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.

The global pandemic of coronavirus disease 2019 (COVID-19) has brought great challenges to the traditional medical model. During the outbreak of COVID-19 in Shanghai, China, from March to May, 2022, there was a significant increase in the number of pediatric cases due to high transmissibility, immune escape, and vaccine breakthrough capacity of Omicron variants. The designated hospitals for children with COVID-19 served as a connecting link between children's specialized hospitals and mobile cabin hospitals. From April 7 to June 2, 2022, a total of 871 children with COVID-19 were admitted to Renji Hospital, Shanghai Jiao Tong University School of Medicine (South Branch), a designated hospital for children with COVID-19. Among these patients, 568 (65.2%) were children under 3 years old, 870 (99.9%) were mild or moderate, and 1 was severe. This article reports the experience in the management of pediatric cases in this designated hospital, which included the following aspects: establishing an optimal case-admission process; strengthening multidisciplinary standardized diagnosis and treatment; optimizing the management, warning, and rescue system for severe COVID-19; implementing family-centered nursing care; formulating an individualized traditional Chinese medicine treatment regimen; optimizing the discharge process and strengthening bed turnover; implementing strict whole-process control to reduce the risk of nosocomial infection; constructing a structured medical record system and using information platforms to adapt to the work mode of large-volume cases; conducting scientific research and sharing the experience in diagnosis and treatment.
COVID-19 ; Child ; Child, Preschool ; China ; Hospitals, Pediatric ; Humans ; SARS-CoV-2

Objective: To explore the safety and feasibility of stereotactic radiation therapy (SBRT) strategy for irradiating porcine ventricular septum, see if can provide a preliminary experimental evidence for clinical SBRT in patients with hypertrophic obstructive cardiomyopathy (HOCM). Methods: Five male pigs (39-49 kg, 6 months old) were used in this study. Pigs were irradiated at doses of 25 Gy (n=2) or 40 Gy (n=3). Delineation of the target volume was achieved under the guidance of 3-dimensional CT image reconstruction, and SBRT was then performed on defined target volume of porcine ventricular septum. Blood biomarkers, electrocardiogram and echocardiography parameters were monitored before and after SBRT. Pathological examination (HE staining, Masson staining) was performed on the target and non-target myocardium at 6 months post SBRT. Results: SBRT was successful and all animals survived to the designed study endpoint (6 months) after SBRT. Serum cardiac troponin T (cTnT) level was significantly higher than the baseline level at 1 day post SBRT, and reduced at 1 week after SBRT, but was still higher than the baseline level(P<0.05). Serum N-terminal pro-B type natriuretic peptide (NT-proBNP) was also significantly increased at 1 day post SBRT (P<0.05) and returned to baseline level at 1 week post SBRT. The serum NT-proBNP level was (249±78), (594±37) and (234±46) pg/ml, respectively, and the cTnT was (14±7), (240±40) and (46±34) pg/ml, respectively at baseline, 1 day and 1 week after SBRT in the 40 Gy dose group. The serum NT-proBNP level was (184±20), (451±49) and (209±36) pg/ml, respectively, the cTnT values ​​were (9±1), (176±29) and (89±27) pg/ml, respectively at baseline, 1 day and 1 week after SBRT in the 25 Gy dose group. Both NT-proBNP and cTnT values tended to be higher post SBRT in the 40 Gy dose group as compared with the 25 Gy dose group, but the difference was not statistically significant (P>0.05). The left ventricular ejection fraction and the left ventricular end-diastolic diameter remained unchanged before and after SBRT (P>0.05). The interventricular septum thickness showed a decreasing trend at 6 months after SBRT, but the difference was not statistically significant ((9.54±0.24) mm vs. (9.82±8.00) mm, P>0.05). The flow velocity of the left ventricular outflow tract, and the valve function and morphology were not affected by SBRT. At 6 months after SBRT, HE staining revealed necrosis in the irradiated target area of ​​the myocardium in the 40 Gy dose group and the 25 Gy dose group, and the degree of necrosis in the irradiated interventricular septum was more obvious in the 40 Gy dose group as compared with the 25 Gy group. The combined histological analysis of the two groups showed that the necrotic area of ​​the irradiated target area accounted for (26±9)% of the entire interventricular septum area, which was higher than that of the non-irradiated area (0) (P<0.05). There was no damage or necrosis of myocardial tissue outside the target irradiation area in both groups. The results of Masson staining showed that the percentage area of myocardial fibrosis was significantly higher in the irradiated target area than non-irradiated area ((12.6±5.3)% vs. (2.5±0.8)%, P<0.05). Conclusion: SBRT is safe and feasible for irradiating porcine ventricular septum.
Animals ; Feasibility Studies ; Male ; Necrosis ; Radiosurgery/methods* ; Stroke Volume ; Swine ; Ventricular Function, Left ; Ventricular Septum

Aortic Valve/surgery* ; Aortic Valve Insufficiency/surgery* ; Aortic Valve Stenosis/surgery* ; Heart Valve Prosthesis ; Humans ; Perioperative Period ; Severity of Illness Index ; Transcatheter Aortic Valve Replacement ; Treatment Outcome

Objective: To investigate the advantages and disadvantages of point electro-cauterization (PEC) and holmium laser cauterization (HLC) in the treatment of post-ejaculation hematuria. METHODS: From January 2015 to December 2018, 73 patients with post-ejaculation hematuria, aged 24－63 (36.8 ± 4.2) years, underwent PEC (n = 35) or HLC (n = 38) after failure to respond to 3 months of conservative treatment. We compared the hospital days, total hospitalization expenses, maximum urinary flow rate (Qmax), average urinary flow rate (Qavg), Hamilton Anxiety Rating Scale (HAMA) score, postoperative duration of hematuria, and recurrence rate at 3 and 6 months after surgery. RESULTS: All the patients experienced first ejaculation but no post-ejaculation hematuria at 1 month after operation. The recurrence rates were lower in the PEC than in the HLC group at 3 months (5.71% vs 2.63%, P > 0.05) and 6 months postoperatively (8.57% vs 5.26%, P > 0.05). Compared with the baseline, the Qmax was decreased from (18.56 ± 2.53) ml/s to (13.68 ± 3.31) ml/s (P < 0.05) and the Qavg from (14.35 ± 2.26) ml/s to (9.69±1.84) ml/s in the PEC group at 1 month after surgery (P < 0.01), but neither showed any statistically significant difference in the HLC group. Mild to moderate anxiety was prevalent in the patients preoperatively, particularly in those without job or regular income and those with a long disease course or frequent onset, the severity of which was not correlated with age, education or marital status. The HAMA score was decreased from18.65 ± 4.33 before to 12.35 ± 3.63 after surgery in the PEC group (P < 0.01), and from 16.88 ± 2.11 to 6.87 ± 4.36 in the HLC group (P < 0.01). The mean hospital stay was significantly longer in the former than in the latter group (［5.2 + 1.3］ vs ［3.4 ± 0.5］ d, P < 0.01), while the total cost markedly lower (［6.35 ± 1.20］ vs ［12.72 ± 2.15］ thousand RMB ¥, P < 0.05). CONCLUSIONS Both PEC and HLC are safe and effective for the treatment of post-ejaculation hematuria, with no significant difference in the recurrence rate at 3 and 6 months after operation, but their long-term effect needs further follow-up studies. PEC may increase the risk of negative outcomes of the postoperative urinary flow rate, while HLC has the advantages of better relieving the patient's anxiety, sooner discharge from hospital and earlier recovery from postoperative hematuria, though with a higher total cost than the former.
Adult ; Cautery ; Ejaculation ; Hematuria/surgery* ; Holmium ; Humans ; Laser Therapy ; Lasers, Solid-State/therapeutic use* ; Male ; Middle Aged ; Treatment Outcome ; Young Adult

Objective:To investigate the effect of astragaloside on the macrophage polarization and the possible anti-tumor immunity mechanism of astragaloside. Method:The cytotoxic effect of different concentrations of astragaloside at different time points on macrophage was measured by methylthiazolyldiphenyl-tetrazolium bromide (MTT), in order to choose the suitable concentration of astragaloside, macrophages were co-cultured with tumor cells at the ratio 1:1, and the effect of astragaloside on macrophage-mediated lysis of tumor cells was performed by biophotonic cytotoxicity assay after the mixed cells were effected with 0.1 mg·L^-1 astragaloside for 24 h. Macrophages were dealt with 0.1 mg·L^-1 astragaloside for 24h, the expressions of CD16/32 and CD206 in macrophages were performed by flow cytometry, the mRNA expressions of macrophage inducible nitric oxide synthase (iNOS), Arginine-1 (Arg-1), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), interleukin-12 (IL-12), interleukin-10 (IL-10) and transforming growth factor-β (TGF-β) were measured by Real-time PCR, the protein expressions of macrophage signal transducers and activators of transcription 1 (STAT1) and phosphorylation signal transducers and activators of transcription 1 (p-STAT1) were determined by Western blot. Result:Astragaloside had no effect on the viability of macrophages with 0.1 mg·L^-1. Compared with control group, astragaloside obviously enhanced the macrophage-mediated lysis of tumor cells according to the biophotonic cytotoxicity assay, induced the M1 macrophage marker CD16/32 expression according to flow cytometry, increased the mRNA expressions of iNOS, IL-1β, TNF-α and IL-12 according to the Real-time PCR, and promoted the phosphorylation of STAT1 in macrophages on the basis of Western blot. Conclusion:Astragaloside could induce M1 macrophage polarization by increasing the phosphorylation of STAT1, and initiate macrophage-related anti-tumor immunity response.