Two novel anthracene derivatives were synthesized, and detailed photo-physical and biological investigations were carried out using a variety of spectroscopy techniques. The effect of cyano (–CN) substitution was found to be effective to alter the charge population and frontier orbital energy levels via Density Functional Theory (DFT) calculations. Particularly, the introduction of styryl and triphenylamine groups attached to the anthracene core helped to increase the conjugation relative to the anthracene moiety. The results revealed that the molecules have intramolecular charge transfer (ICT) properties, occurring from the electron donating triphenylamine to the electron accepting anthracene moiety in solutions. In addition, the photo-physical properties are strongly cyano-dependent, where the cyano-substituted (E/Z)-(2-anthracen-9-yl)-3-(4′-(diphenylamino)biphenyl-4yl)acrylonitrile molecule showed stronger electron affinity due to the enhanced internal steric hindrance compared to the (E)-4′-(2-(anthracen-9-yl)vinyl)-N,N-diphenylbiphenyl-4-amine molecule, which resulted in a lower photoluminescence quantum yield (PLQY) value and a shorter lifetime in the molecule. Besides, the Molecular Docking approach was used to investigate possible cellular staining targets to confirm cellular imaging potential of the compounds. Moreover, cell viability analyses put forth that synthesized molecules do not exhibit significant cytotoxicity under 125 μg mL⁻¹ concentration on the human dermal fibroblast cell line (HDFa). Moreover, both of the compounds showed great potential in cellular imaging of HDFa cells. Compared to Hoechst 33258, a common fluorescent dye used for nuclear staining, the compounds showed higher magnification of cellular structure imaging capacity by staining the whole cellular compartment. On the other hand, bacterial staining showed that ethidium bromide has higher resolution in monitoring Staphylococcus aureus (S. aureus) cell culture.

This study provides an extensive analysis of the role of Machine Learning (ML) and Deep Learning (DL) techniques in the early diagnosis of Coronary Heart Disease (CHD), one of the primary causes of cardiovascular morbidity and mortality worldwide. Early diagnosis is crucial to slow disease progression, prevent severe complications such as heart attacks, and enable timely interventions. We examine the impact of dataset variability on model performance by applying various ML and DL algorithms, including Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Support Machine Vector (SVM), Logistic Regression (LR), Decision Tree (DT), k–Nearest Neighbor (kNN), Categorical Naive Bayes (CategoricalNB), and Extreme Gradient Boosting (XGBclassifier) to two distinct datasets: the comprehensive Framingham dataset and the UCI Heart Disease dataset. Before model training, data preprocessing techniques such as Hotdecking, Synthetic Minority Oversampling Technique (SMOTE), and normalization were implemented to enhance data quality. Model performance was evaluated using a range of metrics, including accuracy, precision, recall, F1–score, and area under the curve (AUC). The results reveal that the SVM model achieved the highest accuracy of $$92.42\%$$ 92.42 % on the UCI dataset, while XGBclassifier attained the highest accuracy of $$90.97\%$$ 90.97 % on the Framingham dataset, surpassing the performance reported in existing literature. These findings emphasize the potential of ML and DL methods for the early diagnosis of CHD and demonstrate the significance of dataset selection on model performance. This study offers valuable insights into the effectiveness of ML and DL approaches, underscoring the importance of data–driven strategies in advancing healthcare for the early detection and management of CHD and similar cardiovascular diseases.

We propose the multiplicative explicit Euler, multiplicative implicit Euler, and multiplicative Crank-Nicolson algorithms for the numerical solutions of the multiplicative partial differential equation. We also consider the truncation error estimation for the numerical methods. The stability of the algorithms is analyzed by using the matrix form. The result reveals that the proposed numerical methods are effective and convenient.

Monoamine oxidase B (MAO-B) is an enzyme that metabolizes several chemicals, including dopamine. MAO-B inhibitors are used in the treatment of Parkinson’s Disease (PD), and the inhibition of this enzyme reduces dopamine turnover and oxidative stress. The absence of dopamine results in PD pathogenesis originating from decreased Acetylcholinesterase (AChE) activity and elevated oxidative stress. Here, we performed a molecular docking analysis for the potential use of costunolide and parthenolide terpenoids as potential MAO-B inhibitors in the treatment of PD. Neuroprotective properties of plant-originated costunolide and parthenolide terpenoids were investigated in a cellular PD model that was developed by using MPP⁺ toxicity. We investigated neuroprotection mechanisms through the analysis of oxidative stress parameters, acetylcholinesterase activity and apoptotic cell death ratios. Our results showed that 100 µg/mL and 50 µg/mL of costunolide, and 50 µg/mL of parthenolide applied to the cellular disease model ameliorated the cytotoxicity caused by MPP⁺ exposure. We found that acetylcholinesterase activity assays exhibited that terpenoids could ameliorate and restore the enzyme activity as in negative control levels. The oxidative stress parameter analyses revealed that terpenoid application could enhance antioxidant levels and decrease oxidative stress in the cultures. In conclusion, we reported that these two terpenoid molecules could be used in the development of efficient treatment strategies for PD patients.