Purpose: Zinc finger protein 639 (ZNF639) is often found within the overlapping amplicon of PIK3CA, and previous studies suggest its involvement in the pathogenesis of esophageal and oral squamous cell carcinomas. However, its expression and significance in breast cancer remain uncharacterized. Methods: Immunohistochemical analysis of ZNF639 was performed using tissue microarrays.Functional studies, including colony formation, Transwell cell migration, and in vivo metastasis, were conducted on breast tumor cells with ZNF639 knockdown via small interfering RNA transfection. Results: Reduced ZNF639 immunoreactivity was observed in 82% of the breast cancer samples, independent of hormone receptor and human epidermal growth factor receptor 2 status. In multivariate Cox regression analyses, ZNF639 expression was associated with favorable survival outcomes, including recurrence-free survival (hazard ratio, 0.35; 95% confidence interval [CI], 0.14–0.89) and overall survival (hazard ratio, 0.41; 95% CI, 0.16– 1.05). ZNF639 knockdown increased clonogenicity, cell motility, and lung metastasis in NOD/ SCID mice. Following the ZNF639 knockdown, the expression of Snail1, vimentin, and C-C chemokine ligand 20 (CCL20) was upregulated, and the changes in cell phenotype mediated by ZNF639 were reversed by the subsequent knockdown of CCL20. Conclusion Low ZNF639 expression is a novel prognostic factor for recurrence-free survival in patients with breast cancer.

POEMS syndrome is a rare condition associated with plasma cell disorders， and it often involves multiple systems and has diverse clinical manifestations. This article reports two cases of POEMS syndrome with hepatosplenomegaly as the initial manifestation. During the course of the disease， the patients presented with lower limb weakness， hepatosplenomegaly， lymph node enlargement， ascites， hypothyroidism， positive M protein， and skin hyperpigmentation， and ¹⁸F-FDG PET-CT imaging revealed bone lesions mainly characterized by osteolytic changes and plasma cell tumors. There was an increase in the serum level of vascular endothelial growth factor. The patients were finally diagnosed with POEMS syndrome， and the symptoms were relieved after immunomodulatory treatment.

Purpose: Zinc finger protein 639 (ZNF639) is often found within the overlapping amplicon of PIK3CA, and previous studies suggest its involvement in the pathogenesis of esophageal and oral squamous cell carcinomas. However, its expression and significance in breast cancer remain uncharacterized. Methods: Immunohistochemical analysis of ZNF639 was performed using tissue microarrays.Functional studies, including colony formation, Transwell cell migration, and in vivo metastasis, were conducted on breast tumor cells with ZNF639 knockdown via small interfering RNA transfection. Results: Reduced ZNF639 immunoreactivity was observed in 82% of the breast cancer samples, independent of hormone receptor and human epidermal growth factor receptor 2 status. In multivariate Cox regression analyses, ZNF639 expression was associated with favorable survival outcomes, including recurrence-free survival (hazard ratio, 0.35; 95% confidence interval [CI], 0.14–0.89) and overall survival (hazard ratio, 0.41; 95% CI, 0.16– 1.05). ZNF639 knockdown increased clonogenicity, cell motility, and lung metastasis in NOD/ SCID mice. Following the ZNF639 knockdown, the expression of Snail1, vimentin, and C-C chemokine ligand 20 (CCL20) was upregulated, and the changes in cell phenotype mediated by ZNF639 were reversed by the subsequent knockdown of CCL20. Conclusion Low ZNF639 expression is a novel prognostic factor for recurrence-free survival in patients with breast cancer.

Purpose: Zinc finger protein 639 (ZNF639) is often found within the overlapping amplicon of PIK3CA, and previous studies suggest its involvement in the pathogenesis of esophageal and oral squamous cell carcinomas. However, its expression and significance in breast cancer remain uncharacterized. Methods: Immunohistochemical analysis of ZNF639 was performed using tissue microarrays.Functional studies, including colony formation, Transwell cell migration, and in vivo metastasis, were conducted on breast tumor cells with ZNF639 knockdown via small interfering RNA transfection. Results: Reduced ZNF639 immunoreactivity was observed in 82% of the breast cancer samples, independent of hormone receptor and human epidermal growth factor receptor 2 status. In multivariate Cox regression analyses, ZNF639 expression was associated with favorable survival outcomes, including recurrence-free survival (hazard ratio, 0.35; 95% confidence interval [CI], 0.14–0.89) and overall survival (hazard ratio, 0.41; 95% CI, 0.16– 1.05). ZNF639 knockdown increased clonogenicity, cell motility, and lung metastasis in NOD/ SCID mice. Following the ZNF639 knockdown, the expression of Snail1, vimentin, and C-C chemokine ligand 20 (CCL20) was upregulated, and the changes in cell phenotype mediated by ZNF639 were reversed by the subsequent knockdown of CCL20. Conclusion Low ZNF639 expression is a novel prognostic factor for recurrence-free survival in patients with breast cancer.

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:To retrospectively analyze the detection and diagnosis process of two cases with double rare thalassemia genotypes,explore the causes of missed diagnosis and misdiagnosis of rare thalassemia,and improve the diagnosis level of rare thalassemia.Methods:Base on the family history,hematological phenotype and hemoglobin electrophoretic analysis results,the common genotypes of α and β-thalassemia were detected by PCR+diversion hybridization.DNA sequencing technology was used for rare α and β protein genes sequencing.Results:Both subjects were combined with double rare thalassemia genotypes,and both rare thalassemia gene combinations were reported for the first time.One of them was αβ complex thalassemia with αα*53_55 del TCC/αα heterozygous merger βIVS Ⅱ2(-T)/βN heterozygous,the other was ααIVS-Ⅱ-55(T→G)in α1/αα4,2-Q double azygous heterozygous α-thalassemia,among whichαα*53_55 del TCC/αα genotype was also reported for the first time.Conclusion:The reported rare gene type αα*53_55 del TCC/αα and two cases of rare gene combinations enriches the spectrum of gene mutations in the Chinese population,and provides richer molecular information for thalassemia diagnosis and eugenics counseling.

Objective:To explore the application value of reticulocyte hemoglobin equivalent(Ret-He)for diagnosing latent iron deficiency in female plateletpheresis donors.Methods:A total of 230 female plateletpheresis donors in Fujian Blood Center from January to February 2022 were selected as the research group and divided into three groups:normal group,iron depletion(ID)group and iron deficient erythropoiesis(IDE)group,according to the severity of iron deficiency.The level of hemoglobin(HGB),mean corpuscular volume(MCV),mean corpuscular hemoglobin(MCH),coefficient of variation of red cell distribution width(RDW-CV)and Ret-He were measured by using the Sysmex XN automated hematology analyzer.Chemiluminescence immunoassay was used to detect iron biochemical indexes.Receiver operating characteristic(ROC)curve analysis was performed to evaluate the diagnosic value of relevant indicators in female blood donors with latent iron deficiency.Results:Ret-He in ID group was 32.55(31.15,33.10)pg,which was significantly lower than that in the normal group[33.80(32.73,34.70)pg](P＜0.05),and significantly higher than that in IDE group[30.40(28.70,31.50)pg](P＜0.05).ROC analysis in diagnosis of IDE demonstrated that the area under the curves(AUCs)of HGB,MCV,MCH,RDW-CV and Ret-He were 0.892,0.843,0.909,0.890,0.931,respectively.When the critical value of Ret-He was 32.05 pg,its sensitivity and specificity were 85.90％and 92.60％,respectively.However,all red blood cell parameters had poor diagnostic value for ID.Conclusion:Ret-He is a perfect predictor for latent iron deficiency in female blood donors.Detection of Ret-He can advance the diagnosis of iron deficiency in female blood donors to the IDE stage.

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.