Heavy metal contamination of water sources has long been a silent yet potent threat, endangering environmental and human health. Conventional wastewater treatments are costly due to high infrastructure expenses, energy consumption, and chemical usage. These treatments lead to secondary environmental pollution, such as producing toxic sludge, greenhouse gaseous emissions, and residual pollutants discharges. Therefore, more sustainable and cost-effective wastewater treatment alternatives are needed to overcome these challenges. Microalgae biosorption and bioaccumulation can bioremediate wastewater by effectively removing heavy metals and other contaminants, such as nitrate and phosphate. By utilizing sunlight and CO2 for growth, microalgae cultivation reduces the need for expensive chemicals and energy-intensive operations in wastewater treatment. Additionally, microalgae can potentially convert heavy metal ions from wastewater into metal nanoparticles, providing a dual benefit of bioremediation and resource recovery. The primary objectives of this review are to assess the effectiveness of microalgae in heavy metal bioremediation and nanoparticle synthesis while also identifying critical research gaps and future directions for optimizing this biotechnology. Heavy metal ions in wastewater can be used as a metal precursor, and metal nanoparticles can be synthesized from wastewater. A review methodology was carried out to assess the availability of literature for readers to identify the research trends and gaps. Mechanisms of microalgae for the biogenesis of metal nanoparticles, including activation, growth, and termination phases, were elucidated. Various chemical interactions between metal ions and functional groups of microalgae, including amine (-NH2), carboxyl (-COOH), phosphate (-PO4), and hydroxyl (-OH) groups were evaluated. Nonetheless, this review also identifies the current challenges and future research directions for optimizing microalgae biotechnology in heavy metal bioremediation and nanoparticle biogenesis.
Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.