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.

The adulteration and counterfeiting of herbal ingredients in medicinal and food homology (MFH) have a serious impact on the quality of herbal materials, thereby endangering human health. Compared to pharmaceutical drugs, health products derived from traditional Chinese medicine (TCM) are more easily accessible and closely integrated into consumers' daily life. However, the authentication of the authenticity of TCM ingredients in MFH has not received sufficient attention. The lack of clear standards emphasizes the necessity of conducting systematic research in this area. This study utilized DNA barcoding technology, combining ITS2, psbA-trnH, matK as universal barcode sequences, and DX, HH, JYH as specific barcode sequences, to identify the authenticity of commercial medicinal and food homology scented herbal teas. The aim was to investigate the authenticity of scented herbal tea products circulating in the market. The research results revealed that among 180 scented herbal tea samples, DNA barcodes were successfully obtained from 164 samples, while the remaining samples either lacked the target components or suffered severe DNA degradation, resulting in failed amplification. Combined with morphological identification studies, it was found that out of the 180 samples, 141 were authentic, accounting for 78.33% of the total samples, while 31 samples showed adulteration and 8 samples lacked the target components, accounting for 21.67% of the total samples. Additionally, water testing revealed that 5 samples of Carthamus tinctorius herbal tea exhibited the phenomenon of weight adulteration. This study exposed the presence of adulteration and weight adulteration in scented herbal tea products, highlighting the need for regulatory authorities to expedite the establishment of quality standards in scented herbal tea industry. These findings provide valuable insights for the rapid development of the scented herbal tea industry.

Objective To investigate the bioequivalence of gliclazide sustained-release tablets in Chinese healthy subjects.Methods The study was designed using a single-center,open,randomized,single-dose,two-cycle,two-sequence administration method;subjects were orally administered the test/reference preparation 30 mg on an fasting or fed conditions,with self-cross-dosing.The concentration of gliclazide in human plasma was determined by liquid chromatography tandem mass spectrometry(LC-MS/MS)method.The main pharmacokinetic parameters of gliclazide(Cmax,AUC0-t and AUC0-∞)were analyzed by non-atrioventricular model of WinNonlin.Result In the fasting study,24 subjects were recruited and 22 completed the study.The main pharmacokinetic parameters of gliclazide sustained-release tablets test preparation and reference preparation in the fasting group were as follows:Cmax were(862.48±294.48)and(902.96±259.09)ng·mL-1;AUC0-t were(2.60 × 104±8 930.46)and(2.50 ×104±7 573.42)h·ng-1·mL-1;AUC0-∞ were(3.00 × 104±1.43 × 104)and(2.68 × 104±7 085.99)h·ng·mL-1.In the fed study,twenty-four subjects were enrolled and 23 completed the study.The main pharmacokinetic parameters of gliclazide sustained-release tablets test preparation and reference preparation in fed group:Cmax were(1 531.74±273.49)and(1 510.87±241.08)ng·mL-1;AUC0-t were(2.78 ×104±9 565.89)and(2.76 ×104±9 821.43)h·ng·mL-1;AUC0-∞ were(3.02 ×104±1.24 ×104)and(3.02 × 104±1.30 × 104)h·ng·mL-1 h·ng·mL-1.The 90％confidence intervals of the geometric mean ratios of Cmax,AUC0-t,AUC0-∞ for the test preparation and reference preparation gliclazide sustained-release tablets were all between 80％and 125％.Conclusion The test and the reference preparation of gliclazide sustained-release tablets are bioequivalent in Chinese healthy subjects.

Objective To explore how to achieve isolation of clinical trial fees and general medical expenses of hospitalized subjects through information technology.Methods This article analyzed various methods for separating clinical trials fees from medical insurance and suggested that all methods need to protect subjects'medical insurance reimbursement rights and ensure that they do not occupy medical insurance funds.Based on this principle,A clinical trial cost segregation information module was developed based on inpatient's workstation.Results Through this information module,subjects could choose their medical status according to the actual medical situation.Doctors could issue medical orders in hospital information system(HIS)according to normal medical or trail needs,change the nature of the orders by adding or deleting clinical trial tags.The system automatically intercepts the clinical trial orders for reimbursement of medical insurance,while other expenses are reimbursed according to rules of medical insurance.Conclusion The application of this information module has improved the subjects'enthusiasm and compliance to participate in clinical trials,reduced their economic burden,and ensured their rights to enjoy reimbursement of medical insurance,which can be used as a reference example for newly registered clinical trial institution.

OBJECTIVE: To analyze the risk factors for refracture of adjacent vertebrae after percutaneous vertebroplasty (PVP) in super-old patients with osteoporotic vertebral compression fractures(OVCFs). METHODS: A retrospective analysis was performed on 40 patients(age≥90 years) with OVCFs who underwent PVP between June 2012 and June 2019. There were 7 males and 33 females, age from 90 to 101 years old with an average of (94.6±1.6) years. Patients were divided into two groups according to whether adjacent vertebral refracture occurred after PVP. Among them, 20 patients occurred refracture after PVP (refracture group) and 20 patients did not occur it(control group). The general information, radiological data and pelvic parameters of the two groups were collected. The items included age, gender, body mass index (BMI), fracture site and bone mineral density(BMD) T-value, fracture to operation time, compression degree of injured vertebra, recovery degree of anterior edge of injured vertebra, bone cement injection amount, bone cement leakage, pelvic index(PI), pelvic tilt angle (PT), sacral angle(SS), et al. Factors that may be related to refracture were included in the single-factor study, and multivariate Logistic regression analysis was performed on the risk factors with statistical significance in the single-factor analysis to further clarify the independent risk factors for refracture of adjacent vertebral bodies after PVP. RESULTS: There were no significant differences in age, gender, fracture site, fracture to operation time, compression degree of injured vertebra and recovery degree of anterior edge of injured vertebra between two groups (P>0.05). There were significant differences in BMI, BMD T-value, bone cement injection amount and bone cement leakage rate between two groups(P<0.05). The PI and PT values of the refracture group were higher than those of the control group(P<0.05). There was no significant difference in SS between two groups (P>0.05). Multivariate Logistic regression analysis showed that decreased BMD T-value, bone cement leakage, increased PT and PI values increased the risk of recurrence of adjacent vertebral fractures in OVCFs (P<0.05). CONCLUSION There are many risk factors for the recurrence of adjacent vertebral fractures in super-old patients with OVCFs. Patients with high PI and PT values may be one of the risk factors.
Aged, 80 and over ; Bone Cements ; Female ; Fractures, Compression/surgery* ; Humans ; Male ; Osteoporotic Fractures/surgery* ; Retrospective Studies ; Risk Factors ; Spinal Fractures/surgery* ; Spine ; Treatment Outcome ; Vertebroplasty/adverse effects*

Objective: To explore the safety and feasibility of stereotactic radiation therapy (SBRT) strategy for irradiating porcine ventricular septum, see if can provide a preliminary experimental evidence for clinical SBRT in patients with hypertrophic obstructive cardiomyopathy (HOCM). Methods: Five male pigs (39-49 kg, 6 months old) were used in this study. Pigs were irradiated at doses of 25 Gy (n=2) or 40 Gy (n=3). Delineation of the target volume was achieved under the guidance of 3-dimensional CT image reconstruction, and SBRT was then performed on defined target volume of porcine ventricular septum. Blood biomarkers, electrocardiogram and echocardiography parameters were monitored before and after SBRT. Pathological examination (HE staining, Masson staining) was performed on the target and non-target myocardium at 6 months post SBRT. Results: SBRT was successful and all animals survived to the designed study endpoint (6 months) after SBRT. Serum cardiac troponin T (cTnT) level was significantly higher than the baseline level at 1 day post SBRT, and reduced at 1 week after SBRT, but was still higher than the baseline level(P<0.05). Serum N-terminal pro-B type natriuretic peptide (NT-proBNP) was also significantly increased at 1 day post SBRT (P<0.05) and returned to baseline level at 1 week post SBRT. The serum NT-proBNP level was (249±78), (594±37) and (234±46) pg/ml, respectively, and the cTnT was (14±7), (240±40) and (46±34) pg/ml, respectively at baseline, 1 day and 1 week after SBRT in the 40 Gy dose group. The serum NT-proBNP level was (184±20), (451±49) and (209±36) pg/ml, respectively, the cTnT values ​​were (9±1), (176±29) and (89±27) pg/ml, respectively at baseline, 1 day and 1 week after SBRT in the 25 Gy dose group. Both NT-proBNP and cTnT values tended to be higher post SBRT in the 40 Gy dose group as compared with the 25 Gy dose group, but the difference was not statistically significant (P>0.05). The left ventricular ejection fraction and the left ventricular end-diastolic diameter remained unchanged before and after SBRT (P>0.05). The interventricular septum thickness showed a decreasing trend at 6 months after SBRT, but the difference was not statistically significant ((9.54±0.24) mm vs. (9.82±8.00) mm, P>0.05). The flow velocity of the left ventricular outflow tract, and the valve function and morphology were not affected by SBRT. At 6 months after SBRT, HE staining revealed necrosis in the irradiated target area of ​​the myocardium in the 40 Gy dose group and the 25 Gy dose group, and the degree of necrosis in the irradiated interventricular septum was more obvious in the 40 Gy dose group as compared with the 25 Gy group. The combined histological analysis of the two groups showed that the necrotic area of ​​the irradiated target area accounted for (26±9)% of the entire interventricular septum area, which was higher than that of the non-irradiated area (0) (P<0.05). There was no damage or necrosis of myocardial tissue outside the target irradiation area in both groups. The results of Masson staining showed that the percentage area of myocardial fibrosis was significantly higher in the irradiated target area than non-irradiated area ((12.6±5.3)% vs. (2.5±0.8)%, P<0.05). Conclusion: SBRT is safe and feasible for irradiating porcine ventricular septum.
Animals ; Feasibility Studies ; Male ; Necrosis ; Radiosurgery/methods* ; Stroke Volume ; Swine ; Ventricular Function, Left ; Ventricular Septum