Charles David

Salmonella Typhi is still considered a significant health issue in various developing nations due to poor hygiene, inadequate food safety measures, and limited public health.[1,2] The statistical e...

With the global emphasis on sustainable growth and development, the depletion of natural energy reserves due to reliance on fossil fuels and non-renewable sources remains a critical concern. Despite strides in transitioning to electrical mobility, rural and agricultural communities depend heavily on liquefied petroleum gas and firewood for cooking, lacking viable, sustainable alternatives. This study focuses on community-led efforts to advance biogas adoption, providing an eco-friendly and reliable energy alternative for rural and farming households. By designing and developing balloon-type anaerobic biodigesters, this initiative provides a robust, cost-effective, and scalable method to convert farm waste into biogas for household cooking. This approach reduces reliance on traditional fuels, mitigating deforestation and improving air quality, and generates organic biofertilizer as a byproduct, enhancing agricultural productivity through organic farming. The study focuses on optimizing critical parameters, including the input feed rate, gas production patterns, holding time, biodigester health, gas quality, and liquid manure yield. Statistical tools, such as descriptive analysis, regression analysis, and ANOVA, were employed to validate and predict biogas output data based on experimental and industrial-scale data. Artificial neural networks (ANNs) were also utilized to model and predict outputs, inspired by the information processing mechanisms of biological neural systems. A comprehensive database was developed from experimental and literary data to enhance model accuracy. The results demonstrate significant improvements in cooking practices, health outcomes, economic stability, and solid waste management among beneficiaries. The integration of statistical analysis and ANN modeling validated the biodigester system’s effectiveness and scalability. This research highlights the potential to harness renewable energy to address socio-economic challenges in rural areas, paving the way for a sustainable, equitable future by fostering environmentally conscious practices, clean energy access, and enhanced agricultural productivity.

Regardless to overwhelming quantum of cancer research worldwide, there are few drugs on the market to treat disease conditions. This is owing to multiple process inferences of drug targets in integrated pathways for invasion, growth, and metastasis. Over the past years, the death rate due to breast cancer has been increasing, that set the stage for improved better treatment. Therefore, there is a persistent and vital demand for innovative development of drugs to treat breast cancer. Many studies have reported that more than 60% of breast cancers are Estrogen receptor-α (ERα)-positive tumours and a key transcription factor, Estrogen receptor-α (ERα) was believed to promote proliferation of breast cancer cells. In this study, 150 ns of molecular dynamics was performed for protein-ligand complex to retrieve the potential stable conformations. The most populated dynamics cluster of 4-Hydroxytamoxifen intact with active site amino acid was selected to generate dynamacophore model (dynamic pharmacophore). Further, internal model validation with AU-ROC values ∼0.93 indicate the best model to screen library. The refined hits are funnelled in pharmacokinetics/dynamics, CDOCKER molecular docking, MM-GBSA and density functional theory to identify the promising ERα ligand candidates. Communicated by Ramaswamy H. Sarma

Despite rigorous research on breast cancer has increased in recent decades, only few drugs are in practice to combat against the disease. Due to excessive usage, these drugs attain resistance is an avertable phenomenon resulting from inadequate treatment. A novel, and real-time approaches are expected to overcome to find the solution for the drug resistance. The molecular dynamics based multi-conformational sampling technique via computer-aided drug-designing approach, may be a promising route to identify the lead candidates from real-time generated frames. The estrogenic receptor, being one of the most widely targeted receptors for various breast cancer drugs namely, tamoxifen, raloxifene and GW5 (tamoxifen-resistance inhibitor) was used for simulating the molecular dynamics to obtain various real time frames. The energetically stable frames were funnelled based on Gibbs free binding energy, interaction energy and active site interaction to generate pharmacophores model for virtual screening of compounds. Generated pharmacophores are validated by receiver operating characteristic area under curve greater than 0.8. Further, screening of compounds with validated structure-based pharmacophore model of different estrogen bound drug complex conformations and binding orientations are complement for tamoxifen and tamoxifen-resistance inhibitor frames. Moreover, the best mapped compounds were docked and probed for ADMET, TopKat® and Lipinski’s rule of five is more favourable for compound Andrographidine F sourced from medicinal herbal plant Andrographis paniculata. Hence, this compound had to be further analysed in in-vitro and in-vivo to prove the same. Communicated by Ramaswamy H. Sarma

The distillery spent wash consists of extremely high COD values, high content of pollutants and has an objectionable dark colour. These properties mark the effluent as one of the challenging tasks to treat ahead of exculpating into the surroundings. In this original research work, the raw distillery effluent was electrochemically treated using twisted-type Iron electrodes. Electrochemical treatment was performed for 2 hours and the treated effluent was sampled at defined periods to evaluate the efficiencies of spent wash degradation in terms of colour removal. Optimisation studies were conducted to evaluate the impact of various process parameters viz., applied electric current intensity, pH, mixing speed, electrolysis time and the inter-electrode space on the colour removal efficiencies. The maximum degradation of colour achieved by twisted-type Iron electrodes was found to be 83.75%. Operating conditions at which maximum colour removal efficiencies were achieved at current intensity 1.9 A, pH 3, 2 cm of inter-electrode distance, mixing speed of 100 rpm and for an electrolysis time of 2 h. The experimental results obtained were validated and evaluated using an Artificial Neural Network for modelling and predicting the electrocoagulation process for the degradation of distillery effluent employing twisted-type Iron electrodes.

The distillery effluent causes a crucial ecological impact when disposed untreated into the environment and the removal of the intense brown colour is a much challenging task due to the presence of a polymeric compound – Melanoidin. This research study reports the synthesis and application of a nano ZnO photocatalyst in an immobilised form. The process parameters such as photocatalyst dosage (0.5–2.5 g/L), initial pH (3–11), initial spent wash concentration and treatment time were optimised to attain the maximum spent wash degradation efficiency. The physical and chemical properties of the as-synthesised photocatalyst were characterised by X-ray diffractometer (XRD), high-resolution scanning electron microscopy (HRSEM), energy dispersive analysis X-ray (EDAX), Brunauer–Emmett–Teller (BET) surface area analysis, diffuse reflectance spectroscopy (DRS) and Fourier transform infrared (FTIR) spectroscopy. The percentage colour degradation of spent wash increased from 36.2 to 77.1%, with increasing photocatalyst dosage from 0.5 to 2.5 g/L, respectively. The reaction rate constant (k_obs) decreased from 0.0141 to 0.0022 (1/min) with increasing initial spent wash concentration (% dilution) of 5–25%. The photocatalytic degradation experiment performed at optimised process parameters yielded a maximum of 91.2% of spent wash colour degradation.