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In this study, we synthesized novel α,β-unsaturated 2-cyanoacetamide derivatives (1–5) using microwave-assisted Knoevenagel condensation. Characterization of these compounds was carried out using FTIR and 1H NMR spectroscopy. We then evaluated their in vitro antibacterial activity against both gram-positive and gram-negative pathogenic bacteria. Additionally, we employed in silico methods, including ADMET prediction and density functional theory (DFT) calculations of molecular orbital properties, to investigate these cyanoacetamide derivatives (1–5). Molecular docking was used to assess the binding interactions of these derivatives (1–5) with seven target proteins (5MM8, 4NZZ, 7FEQ, 5NIJ, ITM2, 6SE1, and 5GVZ) and compared them to the reference standard tyrphostin AG99. Notably, derivative 5 exhibited the most favorable binding affinity, with a binding energy of -7.7 kcal mol−1 when interacting with the staphylococcus aureus (PDB:5MM8), while also meeting all drug-likeness criteria. Additionally, molecular dynamics simulations were carried out to evaluate the stability of the interaction between the protein and ligand, utilizing parameters such as Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (Rg), and Principal Component Analysis (PCA). A 50 nanosecond molecular dynamics (MD) simulation was performed to investigate stability further, incorporating RMSD and RMSF analyses on compound 5 within the active binding site of the modeled protein across different temperatures (300, 305, 310, and 320 K). Among these temperatures, compound 5 exhibited an RMSD value ranging from approximately 0.2 to 0.3 nm at 310 K (body temperature) with the 5MM8 target, which differed from the other temperature conditions. The in silico results suggest that compound 5 maintained significant conformational stability throughout the 50 ns simulation period. It is consistent with its low docking energy and in vitro findings concerning α,β-unsaturated cyanoacetamides.Key insights from this study include: • The creation of innovative α,β-unsaturated 2-cyanoacetamide derivatives (1–5) employing cost-effective, licensed, versatile, and efficient software for both in silico and in vitro assessment of antibacterial activity. • Utilization of FTIR and NMR techniques for characterizing compounds 1–5.

The numerous obstacles to large-scale integration of renewable energy sources (RESs) and the mitigating steps that need to be taken to overcome them in smart grid technology implementation are extensively discussed in this review article. Main focus is given on the control techniques in Microgrids, different supporting measures such as electric vehicles (EVs), energy storage systems (ESSs), and the monitoring techniques of Microgrid considering large scale renewable energy integration. The absence of physical factors like reactive power and frequency makes the DC microgrid less difficult than the AC microgrid. A comparison of the characteristics of centralized, decentralized, and distributed control arrangements reveals that the microgrid central controller (MGCC) bears the majority of the computational load and the cost of computation in centralized control, whereas local controllers (LCs) bear the least of the load and the cost of computation in completely decentralized control. Centralized and decentralized control also have less difficult implementations than distributed control, which has more complexity. Due to a number of challenges that are discussed in this article, the EV scenario in Bangladesh is rather challenging. The transportation sector has chosen vehicle to grid (V2G) approaches over other technologies due to a number of its advantages. In the event that a home energy storage system and an emergency backup storage system are required, V2G can reduce the cost of EVs and be modified for the local community. An autonomous power generation and distribution system is the main emphasis of a smart micro grid in this age, and internet of things (IoT) is utilized in various applications, such as micro grids, intelligent buildings, and intelligent control devices, for monitoring and tracking crucial data. Based on the topics that are presented in this article, it clearly demonstrates the state-of-the-art research in the area in question.

This study investigates the potential of 2-(4-butylbenzyl)-3-hydroxynaphthalene-1,4-dione (11) and its 12 derivatives as anticancer and biofilm formation inhibitors for methicillin-resistant staphylococcus aureus using in silico methods. The study employed various computational methods, including molecular dynamics simulation molecular docking, density functional theory, and global chemical descriptors, to evaluate the interactions between the compounds and the target proteins. The docking results revealed that compounds 9, 11, 13, and ofloxacin exhibited binding affinities of −7.6, −7.9, −7.5, and −7.8 kcal mol−1, respectively, against peptide methionine sulfoxide reductase msrA/msrB (PDB: 3E0M). Ligand (11) showed better inhibition for methicillin-resistant staphylococcus aureus msrA/msrB enzyme. The complex of the 3E0M-ligand 11 remained highly stable across all tested temperatures (300, 305, 310, and 320 K). Principal Component Analysis (PCA) was employed to evaluate the behavior of the complex at various temperatures (300, 305, 310, and 320 K), demonstrating a total variance of 85%. Convergence was confirmed by the eigenvector’s cosine content value of 0.43, consistently displaying low RMSD values, with the minimum observed at 310 K. Furthermore, ligand 11 emerges as the most promising candidate among the compounds examined, showcasing notable potential when considering a combination of in vitro, in vivo, and now in silico data. While the naphthoquinone derivative (11) remains the primary candidate based on comprehensive in silico studies, further analysis using Frontier molecular orbital (FMO) suggests while the Egap value of compound 11 (2.980 eV) and compound 13 (2.975 eV) is lower than ofloxacin (4.369 eV), indicating their potential, so it can be a statement that compound 13 can also be investigated in further research.

BackgroundCerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, via a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis.MethodsA dataset of 865 cases - 725 transsphenoidal approach (TSA) and 140 expanded endonasal approach (EEA) - with cerebrospinal fluid rhinorrhoea as the primary outcome, was used. Relevant variables were extracted from the data, and prediction variables were divided into two categories, preoperative risk factors; and repair techniques, with 6 and 11 variables respectively. Three types of machine learning models were developed in order to predict CSFR: logistic regression (LR); decision tree (DT); and neural network (NN). Models were validated using 5-fold cross-validation, compared via their area under the curve (AUC) evaluation metric, and key prediction variables were identified using their Shapley additive explanations (SHAP) score.ResultsCSFR rates were 3.9% (28/725) for the transsphenoidal approach and 7.1% (10/140) for the expanded endonasal approach. NNs outperformed LR and DT for CSFR prediction, with a mean AUC of 0.80 (0.70-0.90) for TSA and 0.78 (0.60-0.96) for EEA, when all risk factor and intraoperative repair data were integrated into the model. The presence of intraoperative CSF leak was the most prominent risk factor for CSFR. Elevated BMI and revision surgery were also associated with CSFR for the transsphenoidal approach. CSF diversion and gasket sealing appear to be strong predictors of the absence of CSFR for both approaches.ConclusionNeural networks are effective at predicting CSFR and uncovering key CSFR predictors in patients following endonasal skull base surgery, outperforming traditional statistical methods. These models will be improved further with larger and more granular datasets, improved NN architecture, and external validation. In the future, such predictive models could be used to assist surgical decision-making and support more individualised patient counselling.

ObjectiveDespite progress in endonasal skull-base neurosurgery, cerebrospinal fluid (CSF) rhinorrhoea remains common and significant. The CRANIAL study sought to determine 1) the scope of skull-base repair methods used, and 2) corresponding rates of postoperative CSF rhinorrhoea in the endonasal transsphenoidal approach (TSA) and the expanded endonasal approach (EEA) for skull-base tumors.MethodsA prospective observational cohort study of 30 centres performing endonasal skull-base neurosurgery in the UK and Ireland (representing 91% of adult units). Patients were identified for 6 months and followed up for 6 months. Data collection and analysis was guided by our published protocol and pilot studies. Descriptive statistics, univariate and multivariable logistic regression models were used for analysis.ResultsA total of 866 patients were included - 726 TSA (84%) and 140 EEA (16%). There was significant heterogeneity in repair protocols across centres. In TSA cases, nasal packing (519/726, 72%), tissue glues (474/726, 65%) and hemostatic agents (439/726, 61%) were the most common skull base repair techniques. Comparatively, pedicled flaps (90/140, 64%), CSF diversion (38/140, 27%), buttresses (17/140, 12%) and gasket sealing (11/140, 9%) were more commonly used in EEA cases. CSF rhinorrhoea (biochemically confirmed or requiring re-operation) occurred in 3.9% of TSA (28/726) and 7.1% of EEA (10/140) cases. A significant number of patients with CSF rhinorrhoea (15/38, 39%) occurred when no intraoperative CSF leak was reported. On multivariate analysis, there may be marginal benefits with using tissue glues in TSA (OR: 0.2, CI: 0.1-0.7, p

Breast cancer has historically been one of the leading causes of death for women worldwide. As of 2020, breast cancer was reported to have overtaken lung cancer as the most common type of cancer globally, representing an estimated 11.3% of all cancer diagnoses. A multidisciplinary approach is taken for the diagnosis and treatment of breast cancer that includes conventional and targeted treatments. However, current therapeutic approaches to treating breast cancer have limitations, necessitating the search for new treatment options. Cancer cells require adequate iron for their continuous and rapid proliferation. Excess iron saturates the iron-binding capacity of transferrin, resulting in non-transferrin-bound iron (NTBI) that can catalyze free-radical reactions and may lead to oxidant-mediated breast carcinogenesis. Moreover, excess iron and the disruption of iron metabolism by local estrogen in the breast leads to the generation of reactive oxygen species (ROS). Therefore, iron concentration reduction using an iron chelator can be a novel therapeutic strategy for countering breast cancer development and progression. This review focuses on the use of iron chelators to deplete iron levels in tumor cells, specifically in the breast, thereby preventing the generation of free radicals. The inhibition of DNA synthesis and promotion of cancer cell apoptosis are the targets of breast cancer treatment, which can be achieved by restricting the iron environment in the body. We hypothesize that the usage of iron chelators has the therapeutic potential to control intracellular iron levels and inhibit the breast tumor growth. In clinical settings, iron chelators can be used to reduce cancer cell growth and thus reduce the morbidity and mortality in breast cancer patients.