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.

ObjectiveTo analyze the expression level of triggering receptor expressed on myeloid cells-1 （TREM-1） in serum and ascites of patients with cirrhotic ascites， and to investigate its correlation with clinical features and inflammatory markers and its role in the diagnosis of infection and prognostic evaluation. MethodsA total of 110 patients with cirrhotic ascites who were hospitalized in The Fifth Hospital of Shijiazhuang from January 2019 to December 2020 were enrolled， and according to the presence or absence of intra-abdominal infection， they were divided into infection group with 72 patients and non-infection group with 38 patients. The patients with infection were further divided into improvement group with 38 patients and non-improvement group with 34 patients. Clinical data and laboratory markers were collected from all patients. Serum and ascites samples were collected， and ELISA was used to measure the level of TREM-1. The independent-samples t test was used for comparison of normally distributed continuous data between two groups； the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the Kruskal-Wallis H test was used for comparison between multiple groups； the chi-square test was used for comparison of categorical data between two groups. A Spearman correlation analysis was used to investigate the correlation between indicators. A multivariate Logistic regression analysis was used to identify the influencing factors for the prognosis of patients with cirrhotic ascites and infection. The receiver operating characteristic （ROC） curve was used to evaluate the diagnostic and prognostic efficacy of each indicator， and the Delong test was used for comparison of the area under the ROC curve （AUC）. ResultsThe level of TREM-1 in ascites was significantly positively correlated with that in serum （r=0.50， P<0.001）. Compared with the improvement group， the non-improvement group had a significantly higher level of TREM-1 in ascites （Z=-2.391， P=0.017） and serum （Z=-2.544， P=0.011）， and compared with the non-infection group， the infection group had a significantly higher level of TREM-1 in ascites （Z=-3.420， P<0.001）， while there was no significant difference in the level of TREM-1 in serum between the two groups （P>0.05）. The level of TREM-1 in serum and ascites were significantly positively correlated with C-reactive protein （CRP）， procalcitonin （PCT）， white blood cell count， and neutrophil-lymphocyte ratio （r=0.288， 0.344， 0.530， 0.510， 0.534， 0.454， 0.330， and 0.404， all P<0.05）. The ROC curve analysis showed that when PCT， CRP， and serum or ascitic TREM-1 were used in combination for the diagnosis of cirrhotic ascites with infection， the AUCs were 0.715 and 0.740， respectively. The multivariate Logistic regression analysis showed that CRP （odds ratio ［OR］=1.019， 95% confidence interval ［CI］： 1.001‍ ‍—‍ ‍1.038， P=0.043） and serum TREM-1 （OR=1.002， 95%CI： 1.000‍ ‍—‍ ‍1.003， P=0.016） were independent risk factors for the prognosis of patients with cirrhotic ascites and infection， and the combination of these two indicators had an AUC of 0.728 in predicting poor prognosis. ConclusionThe level of TREM-1 is closely associated with the severity of infection and prognosis in patients with cirrhotic ascites， and combined measurement of TREM-1 and CRP/PCT can improve the diagnostic accuracy of infection and provide support for prognostic evaluation.

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.

INTRODUCTION: During the coronavirus disease 2019 (COVID-19) pandemic, multiple guidelines have recommended videolaryngoscope (VL) for tracheal intubation. However, there is no evidence that VL reduces time to tracheal intubation, and this is important for COVID-19 patients with respiratory failure. METHODS: To simulate intubation of COVID-19 patients, we randomly assigned 28 elective surgical patients to be intubated with either McGrath™ MAC VL or direct laryngoscope (DL) by specialist anaesthetists who donned 3M™ Jupiter™ powered air-purifying respirators (PAPR) and N95 masks. The primary outcome was time to intubation. RESULTS: The median time to intubation was 61 s (interquartile range [IQR] 37-63 s) and 41.5 s (IQR 37-56 s) in the VL and DL groups, respectively ( P = 0.35). The closest mean distance between the anaesthetist and patient during intubation was 21.6 ± 4.8 cm and 17.6 ± 5.3 cm in the VL and DL groups, respectively ( P = 0.045). There were no significant differences in the median intubation difficulty scale scores, proportion of successful intubations at the first laryngoscopic attempt and proportion of intubations requiring adjuncts. All the patients underwent successful intubation with no adverse event. CONCLUSION There was no significant difference in the time to intubation of elective surgical patients with either McGrath™ VL or DL by specialist anaesthetists who donned PAPR and N95 masks. The distance between the anaesthetist and patient was significantly greater with VL. When resources are limited or disrupted during a pandemic, DL could be a viable alternative to VL for specialist anaesthetists.
Humans ; COVID-19 ; Intubation, Intratracheal ; Laryngoscopes ; Laryngoscopy ; Respiratory Protective Devices ; Video Recording

Colitis-associated cancer(CAC)is a specific type of colorectal cancer that develops from inflammatory bowel disease(IBD).Myeloid-derived suppressor cells(MDSCs)are a group of myeloid cells with immunosuppressive properties,and MDSCs in the tumor microenvironment proliferate and activate during the development of colitis-associated cancer,inhibiting T-cell production and impairing their function,which impedes the immunotherapeutic effect of colitis-associated cancer.In this paper,we review the immunosuppressive mechanisms of MDSCs in the development of inflammatory bowel disease to colitis-associated cancers and the current drugs targeting MDSCs for immunotherapy of inflammatory colorectal cancers,with a view to providing new strategies for the treatment of colitis-associated cancers.

Objective To investigate the effects of cinbufagin(CB)on the proliferation,migration and invasion ability as well as epithelial-mesenchymal transition(EMT)of human colon cells HCT116.Methods Logarithmically grown HCT116 cells were randomly divided into blank group and experimental-L,-M,-H groups;the blank group did not receive any treatment(0 nmol·L-1),and experimental-L,-M,-H groups were cultured in 1 640 medium containing 17.5,35 and 70 nmol·L-1 cinbufagin for 48 h.Cell counting kit-8(CCK-8)was used to detect the effect of cinbufagin on the survival rate of HCT116 cells;cloning assay was used to detect the effect of cinbufagin on the proliferation of HCT116 cells;cell scratch assay and Transwell assay were used to detect the effect of cinbufagin on the migration and invasive ability of HCT116 cells;Western blot was used to detect the expression levels of janus kinase 2(JAK2)/signal transducers and activators of transcription 3(STAT3)pathway and EMT-related proteins of HCT116 cells.Results The number of clone formation in blank group and experimental-L,-M,-H groups were 122.67±24.42,73.67±15.82,44.33±4.51 and 21.67±1.53;the rates of migration of scratches were(44.64±9.15)％,(26.91±2.94)％,(19.28±1.52)％and(6.33±2.30)％;the number of invaded cells were 120.33±1.15,58.33±9.07,33.33±1.53 and 18.33±3.21;the relative protein expression of phosphorylated JAK-2(p-JAK-2)/JAK-2 were 1.02±0.06,0.94±0.05,0.75±0.22 and 0.49±0.22;relative protein expression of phosphorylated STAT3(p-STAT3)/STAT3 were 0.89±0.10,0.72±0.04,0.65±0.06 and 0.52±0.18;relative protein expression of E-cadherin were 0.30±0.14,0.41±0.13,0.49±0.14 and 0.69±0.17;relative protein expression of N-cadherin were 0.96±0.11,0.78±0.04,0.69±0.12 and 0.40±0.15;Snail relative protein expression were 0.89±0.08,0.62±0.15,0.44±0.15 and 0.27±0.09;Vimentin relative protein expression were 0.92±0.09,0.76±0.13,0.63±0.01 and 0.43±0.09,respectively.The above indexes in experimental-H group showed statistically significant differences compared to blank group(all P＜0.05).Conclusion HCT116 can inhibit the invasion and metastasis of human colorectal cancer cells HCT116 by inhibiting epithelial-mesenchymal transition through JAK2/STAT3 pathway.

Objective To explore the mechanism of inhibition of colorectal cancer cells HT29 proliferation,migration and invasion by bufalin through Janus kinase 2(JAK2)/signal transducer and activator of transcription 3(STAT3)pathway.Methods Human colorectal cancer HT29 cells were randomly divided into control group and experimental-L,-M,-H groups.The cells in the control group were not treated,and the cells in the experimental-L,-M,-H groups were treated with 2.5,5.0 and 10.0 μmol·L-1 bufalin for 48 h.After HT29 cells were infected with FLAG STAT3 lentivirus,the cells were divided into lentivirus infection group and experiment-H(10.0 pmol·L-1 bufalin)+lentivirus infection group.Cell viability was detected by cell counting kit 8(CCK-8).Cloning experiment to verify cell proliferation rate;Transwell experiment verified the migration ability of cells after bufalin treatment;the transfection efficiency of lentivirus and the expression of cell-related proteins were detected by Western blot.Results After 48 h of drug action,the number of cells in the control group,experimental-L,-M,-H groups were 1 003.25±255.53,698.00±152.25,562.13±31.56 and 449.50±82.40,respectively;the number of invasive cells were 932.00±188.84,742.22±108.64,514.67±124.82 and 343.56±86.42,respectively;the protein expression level of p-JAK2 were 1.37±0.27,0.97±0.06,0.74±0.06 and 0.39±0.12,respectively.The number of cells in the control group,experimental-H group,lentivirus infection group,and experimental-H+lentivirus infection group were 906.88±211.71,389.00±143.08,1 279.38±210.34 and 604.75±12.52,respectively;the number of invasive cells were 671.22±44.74,246.11±28.16,1 080.78±119.13 and 574.78±16.23,respectively.Compared with the control group,there were statistically significant differences in the number of cell proliferation,the number of cell invasion and the relative levels of p-JAK2 in the experimental-M and-H groups(all P＜0.05).Compared with the control group,the number of cell proliferation and the number of cell invasion in the experimental-H group,the lentivirus infection group,and the high-dose experimental+lentivirus infection group were statistically significant(all P＜0.05).Conclusion Bufalin can inhibit the proliferation,migration and invasion of colorectal cancer by activating the JAK2/STAT3 signalling pathway.