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.

BACKGROUND: Previous evidence showed that ambient air pollution and cardiovascular mortality are related. However, there is a lack of evidence towards the modification effect of long-term lifestyle on the association between short-term ambient air pollution and death from cardiovascular events. METHOD: A total of 14,609 death from major adverse cardiovascular events (MACE) were identified among the China Kadoorie Biobank participants from 2013 to 2018. Ambient air pollution exposure including particulate matter 2.5 (PM_2.5), SO₂, NO₂, CO, and O₃ from the same period were obtained from space-time model reconstructions based on remote sensing data. Case-crossover design and conditional logistic regression was applied to estimate the effect of short-term exposure to air pollutants on MACE mortality. RESULTS: We found MACE mortality was significantly associated with PM_2.5 (relative percent increase 2.91% per 10 µg/m³ increase, 95% CI 1.32-4.53), NO₂ (5.37% per 10 µg/m³ increase, 95% CI 1.56-9.33), SO₂ (6.82% per 10 µg/m³ increase, 95% CI 2.99-10.80), and CO (2.24% per 0.1 mg/m³ increase, 95% CI 1.02-3.48). Stratified analyses indicated that drinking was associated with elevated risk of MACE mortality with NO₂ and SO₂ exposure; physical inactivity was associated with higher risk of death from MACE when exposed to PM_2.5; and people who had balanced diet had lower risk of MACE mortality when exposed to CO and NO₂. CONCLUSIONS The study results showed that short-term exposure to ambient PM_2.5, NO₂, SO₂, and CO would aggravate the risk of cardiovascular mortality, yet healthy lifestyle conduct might mitigate such negative impact to some extent.
Humans ; Cardiovascular Diseases/epidemiology* ; China/epidemiology* ; Male ; Female ; Air Pollution/adverse effects* ; Middle Aged ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Exposure/adverse effects* ; Life Style ; Aged ; Adult ; Risk Factors ; Cross-Over Studies ; East Asian People

Objective To explore the clinical features,treatment and prognosis of glioblastomas(GBM)in adults.Methods A retrospective cohort study was performed on 176 adult GBM patients admitted to our department from January 2015 to December 2021.Chi-square test was used to investigate the clinical differences between isocitrate dehydrogenase(IDH)mutant and wild-type GBM.Kaplan-Meier and Log-Rank tests were employed to plot survival curve and compute the survival analysis.Multivariate Cox regression model was applied to identify the independent prognostic factors.Results IDH wild-type GBM account for 89.2％and had significantly differences from the IDH-mutant GBM in terms of age of onset,Karnofsky(KPS)score at admission,symptoms of neurological deficit,and methylation status of O6-methylguanine-DNA-methyltransferase(MGMT)promoter(P＜0.05).For the IDH wild-type GBM patients receiving conventional therapy,univariate Cox hazard analysis showed gross total resection,methylation of MGMT promoter,initiation of radiation within the 5th to 6th week after surgery,and adjuvant temozolomide(TMZ)chemotherapy ≥6 cycles were favorable prognostic factors for overall survival(OS);GBMs in the left hemisphere,involvement of single lobe,methylation of MGMT promoter,and initiation of radiation within the 5th to 6th week after surgery were favorable prognostic factors for progression free survival(PFS)(all P＜0.05).Moreover,multivariate Cox hazard regression analysis indicated that methylation of MGMT promoter,and initiation of radiation within the 5th to 6th week after surgery,and adjuvant TMZ chemotherapy ≥6 cycles were independent protective factors for OS,and GBMs in the left hemisphere,involvement of single lobe and methylation of MGMT promoter were independent protective factors for PFS in the GBM patients(all P＜0.05).Conclusion The clinical and prognostic features are totally different between IDH mutant and wild-type GBM,and molecular detections are needed for the further pathological classification.Methylation of MGMT promoter is a primary marker of favorite prognosis for IDH wild-type GBM,and slightly delay in radiotherapy(the 5th to 6th week after surgery)can effectively improve the survival prognosis of IDH wild-type GBM.

Objective:To classify the etiology of secondary hemophagocytic syndrome (sHLH) and explore its clinical, laboratory and therapeutic characteristics in order to deepen the understanding of the disease.Method:A retrospective observational study was conducted on sHLH patients who were treated at Peking University People's Hospital from January 2016 to December 2021. Patients under the age of 18 and those with missing clinical data were excluded. The distribution of departments visited and etiologies of sHLH were analyzed. Baseline data, clinical characteristics, complications, laboratory data, treatment, and in-hospital outcomes of sHLH were collected. The sHLH patients were then divided into 3 groups including malignancy group, macrophage activation syndrome (MAS) group and other etiologies (mainly infection) group. Intergroup comparisons were performed using chi-square tests, analysis of variance, Mann-Whitney tests, and other statistical methods.Results:A total 169 patients were enrolled, among these patients, 27.8% were malignancy-related HLH, 47.9% were MAS, and 24.3% were other etiologies related HLH. Statistical analysis revealed that the clinical characteristics of other etiological group was highly consistent with the malignancy group, including more and severer peripheral blood cell reduction, higher sCD25 levels, more Epstein-Barr virus infection, and the prognosis was similar, both were with more than 50% in-hospital mortality. And the incidence of hemophagocytosis was highest in other etiological groups (65.9%). In contrast, MAS group was with an obviously lower mortality of 17.3% ( P<0.05). Meanwhile, treatments including methylprednisolone pulse, cyclosporine A and interleukin-2 were used frequently in MAS group. Conclusion:Malignancy related HLH and other etiologies related HLH exhibit more similar clinical characteristics and prognosis, while the MAS group, has a milder overall condition and better prognosis.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

ObjectiveThis study aimed to analyze the difference in setup error before and after correction of systematic error. To determine the most appropriate image-guided strategy during HT treatment， we use different scanning ranges and image-guidance frequencies in patients with nasopharyngeal carcinoma （NPC） treated with helical tomotherapy （HT）. MethodsFifteen patients with NPC who received HT treatment in Sun Yat-sen University Cancer Center from October 2019 to February 2020 were selected. Megavoltage computed tomography （MVCT） scanning was performed before each treatment. After five times of radiotherapy， system-error correction was performed to adjust the setup center. The setup errors before and after the correction of systematic errors， as well as the setup errors of different scanning ranges and different scanning frequencies， were collected for analysis and comparison. ResultsWhen comparing the setup errors before and after the correction of systematic error， the differences in setup errors in the left–right （LR）， superior–inferior （SI）， and anterior–posterior （AP） directions were statistically significant （P<0.05）.The different scanning ranges of "nasopharynx + neck" and "nasopharynx" were compared， and a statistically significant difference was found in yaw rotational errors （P<0.05）. In the comparison of daily and weekly scan frequency after system-error correction， a significant difference was found in AP direction （P<0.05）. ConclusionDuring radiotherapy for NPC， the systematic error can be corrected according to the first five setup errors， and then small-scale scanning was selected for image-guided radiotherapy every day.