Trajectories of Self-Rated Health in People with Diabetes: Associations with Functioning in a Prospective Community Sample

Background

Self-rated health (SRH) is a single-item measure that is one of the most widely used measures of general health in population health research. Relatively little is known about changes and the trajectories of SRH in people with chronic medical conditions. The aims of the present study were to identify and describe longitudinal trajectories of self-rated health (SRH) status in people with diabetes.

Methods

A prospective community study was carried out between 2008 and 2011. SRH was assessed at baseline and yearly at follow-ups (n=1288). Analysis was carried out through trajectory modeling. The trajectory groups were subsequently compared at 4 years follow-up with respect to functioning.

Results

Four distinct trajectories of SRH were identified: 1) 72.2% of the participants were assigned to a persistently good SRH trajectory; 2) 10.1% were assigned to a persistently poor SRH trajectory; 3) mean SRH scores changed from good to poor for one group (7.3%); while 4) mean SRH scores changed from poor to medium/good for another group (10.4%). Those with a persistently poor perception of health status were at higher risk for poor functioning at 4 years follow-up than those whose SRH scores decreased from good to poor.

Conclusions

SRH is an important predictor for poor functioning in diabetes, but the trajectory of SRH seems to be even more important. Health professionals should pay attention to not only SRH per se, but also changes in SRH over time.     

Systematic Pharmacogenomics Analysis of a Malay Whole Genome: Proof of Concept for Personalized Medicine

Background

With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine.

Methods

Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject''s mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.

Principal Findings

Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.

Conclusions

The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.     

Serum Protein Profile at Remission Can Accurately Assess Therapeutic Outcomes and Survival for Serous Ovarian Cancer

Background

Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer.

Experimental Design

Forty serum proteins were analyzed in 70 serum samples from healthy controls (HC) and 101 serum samples from serous OC patients at three different disease phases: post diagnosis (PD), remission (RM) and recurrence (RC). The utility of serum proteins as OC biomarkers was evaluated using a variety of statistical methods including survival analysis.

Results

Ten serum proteins (PDGF-AB/BB, PDGF-AA, CRP, sFas, CA125, SAA, sTNFRII, sIL-6R, IGFBP6 and MDC) have individually good area-under-the-curve (AUC) values (AUC = 0.69–0.86) and more than 10 three-marker combinations have excellent AUC values (0.91–0.93) in distinguishing active cancer samples (PD & RC) from HC. The mean serum protein levels for RM samples are usually intermediate between HC and OC patients with active cancer (PD & RC). Most importantly, five proteins (sICAM1, RANTES, sgp130, sTNFR-II and sVCAM1) measured at remission can classify, individually and in combination, serous OC patients into two subsets with significantly different overall survival (best HR = 17, p<10⁻³).

Conclusion

We identified five serum proteins which, when measured at remission, can accurately predict the overall survival of serous OC patients, suggesting that they may be useful for monitoring the therapeutic outcomes for ovarian cancer.     

Analysis of evolution of carbonic anhydrases IV and XV reveals a rich history of gene duplications and a new group of isozymes

Carbonic anhydrase (CA) isozymes CA IV and CA XV are anchored on the extracellular cell surface via glycosylphosphatidylinositol (GPI) linkage. Analysis of evolution of these isozymes in vertebrates reveals an additional group of GPI-linked CAs, CA XVII, which has been lost in mammals. Our work resolves nomenclature issues in GPI-linked fish CAs. Review of expression data brings forth previously unreported tissue and cancer types in which human CA IV is expressed. Analysis of collective glycosylation patterns of GPI-linked CAs suggests functionally important regions on the protein surface.     

Chemical evidence of preserved collagen in 54-million-year-old fish vertebrae

Collagens are the most abundant proteins in the animal kingdom. They form the structural framework of connective tissues such as bones, tendons and skin, and play important biomechanical role in supporting tissue functions. The preservation of collagen in deep time is a topic of intense debate. Here we provide indisputable evidence for the presence of collagen in early Eocene fish vertebrae using online pyrolysis comprehensive two dimensional gas chromatography time-of-flight mass spectrometry (py-GC×GC-TOFMS) and immunofluorescence analysis. The presence of cyclic dipeptides such as diketodipyrrole, 2,5-diketopiperazine of proline-proline and 2,5-diketopiperazine of proline-glycine along with other nitrogen-bearing molecules in the pyrolysis products of the studied fossils unequivocally demonstrate that collagen can withstand degradation and diagenetic alteration. Immunofluorescence study also confirms the presence of collagen-I in the fossilized fish vertebrae. Contrary to common opinion, the present findings suggest that the preservation of collagen in fossilized soft tissues is not rare. We propose that one of the essential factors controlling preservation of collagen is the establishment of a suitable microenvironment within the fossil, inhibiting diagenetic alteration including microbial decay.     

Identification of novel candidate genes by exome sequencing in Tunisian familial male breast cancer patients

Molecular Biology Reports - Male Breast Cancer (MBC) is a rare and aggressive disease that is associated with genetic factors. Mutations in BRCA1&nbsp;and&nbsp;BRCA2&nbsp;account for...     

Identifying potential GluN2B subunit containing N-Methyl-D-aspartate receptor inhibitors: an integrative in silico and molecular modeling approach

Abstract

N-methyl-D-aspartate receptors (NMDARs), a class of ligand-gated ion channels, are involved in non-selective cation transport across the membrane. These are contained in glutamatergic synapse and produce excitatory effects leading to synaptic plasticity and memory function. GluN1-GluN2B, a subtype of NMDAR(s), has significant role in neurodegeneration, amyloid β (Aβ) induced synaptic dysfunction and loss. Thus, targeting and inhibiting GluN1-GluN2B may be effective in the management of neurodegenerative diseases including Alzheimer’s disease. In the present study, ligand and structure-based approaches were tried to identify the inhibitors. The pharmacophore, developed from co-crystallised ifenprodil, afforded virtual hits, which were further subjected through drug likeliness and PAINS filters to remove interfering compounds. Further comprehensive docking studies, free energy calculations and ADMET studies resulted in two virtual leads. The leads, ZINC257261614 and ZINC95977857 displayed good docking scores of ?12.90 and ?12.20?Kcal/mol and free binding energies of ?60.83 and ?61.83?Kcal/mol, respectively. The compounds were having acceptable predicted ADMET profiles and were subjected to molecular dynamic (MD) studies. The MD simulation produced stable complexes of these ligands with GluN1-GluN2B subunit having protein and ligand RMSD in acceptable limit. Abbreviations AD Alzheimer's disease

ADME Absorption distribution metabolism and excretion

ATD Amino terminal domain

BBB Blood-brain barrier

CNS Central nervous system

CREB cAMP response element binding protein

CTD Carboxy-terminal domain

Glu Glutamate

GMQE Global model quality estimation

HTVS High throughput virtual screening

HIA Human intestinal absorption

LGA Lamarckian genetic algorithm

MD Molecular dynamics

MM-GBSA Molecular mechanics, the Generalised Born model for Solvent Accessibility

NMDAR N-methyl-D-aspartate receptors

PAINS Pan assay interference compounds

RMSD Root-mean square deviation

RMSF Root-mean-square fluctuation

SMARTS SMILES arbitrary target specification

SP standard precision

XP extra precision

Communicated by Ramaswamy H. Sarma     

Human gingival derived neuronal cells in the optimized caffeic acid hydrogel for hemitransection spinal cord injury model

Spinal cord injury induces scar formation causes axonal damage that leads to the degeneration of axonal function. Still, there is no robust conceptual design to regenerate the damaged axon after spinal injury. Therefore, the present study demonstrates that human gingival derived neuronal stem cells (GNSCs) transplants in the injectable caffeic acid bioconjugated hydrogel (CBGH) helps to bridge the cavity and promote the engraftment and repopulation of transplants in the injured spinal tissue. Our study reports that the bioluminescence imaging in vivo imaging system (IVIS) provides a satisfactory progression in CBGH-GNSCs transplants compare to lesion control and CBGH alone. Immune regulators interleukin-6 (IL-6), tumor necrosis factor-α, neutrophil elastase are decreased, IL-10 is increased. Likewise, immunostaining (TAU/TUJ-1, SOX-2/NeuN, MAP-2/PSD93, NSE, S100b, and GFAP) shown repopulated cells. Also, TRA-1-81 expression confirms the absence of immune rejection in the CBGH-GNSCs transplants. However, locomotor recovery test, gene (IL-6, CASPASE3, p14-ARF, VEGF, LCAM, BDNF, NT3, NGN2, TrKc, FGF2, Sox-2, TUJ-1, MAP-2, Nestin, and NeuN) and protein expression (TAU, TUJ-1, SOX-2 MAP-2, PSD93, NeuN, TRA-1-81, GFAP, TAU, and MBP) shows functional improvements in the CBGH-GNSCs group. Further, GABA and glutamine level demonstrates the new synaptic vesicle formation. Hence, the CBGH scaffold enhances GNSCs transplants to restore the injured spinal tissue.