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 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: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: 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.

Background In mainland China, patients with neovascular age-related macular degeneration (nAMD) have approximately an 40% prevalence of polypoidal choroidal vasculopathy (PCV). This disease leads to recurrent retinal pigment epithelium detachment (PED), extensive subretinal or vitreous hemorrhages, and severe vision loss. China has introduced various treatment modalities in the past years and gained comprehensive experience in treating PCV.Methods A total of 14 retinal specialists nationwide with expertise in PCV were empaneled to prioritize six questions and address their corresponding outcomes, regarding opinions on inactive PCV, choices of anti-vascular endothelial growth factor (anti-VEGF) monotherapy, photodynamic therapy (PDT) monotherapy or combined therapy, patients with persistent subretinal fluid (SRF) or intraretinal fluid (IRF) after loading dose anti-VEGF, and patients with massive subretinal hemorrhage. An evidence synthesis team conducted systematic reviews, which informed the recommendations that address these questions. This guideline used the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach to assess the certainty of evidence and grade the strengths of recommendations. Results The panel proposed the following six conditional recommendations regarding treatment choices. (1) For patients with inactive PCV, we suggest observation over treatment. (2) For treatment-na?ve PCV patients, we suggest either anti-VEGF monotherapy or combined anti-VEGF and PDT rather than PDT monotherapy. (3) For patients with PCV who plan to initiate combined anti-VEGF and PDT treatment, we suggest later/rescue PDT over initiate PDT. (4) For PCV patients who plan to initiate anti-VEGF monotherapy, we suggest the treat and extend (T&E) regimen rather than the pro re nata (PRN) regimen following three monthly loading doses. (5) For patients with persistent SRF or IRF on optical coherence tomography (OCT) after three monthly anti-VEGF treatments, we suggest proceeding with anti-VEGF treatment rather than observation. (6) For PCV patients with massive subretinal hemorrhage (equal to or more than four optic disc areas) involving the central macula, we suggest surgery (vitrectomy in combination with tissue-plasminogen activator (tPA) intraocular injection and gas tamponade) rather than anti-VEGF monotherapy. Conclusions Six evidence-based recommendations support optimal care for PCV patients' management.

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
Infant, Newborn ; Humans ; Male ; Pregnancy ; Female ; Nomograms ; Retrospective Studies ; Cesarean Section ; Risk Factors ; Asphyxia Neonatorum/etiology*