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 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: 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*

Patients with congenital unilateral absence of the vas deferens (CUAVD) manifest diverse symptoms from normospermia to azoospermia. Treatment for CUAVD patients with obstructive azoospermia (OA) is complicated, and there is a lack of relevant reports. In this study, we describe the clinical features and evaluate the treatments and outcomes of CUAVD patients with OA. From December 2015 to December 2020, 33 patients were diagnosed as CUAVD with OA in Shanghai General Hospital (Shanghai, China). Patient information, ultrasound findings, semen analysis, hormone profiles, and treatment information were collected, and the clinical outcomes were evaluated. Of 33 patients, 29 patients were retrospectively analyzed. Vasoepididymostomy (VE) or cross VE was performed in 12 patients, the patency rate was 41.7% (5/12), and natural pregnancy was achieved in one of the patients. The other 17 patients underwent testicular sperm extraction as the distal vas deferens (contralateral side) was obstructed. These findings showed that VE or cross VE remains an alternative treatment for CUAVD patients with OA, even with a relatively low rate of patency and natural pregnancy.
Pregnancy ; Female ; Humans ; Male ; Vas Deferens/abnormalities* ; Azoospermia/surgery* ; Epididymis/surgery* ; Retrospective Studies ; Tertiary Care Centers ; China ; Semen

Objective: To understand the current status of diagnosis and treatment of chronic lymphocytic leukemia (CLL) /small lymphocytic lymphoma (SLL) among hematologists, oncologists, and lymphoma physicians from hospitals of different levels in China. Methods: This multicenter questionnaire survey was conducted from March 2021 to July 2021 and included 1,000 eligible physicians. A combination of face-to-face interviews and online questionnaire surveys was used. A standardized questionnaire regarding the composition of patients treated for CLL/SLL, disease diagnosis and prognosis evaluation, concomitant diseases, organ function evaluation, treatment selection, and Bruton tyrosine kinase (BTK) inhibitor was used. Results: ①The interviewed physicians stated that the proportion of male patients treated for CLL/SLL is higher than that of females, and the age is mainly concentrated in 61-70 years old. ②Most of the interviewed physicians conducted tests, such as bone marrow biopsies and immunohistochemistry, for patient diagnosis, in addition to the blood test. ③Only 13.7% of the interviewed physicians fully grasped the initial treatment indications recommended by the existing guidelines. ④In terms of cognition of high-risk prognostic factors, physicians' knowledge of unmutated immunoglobulin heavy-chain variable and 11q- is far inferior to that of TP53 mutation and complex karyotype, which are two high-risk prognostic factors, and only 17.1% of the interviewed physicians fully mastered CLL International Prognostic Index scoring system. ⑤Among the first-line treatment strategy, BTK inhibitors are used for different types of patients, and physicians have formed a certain understanding that BTK inhibitors should be preferentially used in patients with high-risk factors and elderly patients, but the actual use of BTK inhibitors in different types of patients is not high (31.6%-46.0%). ⑥BTK inhibitors at a reduced dose in actual clinical treatment were used by 69.0% of the physicians, and 66.8% of the physicians had interrupted the BTK inhibitor for >12 days in actual clinical treatment. The use of BTK inhibitors is reduced or interrupted mainly because of adverse reactions, such as atrial fibrillation, severe bone marrow suppression, hemorrhage, and pulmonary infection, as well as patients' payment capacity and effective disease progression control. ⑦Some differences were found in the perceptions and behaviors of hematologists and oncologists regarding the prognostic assessment of CLL/SLL, the choice of treatment options, the clinical use of BTK inhibitors, etc. Conclusion: At present, a gap remains between the diagnosis and treatment of CLL/SLL among Chinese physicians compared with the recommendations in the guidelines regarding the diagnostic criteria, treatment indications, prognosis assessment, accompanying disease assessment, treatment strategy selection, and rational BTK inhibitor use, especially the proportion of dose reduction or BTK inhibitor discontinuation due to high adverse events.
Female ; Humans ; Male ; Aged ; Middle Aged ; Leukemia, Lymphocytic, Chronic, B-Cell/drug therapy* ; Prognosis ; Lymphoma, B-Cell ; Immunohistochemistry ; Immunoglobulin Heavy Chains/therapeutic use*