The emergence of drug resistance in bacterial pathogens is a growing clinical problem that poses difficult challenges in patient management. To exacerbate this problem, there is currently a serious lack of antibacterial agents that are designed to target extremely drug-resistant bacterial strains. Here we describe the design, synthesis and antibacterial testing of a series of 40 novel indole core derivatives, which are predicated by molecular modeling to be potential glycosyltransferase inhibitors. Twenty of these derivatives were found to show in vitro inhibition of Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus. Four of these strains showed additional activity against Gram-negative bacteria, including extended-spectrum beta-lactamase producing Enterobacteriaceae, imipenem-resistant Klebsiella pneumoniae and multidrug-resistant Acinetobacter baumanii, and against Mycobacterium tuberculosis H37Ra. These four compounds are candidates for developing into broad-spectrum anti-infective agents.
Introduction. Cold plasma is frequently utilized for the purpose of eliminating microbial contaminants. Under optimal conditions, it can function as plasma medicine for treating various diseases, including infections caused by Candida albicans, an opportunistic pathogen that can overgrow in individuals with weakened immune system.Gap Statement. To date, there has been less molecular study on cold plasma-treated C. albicans.Research Aim. The study aims to fill the gap in understanding the molecular response of C. albicans to cold plasma treatment.Methodology. This project involved testing a cold plasma generator to determine its antimicrobial effectiveness on C. albicans' planktonic cells. Additionally, the cells' transcriptomics responses were investigated using RNA sequencing at various treatment durations (1, 3 and 5 min).Results. The results show that our cold plasma effectively eliminates C. albicans. Cold plasma treatment resulted in substantial downregulation of important pathways, such as 'nucleotide metabolism', 'DNA replication and repair', 'cell growth', 'carbohydrate metabolism' and 'amino acid metabolism'. This was an indication of cell cycle arrest of C. albicans to preserve energy consumption under unfavourable conditions. Nevertheless, C. albicans adapted its GSH antioxidant system to cope with the oxidative stress induced by reactive oxygen species, reactive nitrogen species and other free radicals. The treatment likely led to a decrease in cell pathogenicity as many virulence factors were downregulated.Conclusion. The study demonstrated the major affected pathways in cold plasma-treated C. albicans, providing valuable insights into the molecular response of C. albicans to cold plasma treatment. The findings contribute to the understanding of the antimicrobial efficiency of cold plasma and its potential applications in the field of microbiology.
It is an urgent need to develop new drugs for Mycobacterium tuberculosis (Mtb), and the enzyme, dihydrofolate reductase (DHFR) is a recognised drug target. The crystal structures of methotrexate binding to mt- and h-DHFR separately indicate that the glycerol (GOL) binding site is likely to be critical for the function of mt-DHFR selective inhibitors. We have used in silico methods to screen NCI small molecule database and a group of related compounds were obtained that inhibit mt-DHFR activity and showed bactericidal effects against a test Mtb strain. The binding poses were then analysed and the influence of GOL binding site was studied by using molecular modelling. By comparing the chemical structures, 4 compounds that might be able to occupy the GOL binding site were identified. However, these compounds contain large hydrophobic side chains. As the GOL binding site is more hydrophilic, molecular modelling indicated that these compounds were failed to occupy the GOL site. The most potent inhibitor (compound 6) demonstrated limited selectivity for mt-DHFR, but did contain a novel central core (7H-pyrrolo[3,2-f]quinazoline-1,3-diamine), which may significantly expand the chemical space of novel mt-DHFR inhibitors. Collectively, these observations will inform future medicinal chemistry efforts to improve the selectivity of compounds against mt-DHFR.
There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.