OBJECTIVE: To study the in vitro and in vivo antitumor effects of the polysaccharide of Alocasia cucullata (PAC) and the underlying mechanism. METHODS: B16F10 and 4T1 cells were cultured with PAC of 40 µg/mL, and PAC was withdrawn after 40 days of administration. The cell viability was detected by cell counting kit-8. The expression of Bcl-2 and Caspase-3 proteins were detected by Western blot and the expressions of ERK1/2 mRNA were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A mouse melanoma model was established to study the effect of PAC during long-time administration. Mice were divided into 3 treatment groups: control group treated with saline water, positive control group (LNT group) treated with lentinan at 100 mg/(kg·d), and PAC group treated with PAC at 120 mg/(kg·d). The pathological changes of tumor tissues were observed by hematoxylin-eosin staining. The apoptosis of tumor tissues was detected by TUNEL staining. Bcl-2 and Caspase-3 protein expressions were detected by immunohistochemistry, and the expressions of ERK1/2, JNK1 and p38 mRNA were detected by qRT-PCR. RESULTS: In vitro, no strong inhibitory effects of PAC were found in various tumor cells after 48 or 72 h of administration. Interestingly however, after 40 days of cultivation under PAC, an inhibitory effect on B16F10 cells was found. Correspondingly, the long-time administration of PAC led to downregulation of Bcl-2 protein (P<0.05), up-regulation of Caspase-3 protein (P<0.05) and ERK1 mRNA (P<0.05) in B16F10 cells. The above results were verified by in vivo experiments. In addition, viability of B16F10 cells under long-time administration culture in vitro decreased after drug withdrawal, and similar results were also observed in 4T1 cells. CONCLUSIONS Long-time administration of PAC can significantly inhibit viability and promote apoptosis of tumor cells, and had obvious antitumor effect in tumor-bearing mice.
Mice ; Animals ; Alocasia/metabolism* ; MAP Kinase Signaling System ; Caspase 3/metabolism* ; Apoptosis ; RNA, Messenger/metabolism*

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.

Objective To investigate the clinical effect of LUO's Nephropathy Recipe Ⅲ(composed of Sargassum,Astragali Radix,Salviae Miltiorrhizae Radix et Rhizoma,Rehmanniae Radix Praeparata,calcined Ostreae Concha,Houttuyniae Herba,Schizonepetae Spica,etc.)combined with conventional western medicine in treating stage 3-5 non-dialysis chronic kidney disease(CKD)of spleen-kidney deficiency with turbidity-toxin-stasis obstruction type.Methods A total of 180 patients with stage 3-5 non-dialysis CKD of spleen-kidney deficiency with turbidity-toxin-stasis obstruction type were randomly divided into observation group and control group,with 90 cases in each group.The control group was given conventional western medicine for symptomatic treatment,and the observation group was treated with LUO's Nephropathy RecipeⅢon the basis of treatment for the control group.The course of treatment for the two groups covered one month.Before and after treatment,the levels of serum inflammatory factors,renal function indicators and urine protein parameters in the two groups were observed.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)After one month of treatment,the total effective rate in the observation group was 95.56%(86/90)and that in the control group was 81.11%(73/90).The intergroup comparison(tested by chi-square test)showed that the efficacy of the observation group was significantly superior to that of the control group(P＜0.01).(2)After treatment,the serum levels of inflammatory factors of transforming growth factor β1(TGF-β1),monocyte chemotactic protein 1(MCP-1),and tumor necrosis factor α(TNF-α)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(3)After treatment,the levels of renal function indicators of blood urea nitrogen(BUN),serum creatinine(Scr),blood uric acid(UA),and cystatin C(Cys-C)in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the levels of 24-hour urine protein quantification and urine microalbumin in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the observation group was significantly superior to that in the control group(P＜0.01).(5)The incidence of adverse reactions in the observation group was 4.44%(4/90),which was significantly lower than that of 15.56%(14/90)in the control group,and the difference was statistically significant between the two groups(P＜0.05).Conclusion LUO's Nephropathy Recipe Ⅲ combined with conventional western medicine exerts satisfactory efficacy in treating stage 3-5 non-dialysis CKD patients with spleen-kidney deficiency with turbidity-toxin-stasis obstruction syndrome type,and the therapy can significantly alleviate the inflammatory response,improve the renal function,decrease the urinary protein excretion of the patients,with high safety profile.

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:To investigate the clinical characteristics and survival analysis of myelodysplastic syndromes(MDS)with RUNX1 gene mutation.Methods:Clinical data of 177 newly diagnosed MDS patients admitted to the Department of Hematology,the Second Affiliated Hospital of Air Force Military Medical University from October 1,2015 to October 31,2022 were retrospectively analyzed.Gene mutation detection was performed by second-generation sequencing technology,and clinical characteristics and prognosis of patients with RUNX1 gene mutation were analyzed.Results:A total of 30 cases(16.95％)of RUNX1 gene mutations were detected,including 15 missense mutations(50.0％),9 frameshift deletion mutations(30.0％),4 splice site mutations(13.3％),1 insertion mutation(3.3％),and 1 nonsense mutation(3.3％).Patients with RUNX1 mutations had a median age of 68.5 years at diagnosis(range:62.25-78.50 years old).There were no significantly differences between RUNX1 mutations and wild type patients in age distribution,gender,peripheral blood white blood cell count,hemoglobin level,bone marrow and peripheral blood blasts ratio,IPSS-R cytogenetics,IPSS-R stage,etc.(P＞0.05).However,there were statistically significant differences in platelet count and whether complicated karyotype.Compared with patients without RUNX1 gene mutation,patients with RUNX1 gene mutation had lower platelet count(P=0.018),and were less likely to have complicated karyotype at initial diagnosis(P=0.01).Cox proportional hazards model analysis showed that when other co variates remained unchanged,the higher the platelet count,the better the survival of patients(HR=0.995,95％CI:0.990-0.999,P=0.036);In the IPSS-M prognostic stratification,keeping other covariates unchanged,the risk of progression or death of myelodysplastic syndrome was significantly lower in the medium to high-risk and low-risk groups compared with the high-risk group(HR=0.149,95％CI:0.031-0.721,P=0.018;HR=0.026,95％CI:0.003-0.234,P=0.001).Survival analysis showed that MDS patients with RUNX1 gene mutation had worse overall survival time(P＜0.001).Patients with RUNX1 mutation had worse OS than non-mutation patients in the early WHO group.RUNX1 mutation and IPSS-M risk stratification mean OS and mean LFS were worse in low-risk patients than in non-mutated patients.Conclusion:RUNX1 gene mutation is an adverse prognostic factor in MDS patients,especially in the IPSS-M prognosis stratification group of low-risk,medium-low risk,medium-high risk and WHO classification of early 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.

Tubulin beta 4A class IVa (TUBB4A) spectrum disorders include autosomal dominant dystonia type 4 or hypomyelination with atrophy of the basal ganglia and cerebellum (H-ABC syndrome). However, in rare cases, only mild hypomyelination in the cortex with no basal ganglia atrophy may be observed. We report a case of a family with TUBB4A mutation and complicated hereditary spasticity paraplegia (HSP). We performed quadro whole-exome sequencing (WES) on the family to identify the causative gene of progressive spastic paraparesis with isolated hypomyelination leukodystrophy. We identified a novel TUBB4A p.F341L mutation, which was present in all three affected patients but absent in the unaffected father. The affected patients presented with adult-onset TUBB4A disorder, predominant spastic paraparesis with/without ataxia, and brain hypomyelination with no cognitive impairment or extrapyramidal symptoms. In the literature, HSP is considered a TUBB4A spectrum disorder.