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.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

OBJECTIVE: To analyze the laboratory detection results of hemolytic disease of the fetus and newborn(HDFN). METHODS: Related test results of 283 newborns and their mothers' blood samples from Kunming Maternal and Child Health Hospital from August 2023 to May 2024 were collected, including mother and child ABO blood group, RhD blood group, as well as 3 tests of HDFN, total bilirubin (TBil) and indirect bilirubin (IBil). RESULTS: 283 were ABO incompatibility, among which 187 were HDFN positive, with a positive rate of 66.08%; the positive rate of HDFN in neonates with antigen-A incompatibility was 74.12%(126/170), the positive rate of HDFN in neonates with antigen-B incompatibility was 53.57%(60/112), which was the highest in neonates with O/A incompatibility ［75.45%(126/167)］, followed by O/B incompatibility［54.55%(60/110)］. Group by age, the positive rates of HDFN in the ≤1 d group, 2 d group, 3 d group, 4 d group, 5 d group and ≥6 d group were 76.03%(111/146), 67.86%(38/56), 57.14%(24/42), 38.46%(5/13), 46.15%(6/13) and 23.08%(3/13), respectively. With the increase of age, the positive rates of HDFN gradually decreased, there was a statistically significant difference between the ≤3 day age group and >3 day age group ( P <0.05). There was no statistically significant difference in TBil and IBil levels between the "direct antibody+indirect antibody+release+" group and the HDFN negative group in newborns. HDFN infants exhibited a rapid increase in bilirubin levels within the first day after birth, with significantly higher TBil and IBil values compared to Non ABO-HDFN infants in the ≤1 day group ( P <0.01). However, the difference of bilirubin levels between the two groups gradually narrowed from 2-6 days after birth, and the difference was not statistically significant (P >0.05). The peak value of TBil and IBil occurred on the 4th day after birth in HDFN infants. CONCLUSION ABO-HDFN is most commonly seen in newborns whose mothers are type-O, and the positive rate was the highest in newborns with O/A incompatibility. The detection rate of HDFN is affected by the age of the newborns, and the two were correlated inversely. ABO-HDFN group developed more rapidly with a higher peak. Therefore, HDFN tests should be carried out as soon as possible for mothers and newborns with incompatible blood types, and appropriate treatment should be provided to prevent complications.
Humans ; Infant, Newborn ; ABO Blood-Group System ; Erythroblastosis, Fetal/epidemiology* ; Female ; China/epidemiology* ; Blood Group Incompatibility ; Male ; Bilirubin/blood*

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Objective:To investigate the efficacy and safety of en-bloc Holmium laser enucleation of the prostate (HoLEP) with an early apical mucosa dissection technique for the treatment of benign prostate hyperplasia (BPH).Methods:The clinical data of 215 patients treated with HoLEP for BPH from January 2020 to January 2023 in the Department of Urology, Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. According to different treatment methods, the patients were divided into study group ( n=112) and control group ( n=103). Patients in the study group were treated by the en-bloc HoLEP with an early apical mucosa dissection technique, while patients in the control group were treated by the classical two or three-lobes HoLEP. The primary endpoints included the rates of urinary incontinence at 1-month, 3-month, and 6-month after surgery in two groups of patients. The secondary endpoints included operative time, hemoglobin decrease, dissected prostate weight, postoperative indwelling catheter time, postoperative hospital stay, and international prostate symptom score (IPSS), quality of life (QoL), Qmax, and postvoid residual urine (PVR) at 3-month and 6-month after surgery. The measurement data were tested by Shapiro-Wilk normality test. The normal distribution of the measurement data were expressed as mean ± standard deviation ( ± s), and independent sample t-test was used for comparison between two groups. Measurement data of skewness distribution were expressed as median (interquartile distance) [ M( Q1, Q3)], and Wilcoxon or Mann-Whitney U test were used for comparison between two groups. The count data in the two groups were compared by the Chi-square test. Results:The incidence of urinary incontinence in the study group was 9.0% (10/112) and 3.6% (4/112) at 1-month and 3-month after surgery, which was significantly lower than those in the control group [18.5% (19/103) and 11.7% (12/103)], and the differences were statistically significant ( P< 0.05). Urinary incontinence in two groups recovered completely 6-month after surgery. The operation time of the study group was (68.74±23.71) min, which was lower than that of the control group [(88.04±25.43) min], and the difference was statistically significant ( P<0.05). There were no significant differences in hemoglobin decrease, dissected prostate weight, postoperative indwelling catheter time, postoperative hospital stay in the two groups ( P> 0.05). The IPSS, QoL, Qmax and PVR of the two groups were significantly improved at 3-month and 6-month after surgery ( P< 0.05), but there was no significant difference between the two groups ( P> 0.05). Conclusion:En-bloc HoLEP with an early apical mucosa dissection technique is safe and reliable in treating BPH, and has advantages over classic HoLEP in terms of short-term urinary continence rates, shortening operation time.