Objective:To explore the material basis and potential mechanism of Sanhuang Yishen Capsules in the treatment of diabetes through network pharmacology, molecular docking and experimental verification.Methods:The active components and targets of Sanhuang Yishhen Capsules were screened using China Natural product chemical composition database and SymMap database, and the related targets of T2DM were screened by GeneCards database. The "Chinese materia medica-active component-target" network was constructed, and the intersection genes were enriched by GO and KEGG using R language. Key active components were selected for molecular docking verification with potential core targets. 60 rats were divided into normal group, model group, and Sanhuang Yishen Capsules group according to random number table method, with 15 rats in each group. In addition to the normal group, the diabetic rat model was prepared in the other groups, and the corresponding drugs were intragastric in each group for 8 weeks. The levels of fasting blood glucose (FBG), fasting insulin (FINS) and insulin resistance index (HOMA-IR) were measured by radioimmunoassay. Western blotting was used to detect protein expressions of epidermal growth factor receptor (EGFR), epidermal growth factor (EGF), Akt serine/threonine kinase 1 (AKT1), recombinant tumor protein p53 (TP53), and recombinant caspase 3 (CASP3).Results:A total of 160 active components and 298 targets of Sanhuang Yishen Capsules, 2194 targets related to T2DM, and 166 intersection targets were obtained. GO and KEGG analyzed a series of biological reaction processes mainly involved in signal transduction, oxidative stress, apoptosis, etc., and mainly involved in the regulation of P13K/Akt, P53, CASP3 and other targets. The results of molecular docking showed that the main active components obtained by screening had strong binding with the target. Compared with model group, FBG, FINS, HOMA-IR, TP53 and CASP3 in Sanhuang Yishen Capsules group decreased ( P<0.05), EGFR, EGF and Akt1 proteins increased ( P<0.05). Conclusion:The mechanism of Sanhuang Yishen Capsules for the treatment of may be related to the regulation of EGF/EGFR/P13K/Akt signaling pathway, TP53 signaling pathway, CASP3 signaling pathway, PPARG signaling pathway, ESR1 signaling pathway, PTGS2 signaling pathway, CAT signaling pathway and CTNNB1 signaling pathway.

Objective Aberrant expression of ATP binding cassette subfamily B member 1(ABCB1)plays a key role in several cancers.However,influence of G protein coupled receptor family C group 5 type A(GPRC5A)-regulated ABCB1 expression on lung adenocarcinoma proliferation remains unclear.Therefore,this study investigated the effect of GPRC5A regulated ABCB1 expression on the proliferation of lung adenocarcinoma. Methods ABCB1 expressions in lung adenocarcinoma cell lines,human lung adenocarcinoma tissues,and tracheal epithelial cells and lung tissues of GPRC5A knockout mice and wild-type mice were analyzed with RT-PCR,Western blot,or immunohistochemical analysis.Cell counting kit-8 assay was performed to analyze the sensitivity of tracheal epithelial cells from GPRC5A knockout mice to chemotherapeutic agents.Subcutaneous tumor formation assay was performed to confirm whether down-regulation of ABCB 1 could inhibit the proliferation of lung adenocarcinoma in vivo.To verify the potential regulatory relationship between GPRC5A and ABCB1,immunofluorescence and immunoprecipitation assays were performed. Results ABCB1 expression was up-regulated in lung adenocarcinoma cell lines and human lung adenocarcinoma tissues.ABCB1 expression in the tracheal epithelial cells and lung tissues of GPRC5A deficient mice was higher than that in the wild type mice.Tracheal epithelial cells of GPRC5A knockout mice were much more sensitive to tariquidar and doxorubicin than those of GPRC5A wild type mice.Accordingly,28 days after injection of the transplanted cells,the volume and weight of lung tumor in ABCB1 knockout cell-transplanted GPRC5A-/-C57BL/6 mice were significantly smaller than those in wild type cell-transplanted mice(P=0.0043,P=0.0060).Furthermore,immunofluorescence and immunoprecipitation assays showed that GPRC5A regulated ABCB1 expression by direct binding. Conclusion GPRC5A reduces lung adenocarcinoma proliferation via inhibiting ABCB1 expression.The pathway by which GPRC5A regulates ABCB1 expression needs to be investigated.

Objective:To identify the relationship between the CT arterial radiomics score and the treatment response to neoadjuvant therapy for pancreatic cancer.Methods:The clinical data of 243 pancreatic cancer patients who received surgical resection after neo-adjuvant therapy in the First Affiliated Hospital of Naval Medical University from March 2017 to March 2023 were retrospectively analyzed. Based on the tumor regression grade (TRG), the patients were divided into good response group (TRG 0-1, n=30) and non-good response group (TRG 2-3, n=213). The clinical, radiological and pathological features were compared between two groups. Fully-automated segmentation tool was used for segmenting the arterial CT scan of pancreatic tumor before and after treatment. Python package was applied to extract the radiomics features of tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the arterial radiomics score. Univariate and multivariate logistic regression models were used to analyze the association between arterial radiomics score and treatment response to neoadjucant therapy. Receiver operating-characteristics (ROC) curve was drawn and area under curve (AUC), specificity, sensitivity and accuracy for evaluating the treatment response were calculated. The clinical usefulness of arterial radiomics score for diagnosing the response of neoadjuvant treatment for pancreatic cancer were determined by decision curve analysis (DCA) . Results:A total of 330 arterial radiomics CT features were obtained, and 9-selected arterial phase features associated with treatment response were determined after being reduced by the Lasso logistic regression algorithm. Univariate analysis showed that the arterial radiomics score, three-dimensional diameter after neoadjuvant therapy, pancreatic contour, T stage, N stage, Peri-pancreatic nerve invasion, lymph-vascular space invasion (LVSI) and invasion of duodenum were all associated with treatment response (all P value <0.05). Multivariate logistic regression analyses confirmed that arterial radiomics score was obviously associated with the neoadjuvant treatment response ( P<0.001). At the cut-off value of 1.93, AUC of the arterial radiomics score for diagnosing neoadjuvant treatment response was 0.92, and the specificity, sensitivity and accuracy was 86.7%, 84.5% and 84.8%. DCA demonstrated that when the percentage for predicting the treatment response by using the arterial radiomics score was >0.2, the patients could benefit from the application of arterial radiomics score for evaluating neoadjuvant therapy response. Conclusions:The arterial radiomics score was strongly correlated with the neoadjuvant treatment response of pancreatic cancer, and can accurately predict neoadjuant treatment efficacy.

Aim To explore the improvement effect of Bazi Bushen capsule on thymic degeneration in SAMP6 mice and the possible mechanism.Methods Twenty 12 week old male SAMP6 mice were randomly divided into the model group(SAMP6)and the Bazi Busheng capsule treatment group(SAMP6+BZBS).Ten SAMR1 mice were assigned to a homologous control group(SAMR1).The SAMP6+BZBS group was oral-ly administered Bazi Bushen capsule suspension(2.8 g·kg-1)daily,while the other two groups were orally administered an equal amount of distilled water.After nine weeks of administration,the morphology of the thymus in each group was observed and the thymus in-dex was calculated;HE staining was used to observe the structural changes of thymus tissue;SA-β-gal stai-ning was used to detect thymic aging;flow cytometry was used to detect the proportion of thymic CD3+T cells in each group;Western blot was used to detect the levels of p16,Bax,Bcl-2,and cleaved caspase-3 proteins in thymus;immunofluorescence was applied to detect the proportion of cortical thymic epithelial cells in each group;ELISA was employed to detect IL-7 lev-els in thymus.Results Compared with the SAMP6 group,the thymic index of the SAMP6+BZBS group significantly increased(P＜0.05);the disordered thy-mic structure was significantly improved;the positive proportion of SA-β-gal staining significantly decreased(P＜0.01);the proportion of CD3+T cells apparently increased(P＜0.05);the level of p16 protein signifi-cantly decreased(P＜0.05);the level of Bcl-2 pro-tein significantly increased(P＜0.05),while the lev-el of cleaved caspase-3 protein markedly decreased(P＜0.05);the proportion of cortical thymic epithelial cells evidently increased;the level of IL-7 significantly increased(P＜0.01).Conclusions Bazi Bushen capsule can delay thymic degeneration,inhibit cell ap-optosis in thymus and promote thymic cell development in SAMP6 mice,which may be related to increasing the proportion of cortical thymic epithelial cells and promoting IL-7 secretion.

OBJECTIVE: To explore the effect of recombinant human thrombopoietin (rhTPO) on hematopoietic reconstruction in allogeneic hematopoietic stem cell transplantation (allo-HSCT) model. METHODS: The C57BL/6 mice were employed as the donors, and BALB/c mice as recipients. The bone marrow mononuclear cells of the donor mice were extracted and pretreated, which then were injected with 5×10⁶ per mouse through the tail vein of the recipient to establish an allo-HSCT model. The implantation of hematopoietic stem cells in the recipient mice was detected by flow cytometry on the 28^th day after transplantation. Next, the successfully modeled recipient mice were randomly divided into experimental group and control group. The rhTPO was injected into mice in the experimental group on the first day after transplantation, while the saline was injected into mice in the control group. Both groups were injected for 14 consecutive days. The peripheral blood and bone marrow hematopoiesis of the two groups were observed on day 1, 3, 7, 14, and 21 after transplantation. RESULTS: The expression rate of H-2K^b in the bone marrow of recipient mice was 43.85% (>20%) on the 28^th day after transplantation, which indicated that the recipient mice were successfully chimerized. Meanwhile, counts of PLTs on the day 3, 7, 14, and 21 after transplantation in the experimental group were higher than those in the control group with statistical significances (P<0.05). In addition, hematopoietic function of bone marrow was suppressed in both groups on day 1, 3 and 7 after transplantation, but hematopoietic bone marrow hyperplasia was better in the experimental group than in the control group. On day 14 and 21 after transplantation, the hematopoietic function of bone marrow in the two groups was recovered, and the experimental group showed more obvious than the control group. CONCLUSION rhTPO can effectively stimulate the production of PLTs and facilitate the recovery of white blood cells and hemoglobin after allo-HSCT, and promote hematopoietic recovery and reconstitution of bone marrow.
Humans ; Animals ; Mice ; Thrombopoietin ; Mice, Inbred C57BL ; Hematopoietic Stem Cell Transplantation ; Hematopoietic Stem Cells ; Bone Marrow ; Recombinant Proteins ; Mice, Inbred BALB C

Amino-containing compounds,including amino acids,aliphatic amines,aromatic amines,small peptides and catecholamines,are involved in various biological processes and play vital roles in multiple meta-bolic pathways.Previous studies indicated that some amino-containing metabolites are significant diagnostic and prognostic biomarkers of gastric cancer.However,the discovery of precise biomarkers for the preoperative diagnosis of gastric cancer is still in an urgent need.Herein,we established a polarity-regulated derivatization method coupled with liquid chromatography-mass spectrometry(LC-MS)for amino-containing metabolites profiling in the serum samples of patients with gastric cancer and healthy controls,based on our newly designed and synthesized derivatization reagent(S)-3-(1-(diisopropox-yphosphoryl)pyrrolidine-2-carboxamido)-N-hydroxysuccinimidyl ester(3-DP-NHS).Enhanced separa-tion efficiency and detection sensitivity for amino-containing metabolites were achieved after derivatization.This method exhibited good linearity,recovery,intra-and inter-day precision and accu-racy.Only 5 μL serum is needed for untargeted analysis,enabling 202 amino-containing metabolites to be detected.Statistical analysis revealed altered amino acid metabolisms in patients with gastric cancer.Furthermore,ultra high performance liquid chromatography coupled with mass spectrometry(UHPLC-MS/MS)analysis quantification revealed increased serum levels of tryptamine and decreased concen-trations of arginine and tryptophan in patients with gastric cancer.Receiver operating characteristic(ROC)curves indicated that an increased tryptamine/tryptophan ratio could serve as a potential biomarker for gastric cancer diagnosis.This study demostrated the possibility of using serum amino acid biomarkers for gastric cancer diagnosis,providing new avenues for the treatment of gastric cancer.

Objective:To investigate the value of machine learning model based on MRI in predicting the abundance of tumor infiltrating CD 8+ T cell and prognosis of pancreatic cancer patients. Methods:The clinical data of 156 patients with pathological confirmed pancreatic cancer who underwent pre-operative MRI within 7 days before surgery in the First Affiliated Hospital of Naval Medical University from January 2017 to April 2018 was retrospectively analyzed. According to the international consensus on the predictive model, a total of 116 patients from January to December 2017 were included in the training set, and a total of 40 patients from January to April 2018 were included in the validation set. With the overall survival of patients as the outcome variable, X-Tile software was used to obtain cut-off values of the percentage of CD 8+ T cells, and all patients were divided into CD 8+ T-high and -low groups. The clinical, pathological and radiological features were compared between two groups. 3D slicer software was used to draw the region of interest in each layer of the primary MR T 1- and T 2-weighted imaging, arterial phase, portal venous phase, and delayed phase images for tumor segmentation. Python package was applied to extract the radiomics features of pancreatic tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the rad-score. The extreme gradient boosting (XGBoost) were used to construct the machine learning predicted model. The models′ performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results:The cut-off value of the CD 8+ T-cell level was 19.09% as determined by the X-tile program. Patients in the high CD 8+ T cell group had a longer median survival than those in the low CD 8+ T cell group (25.51 month vs 22.92 month, P=0.007). The T stage in the training set and tumor size in the validation set significantly differed between the groups (all P value <0.05). A total of 1 409 radiomics features were obtained, and 19-selected features associated with the level of CD 8+ T cell were determined after being reduced by the Lasso logistic regression algorithm. The rad-score was significantly lower in the CD 8- high group (median: -0.43; range: -1.55 to 0.65) than the CD 8- low group (median: 0.22; range: -0.68 to 2.54, P<0.001). The prediction model combined the radiomics features and tumor size. In the training set, the AUC, sensitivity, specificity, accuracy, and positive and negative predictive value were 0.90 (95% CI 0.85-0.95), 75.47%, 90.48%, 0.84, 0.87, and 0.81. In the validation set, the AUC, sensitivity, specificity, accuracy, and positive and negative predictive value were 0.79 (95% CI 0.63-0.96), 90.00%, 80.00%, 0.85, 0.82, and 0.89. The predictive model can accurately distinguish patients with high and low CD 8+ T cells in pancreatic cancer. Conclusions:The radiomics-based machine learning model is valuable in predicting the CD 8+ T cells infiltrating level in pancreatic cancer patients, which could be useful in identifying potential patients who can benefit from immunotherapies.

Humans ; Adenocarcinoma/pathology* ; Lung Neoplasms/pathology* ; Lung/pathology*

A randomized, double-blind, placebo-controlled, multi-center phase Ⅱ clinical trial design was used in this study to recruit subjects who were in line with the syndrome of excess heat and fire toxin, and were diagnosed as recurrent oral ulcers, gingivitis, and acute pharyngitis. A total of 240 cases were included and randomly divided into a placebo group and a Huanglian Jiedu Pills group. The clinical efficacy of Huanglian Jiedu Pills in treating the syndrome of excess heat and fire toxin was evaluated by using the traditional Chinese medicine(TCM) syndrome scale. Enzyme-linked immunosorbent assay(ELISA) was used to determine and evaluate the levels of adenosine triphosphate(ATP), 4-hydroxynonenal(4-HNE), and adrenocorticotropic hormone(ACTH) in plasma of the two groups before and after administration and to predict their application value as clinical biomarkers. The results showed that the disappearance rate of main symptoms in the Huanglian Jiedu Pills group was 69.17%, and that in the placebo group was 50.83%. The comparison between the Huanglian Jiedu Pills group and the placebo group showed that 4-HNE before and after administration was statistically significant(P<0.05). The content of 4-HNE in the Huanglian Jiedu Pills group decreased significantly after administration(P<0.05), but that in the placebo group had no statistical significance and showed an upward trend. After administration, the content of ATP in both Huanglian Jiedu Pills group and placebo group decreased significantly(P<0.05), indicating that the energy metabolism disorder was significantly improved after administration of Huanglian Jiedu Pills and the body's self-healing ability also alleviated the increase in ATP level caused by the syndrome of excess heat and fire toxin to a certain extent. ACTH in both Huanglian Jiedu Pills group and placebo group decreased significantly after administration(P<0.05). It is concluded that Huanglian Jiedu Pills has a significant clinical effect, and can significantly improve the abnormal levels of ATP and 4-HNE in plasma caused by the syndrome of excess heat and fire toxin, which are speculated to be the effective clinical biomarkers for Huanglian Jiedu Pills to treat the syndrome of excess heat and fire toxin.
Humans ; Adrenocorticotropic Hormone ; Hot Temperature ; Medicine, Chinese Traditional ; Adenosine Triphosphate

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.