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: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: 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.

Network pharmacology, molecular docking, and in vivo and in vitro experiments were employed to study the molecular mechanism of Blaps rynchopetera Fairmaire in the treatment of non-small cell lung cancer(NSCLC). The components of B. rynchopetera were collected by literature review, and the active components were screened out through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). PharmMapper was used to obtain the targets of the active components. The targets of NSCLC were obtained from DrugBank, GeneCards, OMIM, TTD, and PharmGKB. The Venn diagram was drawn to identify the common targets shared by the active components of B. rynchopetera and NSCLC. The "drug component-target" network and protein-protein interaction(PPI) network were constructed by Cytoscape, and the key targets were screened by Centiscape. Gene Ontology(GO) annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment of the above key targets were performed by DAVID. AutoDock and PyMOL were used for the molecular docking between the key targets and corresponding active components. A total of 31 active components, 72 potential targets, and 11 key targets of B. rynchopetera against NSCLC were obtained. The active components of B. rynchopetera had good binding activity with key targets. Further, the serum containing B. rynchopetera was prepared and used to culture human lung adenocarcinoma A549 cells. The CCK-8 assay was employed to determine the inhibition rates on the growth of A549 cells in blank control group and those exposed to different concentrations of B. rynchopetera-containing serum, cisplatin, and drug combination(B. rynchopetera-containing serum+cisplatin) for different time periods. The cell migration and invasion of A549 cells were detected by cell scratch assay and Transwell assay, respectively. Western blot was employed to determine the expression levels of B-cell lymphoma-2(Bcl-2), Bcl-2-associated X(Bax), caspase-3, cell division cycle 42(CDC42), proto-oncogene tyrosine-protein kinase SRC, and vascular endothelial growth factor(VEGF) in A549 cells. C57BL/6 mice were inoculated with Lewis cells and randomly assigned into a model control group, a B. rynchopetera group, a cisplatin group, and a drug combination(B. rynchopetera+cisplatin) group, with 12 mice per group. The body weight and the long diameter(a) and short diameter(b) of the tumor were monitored every other day during treatment, and the tumor volume(mm~3) was calculated as 0.52ab~2. After 14 days of continuous medication, the mice were sacrificed for the collection of tumor, spleen, and thymus, and the tumor inhibition rate and immune organ indexes were calculated. The tissue morphology of tumors was observed by hematoxylin-eosin(HE) staining, and the positive expression of Bax, Bcl-2, caspase-3, CDC42, SRC, and VEGF in the tumor tissue was detected by immunohistochemistry. The results indicated that B. rynchopetera and the drug combination regulated the expression levels of Bax, Bcl-2, caspase-3, CDC42, SRC, and VEGF to inhibit the proliferation, migration, and invasion of A549 cells and Lewis cells, thus playing a role in the treatment of NSCLC via multiple ways.
Humans ; Animals ; Mice ; Mice, Inbred C57BL ; Carcinoma, Non-Small-Cell Lung/genetics* ; Caspase 3 ; Network Pharmacology ; Vascular Endothelial Growth Factor A ; Cisplatin ; Molecular Docking Simulation ; bcl-2-Associated X Protein ; Lung Neoplasms/genetics* ; Cell Proliferation ; Drugs, Chinese Herbal/pharmacology* ; Medicine, Chinese Traditional

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*

Objectives: To investigated the safety and efficacy of treating patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) and elevated levels of N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP) with levosimendan within 24 hours of first medical contact (FMC). Methods: This multicenter, open-label, block-randomized controlled trial (NCT03189901) investigated the safety and efficacy of levosimendan as an early management strategy of acute heart failure (EMS-AHF) for patients with NSTEMI and high NT-proBNP levels. This study included 255 patients with NSTEMI and elevated NT-proBNP levels, including 142 males and 113 females with a median age of 65 (58-70) years, and were admitted in the emergency or outpatient departments at 14 medical centers in China between October 2017 and October 2021. The patients were randomly divided into a levosimendan group (n=129) and a control group (n=126). The primary outcome measure was NT-proBNP levels on day 3 of treatment and changes in the NT-proBNP levels from baseline on day 5 after randomization. The secondary outcome measures included the proportion of patients with more than 30% reduction in NT-proBNP levels from baseline, major adverse cardiovascular events (MACE) during hospitalization and at 6 months after hospitalization, safety during the treatment, and health economics indices. The measurement data parameters between groups were compared using the t-test or the non-parametric test. The count data parameters were compared between groups using the χ² test. Results: On day 3, the NT-proBNP levels in the levosimendan group were lower than the control group but were statistically insignificant [866 (455, 1 960) vs. 1 118 (459, 2 417) ng/L, Z=-1.25,P=0.21]. However, on day 5, changes in the NT-proBNP levels from baseline in the levosimendan group were significantly higher than the control group [67.6% (33.8%,82.5%)vs.54.8% (7.3%,77.9%), Z=-2.14, P=0.03]. There were no significant differences in the proportion of patients with more than 30% reduction in the NT-proBNP levels on day 5 between the levosimendan and the control groups [77.5% (100/129) vs. 69.0% (87/126), χ²=2.34, P=0.13]. Furthermore, incidences of MACE did not show any significant differences between the two groups during hospitalization [4.7% (6/129) vs. 7.1% (9/126), χ²=0.72, P=0.40] and at 6 months [14.7% (19/129) vs. 12.7% (16/126), χ²=0.22, P=0.64]. Four cardiac deaths were reported in the control group during hospitalization [0 (0/129) vs. 3.2% (4/126), P=0.06]. However, 6-month survival rates were comparable between the two groups (log-rank test, P=0.18). Moreover, adverse events or serious adverse events such as shock, ventricular fibrillation, and ventricular tachycardia were not reported in both the groups during levosimendan treatment (days 0-1). The total cost of hospitalization [34 591.00(15 527.46,59 324.80) vs. 37 144.65(16 066.90,63 919.00)yuan, Z=-0.26, P=0.80] and the total length of hospitalization [9 (8, 12) vs. 10 (7, 13) days, Z=0.72, P=0.72] were lower for patients in the levosimendan group compared to those in the control group, but did not show statistically significant differences. Conclusions: Early administration of levosimendan reduced NT-proBNP levels in NSTEMI patients with elevated NT-proBNP and did not increase the total cost and length of hospitalization, but did not significantly improve MACE during hospitalization or at 6 months.
Male ; Female ; Humans ; Aged ; Natriuretic Peptide, Brain ; Simendan/therapeutic use* ; Non-ST Elevated Myocardial Infarction ; Heart Failure/drug therapy* ; Peptide Fragments ; Arrhythmias, Cardiac ; Biomarkers ; Prognosis