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.

Cardiac fibrosis(CF) is a cardiac pathological process characterized by excessive deposition of extracellular matrix(ECM). When the heart is damaged by adverse stimuli, cardiac fibroblasts are activated and secrete a large amount of ECM, leading to changes in cardiac fibrosis, myocardial stiffness, and cardiac function declines and accelerating the development of heart failure. There is a close relationship between heart yin deficiency and cardiac fibrosis, which have similar pathogenic mechanisms. Heart Yin deficiency, characterized by insufficient Yin fluids, causes the heart to lose its nourishing function, which acts as the initiating factor for myocardial dystrophy. The deficiency of body fluids leads to stagnation of blood flow, resulting in blood stasis and water retention. Blood stasis and water retention accumulate in the heart, which aligns with the pathological manifestation of excessive deposition of ECM, as a tangible pathogenic factor. This is an inevitable stage of the disease process. The lingering of blood stasis combined with water retention eventually leads to the generation of heat and toxins, triggering inflammatory responses similar to heat toxins, which continuously stimulate the heart and cause the ultimate outcome of CF. Considering the syndrome of heart Yin deficiency, traditional Chinese medicine capable of nourishing Yin, activating blood, and promoting urination can reduce myocardial cell apoptosis, inhibit fibroblast activation, and lower the inflammation level, showing significant advantages in combating CF.
Humans ; Fibrosis/drug therapy* ; Animals ; Yin Deficiency/metabolism* ; Myocardium/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use*

PURPOSE: Intertrochanteric fractures undergoing proximal femoral nail antirotation (PFNA) surgery are associated with significant hidden blood loss. This study aimed to explore whether intramedullary administration of tranexamic acid (TXA) can reduce bleeding in PFNA surgery for intertrochanteric fractures in elderly individuals. METHODS: A randomized controlled trial was conducted from January 2019 to December 2022. Patients aged over 60 years with intertrochanteric fractures who underwent intramedullary fixation surgery with PFNA were eligible for inclusion and grouped according to random numbers. A total of 249 patients were initially enrolled, of which 83 were randomly allocated to the TXA group and 82 were allocated to the saline group. The TXA group received intramedullary perfusion of TXA after the bone marrow was reamed. The primary outcomes were total peri-operative blood loss and post-operative transfusion rate. The occurrence of adverse events was also recorded. Continuous data was analyzed by unpaired t-test or Mann-Whitney U test, and categorical data was analyzed by Pearson Chi-square test. RESULTS: The total peri-operative blood loss (mL) in the TXA group was significantly lower than that in the saline group (577.23 ± 358.02 vs. 716.89 ± 420.30, p = 0.031). The post-operative transfusion rate was 30.67% in the TXA group and 47.95% in the saline group (p = 0.031). The extent of post-operative deep venous thrombosis and the 3-month mortality rate were similar between the 2 groups. CONCLUSION We observed that intramedullary administration of TXA in PFNA surgery for intertrochanteric fractures in elderly individuals resulted in less peri-operative blood loss and decreased transfusion rate, without any adverse effects, and is, thus, recommended.
Humans ; Tranexamic Acid/administration & dosage* ; Hip Fractures/surgery* ; Male ; Aged ; Female ; Fracture Fixation, Intramedullary/adverse effects* ; Blood Loss, Surgical/prevention & control* ; Antifibrinolytic Agents/administration & dosage* ; Aged, 80 and over ; Bone Nails ; Middle Aged ; Blood Transfusion/statistics & numerical data*

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 To systematically evaluate the clinical efficacy of the prescriptions for softening hardness to dissipate mass combined with Levothyroxine Sodium in the treatment of postoperative thyroid cancer,and to provide evidence-based medical proof for clinical treatment of postoperative thyroid cancer.Methods Computer search was performed in the major domestic and oversea databases for the retrieval of clinical randomized controlled trials(RCTs)of prescriptions for softening hardness to dissipate mass combined with Levothyroxine Sodium for the treatment of postoperative thyroid cancer.After screening the literature according to the inclusion and exclusion criteria,the quality of the included literature was evaluated using the tools for analysis of the bias recommended by Cochrane Reviewer's Handbook and the modified JADAD rating scale,and meta-analysis was performed using RevMan 5.4 software.Results A total of 11 RCTs involving 749 patients were eventually included.The results of meta-analysis showed that compared with Levothyroxine Sodium alone,prescriptions for softening hardness to dissipate mass combined with Levothyroxine Sodium significantly enhanced the efficacy of postoperative patients with thyroid cancer(RR=1.30,95%CI[1.21,1.41],Z = 6.81,P＜0.000 01),and improved the thyroid functions parameters of serum thyroid stimulating hormone(TSH)(SMD=-1.75,95%CI[-2.38,-1.13],Z = 5.47,P＜0.000 01),thyroglobulin(TG)(SMD=-1.13,95%CI[-1.71,-0.55],Z = 3.81,P = 0.000 1),free triiodothyronine(FT3)(SMD=3.42,95%CI[0.73,6.10],Z = 2.50,P = 0.01),free thyroxine(FT4)(SMD=1.85,95%CI[0.05,3.66],Z = 2.02,P = 0.04),and thyroglobulin antibody(TgAb)(SMD=-0.63,95%CI[-1.11,-0.15],Z = 2.55,P = 0.01),increased Karnofsky Performance Status(KPS)scores(SMD= 2.19,95%CI[1.30,3.08],Z = 4.81,P＜0.000 01),shortened the time for the relief of clinical symptoms after thyroid cancer surgery(MD=-4.67,95%CI[-5.38,-3.96],Z = 12.87,P＜0.000 01),reduced the diameter of the largest thyroid nodule after thyroid cancer surgery(MD=-2.51,95%CI[-3.13,-1.89],Z = 7.94,P＜0.000 01),regulated the immune function indicators of T lymphocyte population CD3+(MD=8.68,95%CI[4.97,12.39],Z = 4.59,P＜0.000 01)and CD4+(MD=10.77,95%CI[5.46,16.08],Z = 3.97,P＜0.000 1)levels,and reduced the incidence of postoperative complications of thyroid cancer(RR=0.34,95%CI[0.18,0.65],Z = 3.26,P = 0.001).The differences were all statistically significant(P＜0.05).Conclusion prescriptions for softening hardness to dissipate mass combined with Levothyroxine Sodium can enhance the efficacy of postoperative patients with thyroid cancer.The combined therapy is superior to Levothyroxine Sodium alone in improving thyroid function indicators,KPS score,time for the relief of clinical symptoms,diameter of the largest thyroid nodule,immune function indicators,and the incidence of postoperative complications.However,due to the small amount of included trials and the fact that the prescriptions for softening hardness to dissipate mass vary in the composition,the conclusions of the analysis need to be confirmed by more high-quality,multi-center,large-sample clinically randomized controlled trials.

Aim To explore the effect of total flavonoid extract(TFE)of Ampelopsis megalophylla on the pro-liferation and apoptosis of human breast cancer cells and its mechanism in autophagy inhibition.Methods For human cervical cancer cell Hela,human lung cancer cell A549,human liver cancer cell SMMC-7721,human breast cancer cell MCF-7,MDA-MB-231 and human normal liver cell L-02,MTT method was used to select sensitive cell lines.The inhibitory effect of TFE combined with autophagy inhibitor 3-methylade-nine(3-MA)on sensitive cell proliferation was detec-ted using MTT assay.The morphological changes of cells were observed using transmission electron micros-copy and Hoechst 33258 single staining method.The changes in cell apoptosis rate were detected using An-nexin V-FITC/PI dual staining method.The expression levels of apoptosis related proteins and pathway pro-teins(death receptor pathway,mitochondrial pathway,endoplasmic reticulum stress pathway)were detected uisng Western blot.The expression of the key protein Cyt-c in mitochondrial pathway was determiend by im-munofluorescence,and the autophagy agonist rapamy-cin was selected for reverse validation.Results TFE could inhibit the proliferation of human breast cancer cells in a concentration-dependent manner,and MCF-7 cells were sensitive cell lines.Compared with the TFE group,the TFE+3-MA group significantly increased the inhibition rate of MCF-7 cells at 24,48,and 72 h(P＜0.01).The number of cells decreased,the gap increased,the number of apoptotic bodies increased,and the apoptosis rate increased(P＜0.01).The ex-pression levels of Bax/Bcl-2(P＜0.01),cleaved-caspase3(P＜0.01),Cyt-c(P＜0.05),FADD,and cleaved-caspase-12 all increased,and the expres-sion of apoptotic protein Cyt-c in nucleus increased.The fluorescence of the TFE+RA group decreased,re-versing the mitochondrial pathway apoptosis induced by TFE.Conclusions TFE can significantly inhibit the proliferation of human breast cancer cells.When inhib-iting autophagy,it may promote the apoptosis of MCF-7 cells through the mitochondrial pathway,and activa-ting autophagy can reverse apoptosis.