Aim To screen and study the expression of long non-coding RNA (IncRNA) in rats with middle cerebral artery occlusion (MCAO) with MCAO treated with Tao Hong Si Wu decoction (THSWD) and determine the possible molecular mechanism of THSWD in treating MCAO rats. Methods Three cerebral hemisphere tissue were obtained from the control group, MCAO group and MCAO + THSWD group. RNA sequencing technology was used to identify IncRNA gene expression in the three groups. THSWD-regulated IncRNA genes were identified, and then a THSWD-regu-lated IncRNA-mRNA network was constructed. MCODE plug-in units were used to identify the modules of IncRNA-mRNA networks. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) were used to analyze the enriched biological functions and signaling pathways. Cis- and trans-regulatory genes for THSWD-regulated IncRNAs were identified. Reverse transcription real-time quantitative pol-ymerase chain reaction (RT-qPCR) was used to verify IncRNAs. Molecular docking was used to identify IncRNA-mRNA network targets and pathway-associated proteins. Results In MCAO rats, THSWD regulated a total of 302 IncRNAs. Bioinformatics analysis suggested that some core IncRNAs might play an important role in the treatment of MCAO rats with THSWD, and we further found that THSWD might also treat MCAO rats through multiple pathways such as IncRNA-mRNA network and network-enriched complement and coagulation cascades. The results of molecular docking showed that the active compounds gallic acid and a-mygdalin of THSWD had a certain binding ability to protein targets. Conclusions THSWD can protect the brain injury of MCAO rats through IncRNA, which may provide new insights for the treatment of ischemic stroke with THSWD.

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 explore the difference in the effectiveness between proximal femoral nail anti-rotation(PFNA)and proximal femoral locking compression plate(PFLCP)of intertrochanteric fracture in the elderly patients combined with knee osteoarthritis.Methods The clinical data of 65 intertrochanteric femoral fractures combined with knee osteoarthritis be-tween June 2015 and February 2021 were retrospectively analyze.They were divided into two groups according to the different surgical methods.PFNA group was composed of 36 patients,12 males and 24 females,aged from 61to 88 years old with an av-erage of(77.0±6.4)years old.There were 17 cases of left injury and 19 cases of right injury.According to modified Evans clas-sification,there were 3 cases of type Ⅱ,19 cases of type Ⅲ,10 cases of type Ⅳ,and 4 cases of type Ⅴ.PFLCP group was com-posed of 29 patients,11 males and 18 females,aged from 60 to 92 years old with an average of(78.8±6.5)years old.There were 14 cases of left injury and 15 cases of right injury.According to modified Evans classification,there were 2 cases of typeⅡ,18 cases of type Ⅲ,7 cases of type Ⅳ,and 2 cases of type Ⅴ.Comparison of operation time,intraoperation blood loss,postoperative bed time,incidence of postoperative complications,Harris score at 6 months and 1 year postoperation.Results All 65 patients were followed up ranging from 12 to 24 months with an average of(16.9±3.6)months.In the PFNA and PFLCP groups,the operation time was respectively(57.6±6.8)min and(77.4±6.5)min,the intraoperative blood loss was(128.3±50.3)ml and(156.3±23.9)ml,postoperative bed time was(4.0±2.5)days and(8.1±2.0)days,Harris score at 6 months post-operative was(45.3±8.6)points and(36.3±7.0)points.There were significant differences between two groups(P＜0.05).Inci-dence of postoperative complications was 19.4％(7/36)and 34.5％(10/29),Harris score at 1 year postoperative was(60.8±6.7)points and(59.0±8.1)points.There was no significant difference between the two groups(P＞0.05).Conclusion Compared with PFLCP,PFNA treatment of elderly patients with knee osteoarthritis between the femoral intertrochanteric fractures shorter surgical time,less intraoperative blood loss,bed rest after surgery,short-term hip function recovery better,when the affected knee joint can tolerate traction,can be used as a priority.

Objective:To investigate the frequencies and subset distribution of peripheral helper(Tph)T cells in patients with immune thrombocytopenia(ITP),and explore the pathogenesis of ITP.Methods:A total of 25 newly diagnosed ITP patients treated in The Second Affiliated Hospital of Dalian Medical University from January to December 2022 were selected,and 25 healthy volunteers(age-and sex-matched)were recruited as the control group.Flow cytometry was used to detect the subsets of CD4+T cells and Tph cells.Results:The frequency of effector memory(CCR7-CD45RO+CD4+)T cells in ITP patients was significantly higher than that in healthy controls(P＜0.05).The frequency of Tph cells in ITP patients was also significantly higher than that in healthy controls(P＜0.001),and most of the Tph cells in ITP patients were effector memory T cells.Furthermore,the expressions of T-cell costimulatory molecules in Tph cells,including ICOS and CD84,were similar to those in follicular helper T(Tfh)cells.CXCR3-CCR6-Tph(Tph2)subgroup was dominant in Tph cells,but the frequency of CXCR3+CCR6-Tph(Tph1)cells in ITP patients was much higher than that in healthy controls(P＜0.05).Conclusion:Tph cells,especially Tph1 cells,were abnormally expanded in ITP patients,which may be a potential etiology of ITP.

Aim To screen and study the effects of Tao Hong Si Wu decoction(THSWD)-mediated treat-ment on circular RNA(circRNA)expression profiles in rats with middle cerebral artery occlusion(MCAO),and investigate the possible roles and molecular mecha-nisms of THSWD.Methods Next-generation RNA sequencing was conducted to identify circRNA expres-sion profiles in MCAO rats after treatment with THSWD and compared with the MCAO model group and control group.Bioinformatics analysis was performed to predict the potential target microRNAs and mRNAs.Gene On-tology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses for the potential target mRNAs were applied to explore the potential roles of differentially expressed circRNAs.RT-qPCR was performed to verify circRNAs with significant differences in expression.Results We identified 87 significantly differentially expressed circRNAs between the MCAO group versus the control group,and 86 sig-nificantly differentially expressed circRNAs between the MCAO group versus the THSWD group.respective-ly.Among them,17 circRNAs induced by the MCAO model were reversed via treatment with THSWD.To demonstrate the roles of mRNAs targeted by DECs,the GO and KEGG databases were used.Further analysis revealed that five circRNAs may play important roles in the development of MCAO.Conclusions The com-prehensive expression profile of circRNAs in rats with middle cerebral artery occlusion after THSWD treat-ment is determined for the first time,suggesting that the therapeutic effect of THSWD on MCAO may be a-chieved by regulating the expression of circRNAs.

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.