Brain neurotransmitter receptor antagonist found to prevent opioid addiction in mice

UCLA Health study shows that insomnia drug prevents opioid addiction in mice at opioid doses that provide potent pain relief

UNIVERSITY OF CALIFORNIA - LOS ANGELES HEALTH SCIENCES

New research led by UCLA Health has found a drug that treats insomnia works to prevent the addictive effects of the morphine opioids in mice while still providing effective pain relief.  

The study, published in the journal Nature Mental Health, concluded that suvorexant, which blocks brain receptors for a neurotransmitter called hypocretin, prevents opioid addiction. At high doses in humans, suvorexant induces sleep and is used to treat insomnia. But sleep was not induced, and behavioral alertness was maintained, at the much lower doses effective in preventing opioid addiction in mice. 

Hypocretin, also called orexin, is a peptide that is linked to mood, with hypocretin release in humans being maximal during pleasurable activities and minimal during pain or sadness. The loss of hypocretin neurons is the cause of narcolepsy, which is thought to be an autoimmune disease. People with narcolepsy and mice made narcoleptic have a greatly diminished susceptibility to opiate addiction.  

Researchers have found both humans addicted to heroin and mice addicted to morphine develop higher numbers of hypocretin producing neurons. Morphine causes hypocretin neurons to increase their anatomical connections to pleasure related brain regions. 

The latest study in mice found that administering opioids with suvorexant prevents opioid-induced changes in hypocretin neurons, prevents hypocretin neurons from increasing their connections to the brain’s reward related regions, greatly reduces opioid induced brain inflammation and prevents addictive behavior, such as running in mice expecting to receive their daily morphine dose. Suvorexant given with morphine also greatly reduces morphine withdrawal symptoms, according to the study. 

“The annual US rate of opioid overdose deaths now exceeds 80,000, greater than the annual rates of automobile or gun deaths,” said the study’s senior author, Dr. Jerome Siegel of UCLA Health’s Jane & Terry Semel Institute for Neuroscience and Human Behavior, the UCLA Brain Research Institute and U.S. Department of Veterans Affairs. “Non-opioid analgesics are able to relieve relatively minor pain. But severe burns, cancer, joint inflammation, sickle cell disease, bone damage and many other painful conditions often cannot be effectively treated with non-opioid analgesics.  

“Further studies are needed to determine if the addiction suppressive results seen in mice given suvorexant with morphine are also seen in humans, potentially allowing safer, more effective treatment of pain without the risk of addiction and opioid overdose death,” Siegel continued 

The study included 170 mice that were administered morphine for 14-day periods, 5 postmortem brains of humans with opiate use disorder and 5 control human brains. Trials are necessary to determine whether suvorexant will be as effective in suppressing addiction in humans using opioids for pain relief as it is in mice, Siegel said. 

“The annual US rate of opioid overdose deaths now exceeds 80,000, greater than the annual rates of automobile or gun deaths,” Siegel said. “Non-opioid analgesics are able to relieve relatively minor pain. But severe burns, cancer, joint inflammation, sickle cell disease, bone damage and many other painful conditions often cannot be effectively treated with non-opioid analgesics.  

“Further studies are needed to determine if the addiction suppressive results seen in mice given suvorexant with morphine are also seen in humans, potentially allowing safer, more effective treatment of pain without the risk of addiction and opioid overdose death,” Siegel continued 

Article Citation: McGregor R., Wu M.-F., Thannickal T.C., Li S., and Siegel J.M. (2024). Suvorexant blocks opiate induced anatomical and behavioral changes without diminishing opiate analgesia. Nature Mental Health, 2024. https://doi.org/10.1038/s44220-024-00278-2 

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JOURNAL

Nature Mental Health

DOI

10.1038/s44220-024-00278-2 

SUBJECT OF RESEARCH

Animals

ARTICLE TITLE

Opioid-induced neuroanatomical, microglial and behavioral changes are blocked by suvorexant without diminishing opioid analgesia

ARTICLE PUBLICATION DATE

9-Jul-2024

 

Tensor-force effects on nuclear matter in relativistic ab initio theory

Peer-Reviewed Publication

SCIENCE CHINA PRESS

 

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THE BINDING ENERGIES PER PARTICLE FOR (A) SYMMETRIC NUCLEAR MATTER (SNM) AND (B) PURE NEUTRON MATTER (PNM) CALCULATED WITH (SOLID LINES AND FULL SYMBOLS) AND WITHOUT (DOTTED LINES AND EMPTY SYMBOLS) TENSOR FORCES. (C) THE SYMMETRY ENERGY DIFFERENCE OBTAINED BY PERFORMING THE RBHF CALCULATIONS WITH AND WITHOUT TENSOR FORCE.

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CREDIT: ©SCIENCE CHINA PRESS

This study is led by Prof. Jie Meng (State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University). Numerical modeling and theoretical analyses were conducted mainly by Dr. Sibo Wang (Department of Physics and Chongqing Key Laboratory for Strongly Coupled Physics, Chongqing University).

Tensor force is a crucial ingredient of the nucleon-nucleon (NN) interaction, and has an important impact on the structural and dynamical properties of the nuclear many-body system. Many efforts have been devoted to studying the influence of the tensor force in the effective NN interaction in the nuclear medium. But less is known for the tensor-force effects from realistic NN interactions.

Starting from realistic NN interaction, the authors systematically study the tensor-force effects on the equation of state and symmetry energy of the nuclear matter within the relativistic Brueckner-Hartree-Fock (RBHF) theory, which is one of the most important relativistic ab initio methods. For the binding energies per particle of symmetric nuclear matter (SNM) and the symmetry energy, the tensor-force effects are attractive and are more pronounced around the empirical saturation density. For pure neutron matter, the tensor- force effects are marginal.

This study also shows that the strong tensor force make the neutron-proton system deviate from the unitary limit. By tuning the tensor-force strength, the dilute SNM is located at the unitary limit. With only the interaction in the 3S1-3D1 channel considered, the ground-state energy of dilute SNM is found proportional to that of a free Fermi gas with a scaling factor 0.38, which reveals good universal properties for four-component unitary Fermi gas (spin-1/2 and isospin-1/2).

This work paves the way to study the tensor-force effects in neutron stars as well as finite nuclei from realistic nucleon-nucleon interactions. This work also highlights the role of the tensor force on the deviation of the nuclear physics to the unitary limit and provides valuable reference for studies of the four-component unitary Fermi gas.

Tensor-force effects on the deviation of the neutron-proton system to the unitary limit.

(a) The dimensionless ratio of the effective range rt  to the scattering length at  in spin triplet (left axis) and the binding energy for the deuteron Bd  (right axis) with variation of the tensor force quenching factor λ . The upper (lower) horizontal dashed line denotes the locale of the unitary (bound) limit. (b) The ground-state energy per particle for SNM calculated with Bonn A, B, and C, where the scattering length at in spin-triplet channel is tuned to −100 fm. Only contributions from the 3S1-3D1 channel are considered. The results of a free Fermi gas with a scaling factor 0.38 is also displayed for comparison.

CREDIT

©Science China Press

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JOURNAL

Science Bulletin

DOI

10.1016/j.scib.2024.05.013 

 

KAIST employs image-recognition AI to determine battery composition and conditions​

THE KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY (KAIST)

 

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FIGURE 1. EXAMPLE IMAGES OF TRUE CASES AND THEIR GRAD-CAM OVERLAYS FROM THE BEST TRAINED NETWORK.

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CREDIT: KAIST MATERIALS IMAGING AND INTEGRATION LAB

An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.

KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.

*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.

The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.

The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate. 

 

 

< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >

 

The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.

Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."

Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."

 

 

< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >

 

This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)

Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. 

CREDIT

KAIST Materials Imaging and Integration Lab

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JOURNAL

npj Computational Materials

DOI

10.1038/s41524-024-01279-6 

METHOD OF RESEARCH

Meta-analysis

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images

Novel strategy stabilizes zinc-ion batteries

HEFEI INSTITUTES OF PHYSICAL SCIENCE, CHINESE ACADEMY OF SCIENCES

 

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SCHEMATIC DIAGRAMS FOR ZN DEPOSITION IN ZNSO4 (A) AND SDBS/ZNSO4 (B) ELECTROLYTES.

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CREDIT: LI ZHAOQIAN

According to research published in Energy Storage Materials recently, a team led by Prof. HU Linhua from Hefei Institutes of Physical Science of Chinese Academy of Sciences proposed a general principle through evaluating the highest occupied molecular orbital (HOMO) energy level of molecules and employed it as a critical descriptor to select non-sacrificial anionic surfactant electrolyte additives for stabilizing Zn anodes, realizing sustainable regulation effect with inhibited Zn dendrite growth and side-reactions.

"That means the anionic surfactant electrolyte additive sodium dodecyl benzene sulfonate (SDBS) with high HOMO energy level can stop harmful zinc dendrites from growing and make the batteries better at being recharged and reused," said Dr. LI ZHAO Qian, member of the team.

Aqueous zinc-ion batteries (AZIBs) have nowadays stimulated widespread attention for their safety, reliability, and cost-effectiveness. The severe Zn dendrite growth and severe side-reactions have become the major roadblock to the widespread commercialization of AZIBs. Anionic surfactants, as a category of typical non-sacrificial additives, have a long history of application in metallurgy as corrosion-inhibiting and deestering agents for Zn plating. Therefore, choosing a suitable anionic surfactant additive promises to fundamentally obtain highly stable and reversible metal anodes.

In this study, researchers proposed and demonstrated a general descriptor by evaluating HOMO energy level to select non-sacrificial surfactant additives of electrolyte for stabilizing Zn anodes. Three typical anionic surfactants molecules including SDBS, sodium dodecyl sulfonate (SDS), and sodium p-ethylbenzene sulfonate (SEBS) with non-sacrificial behaviors and different HOMO energy levels are chosen as additives, and the influence of HOMO energy levels on coordination and adsorption effects are investigated for the first time. Experimental and calculational results demonstrate that SDBS with the highest HOMO energy level exhibits the strongest coordination and adsorption effects, which extremely improves the stability and reversibility of Zn anode.

"The battery worked for over 3200 hours in the test, even at high power levels, which is 30 times longer than with the original electrolyte," explained Dr. LI Zhaoqian.

They also tested the battery with different materials and found that it worked well with them, even after many cycles. They assembled Zn//Cu batteries with average Coulomb efficiency of 98.15% after 800 cycles. Meanwhile, the Zn//NH4V4O10 full battery delivers a long-term stability with capacity retention of 93.5% after 8000 cycles.

This research provided a promising strategy for screening optimal electrolyte additives for high performance AZIBs, and was expected to be applied to other metal batteries, according to the team.

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JOURNAL

Energy Storage Materials

ARTICLE TITLE

Non-sacrificial anionic surfactant with high HOMO energy level as a general descriptor for zinc anode

Battery4Life: New COMET centre for battery safety led by Graz University of Technology

Researchers will team up with national and international partners to make batteries safer, more sustainable and to extend their service life. The FFG, the provinces of Styria and Upper Austria and companies are investing about 19 million euros in total

GRAZ UNIVERSITY OF TECHNOLOGY

 

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JÖRG MOSER (LEFT) AND CHRISTIAN ELLERSDORFER FROM THE VEHICLE SAFETY INSTITUTE AT TU GRAZ AT A TEST BENCH OF THE BATTERY SAFETY CENTER GRAZ.

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CREDIT: LUNGHAMMER - TU GRAZ

It is a great success for Graz University of Technology (TU Graz) and proof of the outstanding expertise of its researchers in the rapidly developing field of battery technology: the Austrian Research Promotion Agency FFG has approved the application for the new COMET K1 centre "Battery4Life". Together with international partners from science and industry, a team led by Christian Ellersdorfer from the Vehicle Safety Institute will work on improving the safety, service life and sustainability of batteries. The FFG is funding the project with a total of around 6.5 million euros, the Province of Styria is contributing 2.6 million euros and Upper Austria 0.6 million euros. In addition, the partners from the automotive and electronics industries are investing around nine million euros over the scheduled project term of four years.

By obtaining approval for the K1 centre Battery4Life, TU Graz is further expanding its position as Austria's most successful university in the FFG's COMET programme. "I am very pleased about the funding for Battery4Life," says TU Graz Rector Horst Bischof. "It demonstrates the outstanding expertise in battery research that we have been able to build up at Graz University of Technology together with industrial partners over many years. Together with the HyCentA hydrogen research centre, our Inffeld campus is becoming a hub for energy storage technology.

Safe battery operation over the entire life cycle

Driven by the expansion of e-mobility, the demand for batteries is increasing rapidly, and large sums of money are being invested in research projects worldwide to increase the capacity of batteries and develop new storage materials. " Given the large number of battery types, there is a growing need for research into their safe operation in a wide range of applications and throughout their entire life cycle," says Christian Ellersdorfer. The COMET centre Battery4Life builds on the COMET project SafeLIB in its work and can also draw on a state-of-the-art test centre in the field of battery safety (Battery Safety Center Graz) located at campus Inffeld. In the SafeLib project, the Institute for Vehicle Safety has developed new experimental approaches and virtual processes. The researchers want to further optimise these in Battery4Life and expand them to include artificial intelligence approaches in order to achieve even more precise predictions with a smaller number of experiments and less computing power.

Second lives for used batteries

The competence centre is also exploring methods to reliably assess the safety status of used batteries in particular. Suitable decommissioned batteries, for example from electric cars, could then be reused as stationary power storage units and would not have to be scrapped, which would greatly improve sustainability. When developing the assessment procedures, the researchers will take into account not only technical aspects but also economic efficiency and legal issues relating to data protection, warranty and liability.

Partners from eight countries

The scientific partners include a number of Graz University of Technology institutes as well as universities and research centres from Austria, Germany, Belgium, France, Switzerland and the USA. Corporate partners include AVL List, AVL DiTEST, Infineon, Fronius, Magna Steyr, Audi, BMW and Porsche.

Two other COMET centres received funding approval

In addition to Battery4Life, two existing COMET K1 centres in which TU Graz is involved have been awarded funding for a further four years: Pro2Future and the Polymer Competence Center Leoben.

This research is anchored in the Field of Expertise "Advanced Materials Science" and "Mobility & Production", two of five strategic foci of TU Graz.

 

Latest Review: Advances of Surgical Robotics: Image-guided Classification and Application

SCIENCE CHINA PRESS

 

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CLASSIFICATION OF THE GUIDANCE IMAGE APPLIED FOR SURGICAL ROBOT.

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CREDIT: ©SCIENCE CHINA PRESS

Recently, the National Science Review published an online review paper by Professor Lihai Zhang from the Orthopedics Department of Chinese PLA General Hospital. The paper, titled "Advances of Surgical Robotics: Image-Guided Classification and Application," summarizes the latest advances in image-guided methods for surgical robots over recent years. These advancements have enabled surgical robots to become more intelligent and efficient in clinical applications, providing new possibilities for precise surgical operations.

Significant achievements have been made in the application of surgical robots in minimally invasive surgeries. In recent years, thousands of surgical robot systems have been installed in hospitals worldwide, performing millions of surgeries. By integrating sensing, control, and execution units, surgical robots assist surgeons in completing precise and efficient operations, thereby reducing trauma, alleviating postoperative pain, and shortening recovery time. The ability of surgical robot systems to perceive their environment is crucial for achieving high autonomy and intelligent operation, with image processing technology being at the core of this environmental perception.

Traditional image-guided methods often rely on fixed equipment within the operating room. In contrast, modern surgical robot systems can acquire and process high-precision images in real-time during surgery, providing intuitive operational guidance. Through algorithms such as data augmentation, target segmentation, and instrument tracking, surgical robots can quickly and accurately understand the surgical environment and respond accordingly, thereby improving the efficiency and accuracy of surgeries.

In the review, the research team categorizes navigational images based on the method of data acquisition into direct and indirect images, and based on the method of target tracking into continuous, intermittent continuous, and non-continuous images. Based on these two dimensions, they establish a new classification system for the navigational images of surgical robots, introducing the main characteristics and application scenarios of each category. This classification also serves as a basis for dividing various surgical robot systems that have been applied clinically, summarizing the general rules for the application of navigational images in surgical robots. This will provide guidance for developing more advanced surgical robot systems.

Despite significant achievements, image-guided technology for surgical robots still faces many challenges. Future research directions include image enhancement and surgical scene reconstruction, high-fidelity surgical simulation and intelligent planning, multimodal image registration and accurate localization for deformable tissues, and augmented reality-enhanced navigation. The review concludes with an outlook on the future development of surgical robot technology, aiming to enhance and surpass human surgical capabilities through improved intelligence and autonomy of surgical robots.

This work was supported by the National Natural Science Foundation of China. Professor Lihai Zhang from Chinese PLA General Hospital is the corresponding author, with Professor Changsheng Li from Beijing Institute of Technology, Dr. Gongzi Zhang from Chinese PLA General Hospital, and Dr. Baoliang Zhao from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, as co-first authors.

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JOURNAL

National Science Review

DOI

10.1093/nsr/nwae186 

 

First Journal Impact Factor for One Ecosystem: the ecology and sustainability data journal

PENSOFT PUBLISHERS

 

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ONE ECOSYSTEM - THE ECOLOGY AND SUSTAINABILITY DATA JOURNAL - RECEIVED ITS JOURNAL IMPACT FACTOR BY CLARIVATE'S WEB OF SCIENCE IN THIS YEAR'S JOURNAL CITATION REPORTS.

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CREDIT: PENSOFT

For the first time, the Journal Citation Reports™ - released by Clarivate in late June 2024 - features the open-access scientific journal One Ecosystem. The inaugural Journal Impact Factor for One Ecosystem stands at 1.8.

The 2024 report reflects how many times content published in a particular journal in 2021 and 2022 was cited in the last complete year: 2023. Then, this total count is divided by the number of “citable” (i.e. research and review) articles to estimate the JIF for 2023.

The news comes shortly after the journal specialised in ecology and sustainability data received a Scopus CiteScore of 4.6. In comparison to Clarivate and the Journal Impact Factor, Scopus uses data from its own database and calculates its CiteScore based on publications and citations from the last four complete years.

One Ecosystem was launched in 2016 by open-access scholarly publisher and technology provider Pensoft in collaboration with Editor-in-Chief Prof. Dr. Benjamin Burkhard (Head of the Physical Geography & Landscape Ecology section at Leibniz University Hannover, Germany), and Deputy Editors-in-Chief Prof. Dr. Davide Geneletti (Department of Civil, Environmental and Mechanical Engineering, University of Trento, Italy) and Dr. Joachim Maes, (Directorate-General for Regional and Urban Policy of the European Commission). Since the beginning it has been associated with and endorsed by the global Ecosystem Services Partnership.

Amongst the unique features of the journal are the collaborative writing and review environment integrated within the manuscript submission workflow to allow for heavily automated structured data import; semantically enriched publications; and field-specific article formats, such as Ecosystem Service Mapping; Ecosystem Service Models; Ecosystem Accounting Table; Monitoring Schema.

“Since day one, One Ecosystem has been widely praised in the community for its novel and data-driven approach to ecology and sustainability science, coupled with a straight-forward and low-cost open-access scholarly publishing strategy for any researcher in the world. Now, the recognition by Web of Science and Scopus provides the journal with further proof of its top quality and research integrity that - I expect - will attract even more researchers in the field to submit their work to the journal”

says Editor-in-Chief Benjamin Burkhard. 

Content published in One Ecosystem can be found in over 60 leading academic indexing databases, including Scopus, Research Gate, DOAJ, Cabell’s Directory, CABI, ERIH PLUS, CNKI, Unpaywall and OpenAIRE. It is also archived in CLOCKSS, Zenodo, Portico and Zendy.

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Visit the One Ecosystem journal website at: https://oneecosystem.pensoft.net. You can also follow One Ecosystem on X (formerly Twitter) and Facebook.

 

Novel sensor developed for rapid detection of harmful insecticides

HEFEI INSTITUTES OF PHYSICAL SCIENCE, CHINESE ACADEMY OF SCIENCES

 

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SCHEMATIC DIAGRAM OF THE RAPID VISUAL AND QUANTITATIVE DETECTION OF ORGANOPHOSPHATE INSECTICIDE RESIDUES BY MULTICOLOR APTAMER-BASED SENSOR.

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CREDIT: LIN DAN

Recently, a research team led by Prof. JIANG Changlong from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has constructed a visual sensing platform based on DNA aptamer-based sensing system. This sensor can be used for rapid and quantitative detection of organophosphate insecticides, such as profenofos and isocarbophos, in the environment and food.

The research findings have been published in Analytical Chemistry.

Organophosphate insecticides, like profenofos and isocarbophos, are widely used for their effectiveness in controlling agricultural pests. However, excessive use can leave harmful residues, posing serious health risks. Developing accurate, efficient methods for detecting these residues is crucial for environmental and food safety. Current large-scale detection instruments are inadequate for on-site, rapid, and quantitative analysis. Therefore, creating a rapid, sensitive, and selective method for detecting organophosphate residues is vital for protecting human health.

In this research, researchers developed a new sensor that uses color changes to detect organophosphate insecticides like profenofos and isocarbophos. This sensor works because of a special interaction between the insecticides and designed DNA strands called aptamers.

The sensor includes a green dye (SG-I) that fits into a G-quadruplex structure in the aptamer, making the sensor glow green. When organophosphate insecticides are present, they bind tightly to the aptamers, disrupting this green glow. This binding causes the green fluorescence to fade and enhances the blue fluorescence from the G-quadruplex, changing the color from green to blue. This color change allows for the visual detection of these insecticides, with very low detection limits of 2.48 nM for profenofos and 3.01 nM for isocarbophos.

In addition, researchers have combined 3D printing technology and color-identifier application on smartphone to develop a portable detection platform.

"It can rapidly and visually quantify the organophosphate insecticides, providing a new strategy for on-site rapid detection of pesticide residues," said Prof. JIANG.

Visual and quantitative detection of organophosphate insecticides based on smartphone-based monitoring platform. 

CREDIT

LIN Dan

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JOURNAL

Analytical Chemistry

ARTICLE TITLE

Ultrasensitive Fluorescent Microsensors Based on Aptamers Modified with SYBR Green I for Visual Quantitative Detection of Organophosphate Pesticides