Despite wide application of sodium nitrite (SN) as food additive, it exhibits considerable side effects on various body organs at high dose or chronic exposure. The aim of this study was to test whether Glycyrrhizic acid (GA) could ameliorate SN-induced toxicity in lung and submandibular salivary gland (SMG). A sample size of 30 adult male albino rats was randomly allocated into 3 groups. Group 1 served as control group. Rats were treated orally with 80 mg/kg of SN in group 2 or SN preceded by (15 mg/kg) GA in group 3. Lung & SMG tissues were used for oxidative stress assessment, examination of histopathological changes, fibrosis (MTC, TGF-β and α-SMA) and inflammation (TNF-α, IL-1β and CD-68). Concurrent administration of GA ameliorated pulmonary and salivary SN-induced toxicity via restoring the antioxidant defense mechanisms with reduction of MDA levels. GA reduced the key regulators of fibrosis TGF-β and α-SMA and collagen deposition. In addition to reduction of inflammatory cytokine (TNF-α, IL-1β) and macrophages recruitments, GA amended both pulmonary and salivary morphological changes. The present study proposed GA as a promising natural herb with antioxidant, anti-inflammatory and antifibrotic effects against pulmonary and salivary SN-induced toxicity.

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During the current pandemic of COVID-19, numerous manifestations and complications have developed. Patients with COVID-19 are at high risk of fungal infections, such as mucormycosis, that may result directly from COVID-19 infection and/or as a side effect of the drugs used in COVID-19 treatment protocol, such as dexamethasone, hydroxychloroquine, and antibiotics. In this report, we described a series of 14 cases with maxillary mucormycosis osteomyelitis in immediate post-COVID-19 patients.

The burden of diabetes mellitus (DM) and associated complications is increasing worldwide, affecting many organ functionalities including submandibular glands (SMG). The present study aims to investigate the potential ameliorative effect of glycyrrhizic acid (GA) on diabetes-induced SMG damage. Experimental evaluation of GA treatment was conducted on a rat model of type I diabetes. Animals were assigned to three groups; control, diabetic and GA treated diabetic groups. After 8 weeks, the SMG was processed for assessment of oxidative stress markers, autophagy related proteins; LC3, Beclin-1 and P62, vascular regulator ET-1, aquaporins (AQPs 1.4 and 5), SIRT1 protein expressions in addition to LC3 and AQP5 mRNA expressions. Also, parenchymal structures of the SMG were examined. GA alleviated the diabetes-induced SMG damage via restoring the SMG levels of oxidative stress markers and ET-1 almost near to the normal levels most probably via regulation of SIRT1, AQPs and accordingly LC-3, P62 and Beclin-1levels. GA could be a promising candidate for the treatment of diabetes-induced SMG damage via regulating oxidative stress, autophagy and angiogenesis.

As a complex endocrine and metabolic condition, polycystic ovarian syndrome (PCOS) affects women’s reproductive health. These common symptoms include hirsutism, hyperandrogenism, ovulatory dysfunction, irregular menstruation, and infertility. No one knows what causes it or how to stop it yet. Alterations in gut microbiota composition and disruptions in secondary bile acid production appear to play a causative role in developing PCOS. PCOS pathophysiology and phenotypes are tightly related to both enteric and vaginal bacteria. Patients with PCOS exhibit changed microbiome compositions and decreased microbial diversity. Intestinal microorganisms also alter PCOS patient phenotypes by upregulating or downregulating hormone release, gut-brain mediators, and metabolite synthesis. The human body’s gut microbiota, also known as the “second genome,” can interact with the environment to improve metabolic and immunological function. Inflammation is connected to PCOS and may be caused by dysbiosis in the gut microbiome. This review sheds light on the recently discovered connections between gut microbiota and insulin resistance (IR) and the potential mechanisms of PCOS. This study also describes metabolomic studies to obtain a clear view of PCOS and ways to tackle it.

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Abstract:The epigenetic nature of development mandates the observation of the effect of any exogenous substance, especially those with estrogenic activities, during critical phases of development. The submandibular gland (SMG) presents as a great model due to extensive postnatal development, and is known to be regulated and affected by hormones as well as growth factors. Herein, we observed postnatal development following low doses of Biochanin A (BCA) and 17β estradiol (E2) in rats. The pups were randomly divided into four groups: control, BCA, E2, and dimethyl sulfoxide (DMSO), and euthanized at the 6th, 15th, 30th, and 60th postnatal days (PND). SMG morphogenesis was assessed. The nuclear expression of estrogen receptor beta (ERβ) was evaluated immunohistochemically; ERβ expression was up-regulated by BCA and down-regulated by E2. Similarly, caspase three gene expression, assessed by real time polymerase chain reaction was increased in the BCA group but decreased in the E2 group. A significant decrease in epidermal growth factor gene expression was noted at PND 30. The results presented by this study provide evidence that the effect of a postnatal exposure of the SMG to Biochanin A during development could be linked to sex hormone-dependent disorders.

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.