Aim To construct CX3CR1GFP transgenic mice based on the Cre/Loxp system,and to analyze the expression efficiency of CX3CR1GFP.Methods Targeted vectors were designed using restriction enzyme-based cloning technology to create a linearized targeted vector for transfecting embryonic stem cells(ES).The ES cells with a deletion of the neomycin resistance gene(neo)were then cloned into blastocysts to generate chimeric CX3CR1+/GFPmice.These mice were subsequently bred with wild-type mice(WT),and repeated backcrossing was performed to obtain CX3CR1GFP/GFP mice.DNA and mRNA from WT and CX3CR1GFP mice were extracted and genotyped using agarose gel electrophoresis.The expression level of CX3CR1 in various tis-sues of the mice was detected by RT-qPCR.Western blot analy-sis was used to analyze the expression of GFP protein in periph-eral blood mononuclear cells(PBMC)and various tissues.The labeling efficiency of immune cells in bone marrow was detected by flow cytometry.The expression of GFP in different mouse tis-sues was observed by immunofluorescence.Results The results of agarose gel electrophoresis showed that the transgenic mouse genotype was CX3CR1GFP/GFP homozygote.RT-qPCR and West-ern blot showed that EGFP were targeted to replace CX3CR1 gene,so CX3CR1 expression was very low in CX3CR1GFP mice,while GFP expression was significantly upregulated.Flow cytom-etry and immunofluorescence showed that GFP effectively marked CX3CR1GFP mice,expressed in various tissues and cells with different expression levels.Conclusion This study con-structs and identifies the CX3CR1GFP genetic reporter mice,and GFP is stably expressed in mice,which provides a foundation for further research into the potential mechanisms of CX3CR1 in im-mune regulation.

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 conduct a scoping review of research on the application of mobile health(mHealth)in the management of home enteral tube feeding,so as to provide references for future research and clinical practice.Methods A literature search was performed in the PubMed,Cochrane Library,Embase,Web of Science,CINAHL,CNKI,Wanfang,and CMB databases to identify relevant studies.The search period spanned from the establishment of databases until February 18,2024.According to the scoping review framework,2 researchers independently screened the studies,extracted the data of the included studies,and collaborated on the final analysis.Results A total of 24 studies were included.9 studies were conducted with interventions based on nursing models such as discharge planning model,"Hospital to Home"nutrition management model,guided care nursing model.The management forms included application,network communication software,and website.The content elements included education,assessment,consultation,referral,self-management,electronic health archive,appointment service,peer support.The outcome indicators included patients'physical and mental health,self-management ability,caregiver competence,family burden and user assessment.Conclusion The mHealth has played a positive role in the management of home enteral tube feeding.In the future,it is recommended to establish a multidisciplinary team to conduct high-quality research and continuously improve the form and content of mHealth management.

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 expression of N6-methyladenosine(m6A) modification-related genes and their possible roles in peripheral blood mononuclear cells (PBMCs) of patients with primary gouty arthritis (GA).Methods:Forty-five patients each with acute gout (AG), intermittent gout (IG), and age-and gender-matched healthy controls (HC) were collected from the outpatient clinic of the Department of Rheumatology and Immunology of the Affiliated Hospital of Chuanbei Medical College between October and December of 2023. The expression levels of m6A modification-related genes (METTL3、METTL14、WTAP、FTO、ALKBH5、IGF2BP2、IGF2BP3、YTHDF1、YTHDC2) in PBMCs among the 3 groups were detected by RT-qPCR and correlation analysis with clinical indicators was performed. Measurements conforming to normal distribution were analyzed using ANOVA or t-tests, and data were analyzed using the Kruskal-Wallis H-test and Mann-Whitney U-test for data that is not-normaly distributed. The value of m6A modification-related genes for the diagnosis of GA was evaluated using subject characterization curve ROC. Results:①There were statistically significant differences in the expression of IGF2BP2 ( Z=-3.59, P<0.001)、WTAP ( Z=-5.25, P<0.001)、METTL14 ( Z=-3.62, P<0.001)、YTHDF1 ( Z=-2.12, P=0.034)and YTHDC2 ( Z=-2.00, P=0.045) in the disease group and the normal control group. Among them, the expression of IGF2BP2 in the GA group [28.08 (17.99, 47.06)×10 -4] was significantly higher than that in the HC group [19.23 (12.90, 25.78)×10 -4], and the expressions of WTAP、METTL14、YTHDF1 and YTHDC2 in the GA group [298.61 (213.61, 377.80)×10 -4, 9.94 (6.43, 13.46)×10 -4, 52.63 (28.22, 72.77)×10 -4, 40.24 (20.74, 73.32)×10 -4] were significantly lower than those in the HC group [398.45(339.88, 454.89)×10 -4, 13.27(11.07, 15.85)×10 -4, 64.43(43.61, 87.10)×10 -4, 53.11(36.37, 79.28)×10 -4]. Further subgroup analysis revealed statistically significant differences in the expression of IGF2BP2、WTAP、METTL14、YTHDF1 and YTHDC2 among the 3 groups ( H=19.62、31.73、13.14、16.64、28.90, all P≤0.001). The expressions of WTAP and METTL14 in the AG group [311.13(234.96, 426.67)×10 -4, Z=-3.27, P=0.001; 9.64 (5.21, 15.21)×10 -4, Z=-2.71, P=0.008] and IG group [272.27 (203.29, 347.95)×10 -4, Z=-5.78, P<0.001; 10.40(6.88, 12.88)×10 -4, Z=-3.54, P=0.003] were lower than those in the HC group [398.45 (339.88, 454.89)×10 -4, 13.27(11.07, 15.85)×10 -4]. However, there was no significant difference between AG and IG group ( P>0.05). Both YTHDF1 and YTHDC2 were significantly lower in the AG group [38.10(16.19, 56.78)×10 -4, 24.31 (14.35, 42.77)×10 -4] than those in the IG group [64.13 (48.28, 74.40)×10 -4(Z=-3.54, P<0.001, 65.49 (39.89, 91.23)×10 -4(Z=-4.96, P<0.001)] and HC group [64.43 (43.61, 86.92)×10 -4(Z=-3.51, P<0.001), 53.11 (36.37, 79.28)×10 -4(Z=-4.25, P<0.001)]. But there was no statistically significant difference between IG and HC groups ( P>0.05); IGF2BP2 was significantly lower in the AG group [25.32(16.40, 40.43)×10 -4, Z=-2.46, P=0.014] and HC group [19.23 (12.90, 25.78)×10 -4, Z=-4.54, P<0.001] than in the IG group [31.10(22.60, 49.58)×10 -4], but the comparison between AG and HC showed no statistically significant difference( P>0.05). ②Spearman correlation analysis showed that in GA patients, the expression of IGF2BP2、METTL14 and YTHDF1 was positively correlated with plasma glucose、blood uric acid(sUA) and total cholesterol level respectively ( r=0.22, P=0.037; r=0.38, P=0.003; r=0.23, P=0.034), and WTAP was negatively correlated with GLU ( r=-0.25, P=0.020). ③The ROC curve for the joint prediction of the five differential genes showed that the 95% CI for area under the curve in GA was 0.90 (0.84, 0.95). Conclusion:The m6A modification-related genes are abnormally expressed in GA and are correlated with clinical indicators such as GLU and UA, which are hypothesized to be involved in the pathogenesis of GA and have a certain reference value for the evaluation of metabolism in GA 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.

This study aimed to investigate the effective substances and mechanism of Yishen Guluo Mixture in the treatment of chronic glomerulonephritis(CGN) based on metabolomics and serum pharmacochemistry. The rat model of CGN was induced by cationic bovine serum albumin(C-BSA). After intragastric administration of Yishen Guluo Mixture, the biochemical indexes related to renal function(24-hour urinary protein, serum urea nitrogen, and creatinine) were determined, and the efficacy evaluations such as histopathological observation were carried out. The serum biomarkers of Yishen Guluo Mixture in the treatment of CGN were screened out by ultra-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry(UPLC-Q-TOF-MS) combined with multivariate statistical analysis, and the metabolic pathways were analyzed. According to the mass spectrum ion fragment information and metabolic pathway, the components absorbed into the blood(prototypes and metabolites) from Yishen Guluo Mixture were identified and analyzed by using PeakView 1.2 and MetabolitePilot 2.0.4. By integrating metabolomics and serum pharmacochemistry data, a mathematical model of correlation analysis between serum biomarkers and components absorbed into blood was constructed to screen out the potential effective substances of Yishen Guluo Mixture in the treatment of CGN. Yishen Guluo mixture significantly decreased the levels of 24-hour urinary protein, serum urea nitrogen, and creatinine in rats with CGN, and improved the pathological damage of the kidney tissue. Twenty serum biomarkers of Yishen Guluo Mixture in the treatment of CGN, such as arachidonic acid and lysophosphatidylcholine, were screened out, involving arachidonic acid metabolism, glycerol phosphatide metabolism, and other pathways. Based on the serum pharmacochemistry, 8 prototype components and 20 metabolites in the serum-containing Yishen Guluo Mixture were identified. According to the metabolomics and correlation analysis of serum pharmacochemistry, 12 compounds such as genistein absorbed into the blood from Yishen Guluo Mixture were selected as the potential effective substances for the treatment of CGN. Based on metabolomics and serum pharmacochemistry, the effective substances and mechanism of Yishen Guluo Mixture in the treatment of CGN are analyzed and explained in this study, which provides a new idea for the development of innovative traditional Chinese medicine for the treatment of CGN.
Animals ; Rats ; Arachidonic Acid ; Biomarkers/blood* ; Blood Proteins ; Chromatography, High Pressure Liquid ; Creatinine ; Drugs, Chinese Herbal/therapeutic use* ; Glomerulonephritis/metabolism* ; Metabolomics ; Urea ; Chronic Disease ; Disease Models, Animal ; Complex Mixtures/therapeutic use*

Since Curcumae Radix decoction pieces have multiple sources, it is difficult to distinguish depending on traditional cha-racters, and the mixed use of multi-source Curcumae Radix will affect its clinical efficacy. Heracles Neo ultra-fast gas phase electronic nose was used in this study to quickly identify and analyze the odor components of 40 batches of Curcumae Radix samples from Sichuan, Zhejiang, and Guangxi. Based on the odor fingerprints established for Curcumae Radix decoction pieces of multiple sources, the odor components was identified and analyzed, and the chromatographic peaks were processed and analyzed to establish a rapid identification method. Principal component analysis(PCA), discriminant factor analysis(DFA), and soft independent modeling cluster analysis(SIMCA) were constructed for verification. At the same time, one-way analysis of variance(ANOVA) combined with variable importance in projection(VIP) was employed to screen out the odor components with P<0.05 and VIP>1, and 13 odor components such as β-caryophyllene and limonene were hypothesized as the odor differential markers of Curcumae Radix decoction pieces of diffe-rent sources. The results showed that Heracles Neo ultra-fast gas phase electronic nose can well analyze the odor characteristics and rapidly and accurately discriminate Curcumae Radix decoction pieces of different sources. It can be applied to the quality control(e.g., online detection) in the production of Curcumae Radix decoction pieces. This study provides a new method and idea for the rapid identification and quality control of Curcumae Radix decoction pieces.
Drugs, Chinese Herbal/analysis* ; Electronic Nose ; China ; Plant Roots/chemistry* ; Limonene/analysis* ; Chromatography, High Pressure Liquid