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.

AIM To explore the clinical effects of Yiqi Huoluluo Mingmu Decoction combined with Conbercept Ophthalmic Injection on patients with non-proliferative diabetic retinopathy.METHODS One hundred and sixty-seven patients were randomly assigned into control group(83 cases)for administration of Conbercept Ophthalmic Injection,and observation group(84 cases)for administration of both Yiqi Huoluluo Mingmu Decoction and Conbercept Ophthalmic Injection.The changes in clinical effects,miR-15b,CTGF,VEGF,hemodynamic indices(PSV,EDV,RI),visual field gray value,CMT,hemangioma count,blood spot area,BCVA,electroretinograms(amplitudes of a wave,b wave under light-dark adaptation),TCM symptom scores and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed increased miR-15b,PSV,EDV,BCVA,electroretinograms(P＜0.05),and increased CTGF,VEGF,RI,visual field gray value,CMT,hemangioma count,blood spot area,TCM symptom scores(P＜0.05),especially for the observation group(P＜0.05).No significant difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with non-proliferative diabetic retinopathy,Yiqi Huoluluo Mingmu Decoction combined with Conbercept Ophthalmic Injection can improve hemodynamics,reduce fundus lesions,and regulate miR-15b,VEGF expressions.

Objective To evaluate the bioequivalence and safety of ezetimibe tablets in healthy Chinese subjects.Methods The study was designed as a single-center,randomized,open-label,two-period,two-way crossover,single-dose trail.Subjects who met the enrollment criteria were randomized into fasting administration group and postprandial administration group and received a single oral dose of 10 mg of the subject presparation of ezetimibe tablets or the reference presparation per cycle.The blood concentrations of ezetimibe and ezetimibe-glucuronide conjugate were measured by high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS),and the bioequivalence of the 2 preparations was evaluated using the WinNonlin 7.0 software.Pharmacokinetic parameters were calculated to evaluate the bioequivalence of the 2 preparations.The occurrence of all adverse events was also recorded to evaluate the safety.Results The main pharmacokinetic parameters of total ezetimibe in the plasma of the test and the reference after a single fasted administration:Cmax were(118.79±35.30)and(180.79±51.78)nmol·mL-1;tmax were 1.40 and 1.04 h;t1/2 were(15.33±5.57)and(17.38±7.24)h;AUC0-t were(1 523.90±371.21)and(1 690.99±553.40)nmol·mL-1·h;AUC0-∞ were(1 608.70±441.28),(1 807.15±630.00)nmol·mL-1·h.The main pharmacokinetic parameters of total ezetimibe in plasma of test and reference after a single meal:Cmax were(269.18±82.94)and(273.93±87.78)nmol·mL-1;Tmax were 1.15 and 1.08 h;t1/2 were(22.53±16.33)and(16.02±5.84)h;AUC0_twere(1 463.37±366.03),(1 263.96±271.01)nmol·mL-1·h;AUC0-∞ were(1 639.01±466.53),(1 349.97±281.39)nmol·mL-1·h.The main pharmacokinetic parameters Cmax,AUC0-tand AUC0-∞ of the two preparations were analyzed by variance analysis after logarithmic transformation.In the fasting administration group,the 90％CI of the log-transformed geometric mean ratios were within the bioequivalent range for the remaining parameters in the fasting dosing group,except for the Cmax of ezetimibe and total ezetimibe,which were below the lower bioequivalent range.The Cmax of ezetimibe,ezetimibe-glucuronide,and total ezetimibe in the postprandial dosing group was within the equivalence range,and the 90％CI of the remaining parameters were not within the equivalence range for bioequivalence.Conclusion This test can not determine whether the test preparation and the reference preparation of ezetimibe tablets have bioequivalence,and further clinical trials are needed to verify it.

ObjectiveTriple-negative breast cancer (TNBC) is the breast cancer subtype with the worst prognosis, and lacks effective therapeutic targets. Colony stimulating factors (CSFs) are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells, playing an important role in the malignant progression of TNBC. This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes (CRGs), and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy. MethodsWe downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database. Through LASSO Cox regression analysis, we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score (CRRS). We further analyzed the correlation between CRRS and patient prognosis, clinical features, tumor microenvironment (TME) in both high-risk and low-risk groups, and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy. ResultsWe identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model. Kaplan-Meier survival curves, time-dependent receiver operating characteristic curves, and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival, and the predictive ability of CRRS prognostic model was further validated using the GEO dataset. Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients. Moreover, patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil, ipatasertib, and paclitaxel. ConclusionWe have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs, which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment. Moreover, the key genes within this model may represent potential molecular targets for future therapies of TNBC.

Objective To investigate the clinical efficacy and safety of Yiqi Huatan Tongluo Prescription(mainly composed of Fici Simplicissimae Radix,Notoginseng Radix et Rhizoma,Pinelliae Rhizoma Praeparatum,Poria,Nelumbinis Folium,and Glycyrrhizae Radix et Rhizoma,etc.)combined with drug-coated balloon(DCB)in the treatment of coronary heart disease(CHD)and to observe its effect on low-shear related serological indicators.Methods A total of 106 patients with CHD of qi deficiency and phlegm stasis obstructing collateral type who were scheduled to undergo percutaneous coronary intervention were randomly divided into a treatment group and a control group,with 53 cases in each group.The control group was treated with drug-eluting stent implantation,and the treatment group was treated with DCB.After the operation,the control group was given conventional antiplatelet aggregation drugs,and the treatment group was given oral administration of Yiqi Huatan Tongluo Prescription.The medication for the two groups lasted for 12 weeks.The changes in the serum levels of monocyte chemoattractant protein 1(MCP-1),interleukin 1 β(IL-1β)and vascular endothelial growth factor(VEGF)in the two groups were observed before and after treatment.Moreover,the traditional Chinese medicine(TCM)syndrome efficacy after treatment and the incidence of adverse events one year after operation were compared between the two groups.Results(1)After 12 weeks of treatment,the total effective rate for TCM syndrome efficacy of the treatment group was 88.68％(47/53),and that of the control group was 75.47％(40/53).The intergroup comparison(tested by chi-square test)showed that the TCM syndrome efficacy in the treatment group was significantly superior to that in the control group(P＜0.05).(2)The analysis of indicators related to endothelial dysfunction in the blood flow with low shear stress showed that after treatment,the levels of serum MCP-1,IL-1βand VEGF in the control group presented no obvious changes(P＞0.05),but the serum levels of MCP-1 and IL-1β in the treatment group were significantly lowered compared with those before treatment(P＜0.05).The intergroup comparison showed that the decrease of serum MCP-1,IL-1β and VEGF levels in the treatment group was significantly superior to that in the control group(P＜0.05).(3)The one-year follow-up after the operation showed that the total incidence of adverse events in the treatment group was 18.87％(10/53),and that in the control group was 20.75％(11/53).There was no significant difference between the two groups(P＞0.05).Conclusion Yiqi Huatan Tongluo Prescription combined with DCB has definite action on the targets related to endothelial dysfunction in coronary blood flow with low shear stress,which is conducive to reducing inflammatory response,improving the symptoms of angina pectoris and enhancing clinical efficacy.The incidence of adverse events did not increase one year after operation,indicating good safety and effectiveness.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.