Objective The volume and cortical thickness of gray matter in patients with multiple sclerosis (MS) and neuromyelitis optica (NMO) were compared and analyzed by voxel⁃based morphometry (VBM) and surface⁃based morphometry (SBM), and the differences in the structural changes of gray matter in the two diseases were discussed. Methods A total of 21 MS patients, 16 NMO patients and 19 healthy controls were scanned by routine MRI sequence. The data were processed and analyzed by VBM and SBM method based on the statistical parameter tool SPM12 of Matlab2014a platform and the small tool CAT12 under SPM12. Results Compared with the normal control group (NC), after Gaussian random field (GRF) correction, the gray matter volume in MS group was significantly reduced in left superior occipital, left cuneus, left calcarine, left precuneus, left postcentral, left central paracentral lobule, right cuneus, left middle frontal, left superior frontal and left superior medial frontal (P<0. 05). After family wise error (FWE) correction, the thickness of left paracentral, left superiorfrontal and left precuneus cortex in MS group was significantly reduced (P<0. 05). Compared with the NC group, after GRF correction, the gray matter volume in the left postcentral, left precentral, left inferior parietal, right precentral and right middle frontal in NMO group was significantly increased (P<0. 05). In NMO group, the volume of gray matter in left middle occipital, left superior occipital, left inferior temporal, right middle occipital, left superior frontal orbital, right middle cingulum, left anterior cingulum, right angular and left precuneus were significantly decreased (P<0. 05). Brain regions showed no significant differences in cortical thickness between NMO groups after FWE correction. Compared with the NMO group, after GRF correction, the gray matter volume in the right fusiform and right middle frontal in MS group was increased significantly(P<0. 05). In MS group, the gray matter volume of left thalamus, left pallidum, left precentral, left middle frontal, left middle temporal, right pallidum, left inferior parietal and right superior parietal were significantly decreased (P<0. 05). After FWE correction, the thickness of left inferiorparietal, left superiorparietal, left supramarginal, left paracentral, left superiorfrontal and left precuneus cortex in MS group decreased significantly (P<0. 05). Conclusion The atrophy of brain gray matter structure in MS patients mainly involves the left parietal region, while NMO patients are not sensitive to the change of brain gray matter structure. The significant difference in brain gray matter volume between MS patients and NMO patients is mainly located in the deep cerebral nucleus mass.

The column chromatography and semi-preparative liquid phase chromatography with several chromatographic packing materials, including macroporous adsorbent resin, silica gel, ODS, and Sephadex LH-20, were used for the separation and purification of n-butanol-soluble portion of ethanol extract of Leonurus japonicus Houtt. The structures of isolates were identified by HR-MS, IR, NMR, and acid hydrolysis reaction. Six phenylethanol glycosides were obtained from L. japonicus and identified as leonoside G (1), leonoside E (2), leonoside B (3), leonoside F (4), cistanoside G (5), and salidroside (6). Compound 1 is a new phenylethanol glycoside.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

This study aimed to evaluate the ability of rete testis thickness (RTT) and testicular shear wave elastography (SWE) to differentiate obstructive azoospermia (OA) from nonobstructive azoospermia (NOA). We assessed 290 testes of 145 infertile males with azoospermia and 94 testes of 47 healthy volunteers at Shanghai General Hospital (Shanghai, China) between August 2019 and October 2021. The testicular volume (TV), SWE, and RTT were compared among patients with OA and NOA and healthy controls. The diagnostic performances of the three variables were evaluated using the receiver operating characteristic curve. The TV, SWE, and RTT in OA differed significantly from those in NOA (all P ≤ 0.001) but were similar to those in healthy controls. Males with OA and NOA were similar at TVs of 9-11 cm 3 ( P = 0.838), with sensitivity, specificity, Youden index, and area under the curve of 50.0%, 84.2%, 0.34, and 0.662 (95% confidence interval [CI]: 0.502-0.799), respectively, for SWE cut-off of 3.1 kPa; and 94.1%, 79.2%, 0.74, and 0.904 (95% CI: 0.811-0.996), respectively, for RTT cut-off of 1.6 mm. The results showed that RTT performed significantly better than SWE in differentiating OA from NOA in the TV overlap range. In conclusion, ultrasonographic RTT evaluation proved a promising diagnostic approach to differentiate OA from NOA, particularly in the TV overlap range.
Male ; Humans ; Azoospermia ; Rete Testis ; China ; Testis

[Abstract] ObjectVisualizing the superficial cerebellar vein and its tributaries on suscepxibility weighted imaging (SWI), and to construct superficial cerebellar vein network. Methods According to the inclusion criteria, 80 healthy volunteers (40 males and 40 females) were selected for 3. 0 T MRI scans to obtain conventional sequence cross-section, sagittal tomographic images, and SWI image data. Post-processing was performed on the Extended MR workspace 2. 6. 3. 4 image workstation to reconstruct minimum intensity projection(mIP) images. SPSS 21. 0 statistical software was used to analyze and process each data, and the diameter measurement result were expressed as mean ± standard deviation. Results Both SWI and mIP could image the structures of the cerebellum and its veins. The cerebellar veins were divided into deep and superficial parts. The superficial cerebellar veins were divided into two groups: the vermis and the cerebellar hemispheres. The superficial vein of the cerebellar vermis consisted of superior vermis vein [diameter: (1. 21±0. 24)mm, occurrence rate: 92. 16%], summit vein [ diameter: (0. 66 ± 0. 05) mm, occurrence rate: 95%], mountain vein [diameter: (0. 76±0. 03)mm, occurrence rate: 100%], inferior vermis vein [diameter: (1. 40±0. 27)mm, occurrence rate: 99. 02%]. The superficial cerebellar hemisphere vein consists of anterior superior cerebellar vein [diameter: (1. 09± 0. 12)mm, occurrence rate: 100%], posterior superior cerebellar vein [diameter: (0. 88±0. 13) mm, occurrence rate: 70%], anterior inferior cerebellar vein [ diameter: (1. 34 ± 0. 15) mm, occurrence rate: 100%], posterior inferior cerebellar vein [ diameter: (1. 11 ± 0. 09) mm, occurrence rate: 92. 5%]. The deep veins were divided into cerebellomesencephalic fissure group, cerebellopontine fissure group, and cerebellomedullary fissure group. Conclusion SWI can display the microstructure and venules of the cerebellum, and can construct a network of superficial cerebellar veins.

Objective To investigate the spontaneous neural activity in the brain of patients with Alzheimer' s disease (AD) used 3 indicators of resting state-functional magnetic resonance (rs-fMRI) amplitude of low frequency fluctuation (ALFF), fractional amplitude of low frequency fluctuation (fALFF) and percentage amplitude fluctuation (PerAF). Methods Totally 36 clinically diagnosed AD patients and 40 healthy volunteers were scanned by fMRI in resting state respectively. ALFF, fALFF and PerAF were used to calculate and compare the changes of brain regions between the two groups. Results Compared with the normal control group, mALFF value in AD group increased significantly in bilateral caudate nucleus, medial frontal gyrus, superior frontal gyrus, gyrus rectus, anterior cingulate gyrus, olfactive cortex, left middle frontal gyrus and inferior frontal gyrus (P<0. 05). mALFF values decreased significantly in the right middle temporal gyrus, inferior temporal gyrus, inferior occipital gyrus, middle occipital gyrus, bilateral calcarine, cuneus, lingual gyrus, superior occipital gyrus, vermis, precuneus and other regions (P<0. 05). In AD group, mfALFF value of right inferior temporal gyrus, anterior cerebellar lobe, fusiform gyrus, left superior frontal gyrus, medial frontal gyrus, middle frontal gyrus, inferior frontal gyrus, gyrus rectus and anterior cingulate gyrus increased significantly (P<0. 05); mfALFF values decreased significantly in bilateral lingual gyrus, left calcarine, cuneus, superior occipital gyrus, middle occipital gyrus and vermis (P<0. 05). In AD group, mPerAF value increased significantly in bilateral gyrus rectus, anterior cingulate gyrus, medial frontal gyrus, left superior frontal gyrus, caudate nucleus, middle frontal gyrus, inferior frontal gyrus, olfactive cortex and insula (P<0. 05); mPerAF values decreased significantly in bilateral calcarine, cuneus, superior occipital gyrus, lingual gyrus, precuneus, left fusiform gyrus, inferior occipital gyrus, right superior parietal lobule, angular gyrus, middle temporal gyrus, inferior temporal gyrus and middle occipital gyrus (P < 0. 05). Conclusion The default mode network (DMN) and visual network of AD patients are characterized by abnormal brain activity, with the most significant neural activity in the prefrontal cortex and visual cortex.