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.

Fluorescent carbon dots(CDs)were synthesized via a solvothermal method with citric acid and urea as raw materials,and ethylene glycol as reaction solvent.The micromorphology,crystal structure,elemental composition,surface functional group,and optical property of as-synthesized CDs were characterized.The excitation-dependent fluorescence property of CDs was investigated,and the effects of synthesis conditions including reaction temperature,reaction time and raw materials on excitation and emission wavelengths of the CDs were also discussed.Then,a series of CDs-based fluorescent composites were prepared by combining CDs with starch,nano-silica,montmorillonite,kaoline,kieselguhr and magnesium oxide,respectively.Finally,the CDs-starch composites were used for latent fingerprint development on smooth substrates,and the qualitative as well as quantitative evaluation of the contrast,sensitivity and selectivity in fingerprint development were also made.Enhanced development of latent fingerprints was thus achieved by the aid of the excitation-dependent fluorescence property of CDs-starch composite combined with the optical filtering technique,which could decrease the background noise interference to a great extent.Experimental results showed that,the contrast between fingerprint(developing signal)and substrate(background noise)was obvious,exhibiting a strong contrast;the minutiae of papillary ridges were clear,indicating a high sensitivity;the adsorption between CDs-starch composites and fingerprint residues was specific,showing a good selectivity.

A serious of rare earth luminescent micro/nano-materials with various properties were synthesized via chemical method for fluorescent development of latent fingerprints(LFPs).Three evaluation indexes namely contrast,sensitivity and selectivity were introduced to evaluate the effects of LFP development.Quantitative formulas for calculating the contrast,sensitivity and selectivity were further put forward,and a quality evaluation system based on Python was thus established.In addition,the objective evaluation value was finally confirmed to be consistent with the subjective visual judgment.The reproducibility of this evaluation method was finally confirmed.The effects of luminescence intensity and color of developing materials on the contrast,particle size of developing materials on the sensitivity,and micromorphology and surface property of developing materials on the selectivity were discussed in detail.Five effective ways were also proposed to promote the quality of LFP development,such as increasing the luminescence intensity,tuning the luminescence color,decreasing the particle size,adjusting the micromorphology,and modifying the surface property.This quality evaluation system based on Python could evaluate the effects of LFP development objectively,accurately and comprehensively,exhibiting easy operability,high efficiency,sensitive response,accurate and reliable results,and wide applicability,which would provide beneficial references for the reasonable selection of LFP development methods as well as objective evaluation of evidence value.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To study the effect of a modified behavioral treatment(MBT)on functional anejaculation and analyze the factors influencing the therapeutic efficacy.Methods:We enrolled in this study 59 men aged 24-45 years visiting the Andrology Clinic of Shanghai First Maternity and Infant Hospital from August 2019 to May 2021 and complaining of aejaculation in sexual inter-course but normally ejaculating during masturbation.Thirty-nine of the patients underwent conventional behavioral treatment(the CBT group)and the other 20 received MBT,namely,changing the masturbation method combined with audiovisual stimulation during sexual intercourse(the MBT group).We compared the therapeutic effects between the two groups of patients,and analyzed the correlation of the outcomes of MBT with age,abstinence duration,use of audiovisual stimulation,change of the sexual position,mean bilateral testis volume and sex hormone levels.Results:After treatment,22(37.29％)of the patients achieved successful ejaculation at least once in sexual intercourse,11(55.00％)in the MBT group,and the other 11(28.21)in the CBT group,with a significantly higher effec-tiveness rate in the former than in the latter(P＜0.05).The effectiveness rate was significantly correlated to the method of standing-position masturbation plus sexual intercourse and reduction in the frequency of masturbation among various strategies of behavioral treat-ment(P＜0.05).Conclusion:MBT has a certain effect on functional anejaculation,and targeting the previous events of the patient is the key to the therapeutic efficacy.Further exploration of more effective strategies of behavioral treatment will become the trend of de-velopment in the management of functional anejaculation.

Objective:To investigate the effect of high-fat and low-carbohydrate diet combined with radiotherapy on the tumor microenvironment of mice with lung xenografts.Methods:C57BL/6J mice were selected to establish the Lewis lung cancer model, and they were divided into the normal diet group, the high-fat and low-carbohydrate diet group, the normal diet + radiotherapy group, and the high-fat and low-carbohydrate diet + radiotherapy group, with 18 mice in each group. The mice in the normal diet group and the normal diet + radiotherapy group were fed with the normal diet with 12.11% fat for energy supply, and the mice in the high-fat and low-carbohydrate diet group and the high-fat and low-carbohydrate diet + radiotherapy group were fed with high-fat and low-carbohydratediet with 45.00% fat for energy. On the 12th to 14th days, the tumor sites of the mice in the normal diet + radiotherapy group and the high-fat and low-carbohydrate diet + radiotherapy group were treated with radiotherapy, and the irradiation dose was 24 Gy/3f. The body weight, tumor volume, blood glucose and blood ketone level, liver and kidney function, and survival status of the mice were observed and monitored. Immunohistochemical staining was used to detect the tumor-associated microangiogenesis molecule (CD34) and lymphatic endothelial hyaluronan receptor 1 (LYVE-1), Sirius staining was used to detect collagen fibers, and multiplex immunofluorescence was used to detect CD8 and programmed death-1 (PD-1). Expression of immune cell phenotypes (CD3, CD4, CD8, and Treg) was detected by flow cytometry.Results:On the 27th day after inoculation, the body weigh of the common diet group was(24.78±2.22)g, which was significantly higher than that of the common diet + radiotherapy group [(22.15±0.48)g, P=0.030] and high-fat low-carbohydrate diet + radiotherapy group [(22.02±0.77)g, P=0.031)]. On the 15th day after inoculation, the tumor volume of the high-fat and low-carbohydrate diet + radiotherapy group was (220.88±130.05) mm 3, which was significantly smaller than that of the normal diet group [(504.37±328.48) mm 3, P=0.042)] and the high-fat, low-carbohydrate diet group [(534.26±230.42) mm 3, P=0.016], but there was no statistically significant difference compared with the normal diet + radiotherapy group [(274.64±160.97) mm 3]. In the 4th week, the blood glucose values of the mice in the high-fat and low-carbohydrate diet group were lower than those in the normal diet group, with the value being (8.00±0.36) mmol/L and (9.57±0.40) mmol/L, respectively, and the difference was statistically significant ( P＜0.05). The blood ketone values of the mice in the high-fat and low-carbohydrate diet group were higher than those in the normal diet group, with the value being (1.00±0.20) mmol/L and (0.63±0.06) mmol/L, respectively, in the second week. In the third week, the blood ketone values of the two groups of mice were (0.90±0.17) mmol/L and (0.70±0.10) mmol/L, respectively, and the difference was statistically significant ( P＜0.05). On the 30th day after inoculation, there were no significant differences in aspartate aminotransferase, alanine aminotransferase, creatinine, and urea between the normal diet group and the high-fat, low-carbohydrate diet group (all P＞0.05). The hearts, livers, spleens, lungs, and kidneys of the mice in each group had no obvious toxic changes and tumor metastasis. In the high-fat and low-carbohydrate diet + radiotherapy group, the expression of CD8 was up-regulated in the tumor tissues of mice, and the expressions of PD-1, CD34, LYVE-1, and collagen fibers were down-regulated. The proportion of CD8 + T cells in the paratumoral lymph nodes of the high-fat and low-carbohydrate diet + radiotherapy group was (25.13±0.97)%, higher than that of the normal diet group [(20.60±2.23)%, P＜0.050] and the normal diet + radiotherapy group [(19.26±3.07)%, P＜0.05], but there was no statistically significant difference with the high-fat and low-carbohydrate diet group [(22.03±1.75)%, P＞0.05]. The proportion, of CD4 + T cells in the lymph nodes adjacent to the tumor in the normal diet + radiotherapy group (31.33±5.16)% and the high-fat and low-carbohydrate diet + radiotherapy group (30.63±1.70)% were higher than that in the normal diet group [(20.27±2.15)%, P＜0.05] and the high-fat and low-carbohydrate diet group (23.70±2.62, P＜0.05). Treg cells accounted for the highest (16.58±5.10)% of T cells in the para-tumor lymph nodes of the normal diet + radiotherapy group, but compared with the normal diet group, the high-fat and low-carbohydrate diet group, and the high-fat and low-carbohydrate diet + radiotherapy group, there was no statistically significant difference (all P＞0.05). Conclusion:High-fat and low-carbohydrate diet plus radiotherapy can enhance the recruitment and function of immune effector cells in the tumor microenvironment, inhibit tumor microangiogenesis, and thus inhibit tumor growth.