MicroRNA biosensors for detection of gastrointestinal cancer

Hybrid Nanomaterials: A Brief Overview of Versatile Solutions for Sensor Technology in Healthcare and Environmental Applications

The integration of nanomaterials into sensor technologies not only poses challenges but also opens up promising prospects for future research. These challenges include assessing the toxicity of nanomaterials, scalability issues, and the seamless integration of these materials into existing infrastructures. Future development opportunities lie in creating multifunctional nanocomposites and environmentally friendly nanomaterials. Crucial to this process is collaboration between universities, industry, and regulatory authorities to establish standardization in this evolving field. Our perspective favours using screen-printed sensors that employ nanocomposites with high electrochemical conductivity. This approach not only offers cost-effective production methods but also allows for customizable designs. Furthermore, incorporating hybrids based on carbon-based nanomaterials and functionalized Mxene significantly enhances sensor performance. These high electrochemical conductivity sensors are portable, rapid, and well-suited for on-site environmental monitoring, seamlessly aligning with Internet of Things (IoT) platforms for developing intelligent systems. Simultaneously, advances in electrochemical sensor technology are actively working to elevate sensitivity through integrating nanotechnology, miniaturization, and innovative electrode designs. This comprehensive approach aims to unlock the full potential of sensor technologies, catering to diverse applications ranging from healthcare to environmental monitoring. This review aims to summarise the latest trends in using hybrid nanomaterial-based sensors, explicitly focusing on their application in detecting environmental contaminants.

Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics

BACKGROUND

Specific detection of the type and severity of plant abiotic stresses helps prevent yield loss by considering timely actions. This study introduces a novel method to detect the type and severity of stress in cucumber plants under salinity and drought conditions. Various features, i.e., morphological (image textural features), physiological/biochemical (relative water content, chlorophyll, catalase activity, anthocyanins, phenol content, and proline), as well as miRNA characteristics (the concentration of miRNA-156a, miRNA-166i, miRNA-399g, and miRNA-477b) were extracted from plant leaves, and machine learning methods were used to predict the type and severity of stress by having these features. Support vector machine (SVM) with parameters optimized by genetic algorithm (GA) and particle swarm optimization (PSO) was used for machine learning.

RESULTS

The coefficient of determination of predicting the stress type and severity in plants under both stresses was 0.61, 0.82, and 0.99 using morphological, physiological/biochemical, and miRNA characteristics, respectively. This reveals machine learning methods optimized by metaheuristic optimization techniques can provide specific detection of salt and drought stresses in cucumber plants based on miRNA characteristics. Among the study miRNAs, miRNA-477b and miRNA-399g had the highest and lowest contribution to salt and drought stresses, respectively.

CONCLUSIONS

Comapred to conventional plant traits, miRNAs are more reliable features for providing us with valuable information about plant abiotic diseases at early stages. Using an electrochemical miRNA biosensor similar to one used in this work to measure the miRNA concentration in plant leaves and using a machine learning algorithm such as SVM enable farmers to detect the salt and drought stress at early stages in cucumber plants with very high accuracy.