Classification of pathogenic E. coli has been focused either in mammalian host or infection site, which offers limited resolution. This review presents a comprehensive framework for classifying all E. coli branches within a single, unifying figure. This approach integrates established methods based on virulence factors, serotypes and clinical syndromes, offering a more nuanced and informative perspective on E. coli pathogenicity. The presence of the LEE island in pathogenic E. coli is a key genetic marker differentiating EHEC from STEC strains. The coexistence of stx and eae genes within the bacterial genome is a primary characteristic used to distinguish STEC from other pathogenic E. coli strains. The presence of the inv plasmid, Afa/Dr adhesins, CFA-CS-LT-ST and EAST1 are key distinguishing features for identifying pathogenic E. coli strains belonging to EIEC, DAEC, ETEC and EAEC pathotypes respectively. Food microbiological criteria differentiate pathogenic E. coli in food matrices. ‘Zero-tolerance’ applies to most ready-to-eat (RTE) foods due to high illness risk. Non-RTE foods' roles may allow limited E. coli presence, which expose consumers to potential risk; particularly from the concerning Shiga toxin-producing E. coli (STEC) strains, which can lead to life-threatening complications in humans, including haemolytic uremic syndrome (HUS) and even death in susceptible individuals. These findings suggest that decision-makers should consider incorporating the separate detection of STEC serotypes into food microbiological criteria, in addition to existing enumeration methods. Contamination of STEC is mainly linked to food consumption, therefore, outbreaks of E. coli STEC has been reviewed here and showed a link also to water as a potential contamination route. Since their discovery in 1982, over 39,787 STEC cases associated with 1,343 outbreaks have been documented. The majority of these outbreaks occurred in the Americas, followed by Europe, Asia and Africa. The most common serotypes identified among the outbreaks were O157, the ‘Big Six’ (O26, O45, O103, O111, O121, and O145), and other serotypes such as O55, O80, O101, O104, O116, O165, O174 and O183. This review provides valuable insights into the most prevalent serotypes implicated in STEC outbreaks and identifies gaps in microbiological criteria, particularly for E. coli non-O157 and non-Big Six serotypes.

Malaria is a mosquito-borne infectious disease that is caused by the Plasmodium parasite. Most of the available medication are losing their efficacy. Therefore, it is crucial to create fresh leads to combat malaria. Green silver nanoparticles (AgNPs) have recently attracted a lot of attention in biomedical research. As a result, green mediated AgNPs from leaves of Terminalia bellirica, a medicinal plant with purported antimalarial effects, were used in this investigation. Initially, cysteine-rich proteins from Plasmodium species were studied in silico as potential therapeutic targets. With docking scores between -9.93 and -11.25 kcal/mol, four leaf constituents of Terminalia bellirica were identified. The green mediated silver nanoparticles were afterward produced using leaf extract and were further examined using UV-vis spectrophotometer, DLS, Zeta potential, FTIR, XRD, and FESEM. The size of synthesized TBL-AgNPs was validated by the FESEM results; the average size of TBL-AgNPs was around 44.05 nm. The zeta potential study also supported green mediated AgNPs stability. Additionally, Plasmodium falciparum (3D7) cultures were used to assess the antimalarial efficacy, and green mediated AgNPs could effectively inhibit the parasitized red blood cells (pRBCs). In conclusion, this novel class of AgNPs may be used as a potential therapeutic replacement for the treatment of malaria.

Vibrio vulnificus is a rod shape, Gram-negative bacterium that causes sepsis (with a greater than 50% mortality rate), necrotizing fasciitis, gastroenteritis, skin, and soft tissue infection, wound infection, peritonitis, meningitis, pneumonia, keratitis, and arthritis. Based on pathogenicity V. vulnificus is categorized into three biotypes. Type 1 and type 3 cause diseases in humans while biotype 2 causes diseases in eel and fish. Due to indiscriminate use of antibiotics V. vulnificus has developed resistance to many antibiotics so curing is dramatically a challenge. V. vulnificus is resistant to cefazolin, streptomycin, tetracycline, aztreonam, tobramycin, cefepime, and gentamycin. Subtractive genome analysis is the most effective method for drug target identification. The method is based on the subtraction of homologous proteins from both pathogen and host. By this process set of proteins present only in the pathogen and perform essential functions in the pathogen can be identified. The entire proteome of Vibrio vulnificus strain ATCC 27562 was reduced step by step to a single protein predicted as the drug target. AlphaFold2 is one of the applications of deep learning algorithms in biomedicine and is correctly considered the game changer in the field of structural biology. Accuracy and speed are the major strength of AlphaFold2. In the PDB database, the crystal structure of the predicted drug target was not present, therefore the Colab notebook was used to predict the 3D structure by the AlphaFold2, and subsequently, the predicted model was validated. Potent inhibitors against the new target were predicted by virtual screening and molecular docking study. The most stable compound ZINC01318774 tightly attaches to the binding pocket of bisphosphoglycerate-independent phosphoglycerate mutase. The time-dependent molecular dynamics simulation revealed compound ZINC01318774 was superior as compared to the standard drug tetracycline in terms of stability. The availability of V. vulnificus strain ATCC 27562 has allowed in silico identification of drug target which will provide a base for the discovery of specific therapeutic targets against Vibrio vulnificus.

Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process- Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.