Size-classified monitoring of ATP bioluminescence for rapid assessment of biological distribution in airborne particulates.

The COVID-19 pandemic ignited massive research into the rapid detection of bioaerosols. In particular, nanotechnology-based detection strategies are proposed as alternatives because of issues in bioaerosol enrichment and lead time for molecular diagnostics; however, the practical implementation of such techniques is still unclear due to obstacles regarding the large research and development effort and investment for the validation. The use of adenosine triphosphate (ATP) bioluminescence (expressed as relative luminescence unit (RLU) per unit volume of air) of airborne particulate matter (PM) to determine the bacterial population as a representative of the total bioaerosols (viruses, bacteria, and fungi) has been raised frequently because of the high reponse speed, resolution, and compatibility with culture-based bioaerosol monitoring. On the other hand, additional engineering attempts are required to confer significance because of the size-classified (bioluminescence for different PM sizes) and specific (bioluminescence per unit PM mass) biological risks of air for providing proper interventions in the case of airborne transmission. In this study, disc-type impactors to cut-off aerosols larger than 1 µm, 2.5 µm, and 10 µm were designed and constructed to collect PM1, PM2.5, and PM10 on sampling swabs. This engineering enabled reliable size-classified bioluminescence signals using a commercial ATP luminometer after just 5 min of air intake. The simultaneous operations of a six-stage Andersen impactor and optical PM spectrometers were conducted to determine the correlations between the resulting RLU and colony forming unit (CFU; from the Andersen impactor) or PM mass concentration (deriving specific bioluminescence).

Use of ATP bioluminescence to survey the contamination of dental goggles in surgical removal of impacted mandibular third molars

The importance of protecting the eyes from infectious agents in patients' blood and saliva during dental surgery has long been known, but the global COVID-19 pandemic has made this even more important. The use of ATP bioluminescence to investigate the contamination of dental goggles during the surgical removal of impacted teeth in the present study indicates their importance for protecting the eyes from aerosols from the front, from above, and from the sides.Copyright © 2021 The Author(s)

Prediction of Survival COVID-19 Patients by Using Backpropagation Neural Network Algorithm

COVID-19 is a disease caused by a virus and increasing in cases every day. This is because the large number of patients makes it difficult to be treated at the hospital. This is behind the need for survival prediction of COVID-19 patients within 48 days so that the medical team can prioritize patients who are predicted to not survive on that period. In this research, the firefly algorithm is used which aims to select attributes and will perform comparisons for data that is balance or imbalance and combined with data that do feature selection and does not feature selection. The data that will be used are age, asthma, diabetes, gender, COPD, pregnancy, hypertension, obesity, ICU, chronic kidney disease, smoking, heart disease, immune deficiency, pneumonia, and other medical history. In this research, the selected attributes were gender, type of patient, intubation, pneumonia, age, pregnancy, diabetes, COPD (Chronic Obstructive Pulmonary Disease), asthma, hypertension, other diseases, obesity, chronic kidney disease, smokers, contact with COVID patients, and ICU. The prediction model with the highest level of performance is a model with balanced data with a recall value of 0.79, then a precision value of 0.93, then an f score of 0.85, then an accuracy value of 0.86, then a specificity 0,93, then a NPV 0,82 and a geometric mean value of 0.87 © 2022 IEEE.

Firefly Optimization with Bidirectional Gated Recurrent Unit for COVID-19 Diagnosis on Chest Radiographs

The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches. © 2022 IEEE.

A Decision-Making System for Dynamic Scheduling and Routing of Mixed Fleets with Simultaneous Synchronization in Home Health Care

Globally, the growing number of elderly people, chronic disorders and the spread of COVID-19 have all contributed to a significant growth of Home Health Care (HHC) services. One of HHC's main goals is to provide a coordinated set of medical services to individuals in the comfort of their own homes. On the basis of the current demand for HHC services, this paper attempts to develop a novel and effective mathematical model and a suitable decision-making technique for reducing costs associated with HHC service delivery systems. The proposed system of decision making identifies the real needs of HHCs which incorporate dynamic, synchronized services and coordinates routes by a group of caregivers among a mixed fleet of services. Initially, this study models the optimization problem using Mixed Integer Linear Programming (MILP). The Revised Version of the Discrete Firefly Algorithm is designed to address the HHC planning decision-making problem due to its unique properties and its computational complexity. To evaluate the scalability of this proposed approach, random test instances are generated. The results of the experiments revealed that the algorithm performed well even with the different scenarios such as dynamic and synchronized visits. Furthermore, the improved version of nature-inspired solution methodology has proven to be effective and efficient. As a result, the proposed algorithm has significantly reduced costs and time efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lumit: A Homogeneous Bioluminescent Immunoassay for Detecting Diverse Analytes and Intracellular Protein Targets.

Traditional immunoassays to detect secreted or intracellular proteins can be tedious, require multiple washing steps, and are not easily adaptable to a high-throughput screening (HTS) format. To overcome these limitations, we developed Lumit, a novel immunoassay approach that combines bioluminescent enzyme subunit complementation technology and immunodetection. This bioluminescent immunoassay does not require washes or liquid transfers and takes less than 2 h to complete in a homogeneous "Add and Read" format. In this chapter, we describe step-by-step protocols to create Lumit immunoassays for the detection of (1) secreted cytokines from cells, (2) phosphorylation levels of a specific signaling pathway node protein, and (3) a biochemical protein-protein interaction between a viral surface protein and its human receptor.

A New Hybrid Genetic with Firefly Algorithm for Solving Cyber-Criminal's Attacks

For a long time, optimization has been part of our lives and the most recent literature shows a tremendous increase of the number of articles using Revolutionary algorithms in particular Firefly algorithm (FA) and Genetic algorithm. This tendency can be observed nearly in all areas of Computer Sciences and Engineering domain. Some of them are hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used (FA) and (GA) techniques have outperformed compare to other metaheuristic algorithms. And because of the extraordinary impact of the COVID-19 pandemic on society and business as a whole, the pandemic generated an increase in the number and range of cybercriminal attacks due to the extensive use of computer networks. As result, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. The aim of this article is to give the main mechanisme of those approachs and their application alone and hybrided to solve cybercrime problems. © 2022 IEEE.

Automatic detection of covid-19 using CNN model combined with Firefly algorithm

Coronavirus has already been spread around the world, in many countries, and it has already claimed many lives. Further, the World Health Organization (WHO) has notified public health officials that COVID-19 has reached global epidemic status. Therefore, an early diagnosis using a chest CT scan can aid medical specialists in critical situations. This study aims to develop a web-based service for detecting COVID-19 online. To achieve our goal, we merged the convolutional neural network (CNN) model with the Firefly algorithm (FA). This combination ameliorate definitely the performance and efficiency of the CNN proposed model. Furthermore, the experiments revealed that the proposed FACNN framework enables us to reach high performance with regard to precision, accuracy, sensitivity, F-measure, recall and specificity (1.0%, 1.0%, 1.0%, 1.0%, 1.0% and 1.0%). In addition, a web-based interface was developed to identify and recogonize COVID-19 in chest radiographs in just few seconds. We anticipate that this web predictor will potentially save precious lives, and therefore contribute to society positively. © 2022 IEEE.

Versatile gene delivery targeting via Ultrasound and gas Microbubbles

The COVID-19 crisis and the rapid development of highly effective mRNA vaccines opened a new era for gene therapy. While viral vectors were for a long time the only tool for efficient delivery, new non-viral vectors have recently emerged, spawning new opportunities (indications, tissues, etc.). A new one is set to take off thanks to its safety profile, its specificity toward tissues, and its versatility toward both genetic materials and indications. Gas-filled microbubbles (MB), clinically used as ultrasound (US) contrast agents, have proven their benefits in various animal models and clinical applications for targeted delivery of drugs/genes. Herein, we disclose the development of new MB formulations allowing the delivery of various genetic materials at a specific location under the control of an ultrasound probe. We set forth a study to elicit the expression of a foreign enzyme in a liver mouse model. To this aim, MB were systemically co-injected with a Luciferase pDNA (6 to 65 mug) in the tail vein, then Ultrasound were delivered at MB arrival in the liver. The effective pDNA transfection was observed by bioluminescence 24 hours after treatment. Mice were divided into three groups: pDNA alone;pDNA with US;pDNA with US and MBs (n >= 5). The use of our MB allowed increasing the signal up to 5 folds in comparison to the US alone. These results highlight the potential of MB plus US to efficiently deliver locally genetic material without any safety concerns.

Calcium Phosphate Biomaterials Enhance Immunotherapeutic Mrna Delivery in Melanoma

Background Messenger ribonucleic acid (mRNA) is a powerful tool for transferring genetic information. Its advantages include potent but transient gene expression without risk of genomic insertion, tailorable immunogenicity to match therapeutic application, and the potential for efficient, scalable manufacturing.1 The recent success of mRNA-based SARSCoV- 2 vaccines has inspired interest in mRNA as a cancer therapy to deliver immunostimulatory molecules and tumor antigens. However, clinical translation is limited by mRNA instability at physiological conditions and inefficient in vivo delivery.2 A reliable, non-toxic, and stabilizing in vivo delivery system for immunotherapeutic mRNA would help to advance mRNA as a viable cancer therapy. Here, we utilized calcium phosphate mineral-coated microparticles (MCMs) as a delivery system for mRNA-lipid complexes (lipoplexes) to transfect melanoma cells. Methods MCMs were prepared as previously described3 by suspending beta-tricalcium phosphate particles in modified simulated body fluid under rotation for 7 days at 37degreeC, refreshing the media daily. MCMs were then washed in deionized water and freeze dried. Custom-synthesized reporter or therapeutic mRNA constructs were complexed with a lipidic transfecting agent through mixing, then resulting lipoplexes were incubated briefly with MCMs to facilitate electrostatic binding to the porous CaP coating (figure 1a). Loaded MCMs or soluble lipoplexes were added to B16F10 murine melanoma cell culture, and transfection was measured through various assays, including fluorescence microscopy, bioluminescence, and enzymelinked immunosorbent assays. Results Scanning electron microscopy was used to verify platelike, porous coating morphology following MCM fabrication (figure 1b). MCMs enhanced transfection of B16F10 melanoma cells compared to soluble mRNA lipoplex delivery. This was demonstrated with reporter constructs encoding enhanced green fluorescent protein (eGFP, figure 1c) and Gaussia luciferase (G-Luc), as well as with a therapeutic construct encoding interleukin 15 (IL-15), a T cell growth factor. Timelapse imaging also revealed more rapid transfection with MCMs. A close proximity of cells to MCMs was observed as necessary for transfection. Conclusions We demonstrated that MCMs efficiently and locally deliver mRNA lipoplexes to melanoma cells and cause elevated levels of protein expression compared to soluble lipoplex delivery. This enhanced delivery profile makes MCMs a potential drug delivery platform for future in vivo tumor studies and clinical translation. (Figure Presented).

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches. © 2023 Tech Science Press. All rights reserved.

Development of a rapid, simple, and sensitive point-of-care technology platform utilizing ternary NanoLuc.

Point-of-care tests are highly valuable in providing fast results for medical decisions for greater flexibility in patient care. Many diagnostic tests, such as ELISAs, that are commonly used within clinical laboratory settings require trained technicians, laborious workflows, and complex instrumentation hindering their translation into point-of-care applications. Herein, we demonstrate the use of a homogeneous, bioluminescent-based, split reporter platform that enables a simple, sensitive, and rapid method for analyte detection in clinical samples. We developed this point-of-care application using an optimized ternary, split-NanoLuc luciferase reporter system that consists of two small reporter peptides added as appendages to analyte-specific affinity reagents. A bright, stable bioluminescent signal is generated as the affinity reagents bind to the analyte, allowing for proximity-induced complementation between the two reporter peptides and the polypeptide protein, in addition to the furimazine substrate. Through lyophilization of the stabilized reporter system with the formulated substrate, we demonstrate a shelf-stable, all-in-one, add-and-read analyte-detection system for use in complex sample matrices at the point-of-care. We highlight the modularity of this platform using two distinct SARS-CoV-2 model systems: SARS-CoV-2 N-antigen detection for active infections and anti-SARS-CoV-2 antibodies for immunity status detection using chemically conjugated or genetically fused affinity reagents, respectively. This technology provides a simple and standardized method to develop rapid, robust, and sensitive analyte-detection assays with flexible assay formatting making this an ideal platform for research, clinical laboratory, as well as point-of-care applications utilizing a simple handheld luminometer.

Protein A-Nanoluciferase fusion protein for generalized, sensitive detection of immunoglobulin G.

Detection and quantification of antibodies, especially immunoglobulin G (IgG), is a cornerstone of ELISAs, many diagnostics, and the development of antibody-based drugs. Current state-of-the-art immunoassay techniques for antibody detection require species-specific secondary antibodies and carefully-controlled bioconjugations. Poor conjugation efficiency degrades assay performance and increases the risk of clinical false positives due to non-specific binding. We developed a generic, highly-sensitive platform for IgG quantification by fusing the IgG-Fc binding Z domain of Staphylococcal Protein A with the ultrabright bioluminescence reporter Nanoluc-luciferase (Nluc). We demonstrated the application of this fusion protein in a sandwich IgG detection immunoassay using surface-bound antigens to capture target IgG and protein A-Nanoluc fusion as the detector. We optimized the platform's sensitivity by incorporating multiple repeats of the Z domain into the fusion protein constructs. Using rabbit and mouse anti-SARS-CoV-2 Nucleoprotein IgGs as model analytes, we performed ELISAs in two different formats, either with SARS-CoV-2 Nucleoprotein as the capture antigen or with polyclonal chicken IgY as the capture antibody. Using standard laboratory equipment, the platform enabled the quantitation of antibody analytes at concentrations as low as 10 pg/mL (67 fM).

Pandemic Preparedness and Response: A Foldable Tent to Safely Remove Contaminated Dental Aerosols—Clinical Study and Patient Experience

The D-DART (Droplet and Aerosol Reducing Tent) is a foldable design that can be attached to the dental chair to prevent the spread of contaminated dental aerosols. The objective of this study was to evaluate the ability of the D-DART to reduce spread of aerosols generated during dental treatment. Thirty-two patients (sixteen per group) undergoing deep ultrasonic scaling were recruited and randomly allocated to groups D-DART or Control (no D-DART). After 20 min from the start of the treatment, the clinician’s face shield and dental chair light were swabbed and the viable microbial load was quantified (ATP bioluminescence analysis, blinded operator). Statistical analyses were performed with Tukey’s Honest Test with a level of significance pre-set at 5%. There were significant increases in ATP values obtained from the operator’s face shield and dental chair light for the Control compared with baseline (31.3 ± 8.5 and fold increase). There was no significant change in microbial load when the D-DART was used compared with baseline (1.5 ± 0.4 fold increase). The D-DART contained and prevented the spread of aerosols generated during deep scaling procedures.

Modified mRNA delivery to the heart using Lipid Nanoparticles

Myocardial infarction is a global health burden for which there is no treatment available that aims to recover the damaged tissue after the ischemic event. Lipid nanoparticles (LNPs) represent a well characterized class of mRNA delivery systems, which were recently approved for clinical usage in their application for mRNA-based covid-19 vaccines. After myocardial infarction, endogenous mechanisms that enable repair of the functional damaged tissue can be triggered by modified mRNA (modRNA) delivery, locally in the infarcted area. As a first step, in order to optimize the LNP formulation for effective myocardial delivery and study cellular tropism of the LNPs in the heart, different LNPs formulations will be evaluated as delivery systems in a murine healthy heart model. Different LNP formulations varying in type and amount of helper lipid were used as delivery systems for modRNA encoding the reporter genes luciferase or eGFP. In vitro, LNPs were evaluated for modRNA delivery in a human endothelial cell line (HMEC-1), induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs) and induced pluripotent stem cell -derived fibroblasts (iPS-FBs). In vivo, modRNA delivery was evaluated in C57BL-6 mice, undergoing open chest heart surgery under general anaesthesia in order to infuse LNPs into the left ventricular wall. For determination of luciferase expression levels, animals were infused with luciferin substrate intraperitoneally 24 hrs after injection. Heart, liver, lungs, spleen and kidneys were extracted for imaging in a bioluminescence imaging system. The organs were then stored in liquid nitrogen for further ex-vivo modRNA delivery analysis. For determining cellular tropism, histology was performed on mice treated with eGFP modRNA. Both bioluminescence imaging and luminescence analysis in tissue lysates showed that mRNA transfection is achieved in the myocardium 24 hours after LNP intramyocardial administration. However, all LNP formulations also resulted in high expression levels in other organs, including liver and spleen. Changes in type or amount of helper lipid in LNPs strongly affected transfection levels. Histology of the treated hearts revealed a distinct transfection pattern. The targeted, interstitial cells were negative for CD31 (marker for endothelial cells and monocytes) and Troponin I3 (marker for cardiomyocytes) (Figure 1). We show that, using an optimized LNP formulation, a significant degree of modRNA local transfection of the heart can be achieved. However, despite the local route of administration (into the left ventricular wall), the highest LNP transfection is shown in remote organs such as liver and spleen. More improvements of the LNP formulations must be done to increase their tropism towards the heart tissue for their optimization as cardiac delivery systems. Determining which cell types are being targeted is also important in order to establish a therapeutic target when applying the LNPs for cardiac therapy. (Figure Presented).

MAEBL Contributes to Plasmodium Sporozoite Adhesiveness.

The sole currently approved malaria vaccine targets the circumsporozoite protein-the protein that densely coats the surface of sporozoites, the parasite stage deposited in the skin of the mammalian host by infected mosquitoes. However, this vaccine only confers moderate protection against clinical diseases in children, impelling a continuous search for novel candidates. In this work, we studied the importance of the membrane-associated erythrocyte binding-like protein (MAEBL) for infection by Plasmodium sporozoites. Using transgenic parasites and live imaging in mice, we show that the absence of MAEBL reduces Plasmodium berghei hemolymph sporozoite infectivity to mice. Moreover, we found that maebl knockout (maebl-) sporozoites display reduced adhesion, including to cultured hepatocytes, which could contribute to the defects in multiple biological processes, such as in gliding motility, hepatocyte wounding, and invasion. The maebl- defective phenotypes in mosquito salivary gland and liver infection were reverted by genetic complementation. Using a parasite line expressing a C-terminal myc-tagged MAEBL, we found that MAEBL levels peak in midgut and hemolymph parasites but drop after sporozoite entry into the salivary glands, where the labeling was found to be heterogeneous among sporozoites. MAEBL was found associated, not only with micronemes, but also with the surface of mature sporozoites. Overall, our data provide further insight into the role of MAEBL in sporozoite infectivity and may contribute to the design of future immune interventions.

Bioluminescent and Fluorescent Reporter-Expressing Recombinant SARS-CoV-2.

Reporter-expressing recombinant severe acute respiratory syndrome coronavirus 2 (rSARS-CoV-2) represents an excellent tool to understand the biology of and ease studying viral infections in vitro and in vivo. The broad range of applications of reporter-expressing recombinant viruses is due to the facilitated expression of fluorescence or bioluminescence readouts. In this chapter, we describe a detailed protocol on the generation of rSARS-CoV-2 expressing Venus, mCherry, and NLuc that represents a valid surrogate to track viral infections.

IN VIVO EFFICACY of ENGINEERED ACE2-Fc in PREVENTING LETHAL SARS-CoV-2 INFECTION

Background: Soluble Angiotensin Converting Enzyme 2 (ACE2) constitutes an attractive therapeutic candidate with natural resistance to viral escape. To date, ACE2-Fcs, dimeric forms of soluble ACE2, were mostly tested as robust SARS-CoV-2 neutralizers but their potential as antiviral agents capable of Fc-effector functions is largely unknown and has not been tested for effectiveness in vivo, in any model of SARS-CoV2 infection. Methods: We used structure-guided design to select ACE2 mutations that improve SARS-CoV-2 spike (S) affinity and remove angiotensin enzymatic activity. ACE2-Fc variants were engineered into a human IgG1 or IgG3 backbone and produced in mammalian HEK293 cells. S binding was tested by ELISA and surface plasmon resonance (SPR). Mutational effects were validated by X-Ray crystallography. Neutralization activities were measured against SARS-CoV-2 variants of concern (VOCs) using an in vitro pseudovirus (PsV) assay and dynamic bioluminescence imaging (BLI). Antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) were also quantified using established methods (1, 2). A K18-hACE2 transgenic mouse model challenged by lethal SARS-CoV-2 nLuc infection (3) was used for in vivo evaluation of prophylactic and therapeutic administration of engineered ACE2-Fcs, as monitored by dynamic BLI. Results: Our lead variant, ACE2740 LFMYQY2HA-Fc GASDALIE, increased RBD binding by ∼7-13 fold as compared to wild type, cross-neutralized SARS-CoV-2 VOCs with an IC50 range of 0.23-2.06 nM and mediated robust ADCC and ADCP in vitro. When tested in humanized K18-hACE2 mice, in either a prophylatic or a multi-dosage therapeutic setting, our lead ACE2-Fc variant provided protection from lethal SARS-CoV-2 infection. Our studies in K18-hACE2 mouse model revealed that efficient in vivo efficacy of ACE2-Fcs under prophylaxis or therapeutic settings required Fc-effector functions in addition to neutralization. Conclusion: Our data confirm the utility of engineered ACE2-Fcs as valuable SARS-CoV-2 antivirals and demonstrate that the efficient ACE2-Fc therapeutic activity required both neutralization and Fc-effector functions.

Engineering protein activity into off-the-shelf DNA devices.

DNA-based devices are straightforward to design by virtue of their predictable folding, but they lack complex biological activity such as catalysis. Conversely, protein-based devices offer a myriad of functions but are much more difficult to design due to their complex folding. This study combines DNA and protein engineering to generate an enzyme that is activated by a DNA sequence of choice. A single protein switch, engineered from nanoluciferase using the alternate-frame-folding mechanism and herein called nLuc-AFF, is paired with different DNA technologies to create a biosensor for specific nucleic acid sequences, sensors for serotonin and ATP, and a two-input logic gate. nLuc-AFF is a genetically encoded, ratiometric, blue/green-luminescent biosensor whose output can be quantified by a phone camera. nLuc-AFF retains ratiometric readout in 100% serum, making it suitable for analyzing crude samples in low-resource settings. This approach can be applied to other proteins and enzymes to convert them into DNA-activated switches.