With Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum drug pair as the research object, supramolecular chemistry of traditional Chinese medicine(TCM) was used to study differences between the compatibility of herbal medicine Glycyrrhizae Radix et Rhizoma with mineral medicine Gypsum Fibrosum and its main component CaSO_4·2H_2O, so as to preliminarily discuss the scientific connotation of compatibility of Gypsum Fibrosum in clinical application. A Malvern particle sizer, a scanning electron microscope(SEM), and a conductivity meter were used to observe and determine the physical properties such as microscopic morphology, particle size, and conductivity of Gypsum Fibrosum, CaSO_4·2H_2O, and water decoctions of them with Glycyrrhizae Radix et Rhizoma. An inductively coupled plasma optical emission spectrometer(ICP-OES) was employed to detect the inorganic metal elements in Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. Isothermal titration calorimetry(ITC) was conducted to quantify the interactions of Gypsum Fibrosum and CaSO_4·2H_2O with Glycyrrhizae Radix et Rhizoma. A Fourier transform infrared spectrometer(FTIR) was used to analyze the characteristic absorption peak change of Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. X-ray diffraction(XRD) was performed to determine the crystal structure and phase composition of Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. Further, glycyrrhizic acid(GA) was substituted for Glycyrrhizae Radix et Rhizoma to co-decoct with Gypsum Fibrosum, CaSO_4·2H_2O, and freeze-dried powder of their respective water decoctions. The results of XRD were used for verification analysis. The results showed that although CaSO_4·2H_2O is the main component of Gypsum Fibrosum, there were significant differences between their decoctions and between the decoctions of them with Glycyrrhizae Radix et Rhizoma. Specifically,(1) Both CaSO_4·2H_2O and Gypsum Fibrosum were amorphous fibrous. However, the particle size and conductivity were significantly different between the decoctions of CaSO_4·2H_2O and Gypsum Fibrosum alone.(2) Under SEM, Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O was a hybrid system with various morphologies, while Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum presented uniform nanoparticles.(3) The particle sizes and conductivities of Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O and Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum were significantly different and did not follow the same tendency as those of the decoctions of CaSO_4·2H_2O and Gypsum Fibrosum alone.(4) Compared with Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O, Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum had stronger molecular binding ability and functional group structure change.(5) The crystal form was largely different between the freeze-dried powder of CaSO_4·2H_2O decoction and Gypsum Fibrosum decoction, and their crystal forms were also significantly different from those of the freeze-dried powder of Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O and Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum decoctions. The reason for the series of differences is that Gypsum Fibrosum is richer in trace elements than CaSO_4·2H_2O. The XRD results of GA-Gypsum Fibrosum and GA-CaSO_4·2H_2O decoctions further prove the importance of trace elements in Gypsum Fibrosum for supramolecule formation. This research preliminarily reveals the influence of compatibility of Gypsum Fibrosum or CaSO_4·2H_2O on decoction phase state, material form, and crystal form, providing a basis for the rational clinical application of Gypsum Fibrosum.
Drugs, Chinese Herbal/chemistry* ; Calcium Sulfate/chemistry* ; Glycyrrhiza/chemistry* ; Crystallization ; Particle Size ; Medicine, Chinese Traditional ; Rhizome/chemistry*

OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

Eightly-four novel thioheterocyclic nucleoside derivatives were designed, synthesized, and evaluated for antitumor activity in vitro and in vivo. Most of the compounds inhibited the growth of HCT116 and HeLa cancer cells in vitro, among them 33a and 36b exhibited potent activity against HCT116 cells (IC₅₀ = 0.27 and 0.49 μmol/L, respectively). Both compounds 33a and 36b inhibited cell metastasis, arrested the cell cycle in the G₂/M phase, and induced apoptosis in vitro. Mechanistic studies revealed that 33a and 36b increased ROS levels, led to DNA damage, ER stress, and mitochondrial dysfunction, and inhibited autophagy in HCT116 cells. Biological information analysis, RNA-sequencing, Gene Set Enrichment Analysis (GSEA), drug affinity responsive target stability (DARTS) assay, cellular thermal shift assay (CETSA), and SPR experiments identified that compounds 33a and 36b showed antitumor activity by suppressing the c-MYC pathway. c-MYC silencing assays indicated that c-MYC proteins participated in 33a-mediated anticancer activities in HCT116 cells. More importantly, compound 33a presented favorable pharmacokinetic properties in mice (T _1/2 = 6.8 h) and showed significant antitumor efficacy in vivo without obvious toxicity, showing promising potential for further clinical development.

Objective To evaluate the feasibility and accuracy of virtual preoperative planning and 3D-printed templates for pre-contoured plates for the treatment of posterior wall fractures of the acetabulum.Methods A retrospective analysis of 29 patients with posterior acetabular wall fractures treated between August 2017 and March 2021 were divided into 2 groups based on whether to use preoperative virtual planning and 3D printed template.In 3D-printing group,there were 14 patients,includ-ing 10 males and 4 females;aged from 21 to 53 years old;CT-based virtual surgical planning was done using Mimics and 3-Matic software and 3D-printed templates for pre-contoured plates were adopted.In conventional group,there were 15 patients,including 10 males and 5 females;aged from 19 to 55 years old;conventional method of intra-operative contouring to adapt the plate to the fracture region was adopted.Blood loss,surgical time,radiographic quality of reduction,and hip function were compared between groups.Results The difference in operation time and intraoperative blood loss was significant(P＜0.05).Twenty-three patients were followed up from 12 to 30 months,and the fractures in both groups healed with a healing time of 3 to 6 months.At the last follow-up,the Merle d'Aubign-Postel score of the 3D printed group was lower than that of the conven-tional group(P＜0.05),with no significant differences in walking ability,hip mobility and total score(P＞0.05).In 3D printing group,6 cases were excellent,5 cases were good,3 cases were fair;in conventional group,5 cases were excellent,5 cases were good,4 cases were fair,1 case was worse;no significant difference between two groups(P＞0.05).Conclusion Virtual preopera-tive planning and 3D-printed templates for pre-contoured plates can reduce operative time and the blood loss of surgery,im-prove the quality of reduction.This method is efficient,accurate and reliable to treat acetabular posterior wall fractures.

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.

Seven compounds were isolated from Onychium japonicum by macroporous resin, silica gel, ODS, Sephadex LH-20 column chromatography and semi-preparative HPLC. Their structures were identified by NMR, MS and other spectroscopic methods as onychone A (1), quercetin (2), quercetin-3-O-α-L-rhamnoside (3), kaempferol-7-O-β-D-glucopyranoside (4), kaempferol-3-O-α-L-rhamnopyranoside (5), (-)-prunin (6), and norathyriol (7). Compound 1 is a novel macrocyclic flavonoid, and all the others are reported from this plant for the first time. In vitro cytotoxic activities of compounds 1-7 were evaluated by MTS testing with five cancer cell lines. Compound 7 exhibited weak cytotoxicity against tumor cell lines A549, SMMC-7721, and SW480.