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:To explore the neurophysiological features of the prefrontal-limbic-striatal circuit in patients with early-stage bipolar disorder without manic or hypomanic episodes, and its role in identifying early-stage bipolar disorder.Methods:From 2009 to 2019, a total of 155 hospitalized patients with major depressive disorder (MDD) from Nanjing Brain Hospital were selected after at least 5 years of follow-up, 31 patients with depression transitioned to bipolar disorder(ctBD group) and 76 patients remained the diagnosis of MDD(MDD group) were recruited.Sixty-two healthy controls matched for age, gender, and education years were selected as control group(HC group). Resting-state magnetoencephalography (MEG) data in eyes-open state of all subjects were collected.Data were analyzed based on the fieldtrip toolkit on the MATLAB platform. The key brain area of the prefrontal-limbic-striatal circuit were selected. Inter-group statistical analysis were conducted on the spectral energy and power-correlated functional connectivity at the theta, alpha, beta, and gamma frequency bands in the brain area of interest. In addition, the prediction model was constructed to early recognize bipolar disorder.Results:(1)There were statistically significant differences in the spectral energy of theta and alpha frequency bands in the prefrontal-limbic-striatal circuit among the 3 groups (cluster- F=120.50, 112.39, both P<0.05). The spectral energy of theta and alpha frequency bands in interest brain regions of prefrontal-limbic-striatal circuit in MDD group was lower than that in HC group (cluster- t=89.52, P<0.05). The spectral energy of theta band in prefrontal-limbic-striatal circuit in ctBD group was lower than that in HC group(cluster- t=105.82, P<0.05), and the spectral energy of alpha band in inferior frontal gyrus, orbitofrontal gyrus and caudate nucleus was lower than that in HC group (cluster- t=75.78, P<0.05), while there was no significant difference between the MDD group and the ctBD group ( P>0.05).(2)After FDR correction, there were statistically significant differences in functional connectivity between the left orbitofrontal gyrus and the right ventral striatum among the three groups (0.26 (0.13, 0.34), 0.12 (0.09, 0.24), 0.27 (0.20, 0.37), H=13.51, P<0.05, FDR correction). The strength of functional connectivity between the left orbitofrontal gyrus and the right ventral striatum in the MDD group was weaker than that in the HC group and the ctBD group (all P<0.05).(3)Binary Logistic regression analysis showed that the functional connectivity of beta frequency band between the left orbitofrontal gyrus and the right ventral striatum ( B=1.50, OR=4.50, 95% CI=1.73-11.70), the functional connectivity between the right orbitofrontal gyrus and the right amygdala( B=0.98, OR=2.68, 95% CI=1.18-6.13), the total HAMD score ( B=0.80, OR=2.28, 95% CI=1.36-3.67), the body weight factor score ( B=-1.99, OR=0.14, 95% CI=0.04-0.45), the anxiety factor score ( B=-0.99, OR=0.37, 95% CI=0.19-0.71), and sleep factor score( B=-1.14, OR=0.32, 95% CI=0.16-0.65)were the influencing factors for depression transitioned to bipolar disorder. Conclusion:The decreased resting low-frequency energy in the prefrontal-limbic-striatal circuit may be the common neural basis for the onset of unipolar and bipolar depression, and enhanced functional connectivity may be a potential neural circuit mechanism for depression transitioned to bipolar disorder. Functional connectivity combined with clinical manifestations is helpful for early recognition of bipolar disorder.

Objective:To explore the neurophysiological features of the prefrontal-limbic-striatal circuit in patients with early-stage bipolar disorder without manic or hypomanic episodes, and its role in identifying early-stage bipolar disorder.Methods:From 2009 to 2019, a total of 155 hospitalized patients with major depressive disorder (MDD) from Nanjing Brain Hospital were selected after at least 5 years of follow-up, 31 patients with depression transitioned to bipolar disorder(ctBD group) and 76 patients remained the diagnosis of MDD(MDD group) were recruited.Sixty-two healthy controls matched for age, gender, and education years were selected as control group(HC group). Resting-state magnetoencephalography (MEG) data in eyes-open state of all subjects were collected.Data were analyzed based on the fieldtrip toolkit on the MATLAB platform. The key brain area of the prefrontal-limbic-striatal circuit were selected. Inter-group statistical analysis were conducted on the spectral energy and power-correlated functional connectivity at the theta, alpha, beta, and gamma frequency bands in the brain area of interest. In addition, the prediction model was constructed to early recognize bipolar disorder.Results:(1)There were statistically significant differences in the spectral energy of theta and alpha frequency bands in the prefrontal-limbic-striatal circuit among the 3 groups (cluster- F=120.50, 112.39, both P<0.05). The spectral energy of theta and alpha frequency bands in interest brain regions of prefrontal-limbic-striatal circuit in MDD group was lower than that in HC group (cluster- t=89.52, P<0.05). The spectral energy of theta band in prefrontal-limbic-striatal circuit in ctBD group was lower than that in HC group(cluster- t=105.82, P<0.05), and the spectral energy of alpha band in inferior frontal gyrus, orbitofrontal gyrus and caudate nucleus was lower than that in HC group (cluster- t=75.78, P<0.05), while there was no significant difference between the MDD group and the ctBD group ( P>0.05).(2)After FDR correction, there were statistically significant differences in functional connectivity between the left orbitofrontal gyrus and the right ventral striatum among the three groups (0.26 (0.13, 0.34), 0.12 (0.09, 0.24), 0.27 (0.20, 0.37), H=13.51, P<0.05, FDR correction). The strength of functional connectivity between the left orbitofrontal gyrus and the right ventral striatum in the MDD group was weaker than that in the HC group and the ctBD group (all P<0.05).(3)Binary Logistic regression analysis showed that the functional connectivity of beta frequency band between the left orbitofrontal gyrus and the right ventral striatum ( B=1.50, OR=4.50, 95% CI=1.73-11.70), the functional connectivity between the right orbitofrontal gyrus and the right amygdala( B=0.98, OR=2.68, 95% CI=1.18-6.13), the total HAMD score ( B=0.80, OR=2.28, 95% CI=1.36-3.67), the body weight factor score ( B=-1.99, OR=0.14, 95% CI=0.04-0.45), the anxiety factor score ( B=-0.99, OR=0.37, 95% CI=0.19-0.71), and sleep factor score( B=-1.14, OR=0.32, 95% CI=0.16-0.65)were the influencing factors for depression transitioned to bipolar disorder. Conclusion:The decreased resting low-frequency energy in the prefrontal-limbic-striatal circuit may be the common neural basis for the onset of unipolar and bipolar depression, and enhanced functional connectivity may be a potential neural circuit mechanism for depression transitioned to bipolar disorder. Functional connectivity combined with clinical manifestations is helpful for early recognition of bipolar disorder.

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.

Objective:To explore the efficacy and safety of combining minimally invasive hematoma puncture with urokinase irrigation and continuous drip drainage in patients with spontaneous intracerebral hemorrhage.Methods:A total of 22 patients with spontaneous intracerebral hemorrhage admitted to Department of Functional Neurosurgery, Zhujiang Hospital of Southern Medical University and Department of Neurosurgery of Yuebei People's Hospital from May 2023 to January 2024 were chosen. Using precise localization, a minimally invasive haematoma puncture was used to insert a balloon drainage catheter into the hematoma cord; urokinase irrigation was administered during the procedure, followed by continuous drip drainage postoperatively. A retrospective analysis was performed; their clinical data before and after the procedure were collected; and Glasgow Coma Scale (GCS) score, hematoma residual volume, hematoma evacuation rate and complications were analyzed.Results:In these 22 patients, the residual hematoma volume after intraoperative suction was (20.89±10.74) mL, with a residual rate of (51.86±14.88)%. After the 1 st urokinase injection for continuous drainage, the residual hematoma volume in the 22 patients was (12.72±7.92) mL, with a hematoma evacuation rate of (35.01±30.41)%. After the 2 nd injection, the residual hematoma volume in the 13 patients was (9.48±6.12) mL, with a hematoma evacuation rate of (42.03±20.89)%. After the 3 rd injection, the residual hematoma volume in the 7 patients was (5.84±2.84) mL, with a hematoma evacuation rate of (49.32±11.09)%. After the 4 th injection, the residual hematoma volume in the 3 patients was (3.67±3.79) mL, with a hematoma evacuation rate of (54.44±32.03)%. After the 5 th injection, the residual hematoma volume in the left 1 patient was 5 mL, with a hematoma evacuation rate of 37.50%. Before tube removal, the residual hematoma volume in these 22 patients was (6.73±5.01) mL, with a hematoma evacuation rate of (81.48±13.56)%. During hospitalization, 1 patient experienced postoperative hemorrhage and ultimately died of cardiac arrest; 1 patient developed pulmonary infection after surgery and cured with antibiotics. These patients had GCS scores of 12.23±3.16 at discharge, which was significantly increased than those at admission (9.45±3.19, P<0.05). Conclusion:Minimally invasive hematoma puncture combined with urokinase irrigation and continuous drip drainage is a safe and effective new treatment in spontaneous cerebral hemorrhage.

Objective:To explore the efficacy and safety of combining minimally invasive hematoma puncture with urokinase irrigation and continuous drip drainage in patients with spontaneous intracerebral hemorrhage.Methods:A total of 22 patients with spontaneous intracerebral hemorrhage admitted to Department of Functional Neurosurgery, Zhujiang Hospital of Southern Medical University and Department of Neurosurgery of Yuebei People's Hospital from May 2023 to January 2024 were chosen. Using precise localization, a minimally invasive haematoma puncture was used to insert a balloon drainage catheter into the hematoma cord; urokinase irrigation was administered during the procedure, followed by continuous drip drainage postoperatively. A retrospective analysis was performed; their clinical data before and after the procedure were collected; and Glasgow Coma Scale (GCS) score, hematoma residual volume, hematoma evacuation rate and complications were analyzed.Results:In these 22 patients, the residual hematoma volume after intraoperative suction was (20.89±10.74) mL, with a residual rate of (51.86±14.88)%. After the 1 st urokinase injection for continuous drainage, the residual hematoma volume in the 22 patients was (12.72±7.92) mL, with a hematoma evacuation rate of (35.01±30.41)%. After the 2 nd injection, the residual hematoma volume in the 13 patients was (9.48±6.12) mL, with a hematoma evacuation rate of (42.03±20.89)%. After the 3 rd injection, the residual hematoma volume in the 7 patients was (5.84±2.84) mL, with a hematoma evacuation rate of (49.32±11.09)%. After the 4 th injection, the residual hematoma volume in the 3 patients was (3.67±3.79) mL, with a hematoma evacuation rate of (54.44±32.03)%. After the 5 th injection, the residual hematoma volume in the left 1 patient was 5 mL, with a hematoma evacuation rate of 37.50%. Before tube removal, the residual hematoma volume in these 22 patients was (6.73±5.01) mL, with a hematoma evacuation rate of (81.48±13.56)%. During hospitalization, 1 patient experienced postoperative hemorrhage and ultimately died of cardiac arrest; 1 patient developed pulmonary infection after surgery and cured with antibiotics. These patients had GCS scores of 12.23±3.16 at discharge, which was significantly increased than those at admission (9.45±3.19, P<0.05). Conclusion:Minimally invasive hematoma puncture combined with urokinase irrigation and continuous drip drainage is a safe and effective new treatment in spontaneous cerebral hemorrhage.