The role of glial and neuronal Eph/ephrin signaling in Drosophila mushroom body development and sleep and circadian behavior.

The Eph receptor, a prototypically large receptor protein tyrosine kinase, interacts with ephrin ligands, forming a bidirectional signaling system that impacts diverse brain functions. Eph receptors and ephrins mediate forward and reverse signaling, affecting neurogenesis, axon guidance, and synaptic signaling. While mammalian studies have emphasized their roles in neurogenesis and synaptic plasticity, the Drosophila counterparts are less studied, especially in glial cells, despite structural similarities. Using RNAi to modulate Eph/ephrin expression in Drosophila neurons and glia, we studied their roles in brain development and sleep and circadian behavior. Knockdown of neuronal ephrin disrupted mushroom body development, while glial knockdown had minimal impact. Surprisingly, disrupting ephrin in neurons or glial cells altered sleep and circadian rhythms, indicating a direct involvement in these behaviors independent from developmental effects. Further analysis revealed distinct sleep phenotypes between neuronal and glial knockdowns, underscoring the intricate interplay within the neural circuits that govern behavior. Glia-specific knockdowns showed altered sleep patterns and reduced circadian rhythmicity, suggesting an intricate role of glia in sleep regulation. Our findings challenge simplistic models of Eph/ephrin signaling limited to neuron-glia communication and emphasize the complexity of the regulatory networks modulating behavior. Future investigations targeting specific glial subtypes will enhance our understanding of Eph/ephrin signaling's role in sleep regulation across species.

Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection.

Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.

Electrokinetic Size-Based Spatial Separation of Micro/Nanospheres Using Paper-Based 3D Origami Preconcentrator.

Sample preparation steps (e.g., preconcentration and separation) are key to enhancing sensitivity and reliability in biomedical and analytical chemistry. However, conventional methods (e.g., ultracentrifugation) cause significant loss of sample as well as their contamination. In this study, we developed a paper-based three-dimensional (3D) origami ion concentration polarization preconcentrator (POP) for highly efficient and facile sample preparation. The unique design of POP enables simultaneous preconcentration and spatial separation of target analytes rapidly and economically. The POP comprises accordion-like multifolded layers with convergent wicking areas that can separate analytes based on their sizes in different layers, which can then be easily isolated by unfolding the POP. We first demonstrated 100-fold preconcentration of albumin and its isolation on the specific layers. Then, we demonstrated the simultaneous preconcentration and spatial separation of microspheres of three different sizes (with diameters of 0.02, 0.2, and 2 µm) on the different layers.

Enhanced paper-based ELISA for simultaneous EVs/exosome isolation and detection using streptavidin agarose-based immobilization.

EVs/exosomes are considered as the next generation of biomarkers, including for liquid biopsies. Consequently, the quantification of EVs/exosomes is crucial for facilitating EV/exosome research and applications. Paper-based enzyme-linked immunosorbent assay (p-ELISA) is a portable diagnostic system with low cost that is simple and easy to use; however, it shows low sensitivity and linearity. In this study, we develop p-ELISA for targeting EVs/exosomes by using streptavidin agarose resin-based immobilization (SARBI). This method reduces assay preparation times, provides strong binding, and retains good sensitivity and linearity. The time required for the total assay, including preparation steps and surface immobilization, was shortened to ∼2 h. We evaluated SARBI p-ELISA systems with/without CD63 capture Ab and then with fetal bovine serum (FBS) and EVs/exosome-depleted fetal bovine serum (dFBS). The results provide evidence supporting the selective capture ability of SARBI p-ELISA. We obtain semiquantitative p-ELISA results using an exosome standard (ES) and human serum (HS), with R2 values of 0.95 and 0.92, respectively.

pH Sensing Characteristics of Extended-Gate Field-Effect Transistor with Al2O3 Layer.

A simple and stable pH sensor based on an extended-gate field-effect transistor (EGFET) is demonstrated using electron-beam deposited Al2O3 as a pH sensing layer. The threshold voltage of the EGFET is modulated by different pH values of the buffer solution. A control experiment with a bare Au electrode confirms that the stable pH sensing response with linearity and reproducibility originates from the Al2O3 sensing layer. The minimum area of the pH sensing layer is estimated by considering that the different sizes of the sensing layer are easily modeled with different values of external capacitors connected to the readout transistor. The study verifies that the pH detection accuracy is improved by using the reference electrode with a KCl electrolyte.

Study of Alzheimer's Disease-Related Biophysical Kinetics with a Microslit-Embedded Cantilever Sensor in a Liquid Environment.

A microsized slit-embedded cantilever sensor (slit cantilever) was fabricated and evaluated as a biosensing platform in a liquid environment. In order to minimize the degradation caused by viscous damping, a 300 × 100 µm² (length × width) sized cantilever was released by a 5 µm gap-surrounding and vibrated by an internal piezoelectric-driven self-actuator. Owing to the structure, when the single side of the slit cantilever was exposed to liquid a significant quality factor (Q = 35) could be achieved. To assess the sensing performance, the slit cantilever was exploited to study the biophysical kinetics related to Aß peptide. First, the quantification of Aß peptide with a concentration of 10 pg/mL to 1 µg/mL was performed. The resonant responses exhibited a dynamic range from 100 pg/mL to 100 ng/mL (-56.5 to -774 ΔHz) and a dissociation constant (KD) of binding affinity was calculated as 1.75 nM. Finally, the Aß self-aggregation associated with AD pathogenesis was monitored by adding monomeric Aß peptides. As the concentration of added analyte increased from 100 ng/mL to 10 µg/mL, both the frequency shift values (-813 to -1804 ΔHz) and associate time constant increased. These results showed the excellent sensing performance of the slit cantilever overcoming a major drawback in liquid environments to become a promising diagnostic tool candidate.

A Micro-Preconcentrator Combined Olfactory Sensing System with a Micromechanical Cantilever Sensor for Detecting 2,4-Dinitrotoluene Gas Vapor.

Preventing unexpected explosive attacks and tracing explosion-related molecules require the development of highly sensitive gas-vapor detection systems. For that purpose, a micromechanical cantilever-based olfactory sensing system including a sample preconcentrator was developed to detect 2,4-dinitrotoluene (2,4-DNT), which is a well-known by-product of the explosive molecule trinitrotoluene (TNT) and exists in concentrations on the order of parts per billion in the atmosphere at room temperature. A peptide receptor (His-Pro-Asn-Phe-Ser-Lys-Tyr-Ile-Leu-His-Gln-Arg) that has high binding affinity for 2,4-DNT was immobilized on the surface of the cantilever sensors to detect 2,4-DNT vapor for highly selective detection. A micro-preconcentrator (µPC) was developed using Tenax-TA adsorbent to produce higher concentrations of 2,4-DNT molecules. The preconcentration was achieved via adsorption and thermal desorption phenomena occurring between target molecules and the adsorbent. The µPC directly integrated with a cantilever sensor and enhanced the sensitivity of the cantilever sensor as a pretreatment tool for the target vapor. The response was rapidly saturated within 5 min and sustained for more than 10 min when the concentrated vapor was introduced. By calculating preconcentration factor values, we verified that the cantilever sensor provides up to an eightfold improvement in sensing performance.

A micro-fabricated force sensor using an all thin film piezoelectric active sensor.

The ability to measure pressure and force is essential in biomedical applications such as minimally invasive surgery (MIS) and palpation for detecting cancer cysts. Here, we report a force sensor for measuring a shear and normal force by combining an arrayed piezoelectric sensors layer with a precut glass top plate connected by four stress concentrating legs. We designed and fabricated a thin film piezoelectric force sensor and proposed an enhanced sensing tool to be used for analyzing gentle touches without the external voltage source used in FET sensors. Both the linear sensor response from 3 kPa to 30 kPa and the exact signal responses from the moving direction illustrate the strong feasibility of the described thin film miniaturized piezoelectric force sensor.

Rapid deep learning-assisted predictive diagnostics for point-of-care testing.

Prominent techniques such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and rapid kits are currently being explored to both enhance sensitivity and reduce assay time for diagnostic tests. Existing commercial molecular methods typically take several hours, while immunoassays can range from several hours to tens of minutes. Rapid diagnostics are crucial in Point-of-Care Testing (POCT). We propose an approach that integrates a time-series deep learning architecture and AI-based verification, for the enhanced result analysis of lateral flow assays. This approach is applicable to both infectious diseases and non-infectious biomarkers. In blind tests using clinical samples, our method achieved diagnostic times as short as 2 minutes, exceeding the accuracy of human analysis at 15 minutes. Furthermore, our technique significantly reduces assay time to just 1-2 minutes in the POCT setting. This advancement has the potential to greatly enhance POCT diagnostics, enabling both healthcare professionals and non-experts to make rapid, accurate decisions.

Affordable on-site COVID-19 test using non-powered preconcentrator.

A simple, affordable point of care test (POCT) is necessary for on-site detection of coronavirus disease 2019 (COVID-19). The lateral flow assay (LFA) has great potential for use in POCT mainly because of factors such as low time consumption, low cost, and ease of use. However, it lacks sensitivity and limits of detection (LOD), which are essential for early diagnostics. In this study, we proposed a non-powered preconcentrator (NPP) based on nanoelectrokinetics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Antigen (Ag) lateral flow assay. The non-powered preconcentrator is composed of glass fiber-based composite paper and ion permselective material, and it can be simply operated by force balancing gravitational, capillary, and depletion-induced forces. The proposed approach helps enrich the SARS-CoV-2 viral nucleocapsid (N) proteins based on a 10-min operation, and it improved the LOD by up to 10-fold. The corresponding virus enrichment, which was evaluated using the reverse-transcriptase polymerase chain reaction (RT-PCR), revealed an improvement in ΔCt values > 3. We successfully demonstrated the enhancement of the NPP-assisted LFA, we extended to applying it to clinical samples. Further, we demonstrated an affordable, easy-to-implement form of LFA by simply designing NPP directly on the LFA buffer tube.

Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay.

Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMARTAI-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA. We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.

PCR-like performance of rapid test with permselective tunable nanotrap.

Highly sensitive rapid testing for COVID-19 is essential for minimizing virus transmission, especially before the onset of symptoms and in asymptomatic cases. Here, we report bioengineered enrichment tools for lateral flow assays (LFAs) with enhanced sensitivity and specificity (BEETLES2), achieving enrichment of SARS-CoV-2 viruses, nucleocapsid (N) proteins and immunoglobulin G (IgG) with 3-minute operation. The limit of detection is improved up to 20-fold. We apply this method to clinical samples, including 83% with either intermediate (35%) or low viral loads (48%), collected from 62 individuals (n = 42 for positive and n = 20 for healthy controls). We observe diagnostic sensitivity, specificity, and accuracy of 88.1%, 100%, and 91.9%, respectively, compared with commercial LFAs alone achieving 14.29%, 100%, and 41.94%, respectively. BEETLES2, with permselectivity and tunability, can enrich the SARS-CoV-2 virus, N proteins, and IgG in the nasopharyngeal/oropharyngeal swab, saliva, and blood serum, enabling reliable and sensitive point-of-care testing, facilitating fast early diagnosis.

Multifunctionalized cantilever systems for electronic nose applications.

Multiple target detection using a cantilever is essential for biosensor, chemical sensor, and electronic nose systems. We report a novel microcantilever array chip that includes four microreaction chambers in a chip, which consequently contains four different functionalized surfaces for multitarget detection. For model tests, we designed microcantilever chips and demonstrated the ability of binding of 2,4-dinitrotoluene (DNT) targets onto four different surfaces. We used peptide receptors that are known to have highly selective binding. By simply using four microreaction chambers, we immobilized DNT specific peptide (HPNFSKYILHQRC; SP), DNT nonspecific peptide (TSMLLMSPKHQAC; NSP), and self-assembled monolayer (SAM) as well as a bare cantilever. After flowing DNT gases through the cantilever chip, we could monitor the four different binding signals simultaneously. The shifts in NSP provided information as a negative control because it contained information of temperature fluctuations and mechanical vibration from gas flow. By utilizing the differential signal of the SP and NSP, we acquired 7.5 Hz in resonant responses that corresponds with 160 part per billion (ppb) DNT concentration, showing the exact binding response by eliminating the inevitable thermal noise, vibration noise, as well as humidity effects on the peptide surface.

Rapid and low-cost, and disposable electrical sensor using an extended gate field-effect transistor for cardiac troponin I detection.

Field effect transistor (FET) biosensor is based on metal oxide field effect transistor that is gated by changes in the surface charges induced the reaction of biomolecules. In most cases of FET biosensor, FET biosensor is not being reused after the reaction; therefore, it is an important concept of investigate the biosensor with simplicity, cheap and reusability. However, the conventional cardiac troponin I (cTnI) sensing technique is inadequate owing to its low sensitivity and high operational time and cost. In this study, we developed a rapid and low-cost, and disposable electrical sensor using an extended gate field-effect transistor (EGFET) to detect cTnI, as a key biomarker for myocardiac infarction. We first investigated pH sensing characteristics according to the pH level, which provided a logarithmically linear sensitivity in the pH sensing buffer solution of approximately 57.9 mV/pH. Subsequently, we prepared a cTnI sample and monitored the reaction between cTnI and cTnI antibodies through the changes in the drain current and transfer curves. Our results showed that the EGFET biosensor could successfully detect the cTnI levels as well as the pH with low-cost and rapid detection.

Nanoelectrokinetic-assisted lateral flow assay for COVID-19 antibody test.

A lateral flow assay (LFA) platform is a powerful tool for point-of-care testing (POCT), especially for self-testing. Although the LFA platform provides a simple and disposable tool for Coronavirus disease of 2019 (COVID-19) antigen (Ag) and antibody (Ab) screening tests, the lower sensitivity for low virus titers has been a bottleneck for practical applications. Herein, we report the combination of a microfluidic paper-based nanoelectrokinetic (NEK) preconcentrator and an LFA platform for enhancing the sensitivity and limit of detection (LOD). Biomarkers were electrokinetically preconcentrated onto a specific layer using the NEK preconcentrator, which was then coupled with LFA diagnostic devices for enhanced performance. Using this nanoelectrokinetic-assisted LFA (NEK-LFA) platform for self-testing, the severe acute respiratory syndrome coronavirus 2 Immunoglobulin G (SARS-CoV-2 IgG) sample was preconcentrated from serum samples. After preconcentration, the LOD of the LFA was enhanced by 32-fold, with an increase in analytical sensitivity (16.4%), which may offer a new opportunity for POCT and self-testing, especially in the COVID-19 pandemic and endemic global context.

Origami paper-based sample preconcentration using sequentially driven ion concentration polarization.

Ion concentration polarization (ICP) is one of the preconcentration techniques which can acquire a high preconcentration factor. Still, the main hurdles of ICP are its instability and low efficiency under physiological conditions with high ionic strength and abundant biomolecules. Here, we suggested a sequentially driven ICP process, which enhanced the electrokinetic force required for preconcentration, enabling enrichment of highly ionic raw samples without increasing the electric field. We acquired a 13-fold preconcentration factor (PF) in human serum using a paper-based origami structure consisting of multiple layers for three-dimensional sequential ICP (3D seq-ICP). Moreover, we demonstrated a paper-based enzyme-linked immunosorbent assay (ELISA) by 3D seq-ICP using tau protein, showing a 6-fold increase in ELISA signals.

Longitudinal profiling of oligomeric Aß in human nasal discharge reflecting cognitive decline in probable Alzheimer's disease.

Despite clinical evidence indicating a close relationship between olfactory dysfunction and Alzheimer's disease (AD), further investigations are warranted to determine the diagnostic potential of nasal surrogate biomarkers for AD. In this study, we first identified soluble amyloid-ß (Aß), the key biomarker of AD, in patient nasal discharge using proteomic analysis. Then, we profiled the significant differences in Aß oligomers level between patient groups with mild or moderate cognitive decline (n = 39) and an age-matched normal control group (n = 21) by immunoblot analysis and comparing the levels of Aß by a self-standard method with interdigitated microelectrode sensor systems. All subjects received the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and the Global Deterioration Scale (GDS) for grouping. We observed higher levels of Aß oligomers in probable AD subjects with lower MMSE, higher CDR, and higher GDS compared to the normal control group. Moreover, mild and moderate subject groups could be distinguished based on the increased composition of two oligomers, 12-mer Aß*56 and 15-mer AßO, respectively. The longitudinal cohort study confirmed that the cognitive decline of mild AD patients with high nasal discharge Aß*56 levels advanced to the moderate stage within three years. Our clinical evidence strongly supports the view that the presence of oligomeric Aß proteins in nasal discharge is a potential surrogate biomarker of AD and an indicator of cognitive decline progression.

Highly selective reduced graphene oxide (rGO) sensor based on a peptide aptamer receptor for detecting explosives.

An essential requirement for bio/chemical sensors and electronic nose systems is the ability to detect the intended target at room temperature with high selectivity. We report a reduced graphene oxide (rGO)-based gas sensor functionalized with a peptide receptor to detect dinitrotoluene (DNT), which is a byproduct of trinitrotoluene (TNT). We fabricated the multi-arrayed rGO sensor using spin coating and a standard microfabrication technique. Subsequently, the rGO was subjected to photolithography and an etching process, after which we prepared the DNT-specific binding peptide (DNT-bp, sequence: His-Pro-Asn-Phe-Se r-Lys-Tyr-IleLeu-HisGln-Arg-Cys) and DNT non-specific binding peptide (DNT-nbp, sequence: Thr-Ser-Met-Leu-Leu-Met-Ser-Pro-Lys-His-Gln-Ala-Cys). These two peptides were prepared to function as highly specific and highly non-specific (for the control experiment) peptide receptors, respectively. By detecting the differential signals between the DNT-bp and DNT-nbp functionalized rGO sensor, we demonstrated the ability of 2,4-dinitrotoluene (DNT) targets to bind to DNT-specific binding peptide surfaces, showing good sensitivity and selectivity. The advantage of using the differential signal is that it eliminates unwanted electrical noise and/or environmental effects. We achieved sensitivity of 27 ± 2 × 10-6 per part per billion (ppb) for the slope of resistance change versus DNT gas concentration of 80, 160, 240, 320, and 480 ppm, respectively. By sequentially flowing DNT vapor (320 ppb), acetone (100 ppm), toluene (1 ppm), and ethanol (100 ppm) onto the rGO sensors, the change in the signal of rGO in the presence of DNT gas is 6400 × 10-6 per ppb whereas the signals from the other gases show no changes, representing highly selective performance. Using this platform, we were also able to regenerate the surface by simply purging with N2.

Comparative analyses of plasma amyloid-ß levels in heterogeneous and monomerized states by interdigitated microelectrode sensor system.

Detection of amyloid-ß (Aß) aggregates contributes to the diagnosis of Alzheimer disease (AD). Plasma Aß is deemed a less invasive and more accessible hallmark of AD, as Aß can penetrate blood-brain barriers. However, correlations between biofluidic Aß concentrations and AD progression has been tenuous. Here, we introduce a diagnostic technique that compares the heterogeneous and the monomerized states of Aß in plasma. We used a small molecule, EPPS [4-(2-hydroxyethyl)-1-piperazinepropanesulfonic acid], to dissociate aggregated Aß into monomers to enhance quantification accuracy. Subsequently, Aß levels of EPPS-treated plasma were compared to those of untreated samples to minimize inter- and intraindividual variations. The interdigitated microelectrode sensor system was used to measure plasma Aß levels on a scale of 0.1 pg/ml. The implementation of this self-standard blood test resulted in substantial distinctions between patients with AD and individuals with normal cognition (NC), with selectivity and sensitivity over 90%.

Toward Exosome-Based Neuronal Diagnostic Devices.

Targeting exosome for liquid biopsy has gained significant attention for its diagnostic and therapeutic potential. For detecting neuronal disease diagnosis such as Alzheimer's disease (AD), the main technique for identifying AD still relies on positron-emission tomography (PET) imaging to detect the presence of amyloid-ß (Aß). While the detection of Aß in cerebrospinal fluid has also been suggested as a marker for AD, the lack of quantitative measurements has compromised existing assays. In cerebrospinal fluid, in addition to Aß, T-Tau, and P-Tau, alpha-synuclein has been considered a biomarker of neurodegeneration. This review suggests that and explains how the exosome can be used as a neuronal diagnostic component. To this end, we summarize current progress in exosome preparation/isolation and quantification techniques and comment on the outlooks for neuronal exosome-based diagnostic techniques.