BACKGROUND:Carboxylesterase 1 and inflammatory factors play a crucial role in regulating lipid metabolism and glucose homeostasis.However,the effects of different exercise intensity interventions on carboxylesterase 1 and inflammatory factors in skeletal muscle of type 2 diabetic rats remain to be revealed. OBJECTIVE:To investigate the effects of different exercise intensity interventions on carboxylesterase 1 and inflammatory factors in skeletal muscle of type 2 diabetic rats. METHODS:Thirty-two 8-week-old male Sprague-Dawley rats were randomly divided into normal control group(n=12)and modeling group(n=20)after 1 week of adaptive feeding.Rat models of type 2 diabetes mellitus were prepared by high-fat diet and single injection of streptozotocin.After successful modeling,the rats were randomly divided into diabetic control group(n=6),moderate-intensity exercise group(n=6)and high-intensity intermittent exercise group(n=6).The latter two groups were subjected to treadmill training at corresponding intensities,once a day,50 minutes each,and 5 days per week.Exercise intervention in each group was carried out for 6 weeks.After the intervention,ELISA was used to detect blood glucose and blood lipids of rats.The morphological changes of skeletal muscle were observed by hematoxylin-eosin staining.The mRNA expression levels of carboxylesterase 1 and inflammatory cytokines were detected by real-time quantitative PCR.The protein expression levels of carboxylesterase 1 and inflammatory cytokines were detected by western blot and immunofluorescence. RESULTS AND CONCLUSION:Compared with the normal control group,fasting blood glucose,triglyceride,low-density lipoprotein cholesterol,insulin resistance index in the diabetic control group were significantly increased(P<0.01),insulin activity was decreased(P<0.05),and the mRNA and protein levels of carboxylesterase 1,never in mitosis gene A related kinase 7(NEK7)and interleukin 18 in skeletal muscle tissue were upregulated(P<0.05).Compared with the diabetic control group,fasting blood glucose,triglyceride,low-density lipoprotein cholesterol,and insulin resistance index in the moderate-intensity exercise group and high-intensity intermittent exercise group were down-regulated(P<0.05),and insulin activity was increased(P<0.05).Moreover,compared with the diabetic control group,the mRNA level of NEK7 and the protein levels of carboxylesterase 1,NEK7 and interleukin 18 in skeletal muscle were decreased in the moderate-intensity exercise group(P<0.05),while the mRNA levels of carboxylesterase 1,NEK7,NOD-like receptor heat protein domain associated protein 3 and interleukin 18 and the protein levels of carboxylesterase 1 and interleukin 18 in skeletal muscle were downregulated in the high-intensity intermittent exercise group(P<0.05).Hematoxylin-eosin staining showed that compared with the diabetic control group,the cavities of myofibers in the moderate-intensity exercise group became smaller,the number of internal cavities was reduced,and the cellular structure tended to be more intact;the myocytes of rats in the high-intensity intermittent exercise group were loosely arranged,with irregular tissue shape and increased cavities in myofibers.To conclude,both moderate-intensity exercise and high-intensity intermittent exercise can reduce blood glucose,lipid,insulin resistance and carboxylesterase 1 levels in type 2 diabetic rats.Moderate-intensity exercise can significantly reduce the expression level of NEK7 protein in skeletal muscle,while high-intensity intermittent exercise can significantly reduce the expression level of interleukin 18 protein in skeletal muscle.In addition,the level of carboxylesterase 1 is closely related to the levels of NEK7 and interleukin 18.

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.

ObjectiveThe U6 promoter is an essential element for driving sgRNA expression in the clustered regularly interspaced short palindromic repeat sequences/CRISPR-associated protein 9（CRISPR/Cas9）gene editing system in dicotyledonous plants. Endogenous U6 promoters typically exhibit higher transcriptional activity， which can significantly improve gene editing efficiency. This study aims to identify endogenous U6 promoters in Artemisia annua to optimize its CRISPR/Cas9 gene editing system， which holds significant importance for its molecular breeding. MethodsOn the basis of the highly conserved U6 snRNA sequences in Arabidopsis thaliana， endogenous U6 promoters were screened in the A. annua genome. Expression vectors were constructed with candidate AaU6 promoter driving the firefly luciferase （LUC） reporter gene， and then transiently transformed into Nicotiana benthamiana. Transcriptional activities of the promoters were measured and compared by in vivo imaging and the Dual Luciferase Reporter assay. ResultsEight endogenous U6 promoters were successfully cloned from A. annua. Sequences alignment revealed that all these promoters contained the two conserved cis-acting elements， upstream sequence element （USE） and TATA-box， which affected their transcriptional activity. Dual-luciferase activity assays indicated that the transcriptional activities of AaU6-3， AaU6-1， and AaU6-5 were significantly higher than that of the Arabidopsis AtU6-26 promoter， with AaU6-3 exhibiting the highest activity. ConclusionThis study identified three endogenous AaU6 promoters with high transcriptional activity in A. annua， providing key functional elements for establishing an efficient gene editing system in A. annua. These findings will contribute to advancing precision molecular breeding and high-quality germplasm innovation in A. annua.

OBJECTIVE: To investigate the regulatory effect of electroacupuncture (EA) at "Neiguan" (PC6) on mitochondrial autophagy in rats with myocardial ischemia-reperfusion injury (MIRI) at different phases (ischemia and reperfusion phases), and to explore the bidirectional regulatory effects of EA at "Neiguan" (PC6) and its potential mechanism. METHODS: Forty-five male SD rats were randomly divided into 6 groups according to the random number table method, namely, sham-operation group (n=9), model-A group (n=6), model-B group (n=9), EA-A1 group (n=6), EA-B1 group (n=6), and EA-B2 group (n=9). Except the rats in the sham-operation group, the MIRI model was established in the other groups with the physical ligation and tube pushing method. In the model-A group, the samples were collected directly after ligation, and in the model-B group, the samples were collected after ligation and reperfusion. In the EA-A1 group, EA was delivered while the ligation was performed, and afterwards, the samples were collected. In the EA-B1 group, while the ligation was performed, EA was operated at the same time, and after reperfusion, the samples were collected. In the EA-B2 group, during ligation and the opening of the left anterior descending branch of the coronary artery, EA was delivered, and after reperfusion, the samples were collected. EA was performed at bilateral "Neiguan" (PC6), with a disperse-dense wave, a frequency of 2 Hz/100 Hz, a current of 1 mA, and a duration of 30 min. HE staining was employed to observe the morphology of cardiomyocytes, TUNEL was adopted to detect the apoptosis of cardiomyocytes, transcriptome sequencing was to detect the differentially expressed genes in the left ventricle, JC-1 flow cytometry was to detect the mitochondrial membrane potential (MMP) of cardiomyocytes, Western blot was to detect the protein expression of phosphatase and tensin homolog-induced kinase 1 (Pink1), Parkin and p62 in the left ventricle of rats, and ELISA was to detect the levels of serum creatine kinase isoenzyme (CK-MB) and cardiac troponin I (cTn-I) in the rats. RESULTS: Compared with the sham-operation group, the cardiomyocytes of rats in the model-B group were severely damaged, with disordered arrangement, unclear boundaries, broken muscle fibers, edema and loose distribution; and the cardiomyocytes in the EA-B2 group were slightly damaged, the cell structure was partially unclear, the cells were arranged more regularly, and the intact cardiomyocytes were visible. Compared with the sham-operation group, the apoptosis of cardiomyocytes increased in the model-B group (P<0.001); and when compared with the model-B group, the apoptosis alleviated in the EA-B2 group (P<0.001). The differentially expressed genes among the EA-B2 group, the sham-operation group and the model-B group were closely related to cell autophagy and mitochondrial autophagy. Compared with the sham-operation group, MMP of cardiomyocytes was reduced (P<0.001), the protein expression of Pink1, Parkin, and p62 of the left ventricle and the levels of serum CK-MB and cTn-I were elevated in the model B group (P<0.001). In comparison with model-A group, the MMP of cardiomyocytes and the levels of serum CK-MB and cTn-I were reduced (P<0.001, P<0.05), and the protein expression of Pink1 in the left ventricle rose in the EA-A1 group (P<0.01). Compared with the model-B group, MMP of cardiomyocytes increased (P<0.001), the protein expression of Pink1, Parkin, and p62 of the left ventricle, and the levels of serum CK-MB and cTn-I decreased (P<0.001) in the EA-B1 group and the EA-B2 group. When compared with the EA-A1 group, MMP of cardiomyocytes increased (P<0.001), and the protein expression of Pink1, Parkin, and p62 of the left ventricle, and the levels of serum CK-MB and cTn-I decreased in the EA-B1 group (P<0.01). CONCLUSION EA at "Neiguan" (PC6) can ameliorate MIRI in rats, which may be achieved through the Pink1/Parkin-mediated mitochondrial autophagy pathway. EA can alleviate myocardial injury by enhancing mitochondrial autophagy at the ischemia phase, and it can reduce reperfusion injury by weakening mitochondrial autophagy at the reperfusion phase.
Animals ; Electroacupuncture ; Male ; Myocardial Reperfusion Injury/metabolism* ; Rats, Sprague-Dawley ; Rats ; Acupuncture Points ; Autophagy ; Humans ; Mitochondria/genetics*

OBJECTIVE To construct a double helix model for evaluating the job competency of licensed pharmacists in retail pharmacies， and to provide a reference for building a talented team of licensed pharmacists and promoting the high-quality development of pharmaceutical care. METHODS Based on the competency iceberg model and double helix structure theory， literature analysis and the Delphi method were adopted to explore the model indicators； questionnaires were designed to survey licensed pharmacists in retail pharmacies from Shandong Province. The exploratory factor analysis was used to correct the model indicators and establish a double helix model for evaluating the job competency of licensed pharmacists in retail pharmacies， which was examined by validation factor analysis. RESULTS The recovery rate of the questionnaires in the two rounds of expert consultation was greater than 90%， the expert authority coefficients were both more than 0.86， and the overall Kendall’s W values were 0.288 and 0.510， respectively； after the correction of the exploratory factor analysis， the double helix model for evaluating the job competency of licensed pharmacists in retail pharmacies was established， which contained 4 first-level indexes such as occupational literacy， occupational knowledge， occupational competency and internal motivation， and 23 second-level indexes. The model contained occupational knowledge and occupational competency in the explicit competency chain， occupational literacy and internal motivation in the implicit competency chain， and policy support， incentive mechanism， education and training as the hydrogen bonds connecting the two main chains. The validation factor analysis showed that the model’s goodness of fit index， comparative fit index， and normed fit index all exceeded 0.9； the reliability of the combination of the four first-level indexes ranged from 0.89 to 0.95； the average variance extracted ranged from 0.58 to 0.75， and Cronbach’s α coefficients for both the overall model and the first-level indicators were all greater than 0.84. CONCLUSIONS The constructed double helix model for evaluating the job competency of licensed pharmacists in retail pharmacies is scientific， reasonable and practical.

OBJECTIVE To elucidate the mechanisms responsible for the inhibitory effects of limonene on the proliferation of non-small cell lung cancer(NSCLC) by non-targeted metabolomics and additional approaches. METHODS The CCK-8 assay was utilized to evaluate the inhibitory effects of limonene on NSCLC A549 cell viability and to ascertain the IC50. In vitro experiments, encompassing colony formation, flow cytometry, iron content assessment, and mitochondrial staining, were conducted to assess the anti-lung cancer and iron-induced cell death effects of limonene. Metabolomic analysis was employed to identify potential pathways influenced by limonene, and Western blotting was carried out to validate pivotal proteins within these pathways. RESULTS In comparison to the control group, the limonene-treated group demonstrated a significant, dose-dependent reduction in A549 cell proliferation and colony formation. Optical microscopy revealed cellular detachment and pronounced changes in cellular morphology following exposure to limonene. Limonene induced apoptosis in A549 cells and arrested them in the G0-G1 phase of the cell cycle. Confocal microscopy unveiled diminished mitochondrial fluorescence and an augmented intracellular iron content, indicative of the classical phenomenon of ferroptosis. Metabolomic investigations unveiled divergent metabolic pathways, including glutathione(GSH) metabolism, arginine biosynthesis, D-glutamine and D-glutamate metabolism, as well as cysteine and methionine metabolism, with many of them intricately linked to intracellular GSH synthesis. Western blotting experiments underscored a marked reduction in the levels of SLC40A1, SLC7A11(xCT), and GPX4 proteins within the cells post-limonene treatment. CONCLUSION Limonene may induce ferroptosis in lung cancer cells by reducing GSH synthesis and increasing Fe2+ levels.