The intestinal epithelium is a barrier that composes one of the most immunologically active surfaces of the body due to constant exposure to microorganisms as well as an infinite diversity of food antigens. Disruption of intestinal barrier function and aberrant mucosal immune activation have been implicated in a variety of diseases within and outside of the gastrointestinal tract. With this model in mind, recent studies have shown a link between diet, composition of intestinal microbiota, and type 1 diabetes pathogenesis. In the BioBreeding rat model of type 1 diabetes, comparison of the intestinal microbial composition of diabetes prone and diabetes resistant animals found Lactobacillus species were negatively correlated with type 1 diabetes development. Two species, Lactobacillus johnsonii and L. reuteri, were isolated from diabetes resistant rats. In this study diabetes prone rats were administered pure cultures of L. johnsonii or L. reuteri isolated from diabetes resistant rats to determine the effect on type 1 diabetes development. Findings Results Rats administered L. johnsonii, but not L. reuteri, post-weaning developed type 1 diabetes at a protracted rate. Analysis of the intestinal ileum showed administration of L. johnsonii induced changes in the native microbiota, host mucosal proteins, and host oxidative stress response. A decreased oxidative intestinal environment was evidenced by decreased expression of several oxidative response proteins in the intestinal mucosa (Gpx1, GR, Cat). In L. johnsonii fed animals low levels of the pro-inflammatory cytokine IFNγ were correlated with low levels of iNOS and high levels of Cox2. The administration of L. johnsonii also resulted in higher levels of the tight junction protein claudin. It was determined that the administration of L. johnsonii isolated from BioBreeding diabetes resistant rats delays or inhibits the onset of type 1 diabetes in BioBreeding diabetes prone rats. Taken collectively, these data suggest that the gut and the gut microbiota are potential agents of influence in type 1 diabetes development. These data also support therapeutic efforts that seek to modify gut microbiota as a means to modulate development of this disorder.

Bacterial resistance to antibiotics and the disruption of beneficial microbiota are key problems in contemporary medicine and make the search for new, more efficient infection treatment strategies among the most important tasks in medicine. Multicomponent plant-derived preparations with mild antibacterial activity created by many simultaneous mechanisms together with anti-inflammatory, innate immune and regenerative capacity-stimulating properties are good candidates for this therapy, and proanthocyanidins are among the most promising compounds of this sort. In this study, we have isolated proanthocyanidins from Pelargonium sidoides DC root extract and characterized and compared the composition, antioxidant properties and antibacterial activity of the proanthocyanidin fraction with those of the whole extract. The results revealed that proanthocyanidins had significantly stronger antioxidant capacity compared to the root extract and exhibited a unique antibacterial action profile that selectively targets Gram-negative keystone periodontal and peri-implant pathogenic strains, such as Porphyromonas gingivalis, while preserving the viability of beneficial oral commensal Streptococcus salivarius. The finding suggests that proanthocyanidins from Pelargonium sidoides root extract are good candidates for the prolonged and harmless treatment of infectious diseases.

Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs.

Bioactive glasses (BGs) are attractive materials for bone replacement due to their tailorable chemical composition that is able to promote bone healing and repair. Accordingly, many attempts have been introduced to further improve BGs’ biological behavior and to protect them from bacterial infection, which is nowadays the primary reason for implant failure. Polyphenols from natural products have been proposed as a novel source of antibacterial agents, whereas silver is a well-known antibacterial agent largely employed due to its broad-ranged activity. Based on these premises, the surface of a bioactive glass (CEL2) was functionalized with polyphenols extracted from the Egyptian algae Padina pavonica and enriched with silver nanoparticles (AgNPs) using an in situ reduction technique only using algae extract. We analyzed the composite’s morphological and physical-chemical characteristics using FE-SEM, EDS, XPS and Folin–Ciocalteau; all analyses confirmed that both algae polyphenols and AgNPs were successfully loaded together onto the CEL2 surface. Antibacterial analysis revealed that the presence of polyphenols and AgNPs significantly reduced the metabolic activity (>50%) of Staphylococcus aureus biofilm in comparison with bare CEL2 controls. Finally, we verified the composite’s cytocompatibility with human osteoblasts progenitors that were selected as representative cells for bone healing advancement.

Background: Early detection and screening of breast cancer (BC) might help improve the prognosis of BC patients. This study evaluated the use of serum microRNAs (miRs) as non-invasive biomarkers in BC patients. Methods: Using quantitative real-time polymerase chain reaction, we evaluated the serum expression of four candidate miRs (miR-155, miR-373, miR-10b, and miR-34a) in 99 Egyptian BC patients and 40 healthy subjects (as a control). The miRs expression was correlated with clinicopathological data. In addition, the sensitivity and specificity of the miRs were determined using receiver operating characteristic (ROC) curve analysis. Results: Serum miR-155, miR-373, and miR-10b expression were significantly upregulated (p < 0.001), while serum miR-34a was downregulated (p < 0.00) in nonmetastatic (M0) BC patients compared to the control group. In addition, serum miR-155 and miR-10b were upregulated in BC patients with large tumor sizes and extensive nodal involvement (p < 0.001). ROC curve analysis showed high diagnostic accuracy (area under the curve = 1.0) when the four miRs were combined. Serum miR-373 was significantly upregulated in the human epidermal growth factor 2–negative (p < 0.001), estrogen receptor–positive (p < 0.005), and progesterone receptor (PR)-positive (p < 0.024) in BC patients, and serum miR-155 was significantly upregulated in PR-negative (p < 0.001) BC patients while both serum miR-155 and miR-373 were positively correlated with the tumor grade. Conclusions: Circulating serum miR-155, miR-373, miR-10b, and miR-34a are potential biomarkers for early BC detection in Egyptian patients and their combination shows high sensitivity and specificity.

Purpose: The developed resistance of pathogenic microorganisms towards the currently used antimicrobial agents requires the fast search for newer potent antimicrobials. One of the most important ways to combat the previously mentioned disaster is the use of natural alternatives like medicinal plants. Our study aimed to estimate the anti-inflammatory property, and antibacterial effects of probiotics Lactiplantibacillus plantarum and ethanol extracts of Lawsonia inermis leaves against Staphylococcus aureus when they were used separately or collectively as synergism. Material and Methods: Experimentally induced infected wound model in mice was created and divided into 10 groups then treated for two days by L. plantarum and L. inermis individually or in combination, followed by biochemical assays. The antibacterial, anti-inflammatory, and wound healing activity were evaluated through histopathological sections taken before and after treatment. Results: Our results revealed that L. plantarum and L. inermis mixture could inhibit growth of S. aureus and decrease the minimal inhibitory concentration (MIC) of L. plantarum to 2 mg/mL. The mixture decreased level of both interleukin 6 (IL-6) and interferon-alpha (TNF-α) to a level near the normal uninfected group. Histopathological study showed that animals treated with both L. plantarum and L. inermis had achieved almost 90% healing. Conclusion: These results suggest that L. plantarum and L. inermis mixture has synergistic effect on healing of infected wound.