A research team at Chalmers University of Technology, Sweden, has developed new laser technology that could lead to tiny, cost-effective biosensors. The sensors integrate lasers and optics together on a centimeter-sized chip, which could move testing from hospitals to patients' homes. This, in turn, would free up hospital beds and reduce visits to clinics.

A research team led by Dr. Sung Mook Choi of the Energy & Environment Materials Research Division at the Korea Institute of Materials Science (KIMS), in collaboration with a team headed by Professor Seung-Hwa Lee at Changwon National University, has successfully designed and developed a proprietary non-precious metal oxygen evolution reaction (OER) catalyst featuring a layered structure optimized for anion exchange membrane water electrolysis (AEMWE) environments.

Researchers at Fondazione Policlinico Universitario Agostino Gemelli IRCCS have developed a promising machine learning algorithm capable of predicting survival and cause of death for patients with bladder cancer undergoing radical cystectomy.

Lotta Gränse, ophthalmologist and researcher Lund University and Skåne University Hospital in Sweden has demonstrated that eye drops containing cortisone can prevent the development of the very serious eye disease ROP in infants. This has revolutionised treatment and means that the majority of children in Sweden’s Southern Healthcare Region with severe ROP do not need conventional treatment under anaesthesia to avoid serious visual impairment or blindness. The study has been published in the highly ranked journal Ophthalmology.

Patients with Type 1 diabetes (T1D) require accurate and consistent monitoring of their blood glucose levels. Over the past decade, AI models have been explored to tackle this challenge; however, inter-patient variability and large data volumes remain key challenges. In a new study, researchers present BiT-MAML, a model-agnostic algorithm aimed at personalized blood glucose prediction of patients with T1D. This approach overcomes the limitations of existing models and enables precise predictions in real clinical settings.

Compressed air energy storage (CAES) is an effective technology for mitigating the fluctuations associated with renewable energy sources. In this work, a hybrid cogeneration energy system that integrates CAES with high-temperature thermal energy storage and a supercritical CO2 Brayton cycle is proposed for enhancing the overall system performance. This proposal emphasizes system cost-effectiveness, eco-friendliness, and adaptability. Comprehensive analyses, including thermodynamic, exergoeconomic, economic, and sensitivity evaluations, are conducted to assess the viability of the system. The findings indicate that, under design conditions, the system achieves an energy storage density, a round-trip efficiency, an exergy efficiency, a unit product cost, and a dynamic payback period of 5.49 kWh/m3, 58.39%, 61.85%, 0.1421 $/kWh, and 4.81 years, respectively. The high-temperature thermal energy storage unit, intercoolers, and aftercooler show potential for optimization due to their suboptimal exergoeconomic performance. Sensitivity evaluation indicates that the operational effectiveness of the system is highly sensitive to the maximum and minimum air storage pressures, the outlet temperature of the high-temperature thermal energy storage unit, and the isentropic efficiencies of both compressors and turbines. Ultimately, the system is optimized for maximum exergy efficiency and minimum dynamic payback period. These findings demonstrate the significant potential of this system and provide valuable insights for its design and optimization.