A comprehensive insight into potential roles of Nigella sativa on diseases by targeting AMP-activated protein kinase: a review

ObjectivesNigella sativa (NS) is a known medicinal herb with numerous therapeutic effects such as antidiabetic, anti-proliferative, anti-inflammatory, and anti-cancer activities. It has been indicated that NS can regulate cellular metabolism by adjusting transduction signaling pathways. Adenosine monophosphate-activated protein kinase (AMPK) is one of the main physiological processes, such as energy hemostasis, cellular metabolism, and autophagy regulators. Herb-derived medicines have always been considered as one of the main AMPK activators, and surprisingly recent data has demonstrated that it can be a target for NS and its derivatives.Evidence acquisitionThe literature search was conducted in PubMed, SCOPUS, Embase, ProQuest, and Google Scholar electronic resources. Published articles up to September 2020 were considered, and those of which investigated Nigella sativa effects on the AMPK pathway after meeting the inclusion criteria were included.ResultsThe search was performed on several online databases such as PubMed, Scopus, Embase, ProQuest, and Google Scholar from inception until January 2020. Among the initial search, 245 studies were found. After removing duplicated data and meeting the inclusion criteria, only 14 studies were selected. They included the effects of NS and its bioactive compounds as anti-hyperglycemic (n = 5), on liver function (n = 4), cancers (n = 3), and on Neuroinflammation and Atherosclerosis (n = 2). Most of the included studies are animals or in-vitro investigations.ConclusionIn this review, we discuss the latest findings on the molecular mechanism of NS effecting the AMPK signaling pathway. We also focus on the therapeutic effects of NS, including the prevention and treatment of metabolic and pro-inflammatory disease by targeting the AMPK pathway.Graphical abstract     

The Effect of Oral Administration of PUFAs on the Matrix Metalloproteinase Expression in Gastric Adenocarcinoma Patients Undergoing Chemotherapy

Objective: Gastric cancer is the third-leading cause of cancer-related mortality and the fifth most common cancer globally. Polyunsaturated fatty acids (PUFAs) are considered as functional ingredients that improve the efficacy of chemotherapeutic drugs. The aim of this study is to investigate the effect of PUFAs administration on matrix metalloproteinases (MMPs).

Methods: This study was designed as a randomized, double-blind trial. Thirty-four newly diagnosed patients with gastric cancer were randomly divided into two groups: control group (n?=?17) and case group (n?=17). Both groups received the same dose (75?mg/m²) of cisplatin. Control group received cisplatin plus placebo and the case group received cisplatin plus PUFAs [3600?mg/day, for three courses (each course included 3 weeks)]. The mRNA and protein expression of MMPs determined by real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC), respectively.

Results: The relative gene expression of MMP-1 and MMP-9 was significantly lower in case group than control. The protein expression of MMP-1 and MMP-9 was significantly lower in case group than control.

Conclusion: According to the results of this study, PUFAs reduced the expression of MMPs in gastric cancer cells. It seems that PUFAs may have an inhibitory effect on invasion and metastasis of gastric cancer cells.     

Correction to: Acetyl-L-carnitine confers neuroprotection against lipopolysaccharide (LPS) -induced neuroinflammation by targeting TLR4/NFκB,autophagy, inflammation and oxidative stress

Metabolic Brain Disease - A Correction to this paper has been published: 10.1007/s11011-021-00721-8     

The tear VEGF and IGFBP3 in healthy and diabetic retinopathy

International Journal of Diabetes in Developing Countries - Diabetic retinopathy is an important problem worldwide. The purposes of this study were to measure and analyze VEGF and IGFBP3 level...     

Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks

Electroencephalogram (EEG) microstates that represent quasi‐stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non‐Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long‐short‐term‐memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM‐based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200–2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long‐range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short‐term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.