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.

This study aimed to investigate halofuginone's inhibitory effect and mechanism on the activity of hepatocellular carcinoma cells. HepG2 cells were used to detect the effects of halofuginone. After treatment, cell activity, cell migration, cell cycle, and cell apoptosis were detected by CCK-8, transwell, and flow cytometry, respectively. The expression levels of growth and metabolism-related factors such as citrate synthase (CS), ketoglutarate dehydrogenase (OGDH), and isocitrate deoxygenase (IDH) were detected by real-time quantitative PCR and Western blot. Compared with the control group, the activity of HepG2 cells was significantly inhibited by halofuginone (P < 0.01), the migration rate of HepG2 cells was decreased (P < 0.01), the apoptosis of HepG2 cells was induced (P < 0.01), and the cell cycle was arrested in S phase (P < 0.01). The expression levels of tricarboxylic acid key enzymes CS, IDH3, and OGDH were up-regulated, the expression level of isocitrate dehydrogenase isoenzymes IDH1 and IDH2 were down-regulation. In conclusion, halofuginone can inhibit the proliferation and migration of HepG2 cells and promote apoptosis in a dose-dependent manner, which may be due to the promotion of the aerobic metabolism of cells.

Objective: To explore the effects of high-altitude hypoxia on thyroid hormone(TH) synthesis by quasi-targeted metabolomics technology. Methods: Twenty SPF male SD rats were randomly divided into Control group and Hypoxia group. An acute hypoxia injury model was established in SD rats by simulating hypoxia stress in a hypobaric oxygen chamber at an altitude of 6 km for 48 hours. The body weight, arterial oxygen partial pressure(PaO2) and blood oxygen saturation(SaO2) were detected. KEGG enrichment analysis was performed after the metabolites in the blood of two groups were detected by quasi-targeted metabolomics technology. The expression levels of sodium iodide symporter(NIS), thyroid peroxidase(TPO) and thyroglobulin(TG) were detected by RT-PCR and Western blot in TH synthesis pathway. The content of serum thyroxine(T4) and the expression levels of glutathione peroxidase(GSH-Px), superoxide dismutase(SOD) and malondialdehyde(MDA) were detected by ELISA. The expression levels of toll-like receptors-4(TLR-4), interleukin-6(IL-6), nuclear factor-κB/p65(NF-κB/p65) and tumor necrosis factor-α(TNF-α) were detected by RT-PCR, Western blot and ELISA. The expression levels of Pro apoptotic protein Bcl-2 associated X protein(Bax) and inhibitor of apoptosis protein B-cell lymphoma/leukemia-2(Bcl-2) were detected by RT-PCR and Western blot in rats thyroid tissue. Results: Compared with the Control group, the body weight, PaO2and SaO2of rats in the Hypoxia group significantly decreased(P<0.01). The differential metabolites in arterial serum of hypoxia group rats were significantly enriched in the TH synthesis pathway, and the content of the pathway end product T4decreased significantly(P<0.01). In addition, the mRNA and protein expression levels of NIS, TPO, TG in rats thyroid tissue significantly decreased(P<0.05). The ELISA validation results showed that the changes of T4content were completely consistent with the above results. Compared with the Control group, the enzyme activities of SOD and GSH-Px in the serum of rats in the hypoxia group decreased, while the content of MDA increased(P<0.01); the mRNA, protein expression levels and contents of TLR-4, IL-6, NF-κ B/p65, TNF-α significantly increased(P<0.05), while the mRNA and protein expression levels of Bax in thyroid tissue significantly increased, Bcl-2 significantly decreased(P<0.05). Conclusion Hypoxia stress at high altitude leads to apoptosis of thyroid follicular epithelial cells by promoting oxidative stress and inflammatory response, which effects thyroid function and ultimately reduces thyroid hormone synthesis.

Objective:To compare the efficacy of fractional CO 2 laser combined with topical delivery of fluorouracil versus compound betamethasone injections in the treatment of vitiligo. Methods:Clinical data were collected from 94 patients with localized, non-segmental, and stable vitiligo, who received fractional CO 2 laser combined with drug delivery at the Cosmetological Center, Xijing Hospital, Air Force Medical University from October 2018 to May 2023, and were retrospectively analyzed. Among them, there were 40 cases in the fractional CO 2 laser combined with fluorouracil injection group, and 54 cases in the fractional CO 2 laser combined with compound betamethasone injection group. All the patients received the above treatment once a month for 5 sessions. A 4-level grading scale was used to evaluate the pigmentation improvement, and the clinical efficacy and safety of the two therapeutic regimens were compared. Comparisons between groups were performed using chi-square test, Fisher′s exact test, and t test. Results:In the fractional CO 2 laser combined with fluorouracil injection group, there were 22 males and 18 females, their ages were 21.95 ± 12.88 years, and the disease duration was 25.46 ± 11.42 months; in the fractional CO 2 laser combined with compound betamethasone injection group, there were 36 males and 18 females, their ages were 22.26 ± 8.79 years, and the disease duration was 26.51 ± 12.81 months. One month after the first treatment, no significant difference was observed in the efficacy between the two groups ( χ2 = 1.39, P = 0.238). One month after the fifth treatment, 2 (5.0%) patients showed an excellent response, 4 (10.0%) showed a good response, 12 (30.0%) showed a mild response, and 22 (55.0%) showed a poor response in the fractional CO 2 laser combined with fluorouracil injection group; in the fractional CO 2 laser combined with compound betamethasone injection group, 8 (14.8%) patients showed a good response, 8 (14.8%) showed a mild response, and 38 (70.4%) showed a poor response; there was no significant difference in the efficacy between the two groups after 5 sessions of treatment ( χ2 = 2.35, P = 0.125). After either 1 or 5 sessions of treatment, there were no significant differences in the efficacy for lesions on the face and neck, trunk and limbs, hands and feet between the two therapeutic regimens (all P > 0.05). Comparisons of the efficacy for skin lesions on different body sites showed that one session of the fractional CO 2 laser combined with fluorouracil injection was more effective for the treatment of skin lesions on the face and neck compared with those on the hands and feet ( P = 0.039) ; after 5 sessions of treatment, the two therapeutic regimens both showed better efficacy for facial skin lesions compared with hand and foot skin lesions ( P = 0.005, 0.049). There was no significant difference in the occurrence of adverse reactions such as pigmentation, infection and scarring between the two groups. Conclusion:The fractional CO 2 laser combined with topical delivery of fluorouracil and compound betamethasone injections showed similar efficacy and safety in the treatment of vitiligo, and both can be used as treatment options for vitiligo.

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.