Nanoparticle drug delivery (NDDS) is a novel system in which the drugs are delivered to the site of action by small particles in the nanometer range. Natural or synthetic polymers are used as vectors in NDDS, as they provide targeted, sustained release and biodegradability. Here, we used the chitosan and hepatoma cell-specific binding molecule, glycyrrhetinic acid (GA), to synthesize glycyrrhetinic acid-modified chitosan (GA-CTS). The synthetic product was confirmed by Fourier transformed infrared spectroscopy (FT-IR) and 1H-nuclear magnetic resonance (1H-NMR). By combining GA-CTS and 5-FU (5-fluorouracil), we obtained a GA-CTS/5-FU nanoparticle, with a particle size of 217.2 nm, a drug loading of 1.56% and a polydispersity index of 0.003. The GA-CTS/5-FU nanoparticle provided a sustained release system comprising three distinct phases of quick, steady and slow release. We demonstrated that the nanoparticle accumulated in the liver. In vitro data indicated that it had a dose- and time-dependent anti-cancer effect. The effective drug exposure time against hepatic cancer cells was increased in comparison with that observed with 5-FU. Additionally, GA-CTS/5-FU significantly inhibited the growth of drug-resistant hepatoma, which may compensate for the drug-resistance of 5-FU. In vivo studies on an orthotropic liver cancer mouse model demonstrated that GA-CTS/5-FU significantly inhibited tumor growth, resulting in increased survival time.

Biodegradable polymer nanoparticle drug delivery systems provide targeted drug delivery, improved pharmacokinetic and biodistribution, enhanced drug stability and fewer side effects. These drug delivery systems are widely used for delivering cytotoxic agents. In the present study, we synthesized GC/5-FU nanoparticles by combining galactosylated chitosan (GC) material with 5-FU, and tested its effect on liver cancer in vitro and in vivo. The in vitro anti-cancer effects of this sustained release system were both dose- and time-dependent, and demonstrated higher cytotoxicity against hepatic cancer cells than against other cell types. The distribution of GC/5-FU in vivo revealed the greatest accumulation in hepatic cancer tissues. GC/5-FU significantly inhibited tumor growth in an orthotropic liver cancer mouse model, resulting in a significant reduction in tumor weight and increased survival time in comparison to 5-FU alone. Flow cytometry and TUNEL assays in hepatic cancer cells showed that GC/5-FU was associated with higher rates of G0–G1 arrest and apoptosis than 5-FU. Analysis of apoptosis pathways indicated that GC/5-FU upregulates p53 expression at both protein and mRNA levels. This in turn lowers Bcl-2/Bax expression resulting in mitochondrial release of cytochrome C into the cytosol with subsequent caspase-3 activation. Upregulation of caspase-3 expression decreased poly ADP-ribose polymerase 1 (PARP-1) at mRNA and protein levels, further promoting apoptosis. These findings indicate that sustained release of GC/5-FU nanoparticles are more effective at targeting hepatic cancer cells than 5-FU monotherapy in the mouse orthotropic liver cancer mouse model.

GC/5-FU nanoparticles can significantly inhibit the growth of liver cancer in mice via the p53 apoptosis pathway, and relieve the side effects and immunosuppression of 5-FU.

Background The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow. Main text A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections. Conclusion The review will undoubtedly assist in updating the scientific community’s understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.