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.

Abstract: Objective　To explore the spatial epidemiological characteristics of severe cases hand, foot and mouth disease (HFMD) in Guangxi, China, from 2014 to 2018, and to provide a basis for identifying the high-risk regions as well as the prevention and control of severe cases of HFMD in Guangxi. Methods　Spatial-temporal scanning analysis, global and local spatial autocorrelation analysis were used to analyze the spatial clustering of HFMD. The trend surface analysis was used to evaluate the spatial distribution trend of HFMD. Results　From 2014 to 2018, the incidence and severe case fatality rates of HFMD were 3.89/100 000 and 4.23%, respectively. Monte Carlo scanning analysis showed that the first cluster region was Cenxi City, the second cluster was mainly concentrated in northwest of Guangxi, and the aggregation time was mainly concentrated in April to May and August to October. The global spatial autocorrelation analysis showed that the severe HFMD was significant clustering distribution, and the Moran's I coefficients of the sever cases, severe morbidity and severe case fatality rate were 0.088, 0.118, 0.197, respectively (P<0.05). Local spatial autocorrelation analysis showed that hotspots of severe HFMD cases were concentrated in the southern Guangxi, mainly in Lingshan County. Anselin local Moran's I clustering and outlier analysis indicated that 5 high-high (H-H) clustering regions for fatality were Lingshan, Pubei, Zhongshan, Zhaoping and Pinggui County. There were 6 high-high (H-H) clustering regions for severe incidence rate, namely Lingshan, Qinnan, Lingyun, Youjiang, Bama Yao Autonomous and Pinggui County, and 1 high-low (H-L) clustering region, Cenxi County. The trend surface analysis showed that the overall number of severe cases of death decreased from east or west to the middle, and increased from north to middle, and then decreased to south. Conclusions　Severe HFMD cases in Guangxi have obvious spatial-temporal clustering, and the hop spots are mainly concentrated in southern Guangxi. The prevention and control of HFMD in areas with high incidence of severe cases should be strengthened to reduce the burden of HFMD cases.

Adult ; Humans ; Acute Disease ; Leukemia

Objective: To understand the consciousness of the cancer early treatment and its demographic and socioeconomic factors. Methods: A cross-sectional survey was conducted in 16 provinces covered by the Cancer Screening Program in Urban China (CanSPUC) from 2015 to 2017. A total of 32 257 local residents aged ≥18 years old who could understand the investigation procedure were included in the study by using the cluster sampling method and convenient sampling method. All local residents were categorized into four groups, which contained 15 524 community residents, 8 016 cancer risk assessment/screening population, 2 289 cancer patients and 6 428 occupational population, respectively. The questionnaire collected personal information, the consciousness of the cancer early treatment and relevant factors. The Chi square test was used to compare the difference between the consciousness of the cancer early treatment and relevant factors among the four groups. The logistic regression model was used to analyze the influencing factors related to the consciousness of the cancer early treatment. Results: With the assumption of being diagnosed as precancer or cancer, 89.97% of community residents, 91.84% of cancer risk assessment/screening population, 93.00% of cancer patients and 91.52% of occupational population would accept active treatments (P<0.001). If the immediate family members were diagnosed as precancer or cancer, people who would encourage their family members to receive early treatment in the four groups accounted for 91.96%, 91.94%, 92.44% and 91.55%, respectively (P<0.001). The company employees, annual household income with 40 000 yuan and more and other three groups had a relatively better consciousness of the cancer early treatment (P<0.05). Male, widowed, unemployed and from the central and western regions had a relatively worse consciousness of the cancer early treatment (P<0.05). Conclusion Residents in urban China participants had a good consciousness of the cancer early treatment. The marital status, occupation, annual household income and residential regions were major factors related to the consciousness of the cancer early treatment.

Objective To investigate the potential dosimetric advantages of half jaw volumetric modulated arc therapy ( H-VMAT) applied to the Oropharyngeal Cancer, comparing with full jaw VMAT (F-VMAT) and intensity modulated radiotherapy ( IMRT ). Methods Planning CT images of 10 oropharyngeal cancer patients were retrospectively chosen and transferred to Eclipse treatment planning system v. 11. 0 (Varian Medical Systems, Pala Alto, USA), based on which H-VMAT, W-VMAT, and IMRT plans were created. Two full arcs (360°) were adopted for VMAT planning, and the 7 beams were equally distributed for IMRT planning. The optimization constraints remained the same for the three kinds of plans. The dosimetric parameters such as D2 , D98 , D50 , HI, and CI were evaluated for PGTV, PCTV1, PCTV2, PGTVln, and PCTVln. In addition, the maximum dose (Dmax) and D1 cc(minimum dose received by 1cc) of the brainstem and spinal cord were analyzed respectively. The mean dose ( Dmean ) to the parotids, oral cave, larynx, and cervical normal tissues were also reviewed. The monitor units ( MU) for all treatment plans were recorded. Results Comparisons of the three planning techniques showed that H-VAMT improved the HI and CI of the targets (except PCTV2) significantly (HI: F =3. 959, 6. 764, 10. 581, 6. 770, 13. 040, P<0. 05;CI:F=6. 594, 4. 138, 0. 842, 4. 031, 5. 388, P<0. 05);reduced Dmax(F=4. 509, 20. 331, P<0. 05) and D1 cc for brainstem and spinal cord (F=27. 432, 26. 314, P<0. 05) significantly;reduced Dmean(F=4. 279, 29. 498, 19. 295, P<0. 05) to the normal tissues of the mouth, throat and neck significantly. The V50 of the mouth and throat were slightly lower in IMRT plans (F=8. 140, P<0. 05). IMRT was slightly better than W-VMAT in sparing oral cavity and larynx, but the dose distribution was the worst. The H-VMAT plans showed the best dose distribution in the cervical normal tissues, especially for the lower and posterior parts, where IMRT plans displayed high dose curves. Conclusions H-VMAT is dosimetrically superior than W-VMAT and IMRT for oropharyngeal cancer, which could be considered for clinical applications.

Objective To compare the effective dose deposited in patients between helical tomotherapy (HT) and multi-ISO radiotherapy (M-ISO) in carniospinal irradiation (CSI).Methods Nine children with craniospinal irradiation were selected .For these patients , new plans were designed with HT and M-ISO centers planning method on the treatment planning system ( TPS) .The effective dose of the nine patients from 18 treatment plans were calculated ,and the difference of the effective dose between HT and M-ISO was compared using paired t-test.Results The plans designed in two groups were both satisfied all clinical requirements .For the planning target volume ( PTV ) , no statistically significant difference was found in D95% between two groups ( P>0.05 ) , while D98%, D2% and homogeneity index (HI) in HT group were superior to M-ISO group (t=2.762, 2.413, 4.563, P<0.05), D50%, Dmean and CI in M-ISO group were superior to HT group (t=5.259, 3.685, 7.815, P<0.05).HT and M-ISO had different advantages in the protection of the OARs .The effective dose of patients in M-ISO group was superior to HT group (t=5.921, P<0.05).Conclusions HT and M-ISO have different advantages in CSI.The low dose area has greater influence on the effective dose in HT group compared to M-ISO group. The low dose area should be concerned while designing the treatment planning for CSI .