identify potential biomarkers. The differential metabolites were putatively identified using the metabolome database and bioinformatics analysis was conducted via Ingenuity Pathway Analysis (IPA) to predict the likely functional alterations. In total, 21 differential metabolites were found in SMD and these were involved in phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, glycine, serine and threonine metabolism, and so on. The analysis revealed that the metabolites were strongly related to molecular transport, and small molecule biochemistry metabolic pathways. The present study indicates that high-throughput metabolic profiling, combined with chemometrics and a bioinformatic platform, can reveal the likely functional alterations in disease and could provide more precise and credible information in the basic research of disease pathogenesis.

To investigate the effects and the mechanism of compound WS090152 on non-alcoholic fatty liver (NAFL), the compound was administrated in C57BL/6J mice fed a high fat diet at 50 mg·kg(-1) by lavage. The lipid accumulation in liver was determined by the content of hepatic triglyceride (TG) and the histological pathological analysis. The levels of body weight gain, serum total cholesterol (TC) and TG were measured to evaluate lipid metabolism. Insulin sensitivity was determined by glucose infusion rate (GIR) value in hyperinsulinemic-euglycemic clamp test. The expression of related proteins in liver was measured by Western blot. The effect on the target protein tyrosine phosphatase 1B (PTP1B) was assessed by the activity of recombinate human PTP1B in vitro, and by the expressions of PTP1B in vivo, respectively. The content of hepatic TG (P0.05) and the pathological changes of the livers (P0.01) were attenuated, insulin resistance was improved, (P0.01), and the levels of serum TC (P0.01) and serum TG (P0.05) were reduced by WS090152 treatment in the mice. The recombinant h PTP1B activity was significantly inhibited with IC50 value of 0.34 μmol·L(-1); the expression of PTP1B was significantly downregulated, and the phosphorylation of its downstream insulin receptor (IR), and AKT was upregulated by WS090152 administration in the livers of NAFL mice. The expression of hepatic lipogenesis-related proteins-1c (SREBP-1c), fatty acid synthase (FAS), and acetyl-CoA carboxylase (ACC) was attenuated. These results suggest that compound WS090152 can ameliorate NAFL by increasing insulin sensitivity and decreasing hepatic lipogenesis probably through inhibition of PTP1B.

Antibody-drug conjugates (ADCs) are complex molecules with cytotoxic small molecular drugs covalently bound to monoclonal antibodies via a linker and can improve the targeted drug delivery with minimizing the systemic toxicity. ADCs are heterogeneous mixtures with different drug-to-antibody ratios (DARs) and the DAR distribution is dynamically changing in vivo, therefore the bioanalysis of the ADCs is challenging. Enzyme-linked immunosorbent assay(ELISA) and LC-MS have been widely used in the ADCs bioanalytical assays. Just like other biotherapeutics, ADCs may elicit the host immune response and produce the anti-therapeutic antibody (ATA), which could affect its efficacy, pharmacokinetics, and safety. It is thereby important to investigate its immunogenicity in the ADC development. In this review, we summarized the ELISA- and LC-MS-based bioanalysis strategies for the development of ADCs, including DAR distribution, the determination of total antibody, conjugated antibody, conjugated drug, free drug, and ATA, with the expectation of providing insights and reference for the ADC development in China.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial fibrotic lung disease with an undefined etiology and no effective treatments. By binding to cell surface receptors, transforming growth factor-β (TGF-β) plays a pivotal role in lung fibrosis. Therefore, the screening of microRNAs (miRNAs), especially those interrupting the effects of TGF-β, may provide information not only on the pathomechanism, but also on the treatment of this disease. In the present study, we found that miR-153 expression was dysregulated in the lungs of mice with experimental pulmonary fibrosis and TGF-β1 decreased miR-153 expression in pulmonary fibroblasts. Moreover, increased miR-153 levels attenuated, whereas the knock down of miR-153 promoted the pro-fibrogenic activity of TGF-β1, and miR-153 reduced the contractile and migratory activities of fibroblasts. In addition, TGFBR2, a transmembrane serine/threonine kinase receptor for TGF-β, was identified as a direct target of miR-153. Furthermore, by post-transcriptional regulation of the expression of TGFBR2, phosphorylation of SMAD2/3 was also influenced by miR-153. These data suggest that miR-153 disturbs TGF-β1 signal transduction and its effects on fibroblast activation, acting as an anti-fibrotic element in the development of pulmonary fibrosis.

Cubital tunnel syndrome is often accompanied by paresthesia in ulnar nerve sites and hand muscle atrophy. When muscle weakness occurs, or after failure of more conservative treatments, anterior transposition is used. In the present study, the ulnar nerve and its blood vessels were examined in the elbows of 18 adult cadavers, and the external diameter of the nutrient vessels of the ulnar nerve at the point of origin, the distances between the origin of the vessels and the medial epicondyle of the humerus, and the length of the vessels accompanying the ulnar nerve in the superior ulnar collateral artery, the inferior ulnar collateral artery, and the posterior ulnar recurrent artery were measured. Anterior transposition of the vascularized ulnar nerve was performed to treat cubital tunnel syndrome. The most appropriate distance that the vascularized ulnar nerve can be moved to the subcutaneous tissue under tension-free conditions was 1.8 ± 0.6 cm (1.1-2.5 cm), which can be used as a reference value during the treatment of cubital tunnel syndrome with anterior transposition of the vascularized ulnar nerve.

Finite element analysis on the strength and stability with the full extension job is researched for some special truck crane. The different telescopic parts are connected by the slider, which can transfer load through mutual contact and deform. Stress concentration always appear with the traditional method, so the contact algorithm is vital to the simulation. The intensity and stability of the boom is simulated with finite element analysis with the degree coupling technology, the simulation results have significance for further optimal design of the boom.

The course of information retrieval in higher vocational schools can train students to make use of the consciousness of document information resources, grasp the methods of using computer to document and check libraries, and improve self-study ability.

Through the analysis of development trend of the information era, and points out that the university library should strengthen the comprehensive quality management, pay attention to the development of literature information resources, raise awareness of information literacy of librarians, in order to adapt to the trend of development in this paper.

Based on the concept of MOOC, we can excavate, integrate and publicize the valuable advantage resources, and create the information database of the humanities tourism resources, which can promote the development of the humanities tourism in Changbai Mountain, and promote the Humanities Taste.

To synthesize 3'-deoxy-3'-¹⁸F-fluorotyhymidine)(¹⁸F-FLT) using CFN-MPS-200 automatic synthesis module, and evaluate its distribution in Wistar rats.We used 3-N-Boc-5!d-O-dimethoxytrityl-3!d-O-nosyl-thymidine (Boc-FLT)-percursor as raw material to synthesize ¹⁸F-FLT without residual solvents. Its radiochemical purity was confirmed with radio-HPLC and thin layer chromatography (TLC). Normal Wistar rats were injected withThe entire preparation procedure took about 60 min, which resulted in a radio chemical yield of (24±5)% (after attenuation correction,Automated synthesis of ¹⁸F-FLT using CFN-MPS-200 is a stable method, with high yield, safety without solvent, and acceptable quality.

SUMMARY As the number of young people suffering from diabetes increases worldwide, the impact of this disease on human reproduction urgently needs to be addressed. Here we compared the proteomes of cumulus cells of super-ovulated cumulus–oocyte complexes from diabetic and normal mice. We identified 57 up-regulated and 74 down-regulated proteins in diabetic cumulus cells; among these groups were proteins associated with cell cycle, cellular communication, epigenetic regulation, protein localization, and chromatin organization — all in accordance with type I diabetes. The poor-quality follicles derived from diabetic mice were further enforced by the presence of glycoproteins that are specifically expressed by the oocyte or oviductal epithelial cells in the cumulus-cell samples. In conclusion, the proteomic differences between diabetic and normal cumulus cells provide targets for improving the reproduction health of type I diabetic patients. Mol. Reprod. Dev. 81: 1080–1085, 2014. © 2014 Wiley Periodicals, Inc.

Sewage sludge proves to be difficult to dewater due to its composition and biological nature, and it is unsuitable for disposal with high moisture content. In the present work, sewage sludge is dewatered by the thermal compression, which shows unique separation advantages of various materials, and the effects of applied temperature and pressure on the dewatering performance are investigated. The filtrate SCOD reveals that higher temperature benefits the disruption of sludge flocs, resulting in the release of organic content. According to the accumulative filtrate volume, higher temperature and pressure facilitate the dewatering process, obtaining sludge cake with lower moisture content.

Stress of the cylindrical helical torsion spring is researched with finite element method when torsional degree changes. For spring has great resilience, large deformation effect is considered in the simulations. Analysis on the stiffness shows that the model built is credible, although torsional stiffness is not constant for large torsional angle, the strength is enough whose variation trend is consistent with the spring stiffness with different working torsional angle.