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.

Objective To investigate the toxicokinetic differences of 3,4-methylenedioxy-N-methylamphetamine(MDMA)and its metabolite 4,5-methylene dioxy amphetamine(MDA)in rats af-ter single and continuous administration of MDMA,providing reference data for the forensic identifica-tion of MDMA.Methods A total of 24 rats in the single administration group were randomly divided into 5,10 and 20 mg/kg experimental groups and the control group,with 6 rats in each group.The ex-perimental group was given intraperitoneal injection of MDMA,and the control group was given intraperi-toneal injection of the same volume of normal saline as the experimental group.The amount of 0.5 mL blood was collected from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.In the continuous administration group,24 rats were randomly divided into the experi-mental group(18 rats)and the control group(6 rats).The experimental group was given MDMA 7 d by continuous intraperitoneal injection in increments of 5,7,9,11,13,15,17 mg/kg per day,respectively,while the control group was given the same volume of normal saline as the experimental group by in-traperitoneal injection.On the eighth day,the experimental rats were randomly divided into 5,10 and 20 mg/kg dose groups,with 6 rats in each group.MDMA was injected intraperitoneally,and the con-trol group was injected intraperitoneally with the same volume of normal saline as the experimental group.On the eighth day,0.5 mL of blood was taken from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.Liquid chromatography-triple quadrupole tandem mass spectrometry was used to detect MDMA and MDA levels,and statistical software was employed for data analysis.Results In the single-administration group,peak concentrations of MDMA and MDA were reached at 5 min and 1 h after administration,respectively,with the largest detection time limit of 12 h.In the continuous administration group,peak concentrations were reached at 30 min and 1.5 h af-ter administration,respectively,with the largest detection time limit of 10 h.Nonlinear fitting equations for the concentration ratio of MDMA and MDA in plasma and administration time in the single-administration group and continuous administration group were as follows:T=10.362C-1.183,R2=0.974 6;T=7.397 3C-0.694,R2=0.961 5(T:injection time;C:concentration ratio of MDMA to MDA in plasma).Conclusions The toxicokinetic data of MDMA and its metabolite MDA in rats,obtained through single and continuous administration,including peak concentration,peak time,detection time limit,and the relationship between concentration ratio and administration time,provide a theoretical and data foundation for relevant forensic identification.

With the increasing proportion of human papilloma virus(HPV)infection in the pathogenic factors of oro-pharyngeal cancer,a series of changes have occurred in the surgical treatment.While the treatment mode has been im-proved,there are still many problems,including the inconsistency between diagnosis and treatment modes,the lack of popularization of reconstruction technology,the imperfect post-treatment rehabilitation system,and the lack of effective preventive measures.Especially in terms of treatment mode for early oropharyngeal cancer,there is no unified conclu-sion whether it is surgery alone or radiotherapy alone,and whether robotic minimally invasive surgery has better func-tional protection than radiotherapy.For advanced oropharyngeal cancer,there is greater controversy over the treatment mode.It is still unclear whether to adopt a non-surgical treatment mode of synchronous chemoradiotherapy or induction chemotherapy combined with synchronous chemoradiotherapy,or a treatment mode of surgery combined with postopera-tive chemoradiotherapy.In order to standardize the surgical treatment of oropharyngeal cancer in China and clarify the indications for surgical treatment of oropharyngeal cancer,this expert consensus,based on the characteristics and treat-ment status of oropharyngeal cancer in China and combined with the international latest theories and practices,forms consensus opinions in multiple aspects of preoperative evaluation,surgical indication determination,primary tumor re-section,neck lymph node dissection,postoperative defect repair,postoperative complication management prognosis and follow-up of oropharyngeal cancer patients.The key points include:① Before the treatment of oropharyngeal cancer,the expression of P16 protein should be detected to clarify HPV status;② Perform enhanced magnetic resonance imaging of the maxillofacial region before surgery to evaluate the invasion of oropharyngeal cancer and guide precise surgical resec-tion of oropharyngeal cancer.Evaluating mouth opening and airway status is crucial for surgical approach decisions and postoperative risk prediction;③ For oropharyngeal cancer patients who have to undergo major surgery and cannot eat for one to two months,it is recommended to undergo percutaneous endoscopic gastrostomy before surgery to effectively improve their nutritional intake during treatment;④ Early-stage oropharyngeal cancer patients may opt for either sur-gery alone or radiation therapy alone.For intermediate and advanced stages,HPV-related oropharyngeal cancer general-ly prioritizes radiation therapy,with concurrent chemotherapy considered based on tumor staging.Surgical treatment is recommended as the first choice for HPV unrelated oropharyngeal squamous cell carcinoma(including primary and re-current)and recurrent HPV related oropharyngeal squamous cell carcinoma after radiotherapy and chemotherapy;⑤ For primary exogenous T1-2 oropharyngeal cancer,direct surgery through the oral approach or da Vinci robotic sur-gery is preferred.For T3-4 patients with advanced oropharyngeal cancer,it is recommended to use temporary mandibu-lectomy approach and lateral pharyngotomy approach for surgery as appropriate;⑥ For cT1-2N0 oropharyngeal cancer patients with tumor invasion depth＞3 mm and cT3-4N0 HPV unrelated oropharyngeal cancer patients,selective neck dissection of levels ⅠB to Ⅳ is recommended.For cN+HPV unrelated oropharyngeal cancer patients,therapeutic neck dissection in regions Ⅰ-Ⅴ is advised;⑦ If PET-CT scan at 12 or more weeks after completion of radiation shows intense FDG uptake in any node,or imaging suggests continuous enlargement of lymph nodes,the patient should undergo neck dissection;⑧ For patients with suspected extracapsular invasion preoperatively,lymph node dissection should include removal of surrounding muscle and adipose connective tissue;⑨ The reconstruction of oropharyngeal cancer defects should follow the principle of reconstruction steps,with priority given to adjacent flaps,followed by distal pedicled flaps,and finally free flaps.The anterolateral thigh flap with abundant tissue can be used as the preferred flap for large-scale postoperative defects.

Monosodium urate (MSU)-induced the gouty arthritis (GA) model was used to investigate the effect of Nod-like receptor protein 3 (NLRP3) inhibitor N14 in alleviating GA. Firstly, the effect of NLRP3 inhibitor N14 on the viability of mouse monocyte macrophage J774A.1 was examined by the cell counting kit-8 (CCK-8) assay. The expression of mature interleukin 1β (IL-1β) and cysteinyl aspartate specific proteinase-1 (caspase-1) p20 in the cell supernatant and the expression of NLRP3, caspase-1 and pro-IL-1β proteins in the cell lysates was detected by Western blot for the inhibitory effect of N14 on the MSU-induced NLRP3 inflammasome activation in J774A.1 cells. Animal behavioral tests were used to detect redness, swelling, heat and pain in mice with gouty arthritis. Hematoxylin-eosin (H&E) staining revealed pathologic changes and inflammatory infiltration in foot sections. Protein expression of NLRP3, caspase-1, and pro-IL-1β in mouse hind paw tissues were assessed by Western blot. The effect of N14 on the plasma levels of alanine transaminase (ALT), aspartate transaminase (AST), creatinine (CRE), urea, and uric acid (UA) was investigated by the MSU-induced gouty arthritis model in mice. All animal experiments in this paper were approved by the Scientific Ethics Review Board of Qingdao Marine Biomedical Research Institute (grant No. E-MBWNL-2024-20). The experimental results showed that N14 did not exhibit cytotoxicity in mouse monocyte macrophage J774A.1 cells at concentrations up to 100 μmol·L^-1, and N14 effectively prevented MSU-induced activation of NLRP3 inflammasome. In the mouse gouty arthritis model, N14 significantly ameliorated the redness, swelling, heat and pain caused by GA, and down-regulated the levels of NLRP3 inflammasome-associated proteins in mouse hind paw tissues. Meanwhile, N14 appeared to be well tolerated, as it did not significantly affect various biochemical indices in mouse plasma. In conclusion, N14 effectively alleviated GA in mice by inhibiting the NLRP3 inflammasome pathway, which is important for both prevention and treatment of related diseases.

Objective To investigate the effect of varying blood pressure stratification on renal function in the diabetic population.Methods A prospective cohort study was conducted,enrolling 9 489 diabetic patients from a total of 101 510 Kailuan Group employees who underwent health examinations between July 2006 and October 2007.The follow-up period was(8.6±4.0)years.Participants were categorized into four groups based on their baseline blood pressure levels:normal blood pressure(systolic blood pressure<120 mmHg and diastolic blood pressure<80 mmHg),elevated blood pressure(systolic blood pressure 120-130 mmHg and diastolic blood pressure<80 mmHg),stage 1 hypertension(systolic blood pressure 130-140 mmHg and/or diastolic blood pressure 80-90 mmHg),and stage 2 hypertension(systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg).The incidence density of chronic kidney disease(CKD)was compared among these groups.A multivariate Cox proportional hazards regression model was employed to assess the effects of different blood pressure levels on renal function in diabetic patients,with the stability of the results confirmed using a multivariate time-dependent Cox proportional hazards model.Sensitivity analysis was conducted after excluding cases of cardiovascular disease(CVD)during follow-up,and cases using antihypertensive and antidiabetic medications at baseline.Results(1)At baseline,stage 1 hypertension patients demonstrated statistically significant higher differences with age and body mass index(BMI)compared to normal blood pressure group(P<0.05).(2)By the end of the follow-up,2 294 cases of CKD were identified,including 1 117 cases of estimated glomerular filtration rate(eGFR)decline and 1 575 cases of urinary protein.The incidences density of CKD,eGFR decline and urinary protein for stage 1 hypertension group were 39.4,16.3 and 25.5 per thousand person-years,respectively,all of which were statistically significant different from normal blood pressure group(log-rank test,P<0.01).(3)Multivariate Cox regression analysis revealed that,compared to the normal blood pressure group,stage 1 hypertension was associated with a 29%increased risk of CKD(HR=1.29,95%CI 1.09-1.52)and a 40%increased risk of eGFR decline(HR=1.40,95%CI 1.08-1.80)in diabetic individuals.Conclusion Stage 1 hypertension significantly increases the risk of CKD and eGFR decline in diabetic individuals,with a particularly notable effect on the risk of eGFR decline.

Objective:To explore the clinical application of Hisense Computer-Assisted Sur-gery System(CAS)in the perioperative period of hepatectomy for liver cancer.Methods:Clinical data of patients undergoing laparoscopic hepatectomy(LH)for liver cancer from January 2021 to December 2022 were collected.Patients were divided into three groups based on surgical difficulty(low,medium,high)and further stratified into CAS-assisted subgroup and control subgroup ac-cording to whether the CAS system was used.Demographic and perioperative data were com-pared among different groups.Results:A total of 317 patients'clinical data were collected,in-cluding 31 cases in the low difficulty group,132 cases in th medium difficulty group,and 154 cases in the high difficulty group,with 108 cases(34.1％)in the CAS-assisted subgroup and 209 cases(65.9％)in the control group.In the medium difficulty group,the CAS-assisted subgroup had shorter operation time,drainage tube duration,and postoperative hospital stay compared to the control group(P＜0.001),and the AFP levels at 1 month postoperatively in the CAS-assisted sub-group were lower than those in the control group(P＜0.001).In the high difficulty group,the CAS-assisted subgroup showed shorter operation time,drainage tube duration,and postoperative hospi-tal stay,less intraoperative blood loss,and lower AFP levels 1 month post-operation compared to the control group(P＜0.001 for all).Conclusion:Preoperative CAS in medium and high difficulty laparoscopic liver resections improves perioperative outcomes.Hisense CAS effectively assists general surgeons in accurately identifying the anatomical site of liver tumors,providing precise pre-operative simulation and intraoperative navigation,thereby optimizing surgical strategies for pa-tients.