This study developed and evaluated a novel transferosomal gel formulation incorporating luliconazole to enhance topical antifungal therapy. Luliconazole, an imidazole antifungal drug, is widely prescribed for dermatophytic infections such as tinea pedis, tinea cruris, and tinea corporis. However, conventional topical formulations often suffer from poor skin penetration, low drug bioavailability, and the need for frequent application, which may reduce patient compliance.

In this work, transferosomes were prepared using thin film hydration with lecithin and Tween 80 at varying concentrations, followed by incorporation into a carbopol gel base. The transferosomes demonstrated high entrapment efficiency of 74.45% and 92.75% with particle sizes ranging between 60–200 nm, and scanning electron microscopy confirmed their spherical morphology. The in vitro release study revealed an inverse relationship between entrapment efficiency and release rate, indicating a controlled and sustained release behavior.

The formulated transferosomal gel was further characterized for pH, drug content, spreadability, and viscosity, all of which fell within acceptable ranges for topical application. Importantly, antifungal activity testing confirmed that the luliconazole transferosomal gel exerted a strong therapeutic effect against dermatophytes.

Overall, the study highlights the potential of transferosomal technology to overcome the limitations of conventional antifungal formulations. By improving drug penetration and sustaining drug release, the luliconazole transferosomal gel reduces the frequency of application required, which may enhance treatment adherence and patient outcomes. These findings underscore the promise of transferosome-based gels as a novel drug delivery strategy for managing dermatophytic skin infections effectively.

A Singaporean research team has developed CAN-Scan (short for Cancer Scan), a next-generation precision oncology platform designed to “scan” the molecular features of each patient’s cancer. Published in Cell Reports Medicine, CAN-Scan integrates patient-derived tumour models, multi-omics profiling, and machine learning to predict disease progression and therapeutic response, with the potential to personalise treatment for colorectal cancer (CRC) patients in the future.

In a leap for biomedical science, researchers from the A*STAR Genome Institute of Singapore (A*STAR GIS) and A*STAR Bioinformatics Institute (A*STAR BII), in collaboration with the University of Zagreb, have developed RiNALMo, an artificial intelligence (AI) model that can “read" and interpret RNA sequences like a language.  This breakthrough helps scientists predict how RNA behaves in the body, potentially accelerating the development of RNA-targeted and RNA-based treatments.

The interest in Mn-based layered oxides for potassium-ion batteries (PIBs) cathodes stems primarily from their impressive capacity and economic viability. However, in Mn-based layered oxides, Mn usually exhibits oxidation states between 3+ and 4+. The existence of Mn³⁺ makes these materials susceptible to substantial Jahn-Teller distortions when K⁺ are inserted or extracted. This structural instability leads to an irreversible multiphase transformation, which in turn severely impacts the cycling performance and causes significant degradation. Therefore, suppressing the Jahn-Teller distortion to reduce the phase transition and improve cyclability is of great importance.