Metabolic and Inorganic Elemental Profiling Analysis of Tortoise Shell for the Identification of Tortoise Strain

Tao-Hong-Si-Wu decoction improves depressive symptoms in model rats via amelioration of BDNF-CREB-arginase I axis disorders

CONTEXT

The traditional Chinese medicine formula Tao-Hong-Si-Wu decoction (TSD), used for treating ischaemic stroke, has the potential to treat depressive disorder (DD).

OBJECTIVE

To explore the effective targets of TSD on DD animal models.

MATERIALS AND METHODS

Sprague-Dawley (SD) rats were modelled by inducing chronic unpredictable mild stress (CUMS) during 35 days and treated with three dosages of TSD (2.5, 5 and 10 g/kg) or fluoxetine (10 mg/kg) by oral gavage for 14 days. Bodyweight measurements and behavioural tests were performed to observe the effect of TSD on the CUMS animals. A gas chromatography coupled with mass spectrometry (GC-MS)-based metabolomic analysis was conducted to reveal the metabolic characteristics related to the curative effect of TSD. Levels of the proteins associated with the feature metabolites were analysed.

RESULTS

Reduced immobile duration and crossed squares in the behavioural tests were raised by 48.6% and 32.9%, on average, respectively, by TSD treatment (ED₅₀=3.2 g/kg). Antidepressant effects of TSD were associated with 13 decreased metabolites and the restorations of ornithine and urea in the serum. TSD (5 g/kg) raised serum serotonin by 54.1 mg/dL but suppressed arginase I (Arg I) by 47.8 mg/dL in the CUMS rats. Proteins on the brain-derived neurotrophic factor (BDNF)-cAMP response element-binding protein (CREB) axis that modulate the inhibition of Arg I were suppressed in the CUMS rats but reversed by the TSD intervention.

DISCUSSION AND CONCLUSIONS

TSD improves depression-like symptoms in CUMS rats. Further study will focus on the antidepressant-like effects of effective compounds contained in TSD.

Study of the Unique Characteristics of Multi-Elements of the Wild Astragali Radix from Shanxi Province by Inductively Coupled Plasma Mass Spectrometry

BACKGROUND

Astragali Radix (AR) is widely used because of its dual use in medicine and food. Wild Astragali Radix from Hunyuan county of Shanxi Province in China is accepted as a geo-authentic medicine with high quality and good medicinal effects. Multi-elements of Astragali Radix partially reflect its efficacy and safety. However, there is no systemic research about the elemental analysis of geo-authentic Astragali Radix until now.

OBJECTIVE

In this paper, multi-elemental profiling of Astragali Radix from Gansu, Jilin, Inner Mongolia, Shaanxi and Shanxi provinces in China was implemented.

METHODS

A microwave digestion coupled with ICP-MS, principle component analysis and partial-least square-discriminate analysis were used for the analysis of unique elemental accumulation ability of Shanxi wild-type.

RESULTS

For 53 stably detected elements, the contents of most elements (Ba, Cs, Ga, La, Pr and so on) were significantly higher while some others (Cd, Cu, P, W and Zn) were significantly lower in wild Astragali Radix from Shanxi than those of the samples from Gansu, Jilin, Inner Mongolia, Shaanxi provinces and the cultivated samples from Shanxi. After binary logistic regression, combinational variable Ba-P was found to be a good marker to identify wild Astragali Radix of Shanxi Province from the samples with other origins, and the total positive prediction probability of the test samples from both market and their original field could reach 93.8% through external validation using the model.

CONCLUSIONS

Multi-elemental analysis coupled with PCA, PLS-DA, nonparametric analysis and binary logistic regression can be a good tool for the identification of wild Astragali Radix from Shanxi Province.

HIGHLIGHTS

An ICP-MS method was developed and validated for multi-elements. Fifty-three elements in Astragali Radix from differential origins were compared. The wild Astragali Radix from Shanxi had unique elemental characteristics. Combinational variable Ba-P is a good marker to identify wild-type from Shanxi.