Nowadays, sustainable materials are receiving significant attention due to the fact that they will be crucial for the development of the next generation of products and devices. In the present work, hydrogels have been successfully synthesized using lignin which is non-valorized biopolymer from the paper industry. Hydrogels were prepared via crosslinking with Poly(ethylene) glycol diglycidyl ether (PEGDGE). Different crosslinker ratios were used to determine their influence on the structural and chemical properties of the resulting hydrogels. It has been found that pore size was reduced by increasing crosslinker amount. The greater crosslinking density increased the swelling capacity of the hydrogels due to the presence of more hydrophilic groups in the hydrogel network. Paracetamol release test showed higher drug diffusion for hydrogels produced with a ratio lignin:PEGDGE 1:1. The obtained results demonstrate that the proposed approach is a promising route to utilize lignocellulose waste for producing porous materials for advanced biomedical applications in the pharmacy industry.

The influence of lignin modification on drug release and pH-dependent releasing behavior of oral solid dosage forms was investigated using three different formulations. The first formulation contains microcrystalline cellulose (MCC 101) as the excipient and paracetamol as the active pharmaceutical ingredient (API). The second formulation includes Alcell lignin and MCC 101 as the excipient and paracetamol, and the third formulation consists of carboxylated Alcell lignin, MCC 101 and paracetamol. Direct compaction was carried out in order to prepare the tablets. Lignin can be readily chemically modified due to the existence of different functional groups in its structure. The focus of this investigation is on lignin carboxylation and its influence on paracetamol control release behavior at varying pH. Results suggest that carboxylated lignin tablets had the highest drug release, which is linked to their faster disintegration and lower tablet hardness.

Cancer-associated angiogenesis is a fundamental process in tumor growth and metastasis. A variety of signaling regulators and pathways contribute to establish neovascularization, among them as small endogenous non-coding RNAs, microRNAs (miRNAs) play prominent dual regulatory function in breast cancer (BC) angiogenesis. This review aims at describing the current state-of-the-art in BC angiogenesis-mediated by angioregulatory miRNAs, and an overview of miRNAs dysregulation association with the anti-angiogenic response in addition to potential clinical application of miRNAs-based therapeutics. Angioregulatory miRNA–target gene interaction is not only involved in sprouting vessels of breast tumors but also, trans-differentiation of BC cells to endothelial cells (ECs) in a process termed vasculogenic mimicry. Using canonical and non-canonical angiogenesis pathways, the tumor cell employs the oncogenic characteristics such as miRNAs dysregulation to increase survival, proliferation, oxygen and nutrient supply, and treatment resistance. Angioregulatory miRNAs in BC cells and their microenvironment have therapeutic potential in cancer treatment. Although, miRNAs dysregulation can serve as tumor biomarker nevertheless, due to the association of miRNAs dysregulation with anti-angiogenic resistant phenotype, clinical benefits of anti-angiogenic therapy might be challenging in BC. Hence, unveiling the molecular mechanism underlying angioregulatory miRNAs sparked a booming interest in finding new treatment strategies such as miRNA-based therapies in BC.

Paracetamol crystals often exhibit poor compressibility properties, which results in capping issues. The Particle Size Distribution (PSD) of paracetamol was engineered to improve the compressibility of paracetamol crystals. This was accomplished by growing paracetamol crystals in the presence of additives. The active pharmaceutical ingredient Phenacetin and impurity 4-chloroacetanalide were used to modify the crystal properties of paracetamol. In solution, the phenacetin or 4-chloroacetanalide molecules adsorb onto the paracetamol crystal faces selectively (110 or 011) and inhibit the further growth of the paracetamol crystal and consequently, the paracetamol crystal growth is reduced substantially. For controlling the PSD of crystal to improve the compressibility of paracetamol crystals, a set of cooling crystallization experiments in the presence of additive was designed. According to a statistical experimental design, the cooling rate was the most effective parameter. The PSD was reduced when paracetamol crystallized from the controlled crystallization in the presence of less than 3 mol% of both additives. These smaller particles increased almost four-fold the compressibility of paracetamol in comparison to the commercial material. Moreover, tablets were prepared for each formulation using a direct compaction method. The results illustrated that a higher tablet hardness of paracetamol was achieved by tailoring the paracetamol crystal size distribution. In addition, the tablet disintegration time was higher for the formulation with increased hardness. Overall, this work presents the potential use of structurally similar compounds as additives to alter the mechanical properties of an API.

Carbon capture and storage (CCS) has attracted renewed interest in the re-evaluation of the equations of state (EoS) for the prediction of thermodynamic properties. This study also evaluates EoS for Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) and their capability to predict the thermodynamic properties of CO₂-rich mixtures. The investigation was carried out using machine learning such as an artificial neural network (ANN) and a classified learner. A lower average absolute relative deviation (AARD) of 7.46% was obtained for the PR in comparison with SRK (AARD = 15.0%) for three components system of CO₂ with N₂ and CH₄. Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO₂ with N₂, CH₄, Ar, and O₂) case. In addition, applying machine learning provided promise and valuable insight to deal with engineering problems. The implementation of machine learning in conjunction with EoS led to getting lower predictive AARD in contrast to EoS. An of AARD 2.81% was achieved for the three components and 12.2% for the respective five components mixture.