The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Objective To evaluate the short-term clinical efficacy of unilateral biportal endoscopic(UBE)technique in the treatment of adjacent segment disease after lumbar interbody fusion.Methods The clinical data of 21 patients with adjacent segment disease after lumbar interbody fusion who treated with UBE technique and followed up for more than 3 months in our hospital from March 2020 to January 2023 were retrospectively analyzed.The operation time,decrease value of hemoglobin 1 day before and after operation,drainage volume of the operation area,time of bed rest and complications were recorded.The Japanese Orthopaedic Association(JOA)score was used to evaluate lumbar function 1 day before operation,3 days and 3 months after operation to determine the improvement.The visual analogue scale(VAS)score was used to evaluate the pain 1 day before operation,3 days and 3 months after operation.Results The operation time was 60～175 minutes,with an average of(95.38±18.64)minutes;the decrease value of hemoglobin was 2～6 g/L,with an average of(1.42±0.18)g/L;the time of bed rest was 27～88 hours,with an average of(36.42±15.33)hours;the drainage volume of the operation area was 50～315 mL,with an average of(85.56±15.65)mL;and one case of dural tear occurred during the operation,who was converted to open surgery for repairing dural sac.There was statistically significant difference in VAS score before operation compared with that 3 days and 3 months after operation(P<0.05).There was statistically significant difference in the JOA score before operation compared with that 3 days and 3 months after surgery(P<0.05).Conclusion UBE technique is effective in the treatment of adjacent segment disease after lumbar interbody fusion,with the advantages of small trauma,little bleeding and short time of bed rest.However,patients with the serious adjacent segment degeneration and severe spinal stenosis may have dural tear during operation.Once it occurs,active repair should be performed to avoid cauda equina herniation and necrosis,or switch to open surgery,if necessary.

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.

The objective of this study was to investigate the protective effect and potential mechanism of action of hemin on bleomycin-induced pulmonary fibrosis in mice. Male C57BL/6 mice were randomly divided into control, bleomycin and bleomycin + hemin groups. Mice in the bleomycin and bleomycin + hemin groups were injected intratracheally with bleomycin to establish the pulmonary fibrosis model.The bleomycin + hemin group mice were injected intraperitoneally with hemin starting 7 days before modeling until the end of Day 21 after modeling. Pathological changes in lung tissue were assessed by HE and Masson staining. Malondialdehyde (MDA), superoxide dismutase (SOD) and catalase (CAT) levels were determined in lung tissue. Immunohistochemistry was performed to assess the expression of α-SMA and collagen I. The serum levels of IL-6 and TNF-α were measured via ELISA.Western blotting was used to determine the expression of TGF-β1, SIRT1, PGC-1α and HO-1 and the phosphorylation levels of p38, ERK1/2, JNK, AMPK and NF-κB p65 in lung tissue. Hemin significantly reduced lung indices, increased terminal body weight. It also significantly increased SOD and CAT activities; decreased MDA, IL-6 and TNF-α levels; reduced the levels of α-SMA and collagen I-positive cells; upregulated SIRT1, PGC-1α and HO-1 expression; promoted AMPK phosphorylation; and downregulated TGF-β1 expression and p38, ERK1/2, JNK and NF-κB p65 phosphorylation. Hemin might attenuate oxidative damage and inflammatory responses and reduces extracellular matrix deposition by regulating the expression and phosphorylation of proteins associated with the TGF-β1/MAPK and AMPK/SIRT1/PGC-1α/HO-1/ NF-κB pathways, thereby alleviating bleomycin-induced pulmonary fibrosis.

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.

The objective of this study was to investigate the protective effect and potential mechanism of action of hemin on bleomycin-induced pulmonary fibrosis in mice. Male C57BL/6 mice were randomly divided into control, bleomycin and bleomycin + hemin groups. Mice in the bleomycin and bleomycin + hemin groups were injected intratracheally with bleomycin to establish the pulmonary fibrosis model.The bleomycin + hemin group mice were injected intraperitoneally with hemin starting 7 days before modeling until the end of Day 21 after modeling. Pathological changes in lung tissue were assessed by HE and Masson staining. Malondialdehyde (MDA), superoxide dismutase (SOD) and catalase (CAT) levels were determined in lung tissue. Immunohistochemistry was performed to assess the expression of α-SMA and collagen I. The serum levels of IL-6 and TNF-α were measured via ELISA.Western blotting was used to determine the expression of TGF-β1, SIRT1, PGC-1α and HO-1 and the phosphorylation levels of p38, ERK1/2, JNK, AMPK and NF-κB p65 in lung tissue. Hemin significantly reduced lung indices, increased terminal body weight. It also significantly increased SOD and CAT activities; decreased MDA, IL-6 and TNF-α levels; reduced the levels of α-SMA and collagen I-positive cells; upregulated SIRT1, PGC-1α and HO-1 expression; promoted AMPK phosphorylation; and downregulated TGF-β1 expression and p38, ERK1/2, JNK and NF-κB p65 phosphorylation. Hemin might attenuate oxidative damage and inflammatory responses and reduces extracellular matrix deposition by regulating the expression and phosphorylation of proteins associated with the TGF-β1/MAPK and AMPK/SIRT1/PGC-1α/HO-1/ NF-κB pathways, thereby alleviating bleomycin-induced pulmonary fibrosis.

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.