BACKGROUND:As a degradable scaffold material for bone tissue engineering,lactic acid is widely used in tissue regeneration and repair research,and plays an important role in promoting tissue healing,new bone formation and angiogenesis. OBJECTIVE:To observe the effect of lactic acid degradation products on osteoclasts and to investigate the effects of lactic-interfered osteoclast conditioned medium on the proliferation,migration and tube-forming capacity of human umbilical vein endothelial cells. METHODS:(1)The mouse monocyte macrophage cell line RAW264.7 at logarithmic growth period was selected,and adherent cells were cultured in the osteoclast induction medium(DMEM medium with nuclear factor-κB receptor-activating factor ligand and 10%fetal bovine serum)containing different concentrations of lactic acid(0,5,10,20 mmol/L).After 5 days of culture,tartrate-resistant acid phosphatase staining and cytoskeletal fibrillar actin staining were conducted.After 24 hours of culture,RT-PCR was used to detect the mRNA expression of tartrate-resistant acid phosphatase 5.(2)RAW264.7 cells at logarithmic growth period were selected and adherent cells were divided into two groups.Control group was cultured in the osteoclast induction medium,while experimental group was cultured in the osteoclast induction medium containing 10 mmol/L lactic acid.After 5 days of culture,the medium in each group was removed and the cells in the two groups were cultured in the serum-free DMEM medium for another 24 hours.Cell supernatant was then collected and used as the conditioned medium after mixed with an equal volume of DMEM medium containing 10%fetal bovine serum.Human umbilical vein endothelial cells at the logarithmic growth phase were taken and separately co-cultured with the conditioned medium of the control and experimental groups.The proliferation,migration and tube-forming ability of human umbilical vein endothelial cells were observed by cell counting kit-8 assay,migration assay,scratch assay and tube-forming assay.The mRNA and protein expression of angiogenesis-related genes and proteins were observed by RT-PCR and western blot. RESULTS AND CONCLUSION:Tartrate-resistant acid phosphatase staining and cytoskeletal fibrillar actin staining showed that 5 and 10 mmol/L lactic acid promoted osteoclastic differentiation of RAW264.7 cells and the promoting effect of 10 mmol/L lactate was more significant.RT-PCR results showed that the expression of tartrate-resistant acid phosphatase-5 mRNA of osteoclast-related genes was the highest when the lactic acid concentration was 5,10,and 20 mmol/L(P<0.05),especially 10 mmol/L.Compared with the control group,the proliferation,migration and tube-forming abilities of human umbilical vein endothelial cells were significantly increased in the experimental group(P<0.05).Compared with the control group,the expression levels of vascular endothelial growth factor and angiogenin 1 mRNA and protein were increased in the experimental group(P<0.05).To conclude,lactate-induced osteoclast conditioned medium could promote the angiogenesis of endothelial cells,and the mechanism may be related to the promotion of the expression of vascular endothelial growth factor and angiogenin 1.

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: To investigate the status and influencing factors of platelet bacterial contamination in China, and to provide theoretical support for relevant policies in blood collection and transfusion institutions. Methods: A meta-analysis by systematically searching studies on platelet bacterial contamination in China published between 1998 and 2023 was conducted. Data analysis was performed using R4.4 software to combine studies that met the inclusion criteria. Results: Twenty-three studies were included after screening. The combined analysis showed that the overall contamination rate of platelets in China was 0.18% (95% CI: 0.12%-0.24%). The contamination rate of manually condensed platelets was significantly higher than that of apheresis platelet concentrates (0.28% vs 0.17%, P<0.01). No significant difference in platelet contamination rates was found between eastern and central regions (0.21% vs 0.15%, P>0.01). The contamination rate of aerobic bacteria was higher than that of anaerobic bacteria (0.11% vs 0.06%, P<0.01). Publication bias analysis indicated robust results, and sensitivity analysis showed minimal impact of excluding individual studies on the overall conclusion. Conclusion: Although the platelet contamination rate in China is generally low, significant differences exist across collection methods and regions.

Objective:To analyze the risk factors for asparaginase-associated pancreatitis (AAP) in children with acute lymphoblastic leukemia (ALL) after treatment with pegaspargase and evaluate the predictive value of pediatric sequential organ failure assessment (SOFA) score, pediatric acute pancreatitis severity (PAPS) score, Ranson′s score and pediatric Ministry of Health, Labour and Welfare of Japan (JPN) score for severe AAP.Methods:Cross-sectional study.The clinical data of 328 children with ALL who received pegaspargase treatment in the Department of Pediatric Hematology, Zhujiang Hospital, Southern Medical University from January 2014 to August 2021, as well as their clinical manifestations, laboratory examinations, and imaging examinations were collected.The SOFA score at the time of AAP diagnosis, PAPS score and Ranson′s score at 48 hours after AAP diagnosis, and JPN score at 72 hours after AAP diagnosis were calculated, and their predictive value for severe AAP was evaluated by the receiver operating characteristic (ROC) curve.Results:A total of 6.7%(22/328) of children had AAP, with the median age of 6.62 years.AAP most commonly occurred in the induced remission phase (16/22, 72.7%). Three AAP children were re-exposed to asparaginase, and 2 of them developed a second AAP.Among the 22 AAP children, 16 presented with mild symptoms, and 6 with severe symptoms.The 6 children with severe AAP were all transferred to the Pediatric Intensive Care Unit (PICU). There were no significant differences in gender, white blood cell count at first diagnosis, immunophenotype, risk stratification, and single dose of pegaspargase between the AAP and non-AAP groups.The age at diagnosis of ALL in the AAP group was significantly higher than that in the non-AAP group ( t=2.385, P=0.018). The number of overweight or obese children in the AAP group was also higher than that in the non-AAP group ( χ2=4.507, P=0.034). The areas under the ROC curve of children′s JPN score, SOFA score, Ranson′s score, and PAPS score in predicting severe AAP were 0.919, 0.844, 0.731, and 0.606, respectively.The JPN score ( t=4.174, P=0.001) and the SOFA score ( t=3.181, P=0.005) showed statistically significant differences between mild and severe AAP. Conclusions:AAP is a serious complication in the treatment of ALL with combined pegaspargase and chemotherapy.Older age and overweight or obesity may be the risk factors for AAP.Pediatric JPN and SOFA scores have predictive value for severe AAP.

BACKGROUND:There are many treatment methods for knee osteoarthritis,among which electroacupuncture,as an important non-drug treatment,is effective in the treatment of knee osteoarthritis,but its exact mechanism is not clear. OBJECTIVE:Effect of electroacupuncture on the expression of p53 and P21 in articular cartilage and subchondral bone of aged rats with knee osteoarthritis. METHODS:Eight 6-month-old male Sprague-Dawley rats were included in the young group and sixteen 24-month-old male Sprague-Dawley rats were randomly divided into old group(n=8)and electroacupuncture group(n=8).The rats in the electroacupuncture group received electroacupuncture stimulation once a day,5 days a week,for 8 continuous weeks,and the other two groups did not do any treatment.Eight weeks later,the level of type Ⅱ collagen C-terminal peptide in peripheral blood was detected by ELISA,the morphology of left knee cartilage and subchondral bone was observed by safranin O-fast green staining,the degree of knee cartilage degeneration was evaluated by modified Mankin's score,the microstructure of left knee cartilage and subchondral bone was detected by micro-CT,and the expression levels of matrix metalloproteinase 13,P53,P21 Mrna and protein were detected by RT-PCR and western blot respectively. RESULTS AND CONCLUSION:Compared with the young group,the level of C-terminal peptide of type Ⅱ collagen in the peripheral blood was increased in the old group(P<0.05).The micro-CT results showed that the bone volume fraction,bone mineral density and the number of bone trabeculae were decreased in the old group compared with the young group(P<0.05),while the trabecular separation increased(P<0.05).Safranin O-fast green staining showed that in the old group,the surface layer of cartilage was uneven with fissures,the morphology of chondrocytes was irregular and stained unevenly,the boundary between the cartilage and subchondral bone was blurred,and the matrix loss was serious.The Mankin's score was higher in the old group than the young group(P<0.05).The expression of matrix metalloproteinase 13,P53,P21 at Mrna and protein levels increased in the old group compared with the young group(P<0.05).Compared with the old group,electroacupuncture decreased the level of C-terminal peptide of type Ⅱ collagen(P<0.05),increased the bone volume fraction,bone mineral density and the number of bone trabeculae(P<0.05),and decreased the trabecular separation(P<0.05).Safranin O-fast green staining showed that in the electroacupuncture group,the surface of cartilage was smooth and red staining was uniform,and the cell morphology and structure were between the young group and the old group.Following electroacupuncture treatment,the Mankin's score(P<0.05),matrix metalloproteinase 13 and P21 Mrna expression(P<0.05),and matrix metalloproteinase 13 and P53 protein expression decreased(P<0.05),while there was a decreasing trend of P53 Mrna and P21 protein expression,but with no statistical significance(P>0.05).To conclude,electroacupuncture may delay articular cartilage degeneration and subchondral osteoporosis in aged rats by inhibiting the expression of P53 and P21,so as to protect joints and delay joint aging.

OBJECTIVE To investigate the mechanism of Yifei xuanfei jiangzhuo formula （YFXF） against vascular dementia （VD）. METHODS The differentially expressed genes of YFXF （YDEGs） were obtained by network pharmacology. High-risk genes were screened from YDEGs by using the nomogram model. The optimal machine learning models in generalized linear， support vector machine， extreme gradient boosting and random forest models were screened based on high-risk genes. VD model rats were established by bilateral common carotid artery occlusion， and were randomly divided into model group and YFXF group （12.18 g/kg， by the total amount of crude drugs）， and sham operation group was established additionally， with 6 rats in each group. The effects of YFXF on behavior （using escape latency and times of crossing platform as indexes）， histopathologic changes of cerebral cortex， and the expression of proteins related to the secreted phosphoprotein 1 （SPP1）/phosphoinositide 3-kinase （PI3K）/protein kinase B （aka Akt） signaling pathway and the mRNA expression of SPP1 in cerebral cortex of VD rats were evaluated. RESULTS A total of 6 YDEGs were obtained， among which SPP1， CCL2， HMOX1 and HSPB1 may be high-risk genes of VD. The generalized linear model based on high-risk genes had the highest prediction accuracy （area under the curve of 0.954）. Compared with the model group， YFXF could significantly shorten the escape latency of VD rats， significantly increase the times of crossing platform （P＜0.05）； improve the pathological damage of cerebral cortex， such as neuronal shrinkage and neuronal necrosis； significantly reduce the expressions of SPP1 protein and mRNA （P＜0.05）， while significantly increase the phosphorylation levels of PI3K and Akt （P＜0.05）. CONCLUSIONS VD high-risk genes SPP1， CCL2， HMOX1 and HSPB1 may be the important targets of YFXF. YFXF may play an anti-VD role by down-regulating the protein and mRNA expressions of SPP1 and activating PI3K/Akt signaling pathway.