Objective:To analyze the clinical characteristics and prognosis of patients with CD5+diffuse large B-cell lymphoma(DLBCL).Methods:The clinical data of 161 newly treated DLBCL patients in Gansu Provincial Hospital from January 2013 to January 2020 were retrospectively analyzed.According to CD5 expression,the patients were divided into CD5+group and CD5-group.The clinical characteristics and prognosis of the two groups were statistically analyzed.Results:The median age of patients in CD5+group was 62 years,which was higher than 56 years in CD5-group(P=0.048).The proportion of women in CD5+group was 62.96％,which was significantly higher than 41.79％in CD5-group(P=0.043).The proportion of patients with IPI score＞2 in CD5+group was 62.96％,which was higher than 40.30％in CD5-group(P=0.031).Survival analysis showed that the median overall survival and progression-free survival time of patients in CD5+group were 27(3-77)and 31(3-76)months,respectively,which were both shorter than 30(5-84)and 32.5(4-83)months in CD5-group(P=0.047,P=0.026).Univariate analysis showed that advanced age,positive CD5 expression,triple or double hit at initial diagnosis,high IPI score and no use of rituximab during chemotherapy were risk factors for the prognosis of DLBCL patients.Further Cox multivariate regression analysis showed that these factors were also independent risk factors except for advanced age.Conclusion:CD5+DLBCL patients have a worse prognosis than CD5-DLBCL patients.Such patients are more common in females,with advanced age and high IPI score,which is a special subtype of DLBCL.

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 investigate the clinical characteristics,treatment,and prognosis of children with perianal fistulizing Crohn's disease(pfCD).Methods A retrospective analysis was conducted on the children,aged 6-17 years,who were diagnosed with Crohn's disease(CD)from April 2015 to April 2023.According to the presence or absence of perianal fistulizing lesions,they were divided into two groups:pfCD(n=60)and non-pfCD(n=82).The two groups were compared in terms of clinical characteristics,treatment,and prognosis.Results The incidence of pfCD was 42.3%(60/142).The proportion of males in the pfCD group was higher than that in the non-pfCD group.Compared with the non-pfCD group,the pfCD group had a significantly higher proportion of children with involvement of the colon and small intestine or those with upper gastrointestinal lesions(P<0.05).Compared with the non-pfCD group,the pfCD group had a significantly higher rate of use of infliximab during both induction and maintenance treatment(P<0.05).In the pfCD group,the children with complex anal fistula accounted for 62%(37/60),among whom the children receiving non-cutting suspended line drainage accounted for 62%(23/37),which was significantly higher than the proportion among the children with simple anal fistula patients(4%,1/23)(P<0.05).There were no significant differences between the two groups in mucosal healing rate and clinical remission rate at week 54 of treatment(P>0.05).The pfCD group achieved a fistula healing rate of 57%(34/60)at week 54,and the children with simple anal fistula had a significantly higher rate than those with complex anal fistula(P<0.05).Conclusions There is a high incidence rate of pfCD in children with CD,and among the children with pfCD,there is a high proportion of children with the use of biological agents.There is a high proportion of children receiving non-cutting suspended line drainage among the children with complex anal fistula.The occurrence of pfCD should be closely monitored during the follow-up in children with CD.

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 investigate the causes and management of long-term persistent pelvic presacral space infection. Methods: Clinical data of 10 patients with persistent presacral infection admitted to the Cancer Hospital of Zhengzhou University from October 2015 to October 2020 were collected. Different surgical approaches were used to treat the presacral infection according to the patients' initial surgical procedures. Results: Among the 10 patients, there were 2 cases of presacral recurrent infection due to rectal leak after radiotherapy for cervical cancer, 3 cases of presacral recurrent infection due to rectal leak after radiotherapy for rectal cancer Dixons, and 5 cases of presacral recurrent infection of sinus tract after adjuvant radiotherapy for rectal cancer Miles. Of the 5 patients with leaky bowel, 4 had complete resection of the ruptured nonfunctional bowel and complete debridement of the presacral infection using an anterior transverse sacral incision with a large tipped omentum filling the presacral space; 1 had continuous drainage of the anal canal and complete debridement of the presacral infection using an anterior transverse sacral incision. 5 post-Miles patients all had debridement of the presacral infection using an anterior transverse sacral incision combined with an abdominal incision. The nine patients with healed presacral infection recovered from surgery in 26 to 210 days, with a median time of 55 days. Conclusions: Anterior sacral infections in patients with leaky gut are caused by residual bowel secretion of intestinal fluid into the anterior sacral space, and in post-Miles patients by residual anterior sacral foreign bodies. An anterior sacral caudal transverse arc incision combined with an abdominal incision is an effective surgical approach for complete debridement of anterior sacral recalcitrant infections.
Humans ; Reinfection ; Rectum/surgery* ; Rectal Neoplasms/surgery* ; Drainage ; Anal Canal/surgery* ; Pelvic Infection

Objective: To identify the risk factors in mortality of pediatric acute respiratory distress syndrome (PARDS) in pediatric intensive care unit (PICU). Methods: Second analysis of the data collected in the "efficacy of pulmonary surfactant (PS) in the treatment of children with moderate to severe PARDS" program. Retrospective case summary of the risk factors of mortality of children with moderate to severe PARDS who admitted in 14 participating tertiary PICU between December 2016 to December 2021. Differences in general condition, underlying diseases, oxygenation index, and mechanical ventilation were compared after the group was divided by survival at PICU discharge. When comparing between groups, the Mann-Whitney U test was used for measurement data, and the chi-square test was used for counting data. Receiver Operating Characteristic (ROC) curves were used to assess the accuracy of oxygen index (OI) in predicting mortality. Multivariate Logistic regression analysis was used to identify the risk factors for mortality. Results: Among 101 children with moderate to severe PARDS, 63 (62.4%) were males, 38 (37.6%) were females, aged (12±8) months. There were 23 cases in the non-survival group and 78 cases in the survival group. The combined rates of underlying diseases (52.2% (12/23) vs. 29.5% (23/78), χ²=4.04, P=0.045) and immune deficiency (30.4% (7/23) vs. 11.5% (9/78), χ²=4.76, P=0.029) in non-survival patients were significantly higher than those in survival patients, while the use of pulmonary surfactant (PS) was significantly lower (8.7% (2/23) vs. 41.0% (32/78), χ²=8.31, P=0.004). No significant differences existed in age, sex, pediatric critical illness score, etiology of PARDS, mechanical ventilation mode and fluid balance within 72 h (all P>0.05). OI on the first day (11.9(8.3, 17.1) vs.15.5(11.7, 23.0)), the second day (10.1(7.6, 16.6) vs.14.8(9.3, 26.2)) and the third day (9.2(6.6, 16.6) vs. 16.7(11.2, 31.4)) after PARDS identified were all higher in non-survival group compared to survival group (Z=-2.70, -2.52, -3.79 respectively, all P<0.05), and the improvement of OI in non-survival group was worse (0.03(-0.32, 0.31) vs. 0.32(-0.02, 0.56), Z=-2.49, P=0.013). ROC curve analysis showed that the OI on the thind day was more appropriate in predicting in-hospital mortality (area under the curve= 0.76, standard error 0.05,95%CI 0.65-0.87,P<0.001). When OI was set at 11.1, the sensitivity was 78.3% (95%CI 58.1%-90.3%), and the specificity was 60.3% (95%CI 49.2%-70.4%). Multivariate Logistic regression analysis showed that after adjusting for age, sex, pediatric critical illness score and fluid load within 72 h, no use of PS (OR=11.26, 95%CI 2.19-57.95, P=0.004), OI value on the third day (OR=7.93, 95%CI 1.51-41.69, P=0.014), and companied with immunodeficiency (OR=4.72, 95%CI 1.17-19.02, P=0.029) were independent risk factors for mortality in children with PARDS. Conclusions: The mortality of patients with moderate to severe PARDS is high, and immunodeficiency, no use of PS and OI on the third day after PARDS identified are the independent risk factors related to mortality. The OI on the third day after PARDS identified could be used to predict mortality.
Female ; Male ; Humans ; Child, Preschool ; Infant ; Child ; Critical Illness ; Pulmonary Surfactants/therapeutic use* ; Retrospective Studies ; Risk Factors ; Respiratory Distress Syndrome/therapy*

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