Intervention effects of Ganoderma lucidum spores on epileptiform discharge hippocampal neurons and expression of Neurotrophin-4 and N-Cadherin

Epilepsy can cause cerebral transient dysfunctions. Ganoderma lucidum spores (GLS), a traditional Chinese medicinal herb, has shown some antiepileptic effects in our previous studies. This was the first study of the effects of GLS on cultured primary hippocampal neurons, treated with Mg2+ free medium. This in vitro model of epileptiform discharge hippocampal neurons allowed us to investigate the anti-epileptic effects and mechanism of GLS activity. Primary hippocampal neurons from <1 day old rats were cultured and their morphologies observed under fluorescence microscope. Neurons were confirmed by immunofluorescent staining of neuron specific enolase (NSE). Sterile method for GLS generation was investigated and serial dilutions of GLS were used to test the maximum non-toxic concentration of GLS on hippocampal neurons. The optimized concentration of GLS of 0.122 mg/ml was identified and used for subsequent analysis. Using the in vitro model, hippocampal neurons were divided into 4 groups for subsequent treatment i) control, ii) model (incubated with Mg2+ free medium for 3 hours), iii) GLS group I (incubated with Mg2+ free medium containing GLS for 3 hours and replaced with normal medium and incubated for 6 hours) and iv) GLS group II (neurons incubated with Mg2+ free medium for 3 hours then replaced with a normal medium containing GLS for 6 hours). Neurotrophin-4 and N-Cadherin protein expression were detected using Western blot. The results showed that the number of normal hippocampal neurons increased and the morphologies of hippocampal neurons were well preserved after GLS treatment. Furthermore, the expression of neurotrophin-4 was significantly increased while the expression of N-Cadherin was decreased in the GLS treated group compared with the model group. This data indicates that GLS may protect hippocampal neurons by promoting neurotrophin-4 expression and inhibiting N-Cadherin expression

A novel route for catalytic activation of peroxymonosulfate by oxygen vacancies improved bismuth-doped titania for the removal of recalcitrant organic contaminant

In this work, bismuth-doped titania (BixTiO2) with improved oxygen vacancies was synthesized by sol-gel protocol as a novel peroxymonosulfate (PMS, HSO5−) activator. HSO5− and adsorbed oxygen molecules could efficiently be transformed into their respective radicals through defect ionization to attain charge balance after their trapping on oxygen vacancies of the catalyst. XRD study of BixTiO2 with 5 wt% Bi (5BiT) revealed anatase, crystalline nature, and successful doping of Bi into TiO2 crystal lattice. The particle size obtained from BET data and SEM observations was in good agreement. PL spectra showed the formation rates of •OH by 3BiT, 7BiT, 5BiTC, and 5BiT as 0.720, 1.200, 1.489, and 2.153 μmol/h, respectively. 5BiT catalyst with high surface area (216.87 m2 g−1) and high porosity (29.81%) was observed the excellent HSO5− activator. The catalytic performance of 0BiT, 3BiT, 5BiT, and 7BiT when coupled with 2 mM HSO5− for recalcitrant flumequine (FLU) removal under dark was 10, 27, 55, and 37%, respectively. Only 5.4% decrease in catalytic efficiency was observed at the end of seventh cyclic run. Radical scavenging studies indicate that SO4•− is the dominant species that caused 62.0% degradation. Moreover, strong interaction between Bi and TiO2 through Bi-O-Ti bonds prevents Bi leaching (0.081 mg L−1) as shown by AAS. The kinetics, degradation pathways, ecotoxicity, and catalytic mechanism for recalcitrant FLU were also elucidated. Cost-efficient, environment-friendly, and high mineralization recommends this design strategy; BixTiO2/HSO5− system is a promising advanced oxidation process for the aquatic environment remediation

Smith-Waterman peak alignment for comprehensive two-dimensional gas chromatography-mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.</p> <p>Results</p> <p>We developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.</p> <p>Conclusions</p> <p>The proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.</p

Mutational analysis of xenobiotic metabolizing genes (CYP1A1 and GSTP1) in sporadic head and neck cancer patients

CYP1A1 is the phase I enzyme that detoxifies the carcinogen or converts it into a more electrophilic form, metabolized by phase II enzymes like GSTP1. These detoxifying genes have been extensively studied in association with head and neck cancer (HNC) in different ethnic groups worldwide. The current study was aimed at screening genetic polymorphisms of genes CYP1A1 and GSTP1 in 388 Pakistani HNC patients and 150 cancer-free healthy controls, using PCR-SSCP. No already known variants of either gene were found, however a novel frameshift mutation due to insertion of T (g.2842_2843insT) was observed in the CYP1A1 gene. A statistically significant number (5.4%) of HNC cases, with the mean age of 51.75 (±15.7) years, presented this frameshift mutation in the conserved domain of CYP1A1. Another novel substitution mutation in was found in the GSTP1 gene, presenting TA instead of AG. The g.2848A > T polymorphism causes a leucine-to-leucine formation, whereas g.2849G > A causes alanine-to-threonine formation at amino acid positions 166 and 167, respectively. These exonic mutations were found in 9.5% of the HNC patients and in none of the controls. In addition, two intronic deletions of C (g.1074delC and g.1466delC) were also found in 11 patients with a mean age of 46.2 (±15.6) years. In conclusion, accumulation of mutations in genes CYP1A1 and GSTP1 appears to be associated with increased risk of developing HNC, suggesting that mutations in these genes may play a role in the etiology of head and neck cancer

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

<p>Abstract</p> <p>Background</p> <p>Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV_H) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV_H status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgV_H mutational status which can accurately predict the survival outcome are yet to be discovered.</p> <p>Results</p> <p>In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgV_H mutation status from the ZAP70 co-expression network.</p> <p>Conclusions</p> <p>We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV_H mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.</p

Finite element analysis and calculation method of residual flexural capacity of post-fire RC beams

Fire tests and subsequent bending tests of fourreinforced concrete (RC) beamswere performed. Based on these tests, the post-fire performance of RCbeams was further studied using finite element simulation through reasonable selection of suitable thermal and thermodynamic parameters of steel and concrete materials. A thermodynamic model of RC beams with three sides under fire was built using finite element analysis(FEA)software ABAQUS. The FEA model was validated with the results of fire tests. Different factors were taken into account for further parametric studies in fire using the proposed FE model.The results show that the main factors affecting the fire resistance of the beamsare the thickness of the concretecover, reinforcement ratio of longitudinal steel,the fire exposure timeandthe fire exposure sides. Based on the strength reduction formula at high temperature of steel and concrete, animproved section method was proposed to develop a calculation formula to calculate the flexural capacity of RC beams after fire. The theoretical calculation method proposed in this paper shows good agreement with FEA results, which can be used to calculate the flexuralcapacity of RC beams after fire

Lactic Acid Induces Aberrant Amyloid Precursor Protein Processing by Promoting Its Interaction with Endoplasmic Reticulum Chaperone Proteins

Lactic acid, a natural by-product of glycolysis, is produced at excess levels in response to impaired mitochondrial function, high-energy demand, and low oxygen availability. The enzyme involved in the production of β-amyloid peptide (Aβ) of Alzheimer's disease, BACE1, functions optimally at lower pH, which led us to investigate a potential role of lactic acid in the processing of amyloid precursor protein (APP).Lactic acid increased levels of Aβ40 and 42, as measured by ELISA, in culture medium of human neuroblastoma cells (SH-SY5Y), whereas it decreased APP metabolites, such as sAPPα. In cell lysates, APP levels were increased and APP was found to interact with ER-chaperones in a perinuclear region, as determined by co-immunoprecipitation and fluorescence microscopy studies. Lactic acid had only a very modest effect on cellular pH, did increase the levels of ER chaperones Grp78 and Grp94 and led to APP aggregate formation reminiscent of aggresomes.These findings suggest that sustained elevations in lactic acid levels could be a risk factor in amyloidogenesis related to Alzheimer's disease through enhanced APP interaction with ER chaperone proteins and aberrant APP processing leading to increased generation of amyloid peptides and APP aggregates

An integrated, self-contained microfluidic cassette for isolation, amplification, and detection of nucleic acids

A self-contained, integrated, disposable, sample-to-answer, polycarbonate microfluidic cassette for nucleic acid-based detection of pathogens at the point of care was designed, constructed, and tested. The cassette comprises on-chip sample lysis, nucleic acid isolation, enzymatic amplification (polymerase chain reaction and, when needed, reverse transcription), amplicon labeling, and detection. On-chip pouches and valves facilitate fluid flow control. All the liquids and dry reagents needed for the various reactions are pre-stored in the cassette. The liquid reagents are stored in flexible pouches formed on the chip surface. Dry (RT-)PCR reagents are pre-stored in the thermal cycling, reaction chamber. The process operations include sample introduction; lysis of cells and viruses; solid-phase extraction, concentration, and purification of nucleic acids from the lysate; elution of the nucleic acids into a thermal cycling chamber and mixing with pre-stored (RT-)PCR dry reagents; thermal cycling; and detection. The PCR amplicons are labeled with digoxigenin and biotin and transmitted onto a lateral flow strip, where the target analytes bind to a test line consisting of immobilized avidin-D. The immobilized nucleic acids are labeled with up-converting phosphor (UCP) reporter particles. The operation of the cassette is automatically controlled by an analyzer that provides pouch and valve actuation with electrical motors and heating for the thermal cycling. The functionality of the device is demonstrated by detecting the presence of bacterial B.Cereus, viral armored RNA HIV, and HIV I virus in saliva samples. The cassette and actuator described here can be used to detect other diseases as well as the presence of bacterial and viral pathogens in the water supply and other fluids

Tandem mass spectrometry data quality assessment by self-convolution

<p>Abstract</p> <p>Background</p> <p>Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on <it>de novo </it>sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.</p> <p>Results</p> <p>The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.</p> <p>Conclusion</p> <p>We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well and could potentially be used as a pre-processing for all mass spectrometry based protein identification tools.</p