Heavy metals are highly toxic at trace levels and their pollution has shown great threat to the environment and public health worldwide where current detection methods require expensive instrumentation and laborious operation, which can only be accomplished in centralized laboratories. Herein, we report a low-cost, paper-based microfluidic analytical device (μPAD) for facile, portable, and disposable monitoring of mercury, lead, chromium, nickel, copper, and iron ions. Triple indicators or ligands that contain ions or molecules are preloaded on the μPADs and upon addition of a metal ion, the colorimetric indicators will elicit color changes observed by the naked eyes. The color features were quantitatively analyzed in a three-dimensional space of red, green, and blue or the RGB-space using digital imaging and color calibration techniques. The sensing platform offers higher accuracy for cross references, and is capable of simultaneous detection and discrimination of different metal ions in even real water samples. It demonstrates great potential for semiquantitative and even qualitative analysis with a sensitivity below the safe limit concentrations, and a controlled error range.
Food recalls due to undeclared allergens or contamination are costly to the food manufacturing industry worldwide. As the industry strives for better manufacturing efficiencies over a diverse range of food products, there is a need for the development of new analytical techniques to improve monitoring of the presence of unintended food allergens during the food manufacturing process. In particular, the monitoring of wash samples from cleaning in place systems (CIP), used in the cleaning of food processing equipment, would allow for the effective removal of allergen containing ingredients in between food batches. Casein proteins constitute the biggest group of proteins in milk and hence are the most common milk protein allergen in food ingredients. As such, these proteins could present an ideal analyte for cleaning validation. In this work, molecularly imprinted polymer nanoparticles (nanoMIPs) with high affinity toward bovine α-casein were synthesized using a solid-phase imprinting method. The nanoMIPs were then characterized and incorporated into label free surface plasmon resonance (SPR) based sensor. The nanoMIPs demonstrated good binding affinity and selectivity toward α-casein (KD ∼ 10 × 10-9 M). This simple affinity sensor demonstrated the quantitative detection of α-casein achieving a detection limit of 127 ± 97.6 ng mL-1 (0.127 ppm) which is far superior to existing commercially available ELISA kits. Recoveries from spiked CIP wastewater samples were within the acceptable range (87-120%). The reported sensor could allow food manufacturers to adequately monitor and manage food allergen risk in food processing environments while ensuring that the food produced is safe for the consumer.
Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.