Objective To explore a method for rapid and differential diagnosis of rejection after kidney transplantation through urine hyperspectral imaging technology. Methods Hyperspectral data information from urine samples of 118 recipients after kidney transplantation was collected, and a deep learning model was constructed to diagnose and classify the types of rejection. Results A deep learning diagnostic model based on the 34-layer residual network (ResNet-34) was constructed, and 118 patients were included and divided into the training set and the test set. Based on the pathological results of the transplanted kidney puncture, the urine samples of the patients were classified into five groups: the non-rejection group, the T-cell-mediated rejection group, the antibody-mediated rejection group, the mixed rejection group and the nephropathy recurrence group. The results showed that the diagnostic sensitivities of the model for the above five groups were 0.960, 0.980, 0.930, 0.940 and 0.943 respectively, and the diagnostic specificities were 0.983, 0.993, 0.997, 0.989 and 0.989 respectively. The overall diagnostic accuracy rate reached 95.7%. Conclusions The study provides a non-invasive, rapid and accurate auxiliary diagnostic method for the differential diagnosis of rejection after kidney transplantation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Objective To explore the expression characteristics of BAIAP2L1 in cervical cancer (CC) and its regulatory role in tumor cell metastasis. Methods The correlation between BAIAP2L1 expression and clinical prognosis was analyzed by using a public database. GO pathway enrichment and clinicopathological correlation analyses were conducted by employing R language. The effect of BAIAP2L1 knockdown on CC cell proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) were further investigated through gene silencing approaches. Results BAIAP2L1 expression was significantly upregulated in CC tissues (P_adj <0.001) and it was identified as an independent risk factor for patient mortality (HR=2.808, P=0.03). Elevated BAIAP2L1 levels showed significant correlations with poor overall survival, advanced T/N stage, recurrence, and metastasis (all P<0.05). Functional enrichment analysis revealed its involvement in tumor metastasis-related pathways. The knockdown of BAIAP2L1 significantly attenuated CC cell proliferation, invasion, and migration and suppressed key EMT processes (all P<0.05). Conclusion BAIAP2L1 is overexpressed in CC tissues and associated with patient prognosis and metastasis. The targeted inhibition of BAIAP2L1 can effectively curb tumor progression.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To investigate the effectiveness of new cryoprotective agents in preserving and transplanting human adipose tissue.Methods:The adipose tissue samples were obtained from healthy adult females who underwent liposuction at the Department of Plastic Surgery of Peking University Third Hospital from January to March 2022. The adipose tissue samples were centrifuged and then randomly divided into 9 groups. These groups were cryopreserved in liquid nitrogen using different cryoprotective agents [group A, group B, and dimethyl sulfoxide (DMSO) group] and cryopreservation times (1-month, 2-month, and 3-month groups), respectively. The cryoprotective agent formulation in group A was dextrose glycoside 40 (DEX), amino acids, vitamins, and inorganic salts. In group B, the formulation included DMSO and DEX. The ratio of cryoprotective agent in the DMSO group was 10% DMSO, 20% fetal bovine serum (FBS), and 70% DMEM-12. For cryopreservation, 5 ml cryogenic tubes were used with a fat to cryoprotective agent ratio of 3∶2, and each group contains 6 tubes for cryopreservation. After thawing the adipose tissue, HE staining was used to observe the histological morphology. Immunohistochemical staining was employed for the quantitative analysis of lipid droplet-encapsulated protein (Perilipin), and the Perilipin positivity rate was calculated by the ratio of the number of positive cells to the total number of cells. Adipocyte viability was assessed using the CCK-8 method. Thirty-eight healthy, clean nude mice were selected and divided into 3 groups of 12 mice each according to the use of different cryoprotective agents (groups A, B, and DMSO), while the other 2 mice were used as the day 0 control group. The mean fat freezing duration for all groups was 3 months. After nude mice were anesthetized intraperitoneally, 0.9 ml of thawed cryopreserved fat was injected into the dorsum bilaterally. The rate of adipose tissue retention was calculated by MRI scanning and three-dimensional software at 1, 2, and 3 months after transplantation, and compared between the groups. The fat grafts were explanted from the mice after they were sacrificed, and then subjected to histological morphology and quantitative analysis of Perilipin by using HE staining and immunohistochemical staining. GraphPad Prism 8.0 software was used for statistical analysis of the data. The data that conformed to a normal distribution were expressed as Mean ± SD. The overall comparison between multiple groups used analysis of variance for repeated measures. The comparison of data between groups at the same time point used Tukey’s multiple comparison test.Results:The morphology of adipose tissue in different cryoprotective agent groups closely resembled that of normal fresh adipose tissue after being cryopreserved in liquid nitrogen for 1-3 months. The difference in the proportion of Perilipin-stained positive cells in each group was not statistically significant ( P>0.05). The CCK-8 method indicated that the effect of the DMSO group was superior to groups A and B at 1 and 3 months of cryopreservation ( P<0.01), and that the DMSO group and group B were superior to group A at 2 months of cryopreservation ( P<0.01). In the animal experiments, there was no statistically significant difference between the groups in the volume retention rate 1-3 months after cryopreserved fat transplantation ( P>0.05). Additionally, the adipose tissues in each group exhibited varying degrees of localized necrosis accompanied by an inflammatory reaction 1-3 months after transplantation. There was no statistically significant difference in the Perilipin staining positivity between the groups ( P>0.05). Conclusion:The use of new cryoprotective agents for cryopreserving adipose tissue does not show a significant difference compared to the traditional cryoprotective agent. However, it is theoretically safer as it avoids the potential toxic effects of using DMSO or FBS on the human body.

Objective:To investigate the effectiveness of new cryoprotective agents in preserving and transplanting human adipose tissue.Methods:The adipose tissue samples were obtained from healthy adult females who underwent liposuction at the Department of Plastic Surgery of Peking University Third Hospital from January to March 2022. The adipose tissue samples were centrifuged and then randomly divided into 9 groups. These groups were cryopreserved in liquid nitrogen using different cryoprotective agents [group A, group B, and dimethyl sulfoxide (DMSO) group] and cryopreservation times (1-month, 2-month, and 3-month groups), respectively. The cryoprotective agent formulation in group A was dextrose glycoside 40 (DEX), amino acids, vitamins, and inorganic salts. In group B, the formulation included DMSO and DEX. The ratio of cryoprotective agent in the DMSO group was 10% DMSO, 20% fetal bovine serum (FBS), and 70% DMEM-12. For cryopreservation, 5 ml cryogenic tubes were used with a fat to cryoprotective agent ratio of 3∶2, and each group contains 6 tubes for cryopreservation. After thawing the adipose tissue, HE staining was used to observe the histological morphology. Immunohistochemical staining was employed for the quantitative analysis of lipid droplet-encapsulated protein (Perilipin), and the Perilipin positivity rate was calculated by the ratio of the number of positive cells to the total number of cells. Adipocyte viability was assessed using the CCK-8 method. Thirty-eight healthy, clean nude mice were selected and divided into 3 groups of 12 mice each according to the use of different cryoprotective agents (groups A, B, and DMSO), while the other 2 mice were used as the day 0 control group. The mean fat freezing duration for all groups was 3 months. After nude mice were anesthetized intraperitoneally, 0.9 ml of thawed cryopreserved fat was injected into the dorsum bilaterally. The rate of adipose tissue retention was calculated by MRI scanning and three-dimensional software at 1, 2, and 3 months after transplantation, and compared between the groups. The fat grafts were explanted from the mice after they were sacrificed, and then subjected to histological morphology and quantitative analysis of Perilipin by using HE staining and immunohistochemical staining. GraphPad Prism 8.0 software was used for statistical analysis of the data. The data that conformed to a normal distribution were expressed as Mean ± SD. The overall comparison between multiple groups used analysis of variance for repeated measures. The comparison of data between groups at the same time point used Tukey’s multiple comparison test.Results:The morphology of adipose tissue in different cryoprotective agent groups closely resembled that of normal fresh adipose tissue after being cryopreserved in liquid nitrogen for 1-3 months. The difference in the proportion of Perilipin-stained positive cells in each group was not statistically significant ( P>0.05). The CCK-8 method indicated that the effect of the DMSO group was superior to groups A and B at 1 and 3 months of cryopreservation ( P<0.01), and that the DMSO group and group B were superior to group A at 2 months of cryopreservation ( P<0.01). In the animal experiments, there was no statistically significant difference between the groups in the volume retention rate 1-3 months after cryopreserved fat transplantation ( P>0.05). Additionally, the adipose tissues in each group exhibited varying degrees of localized necrosis accompanied by an inflammatory reaction 1-3 months after transplantation. There was no statistically significant difference in the Perilipin staining positivity between the groups ( P>0.05). Conclusion:The use of new cryoprotective agents for cryopreserving adipose tissue does not show a significant difference compared to the traditional cryoprotective agent. However, it is theoretically safer as it avoids the potential toxic effects of using DMSO or FBS on the human body.

OBJECTIVE To improve the quality evaluation method of Platycodonis Radix, to study the differences in the quality of three-years-old Platycodonis Radix under different harvesting periods, and to determine the optimal harvesting period of Platycodonis Radix. METHODS The leachate, ash, moisture, refractive index and the content of six saponins were used as the quality evaluation indexes. The differences between the herbs of Platycodonis Radix at different harvesting periods were characterized with the help of mathematical and statistical methods. And link the entropy weight method, gray correlation analysis and TOPSIS method were combined to obtain the statistical analysis of the relevant indexes and the quality ranking information of the herbs in different harvesting periods. RESULTS There were significant differences between the quality evaluation indexes of three-years-old Platycodonis Radix at different harvesting periods. The added multi-indicator testing had improved the quality evaluation system of Platycodonis Radix and enhanced the "Drug properties-Effectiveness" linkage of the herbs. And the results of the comprehensive quality evaluation model showed that the herbs harvested around October 21 (Frost’s Descent) were ranked best in terms of comprehensive index. CONCLUSION In order to ensure the quality of Platycodonis Radix, the best harvesting period for three-years-old Platycodonis Radix is determined around the "Frost’s Descent" season, taking into account the characteristics of the herbs' appearance and the material basis of herbs.

Objective:To summarize the clinicopathologic characteristics of undifferentiated embryonal sarcoma of the liver.Methods:The clinical data of 16 patients with undifferentiated embryonal sarcoma of the liver admitted to Shandong Provincial Hospital and Yantai Yuhuangding Hospital from Jan 2000 to Jan 2023 was retrospectively analyzed.Results:The diagnosis of undifferentiated embryonal sarcoma in all the 16 patients was established by postoperative pathology. The clinical manifestations of the patients were mainly asymptomatic abdominal mass, abdominal pain and discomfort. The laboratory examination was mostly nonspecific, the image exam showed mostly solid cystic mass. 14 cases underwent radical surgical resection.The minimum survival time was 7 months, maximum survival time was 121 months with median survival time of 35.9 months.Conclusions:Undifferentiated embryonal sarcoma of the liver is an extremely rare malignant tumor of the liver. The disease lacks specific clinical manifestations. CT examination can assist in the diagnosis of the disease. Diagnosis of undifferentiated embryonal sarcoma of the liver depends on pathological examination. The prognosis of patients is poor, surgical resection of the tumor is an effective treatment.