In recent years， hyperuricemia （HUA） has shown a rapidly increasing incidence and tends to occur in increasingly young people， with a wide range of cardiac， renal， joint， and cancerous hazards and all-cause mortality associations. Western medicine treatment has limitations such as large liver and kidney damage， medication restriction， and easy recurrence. The intestine is the major extra-renal excretion pathway for uric acid （UA）， and the intestinal microecology can be regulated to promote UA degradation. It offers great potential to develop UA-lowering strategies that target the intestinal microecology， which are promising to provide safer and more effective therapeutic approaches. Traditional Chinese medicine （TCM） can treat HUA via multiple targets and multiple pathways from a holistic view， with low toxicity and side effects. Studies have shown that intestinal microecology is a crucial target for TCM in the treatment of HUA. However， its specific mechanism of action has not been fully elucidated. Focusing on the key role of intestinal microecology in HUA， this review explores the relationship between intestinal microecology and HUA in terms of intestinal flora， intestinal metabolites， intestinal UA transporters， and intestinal barriers. Furthermore， we summarize the research progress in TCM treatment of HUA by targeting the intestinal microecology， with the aim of providing references for the development of TCM intervention strategies for HUA and the direction of future research.

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.

Objective To elucidate the causal relationships between colorectal cancer (CRC) and prevalent psychiatric disorders through a two-sample Mendelian randomization approach. Methods Utilizing publicly available genome-wide association study data, we explored the connections between CRC and various psychiatric disorders, including depression, anxiety, bipolar disorder, and schizophrenia. We applied three statistical analyses: inverse variance weighting, MR-Egger, and median weighting. Sensitivity analyses were conducted to ensure the reliability and validity of the results. Results Inverse variance weighting analysis showed no significant links between CRC and depression (P=0.090), anxiety (P=0.099), or schizophrenia (P=0.899). Conversely, a significant inverse relationship was found with bipolar disorder (P=0.010). Conclusion No causal connection exists between CRC and the psychiatric conditions of depression, anxiety, or schizophrenia. However, CRC may have a causal association with a reduced risk of bipolar disorder, further supporting the existence of the gut-brain axis.

Objective: To analyze the epidemiological characteristics and trends of other infectious diarrhea among children under 18 years old in Guangzhou City from 2014 to 2020, and to explore the correlation between climatic factors and the incidence of the disease, so as to provide reference for the early prevention of infectious diseases. Methods: The data of cases of other infectious diarrhea and meteorological data of children under 18 years old in Guangzhou City from 2014 to 2020 were collected through the Chinese Infectious Disease Reporting System and the Guangzhou Meteorological Bureau. The correlation between meteorological factors and the incidence of other infectious diarrhea was analyzed using negative binomial regression. Results: A total of 104 566 cases of other infectious diarrhea among children under 18 years old were reported in Guangzhou City from 2014 to 2020, with a male to female ratio of 1.48∶1. The incidence rate was the highest in 2017 (980.83 per 100 000) and the lowest in 2020 (388.22 per 100 000). The peak of incidence occurred from October to March of the following year. Children under 5 years old accounted for 87.95% of all cases. The number of cases of other infectious diarrhea was negatively correlated with the temperature of the previous 6 days ( IRR = -0.07 ), and positively correlated with the temperature difference on the day of onset ( IRR =0.02) (both P <0.05). It was also positively correlated with the wind speed of the previous 7 days ( IRR=0.07, P <0.05), but there was no statistically significant correlation with the relative humidity on the day of onset ( IRR=-0.00, P >0.05). Conclusions Low temperature, large temperature difference, and high wind speed can increase the risk of other infectious diarrhea. It is necessary to strengthen the prediction and early warning in conjunction with meteorological changes, and warn kindergartens and schools to enhance preventive measures against the clustering of other infectious diarrhea cases.

ObjectiveTo investigate the clinical efficacy and potential mechanism of Shengmai Jiuxin decoction in the treatment of acute decompensated heart failure （ADHF） with the traditional Chinese medicine （TCM） pattern of Qi and Yin deficiency， Yang deficiency， and blood stasis. MethodsA total of 68 patients diagnosed with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type were randomly assigned to an observation group （34 cases） and a control group （34 cases）. Both groups received conventional Western medical treatment， while the observation group was additionally administered Shengmai Jiuxin decoction. Parameters compared before and after treatment included： TCM syndrome score， TCM syndrome efficacy， New York Heart Association （NYHA） functional classification， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， six-minute walk distance （6MWD）， hypoxia-inducible factor-1 alpha （HIF-1α）， vascular endothelial growth factor A （VEGF-A）， Caspase-3， and the number of rehospitalizations for heart failure within one month after discharge. ResultsThere were no significant differences in sex， age， vital signs， or underlying diseases between the two groups. Compared with baseline， both groups exhibited significant reductions in TCM syndrome scores， NT-proBNP， and HIF-1α levels （P<0.01）， as well as significant increases in 6MWD， LVEF， VEGF-A， and Caspase-3 levels （P<0.05， P<0.01）. After treatment， the observation group showed significantly greater reductions in TCM syndrome score， NT-proBNP， HIF-1α， and Caspase-3 levels compared with the control group （P<0.05） and significantly greater increases in 6MWD， TCM syndrome efficacy， and VEGF-A levels （P<0.05）. No significant differences were observed between the groups in NYHA functional classification， LVEF， or the number of rehospitalizations for heart failure within one month after discharge. No drug-related adverse events were reported in either group during the treatment period. ConclusionShengmai Jiuxin decoction can improve cardiac function and clinical symptoms in patients with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type. Its mechanisms may be related to the regulation of the HIF-1 signaling pathway by modulating targets such as HIF-1α， VEGF-A， and Caspase-3.

ObjectiveTo investigate the clinical efficacy and potential mechanism of Shengmai Jiuxin decoction in the treatment of acute decompensated heart failure （ADHF） with the traditional Chinese medicine （TCM） pattern of Qi and Yin deficiency， Yang deficiency， and blood stasis. MethodsA total of 68 patients diagnosed with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type were randomly assigned to an observation group （34 cases） and a control group （34 cases）. Both groups received conventional Western medical treatment， while the observation group was additionally administered Shengmai Jiuxin decoction. Parameters compared before and after treatment included： TCM syndrome score， TCM syndrome efficacy， New York Heart Association （NYHA） functional classification， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， six-minute walk distance （6MWD）， hypoxia-inducible factor-1 alpha （HIF-1α）， vascular endothelial growth factor A （VEGF-A）， Caspase-3， and the number of rehospitalizations for heart failure within one month after discharge. ResultsThere were no significant differences in sex， age， vital signs， or underlying diseases between the two groups. Compared with baseline， both groups exhibited significant reductions in TCM syndrome scores， NT-proBNP， and HIF-1α levels （P<0.01）， as well as significant increases in 6MWD， LVEF， VEGF-A， and Caspase-3 levels （P<0.05， P<0.01）. After treatment， the observation group showed significantly greater reductions in TCM syndrome score， NT-proBNP， HIF-1α， and Caspase-3 levels compared with the control group （P<0.05） and significantly greater increases in 6MWD， TCM syndrome efficacy， and VEGF-A levels （P<0.05）. No significant differences were observed between the groups in NYHA functional classification， LVEF， or the number of rehospitalizations for heart failure within one month after discharge. No drug-related adverse events were reported in either group during the treatment period. ConclusionShengmai Jiuxin decoction can improve cardiac function and clinical symptoms in patients with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type. Its mechanisms may be related to the regulation of the HIF-1 signaling pathway by modulating targets such as HIF-1α， VEGF-A， and Caspase-3.