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 investigate the effects of palmatine on migration,invasion and epithelial mesenchymal transformation(EMT)in human oral cancer KB cells.Methods KB cells were divided into control group and palmatine-L,-M,-H groups,cultured with 0,4,8 and 16 μmol·L-1 palmatine.After incubation for 48 h,scratch assay was used to detect migration;Traswell assay was used to detect invasion;matrix metalloproteinase 2(MMP-2),MMP-9 and fibronectin(FN)contents were detected by enzyme-linked immunosorbent assay;the expression of Vimentin,N-cadherin and E-cadherin mRNA were detected by real-time quantitative polymerase chain reaction;the expression of Vimentin,N-cadherin,E-cadherin,Wnt3 and β-catenin protein were detected by Western blot.Results Cell mobility in control group and palmatine-L,-M,-H groups were(69.27±8.62)％,(52.94±4.49)％,(45.22±5.05)％and(37.63±4.88)％;the number of transmembrane cells were 197.33±20.26,125.33±12.01,97.00±9.17 and 62.67±7.51;the content of MMP-2 were(2.93±0.21),(1.49±0.13),(1.16±0.15)and(0.95±0.09)ng·mL-1;the content of MMP-9 were(3.51±0.36),(2.37±0.23),(2.06±0.35)and(1.72±0.16)ng·mL-1;the content of FN were(41.28±4.02),(24.03±3.17),(20.67±2.63)and(13.82±2.19)ng·mL-1;the above indexes in palmatine-L,-M,-H groups were compared with the control group,and the differences were statistically significant(P＜0.05,P＜0.01).The mRNA and protein expressions of Vimentin,N-cadherin and E-cadherin,and the expressions of Wnt3 and β-catenin protein in palmatine-L,-M,-H groups were statistically significant compared with those in control group(P＜0.05,P＜0.01).Conclusion Palmatine can inhibit the migration,invasion and EMT of human oral cancer KB cells,and its mechanism is related to the regulation of Wnt/β-catenin signaling pathway.

OBJECTIVE To explore the anti-inflammatory and antifungal action mechanism of taste-masked Lithospermum Saf-flower emulsion in vivo and in vitro.METHODS In vitro,the anti-inflammatory effect was detected by MTT assay,qPCR and ELISA.The anti-fungal effect of the product was investigated by broth dilution experiment,bactericidal kinetics,germ tube inhibition and XTT reduction test.In vivo,the effect was evaluated and the mechanism was investigated on the skin disease model of Candida al-bicans in mice.RESULTS Lithospermum in taste-masked Lithospermum Safflower emulsion had a significant inhibitory effect on the proliferation of RAW264.7 cells,and Safflower inhibited the production of IL-6 induced by LPS in a dose-dependent manner.Litho-spermum significantly inhibited the activity of Candida albicans,and its bactericidal mode is time-and concentration-dependent;Lithospermum significantly reduced the formation of Candida albicans germ tubes and destroyed its biofilm;Safflower had no direct kill-ing effect on Candida albicans,was not able to inhibit its biofilm formation,but significantly reduced the hyphal growth of Candida al-bicans and increased its ROS level.CONCLUSION The combination of Lithospermum and Safflower in the taste-masked Lithosper-mum Safflower emulsion can work synergistically to reduce inflammatory damage and treat Candida albicans infection of the skin.

Our team created the pathogenesis theory of cancer toxin in traditional Chinese medicine on the basis of inheriting the academic thought of "cancer toxins" of ZHOU Zhongying, a Chinese medical master.The pathogenesis theory of cancer toxin suggests that cancer toxins is the key factor leading to the occurrence and development of malignant tumors, the basic pathogenesis of malignant tumor is accumulation of evil and toxins, deficiency of vital qi. This paper proposes that the main pathological factors of carcinoma of prostate are deficiency, dampness, heat, stasis and toxins. The core pathogenesis was spleen and kidney deficiency, dampness-heat stasis toxin accumulation in essence chamber. The disease is located in the essence chamber, closely related to kidney and bladder, and involves liver and spleen. Clinical treatment is based on anti-cancer and detoxification, strengthening vital qi to eliminate pathogenic factor as the basic treatment principles, treatment with anti-cancer detoxification as the core, tonifying the spleen and kidney as the fundamental, clearing heat and removing dampness, removing blood stasis and dispersing is key, accompanied by dispersing liver and regulating qi, the whole syndrome differentiation, to maintain a stable period of time. Strengthening vital qi does not leave evil, eliminating evil does not harm vital qi. Guided by the pathogenesis theory of cancer toxin, this paper expounds the treatment of carcinoma of prostate based on syndrome differentiation and highlights the key role of the pathogenesis theory of cancer toxin in the treatment of this disease, providing reference for the differentiation and treatment of carcinoma of prostate.

Objective Explore a non-contact physiological and psychological detection model based on facial video in simulations of weightlessness effects,research new methods for non-contact heart rate and negative mood state detection in long-term simulations of weightlessness effect analysis.Methods Construct a non-contact physiological and psychological data collection system for fusion analysis of visible light and thermal infrared videos.Collect physiological and psychological data of volunteers in the"Earth Star-Ⅱ"90-day head-down bed rest experiment.A non-contact heart rate detection model based on GCN facial multi-region feature fusion and a non-contact negative mood state detection model considering data reliability were constructed,and the effectiveness of the models were validated with finger clip heart rate and POMS-SF scale as labels.Results The experimental results show that the average difference in the Bland-Altman plot of the non-contact heart rate detection model is-1.26 bpm,and 96.3%of value error detection data falls within the 95%confidence interval,indicating a high consistency between the model detected heart rate and the finger clip heart rate.The non-contact negative mood state detection model achieves an accuracy of>0.85 for detecting tension,depression,anger,and fatigue.Features such as heart rate,AU06,eye gaze,and head pose were observed to be important to mood state detection.Conclusion Non-contact physiological and psychological detection methods not only can be utilized for long-term physiological analysis in simulations of weightlessness effects,but also provide a novel technical approach for on-orbit astronauts health assurance during long-term space flight in the future.