In this study, we explored the pharmacological effects of Siwu Decoction in treating premature ovarian insufficiency(POI) and its molecular mechanism based on the mitophagy pathway modulated and mediated by estrogen receptor(ER) subtypes. Female Balb/c mice were divided into a control group, model group, as well as high-dose and low-dose groups of Siwu Decoction. The POI mice model was constructed by intraperitoneal injection of cisplatin. The high-dose and low-dose groups of Siwu Decoction were administered intragastrically with Siwu Decoction each day for 14 days. During this period, we monitored the estrous cycle and body weight of the mice and calculated the ovarian index. The morphology of the ovaries was detected by hematoxylin-eosin(HE) staining, and the number of primordial follicles was counted. The apoptosis of the ovarian tissue was detected by TUNEL staining. The expression levels of anti-Müllerian hormone(AMH), apoptosis-associated and mitophagy-associated proteins, ER subtypes, and the expression levels of key proteins of its mediated molecular pathways were detected by Western blot and immunohistochemistry. KGN cells were divided into a control group, model group, Siwu Decoction group, and gene silencing group. The apoptosis model was induced by H_2O_2, and PTEN-induced putative kinase 1(PINK1) gene silencing was induced by siRNA transfection. The Siwu Decoction group and gene silencing group were added to the medium containing Siwu Decoction. Cell viability was detected by CCK-8 assay. Cell senescence was detected by senescence-associated-β-galactosidase. The expression levels of apoptosis-associated and mitophagy-associated proteins were detected by Western blot. The results of in vivo experiments showed that compared with the model group, the mice in the high-dose and low-dose groups of Siwu Decoction significantly recovered the rhythm of the estrous cycle, and the levels of ovarian index, number of primordial follicles, and expression of AMH, representative indexes of ovarian function, were significantly higher, suggesting that the level of ovarian function was significantly improved. The expression levels of the apoptosis-related proteins, cytochrome C(Cyt C), cysteinyl aspartate specific proteinase 3(caspase 3), B-cell lymphoma-2(Bcl-2)-associated X(Bax), and mitophagy-associated indicator(Beclin 1) were significantly decreased, and the expression levels of Bcl-2 was significantly elevated. The positive area of TUNEL was significantly reduced, suggesting that the apoptosis level of the ovaries was significantly reduced. The expression levels of PINK1, Parkin, and sequestosome 1(p62) were significantly reduced, suggesting that the level of ovarian mitophagy was significantly down-regulated. The expression levels of ERα and ERβ were significantly elevated, and the ratio of ERα/ERβ was significantly reduced. The expression levels of key proteins in the pathway, phosphoinositide 3-kinase(PI3K) and protein kinase B(Akt), were significantly reduced, suggesting that the regulation of ER subtypes and the mediation of PI3K/Akt pathway were the key mechanisms. In vitro experiments showed that compared with the model group, the proportion of senescent cells in the Siwu Decoction group was significantly reduced. Cyt C, caspase 3, Beclin 1, Parkin, and p62 were significantly reduced, which was in line with in vivo experimental results. The proportion of senescent cells and the expression level of the above proteins were further significantly reduced after PINK1 silencing. It can be seen that Siwu Decoction can regulate the expression level and proportion of ER subtypes in KGN cells, then mediate the PI3K/Akt pathway to inhibit excessive mitophagy and apoptosis, and exert therapeutic effects of POI.
Animals ; Female ; Drugs, Chinese Herbal/administration & dosage* ; Mitophagy/drug effects* ; Primary Ovarian Insufficiency/physiopathology* ; Mice ; Mice, Inbred BALB C ; Humans ; Receptors, Estrogen/genetics* ; Apoptosis/drug effects* ; Ovary/metabolism* ; Signal Transduction/drug effects* ; Anti-Mullerian Hormone/genetics*

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.

Humans ; Multiple Myeloma/genetics* ; Neoplasm, Residual/diagnosis* ; Flow Cytometry ; High-Throughput Nucleotide Sequencing

Objectives: To investigated the safety and efficacy of treating patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) and elevated levels of N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP) with levosimendan within 24 hours of first medical contact (FMC). Methods: This multicenter, open-label, block-randomized controlled trial (NCT03189901) investigated the safety and efficacy of levosimendan as an early management strategy of acute heart failure (EMS-AHF) for patients with NSTEMI and high NT-proBNP levels. This study included 255 patients with NSTEMI and elevated NT-proBNP levels, including 142 males and 113 females with a median age of 65 (58-70) years, and were admitted in the emergency or outpatient departments at 14 medical centers in China between October 2017 and October 2021. The patients were randomly divided into a levosimendan group (n=129) and a control group (n=126). The primary outcome measure was NT-proBNP levels on day 3 of treatment and changes in the NT-proBNP levels from baseline on day 5 after randomization. The secondary outcome measures included the proportion of patients with more than 30% reduction in NT-proBNP levels from baseline, major adverse cardiovascular events (MACE) during hospitalization and at 6 months after hospitalization, safety during the treatment, and health economics indices. The measurement data parameters between groups were compared using the t-test or the non-parametric test. The count data parameters were compared between groups using the χ² test. Results: On day 3, the NT-proBNP levels in the levosimendan group were lower than the control group but were statistically insignificant [866 (455, 1 960) vs. 1 118 (459, 2 417) ng/L, Z=-1.25,P=0.21]. However, on day 5, changes in the NT-proBNP levels from baseline in the levosimendan group were significantly higher than the control group [67.6% (33.8%,82.5%)vs.54.8% (7.3%,77.9%), Z=-2.14, P=0.03]. There were no significant differences in the proportion of patients with more than 30% reduction in the NT-proBNP levels on day 5 between the levosimendan and the control groups [77.5% (100/129) vs. 69.0% (87/126), χ²=2.34, P=0.13]. Furthermore, incidences of MACE did not show any significant differences between the two groups during hospitalization [4.7% (6/129) vs. 7.1% (9/126), χ²=0.72, P=0.40] and at 6 months [14.7% (19/129) vs. 12.7% (16/126), χ²=0.22, P=0.64]. Four cardiac deaths were reported in the control group during hospitalization [0 (0/129) vs. 3.2% (4/126), P=0.06]. However, 6-month survival rates were comparable between the two groups (log-rank test, P=0.18). Moreover, adverse events or serious adverse events such as shock, ventricular fibrillation, and ventricular tachycardia were not reported in both the groups during levosimendan treatment (days 0-1). The total cost of hospitalization [34 591.00(15 527.46,59 324.80) vs. 37 144.65(16 066.90,63 919.00)yuan, Z=-0.26, P=0.80] and the total length of hospitalization [9 (8, 12) vs. 10 (7, 13) days, Z=0.72, P=0.72] were lower for patients in the levosimendan group compared to those in the control group, but did not show statistically significant differences. Conclusions: Early administration of levosimendan reduced NT-proBNP levels in NSTEMI patients with elevated NT-proBNP and did not increase the total cost and length of hospitalization, but did not significantly improve MACE during hospitalization or at 6 months.
Male ; Female ; Humans ; Aged ; Natriuretic Peptide, Brain ; Simendan/therapeutic use* ; Non-ST Elevated Myocardial Infarction ; Heart Failure/drug therapy* ; Peptide Fragments ; Arrhythmias, Cardiac ; Biomarkers ; Prognosis

OBJECTIVE: To compare the mid-term clinical effect of arthroscopic surgery versus conservative treatment on the middle aged early knee osteoarthritis (EKOA) patients, with the hope to provide clinical evidence for their individual therapy. METHODS: A total of 145 middle aged EKOA patients(182 knees) who received arthroscopic surgery or conservative treatment from January 2015 to December 2016 were retrospectively enrolled, including 35 males and 110 females, aged from 47 to 79 years old with an average of (57.6±6.9) years old, and the duration of disease ranged from 6 to 48 months with an average of(14.6±8.9) months. According to treatment method, patients were divided into arthroscopic surgery group (47 patients, 58 knees) and conservative treatment group(98 patients, 124 knees). Before treatment, patients presented with symptoms of knee joint, such as pain, swelling, locking, limited flexion and extension, and weakness, as well as abnormal findings in knee X-ray (without or suspicious joint space narrow, and a few of osteophyte formation) or in knee MRI (injury or degeneration of articular cartilage or meniscus, loose body in the joint cavity and synovial hyperemia edema, etc). Related data were collected, including duration of knee symptoms, presence of meniscus injury, loose body in the joint cavity or mechanical symptoms such as locking, and visual analogue scale (VAS) and Lysholm knee function score before treatment and at the latest follow-up. Statistical analysis was performed to compare the differences in VAS or Lyshilm score before or after treatment between the low groups and within each group. RESULTS: Patients in the two groups were followed up from 60 to 76 months. In the arthroscopic surgery group, the incision healing was good and no surgical complications occurred. There were no significant differences in age, gender, BMI and follow-up time between the two groups(P>0.05). Before treatment, compared with conservative group, duration of symptoms in the arthroscopic group was longer (P<0.001), comorbidity rates of meniscus injury (P<0.001), free body (P=0.001) and mechanical symptoms (P<0.001) were higher, VAS (P<0.001) and Lysholm score (P<0.001) were worse. At the final follow-up, VAS and Lysholm score in either the conservative group or the arthroscopic group were significantly better than before treatment (P<0.05), while no significant differences between the two groups were found. The VAS was (1.5±1.2) scores in the arthroscopic group and (1.6±1.0)scores in the conservative group(P=0.549), and the Lysholm score was (84.9±12.5) scores in the arthroscopic group and (84.2±9.9) scores in the conservative group (P=0.676). CONCLUSION Both arthroscopic surgery and conservative treatment have satisfactory intermediate clinical effect middle- aged patients with EKOA, without statistically differences. However, most of the patients before surgery in the arthroscopic treatment group had mechanical locking symptoms caused by meniscus injury or loose body. Therefore, for the middle-aged EKOA patients with mechanical locking symptoms or without obtaining satisfactory outcome after conservative treatment, arthroscopic surgery may be considered.
Male ; Middle Aged ; Female ; Humans ; Aged ; Osteoarthritis, Knee/surgery* ; Retrospective Studies ; Arthroscopy/methods* ; Treatment Outcome ; Knee Joint/surgery*

ObjectiveTo analyze the clinical features of patients with coexisting GFAP-IgG and AQP4-IgG in myelitis. MethodsWe performed a retrospective analysis of patients with myelitis and GFAP-IgG. ResultsTotally 55 cases of autoimmune GFAP astrocytopathy （GFAP-A） were collected. The clinical manifestations included headache， fever， myelitis， abnormal vision， abnormal behavior， ataxia， disturbance of consciousness， epilepsy， dyskinesia， cognitive dysfunction， and other manifestations. Thirty-one cases were accompanied by myelitis， and 8 cases were GFAP/AQP4 double positive with myelitis. The 8 double-positive cases all showed dysuria and sensory plane disturbance. The MRI of spinal cord showed lesions in eight patients， seven of which had spinal cord lesions more than three segments， and three of which had Gadolinium enhancement. CSF showed the increasing level of white blood cell count or protein in six patients and decreasing level of glucose signally in one patient. In this study， 8 cases of GFAP/AQP4 IgG double positive with myelitis， 16 cases of GFAP IgG single positive （except AQP4 IgG negative， neuron antibody， oligodendrocyte antibody and glial cell antibody are negative） with myelitis， and 47 cases of AQP4 IgG single positive （without other neural related antibodies） with myelitis NMOSD were selected. There was statistically difference between single positive group with GFAP-IgG （sGFAP-A） and double positive group with GFAP-IgG （dGFAP-A） and AQP4-IgG in fever and CSF protein level（P<0.05）. ConclusionsGFAP/AQP4-IgG double-positive myelitis is relatively rare， which is different from the AQP4 single-positive myelitis in clinical features. The clinical manifestations include urination and defecation difficulties， sensory dysfunction. Spinal cord MRI usually manifests as long lesions extending over three vertebral segments， and cerebrospinal fluid examinations often indicate increased levels of white blood cells or protein.