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.

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 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*

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.

OBJECTIVE: To explore the clinical and genetic characteristics of a Chinese pedigree affected with Hypertrophic cardiomyopathy (HCM) due to a truncating variant of ALPK3 gene. METHODS: A 44-year-old male admitted to Taizhou Hospital of Zhejiang Province on December 29, 2018 was selected as the study subject. Whole-exome sequencing (WES) was carried out, and candidate variant was interpreted by following the guidelines from the American College of Medical Genetics and Genomics (ACMG). For ALPK3 was considered an autosomal recessive gene, the WES results was considered insufficient to explain his phenotype. In April 2023, the proband's WES data were re-analyzed using updated annotation pipelines, and peripheral blood samples were collected from his first-degree relatives (mother and brother) for Sanger sequencing validation. Conservation analysis and protein structural modeling were performed to assess the impact of the variant. Clinical evaluation and genetic counseling were provided to the proband's family members. Relevant literature on ALPK3tv-induced HCM patients were searched in Wanfang Data Knowledge Service Platform, CNKI, and PubMed database using "ALPK3" and "hypertrophic cardiomyopathy" as keywords. Clinical characteristics of HCM patients with heterozygous ALPK3tv variants were summarized and compared with the clinical characteristics of HCM patients with positive sarcomere-associated gene variants (SARC+). This study was approved by the Medical Ethics Committee of Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University (Ethics No.: K20230314). RESULTS: The proband was a 44-year-old male who was transferred to our institution on December 29, 2018 due to "chest tightness and pain for 6 months, exacerbated for 2 days". Emergency coronary angiography was performed, which led to a preliminary diagnosis of "acute coronary syndrome", and the patient was admitted to the Cardiology Department for treatment. Based on electrocardiogram and echocardiogram findings, the diagnosis was revised as HCM. The patient's condition has stabilized post-coronary angiography, and he was discharged with improved condition. On January 2019, WES was conducted to determine the etiology of the proband's HCM. WES results identified a novel heterozygous c.2156dupC (p.Pro720ThrfsTer53) truncating variant in the ALPK3 gene. At that time, the inheritance pattern could not explain the phenotype. In 2022, a literature indicated that heterozygous ALPK3tv could lead to autosomal dominant HCM. Consequently, in April 2023, the proband's whole-exome data were re-annotated, revealing changes in the transcript and protein versions, with the updated site annotated as ALPK3 (NM_020778.5): c.1550dupC (p.Pro518ThrfsTer53). Sanger sequencing confirmed that the proband's mother and brother also carried this variant. The mother exhibited obstructive HCM, while the brother showed no related phenotype. Bioinformatics analysis demonstrated conservation of this site across multiple species, and the variant has resulted in the loss of a protein domain. Based on ACMG guidelines, the variant was classified as likely pathogenic. Literature review and Bayesian calculation further elevated the pathogenicity rating, indicating that this variant was the cause of HCM in the patient. Literature study revealed distinctions between HCM caused by this variant type and SARC+ HCM. The age of onset among heterozygous ALPK3tv patients was delayed by approximately 10 years compared to SARC+ patients. Both forms of HCM exhibited a male predominance, which was particularly marked in individuals with ALPK3tv. Electrocardiographic left ventricular hypertrophy was more prevalent in heterozygous ALPK3tv patients than in SARC+ patients. The incidence of apical or concentric hypertrophy patterns was higher in heterozygous ALPK3tv patients compared to asymmetric septal hypertrophy, which predominated in SARC+ patients. ALPK3tv patients exhibited lower penetrance and later onset compared to SARC+ patients. A positive correlation between left ventricular wall thickness and age was noted in female patients only. CONCLUSION In this pedigree, the proband has presented with HCM, characterized by echocardiographic evidence of apical left ventricular hypertrophy without significant outflow tract obstruction or extracardiac phenotypes. Although his mother and brother had carried the same heterozygous ALPK3 (NM_020778.5) c.1550dupC (p.Pro518ThrfsTer53), the mother exhibited severe obstructive HCM, while the brother was asymptomatic, suggesting incomplete or age-dependent penetrance within the family. This study has enriched the evidence for the pathogenicity of ALPK3tv among Chinese HCM pedigrees and underscored the importance of periodic literature reviews and genetic re-analysis for unresolved genetic testing results.
Humans ; Male ; Pedigree ; Adult ; Cardiomyopathy, Hypertrophic/genetics* ; Heterozygote ; Asian People/genetics* ; Exome Sequencing ; Mutation ; China ; Female ; East Asian People