In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

Objective:To explore if miR-133a is involved in the occurrence and development of hepatocellular carcinoma(HCC)via regulating G6PD.Methods:Bioinformatics analysis predicted the binding sites of miR-133a and G6PD;RT-PCR or western blot was used to assess the expres-sion of miR-133a and G6PD in HCC tissues and the adjacent normal tissues;CCK-8 and flow cy-tometry assays were performed to evaluate the effects of miR-133a/G6PD on cell proliferation,apop-tosis;Fluorescent reporter gene and western blot assays were used to assess the effect of miR-133a on G6PD expression.Results:miR-133a expression was decreased in HCC tissues while G6PD was increased(P0.01);Up-regulation of miR-133a significantly reduced G6PD expression(P＜0.01);up-reg-ulation of miR-133a inhibited cell growth and promoted cell apoptosis(P＜0.05),whereas these effects induced by miR-133a over-expression were all abolished when G6PD was up-regulated(P＜0.01).Conclusion:miR-133a represses the occurrence and development of HCC via targeting G6PD.

Objective:To analyze the correlation between chemokine CX3C ligand 1 (CX3CL1), CX3C chemokine receptor 1 (CX3CR1) and heart function grade, prognosis in patients with chronic heart failure (CHF).Methods:The clinical data of 200 patients with CHF from June 2021 to June 2023 in General Hospital of Eastern Theater of the Chinese People′s Liberation Army and Wuhan Asia Heart Hospital were retrospectively analyzed, and all patients received standardized treatment for heart failure. The baseline clinical data were recorded; the levels of CX3CL1 and CX3CR1 were detected by enzyme linked immunosorbent assay; the heart function grade was evaluated by New York Heart Association (NYHA) heart function grade method. The patients were followed up until December 2023, the patients were divided into poor prognosis group (all-cause death and readmission due to heart failure) and good prognosis group based on their prognosis. Pearson method was used for correlation analysis. Multivariate Logistic regression analysis was used to analyze the independent risk factors of poor prognosis in patients with CHF.Results:Among the 200 patients, NYHA heart function grade Ⅰ to Ⅱ was in 80 cases, Ⅲ to Ⅳ in 120 cases. The levels of CX3CL1 and CX3CR1 in patients with NYHA heart function grade Ⅲ to Ⅳ were significantly higher than those in patients with NYHA heart function grade Ⅰ to Ⅱ: (3.34 ± 0.45) mg/L vs. (2.45 ± 0.26) mg/L and (8.71 ± 0.92) mg/L vs. (2.53 ± 0.35) mg/L, and there were statistical differences ( t = 15.99 and 57.34, P<0.01). The proportion of age<60 years old, rate of coronary heart disease, CX3CL1, CX3CR1, body mass index and high-sensitivity C-reactive protein in poor prognosis group (40 cases) were significantly higher than those in good prognosis group (160 cases): 82.50% (33/40) vs. 10.62% (17/160), 90.00% (36/40) vs. 68.12% (109/160), (3.26 ± 0.77) mg/L vs. (2.25 ± 0.27) mg/L, (8.35 ± 2.01) mg/L vs. (2.48 ± 0.31) mg/L, (26.80 ± 3.55) kg/m 2 vs. (24.74 ± 2.76) kg/m 2 and (9.31 ± 2.19) mg/L vs. (3.58 ± 2.28) mg/L, the rate of smoking history and left ventricular ejection fraction were significantly lower than those in good prognosis group: 37.50% (15/40) vs. 46.88% (75/160) and (30.14 ± 5.77)% vs. (59.40 ± 6.58)%, and there were statistical differences ( P<0.01). Pearson correlation analysis result showed that the CX3CL1 and CX3CR1 were positively correlated with NYHA heart function grade ( r = 0.29 and 0.34, P<0.05), and negatively correlated with prognosis ( r = - 0.54 and - 0.36, P<0.05). Multivariate Logistic regression analysis result showed that the CX3CL1 and CX3CR1 were the independent risk factors of poor prognosis in patients with CHF ( OR = 2.110 and 1.566, 95% CI 0.445 to 3.125 and 0.270 to 3.455, P<0.01). Conclusions:The CX3CL1 and CX3CR1 are closely related to the heart function grade in patients with CHF. At the time of CHF patient admission, it may be considered to combine the two indicators for preliminary evaluation of and provide targeted interventions to improve prognosis.

Objective The causes of bleeding after endoscopic duodenal papilloma resection were analyzed and discussed,and the prediction model of nomogram was established.Methods A total of 233 patients who underwent endoscopic duodenal papilloma resection in our hospital from January 2018 to December 2023 were retrospectively analyzed,and they were divided into bleeding group(n=31 cases)and non-bleeding group(n=202 cases)according to whether postoperative bleeding occurred.The clinical data of the two groups were compared,the independent risk factors for postoperative bleeding were analyzed by multi-factor logistic regression,the risk nomogram prediction model was constructed,and the Bootstrap method was used for 1000 repeated samples to carry out internal verification.Results Anticoagulant drugs(OR=9.063,95％CI:2.132-38.525),lesion diameter ≥2 cm(OR=2.802,95％CI:1.073-7.321),intraoperative fragment resection(OR=27.653,95％CI:3.055～619.174)and pancreatic complications(OR=6.859,95％CI:1.930～24.377)were independent risk factors for postoperative bleeding after endoscopic duodenal papilloma resection(P＜0.05).A risk prediction nomogram model was constructed according to the Logistic regression analysis results.The samples were repeatedly sampled 1000 times through Bootstrap method for internal verification.The area under the ROC curve was 0.850,and the 95％CI was 0.780-0.913,indicating good differentiation ability of the model.Calibration curve analysis indicated that the prediction probability of postoperative bleeding predicted by the nomogram prediction model was in good agreement with the actual probability of postoperative bleeding,and Hosmer-Lemeshow showed good goodness of fit(x2=3.304 9,P=0.913 8).Conclusion Taking anticoagulant drugs,lesion diameter ≥2 cm,intraoperative segmentary resection,and postoperative combination of pancreas were independent risk factors for bleeding after endoscopic duodenal papilloma resection.A nomogram prediction model was established to help clinical assessment of postoperative bleeding risk in patients and improve decision-making basis for early prevention.

Aim To investigate the expression level of CBX4 in esophageal squamous cell carcinoma(ESCC)and the effect of CBX4 on ESCC proliferation and un-derlying molecular mechanisms.Methods The ex-pression of CBX4 in different cancers was analyzed in Pan-cancers.The expression level of CBX4 in ESCC was analyzed by t-test based on Gene Expression Omni-bus(GEO)data.The viability of CBX4-overex-pressed/knockdown ESCC cells was detected by MTT assay,colony formation assay and flow cytometry assay.Furthermore,the tumor volumn,tumor weight and Ki67 expression were measured by mouse xenograft assay and immunohistochemistry.The mRNA and protein ex-pression levels of apoptosis-related genes PARP、Bcl-2、Bax were determined by qRT-PCR and Western blot,respectively.In addition,the underlying molecular mechanism of CBX4 in ESCC was revealed by qRT-PCR and Western blot.Results CBX4 was upregulat-ed in various cancers.The expression level of CBX4 in ESCC was higher than that in normal tissues(P＜0.05)based on Gene Expression Omnibus(GEO)da-ta.Compared with the normal group,the proliferation of CBX4 knockdown ESCC cells was significantly in-hibited and the apoptosis was promoted(P＜0.05).Meanwhile,the mRNA and protein expression levels of cleaved PARP and Bax were upregulated while that of Bcl-2 was downregulated.In CBX4 overexpression group,tumor volume in vivo increased(P＜0.05).Immunohistochemical results also showed an increase in Ki67 expression.Furthermore,the results of RNA-seq,bioinformatics analysis and qRT-PCR experiments indicated that CBX4 probably regulated the prolifera-tion and apoptosis of ESCC through p38 MAPK signa-ling pathway.Conclusion CBX4 is highly expressed in ESCC and plays as an oncogene role,which might regulate cell proliferation through the p38 MAPK signa-ling pathway.

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.