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 explore the application value of MR diffusion weighted imaging(DWI)and T2*mapping of parotid gland function in patients with head and neck tumors at different radiotherapy periods.Methods A total of 82 patients with head and neck tumors treated were selected.The data of parotid gland volume,salivary volume and parotid gland function were collected.MR DWI and T2*mapping were used to measure the apparent diffusion coefficient(ADC)value and T2*value of parotid gland tissue,and the correlation between parotid gland function and imaging values were analyzed.Results There were all significant differences in parotid gland volume,salivary volume and secretion index among different radiotherapy periods,and parotid gland volume,salivary volume and secretion index during and after radiotherapy were significantly lower than those before radiotherapy.There were signifi-cant differences in T2*values among different radiotherapy periods,and T2*value during and after radiotherapy was significantly lower than that before radiotherapy.There were significant differences in ADC values among different radiotherapy periods,and the ADC value during and after radiotherapy was significantly higher than that before radiotherapy.ADC value was negatively correlated with parotid gland volume,salivary volume and secretion index(r=-0.436,-0.730,-0.718,respectively,P＜0.001),while the T2*value was positively correlated with parotid gland volume,salivary volume and secretion index(r=0.430,0.720,0.707,respec-tively,P＜0.001).Conclusion Parotid gland volume,secretion index and salivary volume show abnormal levels at different radio-therapy periods.MR DWI is negatively correlated with parotid gland function,while T2*mapping is positively correlated with parotid gland function.

Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control. Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake. Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors. Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.

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.

Objective: To assess the exposure levels of lead, cadmium, mercury, arsenic and aluminum in the diets of residents in Henan Province, so as to provide the basis for strengthening food safety supervision. Methods: Six sampling points were selected using stratified random sampling method in Henan Province, including Hebi City, Xiangfu District of Kaifeng City, Jianxi District of Luoyang City, Yuzhou City, Baofeng County and Tanghe County. Food samples were collected and processed into mixed samples of 12 major food categories. The levels of lead, cadmium, mercury, arsenic and aluminum in the samples were measured using inductively coupled plasma mass spectrometry (ICP-MS). Dietary consumption information in Henan Province was collected. The dietary exposure risks of lead, cadmium, mercury, arsenic and aluminum were analyzed using the point estimation method and distribution point estimation method, based on the health guidance values of the Joint FAO/WHO Expert Committee on Food Additives and the margin of exposure (MOE) as the assessment criteria. Results: The dietary exposure level of lead among residents in Henan Province was 41.89 μg/d, which was equivalent to 18.62% of its provisional tolerable weekly intake (PTWI), with cereals and vegetables being the main sources; the MOE values of lead among residents aged 2 to <7 years and 7 to <13 years were both less than 1. The dietary exposure level of cadmium was 10.79 μg/d, which was equivalent to 20.55% of the provisional monthly tolerable intake, with cereals and vegetables being the main sources. The dietary exposure level of total mercury was 0.45 μg/d, which was equivalent to 1.25% of its PTWI, with cereals, vegetables, and water and beverage categories being the main sources; the dietary exposure level of methylmercury was 0.04 μg/d, which was equivalent to 0.28% of its PTWI, and it was entirely derived from aquatic products. The dietary exposure level of total arsenic was 26.65 μg/d, which was equivalent to 0.89% of the daily allowable intake, with cereals and vegetables being the main sources; the dietary exposure level of inorganic arsenic was 8.41 μg/d, which was equivalent to 6.23% of its PTWI, with an MOE value of 22.47. The dietary exposure level of aluminum was 8.27 mg/d, which was equivalent to 45.94% of its PTWI, with cereals and tubers being the main sources; the P90 and P97.5 of dietary aluminum exposure among residents aged 2 to <7 years and 7 to <13 years were both greater than PTWI. Conclusion The overall dietary exposure risks of lead, cadmium, mercury, arsenic and aluminum among residents in Henan Province are relatively low.

Objective:To evaluate the risk factors for the complexity and surgical prognosis in patients with proliferative diabetic retinopathy (PDR).Methods:A historical cohort study of the CONCEPT trial, including 97 patients (97 eyes) who were diagnosed with PDR and requiring three-channel 23-gauge transconjunctival pars plana vitrectomy (PPV) from June 2017 to January 2018 at the First Affiliated Hospital of Nanjing Medical University.All patients received preoperative intravitreal injection of 0.5 mg conbercpet.Based on the PDR complexity score, patients were divided into >3 group or ≤3 group, and the systematic risk factors were compared between the two groups.The influence of sex, age, hypertension, renal insufficiency, duration of diabetes mellitus, and hemoglobin A1c level on the PDR complexity score was evaluated by multivariate logistic regression analysis.Based on age, patients were divided into <45 years group, 45-<60 years group, and ≥60 years group, and the differences in mean operative time, incidence of intraoperative hemorrhage, surgically induced lacrimation and silicone oil filling, and incidence of hemorrhage on color fundus photos and macular edema by optical coherence tomography at postoperative months 1 and 6 were analyzed among different age groups.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Nanjing Medical University (No.2017-SR-283).Written informed consent was obtained from each subject.Results:The age of patients with PDR complexity score >3 was 46.5(36.0, 51.8) years, which was less than 54.0(45.5, 61.5) years for PDR complexity score ≤3, and the difference was statistically significant ( Z=1.835, P=0.002).Among the factors predicting PDR complexity score >3, logistic regression analysis indicated that only age was statistically significant ( P=0.005).For each 1-year increase in age, the risk of PDR complexity score >3 would increase by 7.4%( OR: 0.929, 95% CI: 0.883-0.977).Among the systemic factors, there were significant differences in age, history of diabetes, proportion of patients with hypertension and renal insufficiency among the three age groups (all at P<0.05).Among the ocular factors, there were significant differences in the proportion of patients with history of retinal laser treatment, fibrovascular membrane and complexity score >3 among the three groups (all at P<0.05).The proportion of patients with fibrovascular membrane and complexity score >3 in the <45 years group was significantly higher than that in the 45-<60 and ≥60 years groups (all at P<0.05).There were significant differences in the proportion of patients with intraoperative bleeding and silicone oil filling in the three age groups (all at P<0.017).The proportion of intraoperative bleeding and silicone oil filling in <45 years group was significantly higher than that in 45-<60 and ≥60 years groups (all at P<0.05).The macular edema on postoperative month 1 in the <45 years old group was significantly higher than that in the 45-<60 and ≥60 years groups (both at P<0.05). Conclusions:Among systemic factors, age has a significant impact on the increased PDR complexity and contributes to the poor prognosis of patients.There is a higher percentage of intraoperative complications and early postoperative macular edema in patients in the younger age group compared to the older age group.