Improper use of antimicrobials has resulted in the emergence of antimicrobial resistance (AMR), including multi-drug resistance (MDR) among bacteria. Recently, a sudden increase in Carbapenem-resistant Enterobacterales (CRE) has been observed. This presents a substantial challenge in the treatment of CRE-infected individuals. Bacterial plasmids include the genes for carbapenem resistance, which can also spread to other bacteria to make them resistant. The incidence of CRE is rising significantly despite the efforts of health authorities, clinicians, and scientists. Many genotypic and phenotypic techniques are available to identify CRE. However, effective identification requires the integration of two or more methods. Whole genome sequencing (WGS), an advanced molecular approach, helps identify new strains of CRE and screening of the patient population; however, WGS is challenging to apply in clinical settings due to the complexity and high expense involved with this technique. The current review highlights the molecular mechanism of development of Carbapenem resistance, the epidemiology of CRE infections, spread of CRE, treatment options, and the phenotypic/genotypic characterisation of CRE. The potential of microorganisms to acquire resistance against Carbapenems remains high, which can lead to even more susceptible drugs such as colistin and polymyxins. Hence, the current study recommends running the antibiotic stewardship programs at an institutional level to control the use of antibiotics and to reduce the spread of CRE worldwide.

The present study aimed to investigate the potentiality of certain biocontrol agents, namely Bacillus subtilis, B. pumilus, B. megaterium, Pseudomonas fluorescens, Serratia marcescens, Trichoderma album, T. harzianum and T. viride, as well as the synthetic fungicide difenoconazole to control celery powdery mildew caused by Erysiphe heraclei DC, in vitro (against conidia germination and germ tube length of E. heraclei) and in vivo (against disease severity and AUDPC). In vitro, it was found that the antifungal activity of the tested biocontrol agents significantly reduced the germination percentage of the conidia and germ tube length of the pathogen. The reduction in conidia germination ranged between 88.2% and 59.6% as a result of the treatment with B. subtilis and T. album, respectively compared with 97.1% by the synthetic fungicide difenoconazole. Moreover, the fungicide achieved the highest reduction in germ tube length (92.5%) followed by B. megaterium (82.0%), while T. album was the least effective (62.8%). Spraying celery plants with the tested biocontrol agents in the greenhouse significantly reduced powdery mildew severity, as well as the area under the disease progress curve (AUDPC), after 7, 14, 21 and 28 days of application. In this regard, B. subtilis was the most efficient followed by B. pumilus, S. marcescens and B. megaterium, with 80.1, 74.4, 73.2 and 70.5% reductions in disease severity, respectively. In AUDPC, reductions of those microorganisms were 285.3, 380.9, 396.7 and 431.8, respectively, compared to 1539.1 in the control treatment. On the other hand, the fungicide difenoconazole achieved maximum efficacy in reducing disease severity (84.7%) and lowest AUDPC (219.3) compared to the other treatments. In the field, all the applied biocontrol agents showed high efficiency in suppressing powdery mildew on celery plants, with a significant improvement in growth and yield characteristics. In addition, they caused an increase in the concentration of leaf pigments, and the activities of defense-related enzymes such as peroxidase (PO) and polyphenol oxidase (PPO) and total phenol content (TPC). In conclusion, the results showed the possibility of using tested biocontrol agents as eco-friendly alternatives to protect celery plants against powdery mildew.

Enterobacter cloacae is mainly responsible for sepsis, urethritis, and respiratory tract infections. These bacteria may affect the transcription of the host and particularly their immune system by producing changes in their epigenetics. In the present study, four proteins of Enterobacter cloacae were used to predict the epitopes for the construction of an mRNA vaccine against Enterobacter cloacae infections. In order to generate cellular and humoral responses, various immunoinformatic-based approaches were used for developing the vaccine. The molecular docking analysis was performed for predicting the interaction among the chosen epitopes and corresponding MHC alleles. The vaccine was developed by combining epitopes (thirty-three total), which include the adjuvant Toll-like receptor-4 (TLR4). The constructed vaccine was analyzed and predicted to cover 99.2% of the global population. Additionally, in silico immunological modeling of the vaccination was also carried out. When it enters the cytoplasm of the human (host), the codon is optimized to generate the translated mRNA efficiently. Moreover, the peptide structures were analyzed and docked with TLR-3 and TLR-4. A dynamic simulation predicted the stability of the binding complex. The assumed construct was considered to be a potential candidate for a vaccine against Enterobacter cloacae infections. Hence, the proposed construct is suitable for in vitro analyses to validate its effectiveness.

Antimicrobial resistance (AMR) is a leading cause of treatment failure for many infectious diseases worldwide. Improper overdosing and the misuse of antibiotics contributes significantly to the emergence of drug-resistant bacteria. The co-contamination of heavy metals and antibiotic compounds existing in the environment might also be involved in the spread of AMR. The current study was designed to test the efficacy of heavy metals (arsenic) induced AMR patterns in clinically isolated extended-spectrum β-lactamase (ESBL) producing bacteria. A total of 300 clinically isolated ESBL-producing bacteria were collected from a tertiary care hospital in Lahore, Pakistan, with the demographic characteristics of patients. After the collection of bacterial isolates, these were reinoculated on agar media for reidentification purposes. Direct antimicrobial sensitivity testing (AST) for bacterial isolates by disk diffusion methods was used to determine the AST patterns with and without heavy metal. The heavy metal was concentrated in dilutions of 1.25 g/mL. The collected bacterial isolates were isolated from wounds (n = 63, 21%), urine (n = 112, 37.3%), blood (n = 43, 14.3%), pus (n = 49, 16.3%), and aspirate (n = 33, 11%) samples. From the total 300 bacterial isolates, n = 172 were Escherichia coli (57.3%), 57 were Klebsiella spp. (19%), 32 were Pseudomonas aeruginosa (10.6%), 21 were Proteus mirabilis (7%) and 18 were Enterobacter spp. (6%). Most of the antibiotic drugs were found resistant to tested bacteria. Colistin and Polymyxin-B showed the highest sensitivity against all tested bacteria, but when tested with heavy metals, these antibiotics were also found to be significantly resistant. We found that heavy metals induced the resistance capability in bacterial isolates, which leads to higher AMR patterns as compared to without heavy metal tested isolates. The results of the current study explored the heavy metal as an inducer of AMR and may contribute to the formation and spread of AMR in settings that are contaminated with heavy metals.

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CL^pro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina’s generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets.