OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective:To investigate the clinical characteristics and prognosis of primary bone marrow lymphoma.Methods:The clinical data of 6 patients with primary bone marrow lymphoma admitted to Gansu Provincial People's Hospital from February 2011 to March 2023 were collected,and their clinical characteristics and prognosis were retrospectively analyzed and summarized.Results:The median age of 6 patients was 61(52-74)years old.There were 2 males and 4 females.All patients had fever and abnormal blood routine examination.Physical examination and imaging examination showed no lymphadenopathy,no extranodal lesions in lung,gastrointestinal,liver and spleen,skin,etc.After strict exclusion of systemic lymphoma involvement in the bone marrow,the diagnosis was confirmed by bone marrow examination,5 cases were primary myeloid diffuse large B-cell lymphoma and 1 case was primary myeloid peripheral T-cell lymphoma(NOS).1 case abandoned treatment,5 cases received CHOP-like or combined R regimen,including 1 case of autologous stem cell transplantation.4 cases died and 2 case survived.The median OS was 5.5(1-36)months.Conclusion:The prognosis of primary marrow lymphoma is poor,and bone marrow-related examination is an important means of diagnosis.Diffuse large B-cell lymphoma is the most common histomorphologic and immune subtype,and autologous hematopoietic stem cell transplantation may improve the prognosis.

Objective To study the clinical and pathological characteristics,as well as diagnosis,treatment methods and prognosis of adult patients with chronic active Epstein-Barr virus infection(CAEBVI).Methods Clinical and pathological data of 8 adult patients with CAEBVI admitted to a hospital in Gansu Province from January 2017 to December 2022 were collected retrospectively,clinical and histopathological characteristics,EBV-related test re-sults,as well as treatment and prognosis of patients were analyzed.Results Among 8 CAEBVI patients,3 were males and 5 were females,with the median age of 21.5 years.The median time from onset to diagnosis of CAEBVI was 7 months.The main manifestations were fever,pancytopenia(involving two or three peripheral blood lines),as well as lymph node enlargement,hepatomegaly and splenomegaly.The quantifications of plasma EBV nucleic acid(DNA)were all＞1.0 × 103.The sorting results of EBV infected cells showed that 3 cases were T lymphocytes in-fection,2 were NK cell infection,and 3 were co-infection of T lymphocytes and NK cells.Bone marrow cytological examination of 8 patients showed no atypical lymphocytes,while 6 patients showed hemophagocytic cells.Flow cy-tometey(FCM)typing results showed that no abnormal cell population was detected in all the 8 patients,and no myeloid,B lymphocyte,T lymphocyte and NK cell markers were expressed.The positive rate of T cell receptor(TCR)gene rearrangement was 37.5％(n=3).Histopathology showed that most cases(n=6,75.0％)expressed CD3,partial cases expressed CD4,CD8,CD56,TIA-1,and EBV encoded RNA(EBER),all were positive.The survival rate of patients after treatment was 50.0％(n=4),the follow-up time was 6-51 months,the 1-year sur-vival rate was 85.7％,and the median survival time was 24 months.Conclusion CAEBVI is characterized by varia-ble clinical manifestations that may lead to fatal complications.Early diagnosis and individualized treatment should be performed to reduce mortality of 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: 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 pathogenesis of erythrocytosis by detecting the key enzymes of glucose metabolism and glucose transporter in bone marrow erythrocytes of chronic mountain sickness (CMS), and analyzing its correlation with hemoglobin. METHODS: Twenty CMS patients hospitalized in Qinghai Provincial People's Hospital from January 2019 to December 2020 were selected as CMS group. Twenty males with leukocyte count > 3.5×10⁹/L who had accepted bone marrow aspiration and had normal result were taken as control group. The mRNA and protein expression of key enzymes and glucose transporter in glucose metabolism in bone marrow CD71⁺ erythrocytes were detected by real time qPCR and Western blot, respectively. Glucose, lactic acid and 2,3-diphosphoglycerate in the bone marrow supernatant and serum were tested by ELISA. The mRNA and protein expression of key enzymes and glucose transporter, glucose, lactic acid and 2,3-diphosphoglycerate of the two groups were compared. Pearson correlation was used to analyze the correlation between key enzymes, glucose transporter in glucose metabolism in bone marrow CD71⁺ erythrocytes and hemoglobin. RESULTS: The expression of HK2, GLUT1 and GLUT2 mRNA in the CMS group were higher than those in the control group (P<0.001), while the expression of HK1, OGDH and COX5B mRNA were not different. The expression of HK2, GLUT1 and GLUT2 protein in the CMS group were higher than those in the control group (P<0.05). The levels of glucose and lactic acid in the bone marrow supernatant and serum in the CMS group were not different from those in the control group, while the level of 2,3-diphosphoglycerate was higher (P<0.001). Both HK2 and GLUT2 proteins were positively correlated with hemoglobin (r=0.511, 0.717). CONCLUSION CMS patients may increase glycolysis by increasing the expression of HK2, and promote the utilization of glucose through high expression of GLUT1 and GLUT2 to meet the need of energy supply.
Male ; Humans ; Altitude Sickness/metabolism* ; Glucose Transporter Type 1 ; 2,3-Diphosphoglycerate ; Hemoglobins ; Chronic Disease ; RNA, Messenger ; Phenotype ; Glucose

Phosphodiesterase 4 (PDE4) is an important member of the phosphodiesterase enzyme family that specifically catalyzes the hydrolysis of cyclic adenosine monophosphate (cAMP), activates the downstream phosphorylation cascade pathway by altering cAMP concentration, and is strongly associated with multiple diseases. Inhibition of PDE4 is clinically investigated as a therapeutic strategy in a broad range of disease areas, including respiratory system diseases, autoimmune disorders, central nervous system diseases, and dermatological conditions. However, the incidence of adverse reactions such as nausea and vomiting is relatively high in the marketed PDE4 inhibitors, which has stalled their clinical development. In this review, we provide an overview of the clinical progression and safety issues of the marketed PDE4 inhibitors. We also review the main causes underlying PDE4-mediated adverse effects by combining the structural analysis of the PDE4 protein, the mechanism of action of PDE4 inhibitors, and the related side effect mechanism research, aiming to provide a reference for the development of safe and effective PDE4 inhibitors.