Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients

Broadening of Neutralization Activity to Directly Block a Dominant Antibody-Driven SARS-Coronavirus Evolution Pathway

Phylogenetic analyses have provided strong evidence that amino acid changes in spike (S) protein of animal and human SARS coronaviruses (SARS-CoVs) during and between two zoonotic transfers (2002/03 and 2003/04) are the result of positive selection. While several studies support that some amino acid changes between animal and human viruses are the result of inter-species adaptation, the role of neutralizing antibodies (nAbs) in driving SARS-CoV evolution, particularly during intra-species transmission, is unknown. A detailed examination of SARS-CoV infected animal and human convalescent sera could provide evidence of nAb pressure which, if found, may lead to strategies to effectively block virus evolution pathways by broadening the activity of nAbs. Here we show, by focusing on a dominant neutralization epitope, that contemporaneous- and cross-strain nAb responses against SARS-CoV spike protein exist during natural infection. In vitro immune pressure on this epitope using 2002/03 strain-specific nAb 80R recapitulated a dominant escape mutation that was present in all 2003/04 animal and human viruses. Strategies to block this nAb escape/naturally occurring evolution pathway by generating broad nAbs (BnAbs) with activity against 80R escape mutants and both 2002/03 and 2003/04 strains were explored. Structure-based amino acid changes in an activation-induced cytidine deaminase (AID) “hot spot” in a light chain CDR (complementarity determining region) alone, introduced through shuffling of naturally occurring non-immune human VL chain repertoire or by targeted mutagenesis, were successful in generating these BnAbs. These results demonstrate that nAb-mediated immune pressure is likely a driving force for positive selection during intra-species transmission of SARS-CoV. Somatic hypermutation (SHM) of a single VL CDR can markedly broaden the activity of a strain-specific nAb. The strategies investigated in this study, in particular the use of structural information in combination of chain-shuffling as well as hot-spot CDR mutagenesis, can be exploited to broaden neutralization activity, to improve anti-viral nAb therapies, and directly manipulate virus evolution

Vacancy-Mediated Magnetism in Pure Copper Oxide Nanoparticles

Room temperature ferromagnetism (RTF) is observed in pure copper oxide (CuO) nanoparticles which were prepared by precipitation method with the post-annealing in air without any ferromagnetic dopant. X-ray photoelectron spectroscopy (XPS) result indicates that the mixture valence states of Cu1+ and Cu2+ ions exist at the surface of the particles. Vacuum annealing enhances the ferromagnetism (FM) of CuO nanoparticles, while oxygen atmosphere annealing reduces it. The origin of FM is suggested to the oxygen vacancies at the surface/or interface of the particles. Such a ferromagnet without the presence of any transition metal could be a very good option for a class of spintronics

An Updated Search of Steady TeV γ−\gamma-Ray Point Sources in Northern Hemisphere Using the Tibet Air Shower Array

Using the data taken from Tibet II High Density (HD) Array (1997 February-1999 September) and Tibet-III array (1999 November-2005 November), our previous northern sky survey for TeV $\gamma-$ray point sources has now been updated by a factor of 2.8 improved statistics. From $0.0^{\circ}$ to $60.0^{\circ}$ in declination (Dec) range, no new TeV $\gamma-$ray point sources with sufficiently high significance were identified while the well-known Crab Nebula and Mrk421 remain to be the brightest TeV $\gamma-$ray sources within the field of view of the Tibet air shower array. Based on the currently available data and at the 90% confidence level (C.L.), the flux upper limits for different power law index assumption are re-derived, which are approximately improved by 1.7 times as compared with our previous reported limits.Comment: This paper has been accepted by hepn

VKORC1 Pharmacogenetics and Pharmacoproteomics in Patients on Warfarin Anticoagulant Therapy: Transthyretin Precursor as a Potential Biomarker

Recognizing specific protein changes in response to drug administration in humans has the potential for the development of personalized medicine. Such changes can be identified by pharmacoproteomics approach based on proteomic technologies. It can also be helpful in matching a particular target-based therapy to a particular marker in a subgroup of patients, in addition to the profile of genetic polymorphism. Warfarin is a commonly prescribed oral anticoagulant in patients with prosthetic valve disease, venous thromboembolism and stroke.We used a combined pharmacogenetics and iTRAQ-coupled LC-MS/MS pharmacoproteomics approach to analyze plasma protein profiles of 53 patients, and identified significantly upregulated level of transthyretin precursor in patients receiving low dose of warfarin but not in those on high dose of warfarin. In addition, real-time RT-PCR, western blotting, human IL-6 ELISA assay were done for the results validation.This combined pharmacogenomics and pharmacoproteomics approach may be applied for other target-based therapies, in matching a particular marker in a subgroup of patients, in addition to the profile of genetic polymorphism

Plasma metabolomics and clinical predictors of survival differences in COPD patients

Background: Plasma metabolomics profile (PMP) in COPD has been associated with clinical characteristics, but PMP’s relationship to survival has not been reported. We determined PMP differences between patients with COPD who died an average of 2 years after enrollment (Non-survivors, NS) compared to those who survived (S) and also with age matched controls (C). Methods: We studied prospectively 90 patients with severe COPD and 30 controls. NS were divided in discovery and validation cohorts (30 patients each) and the results compared to the PMP of 30 S and C. All participants completed lung function tests, dyspnea scores, quality of life, exercise capacity, BODE index, and plasma metabolomics by liquid and gas chromatography / mass spectometry (LC/MS, LC/MS2 , GC/MS). Statistically, we used Random Forest Analysis (RFA) and Support Vector Machine (SVM) to determine metabolites that differentiated the 3 groups and compared the ability of metabolites vs. clinical characteristics to classify patients into survivors and non-survivors. Results: There were 79 metabolites statistically different between S and NS [p < 0.05 and false discovery rate (q value) < 0.1]. RFA and SVM classification of COPD survivors and non-survivors had a predicted accuracy of 74 and 85% respectively. Elevation of tricyclic acid cycle intermediates branched amino acids depletion and increase in lactate, fructose and xylonate showed the most relevant differences between S vs. NS suggesting alteration in mitochondrial oxidative energy generation. PMP had similar predictive power for risk of death as information provided by clinical characteristics. Conclusions: A plasma metabolomic profile characterized by an oxidative energy production difference between survivors and non-survivors was observed in COPD patients 2 years before death

Mimotopes selected with neutralizing antibodies against multiple subtypes of influenza A

<p>Abstract</p> <p>Background</p> <p>The mimotopes of viruses are considered as the good targets for vaccine design. We prepared mimotopes against multiple subtypes of influenza A and evaluate their immune responses in flu virus challenged Balb/c mice.</p> <p>Methods</p> <p>The mimotopes of influenza A including pandemic H1N1, H3N2, H2N2 and H1N1 swine-origin influenza virus were screened by peptide phage display libraries, respectively. These mimotopes were engineered in one protein as multi- epitopes in Escherichia coli (E. coli) and purified. Balb/c mice were immunized using the multi-mimotopes protein and specific antibody responses were analyzed using hemagglutination inhibition (HI) assay and enzyme-linked immunosorbent assay (ELISA). The lung inflammation level was evaluated by hematoxylin and eosin (HE).</p> <p>Results</p> <p>Linear heptopeptide and dodecapeptide mimotopes were obtained for these influenza virus. The recombinant multi-mimotopes protein was a 73 kDa fusion protein. Comparing immunized infected groups with unimmunized infected subsets, significant differences were observed in the body weight loss and survival rate. The antiserum contained higher HI Ab titer against H1N1 virus and the lung inflammation level were significantly decreased in immunized infected groups.</p> <p>Conclusions</p> <p>Phage-displayed mimotopes against multiple subtypes of influenza A were accessible to the mouse immune system and triggered a humoral response to above virus.</p

Inferring MicroRNA Activities by Combining Gene Expression with MicroRNA Target Prediction

MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments