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 summarize the clinical experience of Professor LIU Shangyi in treating pruritus vulvae. It is believed that women have the physiological characteristics of liver and kidney as the root， and their pubic area is easily attacked by wind-dampness pathogenic qi， so the core mechanism of pruritus vulvae is proposed as wind-dampness accumulation and deficiency of liver and kidney. The core treatment method is to dispel wind-dampness and nourish the liver and kidneys， and modify the Danggui Decoction （当归饮子） to form a self-prescribed Yinyang Formula （阴痒方） as the basic prescription to treat pruritus vulvaen.

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 promote the construction of a national party building indicator system for public hospitals,provide reference for the development of system documents such as quality evaluation methods for party building work in public hospitals.Methods Conduct a comprehensive and systematic collection,organization,and analysis of policy documents related to party building in public hospitals in China since 2018,and organize expert discussions.Results The positive coefficients of the three rounds of expert discussions were all 100％,and the expert authority coefficient was 0.878.A party building indicator system was constructed with 7 primary indicators and 40 secondary indicators,including leadership system and decision-making mechanism,leadership team and cadre talent team construction,grassroots party organization construction,party member team construction,medical ethics and clean governance construction,party building work guarantee,and others.Conclusion The indicator system has been unanimously recognized by experts and is authoritative and feasible,laying the foundation for the refined party building work in public hospitals.

An innovative on-site real-time avian influenza virus(AIV)detection method was estab-lished by integratingrecombinase-aided amplification(RAA)with the clustered regularly inter-spaced short palindromic repeats(CRISPR)/CRISPR-associated protein(Cas)system.After analy-zing 120 sequences of the M gene of avian influenza viruses of different subtypes publicly available on NCBI,the RAA primers and crRNA were designed based on the identified highly conserved segment and used for RAA nucleic acid amplification.After the amplified products were transferred to a CRISPR/Cas13a detection system,the fluorescence values were monitored throughout the re-action process to indicate the results.The sensitivity and specificity of the RAA-CRISPR/Cas13a method were validated using gradient dilutions(106-100 copies/μL)of positive plasmids and sev-en other avian viruses.Fifty clinical samples were tested using this method and compared with the national standard fluorescence RT-PCR method.The results indicated that the detection limit for RAA-CRISPR/Cas13a method was 102 copies/μL,a two-fold improvement over the standard RAA.Specificity assay showed the established method only detected AIV with no cross-reactivity with other seven avian viruses.Compared to the national standard fluorescence RT-PCR method,this method exhibited 100％specificity,95.24％accuracy,and 98.00％consistency in detection of clinical samples.In conclusion,a universal and rapid RAA-CRISPR/Cas13a for detection of AIV was established with the capacity of achieving detection within 60 minutes at 37 ℃,which provides a rapid,sensitive,and specific on-site detection method for AIV.

Objective:To perform molecular diagnosis and pedigree analysis for one case with α-thalassemia who does not conform to the genetic laws,and explore the effects of a newly discovered rare mutation(HBA2:c.*12G＞A)on clinical phenotypes.Methods:Blood samples of the proband and her family members were collected for blood routine analysis,and the hemoglobin components were analyzed by capillary electrophoresis.The common α-and β-globin gene loci in Chinese population were detected by conventional techniques(Gap-PCR,RDB-PCR).The α-globin gene sequences(HBA1,HBA2)were analyzed by Sanger sequencing.Results:By analyzing the test results of proband and her family members,the genotype of the proband was-α3,7/HBA2:c.*12G＞A,her father was HBA2:c.*12G＞A heterozygous mutation carrier.Conclusion:This study identifies a rare α-globin gene mutation(HBA2:c.*12G＞A)that has not been reported before.It is found that heterozygous mutation carriers present with static α-thalassemia.

Objective To explore the effect of auto-segmentation based on deep learning(DL)and Atlas during online adaptive MRI-guided radiotherapy.Methods Totally 15 prostate cancer patients undergoing MRI-guided online adaptive radiotherapy at some hospital from January 2020 to September 2021 were selected and divided into a training set(12 cases)and a test set(3 cases)by random sampling method.With the training set data the models of clinical target volume(CTV)and organs at risk(OAR)by DL and Atlas segmentation were established,and with the test set data the two segmentation models were modified and the modification lengths were recorded.DL and Atlas segmentation methods were compared on segmentation efficiency and accuracy in terms of Dice similarity coefficient(DSC),Hausdorff distance(HD)and mean distance to agreement(MDA).A joint auto-segmentation scheme based on combined DL and Atlas was constructed with considerations on the advantages and characteristics of the two methods,which was compared with the schemes respectively based on DL or Atlas from the aspect of the time consumed for segmentation.Results Accuracy comparison showed Atlas segmentation model behaved better significantly than DL model for CTV(P＜0.05),while obviously worse than the latter for DSC and MDA in bladder and rectum(P＜0.05).The doctor took 9.4 min in average for CTV and OAR modification based on DL model and 12 min in average for Atlas-model-based modification.The joint auto-segmentation scheme only needed 8 min in average for CTV and OAR modification,which gained advantages over the schemes based on DL or Atlas.Conclusion The auto-segmentation based on combined DL and Atlas during online adaptive MRI-guided radiotherapy behaves well in low time consumption,high accuracy and efficiency.[Chinese Medical Equipment Journal,2024,45(6):59-64]