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.

ObjectiveIn recent years, with the intensification of environmental issues and the depletion of ozone layer, incidence of skin tumors has also significantly increased, becoming one of the major threats to people’s lives and health. However, due to factors such as high concealment in the early stage of skin tumors, unclear symptoms, and large human skin area, most cases are detected in the middle to late stage. Early detection plays a crucial role in postoperative survival of skin tumors, which can significantly improve the treatment and survival rates of patients. We proposed a rapid non-invasive electrical impedance detection method for early screening of skin tumors based on bioimpedance spectroscopy (BIS) technology. MethodsFirstly, we have established a complete skin stratification model, including stratum corneum, epidermis, dermis, and subcutaneous tissue. And the numerical analysis method was used to investigate the effect of dehydrated and dry skin stratum corneum on contact impedance in BIS measurement. Secondly, differentiation effect of different diameter skin tumor tissues was studied using a skin model after removing the stratum corneum. Then, in order to demonstrate that BIS technology can be used for detecting the microinvasion stage of skin tumors, we conducted a simulation study on the differentiation effect of skin tumors under different infiltration depths. Finally, in order to verify that the designed BIS detection system can distinguish between tumor microinvasion periods, we conducted tumor invasion experiments using hydrogel treated pig skin tissue. ResultsThe simulation results show that a dry and high impedance stratum corneum will bring about huge contact impedance, which will lead to larger measurement errors and affect the accuracy of measurement results. We extracted the core evaluation parameter of relaxed imaginary impedance (Z_imag-relax) from the simulation results of the skin tumor model. When the tumor radius (R_tumor) and invasion depth (h)>1.5 mm, the designed BIS detection system can distinguish between tumor tissue and normal tissue. At the same time, in order to evaluate the degree of canceration in skin tissue, the degree of tissue lesion (ε_worse) is defined by the relaxed imaginary impedance (Z_imag-relax) of normal and tumor tissue (ε_worse is the percentage change in virtual impedance of tumor tissue relative to that of normal tissue), and we fitted a Depth-Z_imag-relax curve using relaxation imaginary impedance data at different infiltration depths, which can be applied to quickly determine the infiltration depth of skin tumors after being supplemented with a large amount of clinical data in the future. The experimental results proved that when ε_worse=0.492 0, BIS could identify microinvasive tumor tissue, and the fitting curve correction coefficient of determination was 0.946 8, with good fitting effect. The simulation using pig skin tissue correlated the results of real human skin simulation with the experimental results of pig skin tissue, proving the reliability of this study, and laying the foundation for further clinical research in the future. ConclusionOur proposed BIS method has the advantages of fast, real-time, and non-invasive detection, as well as high sensitivity to skin tumors, which can be identified during the stage of tumor microinvasion.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

ObjectiveThis study aimed to analyze the difference in setup error before and after correction of systematic error. To determine the most appropriate image-guided strategy during HT treatment， we use different scanning ranges and image-guidance frequencies in patients with nasopharyngeal carcinoma （NPC） treated with helical tomotherapy （HT）. MethodsFifteen patients with NPC who received HT treatment in Sun Yat-sen University Cancer Center from October 2019 to February 2020 were selected. Megavoltage computed tomography （MVCT） scanning was performed before each treatment. After five times of radiotherapy， system-error correction was performed to adjust the setup center. The setup errors before and after the correction of systematic errors， as well as the setup errors of different scanning ranges and different scanning frequencies， were collected for analysis and comparison. ResultsWhen comparing the setup errors before and after the correction of systematic error， the differences in setup errors in the left–right （LR）， superior–inferior （SI）， and anterior–posterior （AP） directions were statistically significant （P<0.05）.The different scanning ranges of "nasopharynx + neck" and "nasopharynx" were compared， and a statistically significant difference was found in yaw rotational errors （P<0.05）. In the comparison of daily and weekly scan frequency after system-error correction， a significant difference was found in AP direction （P<0.05）. ConclusionDuring radiotherapy for NPC， the systematic error can be corrected according to the first five setup errors， and then small-scale scanning was selected for image-guided radiotherapy every day.

Objective: To explore the relationship between low density lipoprotein cholesterol (LDL-C)/high density lipoprotein cholesterol (HDL-C) ratio with the severity of coronary artery disease and 2-yeat outcome in patients with premature coronary heart disease. Methods: This prospective, multicenter, observational cohort study is originated from the PROMISE study. Eighteen thousand seven hundred and one patients with coronary heart disease (CHD) were screened from January 2015 to May 2019. Three thousand eight hundred and sixty-one patients with premature CHD were enrolled in the current study. According to the median LDL-C/HDL-C ratio (2.4), the patients were divided into two groups: low LDL-C/HDL-C group (LDL-C/HDL-C≤2.4, n=1 867) and high LDL-C/HDL-C group (LDL-C/HDL-C>2.4, n=1 994). Baseline data and 2-year major adverse cardiovascular and cerebrovascular events (MACCE) were collected and analyzed in order to find the differences between premature CHD patients at different LDL-C/HDL-C levels, and explore the correlation between LDL-C/HDL-C ratio with the severity of coronary artery disease and MACCE. Results: The average age of the low LDL-C/HDL-C ratio group was (48.5±6.5) years, 1 154 patients were males (61.8%); the average age of high LDL-C/HDL-C ratio group was (46.5±6.8) years, 1 523 were males (76.4%). The number of target lesions, the number of coronary artery lesions, the preoperative SNYTAX score and the proportion of three-vessel coronary artery disease in the high LDL-C/HDL-C group were significantly higher than those in the low LDL-C/HDL-C group (1.04±0.74 vs. 0.97±0.80, P=0.002; 2.04±0.84 vs. 1.85±0.84, P<0.001; 13.81±8.87 vs. 11.70±8.05, P<0.001; 36.2% vs. 27.4%, respectively, P<0.001). Correlation analysis showed that there was a significant positive correlation between LDL-C/HDL-C ratio and preoperative SYNTAX score, the number of coronary artery lesions, the number of target lesions and whether it was a three-vessel coronary artery disease (all P<0.05). The 2-year follow-up results showed that the incidence of MACCE was significantly higher in the high LDL-C/HDL-C group than that in the low LDL-C/HDL-C group (6.9% vs. 9.1%, P=0.011). There was no significant difference in the incidence of all-cause death, cardiac death, myocardial infarction, stroke, revascularization and bleeding between the two groups. Cox multivariate regression analysis showed that the LDL-C/HDL-C ratio has no correlation with 2-year MACCE, death, myocardial infarction, revascularization, stroke and bleeding events above BARC2 in patients with premature CHD. Conclusion: High LDL-C/HDL-C ratio is positively correlated with the severity of coronary artery disease in patients with premature CHD. The incidence of MACCE of patients with high LDL-C/HDL-C ratio is significantly higher during 2 years follow-up; LDL-C/HDL-C ratio may be an indicator for evaluating the severity of coronary artery disease and long-term prognosis in patients with premature CHD.
Male ; Humans ; Adult ; Middle Aged ; Female ; Coronary Artery Disease/complications* ; Cholesterol, HDL ; Cholesterol, LDL ; Prospective Studies ; Myocardial Infarction/etiology* ; Stroke ; Risk Factors

BACKGROUND: All-trans retinoic acid (ATRA) promotes the osteogenic differentiation induced by bone morphogenetic protein 9 (BMP9), but the intrinsic relationship between BMP9 and ATRA keeps unknown. Herein, we investigated the effect of Cyp26b1, a critical enzyme of ATRA degradation, on the BMP9-induced osteogenic differentiation in mesenchymal stem cells (MSCs), and unveiled possible mechanism through which BMP9 regulates the expression of Cyp26b1. METHODS: ATRA content was detected with ELISA and HPLC–MS/MS. PCR, Western blot, and histochemical staining were used to assay the osteogenic markers. Fetal limbs culture, cranial defect repair model, and micro–computed tomographic were used to evaluate the quality of bone formation. IP and ChIP assay were used to explore possible mechanism. RESULTS: We found that the protein level of Cyp26b1 was increased with age, whereas the ATRA content decreased. The osteogenic markers induced by BMP9 were increased by inhibiting or silencing Cyp26b1 but reduced by exogenous Cyp26b1. The BMP9-induced bone formation was enhanced by inhibiting Cyp26b1. The cranial defect repair was promoted by BMP9, which was strengthened by silencing Cyp26b1 and reduced by exogenous Cyp26b1. Mechanically, Cyp26b1 was reduced by BMP9, which was enhanced by activating Wnt/b-catenin, and reduced by inhibiting this pathway. b-catenin interacts with Smad1/5/9, and both were recruited at the promoter of Cyp26b1. CONCLUSIONS Our findings suggested the BMP9-induced osteoblastic differentiation was mediated by activating retinoic acid signalling, viadown-regulating Cyp26b1. Meanwhile, Cyp26b1 may be a novel potential therapeutic target for the treatment of bone-related diseases or accelerating bone-tissue engineering.