Enhanced sensitivity of chimeric insect olfactory co-receptors for detecting odorant molecules.

Insect olfactory receptors (ORs) are seven-transmembrane domain ion channels that function by forming heteromeric complexes with olfactory receptor co-receptors (Orcos). In this study, we investigated the potential for enhancing sensitivity of odor detection and responsivity through genetic modification of Orcos, considering its wider application in odor sensing. First, we measured the intensity of response to 1-octen-3-ol for the mosquito Aedes aegypti OR (AaOR8) when complexed individually with an Orco from the same mosquito (AaOrco), the honeybee Apis mellifera (AmOrco), the silkworm Bombyx mori (BmOrco), or the fruit fly Drosophila melanogaster (DmOrco). Relative to the other Orcos, AmOrco demonstrated higher sensitivity and responsivity, with a 1.8 to 21-fold decrease in the half-maximal effective concentration (EC50) and a 1.6-8.8-fold increase in the maximal effect (Emax), respectively. Furthermore, AmOrco co-expressed with AaOR10, BmOR56, or DmOR47a showed higher sensitivity and responsivity than AaOrco, BmOrco, or DmOrco co-expressed with their respective ORs. To further increase sensitivity and responsivity, we engineered chimeric Orcos by fusing AmOrco with DmOrco, considering the domain characteristics of Orcos. The response to 1-octen-3-ol was evaluated for AaOR8 when complexed individually with AmOrco, as well as for a mutant that combines DmOrco from the N-terminal (NT) to the C-terminal region of the fourth transmembrane domain (TM4) with the region of AmOrco following TM4 (Dm[NT-TM4]AmOrco). When compared to AmOrco, Dm(NT-TM4)AmOrco showed higher sensitivity and responsivity, with a 1.4-fold decrease in the EC50 and a 1.4-fold increase in the Emax, respectively. In addition, Dm(NT-TM4)AmOrco co-expressed with either DmOR47a or BmOR56 demonstrated higher sensitivity and responsivity than AmOrco co-expressed with their respective ORs. These results suggest that AmOrco could be a relatively more sensitive Orco, and further enhancement of sensitivity and responsivity could be achieved through recombination with heterologous Orcos near the TM4 of AmOrco.

Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics.

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

Autonomous Artificial Olfactory Sensor Systems with Homeostasis Recovery via a Seamless Neuromorphic Architecture.

Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human-like neuromorphic sensory system. In this study, a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self-alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40 ppm, respectively. Thin-film-transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long-term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance is also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture.

Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning.

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography-mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize.

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Ability of animals to detect cancer odors.

The olfactory capacity of animals has long been used by humans to help with various activities, e.g., hunting, detecting mines, locating people, and diagnosing diseases. Cancer is among the leading diseases causing death worldwide. Several recent studies have underscored the benefit of using scent to detect cancer, and this paper will review the studies using animals to detect tumor scents. A large variety of animals have been used for this purpose-dogs, rodents, insects, and nematodes-and have shown their capacity to detect cancer, with a success rate close to 90%. Here we discuss these studies, their methodologies, and the animal models used. Finally, we discuss the medical perspectives for cancer diagnosis using odors.

Repetitive Direct Comparison Method for Odor Sensing.

Olfactory sensors are one of the most anticipated applications of gas sensors. To distinguish odors-complex mixtures of gas species, it is necessary to extract sensor responses originating from the target odors. However, the responses of gas sensors tend to be affected by interfering gases with much higher concentrations than target odor molecules. To realize practical applications of olfactory sensors, extracting minute sensor responses of odors from major interfering gases is required. In this study, we propose a repetitive direct comparison (rDC) method, which can highlight the difference in odors by alternately injecting the two target odors into a gas sensor. We verified the feasibility of the rDC method on chocolates with two different flavors by using a sensor system based on membrane-type surface stress sensors (MSS). The odors of the chocolates were measured by the rDC method, and the signal-to-noise ratios (S/N) of the measurements were evaluated. The results showed that the rDC method achieved improved S/N compared to a typical measurement. The result also indicates that sensing signals could be enhanced for a specific combination of receptor materials of MSS and target odors.

Biomimetic integrated gustatory and olfactory sensing array based on HL-1 cardiomyocyte facilitating drug screening for tachycardia treatment.

The ectopic co-expression of taste and olfactory receptors in cardiomyocytes provides not only possibilities for the construction of biomimetic gustatory and olfactory sensors but also promising novel therapeutic targets for tachycardia treatment. Here, bitter taste and olfactory receptors endogenously expressed in HL-1 cells were verified by RT-PCR and immunofluorescence staining. Then HL-1 cardiomyocyte-based integrated gustatory and olfactory sensing array coupling with the microelectrode array (MEA) was first constructed for drugs screening and evaluation for tachycardia treatment. The MEA sensor detected the extracellular field potentials and reflected the systolic-diastolic properties of cardiomyocytes in real time in a label-free and non-invasive way. The in vitro tachycardia model was constructed using isoproterenol as the stimulator. The proposed sensing array facilitated potential drug screening for tachycardia treatment, such as salicin, artemisinin, xanthotoxin, and azelaic acid which all activated specific receptors on HL-1 cells. IC50 values for four potential drugs were calculated to be 0.0036 µM, 309.8 µM, 14.68 µM, and 0.102 µM, respectively. Visualization analysis with heatmaps and PCA cluster showed that different taste and odorous drugs could be easily distinguished. The mean inter-class Euclidean distance between different bitter drugs was 1.681, which was smaller than the distance between bitter and odorous drugs of 2.764. And the inter-class distance was significantly higher than the mean intra-class Euclidean distance of 1.172. In summary, this study not only indicates a new path for constructing novel integrated gustatory and olfactory sensors but also provides a powerful tool for the quantitative evaluation of potential drugs for tachycardia treatment.

Biting Innovations of Mosquito-Based Biomaterials and Medical Devices.

Mosquitoes are commonly viewed as pests and deadly predators by humans. Despite this perception, investigations of their survival-based behaviors, select anatomical features, and biological composition have led to the creation of several beneficial technologies for medical applications. In this review, we briefly explore these mosquito-based innovations by discussing how unique characteristics and behaviors of mosquitoes drive the development of select biomaterials and medical devices. Mosquito-inspired microneedles have been fabricated from a variety of materials, including biocompatible metals and polymers, to mimic of the mouthparts that some mosquitoes use to bite a host with minimal injury during blood collection. The salivary components that these mosquitoes use to reduce the clotting of blood extracted during the biting process provide a rich source of anticoagulants that could potentially be integrated into blood-contacting biomaterials or administered in therapeutics to reduce the risk of thrombosis. Mosquito movement, vision, and olfaction are other behaviors that also have the potential for inspiring the development of medically relevant technologies. For instance, viscoelastic proteins that facilitate mosquito movement are being investigated for use in tissue engineering and drug delivery applications. Even the non-wetting nanostructure of a mosquito eye has inspired the creation of a robust superhydrophobic surface coating that shows promise for biomaterial and drug delivery applications. Additionally, biosensors incorporating mosquito olfactory receptors have been built to detect disease-specific volatile organic compounds. Advanced technologies derived from mosquitoes, and insects in general, form a research area that is ripe for exploration and can uncover potential in further dissecting mosquito features for the continued development of novel medical innovations.

Biosensing with Insect Odorant Receptor Nanodiscs and Carbon Nanotube Field-Effect Transistors.

Insect odorant receptors have been reconstituted into lipid nanodiscs and tethered to carbon nanotube field-effect transistors to function as a biosensor. Here, four different insect odorant receptors (ORs) from Drosophila melanogaster (DmelOR10a, DmelOR22a, DmelOR35a, and DmelOR71a) were expressed in Sf9 cells, purified, and reconstituted into lipid nanodiscs. We have demonstrated that each of these ORs produce a selective and highly sensitive electrical response to their respective positive ligands, methyl salicylate, methyl hexanoate, trans-2-hexen-1-al, and 4-ethylguaiacol, with limits of detection in the low femtomolar range. No detection was observed for each OR against control ligands, and empty nanodiscs showed no specific sensor signal for any of the odorant molecules. Our results are the first evidence that insect ORs can be integrated into lipid nanodiscs and used as primary sensing elements for bioelectronic nose technologies.