Meconium microbial toxins and microbiota: A novel and non-invasive proposed diagnostic sample to anticipate the severity of neonates COVID-19

The novel coronavirus, termed severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) is a major public health challenge all over the world and is the causative agent of coronavirus disease 2019 (COVID-19). Since December 2019 the outbreak of COVID-19 has become a major epidemic issue all over world. In this pandemic, preterm and term neonates with infected mothers are becoming more vulnerable each day. Although we mostly witness asymptomatic neonates, getting symptomatic may not be a rarity in the future. After entrance of COVID-19 into the body it could affect the balance of microbiota combination which can result in microbiota dysbiosis and eventually leads to immune imbalance. Intrauterine microbiome dysbiosis in COVID-19 positive mothers and transmission of lipopolysaccharide (LPS) may restructure the environment of the developing fetus with possible short/long-term impact on the individual’s health and disease. Therefore, assessing the changes in microbiome of neonates from infected mothers via exploring meconium could be valuable. It is also logical to measure LPS level and balanced its levels by using prebiotics and probiotics as a supplemental therapeutic procedure to prevent medical challenges in future. The aim of this essay is to review the potential conception that detection of COVID-19 and the meconium microbiota and LPS quantity could be used as a source of prognostic information about the COVID-19 severity in the fetus of pregnant women with COVID-19

Development and stability comparison of targeted therapeutic nanomolecules of aptamer-miRNA conjugates using two methods of conjugation

Introduction: An important issue in cancer therapy is the achievement of desired therapeutic response with the least adverse effects. To achieve this goal, targeted drug delivery systems were developed. Aptamers, mainly DNA/RNA aptamers, are the attractive affinity ligands for the cancer cell surface specific antigens. Besides, microRNAs are another type of therapeutic and diagnostic oligonucleotides that have been recently studied in various cancers. miRNAs are small double stranded RNAs with important roles in cell regulatory pathways. Profile changes of miRNAs can result in cancer development. External addition of miRNAs or their elimination using antagomiR can lead to the efficient treatment of related disease. Targeted delivery of therapeutic agents to the site of action with less adverse effects is the most challenging issue in anticancer chemotherapeutic agents as well as miRNA therapy. In addition, miRNAs stability in biological systems can be improved by targeting strategy. In this study, a cancer specific aptamer (anti-nucleolin aptamer) and a functional miRNA in cell growth and proliferation (miRNAlet-7d) were used in the development of targeted nano-molecules as an efficient anti-proliferative agent for cancer cells.   Methods and Results: Sequences of A1411 aptamer and miRNA let-7d were extracted from related databanks and were chemically synthesized with amine and thiol modification in the 3' terminals or with a 17 nucleotides sticky extension at 3' terminal.  The sequences were conjugated covalently using SM(PEG)2 hetero-bi-functional cross-linker or un-covalently by annealing the sticky ends. Conjugation was confirmed using polyacrylamide gel electrophoresis 15%. The serum stability of these two types of conjugates were evaluated using up to 48 h incubation of conjugates in human serum (AB+). Stability of covalent conjugate using SM(PEG)2 linker was at least two hours more than the un-covalent one.   Conclusions: Remarkable advantages of this nano-molecule were targeted and relative stable delivery of miRNA as the therapeutic agent with probable synergistic effect of two oligonucleotides of miRNA and aptamer in the proliferation inhibition of cancer cells

Brain neural network, development, microbiome, microbial toxins and COVID-19

Although almost 2 years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various researches in order to eliminate or reduce the problems caused by this disease. Neural network science plays a vital role in this regard. It is important to note the key points of neuro-microbial involvement in the diagnosis and management of COVID-19 therapy by physicians and patients whose nervous systems are challenged. The relationship between COVID-19, microbiome and the profile of microbial toxins in the body is one of the factors that can directly or indirectly play a key role in the body's resistance to Covid-19 and changes in the neural network of the brain. In this article, we introduce the relationship and behavioral and mood problems that can result from neuronal changes. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies to assist health providers to choose best decision based on patient’s history.

New insight in severe acute respiratory syndrome coronavirus 2 consideration: Applied machine learning for nutrition quality, microbiome and microbial food poisoning concerns

Although almost two years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various researches in order to eliminate or reduce the problems caused by this disease. Nutrition is one of the sciences that plays a very important supportive role in this regard. It is important for patients to pay attention to the potential of different diets in preventing or accelerating the healing process. The relationship between nutrition and microbiome regulation or the occurrence of food microbial poisoning is one of the factors that can directly or indirectly play a key role in the body's resilience to COVID-19. In this article, we introduce a link between nutrition, the microbiome, and the incidence of food microbial poisoning that may have great potential in preventing, treating COVID-19, or preventing deterioration in patients. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies and lead to the creation of a conceptual model called "Balance square", which we will introduce

Socialization During the COVID-19 Pandemic: The Role of Social and Scientific Networks During Social Distancing

In the COVID-19 era, while we are encouraged to be physically far away from each other, social and scientific networking is needed more than ever. The dire consequences of social distancing can be diminished by social networking. Social media, a quintessential component of social networking, facilitates the dissemination of reliable information and fighting against misinformation by health authorities. Distance learning, telemedicine, and telehealth are among the most prominent applications of networking during this pandemic. Additionally, the COVID-19 pandemic highlights the importance of collaborative scientific efforts. In this chapter, we summarize the advantages of harnessing both social and scientific networking in minimizing the harms of this pandemic. We also discuss the extra collaborative measures we can take in our fight against COVID-19, particularly in the scientific field