Characterization and genome-informatic analysis of a novel lytic mendocina phage vB_PmeS_STP12 suitable for phage therapy pseudomonas or biocontrol.

Background: A novel lytic bacteriophage (phage) was isolated with Pseudomonas mendocina strain STP12 (P. mendocina) from the untreated site of Sewage Treatment Plant of Lovely Professional University, India. P.

Bacteriophage genome engineering for phage therapy to combat bacterial antimicrobial resistance as an alternative to antibiotics.

Bacteriophages (phages) are viruses that mainly infect bacteria and are ubiquitously distributed in nature, especially to their host. Phage engineering involves nucleic acids manipulation of phage genome for antimicrobial activity directed against pathogens through the applications of molecular biology techniques such as synthetic biology methods, homologous recombination, CRISPY-BRED and CRISPY-BRIP recombineering, rebooting phage-based engineering, and targeted nucleases including CRISPR/Cas9, zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs). Management of bacteria is widely achieved using antibiotics whose mechanism of action has been shown to target both the genetic dogma and the metabolism of pathogens.

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset.

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations.

Predictive Supervised Machine Learning Models for Diabetes Mellitus.

Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases.

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.

Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea.

Status of malondialdehyde, catalase and superoxide dismutase levels/activities in schoolchildren with iron deficiency and iron-deficiency anemia of Kashere and its environs in Gombe State, Nigeria.

Background: Iron-deficiency anemia (IDA) or iron deficiency (ID) is by far the most common form of disorder affecting the cognitive development, physical growth and school performance of children in developing countries including Nigeria.

Objectives: In the present study, we aimed to examine whether IDA or ID, or both are associated with oxidative stress or otherwise by assessing the perturbations in oxidative stress markers including malondialdehyde (MDA), catalase (CAT) and superoxide dismutase (SOD).

Methods: Here, a total of eighty-one IDA, ID, and healthy control subjects of twenty-seven replicates each, were recruited and investigated.