Altered hippocampal function in major depression despite intact structure and resting perfusion

Background: Hippocampal volume reductions in major depression have been frequently reported. However, evidence for functional abnormalities in the same region in depression has been less clear. We investigated hippocampal function in depression using functional magnetic resonance imaging (fMRI) and neuropsychological tasks tapping spatial memory function, with complementing measures of hippocampal volume and resting blood flow to aid interpretation. Method: A total of 20 patients with major depressive disorder (MDD) and a matched group of 20 healthy individuals participated. Participants underwent multimodal magnetic resonance imaging (MRI): fMRI during a spatial memory task, and structural MRI and resting blood flow measurements of the hippocampal region using arterial spin labelling. An offline battery of neuropsychological tests, including several measures of spatial memory, was also completed. Results: The fMRI analysis showed significant group differences in bilateral anterior regions of the hippocampus. While control participants showed task-dependent differences in blood oxygen level-dependent (BOLD) signal, depressed patients did not. No group differences were detected with regard to hippocampal volume or resting blood flow. Patients showed reduced performance in several offline neuropsychological measures. All group differences were independent of differences in hippocampal volume and hippocampal blood flow. Conclusions: Functional abnormalities of the hippocampus can be observed in patients with MDD even when the volume and resting perfusion in the same region appear normal. This suggests that changes in hippocampal function can be observed independently of structural abnormalities of the hippocampus in depression

Fintech's Influence on Green Credit Provision: Empirical Evidence from China’s Listed Banking Sector

We explore the impact of financial technology (fintech) advancements on green credit provision, investigating publicly traded banks in China from 2007 to 2022. We particularly focus on credit modelling innovation, examining the non-linear dynamics between fintech evolution and green credit distribution. Results reveal a positive U-shaped correlation. Initial stages of fintech are associated with increased green credit risk, negatively affecting the volume of green credit. However, more established fintech infrastructures significantly enhance green credit volumes by improving resource allocation and credit risk assessment. Utilizing a multiple linear regression approach, we highlight the transformative nature of fintech in advancing sustainable banking practices, particularly through innovations in credit modeling that enhance green credit risk management and resource allocation efficiency

Photoelectrochemical properties of mesoporous NiOx deposited on technical FTO via nanopowder sintering in conventional and plasma atmospheres

Nanoporous nickel oxide (NiO x ) has been deposited with two different procedures of sintering (CS and RDS). Both samples display solid state oxidation at about 3.1 V vs Li+/Li. Upon sensitization of CS/RDS NiO x with erythrosine b (ERY), nickel oxide oxidation occurs at the same potential. Impedance spectroscopy revealed a higher charge transfer resistance for ERY-sensitized RDS NiO x with respect to sensitized CS NiO x . This was due to the chemisorption of a larger amount of ERY on RDS with respect to CS NiO x . Upon illumination the photoinduced charge transfer between ERY layer and NiO x could be observed only with oxidized CS. Photoelectrochemical effects of sensitized RDS NiO x were evidenced upon oxide reduction. With the addition of iodine RDS NiOx electrodes could give the reduction iodine → iodide in addition to the reduction of RDS NiO x . p-type dye sensitized solar cells were assembled with RDS NiO x photocathodes sensitized either by ERY or Fast Green. Resulting overall efficiencies ranged between 0.02 and 0.04 % upon irradiation with solar spectrum simulator (Iin : 0.1 W cm −2 )

Determining Crustal Structure beneath Seismic Stations Overlying a Low-Velocity Sedimentary Layer using Receiver Functions

The receiver function (RF) technique has been widely applied to investigate crustal and mantle layered structures using P-to-S converted (Ps) phases from velocity discontinuities. However, the presence of low-velocity (relative to that of the bedrock) sediments can give rise to strong reverberations in the resulting RFs, frequently masking the Ps phases from crustal and mantle boundaries. Such reverberations are caused by P-to-S conversions and their multiples associated with the strong impedance contrast across the bottom of the low-velocity sedimentary layer. Here we propose and test an approach to effectively remove the near-surface reverberations and decipher the Ps phases associated with the Moho discontinuity. Autocorrelation is first applied on the observed RFs to determine the strength and two-way traveltime of the reverberations, which are then used to construct a resonance removal filter in the frequency domain to remove or significantly reduce the reverberations. The filtered RFs are time corrected to eliminate the delay effects of the sedimentary layer and applied to estimate the subsediment crustal thickness and VP/VSusing a H-k stacking procedure. The resulting subsediment crustal parameters (thickness and VP/VS) are subsequently used to determine the thickness and VP/VS of the sedimentary layer, using a revised version of the H-k stacking procedure. Testing using both synthetic and real data suggests that this computationally inexpensive technique is efficient in resolving subsediment crustal properties beneath stations sitting on a low-velocity sedimentary layer and can also satisfactorily determine the thickness and VP/VS of the sedimentary layer

Investigation of attentional bias in obsessive compulsive disorder with and without depression in visual search

Copyright: © 2013 Morein-Zamir et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedWhether Obsessive Compulsive Disorder (OCD) is associated with an increased attentional bias to emotive stimuli remains controversial. Additionally, it is unclear whether comorbid depression modulates abnormal emotional processing in OCD. This study examined attentional bias to OC-relevant scenes using a visual search task. Controls, non-depressed and depressed OCD patients searched for their personally selected positive images amongst their negative distractors, and vice versa. Whilst the OCD groups were slower than healthy individuals in rating the images, there were no group differences in the magnitude of negative bias to concern-related scenes. A second experiment employing a common set of images replicated the results on an additional sample of OCD patients. Although there was a larger bias to negative OC-related images without pre-exposure overall, no group differences in attentional bias were observed. However, OCD patients subsequently rated the images more slowly and more negatively, again suggesting post-attentional processing abnormalities. The results argue against a robust attentional bias in OCD patients, regardless of their depression status and speak to generalized difficulties disengaging from negative valence stimuli. Rather, post-attentional processing abnormalities may account for differences in emotional processing in OCD.Peer reviewedFinal Published versio

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets