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Kshirsagar A, Politza AJ, Guan W. Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications. ACS Sens 2024; 9:4017-4027. [PMID: 39010300 PMCID: PMC11421847 DOI: 10.1021/acssensors.4c00860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and compact multiplexable systems remains a substantial challenge. This difficulty largely stems from the inherent need for spectrum separation, which typically requires sophisticated and expensive optical components. Here, we demonstrate a compact, lens-free, and cost-effective fluorescence sensing setup that incorporates machine learning for scalable multiplexed fluorescence detection. This method utilizes low-cost optical components and a pretrained machine learning (ML) model to enable multiplexed fluorescence sensing without optical adjustments. Its multiplexing capability can be easily scaled up through updates to the machine learning model without altering the hardware. We demonstrate its real-world application in a probe-based multiplexed Loop-Mediated Isothermal Amplification (LAMP) assay designed to simultaneously detect three common respiratory viruses within a single reaction. The effectiveness of this approach highlights the system's potential for point-of-care applications that require cost-effective and scalable solutions. The machine learning-enabled multiplexed fluorescence sensing demonstrated in this work would pave the way for widespread adoption in diverse settings, from clinical laboratories to field diagnostics.
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Affiliation(s)
- Aneesh Kshirsagar
- Department of Electrical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Anthony J. Politza
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Weihua Guan
- Department of Electrical Engineering, The Pennsylvania State University, University Park 16802, USA
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
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Puglisi R, Cavallaro A, Pappalardo A, Petroselli M, Santonocito R, Trusso Sfrazzetto G. A New BODIPY-Based Receptor for the Fluorescent Sensing of Catecholamines. Molecules 2024; 29:3714. [PMID: 39125116 PMCID: PMC11314322 DOI: 10.3390/molecules29153714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
The human body synthesizes catecholamine neurotransmitters, such as dopamine and noradrenaline. Monitoring the levels of these molecules is crucial for the prevention of important diseases, such as Alzheimer's, schizophrenia, Parkinson's, Huntington's, attention-deficit hyperactivity disorder, and paragangliomas. Here, we have synthesized, characterized, and functionalized the BODIPY core with picolylamine (BDPy-pico) in order to create a sensor capable of detecting these biomarkers. The sensing properties of the BDPy-pico probe in solution were studied using fluorescence titrations and supported by DFT studies. Catecholamine sensing was also performed in the solid state by a simple strip test, using an optical fiber as the detector of emissions. In addition, the selectivity and recovery of the sensor were assessed, suggesting the possibility of using this receptor to detect dopamine and norepinephrine in human saliva.
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Affiliation(s)
- Roberta Puglisi
- Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; (R.P.); (A.C.); (A.P.)
| | - Alessia Cavallaro
- Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; (R.P.); (A.C.); (A.P.)
| | - Andrea Pappalardo
- Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; (R.P.); (A.C.); (A.P.)
- Research Unit of Catania, National Interuniversity Consortium for Materials Science and Technology (I.N.S.T.M.), Viale Andrea Doria 6, 95125 Catania, Italy
| | - Manuel Petroselli
- Institute of Chemical Research of Catalonia (ICIQ), Av. Països Catalans 16, 43007 Tarragona, Spain;
| | - Rossella Santonocito
- Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; (R.P.); (A.C.); (A.P.)
| | - Giuseppe Trusso Sfrazzetto
- Department of Chemical Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; (R.P.); (A.C.); (A.P.)
- Research Unit of Catania, National Interuniversity Consortium for Materials Science and Technology (I.N.S.T.M.), Viale Andrea Doria 6, 95125 Catania, Italy
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3
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Reyes-Vera E, Valencia-Arias A, García-Pineda V, Aurora-Vigo EF, Alvarez Vásquez H, Sánchez G. Machine Learning Applications in Optical Fiber Sensing: A Research Agenda. SENSORS (BASEL, SWITZERLAND) 2024; 24:2200. [PMID: 38610411 PMCID: PMC11014317 DOI: 10.3390/s24072200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 04/14/2024]
Abstract
The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.
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Affiliation(s)
- Erick Reyes-Vera
- Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia;
| | | | - Vanessa García-Pineda
- Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia;
| | - Edward Florencio Aurora-Vigo
- Escuela Profesional de Ingeniería Agroindustrial y Comercio Exterior, Universidad Señor de Sipán, Chiclayo 14001, Peru;
| | - Halyn Alvarez Vásquez
- Facultad de Ingeniería, Arquitectura y Urbanismo, Universidad Señor de Sipán, Chiclayo 14001, Peru;
| | - Gustavo Sánchez
- Instituto de Investigación y Estudios de la Mujer, Universidad Ricardo Palma, Lima 15074, Peru;
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Hassannia M, Fahimi-Kashani N, Hormozi-Nezhad MR. Machine-learning assisted multicolor platform for multiplex detection of antibiotics in environmental water samples. Talanta 2024; 267:125153. [PMID: 37678003 DOI: 10.1016/j.talanta.2023.125153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/29/2023] [Accepted: 09/01/2023] [Indexed: 09/09/2023]
Abstract
Antibiotic (AB) resistance is one of daunting challenges of our time, attributed to overuse of ABs and usage of AB-contaminated food resources. Due to their detrimental impact on human health, development of visual detection methods for multiplex sensing of ABs is a top priority. In present study, a colorimetric sensor array consisting of two types of gold nanoparticles (AuNPs) were designed for identification and determination of ABs. Design principle of the probe was based on aggregation of AuNPs in the presence of ABs at different buffer conditions. The utilization of machine learning algorithms in this design enables classification and quantification of ABs in various samples. The response profile of the array was analyzed using linear discriminant analysis algorithm for classification of ABs. This colorimetric sensor array is capable of accurate distinguishing between individual ABs and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit demonstrated the potential applicability of the developed sensor array in multiplex detection of ABs. The response profiles of the array were linearly correlated to the concentrations of ABs in a wide range of concentration with limit of detections of 0.05, 0.03, 0.04, 0.01, 0.06, 0.05 and 0.04 μg.mL-1 for azithromycin, amoxicillin, ciprofloxacin, clindamycin, cefixime, doxycycline and metronidazole respectively. The practical applicability of this method was further investigated by analysis of mixture samples of ABs and determination of ABs in river and underground water with successful verification.
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Affiliation(s)
- M Hassannia
- Department of Chemistry, Sharif University of Technology, Tehran, 11155-9516, Iran
| | - N Fahimi-Kashani
- Department of Chemistry, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
| | - M R Hormozi-Nezhad
- Department of Chemistry, Sharif University of Technology, Tehran, 11155-9516, Iran.
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Choi HK, Choi JH, Yoon J. An Updated Review on Electrochemical Nanobiosensors for Neurotransmitter Detection. BIOSENSORS 2023; 13:892. [PMID: 37754127 PMCID: PMC10526534 DOI: 10.3390/bios13090892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023]
Abstract
Neurotransmitters are chemical compounds released by nerve cells, including neurons, astrocytes, and oligodendrocytes, that play an essential role in the transmission of signals in living organisms, particularly in the central nervous system, and they also perform roles in realizing the function and maintaining the state of each organ in the body. The dysregulation of neurotransmitters can cause neurological disorders. This highlights the significance of precise neurotransmitter monitoring to allow early diagnosis and treatment. This review provides a complete multidisciplinary examination of electrochemical biosensors integrating nanomaterials and nanotechnologies in order to achieve the accurate detection and monitoring of neurotransmitters. We introduce extensively researched neurotransmitters and their respective functions in biological beings. Subsequently, electrochemical biosensors are classified based on methodologies employed for direct detection, encompassing the recently documented cell-based electrochemical monitoring systems. These methods involve the detection of neurotransmitters in neuronal cells in vitro, the identification of neurotransmitters emitted by stem cells, and the in vivo monitoring of neurotransmitters. The incorporation of nanomaterials and nanotechnologies into electrochemical biosensors has the potential to assist in the timely detection and management of neurological disorders. This study provides significant insights for researchers and clinicians regarding precise neurotransmitter monitoring and its implications regarding numerous biological applications.
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Affiliation(s)
- Hye Kyu Choi
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;
| | - Jin-Ha Choi
- School of Chemical Engineering, Clean Energy Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Jinho Yoon
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon 14662, Republic of Korea
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Sorooshyari SK, Ouassil N, Yang SJ, Landry MP. Identifying Neural Signatures of Dopamine Signaling with Machine Learning. ACS Chem Neurosci 2023. [PMID: 37267623 DOI: 10.1021/acschemneuro.3c00001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023] Open
Abstract
The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near-infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to decide whether the recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy. This is in light of integrated neuromodulator amount being the conventional metric used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly utilized tool to differentiate between neuroanatomical regions or between neurotypical and disease states, with features not detectable by conventional statistical analysis.
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Affiliation(s)
- Siamak K Sorooshyari
- Department of Integrative Biology, University of California, Berkeley, California 94720, United States
| | - Nicholas Ouassil
- Department of Chemical and Biomolecular Engineering, University of California, , Berkeley, California 94720, United States
| | - Sarah J Yang
- Department of Chemical and Biomolecular Engineering, University of California, , Berkeley, California 94720, United States
| | - Markita P Landry
- Department of Chemical and Biomolecular Engineering, University of California, , Berkeley, California 94720, United States
- Innovative Genomics Institute, Berkeley, California 94720, United States
- California Institute for Quantitative Biosciences, University of California, Berkeley California, 94720, United States
- Chan Zuckerberg Biohub, San Francisco, California 94063, United States
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