Nomogram for predicting overall survival after curative gastrectomy using inflammatory, nutritional and pathological factors / Nomograma para predecir la supervivencia global después de una gastrectomía curativa utilizando factores inflamatorios, nutricionales y patológicos

Purpose: To establish a nomogram for predicting the overall survival (OS) in patients with gastric cancer (GC) based on inflammatory, nutritional and pathological factors. Methods: GC patients underwent curative gastrectomy from January 2012 to June 2017 in our hospital were included, and were classified into training set and validation set with a ratio of 7:3. Then variables associated with OS were analyzed using univariate and multivariate Cox regression analysis. Nomograms predicting OS were built using variables from multivariable Cox models. Finally, Kaplan–Meier curve and Log-rank test were also conducted to analyze the 1-yr, 3-yr and 5-yr OS to validate the efficiency of risk stratification of the nomogram. Results: A total of 366 GC patients were included. After univariate and multivariate Cox regression analysis, age (HR = 1.52, 95% CI = 1.01–2.30, P = 0.044), CA50 (HR = 1.90, 95% CI = 1.12–3.21, P = 0.017), PNI (HR = 1.65, 95% CI = 1.13–2.39, P = 0.009), SII (HR = 1.46, 95% CI = 1.03–2.08, P = 0.036), T stage (HR = 2.26, 95% CI = 1.01–5.05, P = 0.048; HR = 7.24, 95% CI = 3.64–14.40, P < 0.001) were independent influencing factors on the survival time of GC patients. Five factors including CEA, prognostic nutritional index (PNI), systemic immune-inflammation index (SII), ln (tumor size), T stage, and N stage were identified and entered the nomogram, which showed good discrimination and calibration in both sets. On internal validation, 1-yr, 3-yr and 5-yr nomogram demonstrated a good discrimination with an area under the ROC curve (AUC) of 0.77, 0.84 and 0.86, respectively. The AUC for 1-yr, 3-yr and 5-yr nomogram in validation set was 0.77, 0.79 and 0.81, respectively. The OS in low risk group of training cohort and validation cohort was significantly higher than that of intermediate risk group and high risk group, respectively...(AU)

Hedyotis diffusa alleviate aflatoxin B1-induced liver injury in ducks by mediating Nrf2 signaling pathway.

Aflatoxin B1 (AFB1), the most harmful aflatoxins, is a frequent contamination in feed and food items, raising global concerns in animal production and human public health. Also, AFB1 induces oxidative stress, cytotoxicity, mutations, and DNA lesions through its metabolic transformation into aflatoxin B1-8,9-epoxide (AFBO) by cytochrome P450 (CYP450). Hedyotis diffusa (HD) is a traditional Chinese herbal medicine known for its multiple pharmacological activities, including antioxidant, anti-inflammatory, and immunomodulatory. Yet, the influence of HD on AFB1-induced liver injury in ducks is still unknown. Here, we investigated whether HD positively affects AFB1-induced liver injury in ducks. Results revealed that I) AFB1 caused significant changes in serum biochemical indices and decreased growth performance of ducks (such as ALT, AST, ALP, TP, ALB, final body weight, and body weight gain), whereas HD supplementation at 200 mg/kg mitigated these alterations. II) HD alleviated hepatic histopathological changes and liver index induced by AFB1 in ducks. III) HD significantly attenuated AFB1-induced oxidative stress, as measured by increased antioxidant enzyme activities such as SOD, GPx, and T-AOC and decreased MDA levels. Furthermore, HD reduced the level of AFB1-DNA adduct in duck liver. IV) HD significantly promoted the transcriptional expression of NF-E2-related nuclear factor 2 (Nrf2) and associated genes, including heme oxygenase 1 (HO-1), NAD(P)H dehydrogenase quinone 1 (NQO1), glutamate-cysteine ligase catalytic (GCLC). In conclusion, these results demonstrated that HD could activate the Nrf2 pathway in ducks to reduce the hepatotoxicity driven by AFB1. This finding also provides theoretical and data support for a deeper understanding of the toxic mechanisms of AFB1 and its prevention.

Majie Cataplasm Promotes Th1 Response to Fight against Asthmatic Th2 Inflammation through NKs.

Background: Immune cells are tightly bound up with the pathogenesis of asthma. Besides T cells, B cells, macrophages, and mast cells, the mechanism of innate lymphoid cells (ILCs) in asthma is gradually explicit. As a kind of traditional Chinese medicine, Majie cataplasm realizes its potential in the clinical setting as an adjuvant for asthma. In our previous experiments, Majie cataplasm inhibits the increasing Th1 and Th2 in allergic asthma inflammation and reshapes a balance between Th1 and Th2. As ILCs are the reflection of Th cells in lung tissues, we will figure out whether Majie cataplasm could have similar effects on ILCs or not. Methods: A total of 40 female C57/BL6 mice were randomly divided into the control group (n = 10), the asthma model group (n = 10), the dexamethasone group (n = 10), and the Majie cataplasm group (n = 10). Except for the control group, mice were sensitized with ovalbumin (OVA) and excited to establish mice models of asthma. Lung tissue and splenic tissue were collected at 24 h after the last challenge with OVA, and the cell suspension of the lungs and spleen was prepared. The number of ILC1s, ILC2s, ILC3s, and NKs cells in the lungs and Tregs and B10s in the spleen were detected by flow cytometry (FCM). This was followed by simultaneous quantitative detection of 40 inflammatory cytokines and chemokines in the lung by a protein microarray. Results: The dexamethasone and Majie cataplasm could restore the number of ILC1s, ILC2s, and ILC3s in lung tissue. Compared with the control group, these cells remained unchanged in the asthma model group, while ILC1s (P < 0.001, P < 0.01), ILC2s (P < 0.001, P < 0.01), and ILC3s (P < 0.01, P < 0.05) were restored after the intervention of dexamethasone and Majie cataplasm. The number of NKs was low among the control group, the asthma model group, and the dexamethasone group, while the number of NKs rocketed in the Majie cataplasm group (P < 0.0001). For splenic Tregs and B10s, Majie cataplasm could curb the increasing numbers of them in the asthma model group (P < 0.0001, P < 0.01), while only Tregs were suppressed by the dexamethasone (P < 0.0001). For the inflammatory cytokines in the lung, the contents of TNF-α, TNFR2, CXCL-9, CCL-12, CCL-9, CCL-2, and CCL-5 in the asthma model group were higher than those in the control group, while the contents of GM-CSF and IL-1α were decreased. Comparing the asthma model group to the dexamethasone group, the levels of G-CSF, CCL-9, CCL-5, and TNFR2 in the former group were higher. The levels of TNF-α, TNFR2, and CCL-9 in the asthma model group increase, while the levels of IFN-γ, IL-1α, ICAM-1, and IL-4 increased in the Majie cataplasm group, especially IFN-γ and IL-1α. Conclusion: Both the dexamethasone and Majie cataplasm could control the asthmatic inflammation by reducing the inflammatory factors, inhibiting the adaptive inflammation reaction in the latter stage of inflammation and furtherly reversing the inhibition of ILC2s, ILC2s, and ILC3s. In addition, Majie cataplasm can promote the quantity of NKs and the content of IL-1α and IFN-γ, induce IFN-γ +NKs to shut down the Th2 response, and tend to elicit the Th1 response.

Network pharmacology assessment of Qingkailing injection treatment of cholestatic hepatitis.

OBJECTIVE: To investigate the targets and mechanisms of action of Qingkailing injection (，QKL) in the treatment of cholestatic hepatitis. METHODS: A network pharmacology method was implemented using drug and disease databases to target QKL and cholestasis hepatitis, respectively. The functional protein association network STRING database was used to construct a protein-protein interaction network using R language and the Bioconductor toolkit. The org.Hs.eg.db and clusterProfiler packages were used for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, which explored biological functions and pathways of potential targets. Targets were then visualized using Cytoscape 3.6.0 software. RESULTS: We screened 121 compounds in QKL and identified 112 targets for the treatment of cholestatic hepatitis. QKL played a role in the treatment of cholestatic hepatitis through 305 biology process terms, 15 cellular component and 29 molecular function terms. The mechanism of QKL action was mainly related to tumor necrosis factor, mitogen-activated protein kinase, and PI3K-Akt signaling pathways. CONCLUSION: The treatment of cholestatic hepatitis by QKL involved multiple targets, biological functions, and signaling pathways that are closely associated with the disease.