This study investigates the bioremediation potential of Marinobacter-hydrocarbonoclasticus SDK644, a strain that has been isolated from petroleum-contaminated environments, for the degradation of the herbicide metribuzin and the treatment of slaughterhouse effluent. The strain’s bacterial growth and degradation capacity were assessed under varying conditions, including different metribuzin concentrations, pH values, temperatures, and inoculum sizes. The strain demonstrated optimal growth at a metribuzin concentration of 20 mg/L, with an optical density (OD600) of 0.408 after 96 h. At this concentration, 80% of the chemical oxygen demand (COD) was reduced over 144 h. The optimal growth conditions for M. hydrocarbonoclasticus SDK644 were identified as a pH of 7 and a temperature of 30 °C, where the enzymatic activity and degradation efficiency were maximized. Additionally, the treatment of slaughterhouse effluent showed significant reductions in organic pollution, with the COD and biochemical oxygen demand (BOD₅) decreasing by 80% (from 1900 mg/L to 384 mg/L) and 81% (from 1700 mg/L to 320 mg/L), respectively, within seven days. The strain also facilitated ammonium removal and promoted nitrification, indicating its suitability for treating high-organic-load wastewater. Notably, the visual transformation of the effluent, from a dark red color to a clear state, further highlighted the efficiency of the treatment process. This research highlights the adaptability of M. hydrocarbonoclasticus SDK644 to a wide range of environmental conditions and its efficiency in biodegrading metribuzin and treating complex wastewater. The findings demonstrate the strain’s potential as a sustainable solution for mitigating organic pollution in agricultural runoff, pesticide-contaminated water, and industrial effluents.

Purpose: The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. Methods: A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set’s external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). Results: The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. Conclusion: The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model’s accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

The aim of this study consists of the production of a bio-surfactant from a new bacterial strain, Marinobacter hydrocarbono clasticus SF (96.76 % similarity) isolated from soil contaminated by hydrocarbons in Hassi-Messaoud (Southern Algeria) to treat liquid effluent from slaughterhouse water. The characteristics of organic matter biodegradation tests were discussed. Despite the high pollutant load and the unfavorable physicochemical composition of the effluent, the specific growth rate of the isolated strain after 10 days of incubation in the range of 0?30 g L-1 of NaCl was at neutral pH 7.4 and temperature of 45?C. The best bio-surfactant production yield was obtained after 72 h of incubation and under the optimal production conditions such as diesel as carbon source, ammonium chloride as nitrogen source, and a C/N ratio of 5. The bio-surfactant produced is of glycolipid type with a low critical micellar concentration (CMC), good emulsifying power, and chemical and functional stability. Significant pollutant removal efficiency was obtained using the bacterial strain (up to 82 %) and the bio-surfactant (up to 96 %). Several anions, such as nitrates, phosphates, ammonium, and suspended solids, were measured.

Purpose: To evaluate the anti-burn activity of ointment formulation from Morus alba leaves aqueous extract (MAE) stabilized by a biomolecule product of Marinobacter hydrocarbonoclasticus. Methods: Phytochemical study conducted on MAE includes moisture content, ash content, and pH. Prepared Morus alba leaf ointment was evaluated for organoleptic, consistency, pH, spreadability, and skin irritation study. The effect of temperature variations and physiological and environmental factors on stability was also determined. Anti-burn effectiveness of MAE ointment (250 mg) was tested on the burn skin model of male Albino rabbits. Results: Morus alba leaves contain polyphenols, flavonoids, glycosides and tannins as active chemical components that are useful in infection. Flavonoids exhibit antioxidant activity with indirect inhibition of inflammatory markers. The MAE ointment showed good scores in all test parameters that were evaluated including appearance, texture, pH, spreading ability and non-irritant study. The ointment was stable at different temperatures, and physiological and environmental conditions, and was free from any bacterial contaminations that have the capacity to cause allergies, especially after the addition of stabilizers which kept the ointment stable for longer period. Conclusion: Ointment formulated from MAE is physically, chemically and microbiologically stable and is effective in the treatment of burns in rabbit models. The MAE thus has promising therapeutic potential in the management of skin burns.

Purpose Predicting drug half-life is crucial for pharmacokinetics, impacting dosing strategies and drug development. Traditional regression models estimate exact half-life values but often struggle with pharmacokinetic variability, making direct interpretation and clinical decision-making challenging. This study proposes a classification-based approach that categorizes drugs into short and long half-life classes using a 12-hour threshold, providing a more practical and interpretable alternative. Methods Molecular structures were processed using a Convolutional Neural Network (CNN), specifically a fine-tuned AlexNet, to extract high-level features. These extracted features served as inputs for a neural network classifier. A holdout validation strategy was applied, with data split into 70% for training, 15% for validation, and 15% for testing. Model performance was assessed based on classification accuracy and F1-score. Results The proposed model achieved an F1-score of 90.9% for the optimal feature dimension of 10. The accuracy rates were 96.2% on validation data and 92.3% on test data, demonstrating strong generalization capabilities. The classification framework outperformed regression-based approaches by addressing the inherent variability in drug half-life data and providing a more clinically relevant decision-making tool. Conclusion This study introduces an efficient and interpretable method for drug half-life classification, facilitating improved drug formulation and clinical decision-making. The findings highlight the advantages of classification in early-stage drug development, providing a scalable and robust approach for pharmacokinetic applications.

Biosurfactants are environmentally friendly surface-active compounds with promising applications in environmental remediation. In this study, a biosurfactant-producing bacterium, Bacillus (B.) velezensis SHB.28, was isolated from heavy metal-contaminated soil and evaluated for its ability to produce biosurfactants using date syrup as a low-cost agro-industrial substrate. Screening assays including drop-collapse (DC), oil spreading (OS), and emulsification index after 24 h E₂₄ (%) confirmed strong biosurfactant production. Culture conditions were optimized, revealing that 30 °C, pH 6, and a C/N ratio between 10% and 20% provided optimal production. Under optimized conditions, the crude biosurfactant extract yield reached 2.16 g/L within 24 h, accompanied by a reduction in surface tension from 69 to 29.1 dyn/cm and high emulsification activity. Kinetic modeling showed that emulsification activity followed an exponential growth model (R² = 0.985), whereas surface tension dynamics were well described by a spike decay–plateau model (R² = 0.998). Structural characterization using Fourier-transform infrared spectroscopy (FTIR), electrospray ionization–mass spectrometry (ESI–MS), and nuclear magnetic resonance (NMR) spectroscopy revealed that the biosurfactants are surfactin- and iturin-like cyclic lipopeptides composed of a β-hydroxy fatty acid chain (C₁₃–C₁₅) linked to a cyclic peptide moiety. The biosurfactant exhibited a critical micelle concentration of 100 mg/L and an anionic character with a pHpzc of 5.7. Furthermore, it demonstrated high efficiency in removing heavy metals, achieving removal efficiencies of 99.88% for Fe²⁺, 99.69% for Pb²⁺, and 94.72% for Cu²⁺, outperforming conventional surfactants such as SDS and Tween 80. These findings highlight the potential of date syrup-derived surfactin and iturin from B. velezensis SHB.28 as sustainable and efficient biosurfactants for environmental remediation and heavy metal removal applications.