Revolutionizing medicine: Molecularly imprinted polymers as precision tools in cancer diagnosis and antibiotic detection.

Molecularly imprinted polymers (MIPs) are pivotal in medicine, mimicking biological receptors with enhanced specificity and affinity. Comprising templates, functional monomers, and cross-linkers, MIPs form stable three-dimensional polymer networks. Synthetic templates like glycan and aptamers improve efficiency, guiding the molecular imprinting process. Cross-linking determines MIPs' morphology and mechanical stability, with printable hydrogels offering biocompatibility and customizable properties, mimicking native extracellular matrix (ECM) microenvironments. Their versatility finds applications in tissue engineering, soft robotics, regenerative medicine, and wastewater treatment. In cancer research, MIPs excel in both detection and therapy. MIP-based detection systems exhibit superior sensitivity and selectivity for cancer biomarkers. They target nucleic acids, proteins, and exosomes, providing stability, sensitivity, and adaptability. In therapy, MIPs offer solutions to challenges like multidrug resistance, excelling in drug delivery, photodynamic therapy, photothermal therapy, and biological activity regulation. In microbiology, MIPs serve as adsorbents in solid-phase extraction (SPE), efficiently separating and enriching antibiotics during sample preparation. They contribute to bacterial identification, selectively capturing specific strains or species. MIPs aid in detecting antibiotic residues using fluorescent nanostructures and developing sensors for sulfadiazine detection in food samples. In summary, MIPs play a pivotal role in advancing medical technologies with enhanced sensitivity, selectivity, and versatility. Applications range from biomarker detection to innovative cancer therapies, making MIPs indispensable for the accurate determination and monitoring of diverse biological and environmental samples.

Deciphering the impact of microbial interactions on COPD exacerbation: An in-depth analysis of the lung microbiome.

In microbiome studies, the diversity and types of microbes have been extensively explored; however, the significance of microbial ecology is equally paramount. The comprehension of metabolic interactions among the wide array of microorganisms in the lung microbiota is indispensable for understanding chronic pulmonary disease and for the development of potent treatments. In this investigation, metabolic networks were simulated, and ecological theory was employed to assess the diagnosis of COPD, subsequently suggesting innovative treatment strategies for COPD exacerbation. Lung sputum 16S rRNA paired-end data from 112 COPD patients were utilized, and a supervised machine-learning algorithm was applied to identify taxa associated with sex and mortality. Subsequently, an OTU table with Greengenes 99 % dataset was generated. Finally, the interactions between bacterial species were analyzed using a simulated metabolic network. A total of 1781 OTUs and 1740 bacteria at the genus level were identified. We employed an additional dataset to validate our analyses. Notably, among the more abundant genera, Pseudomonas was detected in females, while Lactobacillus was detected in males. Additionally, a decrease in bacterial diversity was observed during COPD exacerbation, and mortality was associated with the high abundance of the Staphylococcus and Pseudomonas genera. Moreover, an increase in Proteobacteria abundance was observed during COPD exacerbations. In contrast, COPD patients exhibited decreased levels of Firmicutes and Bacteroidetes. Significant connections between microbial ecology and bacterial diversity in COPD patients were discovered, highlighting the critical role of microbial ecology in the understanding of COPD. Through the simulation of metabolic interactions among bacteria, the observed dysbiosis in COPD was elucidated. Furthermore, the prominence of anaerobic bacteria in COPD patients was revealed to be influenced by parasitic relationships. These findings have the potential to contribute to improved clinical management strategies for COPD patients.

Immunoinformatics design of multivalent chimeric vaccine for modulation of the immune system in Pseudomonas aeruginosa infection.

Increasing in drug-resistant Pseudomonas aeruginosa and high mortality and morbidity rate have become a health challenge worldwide; therefore, developing the novel therapeutic strategies such as immunogenic vaccine candidate are required. Despite a substantial research effort, the future of immunization against P. aeruginosa due to failure in covering two separate stages of infection, and furthermore, inducing ineffective type of immune response, still remains controversial. In this study, immunoinformatics approach was utilized to design multivalent chimeric vaccine from both stages of infection containing Lectin, HIV TAT peptide, N-terminal fragment of exotoxin A and Epi8 of outer membrane protein F (OprF) with hydrophobic linkers which have a high density of B-cell, T Lymphocytes (HTL), T Lymphocytes (CTL), and IFN-γ epitopes. The physicochemical properties, antigenicity, and allergenicity for designed vaccine were analyzed. 3D model generation and refinement further validation of the final vaccine were followed by computational docking with molecular dynamics analyses that demonstrated high- affinity interaction between vaccine and TLR-4. Finally, designed vaccine was in silico cloned in pET22b. We have expected that the designed vaccine able to elucidate innate, humoral and cellular innate immune responses and control the interaction of P. aeruginosa with host and maybe overcome to P. aeruginosa vaccines drawback.

Evaluation of Synergistic Interactions Between Cell-Free Supernatant of Lactobacillus Strains and Amikacin and Genetamicin Against Pseudomonas aeruginosa.

BACKGROUND: The indiscriminate use of antibiotics in the treatment of infectious diseases can increase the development of antibiotic resistance. Therefore, there is a big demand for new sources of antimicrobial agents and alternative treatments for reduction of antibiotic dosage required to decrease the associated side effects. OBJECTIVES: In this study, the synergistic action of aminoglycoside antibiotics and cell-free supernatant (CFS) of probiotic (Lactobacillus rahmnosus and L. casei) against Pseudomonas aeruginosa PTCC 1430 was evaluated. MATERIALS AND METHODS: A growth medium for culturing of probiotic bacteria was separated by centrifugation. The antimicrobial effects of CFS of probiotic bacteria were evaluated using the agar well diffusion assay. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were evaluated using the micro dilution method. Finally, an interaction between CFS and amikacin or gentamicin against P. aeruginosa PTCC 1430 was examined through the checkerboard method and fractional inhibitory concentration (FIC). Furthermore, CFSs from Lactobacillus strains were analyzed by reversed phase HPLC (RP-HPLC) for antimicrobial compounds. RESULTS: The results showed a significant effect of CFS on the growth of P. aeruginosa. The MIC and MBC of CFS from L. casei were 62.5 µL/mL while the MIC and MBC of CFS from L. rhamnosus were 62.5 µL/mL and 125 µL/mL, respectively. Using the FIC indices, synergistic interactions were observed in combination of CFS and antibiotics. Fractional Inhibitory Concentration indices of CFS from L. casei and aminoglycoside antibiotics were 0.124 and 0.312 while FIC indices of CFS from L. rhamnosus and aminoglycoside antibiotics were 0.124 and 0.56, respectively showing a synergism effect. The results of RP-HPLC showed that CFS of Lactobacillus strains contained acetic acid, lactic acid and hydrogen peroxide (H2O2). CONCLUSIONS: Our findings indicate that probiotic bacterial strains of Lactobacillus have a significant inhibitory effect on the growth of P. aeruginosa PTCC 1430. The antimicrobial potency of this combination can be useful for designing and developing alternative therapeutic strategies against P. aeruginosa infections.