ObjectiveTo investigate the mechanism of action of Kaixinsan in the treatment of Alzheimer's disease （AD） based on network pharmacology， molecular docking， and animal experimental validation. MethodsThe Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform（TCMSP） and the Encyclopedia of Traditional Chinese Medicine（ETCM） databases were used to obtain the active ingredients and targets of Kaixinsan. GeneCards， Online Mendelian Inheritance in Man（OMIM）， TTD， PharmGKB， and DrugBank databases were used to obtain the relevant targets of AD. The intersection （common targets） of the active ingredient targets of Kaixinsan and the relevant targets of AD was taken， and the network interaction analysis of the common targets was carried out in the STRING database to construct a protein-protein interaction（PPI） network. The CytoNCA plugin within Cytoscape was used to screen out the core targets， and the Metascape platform was used to perform gene ontology（GO） functional enrichment analysis and Kyoto encyclopedia of genes and genomes（KEGG） pathway enrichment analysis. The “drug-active ingredient-target” interaction network was constructed with the help of Cytoscape 3.8.2， and AutoDock Vina was used for molecular docking. Scopolamine （SCOP） was utilized for modeling and injected intraperitoneally once daily. Thirty-two male C57/BL6 mice were randomly divided into blank control （CON） group （0.9% NaCl， n=8）， model （SCOP） group （3 mg·kg^-1·d^-1， n=8）， positive control group （3 mg·kg^-1·d^-1 of SCOP+3 mg·kg^-1·d^-1 of Donepezil， n=8）， and Kaixinsan group （3 mg·kg^-1·d^-1 of SCOP+6.5 g·kg^-1·d^-1 of Kaixinsan， n=8）. Mice in each group were administered with 0.9% NaCl， Kaixinsan， or Donepezil by gavage twice a day for 14 days. Morris water maze experiment was used to observe the learning memory ability of mice. Hematoxylin-eosin （HE） staining method was used to observe the pathological changes in the CA1 area of the mouse hippocampus. Enzyme linked immunosorbent assay（ELISA） was used to determine the serum acetylcholine （ACh） and acetylcholinesterase （AChE） contents of mice. Western blot method was used to detect the protein expression levels of signal transducer and activator of transcription 3（STAT3） and nuclear transcription factor（NF）-κB p65 in the hippocampus of mice. ResultsA total of 73 active ingredients of Kaixinsan were obtained， and 578 potential targets （common targets） of Kaixinsan for the treatment of AD were screened out. Key active ingredients included kaempferol， gijugliflozin， etc.. Potential core targets were STAT3， NF-κB p65， et al. GO functional enrichment analysis obtained 3 124 biological functions， 254 cellular building blocks， and 461 molecular functions. KEGG pathway enrichment obtained 248 pathways， mainly involving cancer-related pathways， TRP pathway， cyclic adenosine monophosphate（cAMP） pathway， and NF-κB pathway. Molecular docking showed that the binding of the key active ingredients to the target targets was more stable. Morris water maze experiment indicated that Kaixinsan could improve the learning memory ability of SCOP-induced mice. HE staining and ELISA results showed that Kaixinsan had an ameliorating effect on central nerve injury in mice. Western blot test indicated that Kaixinsan had a down-regulating effect on the levels of NF-κB p65 phosphorylation and STAT3 phosphorylation in the hippocampal tissue of mice in the SCOP model. ConclusionKaixinsan can improve the cognitive impairment function in SCOP model mice and may reduce hippocampal neuronal damage and thus play a therapeutic role in the treatment of AD by regulating NF-κB p65， STAT3， and other targets involved in the NF-κB signaling pathway.

As a patented characteristic medicine of Yi ethnic minority， Pingxuan capsules have the effects of nourishing the liver and kidney， pacifying the liver， and subduing Yang. With the main indications of dizziness， headache， palpitations， tinnitus， insomnia， dreaminess， waist and knee soreness caused by liver-kidney deficiency and liver Yang upward disturbance， Pingxuan capsules are widely used in the treatment of posterior circulation ischemic vertigo， vestibular migraine， benign paroxysmal positional vertigo. However， the current knowledge is limited regarding the efficacy， syndrome differentiation， and safety of this medicine. On the basis of summarizing the experience of clinicians and the existing evidence， this study invites clinical experts of traditional Chinese and Western medicine， pharmaceutical experts， and methodological experts from relevant fields across China to conduct evidence-based evaluation of Pingxuan capsules. The evaluation follows the Specifications for the Development of Clinical Expert Consensus on Chinese Patent Medicines issued by the Standardization Office of the China Association of Chinese Medicine， and reaches 5 recommendations and 16 consensus suggestions. The consensus clarifies the clinical applications， efficacy， dose， course of treatment， combination of medicines， precautions， and contraindications of Pingxuan capsules in the treatment of vertigo and explains the safety of clinical application. This consensus is applicable to clinicians （traditional Chinese medicine， Western medicine， and integrated traditional Chinese and Western medicine） and pharmacists in tertiary hospitals， secondary hospitals， and community-level medical and health institutions across China， providing a reference for the rational use of Pingxuan capsules in the treatment of vertigo. It is hoped that the promotion of this consensus can facilitate the rational use of drugs in clinical practice， reduce the risk of drug use， and give full play to the advantages of Pingxuan capsules in the treatment of vertigo diseases. This consensus has been reviewed and published by the China Association of Chinese Medicine， with the number GS/CACM330-2023.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

BACKGROUND:Previous studies by our research group discovered that Jiawei Xiaoyao San has a significant anti-liver cancer effect,but the specific mechanism of action was unclear. OBJECTIVE:To investigate the regulatory effects of the traditional Chinese medicine formula Jiawei Xiaoyao San on the levels of miRNAs in plasma exosomes of rats with diethylnitrosamine chronically induced primary liver cancer,based on high-throughput sequencing combined with bioinformatics. METHODS:SD rats were randomly divided into a blank control group,a liver cancer model group,and a Jiawei Xiaoyao San treatment group.Liver cancer models were induced by continuous administration of diethylnitrosamine for 12 weeks.Starting from the 17th week,rats in the Jiawei Xiaoyao San treatment group were administered Jiawei Xiaoyao San once daily until the end of the 20th week,while rats in the blank control and liver cancer model groups were given an equivalent volume of saline.Anti-hepatocellular carcinoma effects were validated by assessing the morphological structure of rat liver tissues,along with the expression of the hepatocellular carcinoma markers,Glypican-3 protein and serum alpha-fetoprotein.Plasma exosomes from each group of rats were isolated using ultracentrifugation.High-throughput sequencing technology was used to screen for differentially expressed miRNAs in rat plasma exosomes.Bioinformatics was used to predict the potential biomarkers through which Jiawei Xiaoyao San exerts its anti-liver cancer effects via liver cancer-derived exosomal miRNAs,followed by functional analysis. RESULTS AND CONCLUSION:(1)Jiawei Xiaoyao San significantly improved the morphological structure of liver tissues in a rat model of liver cancer.Compared with the liver cancer model group,the expression of liver cancer markers Glypican-3 protein and serum alpha-fetoprotein was significantly reduced in the Jiawei Xiaoyao San treatment group.(2)Bioinformatics analysis showed that in the Jiawei Xiaoyao San group,upregulated miR-223-3p in the liver cancer model group had target binding sites with genes E2F1 and NCOA1,which were closely related to liver cancer survival and prognosis.Therefore,Jiawei Xiaoyao San has a therapeutic effect on liver cancer,possibly by targeting negative regulation of NCOA1/E2F1 through liver cancer plasma-derived exosomal miR-223-3p,thereby playing anti-liver cancer effect.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.