Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning

IntroductionChronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time.MethodsIn this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately.ResultsAmong the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen.DiscussionThis pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients

Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity

One pot synthesis of Ag-SnO2 quantum dots for highly enhanced sunlight-driven photocatalytic activity

Herein, we reported a plasmonic photocatalyst, Ag-SnO2 quantum dots (QDs) for the abatement of water pollution by a simple and one pot synthesis in water. The Ag-SnO2 QDs were prepared with various concentrations of Ag loading. The as synthesized plasmonic photocatalysts were systematically investigated by X-ray powder diffraction (XRD), high-resolution transmission electron microscopy (HR-TEM), and nitrogen adsorption-desorption isotherm, diffuse reflectance spectroscopy (DRS), X-ray photoelectron spectroscopy (XPS), and photoluminescence spectroscopy (PL). The average crystallite size of pristine SnO2 QDs was achieved below 3 nm. The band gap of Ag-SnO2 QDs plasmonic photocatalyst was shifted from UV to visible region i.e. from 3.02 to 2.54 eV. The surface plasmon resonance (SPR) band of Ag-SnO2 QDs is blue shifted in the visible region and the PL intensity of Ag-SnO2 QDs composites decreases with the increase of Ag loading. The sunlight driven photocatalytic activity of Ag-SnO2 QDs composites was carried out by the degradation of Rhodamine B (RhB) solution and the optimized amount of Ag was significantly enhances the photocatalytic activity. The Ag-SnO2 QDs composite with optimized Ag amount showed the highest photocatalytic performance with 98% degradation of the dye under sunlight within 180 min. The improved photocatalytic activity under sunlight is attributed to the tuning of band gap to visible region, SPR of Ag and its synergetic effect of metal and semiconductor quantum dot. The plausible photocatalytic mechanism was suggested for the degradation of the pollutant under sunlight irradiation

Effects of cross-sectional change on the isotachphoresis process for protein-separation chip design

A 2D finite volume method (FVM)-based computer simulation model has been developed for isota-chophoresis (ITP) in three different 20 mm long micro-channels to assist the design of a protein separation chip. The model is based on three major equations, i.e. the mass conservation, charge conservation and electro-neutrality equations. In this study, the ITP system has four negatively-charged components, namely, hydrochloric acid, caproic acid, acetic acid, and benzoic acid, and one positively-charged component, namely, histidine, for use as a background electrolyte (BE). The calculations were performed under the action of a nominal electric field of -5,000 V/m. For the validation of our model, the results of our simulation in a straight channel are compared with the results of a 1D-based open program (SIMUL5), and all the physico-chemical properties are obtained from the SIMUL5. Unlike 1D ITP separation, spatially-changed micro-channel shapes provided different separation and moving times as well as a quasi steady state time compared to the 1D results obtained during the ITP process. Dispersion analysis is also conducted using a 2D moment analysis to investigate the effect of 2D geometries on ITP separation. © Springer-Verlag 2010.1

Energy transfer (In3+ -> Eu3+) based Polyvinyl Alcohol polymer composites for bright red luminescence

A prominent sensitization effect of In3+ ions is observed in In3++Eu3+: PVA polymer composites under UV excitation. Consequently, it enhances the red emission performance of Eu3+ ions in PVA system. We have successfully synthesized Eu3+: PVA, In3+: PVA and In3++Eu3+: PVA polymer films by traditional solution casting method. The structural and ion-polymer interaction studies have been analyzed from XRD and FTIR spectral profiles. Eu3+ doped PVA polymer composites are exhibited a red emission at 619 nm (D-5(0)-> F-7(2)) under 396 nm (F-7(0)-> L-5(6)) of excitation. Upon co-doping with In3+ ions in different concentrations to the Eu3+: PVA polymer film, it exhibits predominant red emission than singly doped Eu3+: PVA under 396 nm of excitation due to energy migration from In3+ to Eu3+. Successful emission photons of In3+ ions are collectively absorbed by the Eu3+ ions which lead the improvement of red emission. Optimized sensitization concentration of the In3+ ions has been found to be 0.01 wt%. Possible energy migration phenomenon is elucidated by several fluorescent dynamics. The energy transfer process is substantiated by lifetime decay analysis and overlapped spectral studies. The Commission International de I-Eclairage chromaticity coordinates were calculated. The quantum efficiencies of the Eu3+ ions and In3+ ions in singly doped and co-doped polymer systems have been evaluated. From these results, these co-doped In3++Eu3+: PVA composite polymer films might be proposed as encouraging candidates for bright red fluorescent materials for several photonic applications

Sunlight-driven photocatalytic activity of SnO2 QDs-g-C3N4 nanolayers

Herein, we reported a sunlight driven photocatalyst, SnO2 quantum dots (QDs)-g-C3N4 nanolayers (NLs) for the abatement of water pollution. SnO2 QDs were prepared in the presence of g-C3N4 NLs and the average crystallite size of SnO2 QDs was achieved below 3 nm. The interactions between both SnO2 QDs and g-C3N4 NLs are strong, as established by differences in binding energies. The sunlight driven photocatalytic activity of SnO2-g-C3N4 composite was carried out by the degradation of methyl orange (MO) and it shows 94 % degradation under sunlight within 180 min. The improved photocatalytic activity under sunlight is attributed to the synergetic effect of SnO2 QDs and layered g-C3N4