Antibacterial efficacy of copper-based metal-organic frameworks against Escherichia coli and Lactobacillus

The widespread and excessive use of antimicrobial drugs has resulted in a concerning rise in bacterial resistance, leading to a risk of untreatable infections. The aim of this study was to formulate a robust and efficient antibacterial treatment to address this challenge. Previous work focused on the effectiveness of the Cu-BTC metal-organic framework (MOF; BTC stands for 1,3,5-benzenetricarboxylate) in combatting various bacterial strains. Herein, we compare the antibacterial properties of Cu-BTC with our newly designed Cu-GA MOF, consisting of copper ions bridged by deprotonated gallate ligands (H2gal2-), against Escherichia coli (E. coli) and Lactobacillus bacteria. Cu-GA was synthesized hydrothermally from copper salt and naturally derived gallic acid (H4gal) and characterized for antibacterial evaluation. The gradual breakdown of Cu(H2gal) resulted in a significant antibacterial effect that is due to the release of copper ions and gallate ligands from the framework. Both copper MOFs were nontoxic to bacteria at low concentrations and growth was completely inhibited at high concentrations when treated with Cu-BTC (1500 μg for E. coli and 1700 μg for Lactobacillus) and Cu-GA (2000 μg for both bacterial strains). Furthermore, our agarose gel electrophoresis results indicate that both MOFs could disrupt bacterial cell membranes, hindering the synthesis of DNA. These findings confirm the antibacterial properties of Cu-BTC and the successful internalization of Cu2+ ions and gallic acid by bacteria from the Cu-GA MOF framework, suggesting the potential for a sustained and effective therapeutic approach against pathogenic microorganisms.

Immobilization of Cyclodextrin glycosyltransferase onto three dimensional- hydrophobic and two dimensional- hydrophilic supports: A comparative study

Cyclodextrin glycosyltransferase (CGTase) degrades starch into cyclodextrin via enzymatic activity. In this study, we immobilize CGTase from Thermoanaerobacter sp. on two supports, namely graphene nanoplatelets (GNP) consisting of short stacks of graphene nanoparticles and a calcium-based two-dimensional metal organic framework (Ca-TMA). The uptakes of CGTase on GNP and Ca-TMA reached 40 and 21 mg g-1 respectively, but immobilized CGTase on Ca-TMA showed a higher specific activity (38 U mg-1 ) than that on GNP (28 U mg-1 ). Analysis of secondary structures of CGTase, shows that immobilization reduces the proportion of β-sheets in CGTase from 56% in the free to 49% and 51.3% for GNP and Ca-TMA respectively, α-helix from 38.5% to 18.1 and 37.5%, but led to increased β-turns from 5.5 to 40% and 11.2% for GNP and Ca-TMA, respectively. Lower levels of conformational changes were observed over the more hydrophilic Ca-TMA compared to hydrophobic GNP, resulting in its better activity. Increased β-turns were found to correlate with lower β-CD production, while more β-sheets and α-helix favored more β-CD. Reusability studies revealed that GNP retains up to 74% of initial CGTase activity, while Ca-TMA dropped to 33% after eight consecutive uses. The results obtained in this work provide insight on the effect of support's surface properties on CGTase performance and can assist in developing robust CGTase-based biocatalysts for industrial application.

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.

Separation of mercuric ions using 2-thienylbenzimidazole/cucurbit[7]uril/iron-oxide nanoparticles by pH control

2-Thienylbenzimidazole (TBI)/cucurbit[7]uril (CB7) host-guest complex was used as a motif to significantly improve the turnover of γ-Fe₃O₄ magnetic nanoparticles for potential application in the separation of toxic mercuric ions in polluted water samples. The mechanism of restoring the original solid materials is based on applying the pH-controlled preferential binding of the CB7 host to the TBI guest. The analytical application of this concept has not been realized in the literature. The pH-controlled stimuli-responsive abilities were confirmed in aqueous solution by the three-order of magnitudes higher stability constant of the protonated TBIH⁺/CB7 complex (e.g., K = 4.8 × 10⁸ M^-1) when compared to neutral TBI/CB7 complex (e.g., K = 2.4 × 10⁵ M^-1), also manifested in an increase in pK_a values by ~ 3.3 units in the ground state. The supramolecular interaction and adsorption on iron oxide nanoparticles (NPs) were also spectroscopically confirmed in the solid state. The excited-state lifetime values of TBI/CB7NPs increased upon lowering the pH values (e.g., from 0.6 to 1.3 ns) with a concomitant blue shift of ~ 25 nm because of polarity effects. The time-resolved photoluminescent behaviors of the final solids in the presence of CB7 ensured pH-driven reusable systems for capturing toxic mercuric ions. The study offers a unique approach for the controllable separation of mercury ions using an external magnet and in response to pH through preferential binding of the host to guest molecules on the top of magnetic surfaces.

Modelling liver cancer microenvironment using a novel 3D culture system

The tumor microenvironment and its contribution to tumorigenesis has been a focal highlight in recent years. A two-way communication between the tumor and the surrounding microenvironment sustains and contributes to the growth and metastasis of tumors. Progression and metastasis of hepatocellular carcinoma (HCC) have been reported to be exceedingly influenced by diverse microenvironmental cues. In this study, we present a 3D-culture model of liver cancer to better mimic in vivo tumor settings. By creating novel 3D co-culture model that combines free-floating and scaffold-based 3D-culture techniques of liver cancer cells and fibroblasts, we aimed to establish a simple albeit reproducible ex vivo cancer microenvironment model that captures tumor-stroma interactions. The model presented herein exhibited unique gene expression and protein expression profiles when compared to 2D and 3D mono-cultures of liver cancer cells. Our results showed that in vivo like conditions cannot be mimicked by simply growing cancer cells as spheroids, but by co-culturing them with 3D fibroblast with which they were able to crosstalk. This was evident by the upregulation of several pathways involved in HCC, and the increase in secreted factors by co-cultured cancer cells, many of which are also involved in tumor-stroma interactions. Compared to the conventional 2D culture, the proposed model exhibits an increase in the expression of genes associated with development, progression, and poor prognosis of HCC. Our results correlated with an aggressive outcome that better mirrors in vivo HCC, and therefore, a more reliable platform for molecular understanding of HCC.