Lactoferrin (LF) is a non-heme iron-binding glycoprotein involved in the transport of iron in blood plasma. In addition, it has many biological functions, including antibacterial, antiviral, antimicrobial, antiparasitic, and, importantly, antitumor properties. In this study, we have investigated the potential of employing lactoferrin-iron oxide nanoparticles (LF-IONPs) as a treatment modality for gastric cancer. The study confirms the formation of LF-IONPs with a spherical shape and an average size of 5 ± 2 nm, embedded within the protein matrix. FTIR and Raman analysis revealed that the Fe–O bond stabilized the protein particle interactions. Further, we conducted hyperthermia studies to ascertain whether the proposed composite can generate a sufficient rise in temperature at a low frequency. The results confirmed that we can achieve a temperature rise of about 7 °C at 242.4 kHz, which can be further harnessed for gastric cancer treatment. The particles were further tested for their anti-cancer activity on AGS cells, with and without hyperthermia. Results indicate that LF-IONPs (10 µg/ml) significantly enhance cytotoxicity, resulting in the demise of 67.75 ± 5.2% of cells post hyperthermia, while also exhibiting an inhibitory effect on cell migration compared to control cells, with the most inhibition observed after 36 h of treatment. These findings suggest the potential of LF-IONPs in targeted hyperthermia treatment of gastric cancer.

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

The short half-life in the GI tract necessitates an excess of drugs causing side effects of oral formulations. Here, we report the development and deployment of Bacterioboat, which consists of surface-encapsulated mesoporous nanoparticles on metabolically active Lactobacillus reuteri as a drug carrier suitable for oral administration. Bacterioboat showed up to 16% drug loading of its dry weight, intestinal anchorage around alveoli regions, sustained release, and stability in physiological conditions up to 24 hours. In vivo studies showed that oral delivery of 5-fluorouracil leads to increased potency, resulting in improved shrinkage of solid tumors, enhanced life expectancy, and reduced side effects. This novel design and development make this system ideal for orally administrable drugs with low solubility or permeability or both and even making them effective at a lower dose.