Innovative accumulative risk assessment of co-exposure to Cd, As, and Pb in contaminated rice based on their in vivo bioavailability and in vitro bioaccessibility.

The consumption of cadmium (Cd), arsenic (As), and lead (Pb) co-contaminated rice exposes humans to multiple heavy metals simultaneously, with relative bioavailability (RBA) and bioaccessibility (BAc) being important determinants of potential health risks. This study evaluated the relationship between in vivo RBA and in vitro BAc of Cd, As, and Pb in rice and their cumulative risk to humans. A total of 110 rice samples were collected in Zhejiang Province, China, and 10 subsamples with varying concentration gradients were randomly selected to measure RBA using a mouse model (liver, kidney, femur, blood, and urine as endpoints) and BAc using four in vitro assays (PBET, UBM, SBRC, and IVG). Our results indicated that Cd-RBA varied from 21.2 % to 67.5 %, As-RBA varied from 23.2 % to 69.3 %, and Pb-RBA varied from 22.2 % to 68.9 % based on mouse liver plus kidneys. The BAc values for Cd, As, and Pb in rice varied according to the assay. Compared to Cd and As, Pb exhibited a lower BAc in the gastric (GP) and intestinal (IP) phases. According to the relationship between the BAc and RBA values, IVG-GP (R2 = 0.92), SBRC-IP (R2 = 0.73), and UBM-GP (R2 = 0.80) could be used as predictors of Cd-, As-, and Pb-RBA in rice, respectively. The health risks associated with co-exposure to Cd, As, and Pb in contaminated rice for both adults and children exceeded the acceptable threshold, with Cd and As being the primary risk factors. The noncarcinogenic and carcinogenic risks were markedly reduced when the RBA and BAc values were incorporated into the risk assessment. Due to the risk overestimation inherent in estimating the risk level based on total metal concentration, our study provides a realistic assessment of the cumulative health risks associated with co-exposure to Cd, As, and Pb in contaminated rice using in vivo RBA and in vitro BAc bioassays.

Evaluation of the diagnostic utility on 1.5T and 3.0T 1H magnetic resonance spectroscopy for temporal lobe epilepsy.

BACKGROUND: 1H magnetic resonance spectroscopy (1H-MRS) can be used to study neurological disorders because it can be utilized to examine the concentrations of related metabolites. However, the diagnostic utility of different field strengths for temporal lobe epilepsy (TLE) remains unclear. The purpose of this study is to make quantitative comparisons of metabolites of TLE at 1.5T and 3.0T and evaluate their efficacy. METHODS: Our retrospective collections included the single-voxel 1H-MRS of 23 TLE patients and 17 healthy control volunteers (HCs) with a 1.5T scanner, as well as 29 TLE patients and 17 HCs with a 3.0T scanner. Particularly, HCs were involved both the scans with 1.5T and 3.0T scanners, respectively. The metabolites, including the N-acetylaspartate (NAA), creatine (Cr), and choline (Cho), were measured in the left or right temporal pole of brain. To analyze the ratio of brain metabolites, including NAA/Cr, NAA/Cho, NAA/(Cho + Cr) and Cho/Cr, four controlled experiments were designed to evaluate the diagnostic utility of TLE on 1.5T and 3.0T MRS, included: (1) 1.5T TLE group vs. 1.5T HCs by the Mann-Whitney U Test, (2) 3.0T TLE group vs. 3.0T HCs by the Mann-Whitney U Test, (3) the power analysis for the 1.5T and 3.0T scanner, and (4) 3.0T HCs vs. 1.5T HCs by Paired T-Test. RESULTS: Three metabolite ratios (NAA/Cr, NAA/Cho, and NAA/(Cho + Cr) showed the same statistical difference (p < 0.05) in distinguishing the TLE from HCs in the bilateral temporal poles when using 1.5T or 3.0T scanners. Similarly, the power analysis demonstrated that four metabolite ratios (NAA/Cr, NAA/Cho, NAA/(Cho + Cr), Cho/Cr) had similar distinction abilities between 1.5T and 3.0T scanner, denoting both 1.5T and 3.0T scanners were provided with similar sensitivities and reproducibilities for metabolites detection. Moreover, the metabolite ratios of the same healthy volunteers were not statistically different between 1.5T and 3.0T scanners, except for NAA/Cho (p < 0.05). CONCLUSIONS: 1.5T and 3.0T scanners may have comparable diagnostic potential when 1H-MRS was used to diagnose patients with TLE.

A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction.

Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart reconstruction algorithm is required to remove undersampling image artifacts. Traditional algorithms commonly explore the similarity of multi-contrast images through joint sparsity. However, these algorithms are time-consuming due to the iterative process and require adjusting hyperparameters manually. Recently, data-driven deep learning successfully overcome these limitations but the reconstruction error still needs to be further reduced. Here, we propose a Joint Group Sparsity-based Network (JGSN) for multi-contrast MRI reconstruction, which unrolls the iterative process of the joint sparsity algorithm. The designed network includes data consistency modules, learnable sparse transform modules, and joint group sparsity constraint modules. In particular, weights of different contrasts in the transform module are shared to reduce network parameters without sacrificing the quality of reconstruction. The experiments were performed on the retrospective undersampled brain and knee data. Experimental results on in vivo brain data and knee data show that our method consistently outperforms the state-of-the-art methods under different sampling patterns.

Brain metabolic differences between temporal lobe epileptic seizures and organic non-epileptic seizures in postictal phase: a retrospective study with magnetic resonance spectroscopy.

Magnetic resonance spectroscopy (MRS) is employed to investigate the brain metabolites differences between patients with temporal lobe epileptic seizures (TLES) and organic non-epileptic seizures (ONES) that appear to be epileptic seizures. Twenty-three patients with TLES and nine patients with ONES in postictal phase underwent MRS examinations on a clinical 1.5T system, with 15 healthy controls in comparison. Statistical analyses on the ratios of brain metabolites were performed using the Mann-Whitney U test with age as a covariate. The results showed that N-acetyl-aspartate/Creatine (NAA/Cr) ratio of patients with TLES was statistically different from that of patients with ONES in postictal phase, i.e., TLES 1.422±0.037, ONES 1.640±0.061, P=0.012 in left temporal pole, while TLES 1.470±0.052, ONES 1.687±0.084, P=0.023 in the right temporal pole. Besides, compared with healthy controls, patients with TLES in postictal phase present significant differences in ratios of NAA/Cr, N-acetyl-aspartate/Choline (NAA/Cho) and NAA/(Cho + Cr). Experimental results demonstrate that NAA/Cr can be used to discriminate TLES from ONES, which has not been found in the references to the best of our knowledge. Although a prospective controlled validation is needed in the future, this retrospective study reveals that MRS may provide useful metabolites information to facilitate the epilepsy diagnosis.

Preliminary application of 3.0 T magnetic resonance chemical exchange saturation transfer imaging in brain metastasis of lung cancer.

BACKGROUND: Lung cancer brain metastases are very common and one of the common causes of treatment failure. We aimed to examine the clinical use of chemical exchange saturation transfer (CEST) technology in the evaluation of brain metastases for lung cancer diagnosis and prognosis. METHODS: We included26 cases of lung cancer brain metastases, 15 cases of gliomas, and 20 cases with normal tests. The magnetization transfer ratio (MTR;3.5 ppm) image from the GRE-EPI-CEST sequence was analyzed using the ASSET technique and APT technology. The MTR values were measured in the lesion-parenchymal, edema, and non-focus regions, and the MTR image was compared with the conventional MRI. ANOVA and t-test were used for statistical analysis. RESULTS: The lesion-parenchymal, edema, and non-focus areas in the metastatic-tumor-group were red-yellow, yellow-green, and green-blue, and the MTR values were 3.29 ± 1.14%,1.28 ± 0.36%,and 1.26 ± 0.31%, respectively. However, in the glioma-group, the corresponding areas were red, red-yellow, and green-blue, and the MTR values were 6.29 ± 1.58%, 2.87 ± 0.65%, and 1.03 ± 0.30%, respectively. The MTR values of the corresponding areas in the normal-group were 1.07 ± 0.22%,1.04 ± 0.23%, and 1.06 ± 0.24%, respectively. Traditional MR images are in black-white contrast and no metabolic information is displayed. The MTRvalues of the three regions were significantly different among the three groups. The values were also significantly different between the parenchymal and edema areas in the metastatic-tumor-group. There were significant differences in the MTR values between the non-lesion and edema regions, but there was no significant difference between the edema and non-focus areas. In the glioma-group, there were significant differences in the MTR values between the parenchymal and edema areas, between the parenchymal and non-focus areas, and between the edema and non-focus areas. CONCLUSIONS: CEST reflects the protein metabolism; therefore, early diagnosis of brain metastases and assessment of the prognosis can be achieved using molecular imaging.

Synthesis, Fungicidal Activity and SAR of 2-Thiazolamide/Pyrazolamide-Cyclohexylsulfonamides against Botrytis cinerea.

In order to explore more efficient sulfonamides against Botrytis cinereal, 36 novel cyclohexylsulfonamides were synthesized by N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide (EDCI) and 1-hydroxybenzotriazole (HOBt) condensation reaction using chesulfamide as a lead compound, introducing thiazole and pyrazole active groups. Their structures were characterized by 1H-NMR, 13C-NMR, mass spectrum (MS), and elemental analysis. Compound III -31 was further confirmed by X-ray single crystal diffraction. The in vitro and in vivo fungicidal activities against B. cinerea were evaluated by three bioassay methods. The results of mycelial growth demonstrated that median effective concentration (EC50) values of nine compounds were close to boscalid (EC50 = 1.72 µg/mL) and procymidone (EC50 = 1.79 µg/mL) against B. cinerea (KZ-9). In the spore germination experiment, it was found that compounds III-19 and III-31 inhibited germination 93.89 and 98.00%, respectively; at 10 µg/mL, they approached boscalid (95.97%). In the tomato pot experiment, the control effects of two compounds (III-21 and III-27) were 89.80 and 87.90%, respectively, at 200 µg/mL which were significantly higher than boscalid (81.99%). The structure-activity relationship (SAR) was also discussed, which provided a valuable idea for developing new fungicides.

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network.

Single-shot gradient-echo echo-planar imaging (GE-EPI) plays a significant role in applications where high temporal resolution is necessary. However, GE-EPI is susceptible to inhomogeneous magnetic fields that will cause image distortion. Most existing methods either need additional acquisitions for field mapping or cannot correct the distortion at high field. Here, we propose a new algorithm based on a deep convolutional neural network (CNN) to solve this problem without additional acquisitions. The residual learning and the cascaded structure improved the performance of the CNN on distortion correction. A simulated dataset was used for training. The simulated and experimental results demonstrate that the proposed method can correct the image distortion caused by field inhomogeneity.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.

BACKGROUND: Multi-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT). METHODS: First, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method. RESULTS: Experimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images. CONCLUSIONS: The proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

[Effects of Eucalyptus grandis leaf litter at its early stage of decomposition on the growth and photosynthetic characteristics of Cichorium intybus].

From March to May, 2010, a pot experiment was conducted to investigate the effects of Eucalyptus grandis leaf litter at its early stage of decomposition on the growth and photosynthetic characteristics of Cichorium intybus. Four treatments with different application rate of the leaf litter, i.e., 0 g x pot(-1) (CK), 30 g x pot(-1) (A1), 60 g x pot(-1) (A2), and 90 g x pot(-1) (A3), were installed. Each pot contained 12 kg soil mixed with the leaf litter, and then, C. intybus was sown. The growth indicators of the C. intybus were measured at the 30, 45, 60, and 75 d after sowing, and the photosynthetic characteristics of the C. intybus in treatment A3 were studied after the seedlings third leaf fully expanded. At each measured time, the biomass accumulation and leaf area growth of C. intybus in treatments A1, A2, and A3 were inhibited significantly. At the early stage of the leaf litter decomposition, the synthesis of photosynthetic pigments of the C. intybus seedlings was inhibited significantly, and the inhibition effect was getting stronger with the increasing amount of the leaf litter addition. The diurnal change of the seedlings photosynthetic rate in all treatments showed a bimodal curve with midday depression, the stomatal conductance and water use efficiency had the same variation trend with the net photosynthetic rate, and the total diurnal photosynthesis decreased in the order of CK > A1 > A2 > A3. The GC-MS analysis showed there were 33 kinds of small molecule compounds released gradually with the decomposition of the leaf litter, among which, allelopathic substance terpenoid dominated.

Validation of hyperintense middle cerebral artery sign in acute ischemic stroke: Comparison between magnetic resonance imaging and angiography.

We performed a retrospective analysis of non-contrast computed tomography (CT) scans, immediately subsequent magnetic resonance imaging (MRI), and cerebral angiography data from 30 consecutive patients with acute ischemic stroke within 6 hours after symptom onset. Results showed that eleven patients developed subsequent hemorrhagic transformation at follow-up. A hyperintense middle cerebral artery sign on MRI was found in six hemorrhagic patients, all of who had acute thrombosis formation on magnetic resonance angiography and digital subtraction angiography. No patients in the non-hemorrhagic group had hyperintense middle cerebral artery sign on MRI. The sensitivity, specificity, and positive predictive values of the hyperintense middle cerebral artery sign on MRI T1-weighted image for subsequent hemorrhagic transformation were 54.5%, 100%, and 100% respectively. Hyperdense middle cerebral artery sign on non-contrast CT was observed in nine patients, five of who developed hemorrhagic transformation. These data suggest that hyperintense middle cerebral artery sign on MRI T1-weighted image is a highly specific and moderately sensitive indicator of subsequent hemorrhagic transformation in patients after acute ischemic stroke, and its specificity is superior to CT.