ObjectiveTo investigate the effect of Homebox A6 （HOXA6） on the proliferation， invasion， metastasis， and apoptosis of HepG2 hepatoma cells and its association with the PI3K/AKT signaling pathway. MethodsHepG2 hepatoma cells were cultured， and HOXA6 overexpression plasmid and siRNA were constructed and transfected into cells. The cells were randomly divided into empty plasmid group， HOXA 6 overexpression group， siRNA negative control group， and siRNA HOXA6 interference group. CCK8 assay was used to measure cell proliferation， Transwell assay was used to observe cell invasion， and wound healing assay was used to observe cell migration （related proteins TIMP3， MMP9， and MMP3）. Flow cytometry was used to measure cell apoptosis （related proteins BAX and BCL2）， the BCA method was used to measure protein concentration， and Western Blot was used to measure the expression of related proteins. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the SNK-q test was used for further comparison between two groups. ResultsCompared with the empty plasmid group， HOXA6 overexpression significantly promoted the proliferation， invasion， and migration of HepG2 hepatoma cells （all P<0.001）， and there was a significant reduction in the protein expression of TIMP3 （P<0.001）， while there were significant increases in the expression levels of MMP9 and MMP3 （both P<0.001）. Compared with the siRNA negative control group， HOXA6 interference significantly inhibited the proliferation， invasion， and migration of HepG2 hepatoma cells （all P<0.001）， and there was a significant increase in the protein expression of TIMP3 （P<0.001）， while there were significant reductions in the expression levels of MMP9 and MMP3 （both P<0.001）. Flow cytometry showed that compared with the empty plasmid group， HOXA6 overexpression inhibited the apoptosis of HepG2 hepatoma cells （P<0.001）， with a significant reduction in the expression of the apoptosis-related protein BAX and a significant increase in the expression of BCL2 （both P<0.001）. Compared with siRNA negative control group， HOXA6 interference promoted the apoptosis of HepG2 hepatoma cells （P<0.001）， with a significant increase in the expression of BAX and a significant reduction in the expression of BCL2 （both P<0.001）. Compared with the empty plasmid group， the HOXA6 overexpression group had significantly higher ratios of p-AKT/AKT and p-PI3K/PI3K （both P<0.001）， and compared with the siRNA negative control group， the siRNA HOXA6 interference group had significantly lower ratios of p-AKT/AKT and p-PI3K/PI3K （both P<0.001）. ConclusionHOXA6 can promote the proliferation， invasion， and metastasis of HepG2 hepatoma cells and inhibit their apoptosis by activating the PI3K/AKT signaling pathway through phosphorylation.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective Using female mice to investigate the reparative effects of human umbilical cord mesenchymal stem cells on radiation-induced ovarian injury. Methods Mice were randomly divided into three groups: a blank control group, a radiation model group, and a cell therapy group. Mice in the radiation model group and the cell therapy group received a single whole-body irradiation of 5 Gy X-rays. Within 2 hours post-irradiation, mice in the cell therapy group underwent ovarian transplantation of UC-MSCs. On days 1, 7, and 14 post-irradiation, body weight was measured, ovarian index was calculated, histopathological changes in ovarian tissue were examined, serum levels of reproductive hormones (follicle-stimulating hormone, anti-Müllerian hormone, and estradiol) were determined, and the colonization of implanted UC-MSCs in the mice was observed. Results On days 1, 7, and 14 post-irradiation, both the cell therapy group and the radiation model group showed decreased body weight compared to the blank control group (P < 0.05). On day 1 post-irradiation compared to day 1 pre-irradiation within the same group, the radiation model group exhibited a greater decrease in body weight than the cell therapy group (P < 0.05). On days 1, 7, and 14 post-irradiation, the ovarian index decreased in both the radiation model group and the cell therapy group compared to the blank control group (P < 0.05). On days 7 and 14 post-irradiation, the ovarian index in the cell therapy group was significantly higher than that in the radiation model group (P < 0.05). Ovarian tissue in the radiation model group exhibited atrophy and a reduction in the number of follicles at all stages. In contrast, follicles in the cell therapy group were large and abundant. On days 1, 7, and 14 post-irradiation, serum follicle-stimulating hormone levels in the cell therapy group were lower than those in the radiation model group, while anti-Müllerian hormone and estradiol levels were higher than those in the radiation model group (P < 0.01). In vivo fluorescence imaging demonstrated that UC-MSCs successfully colonized the ovarian tissue on days 1, 7, and 14 after transplantation. Conclusion UC-MSCs exert a repair effect on radiation-induced ovarian injury in mice.

[摘 要] 目的：探究组蛋白去乙酰化酶（HDAC）抑制剂帕比司他对黑色素瘤生长和免疫性的影响及其机制。方法：常规培养黑色素瘤细胞B16F0，用不同浓度的帕比司他处理细胞，WB法检测帕比司他对B16F0细胞中HDAC表达的影响，CCK-8法、划痕愈合实验、Transwell实验和流式细胞术分别检测帕比司他对B16F0细胞增殖、迁移和侵袭能力，以及细胞凋亡和周期的影响。转录组学检测帕比司他对B16F0细胞基因表达的影响，用qPCR法加以验证。流式细胞术检测帕比司他对B16F0细胞表面MHC Ⅰ/Ⅱ类分子表达的影响，B16F0与骨髓来源树突状细胞（BMDC）共培养检测帕比司他对BMDC细胞表达CD11c、CD80和CD86的影响，B16F0细胞移植瘤实验检测帕比司他对移植瘤生长和裸鼠免疫功能的影响。结果：帕比司他促进B16F0细胞中组蛋白3（H3）和α-微管蛋白（α-TUB）蛋白乙酰化（P < 0.01或P < 0.001或P < 0.000 1），抑制B16F0细胞增殖、迁移和侵袭能力，促进其凋亡，并使细胞周期阻滞于G1期（P < 0.05或P < 0.001或P < 0.000 1），促进B16F0细胞表面表达MHC Ⅰ/Ⅱ类分子表达并促进共培养BMDC成熟（均P < 0.01）。转录组学检测结果显示，帕比司他促进B16F0细胞中E-cadherin和抗原提呈相关基因的表达，抑制N-cadherin、vimentin、c-Myc和CDK1的表达，qPCR法验证了这些结果。帕比司他抑制裸鼠移植瘤的生长并增强荷瘤裸鼠的免疫功能（P < 0.05, P < 0.000 1）。结论：帕比司他可抑制B16F0细胞的恶性生物学行为，促进其凋亡，调控其免疫性，增强荷瘤裸鼠的免疫功能。

ObjectiveEndothelial cell dysfunction being the core link. This study explores the molecular mechanism of Danshen Tongluo formula in treating coronary artery disease-dominated panvascular disease with endothelial cell changes as the core through animal experiments and single-cell transcriptome sequencing. MethodsA rat model of coronary artery disease-dominated panvascular disease was established by ligating the left anterior coronary artery. Rats were randomized into a blank group， a model group， and a Danshen Tongluo formula （28 mg·kg^-1·d^-1） group. The efficacy was evaluated by examining the cardiac ultrasound， determination of the plasma level of N-terminal pro-brain natriuretic peptide， and pathological staining. After single-cell sequencing， SingleR package， public datasets， and related literature were used for annotation of the cells. Cell chat was used for intercellular communication and ligand-receptor analysis， and scmetabolism was used for metabolic analysis of endothelial cells. ResultsAnimal experiments showed that Danshen Tongluo formula reduced the N-terminal pro-brain natriuretic peptide （ NT-proBNP ） level （P<0.05）， ameliorated myocardial cell damage and fibrosis， and increase left ventricular ejection fractions （LVEF） in the rat model of heart failure after myocardial infarction（P<0.05）. Single-cell sequencing results showed that Danshen Tongluo formula increased the proportion of arterial endothelial cells， venous endothelial cells， and capillary-arterial endothelial cells， while reducing the proportion of capillary-venous endothelial cells. In addition， this formula increased the interaction intensity of endothelial cells with cardiomyocytes and M1 macrophages and reduced the interaction intensity of endothelial cells with fibroblasts and T cells. Danshen Tongluo formula upregulated CXCL12-CXCR4 signaling in endothelium-B cells and Ptprm-Ptprm signaling in endothelial endothelial cells， while downregulating Mif-（CD74⁺CXCR44） signaling in endothelium-M1 macrophages and Mif-（CD74⁺CD44） signaling in endothelium-M2 macrophages. It reduced the citric acid cycle， oxidative phosphorylation， and glycolysis and increased the glycolysis/oxidative phosphorylation ratio in endothelial cells. GO and KEGG enrichment analysis showed that arterial endothelial cells， venous endothelial cells， and venous capillary endothelial cells can all regulate oxidative phosphorylation， cell adhesion molecules， and tyrosine metabolism. Lymphatic endothelial cells regulate immunity and vascular constriction to participate in the metabolism of various amino acids and fatty acids. ConclusionDanshen Tongluo Formula can ameliorate coronary artery disease-dominated panvascular disease by changing the composition of endothelial cells and regulating the communication between myocardial endothelial cells and non-endothelial cells.

OBJECTIVE To investigate the preferences of patients who underwent solid organ transplantation regarding therapeutic drug monitoring （TDM） of mycophenolic acid （MPA） and explore the factors influencing patients’ decision-making process， so as to provide support for the development of individualized medication guidelines for MPA and improvement of clinical decision-making. METHODS The cross-sectional study was used to design the questionnaire on the patients’ preferences to accept MPA TDM， and involved patients who underwent solid organ transplantation and received MPA treatment at two tertiary hospitals in Beijing from April 14， 2022， to June 27， 2022. The Likert 5-level scoring method was used to score the patients’ preferences to accept MPA TDM， the influencing factors and their correlation of the patients’ preferences to accept MPA TDM were analyzed by Pearson correlation analysis and binary Logistic regression analysis， and the nonparametric test and chi-square test were used to rank and analyze the consistency of the factors affecting patients’ preference decision. RESULTS A total of 140 questionnaires were collected， and the effective recovery rate was 77.35%. The average preference score of 140 patients to receive MPA TDM was （4.01±0.65）， and the overall preference value was high. There were 116 （82.86%） patients agreed or strongly agreed with MPA TDM. Significant differences were observed in preference scores between patients who had previously undergone MPA TDM and those who had never undergone it （[ 4.30±0.53） scores vs. （3.80±0.65） scores， P＜0.001]. Additionally， patients’ preference scores were significantly influenced by their understanding level and attention level （P＜0.001）. The ranking of factors contributing to decision-making exhibited consistency （P＜0.001）. The factors were ranked in descending order of clinical efficacy， safety， comfortability， economy and time cost. CONCLUSIONS The patients who underwent solid organ transplantation hold high preferences towards MPA TDM. The primary factors influencing their decisions are their prior experience， understanding level， and attention level.