BMC Chemistry最新文献

Magnetic activated carbon has been proved its separation ability to overcome a main drawback of activated carbon powder. However, effect of magnetization method on characterizations and Chromium (VI) adsorption of this adsorbent from Artocarpus Heterophyllus Peel (jackfruit peel) has not been investigated yet. This study magnetized jackfruit peel activated carbon using thermochemical and co-precipitation methods. Magnetic jackfruit activated carbon (MJAC) were examined and compared to jackfruit activated carbon (JAC) for surface chemistry, texture, morphology and crystalline properties. The isotherm/kinetics of Cr(VI) adsorption on these adsorbents were also analyzed. The results showed that all the adsorbents showed a typical peak of –(COO)_n–Fe of iron oxide particles and functional groups but the adsorbent prepared with thermochemical method had the greatest Fe–O–C bond signal. The thermochemical adsorbent also had various particles of Fe₃O₄, Zero Valent Iron, and α-Fe₂O₃ while the co-precipitation absorbents gave a greater mesoporous structure and specific surface area than their JAC precursor; especially the adsorbent produced at mild temperature was covered by the highest iron oxide distribution on the surface and better magnetite property. As a result of the high specific surface area, these co-precipitation adsorbents were more effective for Cr(VI) adsorption than others. The PSO model best describes Cr(VI) adsorption on all absorbents with and without magnetite. Cr(VI) adsorption on JAC was dominated by intra-particle diffusion while multistep processes, including external mass transfer, governed the overall MJAC adsorption process. This work has created jackfruit peel-based magnetic activated carbon for wastewater treatment to remove toxic heavy metals and promote the circular economy that uses solid wastes as raw materials.

Graphical Abstract

A simple and green chemometrics-assisted spectrophotometric technique has beendeveloped and validated for the determination of antipyrine (ANT) and benzocaine HCl (BEN) along with the official impurity of ANT, antipyrine impurity A (ANT imp-A), and the degradation product of BEN, p-amino benzoic acid (PABA), in their quaternary mixture. Three models were developed and compared: partial least squares (PLS), artificial neural networks (ANN), and multivariate curve resolution-alternating least squares (MCR-ALS) where the four studied drugs were successfully quantified. The quantitative determination of the studied drugs was assessed using percentage recoveries, standard errors of prediction, and root mean square errors of prediction. The ANN model demonstrated the lowest error and the best correlation making it the most accurate method for analysis. The models were constructed in the ranges of 5.0–9.0 µg mL⁻¹ for ANT, 1.0–5.0 µg mL⁻¹ for BEN, 0.5–2.5 µg mL⁻¹ for ANT imp-A, and 0.25–1.25 µg mL⁻¹ for PABA. The established models successfully determined ANT, BEN, ANT imp-A, and PABA with detection limits of 0.312, 0.178, 0.093, and 0.042 µg mL⁻¹ for PLS, 0.185, 0.085, 0.001, and 0.034 µg mL⁻¹ for ANN; and 0.473, 0.240, 0.073, and 0.069 µg mL⁻¹ for MCR-ALS, respectively. The greenness and the whiteness of the proposed method were assessed using two green evaluating approaches: analytical Eco-scale, and AGREE, along with one white analytical chemistry evaluating tool, RGB. The three proposed models were successfully applied for determination of ANT and BEN in their pharmaceutically co-formulated dosage forms. They are also recommended for stability assays and purity testing of these drugs in quality control laboratories.

In virtual drug screening, consensus docking is a standard in-silico approach consisting of a combined result from optimized docking experiments, a minimum of two results combination. Therefore, consensus docking is subjected to a lower success rate than the best docking method due to its mathematical nature, an unavoidable limitation. This study aims to overcome this drawback via random forest, an ensemble machine learning model. First, in vitro beta-lactamase inhibitory screening was performed using an in-house chemical library. The in vitro results were later used as a validation. Consequently, we optimized docking protocols for AutoDock Vina and DOCK6 programs. With an appropriate scoring function, we found that DOCK6 could identify up to 70% of all active molecules, double the inappropriate. Further consensus analysis reduced the success rate to 50%. Simultaneously, a false positive rate was down to 16%, which was experimentally favorable for a drug search. Finally, we trained two quantitative structure-activity relationship (QSAR) models using logistic regression as a reference model and a random forest as a test model. After combining consensus docking results, random forest-based QSAR outperformed a logistic regression by restoring the success rate to 70% and maintaining a low false positive rate of around 21%. In conclusion, this study demonstrated the benefit of using a random forest (machine learning)-based QSAR model to overcome a standard consensus docking limitation in beta-lactamase inhibitor search as a proof-of-concept.

To enhance the accuracy of virtual screening for bromodomain-containing protein 4 (BRD4) inhibitors, two docking protocols and seven scoring functions were compared. A total of 73 crystal structures of BRD4 (BD1) complexes were selected for analysis. Firstly, docking was carried out using both the LibDock and CDOCKER methods. The CDOCKER protocol was shown to be more effective based on the root mean square deviation (RMSD) values (in Å) between the docking positions and the co-crystal structures, achieving a docking accuracy rate of 86.3%. Then, among the various scoring functions (LigScore1, LigScore2, PLP1, PLP2, PMF, PMF04 and Ludi3), PMF showed the highest correlation with inhibition constants (r² = 0.614), while Ludi3 scored lowest (r² = 0.266). Finally, using ligand descriptors from PubChem, a strong correlation (r² > 0.5) with inhibition constants for heavy atom count was found. Based on these comprehensive evaluations, the PMF scoring function emerged as the best tool for docking-based virtual screening of potential BRD4 (BD1) inhibitors. And the correlation between molecular properties and BRD4 (BD1) ligands also provided information for future design strategies.

The antibiotic resistance and biofilm formation by bacterial pathogens has led to failure in infections elimination. This study aimed to assess the antibacterial and anti-biofilm properties of novel synthesized nitroimidazole compounds (8a–8o). In this study, nitroimidazole compounds were synthesized via the A3 coupling reaction of sample substrates in the presence of copper-doped silica cuprous sulfate (CDSCS). Fifteen and two carbapenemase producing Escherichia coli and Klebsiella pneumonia (CP-E. coli and CP-K. pneumonia, respectively) and one methicillin-resistant Staphylococcus aureus (MRSA) and one methicillin-susceptible S. aureus (MSSA) plus standard strain of each isolate were included. The antibacterial effects of these compounds demonstrated that the lowest minimum inhibitory and bactericidal concentrations (MIC/MBC, respectively) levels corresponded to compound 8g against S. aureus (1/2 µg/mL) and K. pneumonia (8/32 µg/mL) standard and clinical strains and confirmed by in silico assessment. This was comparable to those of metronidazole being 32–128 µg/mL against K. pneumonia and 32–64 µg/mL against S. aureus. In comparison to metronidazole, against CP-E. coli, compounds 8i and 8m had significantly higher antibacterial effects (p < 0.001) and against CP-K. pneumonia, compounds 8a–8j and 8l–8o had significantly higher (p < 0.0001) antibacterial effects. Compound 8g exhibited significantly higher antibacterial effects against MSSA and compounds 8b (p < 0.001), 8c (p < 0.001), 8d (p < 0.001), 8e (p < 0.001) and 8g (p < 0.0001) exerted significantly higher antibacterial effects than metronidazole against MRSA. Moreover, potential anti-biofilm effects was corresponded to compounds 8a, 8b, 8c, 8e, 8f, 8g, 8i, 8k, 8m and 8n. Considering the antibacterial and anti-biofilm effects of novel synthesized compounds evaluated in this study, further assessments is warranted to verify their properties in vivo and clinical trials in the future.

A novel electrochemical probe is developed to detect amlodipine (AMD) in unprocessed plasma samples. The fabrication process involves the synthesis of platinum nanocubes (Pt NCs) and Mn(TPDCA)₂ complexes, which are then immobilized them onto the glassy carbon electrode (GCE) surface. The developed electrochemical probe demonstrates exceptional detection performance, with a wide dynamic range, outstanding selectivity, and commendable reproducibility. The linear range and lower limit of detection of the developed method are 53 nM-3.5 µM and 53 nM, respectively. Electrochemical experiments have been conducted to study the kinetics of electrooxidation on the modified electrode, revealing that the process is diffusion-controlled. Furthermore, method validation studies are performed to assess the accuracy, precision, and selectivity of the sensor, demonstrating excellent performance in all these aspects. Consequently, it can be concluded that the sensor is highly suitable for practical applications in drug analysis.

This study investigates the chemical interactions and mechanical characteristics of composites made of polystyrene reinforced with biochar. Polystyrene-based resin (PBR) was combined with plantain peel-derived biochar in different weight ratios (10%, 20%, 30%, and 40%). The Brinell hardness test, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS) were used to evaluate the properties of the composites. The results of the hardness test showed a non-monotonic pattern, with hardness first decreasing at low biochar loadings (10% and 20%), then significantly increasing at 30% biochar. At 40% biochar, the hardness then somewhat dropped, indicating that around 30% filler is the optimal biochar level for hardness. As the biochar loading increased, FTIR measurement showed that hydroxyl groups (-OH) were introduced and that the intensity of carbonyl groups (C = O) increased. According to SEM analysis, a uniform surface was found at lower biochar loadings, but at larger biochar contents, the surface became irregular and rough. In addition to providing insights into the chemical interactions at the interface between the biochar and the polymer matrix, these findings demonstrate the possibility of incorporating biochar to alter the mechanical properties of PBR.

PI3K-α mutation plays a critical role in cancer development, notably in breast cancer, particularly within HR + /HER2- subtypes. These mutations drive tumor growth and survival by activating the PI3K/AKT/mTOR pathway, which is essential for cell proliferation and survival. Our research aimed to identify natural compounds that can inhibit mutant and specific isoforms of PI3K-α to prevent tumor progression. e-Pharmacophore model was generated using Receptor-Ligand complex using the Inavolisib drug (PDB:8EXV) and phase screening was performed using the Molport database of natural compounds. Through molecular docking studies we identified seven promising compounds for further molecular dynamics simulations. Among these, three compounds—STOCK1N-85097, STOCK1N-85998, and STOCK1N-86060—showed significant stability and interaction with PI3K-α. These compounds demonstrated favorable results in several parameters, including RMSD, RMSF, Rg, SASA, PCA, FEL, and total energy evaluations. Therefore, these compounds are projected to function as PI3K-α inhibitors and because of its natural origin it can possess fewer side effects than the conventional medicine, which should be validated by proper in vivo and in vitro models.

Graphical Abstract

In the present study, ohmic heating system was developed for the pasteurization of liquid egg white. A batch reactor system was designed with a capacity of 100 ml and operated at varied gradients of voltage (20, 15, 10 V/cm), frequencies (10, 55, 100 Hz), holding times (1, 2.5, 4 min) at two different waveforms (sine and square). The treated liquid egg white was evaluated for validation parameters viz., heating rate, turbidity, soluble protein content, foaming capacity, and foaming stability. The viscosity of ohmically treated egg white was observed by subjecting the egg white to the shear rate ranges from 0.167 to 68 (s⁻¹) where the viscosity decreased as the shear rate increased. The ohmic heating process variables were optimized using the Box-Behnken design and had a significant effect (P < 0.005) on the responses. The optimized parameters 17.93 V/cm voltage gradient, 10 Hz frequency, and 1.6 min holding time for sine waveform resulted in 19.6 °C/min heating rate, 0.01 turbidity, 98.35% soluble protein, 405.68% foaming capacity, and 31.84% foaming stability with the highest desirability of 78% of liquid egg white. The model developed from the dataset of this design can be used for predicting the responses within the limits of process variables.

This Study focuses on the preparation of sustainable and efficient Chitosan catalyst for the removal of three organic pollutants, 17β-Estradiol (E2), 17α-ethynyl estradiol (EE2) and triclosan (TCS) from water. The prepared nanocomposites were characterized by different techniques which confirmed the presence of the key components Chitosan, Carica Papaya seed and Kaolinite. The optical characterization proved the nanocomposite is photoactive with a band gap of 1.81 eV and 1.77 eV for Chitosan/kaolinite biochar (CS/KBC) and Chitosan/kaolinite biochar/GO (CS/KBC/GO) respectively, confirming the ability of the nanocomposite to be active in the visible light region of the spectrum. The degradation experiment using CS/KBC/GO was observed better with 100% removal for 5 mg/L E2 and EE2 over 60 min and 97.8% over 120 min for 10 mg/L TCS at optimum conditions (pH 3 for E2, and EE2 and pH 7). It was observed that the superoxide radical played a major role in the degradation of the contaminants. Furthermore, the CS/KBC/GO was efficient over four cycles without any decrease in performance, which rules out the question of catalyst deactivation proving the sustainability of the catalyst. The toxicity test shows that the water is safe as it does not harm cerio daphnia silvestrii organism.; CS/KBC/GO efficiently removed the micropollutants from real-life waste samples and the performance was very good with a slight decrease in performance for the wastewater due to the complex matrix of the water sample that competes for the active site.