A rare case report of primary uterine and vaginal lymphoma in the elderly.

Uterine lymphoma is rare and usually occurs in middle-aged women. The clinical symptoms lack any specific characteristics. Imaging characteristics usually include uterine enlargement with density and uniform signal soft tissue masses. Magnetic resonance T2 weighted imaging, enhanced scanning, diffusion weighted imaging and apparent diffusion coefficient values have certain characteristics. The gold standard for diagnosis remains a pathological examination of a biopsy specimen. The special feature of this current case was that the uterine lymphoma occurred in an 83-year-old female patient that presented with a pelvic mass for more than 1 month. Based on the imaging findings, a primary uterine lymphoma was considered, but her advanced age of onset did not match the disease. After pathological confirmation, the patient was diagnosed with uterine lymphoma and she received eight cycles of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone) plus local radiotherapy for the large masses. The patients achieved good results. Follow-up enhanced computed tomography imaging showed that the uterine volume had significantly reduced compared with before treatment. The diagnosis of elderly patients with uterine lymphoma can provide a more accurate plan for subsequent treatment.

Structural elucidation and targeted valorization of poplar lignin from the synergistic hydrothermal-deep eutectic solvent pretreatment.

Elucidating the structural variations of lignin during the pretreatment is very important for lignin valorization. Herein, poplar wood was pretreated with an integrated process, which was composed of AlCl3-catalyzed hydrothermal pretreatment (HTP, 130-150 °C, 1.0 h) and mild deep-eutectic solvents (DES, 100 °C, 10 min) delignification for recycling lignin fractions. Confocal Raman Microscopy (CRM) was developed to visually monitor the delignification process during the HTP-DES pretreatment. NMR characterizations (2D-HSQC and 31P NMR) and elemental analysis demonstrated that the lignin fractions had undergone the following structural changes, such as dehydration, depolymerization, condensation. Molecular weights (GPC), microstructure (SEM and TEM), and antioxidant activity (DPPH analysis) of the lignins revealed that the DES delignification resulted in homogeneous lignin fragments (1.32 < PDI < 1.58) and facilitated the rapid assemblage of lignin nanoparticles (LNPs) with controllable nanoscale sizes (30-210 nm) and excellent antioxidant activity. These findings will enhance the understanding of structural transformations of the lignin during the integrated process and maximize the lignin valorization in a current biorefinery process.

Evaluations of wavefront aberrations and corneal surface regularity in dry eye patients measured with OPD Scan III.

AIM: To compare the wavefront aberrations and corneal surface regularity between dry eye (DE) patients and normal subjects and assess its diagnostic performance for DE measured with OPD Scan-III. METHODS: Fifty right eyes of 50 DE patients and 31 right eyes of normal subjects were included. The examinations for ocular surface including logarithm of the minimum angle of resolution best-corrected distance visual acuity (logMAR BCVA) the ocular surface disease index (OSDI), tear film break-up time (TBUT) and corneal fluorescein staining (CFS). OPD Scan-III was used to measure anterior corneal aberrations including total corneal aberrations, high order aberration (HOA), coma, trefoil, spherical aberration (SA), standard deviation of corneal power (SDP), surface regularity index (SRI) and surface asymmetry index (SAI). Statistical analysis were assessed with nonparametric tests and Spearman's correlations. All parameters were also analyzed for sensitivity, specificity, and receiver operating characteristics (ROC) curves. RESULTS: Wavefront aberrations parameters including total corneal aberrations, HOA, coma, trefoil, and SA in DE group were significantly higher than those in normal group (P<0.001). Corneal surface regularity parameters including SRI and SAI in DE group were significantly higher than both in normal group (P<0.05). All the wavefront aberrations parameters had significant correlations with ocular surface parameters (P<0.05). The logMAR BCVA had positive correlations with SAI and SRI (all P<0.001). CFS scores had positive correlations with SAI and SRI (all P<0.001). All the wavefront aberrations parameters showed good diagnosis sensitivity and specificity, however, the corneal regularity parameters showed only good specificity but poor sensitivity. The cut-off value selected for trefoil in diagnosis DE showed the highest area under the curve (AUC, 0.921) values as compared to the other parameters with sensitivity of 0.955 and specificity of 0.867. CONCLUSION: Wavefront aberrations and corneal surface regularity are increased in DE patients and also correlated with ocular surface parameters. Wavefront aberrations parameters have potential to be indicators to diagnosis and monitor DE.

The diagnostic value of lower glucose consumption for IDH1 mutated gliomas on FDG-PET.

BACKGROUND: Non-invasive diagnosis of IDH1 mutation for gliomas has great clinical significance, and PET has natural advantage to detect metabolism, as IDH mutated gliomas share lower glucose consumption. METHODS: Clinical data of patients with gliomas and 18F-FDG PET were retrospectively reviewed. Receiver operating characteristic curve (ROC) analysis was conducted, and standard uptake value (SUV) was estimated in combination with grades or IDH1 mutation. The glucose consumption was investigated with U251 cells expressing wild-type or mutated IDH1 by glucose assay. Quantification of glucose was determined by HPLC in clinical tissues. Meanwhile, bioinformatics and western blot were applied to analyze the expression level of metabolic enzymes (e.g. HK1, PKM2, PC) in gliomas. RESULTS: Seventy-one glioma cases were enrolled, including 30 carrying IDH1 mutation. The sensitivity and specificity dependent on SUVmax (3.85) predicting IDH1 mutation reached 73.2 and 86.7%, respectively. The sensitivity and specificity of differentiating grades by SUVmax (3.1) were 92.3 and 64.4%, respectively. Glucose consumption of U251 IDH1 mutant cells (0.209 ± 0.0472 mg/ml) was obviously lower than IDH1wild-type cells (0.978 ± 0.0773 mg/ml, P = 0.0001) and astrocyte controls (0.335 ± 0.0592 mg/ml, P = 0.0451). Meanwhile, the glucose quantity in IDH1mutant glioma samples were significantly lower than those in IDH1 wild-type tissues (1.033 ± 1.19608 vs 6.361 ± 4.3909 mg/g, P = 0.0051). Silico analysis and western blot confirmed that HK1 and PKM2 in IDH1 wild-type gliomas were significantly higher than in IDH1 mutant group, while PC was significantly higher in IDH1 mutant gliomas. CONCLUSION: SUVmax on PET can predict IDH1 mutation with adequate sensitivity and specificity, as is supported by reduced glucose consumption in IDH1 mutant gliomas.

Comparison of anti-inflammatory effects of intense pulsed light with tobramycin/dexamethasone plus warm compress on dry eye associated meibomian gland dysfunction.

AIM: To compare the anti-inflammatory effects of intense pulsed light (IPL) with tobramycin/dexamethasone plus warm compress through clinical signs and cytokines in tears. METHODS: Eighty-two patients with dry eye disease (DED) associated meibomian gland dysfunction (MGD) were divided into two groups. Group A was treated with IPL, and Group B was treated with tobramycin/dexamethasone plus warm compress. Ocular Surface Disease Index (OSDI), tear film breakup time (TBUT), corneal fluorescein staining (CFS), meibomian gland expressibility (MGE), meibum quality, gland dropout and tear cytokine levels were evaluated before treatment, 1wk and 1mo after treatment. RESULTS: TBUT in Group A was higher (P=0.035), and MGE score was lower than Group B at 1mo (P=0.001). The changes of interleukin (IL)-17A and IL-1ß levels in tears were lower in Group A compared with that in Group B at 1wk after treatment (P=0.05, P=0.005). CONCLUSION: Treatment with IPL can improve TBUT and MGE and downregulate levels of IL-17A and IL-1ß in tears of patients with DED associated MGD better than treatment with tobramycin/dexamethasone plus warm compress in one-month treatment period.

Spontaneous cerebrospinal fluid rhinorrhea: A case report and analysis.

INTRODUCTION: Spontaneous cerebrospinal fluid leakage is usually caused by developmental abnormalities and is rare, accounting for approximately 5% of the cases of cerebrospinal fluid (CSF) leakage. To the best of our knowledge, clival dysplasia-caused CSF rhinorrhea has never been reported in the neurosurgical field. CONCLUSION: Spontaneous cerebrospinal fluid rhinorrhea is often treated by surgery, and a transsphenoidal approach repair is the main surgical method used, offering the advantages of less trauma, fewer complications, rapid postoperative recovery, and low recurrence rate.

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array.

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · ) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method.

Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm.

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.

Identification of New Candidate Genes and Chemicals Related to Esophageal Cancer Using a Hybrid Interaction Network of Chemicals and Proteins.

Cancer is a serious disease responsible for many deaths every year in both developed and developing countries. One reason is that the mechanisms underlying most types of cancer are still mysterious, creating a great block for the design of effective treatments. In this study, we attempted to clarify the mechanism underlying esophageal cancer by searching for novel genes and chemicals. To this end, we constructed a hybrid network containing both proteins and chemicals, and generalized an existing computational method previously used to identify disease genes to identify new candidate genes and chemicals simultaneously. Based on jackknife test, our generalized method outperforms or at least performs at the same level as those obtained by a widely used method--the Random Walk with Restart (RWR). The analysis results of the final obtained genes and chemicals demonstrated that they highly shared gene ontology (GO) terms and KEGG pathways with direct and indirect associations with esophageal cancer. In addition, we also discussed the likelihood of selected candidate genes and chemicals being novel genes and chemicals related to esophageal cancer.

Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.

Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.

Safety and survival benefit of surgical management for elderly gastric cancer patients.

OBJECTIVE: To study the safety and survival outcome of surgical management for elderly gastric cancer patients. Methods: Patients proven of gastric cancer who aged ≥80 years during November 2002 to July 2011 were retrospectively analyzed. The detailed information of patients' characteristics and surgical management was retrieved. Follow-up of overall survival status was performed to analyze the surgical effectiveness. RESULTS: Totally, 92 (48 in surgery and 44 in non-surgery group) out of 187 eligible patients recorded adequate information and analyzed finally. There were 34 patients undergone radical gastrectomy, 6 palliative gastrectomy, 1 gastrojejunostomy and 7 exploratory laparotomy. Median follow-up durations were 25 (9-111) and 28 (8-114) months in surgery and non-surgery groups, respectively (p=0.797). Clinical-pathological T stage and node status were comparable. Clinical-pathological distal metastasis status was 15 and 26 M1 cases for surgery and nonsurgery, respectively (p=0.006). Incidence of postoperative complications and hospital mortality were 25.0% and 2.1%, respectively. The 2-year survival rates of M0 subgroups were 35.7% and 0% for surgery and nonesurgery, respectively (HR=3.98, p=0.022). CONCLUSIONS: The safety of surgery for well-selected ≥ 80-year elderly gastric cancer patients was potentially acceptable and the patients of early or locally advanced diseases could obtain survival benefits by surgery.

A graphic method for identification of novel glioma related genes.

Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.

Prediction of drugs target groups based on ChEBI ontology.

Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset-the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.

Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins.

Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.

Urinary metabonomics of stomach cancer assessed by rapid resolution liquid chromatography/time-of-fight mass spectrometry.

BACKGROUND: Stomach cancer is among the most commonly occurring malignancies worldwide. It would be beneficial to develop a urine-based assay whereby patients with undiagnosed stomach cancer could be screened and their cancer detected in the earliest stages. METHODS: A urinary metabonomics method based on ultra-performance liquid chromatography combined with quadruple time-of-flight mass spectrometry was used to analyze urine samples from patients with stomach cancer and healthy controls. RESULTS: Statistical analysis revealed a clear separation of patients and healthy controls using the aforementioned methodology. Some significantly changed metabolites were identified. CONCLUSIONS: Use of the metabonomics method in patients with stomach cancer could effectively detect distinct changes in urinary metabolites and had the capacity to detect cancer; therefore, it may be a valuable tool in earlier diagnosis. Furthermore, the detection and identification of altered metabolites in the current study may help elucidate possible mechanisms involved in stomach cancer.

Signal propagation in protein interaction network during colorectal cancer progression.

Colorectal cancer is generally categorized into the following four stages according to its development or serious degree: Dukes A, B, C, and D. Since different stage of colorectal cancer actually corresponds to different activated region of the network, the transition of different network states may reflect its pathological changes. In view of this, we compared the gene expressions among the colorectal cancer patients in the aforementioned four stages and obtained the early and late stage biomarkers, respectively. Subsequently, the two kinds of biomarkers were both mapped onto the protein interaction network. If an early biomarker and a late biomarker were close in the network and also if their expression levels were correlated in the Dukes B and C patients, then a signal propagation path from the early stage biomarker to the late one was identified. Many transition genes in the signal propagation paths were involved with the signal transduction, cell communication, and cellular process regulation. Some transition hubs were known as colorectal cancer genes. The findings reported here may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level.

Clinicopathologic significance of ß-catenin and matrix metalloproteinase-2 expression in non-small cell lung cancer.

The purpose of this study was to analyze ß-catenin and matrix metalloproteinase-2 (MMP-2) expression in non-small cell lung cancer (NSCLC) and to investigate the association between their expression and clinicopathologic characteristics of NSCLC patients. Immunohistochemistry was performed to examine ß-catenin and MMP-2 protein expression in 39 resected NSCLC samples and 8 adjacent normal lung tissues. Statistical analysis with SPSS13.0 software was performed to investigate the association between ß-catenin and MMP-2 expression and clinicopathologic features of the patients. Expression of cytosolic ß-catenin in NSCLC tissue was significantly higher than that in normal tissues (P < 0.001). In addition, cytosolic protein expression of ß-catenin in lung squamous cell carcinoma was significantly elevated compared to that in lung adenocarcinoma (P = 0.02). However, cell membrane protein expression of ß-catenin in squamous cell carcinoma was lower than that in adenocarcinoma (P = 0.041). Cytosolic MMP-2 protein expression in NSCLC samples was significantly higher than that in normal tissues (P = 0.002). MMP-2 expression in N (1-2) NSCLC patients was significantly increased relative to N (0) patients (P = 0.019). However, statistical analysis showed no correlation between ß-catenin and MMP-2 expression in NSCLC samples. Collectively, our results show that cytosolic protein expression of ß-catenin in NSCLC samples is increased relative to normal lung tissues. Also, expression of ß-catenin is significantly elevated in squamous cell carcinoma compared to that in lung adenocarcinoma subtypes. Additionally, MMP-2 expression in N (1-2) NSCLC tissues is higher than that in N (0) lung tissue. There is no correlation between ß-catenin and MMP-2 expression in NSCLC, and our study suggests that evaluation of ß-catenin and MMP-2 expression may have potential in diagnosis and progression in patients with NSCLC.

Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.

Identification of catalytic residues plays a key role in understanding how enzymes work. Although numerous computational methods have been developed to predict catalytic residues and active sites, the prediction accuracy remains relatively low with high false positives. In this work, we developed a novel predictor based on the Random Forest algorithm (RF) aided by the maximum relevance minimum redundancy (mRMR) method and incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility to predict active sites of enzymes and achieved an overall accuracy of 0.885687 and MCC of 0.689226 on an independent test dataset. Feature analysis showed that every category of the features except disorder contributed to the identification of active sites. It was also shown via the site-specific feature analysis that the features derived from the active site itself contributed most to the active site determination. Our prediction method may become a useful tool for identifying the active sites and the key features identified by the paper may provide valuable insights into the mechanism of catalysis.

Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.

The effects of atorvastatin on pulmonary arterial hypertension and expression of p38, p27, and Jab1 in rats.

Statins have recently come under evaluation for the treatment of pulmonary arterial hypertension (PAH). The aim of this study was to examine the effects of atorvastatin on the clinical manifestations and expression of p38, p27 and Jab1 using a rat PAH model. Ninety-six male Wistar rats were divided into control (receiving no surgical treatment), vehicle and treatment groups, among which the last two groups underwent left pneumonectomy and were then treated with monocrotaline (MCT, 60 mg/kg). Both control and vehicle groups subsequently received saline, and the treatment group received atorvastatin (20 mg/kg) by stomach catheter. Rats were sacrificed, and mean pulmonary arterial pressure (mPAP) and right ventricle hypertrophy index (RVHI) were measured. The expression of p38, p27, and Jab1 was evaluated by immunohistochemistry and Western blotting. At 28 days, mPAP and RVHI and expression levels of Jab1 and p38 in the vehicle group were significantly higher than those in the treatment and control groups. However, the expression of p27 was lowest in the vehicle group among the three groups. Atorvastatin reduced PAP and RVHI in the rat PAH model, decreased expression of p38 and Jab1 but increased expression of p27.