Finally, we discuss the disparities and similarities between distinct stakeholders’ objectives and the gaps and challenges of combatting AMR at various levels. In summary, this is a comprehensive but not exhaustive revision of the known unknowns about how to analyze the heterogeneities of hosts, microbes, and hospital patches, the role of surrounding ecosystems, and the challenges they represent for surveillance, antimicrobial stewardship, and infection control programs, which are the traditional cornerstones for controlling AMR in human health.Antimicrobial resistance (AMR) is one of the Global Health challenges of the 21st century. The inclusion of AMR on the global map parallels the scientific, technological, and organizational progress of the healthcare system and the socioeconomic changes of the last 100 years. Available knowledge about AMR has mostly come from large healthcare institutions in high-income countries and is scattered in studies across various fields, focused on patient safety (infectious diseases), transmission pathways and pathogen reservoirs (molecular epidemiology), the extent of the problem at a population level , their management and cost , cultural issues (community psychology), and events associated with historical periods (history of science). However, there is little dialogue between the aspects that facilitate the development, spread, and evolution of AMR and various stakeholders . This study consists of four complementary sections. The first reviews the socioeconomic factors that have contributed to building the current Global Healthcare system, the scientific framework in which AMR has traditionally been approached in such a system, and the novel scientific and organizational challenges of approaching AMR in the fourth globalization scenario. The second discusses the need to reframe AMR in the current public health and global health contexts. Given that the implementation of policies and guidelines are greatly influenced by AMR information from surveillance systems, in the third section, we review the  Antibiotic resistance  structure, the instruments employed to detect and monitor AMR, and the disparities and similarities of the objectives of distinct stakeholders. It also describes the changes in the theoretical framework and the effects of knowledge fragmentation in interpreting the AMR information throughout history.therapeutic solutions to counteract major epidemics of infectious diseases led to the birth of the “chemotherapy era” with the introduction of arsphenamine  in 1910, sulfonamides in the 1930s, and natural antibiotics in the 1940s [globalization”  and “the second globalization” (1945 to 1990) also triggered the development of logistics to control infectious diseases epidemics and illness in international campaigns, which included mass drug administration of antibiotics and antiseptics to troops [Between the end of the 19th century and the early 20th century, medical treatment moved from providing care in particular houses, philanthropic organizations, religious charities , and ad hoc foundations for particular diseases to care centered in advanced hospitals [he 1940s ,16,18. Ahe 1940s ,22,23. To troops ,18,24,25o troops ,16. Soono troops ,27.health given in the 1948 United Nations declaration [The second (1945–1989) and the third globalization (1989–2008) waves reflect two major global socioeconomic timeframes that greatly impacted the healthcare system and AMR in different ways. The bimodal economy models established after WWII and represented by the free markets from Europe and America, and the centralized planning markets from the Soviet Union and China resulted in major differences between the health sectors  of the countries aligned with these two models. The 1978 Alma-Ata declaration’s vision for societal health pinpointed the need to reorientate health systems toward primary care to address the social and environmental determinants of health and inequality . The Almlaration , which alaration . The endlaration ,31,32,33laration . A few ylaration ,35.social atomization, the political polarization, and social inequality [The fourth wave of globalization  brought novel challenges, such as the rapid emergence of ecological threats, the rising of the absolute number of the human population , the increased equality . In 2018equality . The novequality . AMR is equality ,38.social atomization also impacts the increasing knowledge fragmentation, the loss of a comprehensive vision of the sociocultural frame, and the rapid obsolescence of social habits that greatly influence the vision and the way to approach health and scientific questions [Other than inequalities, the current uestions ,39,40.miracle” drugs was soon replaced by increasing evidence that unnecessary antibiotic use contributes to the evolution of infectious pathogens. This warning, originally raised in the mid-1940s by René Dubos [The Mirage of Health [AMR has been analyzed through the lens of different disciplines and conceptual frames. The belief that infectious diseases could be eradicated by novel “né Dubos ,42, contf Health . They stf Health ,45, whicf Health ,47 and tf Health . AdoptinStaphylococcus aureus in the 1950s, followed by a continuing endemic situation, motivated the arrival of public health-based and population-based principles to improve hospital hygiene performance in the United Kingdom and the United States by the mid-1960s [The swift escalation of antibiotic-resistant clinical isolates, especially after the global epidemics of penicillin-resistant id-1960s . Hospitaid-1960s ,52. Moreplasmids as vectors of antibiotic resistance genes (ARGs), enabling AMR to cross species and borders [By the late 1970s, the medical community had realized how rapid bacterial adaptation could occur through  borders ,26,27. A borders ,54, what borders . This se borders . Then, A borders ,57,58. M borders , in partpopulations) in the evolution of AMR [resistome concept [Henle-Koch’s postulates, such as the causality of infectious diseases by a single “etiological organism” [Until the 1990s, the control of infectious diseases had been prioritized over analyzing the biological evolution of microorganisms. During this decade, we witnessed the arrival of molecular epidemiology and “-omics” developments and the adoption of a population genetics lens to recognize the impact of various units of selection  determining pathogen transmission and persistence [disease ecology and, thus, for the adoption of appropriate measures to combat/control infectious diseases and AMR.In summary, interactions between heterogeneous microbial communities vary in non-additive ways that may influence pathogen invasion and persistence at different spatial levels ,78. The sistence  is a penAMR has been a public health priority for decades. In 2019, the WHO included AMR as one of the top ten threats to global health ,38,61. HPublic health principles have been applied to prevent and control HAIs and AMR in the healthcare network . Surveilheterogeneous populations with varying interaction intensities and levels of agency [hothouses”  or associated networks , which accumulate some heterogeneous and synergistic risk factors, such as crowding, selective pressure, vulnerable populations, environmental reservoirs of MDR pathogens, or transmission vectors, such as chronic patients or medical workers with high (inter) healthcare exposure. Wastewater treatment plants are increasingly recognized as AMR hotspots [The term “One Health” focuses on the risk assessment of AMR’s emergence, transmission, and maintenance at the interface between humans, animals, and their related environments. The One Heath sectors comprise f agency ,78,107. f agency ,108 withf agency ,109,110.hotspots ,111  to medium-sized ecosystems , and larger ecosystems  [metasystems, including social norms, such as family status, professional/educational status, culture, behavior, cultural norms, beliefs, and religious practices), policies and interventions, and economic structure [AMR control is heterogeneously influenced by the One Health sectors, their associated economic growth groups, and their loops . The timndustry) ,114. Thetructure . These eOther than the need to include the socioeconomic and cultural context in planning public health interventions mentioned above, the time dimension, barely considered in previous operational conceptual frames, will be a key factor for planning and decisionmaking. Muti-level ecological systems considering social traits, including age and family structure, space and time location of individuals, and social dynamics, in general, have been included in the computational simulation of AMR evolution .scale, scope, and structure [validity and reliability of the AMR information to be applied in various healthcare , demographic, and economic contexts  has been neglected to date.AMR information is mostly provided by hospital records that also feed local and regional surveillance systems. Correlation of these data with demographic information from whom the pathogens are isolated  offers insight into the underlying epidemiology and facilitates the formulation of rational interventions aimed at reducing the burden of AMR  and its monitorization . The avatructure ,123,124.content validity, instrument validity, and criterion validity. “Reliability” (or reproducibility) in statistics refers to consistency in measurement: the capacity to produce the same result for two identical states; or, more operationally, the closeness of the initial estimated value(s) to the subsequent estimated value(s)  defines how well a test, indicator, or experiment measures up to its claims. It refers to whether the operational definition of a variable actually reflects the true theoretical meaning of a concept. In other words, does the indicator or the instrument measure what it is supposed to measure? “Construct validity” comprises all other types of validity evidence, such as e(s) see .unit of analysis and indicators used in AMR and discusses the factors that affect their validity and reliability , healthcare settings , and economic sectors  [Microbial infectious diseases and AMR disproportionally impact populations and settings, and the “who” or “what” that might occur needs analysis. The sectors) .exposome influence host susceptibility, host immunity, and social behavior [Age, sex, body health condition, genetics, and the behavior . The clabehavior ,126,127.behavior ,129.The risk of acquiring and spreading AMR also varies at different healthcare levels. Traditionally, populations are analyzed at tertiary and secondary healthcare centers , where the concentration of the high-risk groups of people and online diagnostic and life-sustaining equipment (such as ventilators), indwelling catheterization, and frequent use of drugs facilitate the emergence and local evolution of AMR. Within hospitals, some wards concentrate on groups and factors at higher risk than others ,131. Priindicators  [sentinel species) from non-categorized patients in large hospitals and with AMU/AMC from selected institutions/departments [In AMR surveillance, e, OUSs)  refer toartments ,122,135.Sentinel microbial species. Only a few bacterial and fungal species acquire and successfully spread ARGs of medical relevance, following the Pareto principle (80% of consequences come from 20% of causes), as occurs for causal agents of many infectious diseases [watch lists” or “priority lists” of antibiotic threats of the WHO and the CDC, which only include a few traditional causes of major “classical” human infectious diseases , all affecting specific risk groups of community-based people and often associated with syndemic processes affected by social structures. The lists also included some commensal opportunistic pathogens, such as Enterobacterales, enterococci, and staphylococci, or environmental organisms (Pseudomonas aeruginosa and Acinetobacter baumannii). Recently, the Division of Healthcare Quality Promotion (DHQP) at the CDC reported that more than one fifth of HAIs are non-Legionella water-related opportunistic pathogens and warned of a possible major underestimation of these numbers [Elizabethkingia, Achromobacter, and Burkholderia) able to cause significant hospital outbreaks [patches of microbial species and host populations and the neglected analysis of the “hospital-built environmental patches”  in the emergence, transmission, and persistence of AMR [diseases . These s numbers . The DHQutbreaks . What ale of AMR .Klebsiella pneumoniae, ST239 S. aureus [Escherichia coli [None of the available “watch lists” consider the heterogeneity within a species. Again (Pareto’s principle), only a limited number of specialized lineages within each species significantly contribute to hosting and transmitting AMR. These have been identified as high-risk clonal complexes (HiRCCs) ,140,141 . aureus , or EHEChia coli , or “jumhia coli . Both dihia coli . It demahia coli .Genes, plasmids, and mobile elements. ARGs can be rapidly spread across bacterial species, genera, or microbial kingdoms of various human, animal, and environmental backgrounds through transposable elements, often embedded in plasmid entities [entities ,151,152.entities ,154. Howentities ,156,157 entities ,159 ofteentities .Microbiomes and resistomes. Although not included in any surveillance program, metagenomics-enabled surveillance methods offer the opportunity to improve the detection of both known and yet-to-emerge pathogens [athogens . The anaathogens ,164,165.athogens ,167. Unmathogens ,168,169.athogens ,171.Antimicrobial consumption (AMC) and antimicrobial use (AMU) reflect how antimicrobials are dispensed and prescribed. Although they are often used interchangeably, they measure different things. AMC reflects the volumes of antimicrobials distributed , and AMU tells us how antimicrobials are used at the patient level . AMC/AMU data are primarily relevant in combination with local AMR trends and the preparation of antibiotic stewardship guidelines. Protocols and tools to properly collect AMC/AMU information are currently being debated [ debated ,131,132. debated . Surprisbystander selection) [AMU/AMC variations and shifts at local levels  and regional levels reflect cultural and socioeconomic differences or secondary public health problems ,94. For lection) ,173,174 scope, data quality, and comparability  to analyze various individuals, populations, or setting levels  and in a balanced and discriminated way . Although AMR surveillance employs different indicators/ OUSs to describe trends for a specific unit of analysis , traditional OUSs information is often too narrow to consider the full richness of such a context. AMR composite indicators are needed to analyze the population  [indexes) ,175, setindexes) , or diffindexes) . A clearData quality issues. The reliability of the information relies on the quality of the underlying data. Variations in the unit of analysis, indicators, measurement methods, definitions . This bias often occurs for AMR in the health sector if the ecology or physiopathology of the sentinel organisms does not match that of the unit of analysis. Blood and cerebrospinal fluids are predominantly screened in AMR surveillance, not only because of their clinical relevance but because this choice prevents some of the inconsistencies that arise from differences in clinical cases, different sampling frames, or heterogeneous healthcare utilization. However, invasive isolates might not represent isolates of the same bacterial species producing other infections. In addition, variations in blood culture frequency  result in an increasing uncertainty when comparing resistance percentages between hospitals, CHCs, and other settings. On the other hand, severe infections are uncommonly reported in CHCs, such as LTCFs, where monitoring AMR usually uses urine or respiratory samples [AMR information is frequently valid and reliable, but it is greatly biased by the structure of the dataset  is one of the most important challenges in AMR and infectious diseases that has traditionally been approached by microbial risk assessment [Finally, the assignation of an observation to a particular class  structural, governance, and specialization differences, which are conceptually linked. Socioeconomic structural differences have been pointed out as affecting AMR [ting AMR ,185,186.ting AMR ,185.Healthcare systems also have relevant semi-structural differences in terms of the structure and functioning of countries. Only the OECD statistics provide useful information for understanding the dynamics of the healthcare business and hospital systems in various countries; however, it is restricted to the last three decades . The avaSpecialization/networking differences. In addition to the structural and governance differences, the units of analyses can also differ due to e.g., patch dependency and feedback loops. Properties that define complex systems are emergence, feedback, and adaptation. Such properties have been analyzed in the context of “microbial complex systems” [Emergence describes the properties of the system but cannot be inferred from the elements that comprise it . Feedback describes the effect of a change on further changes [adaptation refers to the adjustments in behavior in response to intervention . Tools for approaching “complex public health systems” are still in their infancy [systems” ,151 but  changes . Finally infancy ,107,187 AMR research and policies have involved many stakeholders with disparate interests and objectives. Grundmann suggested a wise and interesting transversal frame to approach the problem and join the efforts of stakeholders with common objectives . On thisThe hospital is the most important landscape to detect the emergence and facilitate the evolution of AMR. On average, HAIs occur in 7% and 10% of hospitalized patients in LMICs and HICs, respectively . DiffereOne of the major challenges continues to be the definition and interpretation of HAIs cases because it undermines valid estimates of infection rates. Nosocomial acquisition is arbitrarily defined 48–72h after hospital admission, but it can also occur after the patient’s discharge, creating a flux of microbes between healthcare centers, and other community ecosystems ,196. TheConsiderable investments in surveillance, design of guidelines, and policies to contain the AMR problem in the global arena have been conducted with limited success. In May 2015, the 68th World Health Assembly adopted the “global action plan on AMR”  and callWorld Antimicrobial Awareness Week (WAAW). Results differ between countries, and sound recommendations for tailored campaigns adapted to various cultural models and sectors and involving experts in health communication and social marketing seems crucial [One of the key objectives of the WHO global plan to fight AMR is to improve awareness and understanding of AMR through effective communication, education, and training . Communi crucial .Many fundamental scientific questions relevant to understanding the development, selection, transmission, and persistence of ARGs remain to be unraveled. The Joint Programming Initiative on Antimicrobial Resistance (JPIAMR), an international collaborative platform coordinating and funding research on AMR, provides a research and innovation framework for joint actions, outlining key areas that should be addressed, and providing guidance for countries to align their AMR research agendas at national and international levels . The Sciconsumptogenic systems, intensified livestock practices, exacerbation of diseases by climate change, and loss of biodiversity by environmental pollution. They will open novel research areas and will also require the commitment of economic and social sectors [Although a colossal advance in knowledge has been achieved in recent decades with the support of public and private funding, we face new challenges associated with the 4IR, such as extended life expectancy, economic growth of pharma and food industries see  that led sectors ,206 , which are further impacted by climate change, poverty, conflict, migration, the increased atomization of the society, and knowledge fragmentation. Such a syndemic scenario showcases microbial communities in convergent vulnerable populations and disparate settings interacting with varying intensities that increase the risks to the health and safety of humans. The increasing lack of therapeutic options to treat MDR pathogens, the changes in the global drug market, the consequent decreasing interest of pharmaceutical companies in the development of new antimicrobials, and the spread of AMR across humans and animals worsen this scenario [AMR is one of the Global Health challenges of the 21st century that also threatens the achievement of several SDGs. The “scenario ,48,219. This review has only tried to bring together many aspects that are often considered separately. AMR is a public health problem resulting from social construction in the last centuries. Thus, the understanding of the history, the conceptual scientific frames, and the behavior of humans to face health problems are necessary to identify the current gaps and challenges and to resolve many already unresolved problems (the “known unknowns”).


Neurotransmitters are an important category of substances used inside the nervous system, whose detection with biosensors has been seriously addressed in the last decades. Dopamine, a neurotransmitter from the catecholamine family, was recently discovered to have implications for cardiac arrest or muscle contractions. In addition to having many other neuro-psychiatric implications, dopamine can be detected in blood, urine, and sweat. This review highlights the importance of biosensors as influential tools for dopamine recognition. The first part of this article is related to an introduction to biosensors for neurotransmitters, with a focus on dopamine. The regular methods in their detection are expensive and require high expertise personnel. A major direction of evolution of these biosensors has expanded with the integration of active biological materials suitable for molecular recognition near electronic devices. Secondly, for dopamine in particular, the miniaturized biosensors offer excellent sensitivity and specificity and offer cheaper detection than conventional spectrometry, while their linear detection ranges from the last years fall exactly on the clinical intervals. Thirdly, the applications of novel nanomaterials and biomaterials to these biosensors are discussed. Older generations, metabolism-based or enzymatic biosensors, could not detect concentrations below the micro-molar range. But new generations of biosensors combine aptamer receptors and organic electrochemical transistors, OECTs, as transducers. They have pushed the detection limit to the pico-molar and even femto-molar ranges, which fully correspond to the usual ranges of clinical detection of human dopamine in body humors that cover 0.1 ÷ 10 nM. In addition, if ten years ago the use of natural dopamine receptors on cell membranes seemed impossible for biosensors, the actual technology allows co-integrate transistors and vesicles with natural receptors of dopamine, like G protein-coupled receptors. The technology is still complicated, but the uni-molecular detection selectivity is promising. Understanding the neuro-modulation processes in the human brain or animal correlates with target neurotransmitter monitoring and helps to reveal complex behaviors in neuroscience nowadays ,2. DopamFor in vivo DA measurements, one of the most used methods is the neurotransmitters fluorescent detection that permits the monitoring of the synaptic transmission in a few seconds response time, with a single cell precision . After nIn fact, this review is rather focused on the in vitro detection of dopamine using biosensors. All known neurotransmitters were strongly explored in the last 25 years with in vitro biosensors ,25,26,27Traditional analytical methods for measuring the levels of dopamine or any other neurotransmitter include enzyme assays, capillary electrophoresis , high-peHowever, all these bio-devices have some constraints for DA detection. Firstly, in main biological liquids, DA coexists with other electroactive species, like Uric Acid (UA) or Ascorbic Acid (AA). These acids have similar redox potential, resulting in frequent artifacts in DA monitoring by biosensors ,44. AddiOn the other hand, DA biosensors must change their linear range of detection from mili-molar ,24,28 toThe paper is structured in the following sections: (i) introduction; (ii) review of the main classes of DA receptors as the main engine for the biosensors evolution; and (iii) all sub-classes of organic transistors, with emphasis on OECT.For dopamine detection, some enzymes have been exploited as receptor elements: polyphenol oxidase, tyrosinase, horseradish peroxidase (HRP), tyramine oxidase, and laccase . 2 of the electrode for dopamine concentrations ranging between 0.4–1.6 mM [The dawn of dopamine biosensors began with the immobilization of slices of plant tissue, such as green bananas, which contained polyphenol oxidase as the target enzyme. The hydrogen peroxide produced by dopamine oxidation was correlated with dopamine concentration. The DC current was measured with amperometry in a three-electrode cell, keeping pH = 7 with a phosphate buffer saline (PBS) solution. The results showed a linear current excursion of 0.1–0.8 μA per cm4–1.6 mM . Till to4–1.6 mM . Shinohara and collaborators presented dopamine biosensors based on enzyme receptors and chemiluminescence transducers. The dopamine oxidizes under tyramine oxidase to produce hydrogen peroxide, which reacts with luminol-generating chemiluminescence, generating a limit of detection (LOD) of 10 nM and a linear range of 80 nM–500 nM . The most popular enzyme-based dopamine biosensors used tyrosinase, connected to fluorescent or electrochemical transducers. More recently, a functionalized tungsten disulfide nano-tube was used as a dopamine work electrode for its electrochemical detection by a tyrosinase-based receptor . With a −1 cm−2 and long-term promising stability, keeping 90% of its initial activity for 30 days [Rahman designed a tyrosinase-based biosensor with multi-walled carbon nano-tubes modified with glassy carbon electrodes as work electrodes for dopamine. An important step was performed in terms of long-term stability and robust immobilization of the receptor, while interference with ascorbic acid was negligible at 1 mM dopamine. So, in 2016, the most successful sensor was produced, having a high sensitivity of 1323 mA/M 30 days .Another enzyme that is sensitive to dopamine is laccase. Santos et al. fabricated an amperometric biosensor working with a working electrode made by carbon paste modified with laccase enzyme, which was immobilized in a glutaraldehyde agent. A linear concentration range of 0.8 μM–62 μM, with a minimal detected concentration of 0.06 μM, was accomplished, but the stability was up to 90% from a signal in only 9 days .The field effect transistor-based biosensors for dopamine were extremely rare before 2015. In 2021, an Enzyme-FET with tyrosinase was fabricated for the DA measurements in fish brains in vivo . A lineaAntibodies have also become very popular receptors for biosensor development. They offer many advantages, such as high specificity, mature detection platforms, and selective binding affinity, similar to lateral flow devices or ELISA . NeverthNamkung et al. proposed a competitive indirect method for DA detection, immunoassay based . The ELIThe explanation for the lack of immuno-sensors of dopamine could be: (i) it is difficult to achieve in situ fast detection by immunoassay that becomes critical for dopamine monitoring; (ii) many interferences in the real samples are able to produce artifacts within the detection. Aptamers became better candidates for small molecule recognition, while antibodies have more difficulty for these targets.Aptamers consist of single-stranded DNA or RNA molecules, able to bind analyte molecules with high specificity and affinity . AptamerIn 2014, Kim and Paeng composed a competitive enzyme-linked aptamer assay for dopamine detection, associating RNA and DNA aptamers . The autMany aptasensors are based on fluorescent transducers, being attractive for their direct measurements. These optical sensors continue to use DNA aptamer generated from the RNA-aptamer . The optAnother optical aptasensor for DA detection is based on a colorimetric transducer. The successful transducer uses gold nano-particles (AuNPs) due to its strong extinction coefficient. However, colorimetric sensors provided higher LOD than fluorometric sensors. In 2018, Lin and collab. reported a DA biosensor using the artificial cerebrospinal fluid as a specimen. They fabricated a Dopamine Binding Aptamer (DBA) array that captures DA molecules from a fluid. By releasing the DA from DBA in an alkaline solution to produce DA-quinone, a color signaling was recorded: modifications from colorless to brown or associations with a strong reduction of the fluorescent signal, whether Au nano-particles were stabilized with bovine serum albumin (BSA-Au NP). The limit of detection for DA was 0.1 μg/mL if the colorimetric transducer was used or 0.5 ng/mL if the fluorometric transducer was used . In 2020On the other hand, dopamine, as an electroactive specie, can be oxidized with other catalysts, not necessarily enzymes. So, dopamine can be electrochemically detected with a work electrode enhanced with nanomaterials.This sub-section reveals different oxidation agents for dopamine, such as metal, metal oxide, nanomaterials, conducting polymers, graphene, carbon nano-tubes, and molecularly imprinted polymers (MIP), also suggested in Due to the huge number of studies, we resume here only a few examples to emphasize different work principles, while brief performances of other nanomaterials-based DA biosensors are included in In 2015, homogeneous thiophene graphene was doped with sulfur. The process was based on a solid-state reaction between sulfate and graphene oxide. The doping concentration was adjusted by the sulfate quantity. During this S-doping, some mesopores and micropores were configured on the graphene surface. Thiophene S-doped graphene presents a high electrocatalytic property for dopamine oxidation due to its high S-loading mass and multiple porous structure. Hence, the DA biosensor possessed a high sensitivity of 3.94 μM/μA, a low detection limit of 15 nM, and a signal/noise ratio of 3 .In 2019, another DA biosensor used polyanilineboronic acid (PABA) functional groups as recognizing elements for dopamine. The technological process combines excellent properties of single strands of DNA functionalized on mono-walls of carbon nano-tubes (DNA-CNT) with electrochemically-derived nitrogen-doped exfoliated graphene NEG and PABA, as dopamine receptor . By thisOn the other hand, nanomaterials combined with conducting polymers come with multiple advantages: optimized costs, biocompatibility, simple preparation, and flexible substrates. Therefore, many of them were already utilized in some applications, like, electromagnetic shielding and transparent electrodes, including DA biosensors ,74,75. IMore examples of DA biosensors containing classifications of nanomaterials are collected in another review article from 2021 .On the other hand, the natural receptors bound to cell membranes would be the most specific receptors offered by the living world. Their use in biosensors has been postponed for 20 years due to the technological difficulties of capturing them. But, in 2019, some authors proposed a biosensor for neurotransmitter detection, including dopamine, using their natural receptors . UsuallyThe usual biomaterials, such as enzymes, antibodies, aptamers, or DRD1, have some disadvantages, such as complicated molecules that require further functionalization, possible post-processing inactivation of some active centers, short time activity expressed by fast expiration date for testers. On the other hand, enzyme-free organic electrochemical sensors for dopamine can reach very low LOD (in the nM range), can be used even if AA and UA are present in high concentration, and they are cheap, small, and flexible .In 2021, Patella and collaborators reported a robust electrochemical DA biosensor, replacing the biomaterials with an active electrode made by Au or Pt nano-particles linked to reduced graphene oxide . Here, t2) [On the other hand, metal–organic framework (MOF) materials seem to present promising characteristics for DA detection, plus a further immunity to other interferents than AA and UA, such as sulfuric acid, glucose, sodium hydroxide, and potassium chloride. Generally speaking, MOF consists of a crystalline microporous material containing a metal ion and an organic linker. In 2022, Moallem et al. produced a simple electrochemical DA biosensor that uses Cu as metal ions and 1,3,5-benzene tricarboxylic acid (BTC) as an organic linker . But in 2) .Sometimes, the spontaneous or potential-induced polymerization of dopamine to Polydopamine (PDA) can occur. PDA is an adherent polymer that can be separately deposited on a wide diversity of materials to construct biosensing platforms ,94.For instance, Qu et al. reported a DA biosensor, with almost zero interferents, in terms of AA and UA, due to the acid phosphatase (ACP) used as a polymerization agent for DA . EssentiOrganic electronic devices were developed many years ago, starting from OLED applications in displays , followeMostly, there are three types of Organic Thin Film Transistors (OTFT) as the main exponents of the organic transducers for biosensors: organic field-effect transistors (OFETs), electrolyte-gated organic field-effect transistors (EGOFETs), and organic electrochemical transistors (OECTs) that will be presented and compared in this section in terms of the technological strategies and bioelectronics performance for the dopamine detection. Compared to biosensors with inorganic transducers, their OTFT-based counterparts possess larger surface areas achieved through simpler technology and room temperature for organic material processing, allowing the co-integration of biosensors on flexible substrates.In addition, the transistors must work with liquid media for biosensing. These often cause corrosion or other chemical attacks that disrupt and sometimes irreversibly damage the transistor. That is why OFETs, compared to inorganic FETs, present an additional advantage when their construction itself includes liquid electrolytes, which allows them to operate in aqueous solutions in different configurations.+3 ions [Nevertheless, the selectivity and sensitivity of the OFET-based biosensors still demand supplementary enhancement. One direction is to improve the electronic properties of the organic semiconductor, either by new doping methods, such as the incorporation of Fe+3 ions , or by u+3 ions , or by a+3 ions ,102,103.+3 ions . A seconp-type semiconductor and activated by a negative gate voltage) versus the MOS-FET works with an inversion channel. In the OFET biosensor, in addition to a constant external gate biasing, the variations of the electric charge on the gate are proportional to the analyte concentrations in order to produce deviations of the drain-source current and threshold voltage of the transistor. Frequently, the FET part is separated from the receptor membrane that is deposited over the gate insulator or over the substrate as an Extended Gate OFET transistor, OFETs are mainly based on the same principle as any FET, including MOS-FET, except the semiconductor is organic instead an inorganic one. The general principle is also fulfilled for OFET: the channel charge density is controlled by gate biasing. The difference consists in the type of channel. A cross-section through an OFET in Top Gate Bottom Contacts configuration is available in For instance, a biosensor able to detect C-reactive protein (CRP) used an OFET transducer, determining a LOD under 1 μg/mL . Here, txOy). It was deposited by sol-gel technology on an organic substrate made from PET [For dopamine detection, another OFET with an extended gate was recently proposed. The active film was indium-zinc oxide  layer and gate electrode, −12 M and a Vth deviation of 350 mV for a DA concentration of 10−3 M.Casalini et al. reported the first EGOFET-based dopamine biosensor based on SAM technology to immobilize the receptor on the gate electrode. The SAM receptor was integrated by 4-formylphenyl boronic acid and cysteamine, and it was able to directly bind the target neurotransmitter. As a result, the dopamine adsorption near each interface changes the capacitance of each double layer, transmitting specific signals for selective dopamine detection . All theA next representative is the organic electrochemical transistor (OECT) that was introduced by White et al. in 1984 , with ouCompared to the OFETs and EGOFETs-based biosensors, OECTs deliver higher transconductance due to a stronger electronic–ionic interaction, keeping in touch the ions in electrolyte with the electrical carriers within OSC during the doping and dedoping processes . FurtherThe usual material for OECT is polythiophene-doped polystyrene sulfonate (PEDOT: PSS) as a semiconductor, which is deposited by spin-coating or inkjet printing processes. The extra mixing with poly(ethylene glycol) (PEG) or poly (PVA) of the biofunctionalized PEDOT: PSS is frequently used for biosensing purposes . Then, gThe liquid-ion-gated organic field-effect transistor is one of the most advanced OECT representatives. Park et al. reported in 2020 such a kind of DA biosensor that combines conducting polymer nano-tubes to entrap the receptor element consisting of DA-specific aptamers, interdigitated microelectrodes, and carboxylated polypyrrole nano-tubes (CPNT) as organic nanomaterials . A familAptamers with a phosphate backbone were produced by the systematic evolution of ligands by exponential enrichment (SELEX) . Consequco-PE nanofibers and polypyrrole nanofiber networks [Another exponent of the OECT group is that they serve wearable biosensors based on nanomaterials and textile fibers. This kind of OECT fiber-based transistor was developed in 2019 to detect dopamine as a wearable biosensor . The mainetworks . Both finetworks . As a geRecently, Liang et al. combined an organic electrochemical transistor with a flexible polyimide substrate to facilitate the immobilization of DA aptamers. The gate electrode was performed by Cr/Au/Cr metallic layers, while OSC was Poly doped with polystyrenesulfonate (PEDOT:PSS).A half fragment of the receptor (aptamer1) is covalently bound to Au-electrode, while the second fragment (aptamer2) is attached for DA highlighting , using mFirstly, the Au-electrode surface was incubated through 0.5 µM aptamer1 that binds to the self-assembling monolayer, using thiol–Au bonding. In the next stage, the aptamer-modified electrode was sunk in 2 M NaCl aq. solution to release the aptamer2 and analyte molecules.As soon as the dopamine target molecules were dispersed, they fostered the coupling of the two separate aptamer fragments. The association creates multiple aptamer1/dopamine/aptamer2 sandwich structures that increase the redox concentration at the electrode surface, facilitating the charge transfer. This is so that the OECT-based biosensor exhibits an ultralow detection limit under 0.5 fM, which is one of the lowest reported LOD for DA biosensors . The schA concentration of 0.5 fM produced an offset in the transfer characteristic of the OECT of approximately 6 mV c, demonsPreviously, Liao et al. reported an OECT dopamine biosensor with two types of gate electrodes: graphene-modified gate electrodes and chitosan-modified Pt gate electrodes. In both cases, the detection limit cannot be decreased under 5 nM .Tang et al. also achieved a study of the OECT sensitivity to DA, using PEDOT:PSS organic semiconductor with diverse materials as gate electrodes: graphite, Au, and Pt. Pt offered the lowest limit of detection of 5 nM ,49.Another OECT that is able to measure dopamine in artificial sweat is based on (PEDOT:PSS) semiconductors combined with textile fibers . This OEDS and VGS drop voltages, by a unique power supply, VDD, and some resistors, as in the example from D) as firm as possible and the gate current (IG) as low as possible to stimulate the amplification property. In this case, the voltages, VGS and VDS, can be calculated with the formulas at the top of G << ID or IG negligible. The co-integration of a reference electrode near OECT would be a backup solution. However, there are solutions that are known to reduce the gate leakage current by isolation, as in FETs [Moreover, OECT does not need either three electrodes measurement, nor does it need reference electrodes or counter electrodes to build these devices besides their readout devices. Nevertheless, the measurement of OECT transistors requires the application of two voltages and the measurement of two currents. Fortunately, there are solutions to simultaneously ensure convenient V in FETs , by chan in FETs , or by m in FETs .If years ago, the use of natural receptors seemed more like a Sci-Fi story, increasingly, in recent years, natural receptors could be integrated together with organic OECT transistors. In the previous sections, we already noted these trends ,122, butNew bio-devices inspired by micro-nano-electronic technologies were developed for single-cell or single-protein analysis. They combine transistor schemes of nanometric size with microgrippers, micropipettes, or microneedles—simply to be co-integrated by MEMS or NEMS techniques . The useDS  = −0.3 V and VGS = −0.9 V to exhibit a linear response of 0.002–7 μM and a limit of detection of 1 pM [Moreover, a nano-sized needle-type OECT was recently constructed by a single/double barrel of carbon nanoelectrodes functionalized with a conducting polymer to detect dopamine in a wide concentration range: from pico- to micro-molar range. The device works under an operation bias point V of 1 pM .DS = −0.3 V successfully amplified the FSCV signal, in unshielded conditions, for DA concentrations in the sub-micro-molar range [Another direction of OECT development envisages biosignal amplification using transistor properties. Tybrandt et al. used a PEDOT:PSS OECT to amplify the fast scan cyclic voltammetry (FSCV) signal. The sensing electrode was constructed by a 10 μm gold square plate, offering a channel length of 20 μm and a channel thickness of 60 nm. Biasing this OECT transistor to Var range .Recent research proposed a neuro-device  that opeAt the dawn of biosensors, the first dopamine detectors used stand-alone enzymes, or enzymes from plant tissues, to assist in the oxidation of dopamine. The electrochemical sensors that were used in the past offered linear detection ranges of the order of 1 mM–100 mM and detection limits of up to 1 mM. The replacement of the working electrodes from the electrochemical combined with transistors represented an important step in the miniaturization of dopamine biosensors. In this way, the detection limits were pushed below nano-molar values. All these premises led to the inclusion of organic transistors, such as OFET or OECT, inside the biosensors, including those for dopamine. The inclusion of the newest types of receptors, such as DNA-related aptamers, or natural receptors, such as hDRD1, make some direct DA biosensors from these transistors. High-sensitive and selective measurements for dopamine, with a linear detection range between 0.1 nM–10 nM and frequent LOD of 50 fM, make these biosensors suitable for all types of clinical samples collected from humans: blood, urine, or saliva. Future directions seem to abandon Immuno-FET for dopamine, reduce the Enzyme-FET for DA detection, and lean more on OECT-based aptasensors.Dopamine wearable biosensors tend not to be a negligible sector, combining textile materials made of polymers with configurations of organic transistors and nanomaterials activated by last-generation receptors, which have proven equally promising detection ranges and excellent time response of 1 s. Advantages, like ultra-low power consumption , could be the next key points toward an ecological economy, which will include the biosensors sector, too, considering that the actual nano-transistors are getting closer to fulfilling these conditions.Despite the expansion of different types of biosensors and the widest range of receptors and transducers, including OECT transducers, there are still no dopamine biosensors on the market. Possible causes would be too much diversity of biosensors and materials distracts the attention of industry and marketing companies, lack of standardization of biosensors on the market, lack of cohesion between transistor manufacturers and biosensor manufacturers, and possible economic and industrial ambitions. Other times, the rapid expiration of the testers  or their simple exhaustion from markets often leads the client to the electronic device without finding matching testers. The solution must be a stable and standard technology at a low price, which means a large series production, extending the expiration time, and ideally creating recyclables or multi-use testers. Since the testers are inserted into electronic readers, it is desirable that the transducers be transistors, too. The extraordinary success of electronics nowadays consists not of a huge number of transistors, despite hundreds of known transistors. Today, in industry, only two types are series produced: bipolar and MOS-FET transistors. This is why they benefited from ultra-optimized technology and an ultra-large scale of integration that ensured invincible prices. In 2023 the use of an ultra-large pallet of nano-bio-materials coincided with the moment of reaching sub-20 nm dimensions for transistors. A preliminary solution could be conducted by EGOFETs, where only the part with the extended gate on which the receptor is integrated should be removable and re-renewable. All these goals can be touched only by a real interdisciplinary collaboration in the future and stability in technology.


The faster a drug enters the brain, the greater its addictive potential, yet the brain circuits underlying the rate dependency to drug reward remain unresolved. With simultaneous PET-fMRI we linked dynamics of dopamine signaling, brain activity/connectivity, and self-reported ‘high’ in 20 adults receiving methylphenidate orally (results in slow delivery) and intravenously (results in fast delivery) . We estimated speed of striatal dopamine increases to oral and IV methylphenidate and then tested where brain activity was associated with slow and fast dopamine dynamics (primary endpoint). We then tested whether these brain circuits were temporally associated with individual ‘high’ ratings to methylphenidate (secondary endpoint). A corticostriatal circuit comprising the dorsal anterior cingulate cortex and insula and their connections with dorsal caudate was activated by fast (but not slow) dopamine increases and paralleled ‘high’ ratings. These data provide evidence in humans for a link between dACC/insula activation and fast but not slow dopamine increases and document a critical role of the salience network in drug reward. The faster a drug enters the brain, the greater its addictive potential. Using simultaneous PET-fMRI in humans, here the authors report a neural circuit responding to fast but not slow dopamine increases from intravenous versus oral methylphenidate delivery. The rate at which dopamine increases along with the magnitude of the increase determine the intensity of a drug’s rewarding effects and its addictive potential5. As such, routes of drug administration that result in the fastest delivery into the brain  are more rewarding than routes that result in slower delivery 6. Compared to slower brain delivery, faster delivery of cocaine was associated with faster striatal dopamine increases, greater metabolic activity in brain reward circuitry, and more cocaine self-administration in rodents10. Individuals who smoke or inject drugs tend to develop substance use disorders more frequently and more severely, have higher overdose rates, and have more general health problems than individuals who take drugs orally or by insufflation14. Further, the fastest routes of administration evoked the strongest self-reported pleasurable effects to IV cocaine 16. These observations highlight the critical role of pharmacokinetics in drug reward and addiction potential. Therefore, understanding how the speed of drug delivery impacts human brain function and its association with reward could yield promising new targets for addiction treatment.Dopamine increases in brain reward regions are believed to underlie drug reward and to trigger the neuroplastic changes that result in addiction 17 and clear evidence of misuse particularly when injected18, MP is classified by the FDA as a schedule II substance alongside other addictive drugs like cocaine and methamphetamine. Despite MP’s addictiveness, it can be used safely and it is therapeutic when given orally. This is why MP formulations have been developed that make it harder to inject or snort MP 19.Preclinical studies have long used stimulants including methylphenidate (MP) as model drugs to study the relationship between pharmacokinetics and drug reward. But MP also has clinical relevance, for it is widely used to treat attention deficit hyperactivity disorder (ADHD). Because of its addictive potential22. Similarly, despite equivalent levels of dopamine transporter blockade by cocaine in the brain of cocaine users, the rewarding effects were dependent on speed of delivery, with greatest ‘high’ reported for smoked cocaine (time to peak effects: 1.4 min), followed by intravenous (3.1 min) and then intranasal (14.6 min) routes of administration23. Still, the conscious experience of drug reward depends on more than local striatal responses, as dopamine signaling activates large-scale downstream networks via reciprocal cortical connections24. One such circuit that may be sensitive to dopamine dynamics comprises the nucleus accumbens and the ventromedial prefrontal cortex, which are strongly associated with drug reward26, although other circuits may be equally implicated. For example, lesions to regions within the ‘salience network’  and insula) can cause complete remission of addiction,28 leading to hypotheses that this network underpins drug craving31. Thus, several candidate circuits may be sensitive to the speed of drug delivery and its association with drug reward.Yet in humans there is surprisingly little data on how brain function changes based on stimulant drug pharmacokinetics. Early positron emission tomography (PET) studies showed that fast rises in striatal dopamine appear responsible for the feeling of ‘high’ to stimulants such as MP. Specifically, whereas oral MP produced the same total magnitude of striatal dopamine increases as IV administration, only IV MP induced a reliable experience of ‘high’ across healthy controls32 while healthy adults received oral (resulting in slow brain delivery) and intravenous (resulting in fast brain delivery) doses of MP, in a double-blind, counterbalanced, randomized trial , we first tested how cardiovascular (heart rate and systolic blood pressure) responses to oral MP (slow brain delivery), IV MP (fast brain delivery), and placebo differed in intensity and over time, using repeated measures drug condition × time ANOVA. The overall magnitude of systolic blood pressure, but not heart rate, was significantly affected by drug condition  = 2.746, p = 0.077; systolic blood pressure F = 5.122, p = 0.011). However, for both measures there was a significant drug condition × time interaction, as expected  = 3.022, p < 2 × 10−16; systolic blood pressure F = 3.403, p < 2 × 10−16). Visual inspection showed that increases were strongest and fastest in the IV MP condition, whereas they were more modest and gradual in the oral MP condition, as expected  was lower both for IV- and oral-MP relative to placebo for the 90 min scans (corrected p < 0.05). However, there was no significant BPnd difference between oral and IV MP  = 0.6; p = 0.44; within-subjects ANOVA). Thus, there were no significant differences in the overall magnitude of dopamine increases between oral and IV sessions using this ‘static’ approach, which was expected as we used doses for oral and IV MP that were shown to occupy roughly equivalent quantities of striatal dopamine transporters (~70%) in humans37.Conventional ‘static’ analysis of PET imaging revealed significant decreases in relative standardized uptake value (SUVr) to oral (slow) and IV (fast) MP; these changes in [ons Fig. . Hencefo11C]raclopride by taking the minute-by-minute difference in SUVr between the placebo and MP conditions38. Dopamine increases to oral MP compared to IV MP started earlier  and were slower and more modest than the fast and strong increases from IV MP  across subjects reflects the rate of striatal dopamine increases , activity significantly decreased in ventromedial prefrontal cortex Fig. . For theNext, we tested whether any of these significant clusters increased their connectivity with other brain regions in association with the speed of dopamine increases . We performed dynamic functional connectivity, using the aforementioned clusters as seed regions.t-tests, the ventromedial prefrontal cortex cluster did not have any significant dynamic connectivity patterns in association with slow nor fast dopamine increases. However, the left insula and the dACC showed significant functional connectivity with bilateral dorsal caudate in association with fast (IV MP) dopamine increases only  = 10.30, p < 2 × 10−16; Fig. n = 19 reporting feeling ‘high’; t(18) = 3.115; p = 0.006, Fig. Finally, we sought to test whether the circuit identified from the prior analysis was significantly associated with individual differences in subjective drug reward. We first confirmed that ‘high’ ratings significantly differed in intensity and over time based on drug condition, using repeated measures drug condition × time ANOVA: (drug condition × time interaction: Ft-tests showed no significant sex effects for behavior (max high ratings); nor time-to-peak dopamine increases estimated from PET data; nor baseline binding potential (BPnd) in the nucleus accumbens, caudate, or putamen; nor strength of association between rate of dopamine increases and fMRI activations from all significant clusters identified in the manuscript, even at an uncorrected p < 0.05 threshold .Although we were underpowered to formally test sex effects in the current study , we performed exploratory analyses for sex differences. Results from two-sample 28. The dACC and bilateral insula responded to MP when administered intravenously, a route that maximizes the rewarding effects of drugs, but not when administered orally, the route used therapeutically that has much lower addiction potential. Consistently, these regions were among the most responsive, with a similar temporal course in the brain of cocaine users when given IV cocaine (0.6 mg/kg)39. The salience network, especially dACC, appears to be preferentially activated when the drug’s route of administration leads to fast brain delivery and intense rewarding effects. The ACC performs numerous roles in cognition, emotion, and reward41, which could all be relevant for processing the acute effects of stimulant drugs43. However, subregions of ACC are differentially involved in reward-related processes. In contrast to rostral ACC, which has been implicated in (inhibitory) affective processing, dACC, which had the most overlap with our findings, has been implicated in higher-order (drug independent) reward-based decision making but also in the inhibition of drug craving45. However, given that our experiment was performed in resting state without any task, we posit that dACC activation may reflect interoceptive signals mediating the experience of drug reward46. In fact, poor interoceptive awareness is a feature of substance use disorders and theorized to play a role in compulsive drug-seeking47. Moreover, the subregion of the ACC that was significant in our analyses corresponds to the one that underlies the connectivity disruption associated with remission of addiction in patients suffering from brain lesions28.Together, these findings provide insight into how the salience network is critically linked to the pathophysiology of substance use disorder48. The caudate receives extensive dopamine projections from the midbrain, and we previously reported that the speed of MP binding to the caudate strongly correlated with the experience of drug reward12. The current findings provide evidence that downstream cortical targets of the caudate appear necessary for the conscious experience of drug reward. This pattern may be driven by monosynaptic inputs from cortex to caudate, and/or by polysynaptic connections from caudate to cortex via pallidum and thalamus; future preclinical studies could resolve the circuit mechanisms in greater detail.Crucially, after IV MP, the salience network had enhanced functional connectivity with the dorsal caudate, with which it has bidirectional connections49. Further, the ventromedial prefrontal cortex was the only brain region differentiating healthy adults from cocaine users, as MP decreased metabolism in controls but increased it in cocaine users50. Likewise, when controls expected to receive MP, metabolism increased in the ventromedial prefrontal cortex whether they received MP or a placebo51. Together these data point to an intriguing possibility that expectation or sensitization following repeated drug exposure causes enhanced responsivity to drugs in the ventromedial prefrontal cortex. This tracks with preclinical studies suggesting that the infralimbic cortex  is most active after repeated drug use and serves as an “off-switch” for extinction behavior; in contrast, prelimbic cortex (whose homologous region in humans includes dACC) is most active during the initial exposure to drugs and serves as an “on-switch” for drug-seeking52.The ventromedial prefrontal cortex, a key node within the mesolimbic reward circuit, tracked both slow and fast dopamine pharmacokinetics, but its activity did not significantly relate to the experience of drug reward. The lack of an association could be because participants were naïve to stimulant drugs, a theory supported by a series of FDG-PET studies of brain glucose metabolism. In healthy adults, after a single dose of IV MP, metabolism in frontal cortex decreased, whereas after repeated doses it increasedThe opposite pattern of responses in dACC and insula (BOLD increases) and ventromedial prefrontal cortex (BOLD decreases) are consistent with our hypothesis that IV MP but not oral MP would trigger stimulation of D1R with a consequent increase in activation whereas both IV and oral MP would stimulate D2R leading to decreased activation. A question that remains unresolved is the mechanism underlying the greater sensitivity of the salience network to D1R stimulation and that of the ventromedial prefrontal cortex to D2R stimulation.34. Null findings may reflect that energetic effects of stimulation are associated mostly with terminal projections and not the location of cell bodies53. Therefore, while dopamine D1R and D2R would result in activation and deactivation of striatal MSN neurons respectively, the BOLD responses would be observed downstream in projection regions in the direct (D1R) or the indirect (D2R) striato-thalamo-cortical pathway. Further, MP’s effects reflect dopamine and norepinephrine transporter blockade with a consequent increase in dopamine and norepinephrine; and whereas only the dopamine terminals to the striatum express dopamine transporters, terminals to the frontal cortex express norepinephrine transporters54. As such, downstream regions like dACC, which receive convergent projections from dopamine-rich  and norepinephrine-rich  structures may be most relevant for the conscious experience of drug reward. In addition, our hypothesis represented a simplified model that focused primarily on postsynaptic receptor stimulation. In general decreases in striatal D2R binding with [11C]raclopride are interpreted to reflect predominantly postsynaptic D2R receptors since their relative concentration in striatum is estimated to be 4 times higher than that of D2 autoreceptors56. Yet dopamine increases to MP are also likely to evoke presynaptic effects on D2 autoreceptors57, which would have attenuated peak dopamine increases by inhibiting dopamine cell firing and striatal dopamine release, ultimately attenuating striatal BOLD responses. Nonetheless, our null findings in striatum closely parallel a recent PET-fMRI study suggesting that dopamine signaling may more directly impact activation of frontal cortex, including dACC, and fronto-striatal connectivity rather than local striatal activation58.In our study, striatal BOLD did not significantly correlate with dopamine dynamics, in contrast to our predictions based on a prior model59, whether the circuit responses identified here generalize to some or all forms of substance use disorders needs to be further investigated. Second, individuals were administered MP by clinical staff in a laboratory environment, which, while tightly controlled, has low ecological validity. A rich literature in rats63 and humans64 finds that the environmental context during drug exposure has a critical impact on subsequent drug use behavior. For instance, adult males drank significantly more alcohol when they were randomized to exposure in a simulated bar environment relative to a neutral laboratory setting65. PET studies have also demonstrated that personalized cocaine cues elicit dopamine increases in people who misuse cocaine, especially among those with high levels of craving70. Finally, while our behavioral paradigm included ‘high’ ratings as a marker of drug reward, the construct of reward involves much more than the subjective pleasurable effects of drugs. Genetic and epigenetic vulnerability, prior conditioning, availability of alternative rewarding stimuli, and baseline physical/emotional state  all impact the experience of drug reward and deserve further study in this context71.There are several considerations to note regarding the study population and design. First, this study was conducted in participants naïve to stimulant drugs. While the initial subjective experience to stimulant drugs has predictive value in the future development of substance use disorder28. Patients who suffered stroke lesions to brain regions that had positive functional connectivity with dACC and insula (where we observed activation with fast dopamine increases), and lesions to brain regions that had negative functional connectivity with ventromedial prefrontal cortex (where we observed deactivation both with slow and fast dopamine increases) led to remission. Therefore, both studies support interventions to inhibit the ACC and insula and interventions to stimulate the ventromedial prefrontal cortex as strategies for the treatment of substance use disorder. Indeed, the dACC is being tested as a neuromodulation target to combat compulsive drug use72 with preliminary findings showing decreases in cocaine self-administration73, cue-induced alcohol craving74, and heavy drinking days75. Critically, in the latter study, successful stimulation effects were associated with decreased connectivity between dACC and caudate. A key next step is to evaluate if inhibition of this circuit during drug administration blocks the subjective experience of drug reward, which could open new avenues to treat substance use disorders.Notably, our study identified two distinct circuits similar to the pattern of brain lesions leading to clinical remission of addiction38. Here, we applied these data to investigate the relationship between dynamic dopamine changes and brain function assessed with fMRI and all of the primary fMRI results in this manuscript are novel and have not been published.Some of the PET data and the behavioral data (‘high’ ratings) were previously reported in a recent publicationhttps://classic.clinicaltrials.gov/ct2/show/NCT03326245 on October 31, 2017). All participants self-reported no history of nicotine/tobacco use. All participants were compensated for study participation. Our sample is broadly representative of the Washington, D.C. metro area, but it is possible that self-selection bias is present, though it is unclear whether this would affect the primary outcomes of this trial.Twenty healthy individuals  participated in the study  determined that n = 20 would be needed to achieve a medium-to-large effect size (Cohen’s D = 0.65) for a paired t-test with α = 0.05, and 1−β = 0.8.Sample size was determined based on estimates from prior similar studies, since no consensus exists on expected effect sizes for the outcome measures in this trial. However, previous imaging studies that used pharmacological challenges observed large effect sizes for strong within-subject manipulations such as IV drug administration, including the imaging outcomes reported here. Even 20 mg oral MP  affected measures of resting fMRI brain function with Cohen’s D = 0.53 relative to placeboParticipants were screened to exclude major medical and neuropsychiatric disorders that can impact brain function , past or present history of substance use disorders , heart abnormalities (confirmed with electrocardiography), hypertension requiring medication or arrhythmia, pregnancy (confirmed with a urine pregnancy test) or breastfeeding, medications that may interact with methylphenidate , or ferromagnetic body implants that are contraindicated for MRI.The procedure for the study is illustrated in Fig. 11C]raclopride . Simultaneously, fMRI data were acquired continuously for 90 min with a single-shot echo planar imaging (EPI) sequence . Thirty minutes after [11C]raclopride injection, either 0.25 mg/kg MP or placebo was manually injected i.v. as a ~30-s bolus. The participants were instructed to stay as still as possible and keep their eyes open during the scan.The participants underwent simultaneous PET/MRI imaging in a 3T Biograph mMR scanner . All studies were initiated at noon to minimize circadian variability. Venous catheters were placed in the left dorsal hand vein for radiotracer injection, and in the right dorsal hand vein for intravenous injection of medications. Heart rate (HR), systolic and diastolic blood pressures (BPs) were continuously monitored throughout the study with an Expression MR400 patient monitor . Thirty minutes before tracer injection, either 60 mg of MP or placebo was administered orally (p.o). The participant was then positioned in the scanner. Earplugs were used to minimize scanner noise and padding to minimize head motion. A T1-weighted dual-echo image was collected for attenuation correction using an ultrashort-TE (UTE) sequence  for PET attenuation correction, and T1-weighted 3D magnetization-prepared gradient-echo  was used to map brain structure. List mode PET emission data were acquired continuously for 90 min and initiated immediately after a manual bolus injection of raclopride were calculated after normalization for body weight and injected dose, co-registered with the strictrual T1w map, and spatially normalized to MNI space using paramaters obtained from the HCP pipelines79. Relative SUV time series, SUVr(t), were computed in MNI space by normalizing each SUV volume by its mean SUV in cerebellum, as defined in individual FreeSurfer segmentations.A 3-dimensional ordered-subset expectation-maximization (OSEM) algorithmaov function in R. The main effect of drug condition tested for differences in the overall change in intensity in these measures for each session, and the drug-condition × time interaction tested for differences in the temporal dynamics of these measures across the three sessions.We tested how behavioral (self-reported ‘high’) and cardiovascular (heart rate and systolic blood pressure) responses to oral MP (slow delivery), IV MP (fast delivery) and placebo differed in intensity and over time, using repeated measures drug condition × time ANOVA, with the t* = 20 min was used to map the distribution volume ratio (DVR) and non-displaceable binding potential (BPnd)84, independently for each participant and session.Time-activity curves were extracted for putamen, caudate, ventral striatum and cerebellum from SUV time series using individual FreeSurfer segmentations. The Logan Plot graphical analysis for reversible systems using the cerebellum as the reference tissue and equilibration time 11C]Raclopride is sensitive to synaptic dopamine concentration, as it has lower affinity for dopamine D2-like receptors than endogenous dopamine87. Therefore, decreases in [11C]Raclopride binding following administration of a dopamine-boosting drug like MP are a suitable proxy for ‘dopamine increases’89.Decades of clinical and preclinical research have demonstrated that [11C]Raclopride binding to measure the temporal dynamics of dopamine receptor occupancy (and dynamics of dopamine increases in response to dopamine-boosting interventions such as MP). Some of the most popular methods include ‘neurotransmitter PET’ (ntPET)90, the ‘linear simplified reference region model’ (LSSRM)91, and the ‘dynamic binding potential’35.Several groups have further found that one can model the time course of raclopride throughout the 90 min of scanning , which was deemed unsafe.) Therefore, radioactivity counts were lower at the end of the scan than in a paradigm with a continuous infusion. While LSSRM does not strictly require a paradigm with a continuous infusion, in our dataset we found that the relatively low radioactivity counts made dopamine quantification with LSSRM challenging. However, our design had the advantage of an additional placebo scan for each participant. Therefore, we developed an approach that capitalized on the added reliability the placebo scan affords, and could overcome the lack of a continuous [11C]raclopride infusion. While the ΔSUVr approach requires two scans (MP and placebo) it has an important advantage: it only requires the amplitude of ΔSUVr and the time-to-peak of its derivative for fitting the ΔSUVr data, which improved the reliability of dynamic ‘dopamine increases’ estimates over prior methods.The SUVr method can be seen as an approximation of LSSRM (see demonstration below). LSSRM requires only one scan session with an MP challenge to estimate dynamic dopamine increases, but it necessitates five fit parameters, which hindered reliable quantification of dopamine in our data. This may be in part because we were unable to continuously infuse [CT(t) of a target region in relation to the kinetic CR(t) of a reference region92.The Simplified Reference Tissue Model (SRTM) defines the kinetic 91) extended this model by incorporating a time-varying efflux rate γ represents the magnitude of transient effects and the function h(t) characterizes the endogenous neurotransmitter discharge or an exogenous concurrent drug concentration level. Since MP increases extracellular dopamine, it also increases binding competition and reduces tracer concentration in the target region, Eq. (mentclass2pt{minimt), is calculated by dividing the uptake value in a specific region of interest (ROI) by the uptake value in a reference region. The reference region is typically an area of the brain that is considered to have minimal specific binding for the radiotracer used in the PET study. The SUVr is used as a simplified way to quantify the relative accumulation or binding of a radiotracer in a particular brain region compared to the reference region.The standardized uptake value, SUVr, caused by MP-related increases in endogenous dopamine quantifies the change in radiotracer binding with respect to the placebo condition.The SUVr change, ΔSUVr(t) can be expressed asInserting (1) and (2) in (4) ΔSUVr(CR(t) is described by the operational equation of the one-tissue compartment model:K1 = 0.092 mL/min.g and k2 = 0.45 min−1 (see ref. 91). The plasmatic input function can be represented by the tri-exponential functiontpeak = 110 s (see ref. 93). The concentration of the tracer in the striatum can be simulated using specific parameters 30 and Eq. (CR(t) peaks earlier than CT(t), which reaches a maximum near the MP injection time (t = 30 min). In addition, Eq. (h(t) was modeled by a gamma probability distribution function  between ΔSUVr(t) and t) is proportional to the accumulation of endogenous dopamine caused by MP, which in our approach is represented by F(t), and that the instantaneous dopamine change h(u) is equivalent to the rate of dopamine, which in our approach is represented by f(t).The instantaneous tissue concentration in the reference region mentclass2pt{minim1, k2, k2a) to estimate dopamine increases. This enhances the robustness of model fitting as it only requires the amplitude of ΔSUVr and the time-to-peak of its derivative for fitting the ΔSUVr data.The ΔSUVr approach offers a significant advantage over prior methods, such as LSSRM, by eliminating the need for individual-specific SRTM parameters (Rf(t), the PET-derived estimates of the rate of dynamic dopamine increases to oral and IV MP , as the regressors of interest. Because these estimates were of lower temporal resolution (1 min) than the fMRI images (3 s), we first upsampled the dynamic dopamine increases to match the number of fMRI volumes via interpolation, using Python’s interp1d function in the Scipy package with the ‘extrapolate’ method for filling missing values. Then, for each individual and for each session  we used the time course of the rate of oral and IV dopamine increases as regressors against the whole-brain voxelwise maps of BOLD signal intensity, and additionally included a monotonically increasing linear term as of no interest, to account for linear drift in the fMRI signal. This analysis yielded whole-brain maps showing where brain activity was significantly associated with dynamic dopamine increases across time. We then subjected these maps  to second-level analysis in SPM: one-sample t-tests for each drug condition, and then paired t-tests identifying the effects of each MP dose . For paired t-test analyses, we included drug condition order  as a covariate of no interest. We also conducted an additional control analysis to ensure that findings were driven by the rate and not the magnitude of dopamine increases; here he conducted another multiple regression similar to the prior analysis, except we also included the amplitude of dopamine increases (estimated by PET) as an additional regressor -corrected, and a minimum cluster size of k > 50, in line with current reporting guidelines.To identify how brain activity was associated with the differing pharmacokinetic patterns of dopamine increases to slow  vs. fast (IV) MP, we performed whole-brain voxelwise multiple regression analysis of fMRI images in SPM. We used t-test activation analyses to slow  or fast (IV vs. placebo) MP, and using them as seed regions, computed dynamic functional connectivity across the 90-min scanning session . Connectivity, i.e., the z-scored temporal correlation between the bandpass-filtered BOLD signal intensity of the seed region and that of every other voxel in the brain, was computed with a 5-min sliding window with a 4-min overlap, yielding a connectivity estimate for each minute of the scan. Then, as with the activity analysis, for each individual and for each session  we used the time course of both the oral and IV dopamine increases as regressors against the whole-brain voxelwise dynamic connectivity maps. As in the activity analysis, we performed one-sample and paired t-tests on these maps using drug condition order as a covariate.We then tested whether brain regions whose activity significantly associated with slow  or fast (IV) dopamine increases showed dynamic connectivity patterns in association with slow or fast dopamine increases. To do this, we took the significant clusters from the paired n = 19 for the fast (IV) MP session but only n = 13 for the slow  session; therefore, this analysis was only performed for the IV session. Then, second-level analysis consisted of a one-sample t-test for the IV session .Finally, we sought to understand individual differences in the neurocircuitry behind the subjective experience of drug reward. We tested the association between dynamic brain connectivity  and ‘high’ ratings for each individual. We took each individual’s self-reported ‘high’ ratings over the 90-min scan and interpolated the values to match the number of timepoints of dynamic connectivity (82) with the same interpolation method we used for dopamine increases. We then performed time series regression analysis of dynamic functional connectivity with high ratings using the ‘dyn’ and ‘lm’ packages in R. Analysis could only be performed in individuals who rated some change in ‘high’ over the course of the scan . This left Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Oil-contaminated water and industrial oily wastewater discharges have adversely affected aquatic ecosystems and human safety. Membrane separation technology offers a promising solution for effective oil–water separation. Thus, a membrane with high surface area, hydrophilic–oleophobic properties, and stability is a promising candidate. Electrospinning, a straightforward and efficient process, produces highly porous polymer-based membranes with a vast surface area and stability. The main objective of this study is to produce hydrophilic–oleophobic polyacrylonitrile (PAN) and cellulose acetate (CA) nanofibers using core–shell electrospinning. Incorporating CA into the shell of the nanofibers enhances the wettability. The core PAN polymer improves the electrospinning process and contributes to the hydrophilicity–oleophobicity of the produced nanofibers. The PAN/CA nanofibers were characterized by Fourier transform infrared spectroscopy, field emission scanning electron microscopy, X-ray diffraction, and surface-wetting behavior. The resulting PAN/cellulose nanofibers exhibited significantly improved surface-wetting properties, demonstrating super-hydrophilicity and underwater superoleophobicity, making them a promising choice for oil–water separation. Various oils, including gasoline, diesel, toluene, xylene, and benzene, were employed in the preparation of oil–water mixture solutions. The utilization of PAN/CA nanofibers as a substrate proved to be highly efficient, confirming exceptional separation efficiency, remarkable stability, and prolonged durability. The current work introduces an innovative single-step fabrication method of composite nanofibers, specially designed for efficient oil–water separation. This technology exhibits significant promise for deployment in challenging situations, offering excellent reusability and a remarkable separation efficiency of nearly 99.9%. In the current vast, growing industrial age, spoilage of oil-contaminated water and industrial oily wastewater discharges have adversely affected the aquatic ecosystem and human safety . AccordiMembrane separation technology represents a promising approach for oil–water separation. The innovative membrane materials can be classified into two main types for oil–water separation: hydrophobic–oleophilic and hydrophilic–oleophobic membranes. Particularly, hydrophilic–oleophobic materials are widely used as the optimal choice for effective oil–water separation due to limitations in hydrophobic–oleophilic membranes . The latVarious techniques are employed to fabricate these membranes, including phase inversion , coatingSimilarly, CA nanofibers showed unique properties, including biodegradability, thermal stability, and good chemical resistance . HoweverWang et al. 2020) produced electrospun PAN/CA nanofibrous membranes with controllable spatial structures through a multi-fluid-mixing electrospinning process utilizing a multi-spinneret. The research team proposed that the composite nanofibers composed of 75% CA:25% PAN possess promising application in the field of membrane separation, primarily owing to their desirable combination of hydrophobicity and mechanical properties. However, no application was studied in this research . Karki e020 produHowever, studies in the literature that combine PAN/CA to produce nanofibers have focused on the synthesis of nanofibers using each polymer separately and then combining the produced nanofibers and, thus, requiring surface modification  or combi8H10) with a purity of 98.5%, benzene (C6H6) with a purity of 99.7%, and toluene (C7H8) with a purity of 99.5% were procured from SDFCL in Mumbai, India.Polyacrylonitrile (PAN) (molecular weight (MW): 230 kDa) in the form of a copolymer with 99.5% acrylonitrile and 0.5% methyl acrylate and cellulose acetate (CA) (average MW: 50 kDa) were used in this study. PAN and CA were supplied by Sigma–Aldrich, Hamburg, Germany. Acetone and dimethylformamide (DMF) were purchased from Merck Millipore, Darmstadt, Germany. Diesel and gasoline were obtained from a local gas station . Additionally, xylene (CA 10 wt% solution of CA was prepared by dissolving a specific amount of CA in a DMF/acetone (1/1) mixture at 80 °C for 12 h. A 5 wt% PAN solution in DMF was prepared by stirring for 24 h at 50 °C. The fabrication of PAN/CA nanofibers was carried out using an electrospinning device . In this process, the 5 wt% PAN solution and the 10 wt% CA solution were loaded into separate 10 mL syringes, positioned in their respective 1 and 2 positions within the syringe pump device, as illustrated in −1 to 4000 cm−1.To confirm the functional groups on the PAN/CA nanofibers, Fourier transform infrared spectroscopy (FTIR) was conducting using a JASCO FTIR-6300 . This analysis employed the KBr mode, involving 32 scans within a spectral range extending from 500 cmThe observation of the top surface morphology of the PAN/CA nanofibers was conducted through a field emission scanning electron microscope (FESEM), Apreo, Thermo Fisher Scientific, Waltham, MA, USA. To facilitate imaging, a thin layer of gold was coated onto the PAN/CA nanofibers using a vacuum sputter coater. The top surface morphology was observed at both 10 k× and 30 k× magnifications. The average fiber diameter of the PAN/CA nanofibers was determined using ImageJ software . The results are presented as average fiber diameter ± standard deviation, which was calculated using the STDEV formula in Excel.The crystalline properties of PAN/CA nanofibers were evaluated using X-ray diffraction (XRD) employing a D8 Advance X-ray diffractometer  equipped with a CuKα radiation source at a wavelength of 1.5406 Å. The XRD patterns were obtained withing a 2θ scan range from 10° to 70°, utilizing a step size of 0.025° and an exposure rate of 0.1 s/step.The measurement of water contact angle and underwater oil contact angle for each PAN/CA nanofiber was conducted using a contact angle meter  accompanied by Drop Shape Analysis Software (version no. 3.54.164.0). The contact angle was measured at three different spots on the PAN/CA nanofibers’ surface to minimize analytical errors.A 3:1 ratio mixture of 75 mL deionized water and 25 mL targeted oil was prepared for membrane efficiency evaluation. After 24 h of initial agitation, the emulsion was further stabilized by vigorous stirring at 1500 rpm for 30 min. This mechanical agitation, combined with the inherent hydrophilic–oleophobic properties of PAN/CA nanofibers, ensured the formation of a stable and homogeneous oil–water emulsion. Filtration experiments started immediately after stirring, utilizing a gravity-driven system with a 4 cm diameter filtration circle, as depicted in 2/h (LMH) was determined utilizing Equation (1), where ”m” represents the mass of the water permeated through the membrane (g), ”A” denotes the effective area of the membrane (12.57 −4 m2), The membrane’s permeation flux (F) in L/mThe normalized flux was calculated according to Equation (2), where The removal efficiency was calculated according to Equation (3), where Following the separation experiment, the permeates were analyzed for their oil content, employing a turbidity meter model MI415 PRO manufactured by Milwaukee in Romania. These data were then paired with the initial oil concentration of the feed emulsion. This comparative analysis enabled the calculation of both the removal efficiency measurements and the membrane’s separation efficiency, providing a comprehensive assessment of the membrane’s performance in oil–water separation.−1 is associated with the stretching of the C=O ester carbonyl [−1 is attributed to the bending vibration of the C–H bond in the acetyl groups [−1 arises from stretching vibrations of the C–O–C bond in acetyl groups [−1 is characteristic of the C–O–C pyranose ring’s skeletal vibration [The FTIR spectra of PAN/CA nanofibers are presented in carbonyl ,25, thatl groups , and the−1 was ascribed to the stretching vibration of the C≡N group [−1 was attributed to C=N [−1 was attributed to C–N bonds [−1 belonged to the stretching vibration of –OH [−1 was due to the bending vibrations of the CH2 groups of PAN nanofibers, while peaks at 2930 cm−1 and 1079 cm−1 belonged to C–H stretching vibrations [Conversely, PAN within the PAN/CA nanofibers presents a simple chemical composition, characterized by a C–C backbone and an abundance of C≡N groups. The FTIR spectra of pure PAN nanofibers revealed distinctive peaks. Firstly, a sharp and narrow peak at 2244 cmd to C=N . Additio–N bonds . A broadn of –OH . The str−1, 1235 cm−1, and 1042 cm−1 corresponding to the stretching of C=O ester carbonyl, stretching vibrations of C–O–C bonds in acetyl groups, and the C–O–C pyranose ring’s skeletal vibration, respectively, were weakened in PAN/CA nanofibers. On the other hand, the peaks belonging to PAN were strengthened in the PAN/CA nanofibers, found at 3625 cm−1, 2930 cm−1, and 2244 cm−1 and belonging to stretching vibrations of –OH, C–H stretching vibrations, and stretching vibrations of C≡N, respectively. In The differences between the spectra of CA, PAN, and PAN/CA nanofibers indicated that the electrospun nanofibers consist of both CA and PAN, originally present in the two polymer solutions. There is no evidence of a chemical bond formed during the electrospinning process, suggesting that the CA and PAN nanofibers are physically connected.The average fiber diameter of the PAN/CA nanofibers is shown in The XRD pattern of PAN/CA nanofibers is presented in In contrast, CA nanofibers exhibit a primary peak around 7.9°, the principal characteristic peak of semicrystalline acetylated cellulose . The posThe XRD pattern of PAN/CA nanofibers indicates the presence of both CA and PAN peaks. However, the peak at 16.9° is less intense compared to PAN and more intense compared to CA, indicating a higher crystallinity than CA nanofibers, likely due to PAN’s inclusion in the nanofibers’ core. Notably, the intensity of the semicrystalline peak around 7.9° decreases and almost disappears in PAN/CA nanofibers, again suggesting the influence of PAN in the core on the XRD pattern.Surface hydrophilicity–hydrophobicity is an essential factor determining the applications of PAN/CA nanofibers. Conversely, in the case of pure CA nanofibers, the hydrophobic acetyl group was visibly exposed on the surface. This exposure is validated by the higher intensities of the C=O and C–O–C functional groups in the FTIR spectra . The pre−2 h−1 (LMH), implying that the synthesized membranes fall in the range of ultrafiltration and microfiltration membranes. It can be seen that the lowest flux measurements were obtained for the diesel/water mixture, and the maximum flux was obtained for the xylene/water mixture. The differences in flux measurements depend on many factors, such as the physical properties of the oil, including density and viscosity. Moreover, the chemical interaction between the oil droplets/particles and the surface of the membrane and the deposition rate of oil particles onto the membrane surface affect the permeate flux measurements. The membranes’ performance was investigated by separating five different oils  from water. The hydrophilic membrane that showed the best hydrophilic–oleophobic properties, i.e., the membrane with a composition of 1:3 PAN to CA, was utilized for the separation experiments. As visually noticed in the filtration experiments, the total filtration time of the diesel was relatively higher than that of other oils. The filtration time for 100 mL of mixture was in the following order: diesel > gasoline > benzene > toluene > xylene. The total filtration time was around 13 min for diesel, 10 min for gasoline, and 4–5 min for benzene, xylene, and toluene. These results suggested that the deposition of diesel particles onto the membrane surface was more evident than other types of oils. These conclusions can be supported by the decline in permeate flux with filtration cycle number see a. HoweveThe separation mechanism can be attributed to several factors. First is hydrophilic–oleophobic synergy: the electrospun membrane’s composition of PAN and CA generates a synergistic influence. The PAN provides hydrophilic characteristics, while the CA shell adds to the oleophobic nature of the membrane. This synergy allows the membrane to strongly repel oil due to oleophobicity while passing water due to its hydrophilic properties. This synergistic effect also identifies the membrane’s selectivity in passing water droplets and rejecting oil droplets, which can be considered a separation mechanism. As demonstrated in FESEM images, the fabricated membranes showed high porosity and massive permeability potential, which can be considered capillary forces that allow water droplets to flow within the membrane. The interfacial tension also plays a significant role in repelling oil droplets. The composite membrane’s super-hydrophilic and oleophobic properties reduce the interfacial contact between oil and the membrane, keeping the oil phase separated on the top surface of the membrane. In conclusion, hydrophilic–oleophobic PAN/CA nanofibers were successfully produced through one-step core–shell electrospinning. These nanofibers exhibit remarkable wettability properties, characterized by super-hydrophilicity and underwater superoleophobicity. This exceptional surface behavior makes them highly promising for efficient oil–water separation applications. The versatility of these nanofibers is evident as they effectively separate various oils from water, including gasoline, diesel, benzene, xylene, and toluene. The synthesized nanofibers are a reliable substrate, ensuring a high separation efficiency while maintaining stability and durability. The current work introduces a convenient fabrication method for nanofibers designed for on-demand oil–water separation, and these nanofibers show excellent reusability. Notably, a maximum separation efficiency of up to 99.9% and permeate flux values between 429.2 and 857.2 LMH make this technology valuable in scenarios where effective oil–water separation is crucial for environmental protection and safety.