Trimetazidine is the first registered drug belonging to the class of antianginal agents specifically classified as a metabolic modulator. It protects the heart from ischemic damage and oxidative stress. Clinically, it has demonstrated significant benefits for patients with coronary artery disease, showing a prophylactic effect against angina attacks. Its lack of hemodynamic action makes it suitable for combination therapy with other antianginal drugs. Beyond its cardioprotective effects, trimetazidine has also shown therapeutic potential in various renal disorders and the treatment of atherosclerosis. Derivatives of 2-arylpropionic acid represent one of the most widely used classes of nonsteroidal anti-inflammatory drugs (NSAIDs), valued for their anti-inflammatory, analgesic, and antipyretic properties. These compounds exhibit metabolic chiral inversion, and the enantiomers often show distinct pharmacological activities. Compounds containing amide functional groups are of particular interest to both pharmaceutical research and human health due to their fundamental role in the design of new therapeutic agents. This work presents the synthesis and characterization of novel hybrid molecules combining trimetazidine with various profens (ibuprofen, ketoprofen, naproxen, etc.). The target compounds are designed to offer dual pharmacological action—addressing both ischemic cardiovascular conditions and inflammatory diseases. These newly synthesized derivatives show promising potential for future pharmaceutical development and therapeutic application.

In the emerging Pharma 4.0 paradigm, pharmaceutical factories adopt Industry 4.0 digitalization (AI, IoT, etc.) to become “smart” manufacturing sites, improving throughput and enabling right-first-time processes. Integration of AI with Process Analytical Technology (PAT) provides a data-rich platform for real-time process understanding, consistent with Quality by Design (QbD) principles of building quality into manufacturing. For example, spectroscopic PAT methods such as near-infrared (NIR) and Raman spectroscopy are widely employed to monitor blend uniformity, moisture, and crystallinity; AI-driven multivariate models can analyze these data to predict and control critical quality attributes on-the-fly, yielding improved consistency and reduced waste. This AI–PAT synergy has accelerated the development of complex products: for instance, COVID-19 mRNA vaccines were brought to market in months (instead of years) by leveraging machine learning and in-line PAT for rapid formulation and scale-up. Moreover, AI-enabled PAT enhances cost-efficiency and reduces labor needs in manufacturing. High automation can lessen the physical and cognitive demands on a limited workforce, and continuous PAT monitoring reduces the need for highly specialized operators, helping to address skill shortages. Process optimization with AI also allows smaller plant footprints and fewer production steps; these reductions in footprint and complexity lower capital investment and operating costs. Together, these benefits align with the Pharma 4.0 vision of efficient, QbD-driven development—enabling faster, more cost-effective drug development with built-in quality and lower resource use.

This review focuses on the bioactive and functional properties of mulberry (Morus spp., Moraceae) leaves and shoot extracts, highlighting their potential as a rich source of health-promoting compounds. Traditionally valued for feeding Bombyx mori silkworms, mulberry leaves and shoots exhibit significant medicinal potential due to their diverse bioactive composition, including flavonoids, phenolics, anthocyanins, alkaloids, vitamins, and minerals. These compounds contribute to various pharmacological activities such as antioxidant, antidiabetic, antihyperlipidemic, anti-inflammatory, antimicrobial, immune-modulating, and anticancer effects. Some of these effects are linked to mechanisms like α-glucosidase and α-amylase inhibition, reduced foam cell and fat formation, NF-κB signaling suppression, apoptosis promotion, and modulation of oxidative stress and inflammation. Recent studies highlighted the efficacity of key bioactive components like oxyresveratrol or 1-deoxynojirimycin from young mulberry leaves and shoots in preventing and managing metabolic, cardiovascular, skin, and cancer-related conditions, as well as in supporting immune system health. These findings underscore the therapeutic relevance of these phytochemicals in modern preventive and complementary medicine. Bioactive compound extraction commonly involves the use of methanol, acetone, vinegar, or glycerin, resulting in diverse formulations such as tinctures, extracts, tonics, and elixirs. Maceration in hydroalcoholic solutions is particularly effective for preserving the bioavailability of active constituents, with the alcohol-to-water ratio being crucial for extraction efficiency. In pharmaceutical applications, mulberry extracts are valuable in dietary supplements, dermatological formulations, and preventive therapies against degenerative diseases. Moreover, their inclusion in combination therapies may enhance the efficacy of conventional treatments. The promotion of these products can positively impact the valorization of local natural resources, offering an opportunity for sustainable and innovative pharmaceutical development. The novelty of this review lies in its comprehensive approach to mulberry leaf and shoot extracts, highlighting not only their well-documented antioxidant and metabolic health benefits, but also drawing attention to their less-explored properties, supporting future research and applications in health-related fields.

Microplastic (MP) pollution poses an escalating global concern, particularly in the Philippines, which generates over 61,000 metric tons of solid waste daily. This study presents a sustainable and cost-effective method for detecting high-density (HDPE) and low-density polyethylene (LDPE) microplastics using nitrogen-doped carbon dots (N-CDs) synthesized from onion peel and L-cysteine via hydrothermal carbonization. Two precursor ratios (1:1 and 1:0.30 w/w) were evaluated. The resulting N-CDs exhibited bright yellow-green fluorescence (470–500 nm) and excitation-dependent photoluminescence under 365 nm UV light. FTIR and UV-Vis spectroscopy confirmed the presence of nitrogen-containing functional groups and effective graphitization, particularly in the 1:0.30 ratio. Fluorescence imaging revealed stronger intensity and greater stain uniformity in thermally softened MPs treated with 1:0.30 N-CDs, with a peak emission of 10,230.02 a.u. at 2 hours and PMT 11—surpassing the 1:1 ratio. Bandgap and absorbance analyses supported the superior optical behavior of the lower-concentration formulation. Image analysis further indicated increased luminescent area over time, and two-way ANOVA confirmed statistically significant effects of heating time and PMT settings (p < 0.05). Compared to traditional filtration staining, thermal-assisted application offered enhanced and stable fluorescence. These findings demonstrate the efficacy of green-synthesized N-CDs for MP detection, with potential scalability and environmental applicability. Future work should explore alternative biomass sources and assess N-CD performance under field conditions to optimize environmental sensing strategies.

Mesh type plays a pivotal role in the accuracy, efficiency, and stability of CFD-DEM (Computational Fluid Dynamics–Discrete Element Method) simulations. While tetrahedral meshes are frequently adopted for their automation and flexibility, other mesh types—including hexahedral, polyhedral, and hybrid (hexahedral–polyhedral)—offer distinct trade-offs in terms of mesh quality metrics, computational cost, and numerical robustness.

This study compares the effects of four mesh types—tetrahedral, hexahedral, polyhedral, and hybrid—on key CFD-DEM simulation outcomes in a mechanically stirred mixing system. Evaluation criteria include mesh quality indicators such as skewness, non-orthogonality, and aspect ratio, as well as simulation runtime and convergence behavior. By using a standardized impeller-baffled tank configuration, the study ensures fair comparison across all mesh scenarios.

The results reveal that hybrid (hexahedral–polyhedral) meshes strike a favorable balance, offering superior orthogonality and lower skewness, which lead to enhanced numerical stability and reduced simulation divergence. Hexahedral meshes, while yielding the lowest numerical diffusion and highest accuracy in velocity fields, are constrained by limited geometric flexibility and higher meshing effort. Tetrahedral meshes, although easy to generate, suffer from higher skewness and reduced stability in capturing near-wall interactions and vortical structures. Hybrid meshes combine the geometric adaptability of tetrahedrals with the accuracy and stability of polyhedrals or hexahedrals, showing promise for complex geometries without significantly compromising performance. In terms of simulation runtime, polyhedral and hybrid meshes show reduced computational load due to better convergence and fewer iterations per timestep.

This work provides critical guidance on mesh selection strategies in CFD-DEM modeling of multiphase mixing systems. Future investigations will focus on mesh adaptation techniques and their impact on simulations involving non-spherical particles and multiphase turbulence modeling.

The increasing resistance of Candida tropicalis to conventional antifungal agents has necessitated the development of effective, biocompatible alternatives derived from natural sources. Garlic (Allium sativum), known for its potent antimicrobial activity, contains 33 bioactive sulfur compounds, some of them being allicin, ajoene, and diallyl sulfides, which exhibit strong antifungal effects. However, the clinical application of garlic extract in pharmaceutical formulations remains limited due to its chemical instability, rapid degradation, and limited bioavailability. This review highlights recent advancements in pharmaceutical processing and particle engineering approaches to enhance the stability, delivery, and therapeutic efficacy of garlic extract-based antifungal formulations. Emphasis is placed on innovative strategies such as nanoencapsulation, lyophilization, spray drying, and incorporation into biocompatible hydrogels for targeted delivery. Special attention is given to hydrogel-based systems due to their excellent mucoadhesive properties, ease of application, and sustained release potential, making them ideal for treating localized Candida tropicalis infections. This review also discusses formulation challenges and in vitro evaluation parameters, including minimum inhibitory concentration, minimum fungicidal concentration, and biofilm inhibition. By analyzing recent findings and technological trends, this review underscores the potential of garlic extract-based particle-engineered systems as sustainable and effective antifungal therapies. The scope of this review includes an in-depth evaluation of garlic extract-derived formulations, the application of particle processing technologies, and their translational potential in the design of next-generation antifungal delivery systems for managing Candida tropicalis infections.

Introduction: Particulate processes are central to many scientific and industrial systems, from materials synthesis to astrophysical processes. A comprehensive understanding of particle dynamics—nucleation, growth, aggregation, and diffusion—is essential for process design, optimization, and control. This review integrates disparate modelling techniques and control strategies from fields like flame synthesis of nanoparticles, batch and continuous crystallization, and stellar evolution driven by particle motion.

Methodology: Embedded at the center of particulate modelling is the Population Balance Model (PBM) that follows particle size distributions via internal coordinates such as geometry, composition, and age. The model is designed to handle a range of complexities from basic spherical particles to full agglomerate structures. Numerical solutions, such as a method of moments, sectional methods, finite element analysis, and Monte Carlo simulations, are explained with applications to laminar as well as turbulent flows. On the control side, predictive control algorithms—specifically hybrid MPC systems—are presented for stabilizing and optimizing particulate processes. Complementary optical monitoring methods like turbidity and light scattering are reviewed, in addition to stochastic models based on Markov chain theory for the characterization of mesoscopic behaviors.

Results: Model predictive control in batch crystallization demonstrates tangible advantages, e.g., a 13.4% decrease in fines volume using linear vs. nonlinear cooling. Hybrid MPC systems improve the robustness and stability of continuous operations. In flame synthesis, different numerical solutions precisely reproduce particle growth and interaction behavior. In astrophysical applications, novel formulations of diffusion and radiative acceleration information improve stellar evolution models and better match observed phenomena like the lithium gap and chemical stratification in AmFm stars.

Conclusion: This summary emphasizes the interfacing of modeling, simulation, and control methodology required for the predictive analysis of particulate systems. Although rapid progress has been made, overlooked methods like Markov-based stochastic models offer a window of new innovation.

Pharmaceutical processing plays a pivotal role in determining the therapeutic success of antiviral formulations, particularly in the management of hepatitis virus infections. Beyond the discovery of potent antiviral compounds, the formulation strategy and particle processes directly influence drug release behavior, bioavailability, and ultimately, clinical efficacy. In this study, a mathematical model is developed to explore the dynamic interaction between viral load, drug concentration, and immune response, while incorporating the influence of pharmaceutical processing parameters on the drug release profile. The model captures how drug formulation—including particle size, granulation, and controlled-release systems—shapes the release kinetics and enhances antiviral action. By simulating various release scenarios through a pharmaceutical processing-dependent function, this study offers valuable perspective into optimizing antiviral formulations for improved therapeutic outcomes.

Despite advances in drug discovery, the integration of pharmaceutical processing characteristics into predictive viral dynamic models remains underexplored. This research addresses this gap by bridging pharmaceutical engineering and biological modeling, offering a systematic framework to guide the design of more effective and patient-centric antiviral therapies. Furthermore, this approach enables quantitative evaluation of how formulation changes impact viral suppression over time, providing a critical perspective for optimizing treatment schedules. This holistic strategy could significantly enhance personalized medicine in antiviral treatment planning.

Integrating electrochemical fuel cells and internal combustion units can enhance energy power systems' total efficiency and sustainability. This study presents a promising solution by integrating a Proton Exchange Membrane Fuel Cell (PEMFC) with a mini gas turbine system, forming a hybrid system named the "Oya System." This approach aims to mitigate the efficiency losses of gas turbines during high ambient temperatures.
The hybrid model is designed using Aspen Plus for modeling and the EES simulation program for solving mathematical equations. The primary objective of this research is to enhance the efficiency of gas turbine systems, particularly under elevated ambient temperatures. The results demonstrate a notable increase in efficiency, rising from 37.97% to 43.06% at an ambient temperature of 10°C (winter) and from 31.98% to 40.33% at an ambient temperature of 40°C (summer). This improvement, ranging from 5.09% in winter to 8.35% in summer, represents a significant achievement aligned with the goals of the Oya System. Furthermore, integrating PEMFC contributes to environmental sustainability by utilizing hydrogen, a clean energy source, and reducing greenhouse gas emissions. The system also enhances efficiency through waste heat recovery, further optimizing performance and reducing energy losses. This research highlights the critical role of interface engineering in the hybrid system, particularly the interaction between the PEMFC and the gas turbine. Integrating these two systems involves complex interfaces that facilitate the transfer of electrochemistry, energy, and materials, optimizing the overall performance. This aligns with the conference session's focus on green technologies and resource efficiency. The Oya System exemplifies how innovative hybrid systems can enhance performance while promoting environmentally friendly processes.

Lectins are a wide group of proteins found in plants, animals, and marine organisms. These proteins are characterized by their remarkable ability to bind carbohydrates, playing critical roles in various biological processes, such as cell recognition, immune response, and pathogen defense. Plant-derived lectins, in particular, have shown significant medicinal potential, including antifungal, antiviral, and anti-inflammatory properties. However, to fully harness the therapeutic benefits of plant lectins, efficient extraction and purification techniques are essential. Terminalia catappa, commonly known as the tropical almond tree, has been used in traditional medicine for various therapeutic purposes, including antimicrobial, anti-inflammatory, antitumor, and antioxidant effects. The seeds of this plant contain bioactive compounds, among which lectins play a crucial role. It has been reported that lectins are glycoproteins exhibiting specificity towards certain carbohydrate moieties. This property is made use of in the purification of lectins and other glycoproteins using the technique of affinity chromatography. Due to the presence of sugar residues, lectins can demonstrate hemagglutination activity. This study aimed to isolate and purify lectin-like proteins from the seeds of the medicinally important Terminalia catappa plant. Lectin was isolated using phosphate-buffered saline (PBS) and purified through a series of steps including ammonium sulfate precipitation, dialysis, gel filtration, and affinity chromatography. The saline extract had a protein concentration of 1.5mg/ml, while after performing gel filtration and affinity chromatography, the concentration was found to be 0.9 mg/ml and 0.7mg/mL, respectively. A single peak obtained on the affinity column indicated the lectin’s homogeneous form with a simultaneous increase in the specific activity of the lectin.