With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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 explore the clinical value of combination of contrast-enhanced ultrasound(CEUS)and magnetic reso-nance imaging(MRI)in the diagnosis of parotid gland tumors.Methods:167 patients with parotid gland tumors were diagnosed by CEUS and MRI respectively and in combination.With postoperative pathological diagnosis result as the gold standard,the diagnos-tic value of CEUS combined with MRI(CEUS+MRI)was analyzed.Results:171 tumors were confirmed by postoperative pathology in 167 patients,including 143 benign tumors(83.63％)and 28 malignant tumors(16.37％).The benign tumors were mainly pleo-morphic adenoma(65 tumors,45.45％),Warthin tumor(40 tumors,27.97％)and basal cell adenoma(13 tumors,9.09％).Among the malignant tumors,mucoepidermoid carcinoma(7 tumors,25.00％),adenoid cystic carcinoma(6 tumors,21.43％)and acinic cell carcinoma(3 tumors,10.71％)were the most common.CEUS showed 39 cases of malignant parotid gland tumors and 132 cases of benign parotid gland tumors.The sensitivity,specificity and accuracy rate of CEUS were 57.14％,83.92％and 79.53％respectively.The MRI ADC values were manifested as pleomorphic adenoma＞malignant tumor＞Warthin tumor(P＜0.05).MRI showed 53 cases of malignant parotid gland tumors and 118 cases of benign parotid gland tumors,and the sensitivity,specifici-ty and accuracy rate of MRI diagnosis were 67.86％,76.22％and 74.85％respectively.CEUS+MRI revealed 33 cases of malig-nant parotid gland tumors and 138 cases of benign parotid gland tumors,the sensitivity,specificity and accuracy rate of CEUS+MRI diagnosis were 78.57％,92.31％and 90.06％respectively.The specificity and accuracy rate of CEUS+MRI were significantly higher than those of single examination(x2=4.806,13.951,7.348,13.664,allP＜0.05).Conclusion:Both CEUS and MRI have certain clinical value in the diagnosis of parotid gland tumors,and the specificity and accuracy rate of CEUS+MRI are significantly higher than those of single examination.

Objective:To explore the application value of part-cut jejunal transection in digestive tract reconstruction of totally laparoscopic total gastrectomy.Methods:The propensity score matching and retrospective cohort study was conducted. The clinicopathological data of 112 patients with gastric cancer who underwent totally laparoscopic total gastrectomy in The Second Affiliated Hospital of Anhui Medical University from June 2018 to September 2022 were collected. There were 81 males and 31 females, aged (70±8)years. Among the 112 patients, 60 patients undergoing diges-tive tract reconstruction by Roux-en-Y anastomosis with part-cut jejunum were set as the part-cut group, and 52 patients undergoing digestive tract reconstruction by traditional Roux-en-Y anasto-mosis were set as the traditional group. Observation indicators: (1) propensity score matching status and comparison of clinical data of patients between the two groups after matching; (2) intraopera-tive and postoperative conditions; (3) follow-up. Comparison of measurement data with normal dis-tribution between groups was conducted using the independent sample t test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Com-parison of ordinal data was conducted using the nonparametic rank sum test. Propensity score matching was performed using the 1∶1 nearest neighbor matching method, with the caliper value of 0.02. Results:(1) Propensity score matching status and comparison of clinical data of patients between the two groups after matching. Of the 112 patients, 90 patients were successfully matched, with 45 cases in each of the part-cut group and the traditional group. After propensity score matching, the elimination of body mass index, clinical TNM staging confounding bias ensured comparability. (2) Intraoperative and postoperative conditions. After propensity score matching, the total operation time and digestive tract reconstruction time of patients in the part-cut group were (217.0±15.1)minutes and (34.7±1.8)minutes, versus (252.6±21.9)minutes and (52.6±7.4)minutes in the traditional group, respectively, showing significant differences in the above indicators between the two groups ( t=?8.97, ?15.66, P<0.05). (3) Follow-up. After propensity score matching, 90 patients were followed up postoperatively for (47±15)months. During the follow-up, no patient in either group received secondary surgery, and there was no death. There were 3 cases and 10 cases of Roux stasis syndrome in the part-cut group and the traditional group, respectively, showing a significant difference between the two groups ( χ2=4.41, P<0.05). Conclusion:Compared with traditional Roux-en-Y anastomosis, the Roux-en-Y anastomosis with part-cut jejunum in totally laparoscopic total gastrectomy can signifi-cantly shorten the time for digestive tract reconstruction and reduce the incidence of postoperative Roux stasis syndrome.

Objective:To explore the difficulties and needs of wound ostomy incontinence specialist nurses when applying convex baseplates, with the aim of providing a basis for standardizing ostomy care.Methods:Purposeful sampling was used to select 13 wound ostomy incontinence specialist nurses from tertiary hospitals in Hebei, Jilin, Hunan, Beijing, and Tianjin for semi-structured interviews between November and December 2024. Content analysis was used to analyze the data.Results:Two main themes were identified: application dilemmas and application needs. Application dilemmas included four subthemes: insufficient understanding of convex baseplate characteristics, unclear indications and contraindicated populations, lack of unified standards for assessment timing, and inadequate awareness of health education. Application needs included two subthemes: urgent need for related evidence-based evidence and a desire for clinical guidelines and expert consensus.Conclusions:Currently, wound ostomy incontinence specialist nurses in China face challenges in understanding the characteristics of convex baseplates, scope of application, identifying contraindications, determining the optimal timing for application assessment, and providing patient education. Administrators should develop clinical application standards for convex baseplates based on these challenges. Furthermore, researchers should develop relevant clinical guidelines or expert consensus for convex baseplate application in China, providing a basis for the standardized use of ostomy care products.

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.