1.Yimei Baijiang Formula Treats Colitis-associated Colorectal Cancer in Mice via NF-κB Signaling Pathway
Qian WU ; Xin ZOU ; Chaoli JIANG ; Long ZHAO ; Hui CHEN ; Li LI ; Zhi LI ; Jianqin LIU
Chinese Journal of Experimental Traditional Medical Formulae 2026;32(3):119-130
ObjectiveTo explore the effects of Yimei Baijiang formula (YMBJF) on colitis-associated colorectal cancer (CAC) and the nuclear factor kappaB (NF-κB) signaling pathway in mice. MethodsSixty male Balb/c mice of 4-6 weeks old were randomized into 6 groups: Normal, model, capecitabine (0.83 g
2.Yimei Baijiang Formula Treats Colitis-associated Colorectal Cancer in Mice via NF-κB Signaling Pathway
Qian WU ; Xin ZOU ; Chaoli JIANG ; Long ZHAO ; Hui CHEN ; Li LI ; Zhi LI ; Jianqin LIU
Chinese Journal of Experimental Traditional Medical Formulae 2026;32(3):119-130
ObjectiveTo explore the effects of Yimei Baijiang formula (YMBJF) on colitis-associated colorectal cancer (CAC) and the nuclear factor kappaB (NF-κB) signaling pathway in mice. MethodsSixty male Balb/c mice of 4-6 weeks old were randomized into 6 groups: Normal, model, capecitabine (0.83 g
3.Multicenter machine learning-based construction of a model for predicting potential organ donors and validation with decision curve analysis
Xu WANG ; Wenxiu LI ; Fenghua WANG ; Shuli WU ; Dong JIA ; Xin GE ; Zhihua SHAN ; Tongzuo LI
Organ Transplantation 2026;17(1):106-115
Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.
4.Multi-dimensional Holographic Characterization of Zhejiang Characteristic Atractylodis Macrocephalae Rhizoma with Nine-time Repeating Steaming and Processing
Xin WU ; Cuiwei CHEN ; Qiao YU ; Chao FENG ; Hongyan ZHANG ; Yan CHEN ; Caihua SUN ; Gang CAO
Chinese Journal of Experimental Traditional Medical Formulae 2026;32(6):197-205
ObjectiveHistorically documented Zhejiang Atractylodis Macrocephalae Rhizoma(Baizhu) possesses premium characteristics such as phoenix-like head and crane-like neck, pronounced sweetness, and fragrant aroma. However, its current market circulation is low, and the processed products with Zhejiang-style characteristics are at the risk of being lost. This study aims to preserve the ancient Zhejiang-style processing techniques and evaluate them using modern scientific methods. MethodsMultidimensional intelligent sensory evaluation was used to digitally characterize the "quality-structure" of the external appearance of nine-steamed and nine-processed Baizhu medicinal materials(intermediate processed products) and the "odor-taste" of the internal quality of its decoction pieces(slices), and the appearance parameters were digitally characterized by colorimeter, texture analyzer, electronic nose and electronic tongue, the chemical composition was analyzed via ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS/MS). Then, cluster analysis on the differences in odor between the medicinal materials(intermediate processed products) and decoction pieces(slices) of nine-steamed and nine-processed Baizhu was conducted, as well as the differences in taste between water-soluble and alcohol-soluble extracts of the decoction pieces(slices), and the correlation analysis of chroma value-alcohol-soluble extract content-component response value. ResultsThe nine-steamed and nine-processed Baizhu had a dark brown to black epidermis, a brownish-yellow to brownish-gray cross-section, a slightly tough texture, a faint odor, and a slightly sweet, bitter and pungent taste. Texture analyzer measurements revealed minimal adhesion and maximum recovery in the middle section of the characteristic processed Baizhu, consistent with the processing endpoint of thorough steaming and cooking. The head section showed the highest internal hardness, elasticity and chewiness, indicating a denser texture in this area. The electronic nose sensor could clearly distinguish the difference between the medicinal materials and its decoction pieces, with a more significant clustering effect at 60 ℃ for 30 minutes compared to ambient temperature headspace for 2 hours, highlighting the significant impact of the baking degree before slicing on the quality. The electronic tongue taste signal map clearly distinguished the differences between water-soluble and alcohol-soluble extracts of nine-steamed and nine-processed Baizhu decoction pieces, and the addition of auxiliary materials during processing could enhance its alcohol-soluble extract content. A total of 82 chemical components were identified in the characteristic processed Baizhu. After processing, the contents of 58 components increased, while 24 components decreased. Correlation analysis revealed significant negative correlations(P<0.01) between ethanol-soluble extract content and colorimetric values of brightness(L*), yellow-bule value(b*), and total color difference(E*ab). E*ab showed marked negative correlations(P<0.05) with the response values of isochlorogenic acid A and C. ConclusionThis study establishes a modern intelligent sensory evaluation model for multidimensional holographic characterization of nine-steamed and nine-processed Baizhu, clarifying the correlation between increased isochlorogenic acid content and the visual color appearance after different steaming cycles, as well as its intrinsic alcohol-soluble extracts. This provides a reference for quality evaluation and processing standards of the Zhejiang-style characteristic processed products.
5.Current status of research on the mechanism of action of emodin in the prevention and treatment of chronic liver diseases
Yajie CHEN ; Xin WANG ; Yunjuan WU ; Ying SU ; Yuhan WANG ; Jinxue ZHANG ; Ning YAO ; Ying QIN ; Xiaoning ZUO
Journal of Clinical Hepatology 2026;42(1):228-234
Chronic liver diseases are a group of diseases in which the liver is subjected to a variety of injuries over a long period of time, resulting in irreversible pathological changes that last longer than 6 months. Emodin (EMO) is a natural anthraquinone derivative derived from Rheum officinale, and its pharmacological effect has been extensively studied, exhibiting a variety of biological properties and involving multiple signaling molecules and pathways. Western medicine or surgical treatment is currently the main treatment regimen for chronic liver diseases, and the advance in treatment is limited by various reasons such as side effects and high costs. Due to its natural origin and efficacy, EMO has unique advantages in the treatment of chronic liver diseases and has now become a research hotspot. This article summarizes the therapeutic effect of EMO on chronic liver diseases and its mechanism, in order to provide a certain scientific basis for the traditional Chinese medicine treatment of chronic liver diseases and the development of drugs in clinical practice.
7.Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
Xue-han WU ; Yu-tao QUE ; Xin-yue YANG ; Zi-qiang WEN ; Yu-ru MA ; Zhi-wen ZHANG ; Quan-meng LIU ; Wen-jie FAN ; Li DING ; Yue-jiao LANG ; Yun-zhu WU ; Jian-peng YUAN ; Shen-ping YU ; Yi-yan LIU ; Yan CHEN
Korean Journal of Radiology 2025;26(5):400-410
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.
8.Overview and implications of the cohort construction for autism spectrum disorders based on Internet recruitment
CHEN Xin, GAO Hui, WU De, TAO Fangbiao
Chinese Journal of School Health 2025;46(2):157-161
Abstract
The construction of autism spectrum disorders (ASD) specialty cohorts in China is still in its infancy, and the cost effectiveness is insufficient when relying on diagnostic and treatment processes of child health care to collect ample and high quality data. After 2000, the United States Simons Foundation s ASD Research Initiative, the Early ASD Risk Longitudinal Investigation (EARLI), and the British ASD Study of Infant Siblings (BASIS), which have been built based on Internet recruitment, have provided new insight for the construction of large sample ASD specialty cohorts in China. Future research can further explore and optimize the methods of Internet recruitment, and establish a more comprehensive and accurate ASD specialty cohorts.
9.Study of adsorption of coated aldehyde oxy-starch on the indexes of renal failure
Qian WU ; Cai-fen WANG ; Ning-ning PENG ; Qin NIE ; Tian-fu LI ; Jian-yu LIU ; Xiang-yi SONG ; Jian LIU ; Su-ping WU ; Ji-wen ZHANG ; Li-xin SUN
Acta Pharmaceutica Sinica 2025;60(2):498-505
The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure
10.Study on the modeling method of general model of Yaobitong capsule intermediates quality analysis based on near infrared spectroscopy
Le-ting SI ; Xin ZHANG ; Yong-chao ZHANG ; Jiang-yan ZHANG ; Jun WANG ; Yong CHEN ; Xue-song LIU ; Yong-jiang WU
Acta Pharmaceutica Sinica 2025;60(2):471-478
The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.


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