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 investigate the effectiveness of the integrated schistosomiasis control programme in Sichuan Province during the stage moving from transmission interruption to elimination (2015—2023), so as to provide insights into formulation of the schistosomiasis control measures during the post-elimination stage. Methods Schistosomiasis control data were retrospectively collected from departments of health, agriculture and rural affairs, forestry and grassland, water resources, and natural resources in Sichuan Province from 2015 to 2023, and a database was created to document examinations and treatments of human and livestock schistosomiasis, and snail survey and control, conversion of paddy fields to dry fields, ditch hardening, rivers and lakes management and building of forests for snail control and schistosomiasis prevention. The completion of schistosomiasis control measures was investigated, and the effectiveness was evaluated. Results A total of 20 545 155 person-times received human schistosomiasis examinations in Sichuan Province during the period from 2015 to 2023, and 232 157 person-times were seropositive, with a reduction in the seroprevalence from 2.10% (44 299/2 107 003) in 2015 to 1.12% (9 361/837 896) in 2023 (χ² = 7.68, P < 0.001). The seroprevalence of human schistosomiasis appeared a tendency towards a decline in Sichuan Province over years from 2015 to 2023 (b = −8.375, t = −10.052, P < 0.001); however, no egg positive individuals were identified during the period from 2018 to 2023, with the prevalence of human Schistosoma japonicum infections maintained at 0. Expanded chemotherapy was administered to 2 754 515 person-times, and medical assistance of advanced schistosomiasis was given to 6 436 persontimes, with the treatment coverage increasing from 46.80% (827/1 767) in 2015 to 64.87% (868/1 338) in 2023. Parasitological tests for livestock schistosomiasis were performed in 35 113 herd-times, and expanded chemotherapy was administered to 513 043 herd-times, while the number of fenced livestock decreased from 121 631 in 2015 to 103 489 in 2023, with a reduction of 14.92%. Snail survey covered 433 621.80 hm² in Sichuan Province from 2015 to 2023, with 204 602.81 hm² treated by chemical control and 4 637.74 hm² by environmental modifications. The area of snail habitats decreased from the peak of 5 029.80 hm² in 2016 to 3 709.72 hm² in 2023, and the actual area of snail habitats decreased from the peak of 8 585.48 hm² in 2016 to 473.09 hm² in 2023. The mean density of living snails remained low across the study period except in 2017 (0.62 snails/0.1 m²). Schistosomiasis control efforts by departments of agriculture and rural affairs in Sichuan Province included conversion of paddy fields to dry fields covering 153 346.93 hm², hardening of 6 110.31 km ditches, building of 70 356 biogas digesters, replacement of cattle with 227 161 sets of machines, and captive breeding of 21 161 070 livestock from 2015 to 2023, and the control efforts by departments of water resources included rivers and lakes management measuring 5 676.92 km and renovation of 2 331 irrigation areas, while the control efforts by departments of forestry and grassland included building of forests for snail control and schistosomiasis prevention covering 23 913.33 hm², renovation of snail control forests covering 8 720 hm² and newly building of shelterbelts covering 764 686.67 hm². All 63 endemic counties (cities and districts) had achieved the criterion for schistosomiasis elimination criteria in Sichuan Province by the end of 2023. Conclusion Following the integrated control efforts from 2015 to 2023, remarkable achievements have been obtained in the schistosomiasis control programme in Sichuan Province, with all endemic counties successfully attaining the schistosomiasis elimination target at the county level.

OBJECTIVE To investigate the mechanism of Yifei xuanfei jiangzhuo formula （YFXF） against vascular dementia （VD）. METHODS The differentially expressed genes of YFXF （YDEGs） were obtained by network pharmacology. High-risk genes were screened from YDEGs by using the nomogram model. The optimal machine learning models in generalized linear， support vector machine， extreme gradient boosting and random forest models were screened based on high-risk genes. VD model rats were established by bilateral common carotid artery occlusion， and were randomly divided into model group and YFXF group （12.18 g/kg， by the total amount of crude drugs）， and sham operation group was established additionally， with 6 rats in each group. The effects of YFXF on behavior （using escape latency and times of crossing platform as indexes）， histopathologic changes of cerebral cortex， and the expression of proteins related to the secreted phosphoprotein 1 （SPP1）/phosphoinositide 3-kinase （PI3K）/protein kinase B （aka Akt） signaling pathway and the mRNA expression of SPP1 in cerebral cortex of VD rats were evaluated. RESULTS A total of 6 YDEGs were obtained， among which SPP1， CCL2， HMOX1 and HSPB1 may be high-risk genes of VD. The generalized linear model based on high-risk genes had the highest prediction accuracy （area under the curve of 0.954）. Compared with the model group， YFXF could significantly shorten the escape latency of VD rats， significantly increase the times of crossing platform （P＜0.05）； improve the pathological damage of cerebral cortex， such as neuronal shrinkage and neuronal necrosis； significantly reduce the expressions of SPP1 protein and mRNA （P＜0.05）， while significantly increase the phosphorylation levels of PI3K and Akt （P＜0.05）. CONCLUSIONS VD high-risk genes SPP1， CCL2， HMOX1 and HSPB1 may be the important targets of YFXF. YFXF may play an anti-VD role by down-regulating the protein and mRNA expressions of SPP1 and activating PI3K/Akt signaling pathway.

ObjectiveTo validate the efficacy of Yunpi Huatan Tongqiao prescription （YHTP） in down-regulating glycolysis to modulate microglia phenotype and improve inflammation and cognitive memory deficits in obstructive sleep apnea （OSA） mice. MethodForty-eight male Balb/C mice were randomly divided into a normal group， a model group， a montelukast sodium group （30 mg·kg^-1）， and low， medium， and high dose groups of YHTP （8.28， 16.56， and 33.12 g·kg^-1）， with 8 mice in each group. All groups， except the normal group， received intraperitoneal injections of lipopolysaccharide （LPS） and underwent chronic intermittent hypoxia （CIH） modeling for 4 weeks. Subsequently， the mice were treated with medications for 4 weeks and then sampled. Animal behavioral tests assessed memory impairment due to hypoxia. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to measure mRNA expression levels of M1-associated inflammatory factors interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， and markers such as T lymphocyte activation antigen （CD86） and inducible nitric oxide synthase （iNOS）， as well as M2-associated inflammatory factors interleukin-10 （IL-10）， transforming growth factor-β （TGF-β）， and the marker mannose receptor （CD206） in hippocampal tissue. Western blot was employed to detect differences in the expression of M1 and M2 microglia phenotypic markers （CD86， CD206） and glycolysis-related proteins glucose transporter type 1 （GLUT1）， hexokinase 2 （HK2）， phosphofructokinase （PFKM）， pyruvate kinase 2 （PKM2）， and monocarboxylic acid transporter 1 （MCT1）. ResultBehavioral tests showed that compared to the results in the normal group， the Y-maze autonomous alternation rate was significantly reduced in the model group （P<0.01）. The latency time for the target hole in the Barnes' maze during the training period （days 2， 3， 4） and testing period （days 5， 12） was significantly increased （P<0.05， P<0.01）. M1 glial cell markers CD86 and iNOS， as well as inflammatory factors IL-1β and TNF-α mRNA， were significantly elevated （P<0.01）. In contrast， the mRNA expression of M2 glial cell markers IL-10， CD206， and TGF-β was significantly reduced （P<0.01）. The protein expression of glycolytic proteins HK2， PFKM， PKM2， MCT1， and the M1 marker CD86 was significantly increased （P<0.05， P<0.01）， while M2 marker CD206 protein expression was significantly decreased （P<0.01）. Compared to the results in the model group， the Y-maze autonomous alternation rate was significantly increased in the medium and high dose groups of YHTP （P<0.05， P<0.01）. The latency time for the target hole during the training （day 4） and testing periods （days 5， 12） was significantly reduced （P<0.01）. Real-time PCR results indicated that mRNA expression levels of M1-related pro-inflammatory factors in the hippocampal tissue were significantly reduced in the low， medium， and high dose groups of YHTP （P<0.01）， while M2-related inflammatory factors' mRNA expression was significantly increased （P<0.01）. Western blot results showed that in the medium and high dose groups of YHTP， the expression of the M1 marker CD86 in the hippocampus was reduced， whereas the expression of the M2 marker CD206 was significantly increased （P<0.01）， with a significant decrease in the expression of glycolysis-related proteins （P<0.01）. ConclusionYHTP can improve inflammation and cognitive impairment induced by hypoxia in OSA model mice. This is achieved by downregulating glycolysis in brain microglia， inhibiting M1 activation， reducing pro-inflammatory factor release， and promoting M2 activation， thereby exerting a therapeutic effect on inflammation and cognitive impairment caused by OSA.

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.

Pleural mesothelioma （PM） is a rare malignant tumor originating from the pleura. Most patients are already in the advanced stage at the time of diagnosis， resulting in a low overall survival rate. MicroRNA （miRNA）， as key regulators of tumor epigenetic modification， have an intertwined interactive network with PM drug resistance. The mechanisms of drug resistance in PM to chemotherapeutic drugs include increasing drug efflux， reducing drug intake， enhancing DNA repair， and altering drug targets. The mechanisms of resistance to targeted therapy drugs include activating alternative signaling pathways， establishing a favorable tumor microenvironment， and triggering epithelial-mesenchymal transition. MiRNA plays a key part in the aforementioned resistance mechanisms， with some miRNAs promoting the drug sensitivity of cancer cells， while others contribute to increased drug resistance. In light of these key regulatory functions， targeting the dysregulated expression of endogenous miRNAs in the process of resistance formation using miRNA antagonists or miRNA mimics may be an effective therapeutic strategy to reverse drug resistance in PM.