Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

The angiogenic response is essential for the repair of ischemic brain tissue. Integrin α6 (Itga6) expression has been shown to increase under hypoxic conditions and is expressed exclusively in vascular structures; however, its role in post-ischemic angiogenesis remains poorly understood. In this study, we demonstrate that mice with endothelial cell-specific knockout of Itga6 exhibit reduced neovascularization, reduced pericyte coverage on microvessels, and accelerated breakdown of microvascular integrity in the peri-infarct area. In vitro, endothelial cells with ITGA6 knockdown display reduced proliferation, migration, and tube-formation. Mechanistically, we demonstrated that ITGA6 regulates post-stroke angiogenesis through the PI3K/Akt-eNOS-VEGFA axis. Importantly, the specific overexpression of Itga6 in endothelial cells significantly enhanced neovascularization and enhanced the integrity of microvessels, leading to improved functional recovery. Our results suggest that endothelial cell Itga6 plays a crucial role in key steps of post-stroke angiogenesis, and may represent a promising therapeutic target for promoting recovery after stroke.
Animals ; Nitric Oxide Synthase Type III/metabolism* ; Mice ; Proto-Oncogene Proteins c-akt/metabolism* ; Integrin alpha6/genetics* ; Endothelial Cells/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Stroke/pathology* ; Vascular Remodeling/physiology* ; Vascular Endothelial Growth Factor A/metabolism* ; Mice, Knockout ; Signal Transduction/physiology* ; Mice, Inbred C57BL ; Male ; Neovascularization, Physiologic/physiology*

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective To investigate the clinical efficacy and safety of Zishen Jianpi Quyu Formula in treating patients with migraine without aura in the postmenstrual period of kidney deficiency and blood stasis type.Methods A total of 104 patients with migraine without aura of kidney deficiency and blood stasis type were randomly divided into the control group and the trial group,with 52 cases in each group.The control group was treated with Flunarizine Hydrochloride Capsules,and the trial group was treated with Zishen Jianpi Quyu Formula on the basis of treatment for the control group.One menstrual cycle constituted a course of treatment,and the treatment covered a total of two courses(eight weeks).The changes of traditional Chinese medicine(TCM)syndrome score,migraine attack frequency,duration of migraine headaches,visual analogue scale(VAS)score of migraine headache intensity,Headache Impact Test-6(HIT-6)score and hemorheology indexes in the two groups before and after treatment were observed.Moreover,the efficacy for TCM syndrome and clinical safety in the two groups were evaluated.Results(1)After two courses of treatment,the total effective rate of the trial group was 90.38%(47/52),and that of the control group was 69.23%(36/52),the intergroup comparison(by chi-square test)showed that the efficacy for TCM syndrome in the trial group was significantly superior to that of the control group(P＜0.05).(2)After treatment,the TCM syndrome scores of the patients in both groups were significantly lower than those before treatment(P＜0.05),and the reduction of TCM syndrome score in the trial group was significantly superior to that in the control group(P＜0.05).(3)After treatment,the migraine attack frequency,duration of migraine headaches,VAS scores of migraine headache intensity in the two groups of patients were significantly improved compared with those before treatment(P＜0.05),and the improvement of migraine headache parameters in the trial group was significantly superior to that in the control group(P＜0.05).(4)After treatment,the HIT-6 score in the two groups was decreased significantly compared with those before treatment(P＜0.05),and the decrease of HIT-6 score in the trial group was significantly superior to that in the control group(P＜0.05).(5)After treatment,hemorheology indexes(including plasma viscosity,whole blood high-shear viscosity,whole blood low-shear viscosity,fibrinogen,and hematocrit)in the two groups were improved compared with those before treatment(P＜0.05),and the improvement of each of hemorheology indexes in the trial group was significantly superior to that in the control group(P＜0.05).(6)During treatment,no serious adverse events occurred in the two groups,which was of high safety.Conclusion Zishen Jianpi Quyu Formula exerts remarkable clinical efficacy in treating patients of migraine without aura in the postmenstrual period of kidney deficiency and blood stasis type.The formula is effective on improving the TCM syndromes and migraine attacks of the patients,achieving the efficacy of milder headache,lower attack frequency and shorter duration,more stability hemorheology indexes and higher safety.

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.

Pulmonary fibrosis is a severe lung interstitial disease characterized by the destruction of lung tissue structure, excessive activation and proliferation of fibroblasts, secretion and accumulation of a large amount of extracellular matrix (ECM), and impaired lung function. Due to the complexity of the disease, a suitable animal model to mimic human pulmonary fibrosis has not yet been established. Precision-cut lung slice (PCLS) has been a widely used in vitro method to study lung physiology and pathogenesis in recent years. This method is an in vitro culture technology at the level between organs and cells, because it can preserve the lung tissue structure and various types of airway cells in the lung tissue, simulate the in vivo lung environment, and conduct the observation of various interactions between cells and ECM. Therefore, PCLS can compensate for the limitations of other models such as cell culture. In order to explore the role of discoidin domain receptor 2 (DDR2) in pulmonary fibrosis, Ddr2^flox/flox mice were successfully constructed. The Cre-LoxP system and PCLS technology were used to verify the deletion or knockdown of DDR2 in mouse PCLS. Transforming growth factor β1 (TGF-β1) can induce fibrosis of mouse PCLS in vitro, which can simulate the in vivo environment of pulmonary fibrosis. In the DDR2 knock down-PCLS in vitro model, the expression of various fibrosis-related factors induced by TGF-β1 was significantly reduced, suggesting that knocking down DDR2 can inhibit the formation of pulmonary fibrosis. The results provide a new perspective for the clinical study of DDR2 as a therapeutic target in pulmonary fibrosis.
Animals ; Humans ; Mice ; Discoidin Domain Receptor 2/metabolism* ; Fibroblasts/pathology* ; Fibrosis ; Lung/pathology* ; Pulmonary Fibrosis/metabolism* ; Transforming Growth Factor beta1/metabolism*