Allium hookeri root extract regulates asthmatic changes through immunological modulation of Th1/Th2‑related factors in an ovalbumin‑induced asthma mouse model

Risk prediction model for distinguishing Gram-positive from Gram-negative bacteremia based on age and cytokine levels: A retrospective study

BACKGROUND

Severe infection often results in bacteremia, which significantly increases mortality rate. Different therapeutic strategies are employed depending on whether the blood-borne infection is Gram-negative (G^-) or Gram-positive (G⁺). However, there is no risk prediction model for assessing whether bacteremia patients are infected with G^- or G⁺ pathogens.

AIM

To establish a clinical prediction model to distinguish G^- from G⁺ infection.

METHODS

A total of 130 patients with positive blood culture admitted to a single intensive care unit were recruited, and Th1 and Th2 cytokine concentrations, routine blood test results, procalcitonin and C-reactive protein concentrations, liver and kidney function test results and coagulation function were compared between G⁺ and G^- groups. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to optimize the selection of predictive variables by running cyclic coordinate descent and K-fold cross-validation (K = 10). The predictive variables selected by LASSO regression analysis were then included in multivariate logistic regression analysis to establish a prediction model. A nomogram was also constructed based on the prediction model. Calibration chart, receiver operating characteristic curve and decision curve analysis were adopted for validating the prediction model.

RESULTS

Age, plasma interleukin 6 (IL-6) concentration and plasma aspartate aminotransferase concentration were identified from 57 measured variables as potential factors distinguishing G⁺ from G^- infection by LASSO regression analysis. Inclusion of these three variables in a multivariate logistic regression model identified age and IL-6 as significant predictors. In receiver operating characteristic curve analysis, age and IL-6 yielded an area under the curve of 0.761 and distinguished G⁺ from G^- infection with specificity of 0.756 and sensitivity of 0.692. Serum IL-6 and IL-10 levels were upregulated by more than 10-fold from baseline in the G^- bacteremia group but by less than ten-fold in the G⁺ bacteremia group. The calibration curve of the model and Hosmer-Lemeshow test indicated good model fit (P > 0.05). When the decision curve analysis curve indicated a risk threshold probability between 0% and 68%, a nomogram could be applied in clinical settings.

CONCLUSION

A simple prediction model distinguishing G^- from G⁺ bacteremia can be constructed based on reciprocal association with age and IL-6 level.

Risk prediction model for distinguishing Gram-positive from Gram-negative bacteremia based on age and cytokine levels: A retrospective study

BACKGROUND

Severe infection often results in bacteremia, which significantly increases mortality rate. Different therapeutic strategies are employed depending on whether the blood-borne infection is Gram-negative (G-) or Gram-positive (G+). However, there is no risk prediction model for assessing whether bacteremia patients are infected with G- or G+ pathogens.

AIM

To establish a clinical prediction model to distinguish G- from G+ infection.

METHODS

A total of 130 patients with positive blood culture admitted to a single intensive care unit were recruited, and Th1 and Th2 cytokine concentrations, routine blood test results, procalcitonin and C-reactive protein concentrations, liver and kidney function test results and coagulation function were compared between G+ and G- groups. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to optimize the selection of predictive variables by running cyclic coordinate descent and K-fold cross-validation (K = 10). The predictive variables selected by LASSO regression analysis were then included in multivariate logistic regression analysis to establish a prediction model. A nomogram was also constructed based on the prediction model. Calibration chart, receiver operating characteristic curve and decision curve analysis were adopted for validating the prediction model.

RESULTS

Age, plasma interleukin 6 (IL-6) concentration and plasma aspartate aminotransferase concentration were identified from 57 measured variables as potential factors distinguishing G+ from G- infection by LASSO regression analysis. Inclusion of these three variables in a multivariate logistic regression model identified age and IL-6 as significant predictors. In receiver operating characteristic curve analysis, age and IL-6 yielded an area under the curve of 0.761 and distinguished G+ from G- infection with specificity of 0.756 and sensitivity of 0.692. Serum IL-6 and IL-10 levels were upregulated by more than 10-fold from baseline in the G- bacteremia group but by less than ten-fold in the G+ bacteremia group. The calibration curve of the model and Hosmer-Lemeshow test indicated good model fit (P > 0.05). When the decision curve analysis curve indicated a risk threshold probability between 0% and 68%, a nomogram could be applied in clinical settings.

CONCLUSION

A simple prediction model distinguishing G- from G+ bacteremia can be constructed based on reciprocal association with age and IL-6 level.

Codonopsis laceolata Water Extract Ameliorates Asthma Severity by Inducing Th2 Cells’ and Pulmonary Epithelial Cells’ Apoptosis via NF-κB/COX-2 Pathway

Asthma is an incurable pulmonary disease with several symptoms, including abnormal breathing, coughing, and sleep apnea, which can lead to death, and the population of asthma patients has been increasing worldwide. There are many adverse effects in current drugs, and thus, we have tried to develop anti-asthmatic agents from natural products such as Codonopsis laceolata. To define the anti-asthmatic effect and the mechanism of Codonopsis laceolata, an animal study was conducted considering different cell counts of BALF, serum IgE levels, morphological changes in the pulmonary system, the Th2 cell transcription factor (GATA-3), and the apoptotic pathway (NF-κB/COX-2). Codonopsis laceolata significantly suppressed the representative asthmatic changes, such as airway remodeling, mucous hypersecretion, epithelial hyperplasia, and inflammatory cell infiltration, in the respiratory system. It suppressed the levels of GATA-3, IL-4, and IL-13. The down-regulation of Th2-related factors, such as GATA-3, IL-4, and IL-13, results from the stimulated apoptosis of Th2 cells and epithelial cells via a decrease in the levels of NF-κB and COX-2. We concluded that Codonopsis laceolata might be a promising anti-asthmatic drug.

Natural Antioxidants, Health Effects and Bioactive Properties of Wild Allium Species

BACKGROUND

There is an increasing interest from the pharmaceutical and food industry in natural antioxidant and bioactive compounds derived from plants as substitutes for synthetic compounds. The genus Allium is one of the largest genera, with more than 900 species, including important cultivated and wild species, having beneficial health effects.

OBJECTIVE

The present review aims to unravel the chemical composition of wild Allium species and their healthrelated effects, focusing on the main antioxidant compounds. For this purpose, a thorough study of the literature was carried out to compile reports related to health effects and the principal bioactive compounds. Considering the vast number of species, this review is divided into subsections where the most studied species are presented, namely Allium ampeloprasum, A. flavum, A. hookeri, A. jesdianum, A. neapolitanum, A. roseum, A. stipitatum, A. tricoccum, and A. ursinum, with an additional composite section for less studied species.

METHODS

The information presented in this review was obtained from worldwide accepted databases such as Scopus, ScienceDirect, PubMed, Google Scholar and Researchgate, using as keywords the respective names of the studied species (both common and Latin names) and the additional terms of"antioxidants" "health effects" and "bioactive properties".

CONCLUSION

The genus Allium includes several wild species, many of which are commonly used in traditional and folklore medicine while others are lesser known or are of regional interest. These species can be used as sources of natural bioactive compounds with remarkable health benefits. Several studies have reported these effects and confirmed the mechanisms of action in several cases, although more research is needed in this field. Moreover, considering that most of the studies refer to the results obtained from species collected in the wild under uncontrolled conditions, further research is needed to elucidate the effects of growing conditions on bioactive compounds and to promote the exploitation of this invaluable genetic material.

The mediatory role of Majie cataplasm on inflammation of allergic asthma through transcription factors related to Th1 and Th2

Background

Asthma, a common respiratory disease, is harmful biological effect to our health. As a traditional Chinese medicine for asthma, Majie cataplasm could alleviate the symptoms of asthma and its compositions have immunomodulatory effects. Previous experiments showed that Majie cataplasm was an effective approach to mitigate asthma airway remodeling and had the potential to regulate Th2 cytokines of IL-5 and IL-13. Therefore, our further research focuses on the explanation about the regulatory effect of Majie cataplasm on reshaping Th1/Th2 through their related transcription factors.

Methods

In this experiment, the launch of asthma model was made by inducing with Ovalbumin (OVA) in C57 mice (n = 40), including 4 groups: the untreated control group (n = 10), the asthma model group (n = 10), the dexamethasone group (n = 10) and the Majie cataplasm group (n = 10). After the intervention, all groups of animals got detected for serum IgE levels, and HE staining of lung tissues was to observe and examine pathological changes. Meanwhile, we analyzed the secretion of IL-4⁺ T cells and IFN-γ⁺ T cells in spleen by flow cytometry. The expressions of transcription factor STAT6 mRNA, GATA-3 mRNA and T-bet mRNA in lung tissues was tested by PCR, and western blot had been used to detect levels of JAK2 and STAT3.

Results

We found that Majie cataplasm eased the content of serum IgE and lung inflammation. It could lower the increased number of IL-4⁺ T cells and IFN-γ⁺ T cells (P < 0.0001, P < 0.01) in asthmatic mice and curb the expression of STAT6 mRNA and GATA-3 (P < 0.0001, P < 0.01) mRNA as well as the protein levels of JAK2 (P < 0.001) and the ratio of pSTAT3/STAT3 (P < 0.05). Besides, Majie cataplasm made its mark on T-bet mRNA by improving it (P < 0.0001).

Conclusion

These data suggest that Majie cataplasm exert an anti-inflammatory effect of Th2 by rebalancing Th1/Th2 through corresponding transcription factor STAT6, GATA-3, STAT3, and T-bet, which providing a strong cornerstone for asthma control.