Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Obesity, as a chronic recurrent disease, has become a major public health challenge in China. To implement the requirements of the Healthy China Initiative (2019—2030), under domestic guidelines or consensus statements on overweight and obesity, and in alignment with the latest scientific advances globally, the Quality control protocol for adult overweight and obesity screening in health management (examination) institutions (2025 edition) was developed. This protocol was drafted by the Health Management Center of Shanghai Changzheng Hospital and formulated through multiple rounds of deliberation by experts in China’s health examination quality control field. The protocol establishes unified standards for screening facilities, personnel qualifications, and measurement or testing procedures. It defines specific screening items, outlines a standardized screening pathway, and sets requirements for the final medical review, ensuring the scientific validity, effectiveness, and safety of the screening process. The implementation of this protocol will enhance the consistency of weight management practices for adults across health examination institutions and strengthen the quality control of overweight and obesity screening programs.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To determine 32 anabolic agents in tablets,capsules and injections using liquid chromatography-high resolution mass spectrometry(LC-HRMS),so as to provide technical support in the fields of anti-doping and judicial identification.Methods The tablet grinded into powder,capsule samples extracted using a methanol∶water(7∶3)solution and injection underwent centrifugation and filtration.The separation was performed on a Waters Acquity UPLC HSS T3 column(100 mm×2.1 mm,1.8 μm),followed by gradient elution using ammonium acetate(20 mmol/L)solution containing 0.1%formic acid,5%acetonitrile and acetonitrile system as mobile phases,and detection by heated electrospray ionization in positive mode with a primary full-scan/data-dependent secondary scan mode.The established analytical method was subjected to methodological validation and parameters such as se-lectivity,sensitivity,matrix effect and delay effect were investigated.Meanwhile,the method was ap-plied to the detection of anabolic agents in 20 samples of tablets,capsules and injections provided by the authorities.Results The detection limits for 32 anabolic agents ranged from 1～200 μg/mg in the powder and 1～200 ng/mL in the injection.When this method was applied to the analysis of actual samples,five anabolic agents,including methandrostenolone and stanozolol,were identified among the 20 samples,disconsistent with labels on the package.Conclusion The LC-HRMS method is simple in pretreatment,sensitive and suitable for the detection of anabolic agents in tablet,capsule and injec-tion samples in the fields of anti-doping and judicial identification.

OBJECTIVE To investigate the therapeutic mechanisms of electroacupuncture at"Tianshu"(ST25)and"Sanyinjiao"(SP6)acupoints combined with metformin in the treatment of type 2 diabetes mellitus(T2DM)using serum non-targeted metabolomics.METHODS Male SD rats were randomly divided into blank group,model group,metformin group,electroacupunc-ture group,and acupuncture-medicine combination(electroacupuncture combined with metformin)group.A type 2 diabetes model was established by high-fat diet combined with intraperitoneal injection of streptozotocin.The metformin group was treated with 250 mg·kg-1 metformin by gavage,the electroacupuncture group was treated with bilateral Tianshu and Sanyinjiao,the acupuncture-medicine combination group was treated with metformin by gavage combined with electroacupuncture,and the blank group and model group were treated with normal saline by gavage.All rats were treated 6 times a week for 7 weeks.After the intervention,the blood glucose level in the tail vein of the rats was measured using a blood glucose meter in the fasting state.The blood glucose levels of the rats were measured at 30,60,120,and 240 min after intraperitoneal injection of 50%glucose solution(4 mL·kg-1)to evaluate glu-cose tolerance.The serum insulin level of the rats was detected by ELISA and the insulin resistance index was calculated.The blood biochemical parameters were measured by an automatic blood biochemical analyzer.HE staining was used to evaluate the pathological conditions of the liver and pancreatic tissues of the rats.Ultra-performance liquid chromatography-mass spectrometry(UPLC-MS)technology was used for mass spectrometry detection to identify differential metabolites,and MetaboAnalyst 5.0 was used for pathway enrichment analysis.RESULTS Compared with the blank group,the fasting blood glucose,area under the glucose tolerance curve,and insulin resistance index of the model group rats were significantly increased(P<0.001),blood TP and GLB were significantly de-creased(P<0.01),AST,ALT,and ALP were significantly increased(P<0.05,P<0.01,P<0.001),and obvious inflammatory cell infiltration and pathological damage were observed in the liver and pancreas tissues;compared with the model group,the fasting blood glucose,area under the glucose tolerance curve,and insulin resistance index of the acupuncture-medicine combination group were sig-nificantly decreased(P<0.05,P<0.01,P<0.001),blood ALP was significantly decreased(P<0.01),TP and GLB were significant-ly increased(P<0.05),and the pathological damage of the liver and pancreas was significantly improved.Serum metabolomics showed that the metabolic profiles of the groups were well distinguished.Compared with the blank group,the differential metabolites in the model group were enriched in histidine metabolism,thiamine metabolism,taurine and hypotaurine metabolism,ascorbic acid and alde-hyde ester metabolism,valine,leucine and isoleucine biosynthesis pathways;compared with the model group,237 metabolites such as 3-aminoadipic acid,3-oxocyclobutanecarboxylic acid and phosphorylcholine in the acupuncture-medicine combination group were sig-nificantly reduced,and the pathways were enriched in histidine metabolism,linoleic acid metabolism,thiamine metabolism,taurine and hypotaurine metabolism,valine,leucine and isoleucine biosynthesis pathways.CONCLUSION Electroacupuncture combined with metformin can effectively improve the glucose and lipid metabolism of T2DM rats,and its potential mechanism may be related to the regulation of amino acid metabolism.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

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.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.