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Cytokine monitoring has attracted great attention due to its significance in the diagnosis and treatment of many diseases, such as tumors, microbial infections, and immunological diseases. Enzyme‐linked immunosorbent assay (ELISA) is one of the most popular methods in cytokine detection, ascribing to the lavish signal amplification methods in the ELISA platform. In addition to classical enzymes, other signal amplifiers such as fluorescent probes, artificial nano‐enzymes, and photothermal reagents have been applied to reduce the detection limit and produce more sensitive ELISA kits. Due to the accumulative effect of heat, photothermal reagents are promising materials in the signal amplification of ELISA. However, the lack of efficient photothermal generation material at an aggregate scale may delay the further development of this area. In this contribution, based on an efficient organic photothermal aggregate material, an enzyme‐free photothermally amplified fluorescent immunosorbent assay system consisting of an assay microfluidic chip and detecting platform was developed. The photothermal nanoparticles with highly efficient photothermal conversion by harvesting energy via excited‐state intramolecular motions and enlarging molar absorptivity were successfully prepared. The detection concentration at 50 pg/mL of interleukin‐2 was achieved, realizing a signal improvement of detection limits by 20‐fold compared to that of previously reported photothermal ELISA. The microscopic imaging integrated with plane sweeping technology provided high spatial resolution and precision, indicating the potential of achieving high throughput profiling at the microscale. Moreover, as an alternative excitation source, light‐emitting diode not only provided a more affordable and miniaturized detection system but also revealed the great feasibility of intramolecular motion‐induced photothermy nanoparticles for biological analyses. [ABSTRACT FROM AUTHOR]

The insufficiently long RF saturation duration and relaxation delay in chemical exchange saturation transfer (CEST)‐MRI experiments may result in underestimation of CEST measurements. To maintain the CEST effect without prolonging the saturation duration and reach quasi‐steady state (QUASS), a deep learning method was developed to reconstruct a QUASS CEST image pixel by pixel from non‐steady‐state CEST acquired in experiments. In this work, we established a tumor‐bearing rat model on a 7 T horizontal bore small‐animal MRI scanner, allowing ground‐truth generation, after which a bidirectional long short‐term memory network was formulated and trained on simulated CEST Z‐spectra to reconstruct the QUASS CEST; finally, the ground truth yielded by experiments was used to evaluate the performance of the reconstruction model by comparing the estimates with the ground truth. For quantitation evaluation, linear regression analysis, structural similarity index (SSIM) and peak signal‐to‐noise ratio (peak SNR) were used to assess the proposed model in the QUASS CEST reconstruction. In the linear regression analysis of in vivo data, the coefficient of determination for six different representative frequency offsets was at least R2 = 0.9521. Using the SSIM and peak SNR as evaluation metrics, the reconstruction accuracies of in vivo QUASS CEST were found to be 0.9991 and 46.7076, respectively. Experimental results demonstrate that the proposed model provides a robust and accurate solution for QUASS CEST reconstruction using a deep learning mechanism. [ABSTRACT FROM AUTHOR]

Serum metabolites of healthy controls and esophageal cancer (EC) patients have previously been compared to predict cancer‐specific profiles. However, the association between metabolic alterations in serum samples and esophageal tissues in EC patients remains unclear. Here, we analyzed 50 pairs of EC tissues and distant noncancerous tissues, together with patient‐matched serum samples, using 1H NMR spectroscopy and pattern recognition algorithms. EC patients could be differentiated from the controls based on the metabolic profiles at tissue and serum levels. Some overlapping discriminatory metabolites, including valine, alanine, glucose, acetate, citrate, succinate and glutamate, were identified in both matrices. These results suggested deregulation of metabolic pathways, and potentially revealed the links between EC and several metabolic pathways, such as the tricarboxylic acid cycle, glutaminolysis, short‐chain fatty acid metabolism, lipometabolism and pyruvate metabolism. Perturbation of the pyruvate metabolism was most strongly associated with EC progression. Consequently, an optimal serum metabolite biomarker panel comprising acetate and pyruvate was developed, as these two metabolites are involved in pyruvate metabolism, and changes in their serum levels were significantly correlated with alterations in the levels of some other esophageal tissue metabolites. In comparison with individual biomarkers, this panel exhibited better diagnostic efficiency for EC, with an AUC of 0.948 in the test set, and a good predictive ability of 82.5% in the validation set. Analysis of key genes related to pyruvate metabolism in EC patients revealed patterns corresponding to the changes in serum pyruvate and acetate levels. These correlation analyses demonstrate that there were distinct metabolic characteristics and pathway aberrations in the esophageal tumor tissue and in the serum. Changes in the serum metabolic signatures could reflect the alterations in the esophageal tumor profile, thereby emphasizing the importance of distinct serum metabolic profiles as potential noninvasive biomarkers for EC. [ABSTRACT FROM AUTHOR]

Previous studies have compared fecal metabolites from healthy and colorectal cancer (CRC) patients to predict the pro‐CRC signatures. However, the systemic mechanistic link between feces and colonic tissues of CRC patients is still limited. The current study was a paralleled investigation of colonic tumor tissues and their normal adjacent tissues alongside patient‐matched feces by using 1H nuclear magnetic resonance spectroscopy combined with pattern recognition to investigate how fecal metabolic phenotypes are linked to the changes in colorectal tumor profiles. A set of overlapping discriminatory metabolites across feces and tumor tissues of CRC were identified, including elevated levels of lactate, glutamate, alanine, succinate and reduced amounts of butyrate. These changes could indicate the networks for metabolic pathway perturbations in CRC potentially involved in the disruptions of glucose and glycolytic metabolism, TCA cycle, glutaminolysis, and short chain fatty acids metabolism. Furthermore, changes in fecal acetate were positively correlated with alterations of glucose and myo‐inositol in colorectal tumor tissues, implying enhanced energy production for rapid cell proliferation. Compared to other fecal metabolites, acetate demonstrated the highest diagnostic performance for diagnosing CRC, with an AUC of 0.843 in the training set, and a good predictive ability in the validation set. Overall, these associations provide evidence of distinct metabolic signatures and metabolic pathway disturbances between the colonic tissues and feces within the same individual, and changes of fecal metabolic signature could reflect the CRC tissue microenvironment, highlighting the significance of the distinct fecal metabolic profiles as potential novel and noninvasive relevant indicators for CRC detection. What's new? While fecal testing for altered metabolite levels is a promising tool for colorectal cancer (CRC) screening, associations between specific metabolic disturbances and CRC remain unknown. Here, parallel analysis of CRC tissues and adjacent normal tissues and fecal samples obtained pre‐ and post‐operatively from the same patients reveals a link between fecal metabolic phenotype and metabolite changes in CRC. Discriminatory metabolites were detected across feces and CRC tissues. Among these metabolites was fecal acetate, which showed high diagnostic performance for CRC. The findings warrant further investigation of fecal metabolic signature as a non‐invasive means of early CRC detection. [ABSTRACT FROM AUTHOR]

Background: Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.Purpose: To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (1 H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.Study Type: Prospective.Subjects: Seventy-one patients with breast lesions (50 malignant, 26 benign).Sequence: DKI, DWI, DCE-MRI, and 1 H-MRS were performed at 3.0T.Assessment: Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.Statistical Tests: Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.Results: MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.Data Conclusion: MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856. [ABSTRACT FROM AUTHOR]

Purpose The objective of this study was to compare the clarity of magnetic resonance lymphography ( MRL) images of the thoracic duct ( TD) obtained from subjects following an overnight fast with those obtained from the same subjects after ingestion of a high fat meal. Methods Nineteen healthy volunteers were included in this study. TD images were acquired on a 3·0T MRI system with the imaging sequence of magnetic resonance cholangiopancreatography. TD MRL images were obtained from subjects following an overnight fast and from the same subjects 3-4 h after ingestion of a high fat meal. Images were displayed in maximum intensity projection format and degree of visualization was evaluated using a scoring system. Results The mean TD score obtained following an overnight fast was significantly lower than the mean TD score obtained 3-4 h after ingestion of a high fat meal ( P<0·05). Conclusion The clarity of TD MRL images is improved if subjects have ingested a high fat meal 3-4 h prior to examination. [ABSTRACT FROM AUTHOR]

Nuclear Overhauser enhancement (NOE) is a type of magnetization transfer using cross-relaxation. It originates from mobile macromolecules, which may have relevance to the evaluation of tumor features. We studied the value of NOE imaging at 7 and 3 T and suggest a utility for diagnosing human brain tumors. Two types of protein solution at different concentrations and pH values, and six normal Sprague Dawley (SD) rats, were used to detect NOE signal with a 7 T scanner. Then, six healthy volunteers and 11 patients with brain tumors (six gliomas and five meningiomas) were included at 3 T. Z-spectra were measured and NOE weighted (NOE*) images were acquired with a three-offset measurement. Wide spectral separation was shown at both 7 T and 3 T delineating the NOE peak in the Z-spectrum. The concentration dependence and pH independence of NOE were confirmed in phantom experiments, and NOE values were greater in white matter than in gray matter in vivo. At 3 T, data indicated that NOE* maps were slightly hypointense in gliomas and were not obviously different from meningiomas. Thus, NOE imaging may help distinguish benign from malignant tumors, and as such may contribute to diagnosing brain tumors. [ABSTRACT FROM AUTHOR]

Objectives Bipolar disorder ( BD) is a mental disorder characterized by periods of elevated mood and depression. Many individuals with BD are initially misdiagnosed and treated for unipolar depression ( UD). In this study, we report direct comparisons between medication-free individuals with BD and those with UD in terms of the neurometabolites in the anterior cingulate cortex ( ACC), medial prefrontal cortex ( mPFC), parietal cortex ( PC), and posterior cingulate cortex ( PCC) of the brain. Methods Participants included medication-free patients with BD or UD, and matched healthy controls. All patients were in the depressive state and had similar symptoms. All subjects were subjected to a multi-voxel proton magnetic resonance spectroscopy procedure with a 3.0 T GE Signa MR scanner. After post-processing, the absolute concentrations of glycerophosphocholine + phosphocholine ( GPC + PC), phosphocreatine + creatine ( PCr + Cr), Glx (glutamate + glutamine), myo-inositol ( MI), and N-acetyl aspartate ( NAA) from the above brain regions were compared across the three groups. Results Patients with BD showed significantly higher levels of Glx in their ACC, lower GPC + PC, PCr + Cr, MI, and NAA in their PC, and lower NAA in their mPFC, compared to healthy controls; patients with UD presented significantly lower levels of GPC + PC, PCr + Cr, and NAA in their PCC, and lower Glx in their mPFC. All analyzed brain metabolites, except Glx, were significantly lower in the PC of patients with BD, whereas levels of GPC + PC, PCr + Cr, and NAA were significantly reduced in the PCC of patients with UD. Conclusions These results add to the evidence of brain metabolite differences in brains of patients with UD and BD which may be of help in differentiating these two mood disorders. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) MRI holds great promise for the imaging of pH. However, routine CEST measurement varies not only with the pH-dependent chemical exchange rate, but also with CEST agent concentration, providing pH-weighted information. Conventional ratiometric CEST imaging normalizes the confounding concentration factor by analyzing the relative CEST effect from different exchangeable groups, requiring CEST agents with multiple chemically distinguishable labile proton sites. Recently, a radiofrequency (RF) power-based ratiometric CEST MRI approach has been developed for concentration-independent pH MRI using CEST agents with a single exchangeable group. To facilitate quantification and optimization of the new ratiometric analysis, we quantified the RF power-based ratiometric CEST ratio (rCESTR) and derived its signal-to-noise and contrast-to-noise ratios. Using creatine as a representative CEST agent containing a single exchangeable site, our study demonstrated that optimized RF power-based ratiometric analysis provides good pH sensitivity. We showed that rCESTR follows a base-catalyzed exchange relationship with pH independent of creatine concentration. The pH accuracy of RF power-based ratiometric MRI was within 0.15-0.20 pH units. Furthermore, the absolute exchange rate can be obtained from the proposed ratiometric analysis. To summarize, RF power-based ratiometric CEST analysis provides concentration-independent pH-sensitive imaging and complements conventional multiple labile proton group-based ratiometric CEST analysis. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) MRI is sensitive to labile proton concentration and exchange rate, thus allowing measurement of dilute CEST agent and microenvironmental properties. However, CEST measurement depends not only on the CEST agent properties but also on the experimental conditions. Quantitative CEST (qCEST) analysis has been proposed to address the limitation of the commonly used simplistic CEST-weighted calculation. Recent research has shown that the concomitant direct RF saturation (spillover) effect can be corrected using an inverse CEST ratio calculation. We postulated that a simplified qCEST analysis is feasible with omega plot analysis of the inverse CEST asymmetry calculation. Specifically, simulations showed that the numerically derived labile proton ratio and exchange rate were in good agreement with input values. In addition, the qCEST analysis was confirmed experimentally in a phantom with concurrent variation in CEST agent concentration and pH. Also, we demonstrated that the derived labile proton ratio increased linearly with creatine concentration ( P < 0.01) while the pH-dependent exchange rate followed a dominantly base-catalyzed exchange relationship ( P < 0.01). In summary, our study verified that a simplified qCEST analysis can simultaneously determine labile proton ratio and exchange rate in a relatively complex in vitro CEST system. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

After Newman and Twieg ([]: Hum Brain Mapp 14:39-47) and others, we used a fast event-related functional magnetic resonance imaging (fMRI) design and contrasted the lexical processing of pseudowords and real words. Participants carried out an auditory lexical decision task on a list of randomly intermixed real and pseudo Chinese two-character (or two-syllable) words. The pseudowords were constructed by recombining constituent characters of the real words to control for sublexical code properties. Processing of pseudowords and real words activated a highly comparable network of brain regions, including bilateral inferior frontal gyrus, superior, middle temporal gyrus, calcarine and lingual gyrus, and left supramarginal gyrus. Mirroring a behavioral lexical effect, left inferior frontal gyrus (IFG) was significantly more activated for pseudowords than for real words. This result disconfirms a popular view that this area plays a role in grapheme-to-phoneme conversion, as such a conversion process was unnecessary in our task with auditory stimulus presentation. An alternative view was supported that attributes increased activity in left IFG for pseudowords to general processes in decision making, specifically in making positive versus negative responses. Activation in left supramarginal gyrus was of a much larger volume for real words than for pseudowords, suggesting a role of this region in the representation of phonological or semantic information for two-character Chinese words at the lexical level. Hum Brain Mapp 25:212-221, 2005. © 2005 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (1H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel 1H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel 1H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference (p < 0.05). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from 1H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE. [ABSTRACT FROM AUTHOR]