Sci

Kalanchoe laetivirens is widely consumed as a medicinal plant in rural and urban communities, traditionally used in folk medicine for treating inflammatory conditions and cancer. However, little is known about its elemental composition and the potential health risks associated with different preparation methods. This study aimed to evaluate concentrations of Al, As, Ba, Co, Cu, Fe, Mg, Mn, Mo, Na, Ni, P, Pb, Se, V, and Zn in raw leaves, tea infusions, and aqueous extracts, and to assess associated health risks. Elemental analysis revealed significant differences among preparations, with raw leaves presenting the highest concentrations, tea showing intermediate values, and aqueous extracts the lowest. For example, potassium (K) reached 15,399.31 ± 131.55 mg/kg in leaves and 12,249.97 ± 240.17 mg/L in tea, while arsenic (As) and lead (Pb) were also detected at concerning levels, with As at 5.98 ± 1.64 mg/L and Pb at 3.82 ± 0.179 mg/L in tea. Risk assessment was performed using the Chronic Daily Intake (CDI), Hazard Quotients (HQs), Hazard Index (HI), and Incremental Lifetime Cancer Risk (ILCR), considering different exposure frequencies. Results indicated phosphorus (P) as the dominant contributor to non-carcinogenic risk, with HI values exceeding safety thresholds in all scenarios, while arsenic was the primary carcinogenic element, with ILCR values up to 10⁻³ in tea. These findings highlight the influence of preparation methods on exposure levels and reinforce the need for continuous monitoring and regulatory guidelines to ensure the safe medicinal use of K. laetivirens.

Artificial intelligence (AI) is transforming pharmaceutical science by shifting drug delivery research from empirical experimentation toward predictive, data-driven innovation. This review critically examines the integration of AI across formulation design, smart drug delivery systems (DDSs), and sustainable pharmaceutics, emphasizing its role in accelerating development, enhancing personalization, and promoting environmental responsibility. AI techniques—including machine learning, deep learning, Bayesian optimization, reinforcement learning, and digital twins—enable precise prediction of critical quality attributes, generative discovery of excipients, and closed-loop optimization with minimal experimental input. These tools have demonstrated particular value in polymeric and nano-based systems through their ability to model complex behaviors and to design stimuli-responsive DDS capable of real-time therapeutic adaptation. Furthermore, AI facilitates the transition toward green pharmaceutics by supporting biodegradable material selection, energy-efficient process design, and life-cycle optimization, thereby aligning drug delivery strategies with global sustainability goals. However, challenges persist, including limited data availability, lack of model interpretability, regulatory uncertainty, and the high computational cost of AI systems. Addressing these limitations requires the implementation of FAIR data principles, physics-informed modeling, and ethically grounded regulatory frameworks. Overall, AI serves not as a replacement for human expertise but as a transformative enabler, redefining DDS as intelligent, adaptive, and sustainable platforms for future pharmaceutical development. Compared with previous reviews that have considered AI-based formulation design, smart DDS, and green pharmaceutics separately, this article integrates these strands and proposes a dual-framework roadmap that situates current AI-enabled DDS within a structured life-cycle perspective and highlights key translational gaps.

This study is motivated by the severe tribological regime of PA6 composites in VR platforms operating under dry or boundary lubrication, where alternating shear during foot rotation, localised contact pressures, and third-body abrasion concurrently challenge wear resistance and retention of strength. This paper presents the results of research into the properties of composites based on polyamide PA6 and molybdenum disulphide, obtained by combining the components through high-intensity mechanochemical activation in a planetary mill and classical mixing in a turbulence mixer. We demonstrate that varying the energy of the premixing stage (mechanochemical activation versus low-energy premixing) serves as an effective means of interfacial engineering in PA6/MoS₂ composites, enabling simultaneous enhancement of mechanical and tribological properties at low filler contents. Analysis of experimental composite samples using Fourier-transform infrared spectroscopy (FTIR) indicates the interaction between MoS₂ and oxygen-containing groups of polyamide while maintaining its overall chemical composition. According to the TG-DSC curves, modification of polyamide leads to an increase in the melting temperature by 2 °C, while mechanical activation ensures stronger interaction between the matrix and the filler. Compared to pure PA6, the tensile strength of composites increases by 10–20% for mechanoactivated materials and by 5–10% for materials obtained by conventional methods. The mechanical activation effect is observed even at minimal amounts (0.25 and 0.5%) of MoS₂ in composites. The toughness of all composites, regardless of the mixing method, increases by 5–7% compared to pure polyamide. All composites show a 10–20% reduction in the coefficient of friction on steel. Simultaneously, the water absorption of composites becomes 5–20% higher than that of the original material, which indicates a change in structure and an increase in porosity. The obtained composite materials are planned to be used for manufacturing platforms for the movement of virtual reality (VR) operators.