Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Comparative Evaluation of GC Gold Label IX with GC Gold Label IX Extra and GC Gold Label Hybrid in Cavitated Lesions Prepared by Hand Instrumentation Technique in Primary Molars: An In Vivo Study.

Aim: The purpose of the present study was to compare the clinical efficacy of GC Gold Label IX, GC Gold Label IX Extra, and GC Gold Label Hybrid in occlusal surface cavities in primary molars prepared by hand instrumentation technique. Materials and methods: Ninety primary molars were selected from the children according to the inclusion and exclusion criteria. The selected teeth were randomly allocated into the following three groups, with 30 teeth in each group, according to the restorative material used: group I: GC Gold Label IX, group II: GC Gold Label IX Extra, and group III: GC Gold Label Hybrid. Occlusal cavities were prepared using hand instruments. Caries removal efficacy was verified using caries detector dye. The restorative materials were mixed and condensed into the prepared cavities. The restorations were assessed using modified United States Public Health Service (USPHS) evaluation criteria at 1, 3, and 6 months. Results: The results were tabulated and statistically analyzed using the Chi-squared test. No significant difference was found in terms of color match, marginal discoloration, marginal integrity, surface roughness, and secondary caries, but a significant difference was observed in terms of retention and anatomic form between group I and group III. Conclusion: GC Gold Label Hybrid exhibited better clinical performance with respect to GC Gold Label IX and comparable clinical performance with respect to GC Gold Label IX Extra. Clinical significance: GC Gold Label Hybrid is a newly developed glass ionomer cement (GIC), and its clinical performance needs to be studied. How to cite this article: Mittal M, Bajaj N, Bhola M, et al. Comparative Evaluation of GC Gold Label IX with GC Gold Label IX Extra and GC Gold Label Hybrid in Cavitated Lesions Prepared by Hand Instrumentation Technique in Primary Molars: An In Vivo Study. Int J Clin Pediatr Dent 2024;17(5):570-575.

BDPapayaLeaf: A dataset of papaya leaf for disease detection, classification, and analysis.

Papaya is a popular vegetable and fruit in both developing and developed countries. Nonetheless, Bangladesh's agricultural landscape is significantly influenced by papaya cultivation. However, disease is a common impediment to papaya productivity, adversely affecting papaya quality and yield and leading to substantial economic losses for farmers. Research suggests that computer-aided disease diagnosis and machine learning (ML) models can improve papaya production by detecting and classifying diseases. In this line, a dataset of papaya is required to diagnose the disease. Moreover, like many other fruits, papaya disease may vary from country to country. Therefore, the country-based papaya disease dataset is required. In this study, a papaya dataset is collected from Dhaka, Bangladesh. This dataset contains 2159 original images from five classes, including the healthy control class and four papaya leaf diseases: Anthracnose, Bacterial Spot, Curl, and Ring spot. Besides the original images, the dataset contains 210 annotated data for each of the five classes. The dataset contains two types of data: the whole image and the annotated image. The image will interest data scientists who apply disease detection through a convolutional neural network (CNN) and its variants. Furthermore, the annotated images, such as You Only Look Once (YOLO), U-Net, Mask R-CNN, and Single Shot Detection (SSD), will be helpful for semantic segmentation. Since firm-applicable AI devices and mobile and web applications are in demand, the dataset collected in this study will offer multiple options for integrating ML models into AI devices. In countries with weather and climate similar to Bangladesh, data scientists may use their dataset in that context.

Adenanthin exhibits anti-inflammatory effects by covalently targeting the p65 subunit in the NF-κB signaling pathway.

Adenanthin is a structurally unique ent-kaurane diterpenoid isolated from Rabdosia adenantha, a traditional Chinese medicinal plant with potent anti-cancer and anti-inflammatory activities. However, its anti-inflammatory molecular mechanism remains largely elusive to date. Here, we developed an affinity-based label-free protein profiling (ALFPP) to identify potential covalent targets of electrophilic natural products with ketone or aldehyde groups. Using ALFPP, we identified 27 potential covalent targets of adenanthin, among which p65 (RelA) has been associated with its anti-inflammatory activities. Through a series of experiments, including LC-MS/MS, molecular docking, electrophoretic mobility shift assays (EMSA), and genome editing, we demonstrated that adenanthin could covalently modify the Cys38 residue of p65 to affect the binding of DNA to p65, thereby inhibiting the NF-κB signaling pathway. ALFPP will facilitate the target identification of electrophilic carbonylated natural products, especially those containing α, ß-unsaturated keto groups. Furthermore, the elucidation of the molecular mechanism of adenanthin will contribute to new drug development of adenanthin to treat inflammations and cancers, enhancing the possibility for its clinical application.

Comparison of the nutritional composition and calculated Nutri-score classifications of the Dutch food retail supply in 2018 and 2020.

OBJECTIVE: In the Netherlands, reformulation strategies have been established for several years, whereas Nutri-Score was implemented in 2024. Besides being a helpful tool for consumers to make healthier food choices, Nutri-Score also aims to stimulate food reformulation by food manufacturers. The present study investigates whether changes in food composition could have led to different calculated Nutri-Score classifications. DESIGN: Food compositions and Nutri-Score classifications were calculated using the updated Nutri-Score algorithm. Food groups with the largest change in the distribution of Nutri-Score classifications were analysed in-depth by plotting frequency distributions and calculating median contents for nutrient contents that relatively changed the most in 2020. SETTING: Food composition data were available from the Dutch Branded Food database in 2018 (n 38 295) and 2020 (n 48 091). PARTICIPANTS: Not applicable. RESULTS: In general, median nutrient contents and calculated Nutri-Score classifications were similar for 2018 and 2020. The median sugar and SFA contents were lower for some food groups (e.g. breakfast cereals, meat preserves, sweets and sweet goods) in 2020 compared to 2018. The median SFA content for meat preserves and sweets and sweet goods was relatively low in Nutri-Score classification A ascending towards higher median content in Nutri-Score classification E. CONCLUSIONS: Although food reformulation was not substantial in the Dutch food retail supply in 2018 and 2020, some differences in Nutri-Score classifications were observed. When implemented, Nutri-Score may encourage food manufacturers to increase their reformulation efforts. Repeated monitoring of food compositions and Nutri-Score classifications is recommended to establish reformulation efforts by food manufacturers.

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.

Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-024-00166-4.

Agomelatine in pediatric patients with moderate to severe major depressive disorder: an open-label extension study.

Major depressive disorder (MDD) in young people is a common psychiatric disorder, but treatment options are limited. Agomelatine has demonstrated short-term efficacy and safety in pediatric patients. We report here the results of a 92-week open-label extension (OLE). The international, multicenter, double-blind, study randomized 400 patients (80 children, 320 adolescents) with moderate-to-severe MDD to one of four treatment groups: agomelatine 10 mg (n = 102), agomelatine 25 mg (n = 95), placebo (n = 103), and fluoxetine 10-20 mg (n = 100). After 12 weeks, patients who could benefit from treatment continuation were offered entry into an optional OLE during which they received agomelatine 10 or 25 mg for a further 92 weeks. A total of 339 patients (271 adolescents) entered the OLE. Treatment groups considered for the OLE analysis reflected those received in the double-blind and OLE periods: agomelatine (10 or 25 mg) in both (ago/ago, n = 170); placebo then agomelatine 10-25 mg (pcb/ago, n = 85); or fluoxetine then agomelatine 10-25 mg (fluox/ago, n = 84). Mean age (± SD) at entry into the double-blind phase (Week 0) was 13.6 ± 2.7 years and 61.9% were female. Mean changes in Children's Depression Rating Scale revised (CDRS-R) raw total score from Week 12 to last post-Week 12 value in the three groups were - 16.3 ± 12.2 (ago/ago), - 18.9 ± 16.1 (pcb/ago), and - 16.1 ± 15.5 (fluox/ago), reflecting the difference in efficacy between treatments during the double-blind period, and heterogeneity at W12 between the treatment groups. Adverse events considered related to treatment occurred in 14.5% of patients: 15.3% ago/ago, 16.5% pcb/ago, and 10.7% fluox/ago. Three patients (all adolescents) experienced treatment-related severe adverse events: two treated with ago/ago and one treated with pcb/ago. Among the adolescents, one treatment-related severe adverse event in a patient in the pcb/ago group led to study withdrawal. Agomelatine was associated with continuous improvement in depressive symptoms without unexpected safety signals. These findings support the safe use of agomelatine in a pediatric population with moderate-to-severe MDD for up to 104 weeks.Trial registration No: EUDRACT No. 2015-002181-23.

The temporal profile of self-prioritization.

Personal relevance exerts a powerful influence on decisional processing, such that arbitrary stimuli associated with the self are classified more rapidly than identical material linked with other people. Notwithstanding numerous demonstrations of this facilitatory effect, it remains unclear whether self-prioritization is a temporally stable outcome of decision-making. Accordingly, using a shape-label matching task in combination with computational modeling, the current experiment investigated this matter. The results were informative. First, regardless of the target of comparison (i.e., friend or stranger), self-prioritization was a persistent product of decision-making across the testing session. Second, a variant of the standard drift diffusion model in which decisional boundaries collapsed gradually over the course of the task best fit the observed data. Third, whereas the efficiency of stimulus processing increased for other-related stimuli during the task, it decreased for self-related material. Collectively, these findings advance understanding of the temporal profile of self-prioritization.

People are skeptical of headlines labeled as AI-generated, even if true or human-made, because they assume full AI automation.

The rise of generative AI tools has sparked debates about the labeling of AI-generated content. Yet, the impact of such labels remains uncertain. In two preregistered online experiments among US and UK participants (N = 4,976), we show that while participants did not equate "AI-generated" with "False," labeling headlines as AI-generated lowered their perceived accuracy and participants' willingness to share them, regardless of whether the headlines were true or false, and created by humans or AI. The impact of labeling headlines as AI-generated was three times smaller than labeling them as false. This AI aversion is due to expectations that headlines labeled as AI-generated have been entirely written by AI with no human supervision. These findings suggest that the labeling of AI-generated content should be approached cautiously to avoid unintended negative effects on harmless or even beneficial AI-generated content and that effective deployment of labels requires transparency regarding their meaning.

Influence of qualitative menu labeling on diners' food choices: A controlled quasi-experiment in self-service buffet restaurants.

Menu labeling is a strategy to promote healthier food choices in restaurants. This study aimed to analyze the influence of a qualitative menu labeling on the healthfulness of diners' food choices in self-service buffet restaurants. A controlled quasi-experiment comparing parallel groups at baseline and intervention periods was conducted in a control restaurant and an intervention restaurant. Qualitative labels provided information on the name and ingredients list of the dish, highlighting the use of organic vegetables. The same food menu was served in both restaurants. Menu labeling was implemented at the intervention restaurant for six dishes each day, three considered healthier and three less healthy. Dishes were weighed at the beginning and at the end of the service to estimate the average portion size (g) of diners, during five consecutive days at baseline and intervention. Diners who had lunch at the intervention restaurant were invited to participate in a survey on the use of menu labels. Of the 153 interviewed diners, 31% reported noticing menu labels. Of these, 57% said menu labeling had influenced their food choices at the restaurant. No changes in portion sizes were observed between baseline and intervention periods within control and intervention restaurants. However, it was found that the provision of qualitative menu labeling sparked the interest of restaurant managers in improving meal quality and modifying recipes to eliminate ultra-processed ingredients. Thus, the implementation of the proposed menu labeling model can promote healthier food choices through the reformulation of culinary recipes. However, it is essential to adopt strategies to enable its direct impact.

Real-time, specific, and label-free transistor-based sensing of organophosphates in liquid.

Organophosphates (OP), commonly used in agriculture and as chemical warfare agents, pose significant environmental risks, necessitating real-time, low-cost OP detection methods. In particular, liquid-phase OP sensing with minimal sample volumes is crucial. While several methods allow rapid detection of low concentrations of OP vapors, they are effective only in the short term, while vapors are still being produced. Many OP compounds are semi-volatile, leading to the contamination of water, soil, and surfaces, posing a risk of secondary, long-term exposure. Detecting this contamination requires methods that can be directly applied to droplets of the affected medium. Currently, no method provides the desired combination of ultra-sensitivity, quantitative detection, rapid response, and low-cost for detecting OPs in liquid samples. This study aims to demonstrate quantitative, low-cost, real-time, specific, and label-free OP sensing in ultra-small samples using a transistor-based approach. The current work employs the 2-(4-Aminophenyl)-1,1,1,3,3,3-hexafluoro-2-propanol (aminophenyl-HFIP) functionalized meta-nano-channel field-effect chemical sensor (MNChem sensor) to monitor the organophosphate, diethyl cyanophosphonate (DCNP), in liquid samples. The silicon component of the MNChem is fabricated using a complementary metal-oxide semiconductor (CMOS) process, and the amine-based chemical functionalization of the sensing area is performed post-fabrication. The MNChem sensor provides electrostatic control over the source-drain current (IDS), allowing an optimized channel configuration that efficiently transduces localized OP recognition events into significant IDS variations. Sensing is performed using 0.5 µL buffer solution to simulate a miniature field-deployable sensor for on-site liquid analysis. We report the sensing of DCNP with a limit-of-detection of 100 fg/mL, a dynamic range of 9 orders of magnitude, and excellent linearity (≥0.97) and sensitivity. Control measurements confirm the specificity and reliability of the sensor's response, validating its applicability. This study introduces a novel method for OP detection in contaminated droplets using a low-cost disposable transistor technology.

Responses to the updated Nutri-Score algorithms in Norway: A qualitative study among food system actors in the NewTools-project.

Background: Nutri-Score is a front-of-pack label grading foods and beverages from A to E indicating nutritional quality based on the foods' favorable and unfavorable components, and a contender in the ongoing debate on the possible implementation of a harmonized mandatory front-of-pack nutrition label in the European Union. NewTools is a research project on scoring systems for foods involving 28 partners representing actors involved in the Norwegian food system. Objective: This study aimed to explore views reported by Norwegian food system actors on the advantages and disadvantages with the updated Nutri-Score algorithms for food and beverages (2022-2023). This included Nutri-Score's performance in ranking foods according to the national food-based dietary guidelines and to the nutritional challenges in Norway. Design: A total of 28 project partners and 15 other food system stakeholders following the NewTools-project were invited to provide responses on the Nutri-Score algorithms and their application on foods and beverages in the Norwegian food composition table. Thirteen written responses were received and analyzed with qualitative content analysis. Results: The responses to the updated Nutri-Score varied in content, reflecting mainly concerns. Examples of perceived concerns included excessive penalty of salt content; insufficient differentiation based on fat content in meat, sausages, cheese, and milk; and several unreasonable comparisons across food categories. They also expressed a concern that Nutri-Score may stimulate to increased food processing, and some reported inconsistencies between Nutri-Score's classification of foods and national nutrition guidelines and policies. Discussion and conclusion: Several concerns with the updated Nutri-Score algorithms were raised, including the weighting of specific nutrients, unfair outcomes when comparing across food categories, and inconsistencies with established Norwegian nutrition guidelines and policies. The results should be interpreted with caution, as some perspectives from the Norwegian food system may be missing.

Eliminating quantization errors in classification-based sound source localization.

Sound Source Localization (SSL) involves estimating the Direction of Arrival (DOA) of sound sources. Since the DOA estimation output space is continuous, regression might be more suitable for DOA, offering higher precision. However, in practice, classification often outperforms regression, exhibiting greater robustness. Conversely, classification's drawback is inherent quantization error. Within the classification paradigm, the DOA output space is discretized into several intervals, each treated as a class. These classes show strong inter-class correlations, being inherently ordered, with higher similarity as intervals grow closer. Nevertheless, this characteristic has not been fully exploited. To address this, we propose Unbiased Label Distribution (ULD) to eliminate quantization error in training targets. Furthermore, we introduce Weighted Adjacent Decoding (WAD) to overcome quantization error during the decoding stage. Finally, we tailor two loss functions for the soft labels: Negative Log Absolute Error (NLAE) and Mean Squared Error without activation (MSE(wo)). Experimental results show our approach surpasses classification quantization limits, achieving state-of-the-art performance. Our code and supplementary material are available at https://github.com/linfeng-feng/ULD.

Self-adaptive label discovery and multi-view fusion for complementary label learning.

Unlike traditional supervised classification, complementary label learning (CLL) operates under a weak supervision framework, where each sample is annotated by excluding several incorrect labels, known as complementary labels (CLs). Despite reducing the labeling burden, CLL always suffers a decline in performance due to the weakened supervised information. To overcome such limitations, in this study, a multi-view fusion and self-adaptive label discovery based CLL method (MVSLDCLL) is proposed. The self-adaptive label discovery strategy leverages graph-based semi-supervised learning to capture the label distribution of each training sample as a convex combination of all its potential labels. The multi-view fusion module is designed to adapt to various views of feature representations. In specific, it minimizes the discrepancies of label projections between pairwise views, aligning with the consensus principle. Additionally, a straightforward mechanism inspired by a teamwork analogy is proposed to incorporate view-discrepancy for each sample. Experimental results demonstrate that MVSLDCLL learns more discriminative label distribution and achieves significantly higher accuracies compared to state-of-the-art CLL methods. Ablation study has also been performed to validate the effectiveness of both the self-adaptive label discovery strategy and the multi-view fusion module.

One-step cascade amplification system based on entropy-driven catalysis and DNAzyme triggered DNA walker for label-free detection of acetamiprid.

Herein, an electrochemical aptasensor for highly sensitive detection of acetamiprid (ACE) was constructed based on a one-step cascade amplification strategy. This innovative strategy integrated DNA walker containing DNAzyme sequence into entropy-driven catalysis (EDC) system. The trigger strand was released by aptamer-specific binding to ACE, initiating the EDC amplification circuit and delivering DNA walker strands. The dangling DNA walker continuously bound and cleaved hairpin substrate to form G-quadruplex fragments with the assistance of Mg2+. The G-quadruplex fragments folded and captured hemin to form multitudinous G-quadruplex/hemin complexes in the presence of K+, generating significantly enhanced current, enabling enzyme-free, label-free and highly sensitive detection of ACE, with a linear detection range of 100 fM to 50 nM and a detection limit of 68.36 fM (S/N = 3). The constructed aptasensor achieved the reliable detection of ACE in vegetable soil and cucumber samples, demonstrating its potential application prospects in environmental protection and food supervision.

Bioreceptors' immobilization by hydrogen bonding interactions and differential pulse voltammetry for completely label-free electrochemical biosensors.

Label-free electrochemical biosensors show great potential for the development of point-of-care devices (POCDs) for environmental and clinical applications. These sensors operate with shorter analysis times and are more economic than the labelled ones. Here, four completely label-free biosensors without electron transfer mediators were developed for hepatitis B virus (HBV) detection. The approach consisted in (i) the modification of gold surfaces with cysteamine (CT) or cysteine (CS) linkers, (ii) the subsequent antibody (Ab) immobilization, either directly by hydrogen bonding (HB) interactions or by covalent bonds (CB) using additional reagents, and (iii) measuring the biosensor response by electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV). The electrode surfaces at each stage of the modification process were characterised by X-ray photo-electron spectroscopy (XPS) and atomic force microscopy (AFM). The combination of Ab immobilization by HB with the DPV analysis displayed improved repeatability, lower interference to serum matrix and similar limits of detection and quantification than the traditional biosensors that immobilize the Ab via CB and use EIS as readout technique. The Ab immobilization by HB is shown as a simple, efficient and low-cost alternative to CB ones, while DPV was faster and showed better performance than EIS. The CT-HB biosensor displayed the lowest limits of detection and quantification of 0.14 and 0.46 ng/mL, respectively, a 0.46-12.5 ng/mL linear analytical range, and 100% of recovery for 1/10 human serum media during HBV surface antigen detection by DPV. Even, it preserved the initial sensing capability after 7 days of its fabrication.

Development of a label-free electrochemical aptasensor for Rift Valley fever virus detection.

In this research, we describe the first aptasensor for the detection of the Rift Valley Fever virus (RVFV). The process involved the selection of aptamers through the systematic evolution of ligands by the exponential enrichment (SELEX) technique. After 12 rounds of selection, 6 aptamers were selected and the corresponding binding affinities were assessed using fluorescence binding assays, revealing dissociation constants ranging from 15.45 to 40.98 nM. Notably, among the aptamers, RV2 and RV3 exhibited the highest binding affinities toward RVFV, with dissociation constants of 15.45 and 18.62 nM, respectively. Thiol-modified aptamers were subsequently immobilized onto screen-printed gold electrodes, facilitating the label-free detection of RVFV through square wave voltammetry. The voltammetric aptasensor demonstrated an excellent sensitivity, with a detection limit of 0.015 ng/mL. In addition, cross-reactivity assessments were conducted, where negligible response was obtained when the aptasensor was exposed to non-specific proteins.

Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico.

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net ('bead') architectures (termed 'Θ-Net' in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology).

Electrode Droplet Microarray (eDMA): An Impedance Platform for Label-Free Parallel Monitoring of Cellular Drug Response in Nanoliter Droplets.

Label-free real-time monitoring of cellular behavior using impedance spectroscopy is important for drug development and toxicological assessments. Parallelization and miniaturization of such experiments are essential for increasing throughput and enabling experiments with low abundant stem or primary cells. Traditional methods are not miniaturized and require large volumes of reagents and number of cells, limiting their suitability for cost effective high-throughput screening of cells of limited availability. Here, the fabrication, optimization, and application of a bioelectrical signaling monitoring system - electrode droplet microarray (eDMA) are demonstrated. The eDMA platform is based on preparation of a hydrophilic-superhydrophobic patterns covering an array of individually addressable microelectrodes, which confines cells to individual microelectrodes, allowing for parallel, real-time, and label-free detection of cellular responses to drug treatments in nanoliter droplets. The real-time monitoring of cytotoxic effect of an anticancer drug is demonstrated over 48 h with real-time calculation of the half-inhibitory concentration (IC50) values through impedance spectroscopy. This demonstrates eDMA's ability to dynamically assess responses to various drugs in parallel at any given time point, which is crucial for functional personalized oncology. Specifically, the platform can be employed for monitoring anticancer drug toxicity using limited patient samples, where the miniaturization provided by eDMA is essential.