Response Mechanism of Hyperpolarization-Based Polyion Nanosensors

Colorimetric Optode Sensor with Tripodal Ionophore for Rapid Urinary Ammonium Determination

The amount of ammonium excreted in urine is a crucial indicator for assessing renal metabolic acidosis. Therefore, there is a significant demand for chemical sensors capable of accurately determining urinary ammonium concentrations with high throughput. In this study, we developed an ammonia chemical sensor based on the ion-selective optode principle. This sensor employs strategically designed ionophores and a PTFE gas-permeable film to enhance selectivity for free NH3. The low basicity of the chromoionophore indicator and the weak affinity of the tripodal ammonium receptor (L, log β = 4.62 ± 0.18) contributed to a markedly reduced response time, achieving detection within seconds, a significant improvement over previous sensors. A limit of detection (LOD) of approximately 15 nM was obtained in colorimetric mode. Total inorganic ammonia content in undiluted human urine was successfully determined using the sensor. Additionally, the optode was adapted into a multiwell format, offering potential for high-throughput point-of-care testing of urinary ammonium concentrations and other samples.

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling

Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artificial intelligence (AI). It is possible to identify disease-specific biomarkers using real-time and sensitive molecular interactions. These technologies are precious for genetic, epigenetic, and proteomic changes critical to disease progression and treatment response. AI improves multi-omics profiling by analyzing large, diverse data sets and common patterns traditional methods overlook. Machine learning tools Biomarkers Discovery is revolutionizing, drug resistance is being understood, and medicine is being personalized as the combination of AI and nanosensors has advanced the detection of DNA methylation and proteomic signatures and improved our understanding of cancer, cardiovascular disease and vascular disease. Despite these advances, challenges still exist. Difficulties in integrating data sets, retaining sensors, and building scalable computing tools are the biggest obstacles. It also examines various solutions with advanced AI algorithms and innovations, including fabrication in nanosensor design. Moreover, it highlights the potential of nanosensor-assisted, AI-driven multi-omics profiling to revolutionize disease diagnosis and treatment. As technology advances, these tools pave the way for faster diagnosis, more accurate treatment and improved patient outcomes, offering new hope for personalized medicine.

Optical Detection of Heparin in Whole Blood Samples Using Nanosensors Embedded in an Agarose Hydrogel

Point-of-care quantification of the anticoagulant heparin still remains a significant clinical challenge as the reference method (colorimetric anti-factor Xa assay) cannot be performed in whole blood. Our group recently put forth the novel optical nanosensing principle using an ionic solvatochromic dye as a signal transducer. These nanosensors demonstrated significantly improved selectivity and sensitivity compared to ion-exchange-type polyion nanosensors and enabled protamine/heparin quantification in blood plasma samples. However, because the readout is absorbance-based, they are still not suitable for whole blood measurements. To overcome the background absorbance of blood, the nanosensors were here embedded in an agarose hydrogel capable of filtering out red blood cells while allowing plasma components to diffuse into the gel. Calibration curves for both protamine and heparin were successfully obtained in buffer, undiluted plasma, and undiluted whole blood using different colorimetric image analysis methods and a simple experimental setup.