The shortage of effective chemotherapeutic agents poses a significant challenge to the global public health system. Cancer is among the leading diseases affecting the human population worldwide. Issues such as drug resistance, toxicity, lack of specificity, poor bioavailability and water solubility, and severe side effects reduce the effectiveness of many existing anticancer drugs. As a result, there is growing interest in discovering a new generation of therapeutic agents to overcome these limitations. Phenolic acids, including ferulic and caffeic acids, are cinnamic acid derivatives with numerous biological effects, including anti-inflammatory, antibacterial, antifungal, antioxidant, antiviral, cytotoxic, and antiproliferative effects. In recent years, drug repurposing and hybridization strategies have emerged as attractive approaches in medicinal chemistry because they may reduce both the cost and time associated with conventional drug discovery. As a result, several researchers have combined ferulic acid and caffeic acid scaffolds with different pharmacophores to generate hybrid compounds with enhanced anticancer potential. This review summarizes recent in vitro and in silico studies published between 2022 and 2025 on ferulic and caffeic acid hybrid compounds that exhibit cytotoxic and antiproliferative effects. Furthermore, the review discusses structure–activity relationship trends, synthetic approaches, and structural modifications associated with improved biological activity. Collectively, the findings highlight the significant potential of ferulic acid and caffeic acid scaffolds in the development of multifunctional anticancer agents.

This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10–20 mg/L). According to these findings, cadmium (II) breakthrough occurred more quickly at lower bed heights, higher flow rates, and higher cadmium (II) concentrations. The Thomas model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (backpropagation and least squares estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solutions via modified cellulose nanocrystals. To compare the model’s predicted results with experimental data, statistical approaches were employed, including calculating the coefficient of determination (R²) and mean square error (MSE). The ANFIS model used to predict cadmium (II) adsorption via modified cellulose nanocrystals had a strong correlation value of 0.997 for least squares estimation (LSE) and 0.999 for the gradient descent (backpropagation) method, indicating the effectiveness of the trained model in predicting the cadmium (II) adsorption process.

Cellulose nanocrystals (CNCs) have emerged as highly efficient adsorbents for heavy metal removal owing to their biodegradability, wide availability, and rich surface chemistry. Their abundant hydroxyl and other reactive functional groups provide a high density of active sites, significantly enhancing their affinity and adsorption capacity for toxic metal ions such as chromium (VI). The green adsorbent was characterised using FTIR to identify the functional groups. The optimum conditions were pH 6, concentration 140 mg/L, time 120 min, and adsorbent dosage 6 g/L, with a percentage removal of 95%. Deep machine learning was employed to predict the removal capacity of green and biodegradable adsorbents for chromium (VI) removal from wastewater. The findings show that adaptive neuro-fuzzy inference systems effectively model the prediction of Chromium (VI) adsorption. The Levenberg–Marquardt algorithm (LM) was used to train the network through feedforward propagation. In the training dataset, R² was 0.966, Mean Square Error (MSE) 0.042, Absolute average relative error (AARE) 0.053, Root means square error (RMSE) 0.077, and average relative error (ARE) 0.053 for the artificial neural network. The RMSE of 0.021, AARE of 0.015, ARE of 0.01, MSE of 0.017, and R² of 0.998 for the adaptive neuro-fuzzy inference system. These findings confirm the strong adsorption potential of CNCs and the suitability of advanced machine learning models for forecasting heavy metal removal efficiency.