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.

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.

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.

To perform a comprehensive analysis of the pathogenic causes of a food poisoning case in a district of Wuhan Cit-y,we investigated the molecular epidemiological relationships among pathogenic bacteria,to aid in traceability analysis of food-borne disease outbreaks,as well as clinical diagnosis and treatment.The pathogenic bacteria in this food poisoning case were i-solated and identified according to GB789.4-2016.The isolated strains were subjected to genotyping with pulsed field gel elec-trophoresis(PFGE).Drug resistance gene analysis,multi-locus sequence typing(MLST),and genome-wide single-nucleotide polymorphism analysis(wgSNP)were conducted via whole genome sequencing(WGS).The evolutionary tree for cluster analy-sis was constructed in fasttree software.Drug susceptibility testing was conducted with the broth microdilution method.A total of 12 strains of Salmonella were detected in seven anal swab samples and two fecal samples from the case,as well as three anal swab samples from unaffected individuals.The serotype of the strains was Salmonella typhimurium.The strain exhibited severe multiple drug resistance,including resistance to amikacin,ampi-cillin,cefazolin,gentamicin,piperacillin,and tetracycline,but susceptibility to other antibiotics.The coincidence rate between drug resistance genes and drug resistance phenotypes was high.PFGE revealed that nine strains from this food poisoning case were highly homologous.WGS revealed that the MLST type was ST19,and varying numbers of SNPs(1-6)were present a-mong strains.The phylogenetic tree revealed nine isolated strains forming a distinct cluster,differing from other Salmonella strains in the database and belonging to a novel clonal branch.The single nucleotide site in the strains was highly homologous to that of GCF in Jiangxi_020221795.1.The food poisoning case was caused by Salmonella typhimurium ST19,and all nine iso-lated strains originated from the same source.The chef is closely connected to this food poisoning case.This strain of Salmo-nella typhimurium belongs to a new clonal branch and exhibits multiple drug resistance.

Objective:To evaluate the diagnostic performance of the Prostate Imaging Reporting and Data System version 2.1(PI-RADS v2.1)for clinically significant prostate cancer(CSPCa)in the peripheral zone(PZ),transitional zone(TZ)and multiple zones(MZs).Methods:We retrospectively studied the clinical data on 108 patients undergoing multiparametric magnetic resonance imaging(mpMRI)and transperineal prostate biopsy in our hospital from January 2021 to January 2023.Using PI-RADS v2.1,we ex-amined the MR images of the patients with suspected PCa,compared the PI-RADS v2.1 scores with the results of prostate biopsy,and analyzed the correlation of the PI-RADS v2.1 scores with CSPCa.We calculated the area under the receiver operating characteristic(ROC)curve(AUC),and described the diagnostic performance of PI-RADS v2.1 for CSPCa in the PZ,TZ and MZs.Results:Transperineal prostate puncture biopsy was successfully completed in all the patients,which revealed 66(61.11％)cases of CSPCa with Gleason score(GS)7-10.Suspected CSPCa was observed in 45(95.74％)of the 47 PZ lesions,8(47.06％)of the 17 TZ le-sions,and 40(90.91％)of the 44 MZ lesions.The PZ,TZ and MZ lesions diagnosed by PI-RADS v2.1 were significantly correlated with CSPCa(r=0.492,P＜0.001).The AUCs of PI-RADS v2.1 for PZ,TZ and MZs were 0.644,0.732 and 0.811,with specificities of 66.8％,57.6％and 62.1％,and sensitivities of 57.2％,78.4％and 93.2％,respectively.The negative predictive values were 46.5％,85.7％and 79.2％,and the positive predictive values 76.2％,43.4％and 84.8％,respectively.Conclusion:The PI-RADS v2.1 score has a high diagnostic value for CSPCa in the PZ,TZ and MZs,with the best performance for that in the MZs.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Chronic refractory wound (CRW) is one of the most challengeable issues in clinic due to complex pathogenesis, long course of disease and poor prognosis. Experts need to conduct systematic summary for the diagnosis and treatment of CRW due to complex pathogenesis and poor prognosis, and standard guidelines for the diagnosis and treatment of CRW should be created. The Guideline forthe diagnosis and treatment of chronic refractory wounds in orthopedic trauma patients ( version 2023) was created by the expert group organized by the Chinese Association of Orthopedic Surgeons, Chinese Orthopedic Association, Chinese Society of Traumatology, and Trauma Orthopedics and Multiple Traumatology Group of Emergency Resuscitation Committee of Chinese Medical Doctor Association after the clinical problems were chosen based on demand-driven principles and principles of evidence-based medicine. The guideline systematically elaborated CRW from aspects of the epidemiology, diagnosis, treatment, postoperative management, complication prevention and comorbidity management, and rehabilitation and health education, and 9 recommendations were finally proposed to provide a reliable clinical reference for the diagnosis and treatment of CRW.