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 explore the characteristics and influencing factors of immune-related adverse events （irAEs） induced by tislelizumab in patients with liver cancer. METHODS A retrospective cohort of 203 liver cancer patients treated with tislelizumab in Guangxi Medical University Cancer Hospital from May 2022 to March 2024 was included. These patients were divided into an irAEs group （58 cases） and a non-irAEs group （145 cases）. Clinical data were collected and compared between the two groups. A multivariate logistic regression model was employed to analyze factors influencing the occurrence of irAEs and establish a predictive model. The receiver operator characteristic （ROC） curve was plotted to evaluate the predictive value of the model for the occurrence of irAEs. The correlation between irAEs and overall survival （OS） as well as progression free survival （PFS） in patients was analyzed using the Kaplan-Meier method. RESULTS The irAEs induced by tislelizumab in liver cancer patients were predominantly grade 1-2 （89.71%）， mainly manifesting as hematological toxicity （42.65%） and hepatotoxicity （20.59%）， and mostly occurred within 1-12 cycles after tislelizumab treatment. Compared with liver cancer patients without underlying liver diseases， those with chronic hepatitis B had a higher incidence of irAEs. Statistically significant differences were observed between the irAEs and non-irAEs groups in terms of the number of patients with a China Liver Cancer Staging （CNLC） stage ≥Ⅱ， white blood cell count， neutrophil count， systemic immune-inflammation index （SII）， and neutrophil-to-lymphocyte ratio （NLR） （P＜0.05）. Multivariate Logistic regression analysis revealed that CNLC stage ≥Ⅱ was an independent risk factor for the occurrence of irAEs （P＝0.027）. The ROC curve indicated that neutrophil count， white blood cell count， NLR， and SII all demonstrated certain predictive potential for the occurrence of irAEs （with area under the curve values of 0.614， 0.592，0.591， and 0.589， respectively）. The Kaplan-Meier survival curve showed no statistically significant differences in PFS and OS between the irAEs and non-irAEs groups， among patients with different grades of irAEs， and among irAEs patients with different CNLC stages （P＞0.05）. CONCLUSION The irAEs induced by tislelizumab in liver cancer patients are relatively mild （grade 1-2），mainly manifesting as hematological toxicity and hepatotoxicity. Liver cancer patients with concurrent chronic hepatitis B are at a higher risk of developing irAEs. CNLC stage ≥Ⅱ is an independent risk factor for irAEs induced by tislelizumab. Neutrophil count， white blood cell count， NLR， and SII have certain predictive value for the occurrence of irAEs.

Objective To investigate the effects of varying durations of overwork on cardiomyocyte pyroptosis in mice.Methods A total of 24 SPF KM mice were randomly divided into four groups(n=6)using a random number table:control group,2-week overwork(W2)group,4-week overwork(W4)group,and 6-week overwork(W6)group.Mice in control group were normally raised,while those in W2,W4,and W6 groups were forced to stand in water for 8 h and then restrained for 3 h daily for 2,4,6 weeks,respectively.The general condition and weekly weight changes of the mice were observed.After modeling,blood samples were collected,and hearts were excised.Myocardial histopathological changes were assessed using hematoxylin and eosin(HE)staining.The localization of gasdermin D(GSDMD)protein in myocardial tissue was detected through immunohistochemical staining,and the expression levels of pyroptosis-related proteins[NOD-like protein receptor 3(NLRP3),Caspase-1,GSDMD]in myocardial tissue were analyzed using Western blotting.The contents of interleukin-1β(IL-1β)and interleukin-18(IL-18)in serum and myocardial tissues were measured using ELISA.Results(1)The weight of control group mice increased steadily within 2 weeks.In W2 group,there was no significant weight change within 2 weeks,while in W4 and W6 groups,the body weights were higher than their initial values from the 2nd to 6th week.Compared with control group,the body weights of W2,W4,and W6 groups were lower than those of control group in the 1st and 2nd week,with statistically significant differences(P<0.05).The activity levels of the mice in W2,W4,and W6 groups initially increased and then decreased,with their fur becoming dull and falling out,and their mental state deteriorating.(2)In control group,cardiomyocytes were neatly arranged,and the nuclear morphology was normal.Compared with control group,in W2 group,cardiomyocyte arrangement was less regular,and capillary congestion was increased.In W4 group,the vascular congestion in the myocardium was significantly increased,the interstitial tissue was hyperplastic,and vacuolization appeared around the nuclei.In W6 group,the myocardial interstitium was loose,fat infiltration was increased,vacuolization around the nuclei was increased,and myocardial fibers were swollen,and the arrangement was disordered.(3)GSDMD was mainly located in the cytoplasm of cardiomyocytes.Compared with control group,the expression levels of NLRP3,Caspase-1,and GSDMD proteins in W2,W4,and W6 groups were significantly increased,and the expression levels were in the order of W6 group>W4 group>W2 group,with significant differences(P<0.05).(4)Compared to control group,the levels of IL-1β in serum and myocardial tissues of W2,W4,and W6 groups were significantly increased.In serum,the level of IL-1β in W6 group was higher than those in W2 and W4 groups,and in myocardial tissue,the levels in W4 and W6 groups were higher than those in the W2 group,with significant differences(P<0.05).There were no significant differences in IL-1β levels in serum among W2 and W4 groups,nor were there significant differences in myocardial tissue between W4 and W6 groups(P>0.05).Compared with control group,the levels of IL-18 in serum and myocardial tissue of W4 and W6 groups were significantly increased(P<0.05).In serum,the levels of IL-18 in W4 and W6 groups were higher than that in W2 group,and in myocardial tissue,the level in W6 group was higher than those in W2 and W4 groups,with the differences being statistically significant(P<0.05).Conclusions Overwork can cause structural damage to mouse myocardial tissue,increase the expression of pyroptosis proteins NLRP3,Caspase-1,GSDMD,and aggravate myocardial inflammatory responses in overworked mice.Cardiomyocyte pyroptosis may be one of the factors contributing to sudden cardiac death induced by overwork.