This study utilized a chiral liquid chromatography-mass spectrometry (LC-MS)-guided isolation strategy to accurately capture angular-type pyranocoumarins (APs) in Peucedani Radix (Chinese name: Qianhu). Sixteen APs were successfully purified from the ethyl acetate extract of Peucedani Radix through deploying various techniques such as silica gel, ODS, Sephadex LH-20, and achiral (chiral) semi-preparative liquid chromatography. After extensive structural measurements, such as ¹H and ¹³C NMR spectroscopy, their structures were identified as (3′S)-3′-(2-methyl-butyroyl)-4′-oxo-3′,4′-dihydroseselin (1A), (3′R)-3′-(2-methyl-butyroyl)-4′-oxo-3′,4′-dihydroseselin (1B), (3′S)-3′-isovaleryl-4′-oxo-lomatin (2A), (3′R)-3′-isovaleryl-4′-oxo-lomatin (2B), (3′S)-3′-angeloyloxy-4′-oxo-3′,4′-dihydroseselin (3A), (3′R)-3′-angeloyloxy-4′-oxo-3′,4′-dihydroseselin (3B), (3′S,4′S)-praeruptorin B (4A), (3′R,4′R)-praeruptorin B (4B), (3′S,4′S)-praeruptorin E (5A), (3′R,4′R)-praeruptorin E (5B), 3′-isovaleryl-4′-angeloyl-cis-khellactone (6), 3′-angeloyl-4′-(2-methyl-butyroyl)-cis-khellactone (7), (3′S,4′S)-praeruptorin A (8A), (3′R,4′R)-praeruptorin A (8B), (3′S,4′S)-khellactone (9A), and (3′R,4′R)-khellactone (9B), respectively. Thereof, compounds 1A and 1B were new compounds, while compound 2A represents a new configuration for a known planar structure. Compounds 2A and 2B were isolated for the first time from Peucedani Radix. Above all, chiral LC-MS-guided isolation strategy is advantageous at rapid capturing new compounds from herbal medicines, providing an effective means for the separation of novel structures, especially new enantiomers.

Objective To investigate the impact of succinate dehydrogenase(SDH)on the modulation of human sperm functions.Methods The isolated human sperm were co-incubated with different concentrations(10,20,40 mmol/L)of SDH inhibitor disodium malonate for one or two hours.The activity of the SDH was measured by commercially available reagent kit,while the protein level of the SDH catalytic subunit SDHA was determined through Western blot analysis.Sperm functions were analyzed:1)The impact of disodium malonate on important mo-tility parameters of un-capcitated sperm including progressive motility rate(PR),total motility(TM),average pathvelocity(VAP)and the ability of capacitated sperm to penetrate viscous media were be assessed using a com-puter aided semen analysis system.2)Effect of disodium malonate on sperm survival rate was evaluated using the Eosin-Nigrosin microscopy.3)The incidence of acrosome reaction in capacitated sperm was be detected by PSA-FITC staining assay following disodium malonate treatment.Results Disodium malonate had no effect on expression of SDH catalytic subunit SHDA protein in human sperm.However,it inhibited the catalytic activity of the SDH,sperm forward motility,total motility,and the ability of sperm to penetrate viscous media.These inhibitory effects were positively correlated with the concentration of disodium malonate.Furthermore,disodium malonate had no any influence on the occurrence of spontaneous acrosome reaction in capacitated sperm.Conclusions Disodium mal-onate impairs human sperm motility by inhibiting succinate dehydrogenase activity.

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.

As a malignant tumor with high incidence and mortality worldwide,lung cancer seriously threatens the life and health of human beings.At present,clinical treatment of lung cancer is mainly based on surgery combined with radiotherapy and chemotherapy and other comprehensive treatments,which can control the progression of lung cancer to a certain extent,but there are still problems such as low survival rate and poor quality of life.Autophagy is a complex intracellular self degradation mechanism.The occurrence of autophagy is closely related to autophagy-related gene proteins and signal pathways.Research shows that reasonable regulation of signal pathway can interfere with autophagy leading to lung cancer cell death and inhibit tumor growth.In recent years,the regulation of auto-phagy-related signaling pathways in Chinese medicine against lung cancer has become a hot spot in the field of oncology research.Therefore,this paper compares and summarizes the research on the regulation of autophagy-related signaling pathways in Chinese medicine against lung cancer in recent years,in order to provide a reference basis for the development of new drugs and clini-cal application of Chinese medicine against lung cancer.

Derived from Curcuma plants, Curcumae Longae Rhizoma, Curcumae Rhizoma, Wenyujin Rhizoma Concisum, and Curcumae Radix are common blood-activating and stasis-resolving medicinals in clinical practice, which are mainly used to treat amenorrhea, dysmenorrhea, chest impediment and heart pain, and rheumatic arthralgia caused by blood stasis block. According to modern research, the typical components in medicinals derived from Curcuma plants, like curcumin, demethoxycurcumin, bisdemethoxycurcumin, curdione, germacrone, curcumol, and β-elemene, have the activities of hemorheology improvement, anti-platelet aggregation, anti-thrombosis, anti-inflammation, anti-tumor, and anti-fibrosis, thereby activating blood and resolving stasis. However, due to the difference in origin, medicinal part, processing, and other aspects, the efficacy and clinical application are different. The efficacy-related substances behind the difference have not yet been systematically studied. Thus, focusing on the efficacy-related substances, this study reviewed the background, efficacy and clinical application, efficacy-related substances, and "prediction-identification-verification" research method of blood-activating and stasis-resolving medicinals derived from Curcuma plants, which is expected to lay a theoretical basis for the future research on the "similarities and differences" of such medicinals based on integrated evidence chain and to guide the scientific and rational application of them in clinical practice.
Curcuma ; Curcumin ; Drugs, Chinese Herbal ; Plant Roots ; Platelet Aggregation ; Rhizome

This study aims to study the effective substance and mechanism of Ziziphi Spinosae Semen extract in the treatment of insomnia based on serum metabolomics and network pharmacology. The rat insomnia model induced by p-chlorophenylalanine(PCPA) was established. After oral administration of Ziziphi Spinosae Semen extract, the general morphological observation, pentobarbital sodium-induced sleep test, and histopathological evaluation were carried out. The potential biomarkers of the extract in the treatment of insomnia were screened by ultra-high performance liquid chromatography-mass spectrometry(UHPLC-MS) combined with multivariate analysis, and the related metabolic pathways were further analyzed. The "component-target-pathway" network was constructed by ultra-high performance liquid chromatography coupled with quadrupole-Exactive mass spectrometry(UHPLC-Q-Exactive-MS/MS) combined with network pharmacology to explore the effective substances and mechanism of Ziziphi Spinosae Semen in the treatment of insomnia. The results of pentobarbital sodium-induced sleep test and histopathological evaluation(hematoxylin and eosin staining) showed that Ziziphi Spinosae Semen extract had good theraputic effect on insomnia. A total of 21 endogenous biomarkers of Ziziphi Spinosae Semen extract in the treatment of insomnia were screened out by serum metabolomics, and the metabolic pathways of phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, and nicotinate and nicotinamide metabolism were obtained. A total of 34 chemical constituents were identified by UHPLC-Q-Exactive-MS/MS, including 24 flavonoids, 2 triterpenoid saponins, 4 alkaloids, 2 triterpenoid acids, and 2 fatty acids. The network pharmacological analysis showed that Ziziphi Spinosae Semen mainly acted on target proteins such as dopamine D2 receptor(DRD2), 5-hydroxytryptamine receptor 1 A(HTR1 A), and alpha-2 A adrenergic receptor(ADRA2 A) in the treatment of insomnia. It was closely related to neuroactive ligand-receptor interaction, serotonergic synapse, and calcium signaling pathway. Magnoflorine, N-nornuciferine, caaverine, oleic acid, palmitic acid, coclaurine, betulinic acid, and ceanothic acid in Ziziphi Spinosae Semen may be potential effective compounds in the treatment of insomnia. This study revealed that Ziziphi Spinosae Semen extract treated insomnia through multiple metabolic pathways and the overall correction of metabolic disorder profile in a multi-component, multi-target, and multi-channel manner. Briefly, this study lays a foundation for further research on the mechanism of Ziziphi Spinosae Semen in treating insomnia and provides support for the development of innovative Chinese drugs for the treatment of insomnia.
Animals ; Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal/chemistry* ; Metabolomics ; Network Pharmacology ; Rats ; Seeds/chemistry* ; Sleep Initiation and Maintenance Disorders/drug therapy* ; Tandem Mass Spectrometry ; Ziziphus/chemistry*