The Application of Nanoparticles of Waste Tires in Remediating Boron from Desalinated Water

A waste tire rubber (WTR) collected from the remains discarded tires has exhibited a noteworthy capacity to adsorb Boron. In the current study, the boron adsorption remediation from water at selected pH values, initial boron concentration, contact time, adsorbent dosage and particle size were examined using the WTR, the chemically modified WTR, and nano-WTR. The adsorption isotherms were best fitted to the Freundlich model with a high correlation coefficient (R2 :0.89-0.99), while the adsorption kinetics were satisfactorily described by the pseudo second order kinetic equation with correlation coefficient (R2: 1).The boron remediation using the WTR, the chemically modified-WTR and nano-WTR at low boron concentration (≤ 17.7 mg/L) were comparable with other adsorbents. The highest adsorption capacities for WTR, chemically modified-WTR and nano-WTR at initial concentration of 17.5 mg/L were 16.7 ± 1.3 mg/g, 13.8 ± 1.9 mg/g and 12.7 ± 1.8mg/g, respectively.This publication was made possible by UREP # (19-171-1-031) from the Qatar National Research Fund (a member of Qatar Foundation)

Nitrite binding to globins: linkage isomerism, EPR silence and reductive chemistry

The nitrite adducts of globins can potentially bind via O- or N- linkage to the heme iron. We have used EPR (electron paramagnetic resonance) and DFT (density functional theory) to explore these binding modes to myoglobin and hemoglobin. We demonstrate that the nitrite adducts of both globins have detectable EPR signals; we provide an explanation for the difficulty in detecting these EPR features, based on uniaxial state considerations. The EPR and DFT data show that both nitrite linkage isomers can be present at the same time and that the two isomers are readily interconvertible in solution. The millisecond-scale process of nitrite reduction by Hb is investigated in search of the elusive Fe(II)-nitrite adduct

Rapid movement and transcriptional re-localization of human cohesin on DNA

The spatial organization, correct expression, repair, and segregation of eukaryotic genomes depend on cohesin, ring-shaped protein complexes that are thought to function by entrapping DNA It has been proposed that cohesin is recruited to specific genomic locations from distal loading sites by an unknown mechanism, which depends on transcription, and it has been speculated that cohesin movements along DNA could create three-dimensional genomic organization by loop extrusion. However, whether cohesin can translocate along DNA is unknown. Here, we used single-molecule imaging to show that cohesin can diffuse rapidly on DNA in a manner consistent with topological entrapment and can pass over some DNA-bound proteins and nucleosomes but is constrained in its movement by transcription and DNA-bound CCCTC-binding factor (CTCF). These results indicate that cohesin can be positioned in the genome by moving along DNA, that transcription can provide directionality to these movements, that CTCF functions as a boundary element for moving cohesin, and they are consistent with the hypothesis that cohesin spatially organizes the genome via loop extrusion

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10−8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10−7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10−6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10−5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.</p

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10−8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10−7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10−6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10−5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.</p

Biomarker candidates of neurodegeneration in Parkinson’s disease for the evaluation of disease-modifying therapeutics

Reliable biomarkers that can be used for early diagnosis and tracking disease progression are the cornerstone of the development of disease-modifying treatments for Parkinson’s disease (PD). The German Society of Experimental and Clinical Neurotherapeutics (GESENT) has convened a Working Group to review the current status of proposed biomarkers of neurodegeneration according to the following criteria and to develop a consensus statement on biomarker candidates for evaluation of disease-modifying therapeutics in PD. The criteria proposed are that the biomarker should be linked to fundamental features of PD neuropathology and mechanisms underlying neurodegeneration in PD, should be correlated to disease progression assessed by clinical rating scales, should monitor the actual disease status, should be pre-clinically validated, and confirmed by at least two independent studies conducted by qualified investigators with the results published in peer-reviewed journals. To date, available data have not yet revealed one reliable biomarker to detect early neurodegeneration in PD and to detect and monitor effects of drug candidates on the disease process, but some promising biomarker candidates, such as antibodies against neuromelanin, pathological forms of α-synuclein, DJ-1, and patterns of gene expression, metabolomic and protein profiling exist. Almost all of the biomarker candidates were not investigated in relation to effects of treatment, validated in experimental models of PD and confirmed in independent studies