Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm.

Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

BACKGROUND: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS: We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS: Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS: Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes.

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

Quantitative morphometric changes in vascular mild cognitive impairment patients: early diagnosis of dementia.

Vascular mild cognitive impairment (VMCI) is an early and reversible stage of dementia. Volume differences in regional gray matter may reveal the development and prognosis of VMCI. This study selected 2 of the most common types of VMCI, namely, periventricular white matter hyperintensities (PWMH, n = 14) and strategic single infarctions (SSI, n = 10), and used the voxel-based morphometry method to quantify their morphological characteristics. Meanwhile, age- and sex-matched healthy volunteers were included (n = 16). All the participants were neuropsychologically tested to characterize their cognitive function and underwent whole-brain magnetic resonance imaging scanning. Our results showed that the volumes of the bilateral temporal lobes and bilateral frontal gray matter were obviously diminished in the PWMH group. The atrophy volume difference was 4,086 voxels in the left temporal lobe, 4,154 voxels in the right temporal lobe, 1,718 voxels in the left frontal lobe, and 1,141 voxels in the right frontal lobe (P ≤ 0.001). Moreover, the characteristics of the gray matter atrophy associated with the PWMH were more similar to those associated with Alzheimer's disease than SSI, which further revealed the susceptibility for escalation from PWMH to dementia. In conclusion, PWMH patients and SSI patients have different morphological characteristics, which explain the different prognoses of VMCI.

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment. To this end, we have constructed a sinus CT dataset, which comprises CT scan data and pathological biopsy results from 192 patients of chronic rhinosinusitis with nasal polyps (CRSwNP), treated at the Second Affiliated Hospital of Shantou University Medical College between 2020 and 2022. To differentiate CRSwNP endotype on preoperative CT and improve efficiency at the same time, we developed a multi-view fusion model that contains a mini-architecture with each network of 10 layers by modifying the deep residual neural network. The proposed model is trained on a training set and evaluated on a test set. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.991, accuracy of 0.965 and F1-Score of 0.970 in test set. We compared the performance of the mini-architecture with other lightweight networks on the same Sinus CT dataset. The experimental results demonstrate that the developed ResMini architecture contribute to competitive CRSwNP endotype identification modeling in terms of accuracy and parameter number.

Deep learning to reconstruct quasi-steady-state chemical exchange saturation transfer from a non-steady-state experiment.

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.

1 H NMR-based metabolomics of paired esophageal tumor tissues and serum samples identifies specific serum biomarkers for esophageal cancer.

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 1 H 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.

Amide signal intensities may be reduced in the motor cortex and the corticospinal tract of ALS patients.

OBJECTIVES: The aim of the study is to assess amide concentration changes in ALS patients compared with healthy controls by using quantitative amide proton transfer (APT) and multiparameter magnetic resonance imaging, and testing its correlation with clinical scores. METHODS: Sixteen ALS patients and sixteen healthy controls were recruited as part of the Canadian ALS Neuroimaging Consortium, and multimodal magnetic resonance imaging was performed at 3 T, including APT and diffusion imaging. Lorentz fitting was used to quantify the amide effect. Clinical disability was evaluated using the revised ALS functional rating scale (ALSFRS-R), and its correlation with image characteristics was assessed. The diagnostic performance of different imaging parameters was evaluated with receiver operating characteristic analysis. RESULTS: Our results showed that the amide peak was significantly different between the motor cortex and other gray matter territories within the brain of ALS patients (p < 0.001). Compared with controls, amide signal intensities in ALS were significantly reduced in the motor cortex (p < 0.001) and corticospinal tract (p = 0.046), while abnormalities were not detected using routine imaging methods. There was no significant correlation between amide and ALSFRS-R score. The diagnostic accuracy of the amide peak was superior to that of diffusion imaging. CONCLUSIONS: This study demonstrated changes of amide signal intensities in the motor cortex and corticospinal tract of ALS patients. KEY POINTS: • The neurodegenerative disease amyotrophic lateral sclerosis (ALS) has a lack of objective imaging indicators for diagnosis and assessment. • Analysis of amide proton transfer imaging revealed changes in the motor cortex and corticospinal tract of ALS patients that were not visible on standard magnetic resonance imaging. • The diagnostic accuracy of the amide peak was superior to that of diffusion imaging.

Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission.

BACKGROUND: To investigate the CT imaging and clinical features of three atypical presentations of coronavirus disease 2019 (COVID-19), namely (1) asymptomatic, (2) CT imaging-negative, and (3) re-detectable positive (RP), during all disease stages. METHODS: A consecutive cohort of 79 COVID-19 patients was retrospectively recruited from five independent institutions. For each presentation type, all patients were classified into atypical vs. typical groups (i.e., asymptomatic vs.symptomatic, CT imaging-negative vs. CT imaging-positive, and RP and non-RP,respectively). The chi-square test, Student's t test, and Kruskal-Wallis H test were performed to compare CT imaging and clinical features of atypical vs. typical patients for all three presentation categories. RESULTS: In our COVID-19 cohort, we found 12.7% asymptomatic patients, 13.9% CT imaging-negative patients, and 8.9% RP patients. The asymptomatic patients had fewer hospitalization days (P=0.043), lower total scores for bilateral lung involvement (P< 0.001), and fewer ground-glass opacities (GGOs) in the peripheral area (P< 0.001) than symptomatic patients. The CT imaging-negative patients were younger (P=0.002), had a higher lymphocyte count (P=0.038), had a higher lymphocyte rate (P=0.008), and had more asymptomatic infections (P=0.002) than the CT imaging-positive patients. The RP patients with moderate COVID-19 had lower total scores of for bilateral lung involvement (P=0.030) and a smaller portion of the left lung affected (P=0.024) than non-RP patients. Compared to their first hospitalization, RP patients had a shorter hospitalization period (P< 0.001) and fewer days from the onset of illness to last RNA negative conversion (P< 0.001) at readmission. CONCLUSIONS: Significant CT imaging and clinical feature differences were found between atypical and typical COVID-19 patients for all three atypical presentation categories investigated in this study, which may help provide complementary information for the effective management of COVID-19.

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study.

OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. METHODS: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. RESULTS: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. CONCLUSIONS: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. KEY POINTS: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

Comprehensive genomic variation profiling of cervical intraepithelial neoplasia and cervical cancer identifies potential targets for cervical cancer early warning.

BACKGROUND: To better understand the pathogenesis of cervical cancer (CC), we systematically analysed the genomic variation and human papillomavirus (HPV) integration profiles of cervical intraepithelial neoplasia (CIN) and CC. METHODS: We performed whole-genome sequencing or whole-exome sequencing of 102 tumour-normal pairs and human papillomavirus probe capture sequencing of 45 CCs, 44 CIN samples and 25 normal cervical samples, and constructed strict integrated workflow of genomic analysis. RESULTS: Mutational analysis identified eight significantly mutated genes in CC including four genes (FAT1, MLL3, MLL2 and FADD), which have not previously been reported in CC. Targetable alterations were identified in 55.9% of patients. In addition, HPV integration breakpoints occurred in 97.8% of the CC samples, 70.5% of the CIN samples and 42.8% of the normal cervical samples with HPV infection. Integrations of high-risk HPV strains in CCs, including HPV16, 18, 33 and 58, also occurred in the CIN samples. Moreover, gene mutations were detected in 52% of the CIN specimens, and 54.8% of these mutations occurred in genes that also mutated in CCs. CONCLUSION: Our results lay the foundation for a deep understanding of the molecular mechanisms and finding new diagnostic and therapeutic targets of CC.

1 H NMR-based metabolomics reveal overlapping discriminatory metabolites and metabolic pathway disturbances between colorectal tumor tissues and fecal samples.

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 1 H 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.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors.

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.

Novel nanomedicine with a chemical-exchange saturation transfer effect for breast cancer treatment in vivo.

BACKGROUND: Nanomedicine is a promising new approach to cancer treatment that avoids the disadvantages of traditional chemotherapy and improves therapeutic indices. However, the lack of a real-time visualization imaging technology to monitor drug distribution greatly limits its clinical application. Image-tracked drug delivery is of great clinical interest; it is useful for identifying those patients for whom the therapy is more likely to be beneficial. This paper discusses a novel nanomedicine that displays features of nanoparticles and facilitates functional magnetic resonance imaging but is challenging to prepare. RESULTS: To achieve this goal, we synthesized an acylamino-containing amphiphilic block copolymer (polyethylene glycol-polyacrylamide-polyacetonitrile, PEG-b-P(AM-co-AN)) by reversible addition-fragmentation chain transfer (RAFT) polymerization. The PEG-b-P(AM-co-AN) has chemical exchange saturation transfer (CEST) effects, which enable the use of CEST imaging for monitoring nanocarrier accumulation and providing molecular information of pathological tissues. Based on PEG-b-P(AM-co-AN), a new nanomedicine PEG-PAM-PAN@DOX was constructed by nano-precipitation. The self-assembling nature of PEG-PAM-PAN@DOX made the synthesis effective, straightforward, and biocompatible. In vitro studies demonstrate decreased cytotoxicity of PEG-PAM-PAN@DOX compared to free doxorubicin (half-maximal inhibitory concentration (IC50), mean ~ 0.62 µg/mL vs. ~ 5 µg/mL), and the nanomedicine more efficiently entered the cytoplasm and nucleus of cancer cells to kill them. Further, in vivo animal experiments showed that the nanomedicine developed was not only effective against breast cancer, but also displayed an excellent sensitive CEST effect for monitoring drug accumulation (at about 0.5 ppm) in tumor areas. The CEST signal of post-injection 2 h was significantly higher than that of pre-injection (2.17 ± 0.88% vs. 0. 09 ± 0.75%, p < 0.01). CONCLUSIONS: The nanomedicine with CEST imaging reflects the characterization of tumors and therapeutic functions has great potential medical applications.

Rapid and precise diagnosis of disseminated T.marneffei infection assisted by high-throughput sequencing of multifarious specimens in a HIV-negative patient: a case report.

BACKGROUND: Talaromyces marneffei, is an opportunistic pathogenic fungus that is most commonly reported in Southeast Asia and disseminated T.marneffei infection predominantly occurs in patients with immunodeficiency. With a potential to invade multiple organs, it can be fatal for patients if diagnosis and treatment are delayed. In current clinical practice, the diagnosis of T.marneffei infection relies heavily on tissue culture and histologic analysis, which may suffer from limited positive rate and is sometimes time consuming. The rapid and accurate diagnosis of disseminated T.marneffei infection remains challenging. CASE PRESENTATION: A 22-year-old man gradually developed fever, cough, lower extremities weakness, jaundice and rash, for which a 3-month extensive investigation failed to reach a diagnosis. After admitted into our hospital, laboratory and radiological tests revealed multiple lesions in the patient's brain, spinal cord, and lungs. We performed next generation sequencing on the patient's skin tissue, bone marrow, blood and cerebrospinal fluid, which all identified numerous Talaromyces marneffei nucleotide sequences and leaded to the rapid diagnosis and treatment of disseminated T.marneffei infection. CONCLUSIONS: This case underline the clinical significance of T.marneffei as a possible pathogen in immune-competent patients. This successful application of the next generation sequencing assisting the rapid diagnosis of disseminated T.marneffei infection provides a new perspective in the clinical approach to the systematic fungi infections and highlights the potential of this technique in rapid etiological diagnosis.

Imaging of nuclear Overhauser enhancement at 7 and 3 T.

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.

Cervical spinal functional magnetic resonance imaging of the spinal cord injured patient during electrical stimulation.

PURPOSE: To evaluate the spatial distribution and signal intensity changes following spinal cord activation in patients with spinal cord injury. METHODS: This study used spinal functional magnetic resonance imaging (fMRI) based on signal enhancement by extra-vascular water protons (SEEP) to assess elicited responses during subcutaneous electrical stimulation at the right elbow and right thumb in the cervical spinal cord. RESULTS: Seven healthy volunteers and seven patients with cervical spinal cord injury (SCI) were included in this study. Significant functional activation was observed mainly in the right side of the spinal cord at the level of the C5-C6 cervical vertebra in both the axial and sagittal planes. A higher percentage of signal changes (4.66 ± 2.08 % in injured subjects vs. 2.78 ± 1.66 % in normal) and more average activation voxels (4.69 ± 2.59 in injured subjects vs. 2.56 ± 1.13 in normal subject) in axial plane at the C5-C6 cervical vertebra with a statistically significant difference. The same trends were observed in the sagittal plane with higher percentage of signal changes and more average activation voxels, though no statistically significant difference compared with the control group. CONCLUSIONS: Spinal SEEP fMRI is a powerful noninvasive method for the study of local neuronal activation in the human spinal cord, which may be of clinical value for evaluating the effectiveness of interventions aimed at promoting recovery of function using electrical stimulation.

Differential neurometabolite alterations in brains of medication-free individuals with bipolar disorder and those with unipolar depression: a two-dimensional proton magnetic resonance spectroscopy study.

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.

Quantitative description of radiofrequency (RF) power-based ratiometric chemical exchange saturation transfer (CEST) pH imaging.

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.