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 therapeutic effect of Huatan Jieyu Anshen Decoction(mainly with the actions of resolving phlegm,relieving depression and calming mind)combined with abdominal vibration tuina manipulations on chronic insomnia in the elderly.Methods Ninety-four cases of elderly patients with chronic insomnia of phlegm-heat harassing the interior type were randomly divided into the observation group and the control group,with 47 cases in each group.The control group was given Huatan Jieyu Anshen Decoction orally,while the observation group was given oral use of Huatan Jieyu Anshen Decoction combined with abdominal vibration tuina manipulations.The course of treatment for the two groups lasted for 4 weeks.Before and after the treatment,the two groups were observed in the changes of traditional Chinese medicine(TCM)syndrome scores,Pittsburgh Sleep Quality Index(PSQI)score,Athens Insomnia Scale(AIS)score,Fatigue Scale-14(FS-14)score,World Health Organization Quality-of-Life Brief Scale(WHOQOL-BREF)score,and the serum levels of melatonin(MT),dopamine(DA),and cortisol(CORT).After treatment,the clinical efficacy of the two groups was evaluated.Results(1)After 4 weeks of treatment,the total effective rate of the observation group was 97.88%(46/47),while that of the control group was 87.23%(41/47),and the intergroup comparison(tested by chi-square test)showed that the therapeutic efficacy of the observation group was superior to that of the control group(P＜0.01).(2)After treatment,the scores of primary and secondary TCM symptoms in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease of the scores of primary and secondary TCM symptoms in the observation group was significantly superior to that in the control group(P＜0.01).(3)After treatment,the PSQI scores,AIS scores,and FS-14 scores in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the WHOQOL-BREF scores were significantly increased compared with those before treatment(P＜0.05).The decrease of the PSQI scores,AIS scores and FS-14 scores as well as the increase of the WHOQOL-BREF scores in the observation group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the serum MT level of both groups was significantly higher than that before treatment(P＜0.05),and the serum DA and CORT levels were significantly lower than those before treatment(P＜0.05).The increase in serum MT level and the decrease in serum DA and CORT levels of the observation group were significantly superior to those of the control group(P＜0.01).Conclusion The combined therapy of Huatan Jieyu Anshen Decoction combined with vibration tuina manipulations can achieve satisfactory efficacy in the elderly patients with chronic insomnia of phlegm-heat harassing the interior syndrome.The therapy is effective on regulating the central nervous system of the patients,improving the quality of the sleep,and promoting the relief of fatigue and the enhancement of the quality of life,which has great significance to the enhancement of the overall therapeutic efficacy of insomnia.

Objective:To explore the clinical application and triage management value of using blood circulating cell-free DNA (cfDNA) (cysteine dioxygenase type 1 gene, CDO1, and Homeobox protein A9 gene, HOXA9) hypermethylation level to detect and diagnose ovarian cancer.Methods:A case-control study was conducted on patients who went for surgery at Chengdu Womens and Childrens Central Hospital from November 2022 to October 2023. Blood samples were collected before surgery for evaluation of cancer antigen 125 (CA125), human epididymis protein 4 (HE4), risk of ovarian malignancy algorithm (ROMA) score, and DNA methylation testing. The basic clinical information, biomarkers, and transvaginal ultrasound (TVS) information were collected simultaneously. Information from a total of 151 patients was collected, including 122 cases with benign pathology and 29 ovarian cancer cases. The pathologic diagnosis of ovarian tissue was defined as the gold standard. The multivariate logistic regression analysis was used to identify high-risk factors for ovarian cancer. The clinical efficacy of DNA methylation detection for ovarian cancer was analyzed using the area under curve (AUC).Results:The results showed that the age, menopausal status, CA125 and HE4 detection, ROMA score, positivity rate of CDO1 gene and HOXA9 gene single or combined testing in ovarian cancer patients were higher than those in the benign group and showed significant differences ( P<0.05). Among these detection protocols, the AUC of CDO1 and HOXA9 dual gene methylation testing for ovarian cancer was the highest at 0.936 (95% CI, 0.878-0.994), with 89.7% (95% CI 73.6%-96.4%) sensitivity and 97.5% (95% CI 93.0%-99.2%) specificity, respectively. The positive detection rate of CDO1 and HOXA9 dual gene methylation in early ovarian cancer FOGO I-II stage is 12/14 higher than other tests. Conclusion:Blood cfDNA methylation detection, a simple, non-invasive, and highly sensitive detection method, is superior to the current ovarian cancer testing in the risk assessment and early detection.

Objective:To explore the prospective associations between physical activity and incident ischemic stroke in adults.Methods:Data of China Kadoorie Biobank study in Tongxiang of Zhejiang were used. After excluding participants with cancers, strokes, heart diseases and diabetes at baseline study, a total of 53 916 participants aged 30-79 years were included in the final analysis. The participants were divided into 5 groups according to the quintiles of their physical activity level. Cox proportional hazard regression models was used to calculate the hazard ratios ( HR) for the analysis on the association between baseline physical activity level and risk for ischemic stroke. Results:The total physical activity level in the participants was (30.63±15.25) metabolic equivalent (MET)-h/d, and it was higher in men [(31.04±15.48) MET-h/d] than that in women [(30.33±15.07) MET-h/d] ( P<0.001). In 595 526 person-years of the follow-up (average 11.4 years), a total of 1 138 men and 1 082 women were newly diagnosed with ischemic stroke. Compared to participants with the lowest physical activity level (<16.17 MET-h/d), after adjusting for socio-demographic factors, lifestyle, BMI, waist circumference, and SBP, the HRs for the risk for ischemic stroke in those with moderate low physical activity level (16.17-24.94 MET-h/d), moderate physical activity level (24.95-35.63 MET-h/d), moderate high physical activity level (35.64-43.86 MET-h/d) and the highest physical activity level (≥43.87 MET-h/d) were 0.93 (95% CI: 0.83-1.04), 0.87 (95% CI: 0.76-0.98), 0.82 (95% CI: 0.71-0.95) and 0.76 (95% CI: 0.64-0.89), respectively. Conclusion:Improving physical activity level has an effect on reducing the risk for ischemic stroke.

A quartz crystal microbalance(QCM)-systematic evolution of ligands by the exponential en-richment(SELEX)technique was developed to screen out aptamers with high affinity for arsenic(Ⅲ).A random single strand DNA library was designed and fixed on the mercaptoethylamine-modified crystal plate with arsenic(Ⅲ)as the target,and the free aptamer was captured in the solution,and the QCM-SELEX screening method was constructed.After 6 rounds of screening,the secondary library was se-quenced with high throughput method,and the 6S1 dissociation coefficient Kd value was 0.36 μmol/L based on QCM resonance frequency.Using 6S1 as a probe,the QCM biosensor was constructed for the detection of arsenic(Ⅲ).The sensor has a good linear relationship in the range of 0.01 μmol/L～0.2μmol/L,and the detection limit of arsenic(Ⅲ)is 5.2 nmol/L(3σ),indicatinggood selectivity.

Objective:To analyze the immune reconstitution after BTKi treatment in patients with chronic lymphocytic leukemia(CLL).Methods:The clinical and laboratorial data of 59 CLL patients admitted from January 2017 to March 2022 in Fujian Medical University Union Hospital were collected and analyzed retrospectively.Results:The median age of 59 CLL patients was 60.5(36-78).After one year of BTKi treatment,the CLL clones(CD5+/CD19+)of 51 cases(86.4％)were significantly reduced,in which the number of cloned-B cells decreased significantly from(46±6.1)× 109/L to(2.3±0.4)× 109/L(P=0.0013).But there was no significant change in the number of non-cloned B cells(CD19+minus CD5+/CD19+).After BTKi treatment,IgA increased significantly from(0.75±0.09)g/L to(1.31±0.1)g/L(P＜0.001),while IgG and IgM decreased from(8.1±0.2)g/L and(0.52±0.6)g/L to(7.1±0.1)g/L and(0.47±0.1)g/L,respectively(P＜0.001,P=0.002).BTKi treatment resulted in a significant change in T cell subpopulation of CLL patients,which manifested as both a decrease in total number of T cells from(2.1±0.1)× 109/L to(1.6±0.4)× 109/L and NK/T cells from(0.11±0.1)× 109/L to(0.07±0.01)× 109/L(P=0.042,P=0.038),both an increase in number of CD4+cells from(0.15±6.1)× 109/L to(0.19±0.4)× 109/L and CD8+cells from(0.27±0.01)× 109/L to(0.41±0.08)× 109/L(both P＜0.001).BTKi treatment also up-regulated the expression of interleukin(IL)-2 while down-regulated IL-4 and interferon(IFN)-γ.However,the expression of IL-6,IL-10,and tumor necrosis factor(TNF)-α did not change significantly.BTKi treatment could also restored the diversity of TCR and BCR in CLL patients,especially obviously in those patients with complete remission(CR)than those with partial remission(PR).Before and after BTKi treatment,Shannon index of TCR in patients with CR was 0.02±0.008 and 0.14±0.001(P＜0.001),while in patients with PR was 0.01±0.03 and 0.05±0.02(P＞0.05),respectively.Shannon index of BCR in patients with CR was 0.19±0.003 and 0.33±0.15(P＜0.001),while in patients with PR was 0.15±0.009 and 0.23±0.18(P＜0.05),respectively.Conclusions:BTKi treatment can shrink the clone size in CLL patients,promote the expression of IgA,increase the number of functional T cells,and regulate the secretion of cytokines such as IL-2,IL-4,and IFN-γ.BTKi also promote the recovery of diversity of TCR and BCR.BTKi treatment contributes to the reconstitution of immune function in CLL patients.