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: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

Objective Utilizing a specially engineered miR-144-GFP-H1 human embryonic stem cell(hESC)reporter line,this study leverages GFP fluorescence as an indicator of miR-144 expression to gauge the progression of erythropoiesis.The investigation is aimed at elucidating the potential roles of lncRNAs within the erythropoietic framework and conducting an initial assessment of their functional impact.Methods The miR-144/451-GFP-H1 cell line(hereafter referred to as 144-H1)was utilized for in vitro erythrocyte induction culture.The subpopulations of cells entering the erythropoiesis stage were characterized by the surface molecules CD71 and GPA.The GFP reporter gene of miR-144 served as a critical determi-nant to distinguish between GFP-positive cells(with a high propensity for erythropoiesis)and GFP-negative cells(with a low propensity for erythropoiesis).Transcriptome sequencing was performed on both groups to identify differentially ex-pressed long non-coding RNAs(lncRNAs).LncRNA entries with potential for validation were selected for preliminary func-tional verification.The CRISPR/Cas9 gene editing technique was employed to design functional interference strategies for the targeted lncRNAs,obtaining 144-H1 cell lines with knocked-out function of the specific lncRNAs.These knockout cell lines,along with non-knockout 144-H1 cell lines,were used for parallel erythrocyte induction culture to identify differential nodes.This approach preliminarily verified their impact on erythropoiesis in an in vitro development model.Results 1)The constructed 144-H1 cell line was capable of expressing GFP fluorescence upon entering the stage of in vitro erythrocyte in-duction,indicating the activation of miR-144/451.2)Within the CD71,GPA double-positive group,significant differences in lncRNA expression were observed between the GFP-positive and GFP-negative subpopulations.3)Gene editing strategies involving the deletion of sequence segments capable of effectively interfering with the function of multiple lncRNA entries were designed and verified for successful editing.In the knockout cell lines,parts of the lncRNA sequences were directly de-leted.4)In parallel validation experiments of erythrocyte induction culture,cell lines with LINC01569 knocked out exhibited significant differences in flow cytometric subpopulations and cell proliferation capabilities compared to the non-knockout cell lines:①The knockout cell lines showed sustained high expression of GFP fluorescence.②The proportion of the CD71-GPA double-positive group in the knockout cell lines continuously decreased during erythrocyte maturation.③No significant ex-pression of hemoglobin was observed in the knockout cell lines,lacking the characteristic red color.④The cell proliferation capability of the knockout cell lines was significantly lower than that of the non-knockout cell lines(P＜0.05).Conclusion The successful employment of the 144-H1 cell line facilitated an exploration into the potential functions of lncRNAs in e-rythropoiesis.This enables the design of more refined in vitro developmental experiments to enhance the precision in captu-ring lncRNA functions.Among the differentially expressed lncRNA entries,LINC01569 was preliminarily validated to play a regulatory role in erythropoiesis.The functional absence of LINC01569 severely impacts the normal differentiation and prolif-eration of erythrocytes.The specific regulatory mechanism of LINC01569 in erythropoiesis warrants further investigation and research.

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.