Tobamoviruses are plant-infecting viruses with an ancient lineage, understood to have arisen during the age of the dinosaurs in the Cretaceous period 145–66 million years ago  and for https://www.mdpi.com/1999-4915/15/3/683 URL accessed 24 October 2023), a virus of concern to growers of cucurbits . Although CGMMV was first identified in 1935, it has in recent years been identified in an increasing number of counties, including Australia in 2014. Little is known about the transmission of CGMMV between weeds and crop species in Australia. Common weed species belonging to a number of plant families were tested for presence of the virus, and some were inoculated with the virus under laboratory conditions. The samples of six weed species were tested using RT-PCR and RT-qPCR methods, with the latter often detecting CGMMV in samples of Amaranthus viridis (family Amaranthaceae). A. viridis plants inoculated in the laboratory were infected at a rate of 50–75%, confirming it as an alternative host for CGMMV. Sap taken from infected A. viridis plants was inoculated in watermelon seedlings, which became infected and symptomatic. This important study highlighted the importance of controlling A. viridis in and around cucurbit crops and called for more research into determining the extent of the CGMMV host range in Australia.The first paper in this Special Issue is concerned with the local host range of cucumber green mottle mosaic virus (CGMMV) (https://www.mdpi.com/1999-4915/15/3/728 URL accessed 24 October 2023) focused on the transmission of the tomato brown rugose fruit virus (ToBRFV), a virus that emerged in Israel and surrounding areas in 2015 and is rapidly spreading around the world. ToBRFV remains infectious in plant debris in the soil from previous tomato crops. When a new crop of tomato seedlings is transplanted into infectious soil, they become infected with the virus through wounds in the roots. These authors tested four root-coating formulations aimed at preventing damaged roots from becoming infected with ToBRFV. The four root-coating formulations were methylcellulose, polyvinyl alcohol, silica Pickering emulsion and super-absorbent polymer, all prepared with the disinfectant chlorinated trisodium phosphate. Seedlings were planted in virus-infected soil and tested for ToBRFV infection after a period of time. All control plants with untreated roots became infected. In contrast, all four formulations reduced soil-mediated ToBRFV infection to 0%, 4.3%, 5.5% and 0%, respectively. The tested formulations had no adverse effect on plant growth when compared to untreated control plants grown under virus-free inoculation conditions.The second paper (https://www.mdpi.com/1999-4915/15/3/743 URL accessed 24 October 2023), also on the topic of CGMMV in Australia, focuses on biosecurity policy. Since the initial incursion of CGMMV into the Northern Territory, detected in 2014, the virus had been detected in three other Australian states despite strict biosecurity restrictions aimed at preventing such spread. The aim of this study was to determine whether new incursions of the virus had occurred from outside sources from contaminated imported seed or whether the initial strain was spreading within the country. All imported cucurbit seed is routinely tested for CGMMV. Plant and pollen samples were collected from across the country and virus genome sequences generated and compared with CGMMV genomes from across the world. All the Australian genomes were tightly grouped together, indicating they most likely originated from one, or possibly two, incursions of very closely related strains, thereby confirming the effectiveness of the biosecurity measures implemented to prevent new CGMMV strains entering Australia.The third paper (https://www.mdpi.com/1999-4915/15/4/883 URL accessed 24 October 2023) also has an Australian biosecurity focus but examines imported seed batches, which are routinely tested for the presence of a number of tobamoviruses. Of 777 large and small seed lots tested from 2019–2021, 154 carried one or more of five Tobamovirus species, including tomato mottle mosaic virus (ToMMV) and ToBRFV, both quarantine pests for Australia. The estimated prevalence of contamination by tobamoviruses ranged from 0.388% to 0.004% in larger contaminated seed lots. In large lots, 20,000 seeds were tested, and this large number aims for a detection confidence level of 99%. The current testing requirement is applied irrespective of the nominal country of consignment origin, recognising that global seed production may involve mixing seed from different jurisdictions. The effectiveness of this approach is that Australia has not recorded an onshore incursion of either ToBRFV or ToMMV.The fourth paper in the Special Issue (https://www.mdpi.com/1999-4915/15/9/1951 URL accessed 24 October 2023) investigates the use of tobamovirus particles to display antibodies. Transgenic turnip vein clearing virus (TVCV) particles were used as vehicles to display IgG-binding domains D and E of Staphylococcus aureus protein A (PA) on every coat protein (CP) subunit to generate TVCVPA. Each TVCV particle is made up of hundreds of subunits, and in this case, a single TVCVPA particle displayed up to 500 IgGs, which could be visualised under an electron microscope. The bi-enzyme ensembles of cooperating glucose oxidase and horseradish peroxidase were tethered together on the TVCVPA carriers via a single antibody type, with one enzyme conjugated chemically to its Fc region, and the other one bound as a target, yielding synthetic multi-enzyme complexes. In microtiter plates, the TVCVPA-displayed sugar-sensing system were reused in further tests. The engineered virus particles replicated in host plants, and 1–3 mg of TVCVPA particles could be purified per gram of plant tissue. The engineered virus particles are promising candidates for use as adapter coatings on detector chips in biosensor setups applied for electrochemical, label-free real-time analytics. Future work might evaluate the TVCVPA concept not only for bio-detection tasks from food analysis to environmental monitoring but also for in situ signal amplification and biomedical uses. The robust structure of the tobamovirus particle is ideally suited to this application. The final paper (This Special Issue provides a fascinating snapshot of tobamovirus research, from protecting crops with PCR-based, ELISA-based and high-throughput sequencing approaches to phylogenetics and biosecurity polity to exciting research where tobamoviruses are turned into nanoparticles with biomedical applications. I have no doubt that more of these ancient viruses will be discovered in both cultivated and wild plants in the coming decades and centuries, some of which will challenge scientists with the protection of our food supply from them and/or to use their nanometre-sized particles and tiny genomes for exciting new applications.


Gammacarmovirus, remains poorly understood in terms of its transmission pathway. This study reveals the complete genome sequence of a seed-transmitted isolate, ST-HB56, contributing to the understanding of SYMMV’s ecological dynamics.Soybean yellow mottle mosaic virus (SYMMV), a member of the genus  Gammacarmovirus, which belongs to the family Tombusviridae, is classified in the recently renamed species Gammacarmovirus glycinis. Since the virus was initially described in Korean soybeans (Soybean yellow mottle mosaic virus (SYMMV), a member of the genus soybeans , it has soybeans  and is tsoybeans .In June 2020, distinct mosaic symptoms  were obs


Defective interfering particles (DIPs) are virus-like particles that occur naturally during virus infections. These particles are defective, lacking essential genetic materials for replication, but they can interact with the wild-type virus and potentially be used as therapeutic agents. However, the effect of DIPs on infection spread is still unclear due to complicated stochastic effects and nonlinear spatial dynamics. In this work, we develop a model with a new hybrid method to study the spatial-temporal dynamics of viruses and DIPs co-infections within hosts. We present two different scenarios of virus production and compare the results from deterministic and stochastic models to demonstrate how the stochastic effect is involved in the spatial dynamics of virus transmission. We compare the spread features of the virus in simulations and experiments, including the formation and the speed of virus spread and the emergence of stochastic patchy patterns of virus distribution. Our simulations simultaneously capture observed spatial spread features in the experimental data, including the spread rate of the virus and its patchiness. The results demonstrate that DIPs can slow down the growth of virus particles and make the spread of the virus more patchy. Defective interfering particles (DIPs) are viral mutants in which a crucial part of the particle’s genome has been lost. DIPs are not infectious but can still co-infect cells with natural viruses. Such mutations are not uncommon. In fact, it has been found in most classes of viruses, including SARS coronavirus and influenza virus. It gives DIPs a promising future as a medium for disease treatment. However, the mechanism by which DIPs affect virus transmission remains unclear. In this paper, we develop a model to study the interaction between viruses and DIPs within host cells and the role stochastic effects play in virus transmission. Our simulations can capture patchy patterns and other spatial spread features observed in experiments and demonstrate that DIPs can slow down the growth of virus particles and make the spread of viruses more patchy. Many diseases such as COVID-19, Ebola virus disease, AIDS, and SARS, are caused by the transmission of viruses. Various antiviral drugs have been proposed to inhibit the gene and protein functions of viruses. Still, a major challenge in drug development is caused by occasional mutations in the viral genomes. However, some of these mutations may help us create a new type of treatment through developing defective interfering particles (DIPs), which are virus-like particles that have been detected in patients infected with influenza A virus . DIPs laIn a recent experimental study , engineeMany mathematical models were built to study the growth of virus –22 and tThe existing computational studies provide a keystone for modeling the interaction of DIPs and wild-type viruses. However, the mechanism by which DIPs affect the spatial distribution of virus expression is still unclear partly due to complicated stochastic effects and nonlinear spatial dynamics. In , the autIn this paper, we develop and analyze a new mathematical model to study the spreading speed and the spatial pattern generated by the interaction of viruses and DIPs. To incorporate the random movements of the virus and the DIPs and the stochastic effect of the interactions due to finite number of particles, we developed a stochastic reaction-diffusion system for the virus and DIP co-infection and built a hybrid method for stochastic simulation. Our stochastic model enables also the study and comparison of two common scenarios of virus production. Our simulation results demonstrated that this model can regenerate simultaneously the patchy patterns and the spread rates observed in wet-lab experiments , which wOur new hybrid model is developed based on the deterministic reaction-diffusion model introduced by Frank , but hasBelow we describe firstly the deterministic part of our model which is a system of partial differential equations, and secondly our stochastic model that incorporates two different scenarios of virus production.x1 max] × . Also, there are six types of cells: uninfected cells, cells infected only by natural viruses but not in the period of virus production, cells infected only by natural viruses and in the period of virus production, cells infected by DIPs only, cells infected by DIPs and natural viruses but not in the period of virus production, and cells infected by both DIPs and natural viruses as well as in the period of virus production. The numbers of the respective cells are denoted by C, CV, CD, CVD and Based on the model in , we propCV and the CVD cells in our model. Ultimately, the DIPs are produced only by the mature CV cells can get to maturity by themselves and produce virus, CD cells cannot produce DIP unless they are co-infected by the virus and become CVD and get to maturity. The latter models the situation that DIPs cannot replicate unless they co-infect a cell with a wild-type virus.Considering infection by DIPs of cells late in the replication cycle is too late to affect the production of the virus, we assume that DIPs cannot infect the cells CV* . There a2 is the Laplacian operator, describing the virus and DIP diffusion.The following two equations are for modeling the dynamics of the virus and DIP:We assume that cells are not moving in the spatial domain and we can model the dynamics of the cell densities by the following system:CVD cells in our model, but there is no age structure for CVD cells in .The compartment-based regime evolves from the Gillespie algorithm (SSA). Consider the propensity function of reactions and diffusion, C−1, presented for diffusion between the deterministic and stochastic domains. This is a compartment adjacent to the interface but within deterministic domain j ∈ {M + 1, ⋯, M + 4}, representing diffusion (four directions of the cross). In order to correctly model the flux over the interface, individuals in the boundary compartment in ps is the PDE solution of density of the s-th species. Then the propensity function for jumping from the pseudo-compartment to the adjoining compartment in The coupling is implemented with a pseudo-compartment, τ, is determined by:r1 is a random variable uniformly distributed in . We then use the second random number r2 to find the corresponding reaction or jump. The algorithm then checks whether the closer update time is for PDE or SSA. If t + τ < tP, then the update is for SSA and t = t + τ; otherwise it is for PDE and t = tP, tP = t + ΔtP.The Gillespie’s direct method  is used tI, by recomparing amounts in a compartment of all reactants of each reaction with the threshold Θ. Similar to . If r < P then we place the individual in compartment Ck; otherwise it is placed in the deterministic domain.The multiple interfaces are updated with time step Δmilar to , after tThe pseudocode for our algorithm is given in Algorithm 1.Algorithm 1 The hybrid algorithm for reaction-diffusion systems.t = t0 and set the final time, T. Specify the PDE-update time step ΔtP and initialize the next PDE time step to be tP = t + ΔtP. Specify the interface-update time step ΔtI and initialize the next interface-update time step to be tI = t + ΔtI.1. Initialize the time, x and the compartment width h. Initialize the amount of each species in each compartment, Ns for k ∈ {1, …, K} and specify the threshold Θ. Compute the density, ps = Ns/h for PDE grid points.2. Specify the PDE spacial step Δj, j ∈ {1, 2, ⋯, M}:3. Determine the initial interface for each reaction k such that Sj contains all species involved in reaction j, then the k-th compartment is part of the stochastic domain  (a) Find all  (b) All compartments adjacent to tC = t + τ.4. Determine the time for the next ‘compartment-based’ event according to the Gillespie algorithm, tC, tP, tI} = tC then the next compartment-based event occurs:5. If min{ (a) Determine which event occurs according to the Gillespie algorithm.C−1, then for the corresponding , Ns = Ns − 1 and x ∈ A and 0 otherwise. (b) If the event is moving from stochastic domain to a pseudo compartment, C−1 to stochastic domain and p > 1/h for all x ∈ C−1, then Ns = Ns + 1 and  (c) If the event is moving from a pseudo compartment  (d) Update the density for the pseudo compartment.t = tC. (e) Update the current time, tC, tP, tI} = tP then the PDE domain is updated:6. If min{ (a) Apply backward Euler for diffusion terms and forward Euler for reaction terms. (b) Update the density for the pseudo compartment.t = tP and set tP = t + ΔtP. (c) Update the current time, tC, tP, tI} = tI then the interfaces are updated, similar to step 3:7. If min{k such that Sj contains all species involved in reaction j, then the k-th compartment is part of the stochastic domain  (a) For each reaction, find all  (b) All compartments adjacent to Ck that change from PDE domain to stochastic domain, let rs ∈ . (c) For the compartment rs < Ps then Ns = ceil ) and ps = ps − (1 − Ps)/ Area   • If Ns = floor ) and ps = ps + Ps/ Area   • otherwise, t = tI and set tI = t + ΔtI. (d) Update the current time, t ≤ T, return to step 4.8. If  Else end.L = 2.552mm, which is the same as the experimental data; for the PDE numerical scheme, we apply the central difference scheme to discretize the Laplace operation with Δx = Δy = 0.058mm; for the temporal numerical scheme, we use the backward Euler method for the Laplace operation and forward Euler method for the other terms with time step Δt = 0.01h. In the SSA approximation, the domain is partitioned into square compartments with dimension h × h = Δx × Δy.We consider the spatial domain as a two-dimensional square with length −6cm2 in [−5s−1. As the diffusion rate varies according to the environment and plays a vital role in spatial distribution, we increased the former dV = dD = 2.38 × 10−3mm2/h to provide qualitative agreement with experimental data and left the latter unchanged δV = δD = 0.144h−1.Diffusion coefficients of virus and DIP are set to be 2.38 × 10−6cm2 in  while thα1 = 758.045 × (68.503 × 10±2d−1) = 2163.682 × 10±2h−1, and α3 = 38.259 × (21.782 × 10±2d−1) = 34.723 × 10±2h−1. The wide range of parameters allows us to choose a suitable value to obtain a good agreement with the experimental results qualitatively. So we set α1 = 6.491h−1 and α3 = 69.446h−1. Since DIPs may exhibit a replication advantage over infectious viruses [α2 = α3/10 in this work.In , the ratαC = 15.217d−1 = 0.634h−1. But the cellular carrying capacity of proliferation varies depending on the experimental environment. We let K = 3.505 × 105 × h2 cells/compartment to achieve qualitative agreement with experimental data, where h2 is the compartment area.Same as , the intCV cells into ±2 d−1 in [ν1 = ν2 = 0.205h−1 because mature infected cells are observed later in experiments. β1 and β2 are considered as the death rate of ±2d−1 in [β1 = β2 = 0.05h−1 in simulations.The rate of maturation of 2 d−1 in . We slig±2d−1 in . We take−10 = 1.02 × 10d−1 = 1.02 × 10−11h−1 in [γ1 = γ2 = 4 × 10−4h−1.Virus and DIP infection rate is 2.45 × 1011h−1 in , which i−2h−1 in [The infected cell death rate is 5.91 × 10−2h−1 in , which iAll parameters are listed in To validate the usefulness of our model, we compare the simulation results with the experiment data published in , the latFluorescent protein labeling is usually used for qualitative purposes, and there is no linear relationship between brightness and intensity. Therefore the experimental images only provide a reference for virus expression in simulations.Since in the following simulations we employed the compartment-based method while experiments provide scatter diagrams, we have done some preprocessing to compare them with the computer simulation results. t = 25h with different initial DIPs, as well as images of experimental data at the same time in [CVD(0) = 200 for the PDE model, then viruses are distributed in a ring while the DIPs are radially distributed in the center. Compared with the experimental results under similar conditions, the PDE model shows good agreement in the speed of infection with experimental data but the spatial distribution does not match and the patchiness of We first study the PDE model in Eqs  and 2)..2). Fig  time in . When thV, denoted by Ci,j(0) = 1000 for all , CVi,j(0) = CVDi,j(0) = 0 for all  except the midpoint CV22,22(0) = 100, and CVD22,22(0) varies from 0 to 400.In stochastic simulations, the same types of particles and cells in different compartments are treated as different species, for example, for Two scenarios are considered in simulations and compared with experimental results:Scenario 1: infected cells produce virus and DIPs through cell bursting;Scenario 2: infected cells keep producing viruses and DIPs continuously.t = 17h to t = 25h. Row 1 and 2 show the spatial distribution of matured infected cells t = 17 h and t = 25 h respectively. The experimental observation has an inherent threshold, and images have been denoised; therefore, we also introduced a cut-off for simulation data. Namely the amount of cells is set to be zero if it is less than the cut-off value 50, which is also applied to all the following simulations. The last column is the evolution of a representative experiment without initial DIP. When there is no DIP in the system initially, there is no DIP growth, and the virus growth is radially symmetric and flat in both scenarios and experiments.In experiments, the radial symmetry disappears as the initial amount of DIP increases. In fact, patchy formation of cells infected by virus is sensitive to the dose of DIP. It can be observed even with a small initial dose of DIP . When thCV represents the amount of cells infected by virus at grid point  at time t. Since a certain amount of virus expression is required to be observed in the experiment and noise is filtered, we also set a cut-off θ = 50 for computing area, i.e.t ≤ 25 (h) with different initial DIP inputs in 2 scenarios simulations and experiments. We can see the radius keeps increasing and viruses keep spreading in all cases. Whereas as initial DIPs increase, in both experiments and simulations, the growth rate of radius goes down, which is due to the inhibitory effect of DIPs on viruses. On the other hand, the Scenario 1 can better match the experimental results, both in terms of the dynamics and the level of fluctuations.To quantify the spread characteristics of viral expression under stochastic effects, we define the virus radius as:ρ is proportional to initial DIPs. x-axis, so it is shifted by 0.01 to avoid troubles when ρ = 0. The growth rate was computed using the data points after t = 13 h to ensure in all cases we have close to linear growth in radius vs. time  denotes the brightness at the -th pixel at time t for the image, which range is {0, 255}. For simulation results, we set a threshold for the binary transformation to approximate the threshold inherent in the experimental methods and offset the loss when denoising the experimental images. Specifically, when M = 0 and the point is black in the image; when M = 255 and the point is red  by Mi,j and qt by q; The denominator of A more intuitive formula for the  follows , here weq ≈ 1, that means the sum of squares within strata is relatively more minor, indicating in each stratum, the virus is concentrated and the sum of squares between strata is somewhat more significant, meaning the differences between strata are large, so the image would appear to be more patchy. If q ≈ 0, the variance in each stratum is large, and the differences between strata are minor, so the picture would appear to be not so patchy. The q-statistic provides a suitable and convenient way to quantify the patchiness that are visibly observed in the experimental data we studied in this work. By choosing different strata according to the data, like rectangular grid in a non-radially symmetric system and triangular mesh for surfaces, this method may be applicable to other studies of spatial temporal systems.So if q-statistic of cells infected by the virus at time t = 25 h, that is ρ is proportional to the initial dose of DIPs.We study the behavior of x-axis is a logarithmic scale so we shift it by 0.01 to the right to avoid trouble when ρ = 0 . On the left, the blue line denotes the q-statistic of Scenario 1 (infected cells produce viruses and DIPs through cell bursting) while the red line denotes that of Scenario 2 (infected cells keep producing viruses and DIPs) at time t = 25 h. The errors are described by the 95% confidence intervals. On the right, we marked the q-statistic for each experimental image at time t = 25 h and plot the average for four groups of experiments. Both scenarios show the same trend as experiments. When there is no DIP in the beginning, the q-statistic is minor, meaning the spatial distribution is uniform. The q-statistic increases as the initial dose of DIPs increases. Taking into account the conclusions of the previous section, DIPs slow down the growth of virus particles and make them more patchy. But when the initial dose of DIP is large enough, we observe a drop of q-statistic. It may be caused by the domination of DIPs. To be specific, when the infection of DIPs is dominated, cells infected by the virus are distributed as sporadic patches and the majority of the domain is homogeneous  in wet-lab experimental data, which was not achieved in previous works. In previous studies, there were not many models considering the spatial effects of DIP-virus interaction. Deterministic models in one or two dimensions were developed but could not simultaneously capture the spread rate and the spatial patterns observed in experiments [We measured the spread rate of the virus and showed the relationship between the spread radius growth rate and the initial dose of DIP. To measure the patchiness, we computed the eriments , 25. A seriments  to inveseriments . Here weeriments , 40. Theeriments  arises feriments  the autheriments  Click here for additional data file.S1 Figt = 17h and t = 17h; B: spatial distribution of those in Scenario 2 (infected cells keep producing viruses and DIPs); C: the representative experimental results.A: spatial distribution of virus in cells ((EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 FigA: the spatial distribution of (EPS)Click here for additional data file.S4 FigA: Time series plot of virus in cells ((EPS)Click here for additional data file.S5 FigRow 1, 2 are time series plots of virus in cells ((EPS)Click here for additional data file.S6 FigRow 1, 2 are time series plots of virus in cells ((EPS)Click here for additional data file.S7 FigRow 1, 2 are time series plots of virus in cells ((EPS)Click here for additional data file.S8 FigRow 1, 2 are time series plots of virus in cells ((EPS)Click here for additional data file.


For social protection policies to be inclusive they must address the extra costs that people with disabilities incur. Studies show that these costs are highly significant and if not taken into account the economic wellbeing of people with disabilities is underestimated. Additionally, disability costs vary significantly by the type and degree of disability. To align the structure of social protection programs with how costs are incurred to promote equal participation requires estimating those costs. The Goods and Services Required approach, it is argued, is better than the often used Standard of Living Approach, and has implications for policy design. Growing evidence suggests that people with disabilities are more likely to experience poverty than people without disabilities . MoreoveIndeed, evidence suggests that people with disabilities incur substantial extra costs of living . These cOne way to reduce these costs is to remove environmental barriers. If infrastructure and communication are more accessible, for example, the expenditures that families must make to overcome barriers to participation will be lower. Even with such efforts, people with disabilities will still incur extra costs to obtain the same standard of living as their nondisabled peers, especially in current environments where accessibility is often quite limited, and even more so in low and middle income countries.If social protection programs do not account for these extra costs, they will fail people with disabilities in their goal of providing the level of well-being that they are designed to provide. Article 28 of the Convention on the Rights of Persons with Disabilities calls for an adequate standard of living. What is needed to maintain the same standard of living can be different for people with and without disabilities. Consider two households that are similar in every way  except one has a member with a disability and one does not. They both have similar needs for housing, food, clothing, and other necessities, but the household with a member with a disability has additional needs, for example assistive technology. They also have additional costs of undertaking basic activities – for example the need for special transportation or special food. Although they have the same level of income, they have different standards of living. Either they pay the extra costs associated with disability and have less for other necessities, or they do not cover those extra costs and thus undermine the wellbeing of the person with a disability. If a social protection program provides the same benefits to both households, it may raise the household without a disability over the poverty line, but still leave the household with a member with a disability effectively living below it.The challenge is to adequately measure the extra expenditures necessitated by disability so that they can be incorporated into the design of social protection programs. Complicating this is the vast diversity of the population of people with disabilities. Disabilities vary significantly not only in the degree of disability, but also by the type of disability. People with vision difficulties face different costs than people with hearing difficulties, mobility difficulties, or complex medical needs . A one-s•Some goods and services required for participation may not be available where people live, especially if they live in a rural area in a low income country.•Some people may not be aware of the types of goods and services that could allow them to participate more fully. Again, this is probably more common in low income countries, and among low income populations.•Some households may be income constrained and not be able to afford all their basic necessities, and so must forego spending on some of them. That is, they can’t afford the things needed by their household members with disabilities and all other household necessities.•Discrimination may exist within a household, and resources are kept from the person with a disability.A common way of assessing the extra costs associated with disability is a method called the Standard of Living (SOL) method  method . This meThe basic idea of SOL is that two families with the same income and other characteristics are expected to have the same standard of living. If one of those households has a member with a disability, then any gap in wellbeing is assumed to result from the increased expenditures associated with the needs of the person with disability. The measure of the standard living could be an asset index but can also be other measures, such as a subjective measure of wellbeing.A recent systematic review found that 18 studies covering 40 countries used the SOL estimated the extra costs of disability from 5.7%  to 155% of household income  , 11. CosSOL is a useful measure for examining the current economic impact of disability on households – and to what extent the current poverty line should be adjusted to get a better sense of how many households with disabled members are living below the effective consumption poverty line. However, it is not appropriate for adjusting social protection benefits for three reasons. First, it does not provide an estimate of the expense of goods and services required for full participation, which should be the goal of an inclusive social protection program. Second, it tends to provide lower estimates of the extra costs of disability in countries  where the reasons for the gap between what is spent and what is required may be most pronounced. This last point is the finding a recent study using the SOL in a group of low income countries in Africa, where the estimated costs of disability were considerably lower than the costs estimated in higher income countries , 20.Finally, the SOL method does not tell us what goods and services are needed. To that end, they can only inform us about the potential size of a cash benefit. It could be more efficient to meet the needs of people with different types of disabilities by targeting their specific needs, for example personal assistance and assistive technology. To do so, however, requires knowledge on how extensively they are needed and how much they cost.When people are asked directly about what they are spending because of their disability, the range and nature of that spending varies dramatically. As can be seen in The obvious questions are: What causes the high variance across disability types? And, what causes the high variance within disability types?.The answer to the first question no doubt lies with the different types of goods and services needed. One answer to the second question (another will be discussed below) is that the degree of support needs might be different. Consider Returning to If a person is deaf-blind and is not participating in the community. That is, not going to school or to work or taking part in community life, they do not need to spend much. They can move about their home and communicate with their family. However, if they desire to participate in the social and economic life of their communities, they would require a full-time interpreter guide. That level of human assistance is expensive. They may choose not to participate exactly because of this expense. This effect may be especially pronounced among people who are deaf-blind, but it applies to people with all types of disability. Participating in society increases the expenditures associated with disability. One need not spend money on a modified vehicle or on taxis instead of public transportation if one is not venturing out of the house on a regular basis. This is truer when the barriers to participation present in the environment are greater. For example, one study from Turkey showed, using the SOL method, that extra expenditures are higher when people work .The method for determining the disability costs—in amount and type—that are needed for equal participation is referred to as the Goods and Services Required (GSR) method . The GSRAfter establishing an advisory group of key stakeholders to oversee the process, and an expert group to undertake the analysis, the expert group has several tasks. First, they must decide on the disability types they will consider and how to define low- and high support needs. Second, they must run focus groups to get wider input from the disability community on what goods and services are required. Then, they can make the initial estimates of the costs of required goods and services by type of disability and level of support needs. To finalize these estimates, they then conduct market research to fill in gaps where prices of needed goods and services may not exist. Early attempts at this method were done in South Africa , 24 and One way to account for disability costs in social protection programs is to simply alter the threshold on means tests. This will make it easier for people with disabilities to qualify. However, as noted above, a means test adjustment pegged to the average costs of disability will be too large for some and too low for others, in particular for those with the greatest needs. If that adjustment is made based on SOL estimates, then it will not be based on the costs needed for participation and would probably be inadequate even for people with below the mean current extra expenditures. The same is true for a Guaranteed Annual Income (GAI) program, if the GAI level is increased for people with disabilities based on some estimate of current average extra expenditures. In fact, currently disability allowances tend to only support basic household consumption and not disability related costs .Another approach is to consider the diversity of disability related costs that are needed for full participation. In fact, the best way to address these extra costs is to target the type of cost. The studies cited in this paper, for example, all point to the high costs of health care, personal assistance, assistive devices, and transportation. After supplying those in-kind, the variance of the remaining costs, across types and degrees of disability, is reduced substantially. They remain, but they are varied and idiosyncratic and could best be covered by a cash benefit.•Health care including, including (re)habilitation and assistive devices. People with disabilities often have extra medical expenses, and (re)habilitation and assistive devices can be essential for undertaking daily activities.•A cash allowance to cover the wide range of different types of disability related costs. These include extra costs of general goods such as food, heat, and housing which people with disabilities often incur. This benefit could be provided at different levels depending on the severity of disability. Many countries already certify people with disabilities at different levels and adjust their benefits accordingly.•Human assistance This would include personal assistance and sign language interpretation. It could be delivered via vouchers for purchasing services, a caregiver allowance to family members, or direct provision. The nature of the program could vary depending on the local context.•Concessions. Subsidies could be provided for public transport, taxis, social housing or identified goods and services that are disproportionately needed by people with disabilities. A GSR study could help identify common items used by people with disabilities that could be subsidized.Already in some high income countries, such as the United States, a suite of programs exists. But to ensure they fully cover the range and type of costs faced by people with different types and degrees of disabilities, without key gaps, a goods and services type study is needed.The ultimate solution could be to offer a suite of programs offered in a progressive combination . These cAnd as noted above, along with these kinds of benefits—and to minimize the cost of providing them—investments should be made in creating a more accessible environment , improving health care systems, and making education systems more inclusive.The question then arises about means testing for these benefits. However, in most low and middle income countries the ratio of extra costs to income is so high that means testing is not relevant. Moreover, it is important that disability benefits do not create a disincentive to work. Some countries, for example the United States, tie benefits to the inability to work . This isPeople with disabilities require additional expenditures to achieve the same opportunities for participation as their peers without disabilities and the same standard of living. If these are not accounted for, their level of wellbeing is being overestimated. For social protection programs to be inclusive they must account for these costs.Simply estimating the average costs that people with disabilities are incurring is insufficient for the design of social protection programs. First, an average cost hides the great variance in costs faced by people with different types and degrees of disability. Some people incur very high costs, others’ costs are much lower. Second, it also hides the variance in the types of costs people incur. Some people have a high need for personal assistance, while others do not require it at all. Some people need assistive devices, while others do not.To efficiently and adequately address disability costs of households, social protection programs must be structured in a way that aligns with those costs. One logical way to accomplish this is to develop a suite of programs that both target the specific types of costs that people face, while at the same time providing a cash benefit to cover idiosyncratic costs. Once the major categories driving the costs of disability are addressed, the residual can most likely be covered adequately by common cash benefits.


To estimate the economic costs, health-related quality-of-life outcomes and cost-effectiveness of subthreshold micropulse laser (SML) versus standard laser (SL) for the treatment of diabetic macular oedema (DMO) with central retinal thickness (CRT) of <400µ.An economic evaluation was conducted within a pragmatic, multicentre, randomised clinical trial, DIAbetic Macular Oedema aNd Diode Subthreshold.18 UK Hospital Eye Services.Adults with diabetes and centre involving DMO with CRT<400µ.Participants (n=266) were randomised 1:1 to receive SML or SL.The base-case used an intention-to-treat approach conducted from a UK National Health Service (NHS) and personal social services (PSS) perspective. Costs (2019–2020 prices) were collected prospectively over the 2-year follow-up period. A bivariate regression of costs and quality-adjusted life-years (QALYs), with multiple imputation of missing data, was conducted to estimate the incremental cost per QALY gained and the incremental net monetary benefit of SML in comparison to SL. Sensitivity analyses explored uncertainty and heterogeneity in cost-effectiveness estimates.One participant in the SL arm withdrew consent for data to be used; data from the remaining 265 participants were included in analyses. Mean (SE) NHS and PSS costs over 24 months were £735.09 (£111.85) in the SML arm vs £1099.70 (£195.40) in the SL arm (p=0.107). Mean (SE) QALY estimates were 1.493 (0.024) vs 1.485 (0.020), respectively (p=0.780), giving an insignificant difference of 0.008 QALYs. The probability SML is cost-effective at a threshold of £20 000 per QALY was 76%.There were no statistically significant differences in EQ-5D-5L scores or costs between SML and SL. Given these findings and the fact that SML does not burn the retina, unlike SL and has equivalent efficacy to SL, it may be preferred for the treatment of people with DMO with CRT<400µ.ISRCTN17742985; NCT03690050. Study is based on a high-quality randomised clinical trial.First study to compare the cost-effectiveness of subthreshold micropulse laser with that of standard laser for the treatment of diabetic macular oedema.The analysis was from a UK National Health Service (NHS) and personal social services perspective and did not consider costs such as productivity losses from a societal perspective.Low rate of missing data for both costs and outcomes.We used published costs of anti-vascular endothelial growth factor drugs, rather than NHS costs which incorporates confidential price discounts for these drugs.Diabetic macular oedema (DMO) is a visual-threatening complication of diabetes, occurring in approximately 7% of people living with diabetes.5In DMO fluid accumulates in the centre of the retina, the macula, which is the area of the retina responsible for providing central vision.DIAbetic Macular Oedema aNd Diode Subthreshold micropulse laser (DIAMONDS) was a pragmatic, allocation-concealed, double-masked, multicentre, randomised, non-inferiority clinical trial which compared the clinical effectiveness and cost-effectiveness of subthreshold micropulse laser (SML) and standard laser (SL) for the treatment of people with DMO with CRT of <400µ.As described previously10 11The study is reported as per Consolidated Health Economic Evaluation Reporting Standards 2022 Statement.et alet al,18As described in detail previously in Lois Resource use data were collected and reported on trial case record forms (CRFs) at scheduled 4-monthly clinic visits . Data were collected on the costs of laser treatment, both at the initial laser session and at subsequent ones if required, outpatient visits and intravitreal anti-VEGF and/or steroid treatment (costs of drugs and administration) if rescue was required. All costs were expressed in UK pounds sterling and valued in 2019–2020 prices. If costs were not in line, they were inflated to 2019–2020 prices using the NHS Cost Inflation Index.10 19The costs of laser treatment included staff and equipment costs . Unit costs for staff were obtained from the Unit Costs of Health and Social Care 2019 compendium10 11Data were also collected on any outpatient attendances or hospital admissions related to DMO or the treatments. Where an outpatient attendance was reported but no procedure undertaken, the average unit cost of an outpatient ophthalmology visit was used (varying between £80 and £101 per consultation depending on whether the consultation was ‘non-consultant’ vs ‘consultant-led’).10CRFs also recorded information on other tests or investigations, medication use including anti-VEGF/steroids or other rescue treatments. Anti-VEGF and steroid drugs were separately costed as these are considered an unbundled HRG. Costing of laser retreatments followed the same approach as costing for the index (first session) laser procedure.Unit costs were derived from national compendia in accordance with NICE’s Guide to the Methods of Technology Appraisal.10.1136/bmjopen-2022-067684.supp1Supplementary dataThe HRQoL of trial participants was assessed at baseline and at 12 and 24 months postrandomisation using the EQ-5D-5L instrument.22HRQoL was also assessed using two vision-specific measures: the National Eye Institute Visual Functioning Questionnaire-25 (NEI-VFQ-25) and Vision and Quality of Life Index (VisQoL).25Summary statistics were generated for resource use variables, health utility values and QALYs by treatment allocation and assessment time point. For resource use and costs, mean values were compared between groups using two sample t-tests. Differences between groups, along with 95% CIs, were estimated using non-parametric bootstrap estimates (10 000 replications). For HRQoL, these were presented as mean values with their associated standard errors. Between-group differences were compared using the two-sample t-test.10 11Multiple imputation by chained equations was used to predict missing health status (utility) scores and costs based on the assumption that data were missing at random.26The base-case cost-effectiveness analysis was performed using an intention-to-treat approach. Mean incremental costs and QALYs were estimated using seemingly unrelated regression (SUR) methods that account for the correlation between costs and outcomes. The SUR adjusted for covariates .As reported elsewhere,Furthermore, there were no statistically significant differences between the two laser treatment groups for any of the VisQoL dimensions or NEI-VFQ-25 subscales at any follow-up time point  , but equOver the 2-year follow-up period, participants in the SML arm, compared with the SL arm, experienced a non-statistically significant increase in QALYs of 0.008 .DIAMONDS was a pragmatic clinical trial carried out in 16 NHS ophthalmology departments throughout the UK. The DIAMONDS trial was powered to detect not only differences in the primary outcome (BCVA) but also in important secondary outcomes .As noted in the main clinical effectiveness manuscript,In DIAMONDS, two vision-specific patient-reported outcome quality-of-life instruments were used in addition to the generic preference-based EQ-5D-5L. Neither showed any statistically significant differences between laser arms. It has sometimes been mentioned that changes in vision that are sufficient to affect some activities of daily living, may not be reflected in changes in EQ-5D, because the EQ-5D may not be as sensitive to detect changes in quality of life as other visual-specific questionnaires such as the NEI-VFQ-25.One limitation in the current analysis is the fact that we had to use published costs of anti-VEGF drugs ,Both SML and SL were successful in 80% of participants.


We present here aninexpensive method for generating a sensitivedirect electronic readout in bead-based immunoassays without the useof any intermediate optical instrumentation . Analyte binding to capture antigen-coated beads or microparticlesis converted to probe-directed enzymatically amplified silver metallizationon microparticle surfaces. Individual microparticles are then rapidlycharacterized in a high-throughput manner via single-bead multifrequencyelectrical impedance spectra captured using a simple and inexpensivemicrofluidic impedance spectrometry system we develop here, wherethey flow through a three-dimensional (3D)-printed plastic microaperturesandwiched between plated through-hole electrodes on a printed circuitboard. Metallized microparticles are found to have unique impedancesignatures distinguishing them from unmetallized ones. Coupled witha machine learning algorithm, this enables a simple electronic readoutof the silver metallization density on microparticle surfaces andhence the underlying analyte binding. Here, we also demonstrate theuse of this scheme to measure the antibody response to the viral nucleocapsidprotein in convalescent COVID-19 patient serum. This hinders their use, especially in thecontext of widespread infectious diseases such as COVID-19. This raisesthe question of what other measurable properties could be linked toanalyte binding in an immunoassay while maintaining the benefits ofbead-based assays and in-flow measurement systems.Bead-based immunoassaysprovide excellent sensitivity, speed, andextended dynamic range due to their enhanced binding kinetics andease of automation.5 However, rather than the enzyme-labeled secondaryprobe catalyzing a color or fluorescence change in solution, we catalyzea metal deposition7 onto a bead surface, thus changingthe electrical properties of the beads. This enables the use of asimple electrical detection system, based on the “Coulter principle,”to measure the impedance of beads in continuous flow.8 Here, an inexpensive in-flow multifrequency impedance characterizationsystem is developed, using a three-dimensional (3D)-printed plasticmicroaperture to measure impedance characteristics of microparticleswhich are then linked to an immunoassay via the enzymatic metallization.Using a model binding assay to study the metal deposition and impedancecharacteristics of metallized beads, we found that they possess aunique impedance spectral signature that enables distinguishing themfrom nonmetallized beads. Further, corroborated with analytical modelsand numerical simulations, these impedance signatures were also foundto provide quantitative information about the amount of metallizationon beads. A machine learning method was then applied to reduce themeasured multivariate bead impedance spectra to define a single metricthat clearly distinguishes metallized and nonmetallized beads anda limit of detection in terms of this reduced metric was then alsomeasured. Finally, we integrate and apply these methods to a clinicalimmunoassay by measuring antibodies directed against the nucleocapsidprotein of SARS-CoV-2 in convalescent COVID-19 patient serum. COVID+and healthy samples were clearly distinguished using this assay. Thus,a fully electronic bead-based immunoassay is realized, which we termthe Bead-based Electronic Bio-Assay Detection using Enzymatic Metallization or BEAD-EM, which provides a sensitive readout of analytebinding without any intermediate optics.Here, wedevelop an inexpensive, electronic alternative to fluorophoresand optics for analyte detection by using a bead-based assay chemistrythat mirrors an enzyme-linked immunosorbent assay (ELISA).22.12 of beads by surface area were taken from stockand suspended in 1 mL of a 1:10 dilution of MES buffer. 40 μLof EDC and 40 μL of NHS at 50 mg/mL were added, and this solutionwas incubated for 20 min on a vortexer. After incubation, two washcycles were performed with a 1:10 dilution phosphate buffer. Next,200 μL of 1:10 dilution phosphate buffer is added with the desiredantigen at 50 μg/mL and incubated for 2 h on a vortexer. Followingthis, 2 washes were performed in 1% PBS with 0.1% Tween 20 and 0.1%bovine serum albumin, and the beads were stored in this same solution.We note here that the streptavidin–biotin-based binding ofthe antigen on the bead was also attempted before choosing the aboveEDC-NHS chemistry, but this resulted in bead aggregate formation andhence was not pursued further. Additionally, antigen concentrationoptimization results are shown in Figure S1.Magnetic andnonmagnetic polystyrene beads functionalized with carboxyl groupswere obtained from Spherotech  and Polysciences, respectively. Note that whilethese magnetic and nonmagnetic beads of different sizes are used forcharacterizing the microfluidic bead impedance measurement system,eventually, only the 8.2 μm magnetic beads are used to performthe immunoassays. To help normalize the surface chemistry stoichiometrybetween different bead sizes and stock concentrations, the microparticlesurface area per solution volume was calculated for each stock solutionassuming spherically shaped beads. Stock solution volumes correspondingto 25 cm2.29For the initialinvestigation of the metal deposition, recombinant protein A/G (ThermoFisherScientific) was immobilized as the antigen. This was incubated for60 min with 200 nM mouse anti-human IgG Fc-Horse Radish Peroxidase(HRP) (SouthernBiotech) in 100 μL volume. Beads were washed3 times in deionized water before the silver deposition was performedwith EnzMet (Nanoprobes). 50 μL each of EnzMet solutions A,B, and C were incubated with beads for 4, 4, and 8 min, respectively.Note that these times are obtained from assay optimization performedin our prior work with enzymatic metallization.2.3n = 5) and healthy serum (n = 4)(Ray Biotech). This was incubated for 60 min with 200 nM mouse anti-humanIgG Fc-HRP in 100 μL volume. Beads were washed 3 times in deionizedwater before performing silver deposition at the same conditions asabove.For clinicalsample characterization, the SARS-CoV-2 nucleocapsid protein  was immobilized on the bead surface, followed by incubationwith 100 μL of 1:900 dilution of individual COVID-19 patient and exported for 3D printing by a Nanoscribe Photonic ProfessionalGT2. After laser exposure, the print was developed by a 10 min bathin an SU-8 developer, followed by a 10 min bath in isopropanol andset under a UV lamp overnight before assembly with other components.2.5A lock-inamplifier and transimpedance amplifier were used for impedance measurements. Impedance spectra were sampledat 57,000 samples per second at 6 frequencies ranging from 45 kHzto 35 MHz. These time-series signals are passed through a high-passfilter before further analysis. For waveform identification, the impedancemagnitude at 45 kHz is used in a peak finding algorithm with a thresholdof 110 Ω. Timestamps of peaks at this frequency are used todirectly index the remaining 5-magnitude and 6-phase time-series measurements.The flow rate of the syringe pump was 10 μL/min. All aggregatemeasurements reported here are for 60 beads each.2.610 To create training data for themodel, mIgG-HRP and mIgG-PE were directly conjugated to the beadsand incubated with the metallization substrate to create “high-metal”and “low-metal” beads, respectively. The impedance andphase of peaks at 11 MHz were used as features in the LDA model afterbeing scaled to a standard distribution (z-score).This model is then used to quantify the limit of detection and clinicalassays. Specifically, the model is applied to each of the 60 beadsmeasured in each trial to yield their principal components, and themean of this is taken to represent that trial and serves as a measureof the degree of metallization on the beads. The Python code usedfor this analysis is made available at https://github.com/MNBEL/BEAD-EM. For dilutions curves, four parameter logistic (4PL) curves werefit using Prism.To reduce themultidimensional data acquired for many beads in each trial to a singlemeasure corresponding to antibody presence in that trial, a subsetof the acquired data was used in a linear discriminant analysis (LDA)model.2.7Finite-element analysis was performed withCOMSOL Multiphysics5.6 with the AC/DC module. To match the baseline of the empiricalsystem, lumped elements were added to the electronic circuit interface.Nonmetallized beads were simulated as nonconducting spheres and metallizedbeads were simulated with the electric shielding feature on the nonconductingsphere surface.33.11112 Overall, these results establish that enzyme-labeledprobe-driven metal deposition on polystyrene beads can be used tocreate selective and specific immunobinding-driven metallization.The distinctive nanostructured morphology of the metal thin film raisesthe possibility of a unique electronic signature of metallized beads,which we explore next by building and using a microfluidic systemfor bead impedance spectrometry.We firstdeveloped a model binding assay to investigate immunobindingand target-probe-driven enzymatic metallization on beads and theireffect on their electrical properties 1A. Carbo3.2Next, we developed and characterized a microfluidic system forhigh-throughput, sensitive, in-flow impedance characterization ofindividual beads where the beads in suspension pass through a microscaleaperture placed between two plated through-hole electrodes 2A. The aFigure S2). We observe that the system shows a flat  magnituderesponse up until a salt concentration-dependent cutoff frequency,after which it declines, indicating that capacitive effects dominatein that frequency range. Additionally, inductive effects appear atthe high-frequency end. Also, as more salt is added, the impedanceis found to decrease, as expected, due to the increasing conductivityof the fluid. To better understand these impedance characteristicsof the system, a COMSOL impedance model of the system, including thefinite-element analysis (FEA) model of the aperture itself, was alsobuilt for comparison with fluid conductivity set to that of 1% saline. This model allows the extractionof the lumped impedance parameters representing both the capacitiveand inductive parasitic effects, and the fitted model output matchesthe corresponding measured impedance spectrum, as shown in Figure S2. This coupled FEA and circuit COMSOLmodel was used hence as the baseline for modeling bead impedancesas well. Next, we used this microfluidic system for bead impedancespectrometry developed here to measure the impedance spectra of nonmetallizedand metallized beads.To characterize the operationof the microfluidic impedance measurementsystem, the frequency response of the system was measured for variouslevels of salt concentrations in the buffer fluid flowing throughthe aperture without any beads (3.3Zp) and phase(Δϕp) values were obtained for each bead (seethe Zp and Δϕp with the bead radius at two selected frequencies (45 kHz& 2 MHz). As expected, ΔZp riseswith bead size as larger nonconductive beads displace more of theconductive fluid filling the sensing zone. Δϕp remains low, showing that the impedance change due to beads is mostlyresistive at these two frequencies, with larger bead sizes showingslightly higher capacitive phase change. These results were comparedwith COMSOL simulation results for nonconducting spheres of differentsizes passing through the aperture, as shown in Impedance magnitude and phase waveforms were recordedfor sevendifferent sizes of nonmetallized polystyrene beads ranging in diameterfrom 3.9 to 20.1 μm flowing through the aperture, and the peakchange in magnitude . We model the system, without thebeads, as a resistance due to the conducting fluid in parallel withcapacitance due to the separation of the electrodes. When a bead isadded to this, the additional resistance increases the impedance magnitude.A capacitive effect of the bead is also implied by the impedance phasechange peaking at 2 MHz and is assumed to be due to the nonconductingbead acting as an additional dielectric between the relatively conductivefluid above and below it. This bead capacitance is modeled in oursimple circuit as parallel to the baseline capacitance.Overall,these nonmetallized bead impedance spectra results andtheir match with COMSOL simulation results and simplified circuitmodels establish the ability of the microfluidic system to characterizeindividual beads in a sensitive and high-throughput manner and distinguishtheir physical properties such as size. Notably, this system enablesthis single-bead impedance spectrometry using a simplified 3D-printedmicroaperture-based sensor assembly without the need for any microelectrodes,microchannels, or associated microfabrication steps.3.4Zp formetallized beads is, overall, significantly lower than that for nonmetallizedbeads and even becomes negative at some frequencies  obtained after enzymatic metallization,as described above, were characterized next. We observed that metallizedbeads have significantly different impedance and phase spectra comparedto nonmetallized beads. Δquencies 4A–C. ThiZp spectra for metallizedbeads, which is distinguishable from that of the nonmetallized beads.Δϕp of metallized beads has a positive peakat a lower frequency than the 2 MHz peak of nonmetallized beads andtakes on more negative values at 2 MHz . Therefore, we concludethat the metallized bead surface cannot be modeled simply as a conformalconductive material layer covering the bead surface as such a modeldoes not show the frequency-specific negative impedance signaturethat is observed for the metallized beads.Tobetter understand the physical basis of these unique impedancecharacteristics of the metallized beads, in COMSOL, the earlier nonmetallizedbead model was altered by adding, around the nonconducting sphere,a thin conductive layer whose conductivity was also varied. We foundthat in these simulations, at high metal-layer conductivities, negativeΔFigure S6A). This model is found to match the general shapeof the impedance and phase spectrum of the metallized beads , including the frequency-specificnegative impedance signature. We, thus, intuit that the many individualmetal spheroids of a “desert rose”-like shape . Hence, we explored if the bead impedance spectracontained subpopulations that show different impedance signaturesand could be related to variations in bead metallization. Using theimpedance spectra of metallized beads from Zp  wastaken and analyzed with linear discriminant analysis see the 6D,E. TheThe limit of detection of the modelassay was next determined usingthis derived metallization metric. The model assay of protein A/G-coatedbeads probed with mIgG-HRP was performed with 5 different mIgG-HRPconcentrations from 0 to 200 nM 6F. The 213 achieved a ∼1–5 pM LOD using a similar metallizationreaction on glass slides when using a simplified optical measurementof metallized spot darkness using a cellphone camera in point-of-careimmunoassays. However, it is possible that here the changes in theimpedance signature of a bead may only occur after significantly higheramounts of metal deposition grow and connect to create a conductinglayer. It may be possible to improve this metallization by immobilizingadditional catalysts, such as gold nanoparticles, on the bead surface.We have earlier9 shown that this techniqueachieves a ∼4–100 pM LOD for electronic detection ofimmunoassays on microelectrode arrays.9 With regards to the measurement method, sensitivity is highly dependenton the electric field focusing in the aperture and could be enhancedby minimizing its relevant dimensions to more closely match the beadsize. Additionally, electrical noise in the system could be reducedif a slower throughput was used, as it would allow the use of a lowercutoff frequency in the low-pass filter inherent in lock-in-amplification.Further, better flow-focusing14 of thebeads can also reduce variation of impedance introduced by the off-centerpositioning of some beads in the aperture. It is also worth consideringsubstantial changes to the measurement methodology, such as electrochemicalmeasurement techniques, which have shown high sensitivities to silveron microbeads.15 While these remain avenuesof future work for the improvement of the assay, we next considerthe question of the clinical applicability of the current assay.Considering the LOD comparison and improvement questions, we expectthat the binding constants of mIgG-HRP and mIgG-PE we used are comparable,indicating that this difference in sensitivity is likely due to limitationsof either the metal deposition or our measurement technique. Withregards to metal deposition, our earlier work3.6n = 5) or prepandemic healthy serum (n = 4) as anegative control. It is worth re-emphasizing for clarity here thatthe beads bound with the N protein are expected to bind anti-N hIgGfrom serum. These beads were then probed with mIgG-HRP directed againsthuman IgG (hIgG) to measure anti-N hIgG levels in the sera. Finally,they were incubated with the metallization substrate solution. TheSEM images in We applied the BEAD-EM technique to measure SARS-CoV-2 antigen-specificantibodies from convalescent COVID-19 patient serum. A schematic ofthe immunoassay is shown in 16 It is also worth emphasizingfor clarity here that these results with clear differences betweenCOVID+ and healthy serum samples inherently establish the specificityof the assay. Additionally, the low metallization metric of healthyserum shows that nonspecific binding from serum does not limit thesensitivity or specificity of this assay.After measuring the impedance signatures of the above beads, thesame machine learning analysis as above was used to classify the clinicalsamples while using the same low and high metallization samples trainingdata. The features for the LDA and the resulting distribution of principalcomponents across all clinical samples are plotted in 9 that a ∼44 pM–1 nM range of antigen-specificIgG concentration is expected in the 1:900 dilution of serum usedfor the clinical BEAD-EM assay above. Strikingly, this is 7–160-foldbelow the BEAD-EM LOD measured in the model assay earlier. It is alsoworth noting here that the 1:900 dilution of serum used for this assay,in fact, leaves significant headroom in the assay dynamic range foreven lower concentrations of antibody-based biomarkers as more concentratedserum up to even neat, i.e., undiluted serum is often used in clinicalimmunoassays. Overall, this result indicates that the sensitivityof the BEAD-EM technique is higher when used with serum than in themodel assay. We hypothesize that this may be due to the polyclonalnature of the human antibody response, which may result in more thanone epitope on the N antigen being targeted, unlike in the model assay.While the exact concentration of anti-N IgG in patient serum isnot known as it can vary based on the stage of infection or host response,it is estimated based on a calibration performed in our earlier work4The BEAD-EM assay was developedas a bead-based immunoassay thatlinks analyte binding to enzymatic metallization on bead surfacesto produce “impedance-labeled” beads in contrast towidely used fluorescently labeled beads. A nanostructured “desert-rose”morphology was found for the enzymatic metallization layer on thebeads, which forms the basis of the unique electronic signature ofmetallized beads. In-flow bead impedance sensing was performed usinga 3D-printed microscale aperture to capture the impedance spectrumfor individual beads in a sensitive and high-throughput yet inexpensivemanner. Metallized beads were found to have a distinct impedance spectralsignature with frequency-specific negative dips in impedance changewhich were not found for nonmetallized beads. Thus, metallized andnonmetallized beads could be clearly distinguished using this electronicsignature. Additionally, distinct subsets of metallized beads withhigh, medium, and low metallization-based impedance signatures arefound.A finite-element model for the bead impedance measurementschemeaccurately predicted nonmetallized bead impedances but not metallizedbead impedances when modeled as nonconductive beads with a purelyconductive layer. Instead, a lumped element circuit model, which modelsthe metallization layer as a combined resistive and capacitive element,matches the measured impedance spectra better for the metallized beads.This, we hypothesize, can be linked to the nanoscale structure ofthe metallization layer.Finally, the BEAD-EM assay was formalizedby using features inthe impedance spectra of beads to produce a single quantitative metallizationmetric. This metric was used to quantify the limit of detection ofthe BEAD-EM technique in our model assay and compare it to a standardfluorescence-based readout. Furthermore, we applied the BEAD-EM techniquein a clinical context to detect SARS-CoV-2 viral antigen-specificantibodies from convalescent COVID-19 patient serum, showing cleardifferences between COVID+ vs healthy sera. Thus, a clinical immunoassayfor electronic biomarker detection without the use of any expensiveand bulky intermediate optics was developed and demonstrated.18 Such a multiplexedBEAD-EM scheme, which remains beyond the scope of this work but couldbe pursued in future work, would allow the measurement of multiplexedbiomarkers, including those that allow prediction of the outcome insevere COVID-19.Overall, while the BEAD-EM assay shows enough sensitivity alreadyto detect antibody-based biomarkers, these results remain a preliminarydemonstration of the eventual power and utility of this electronicdetection technique. Further work is needed to optimize the sensitivityachieved here, with multiple routes of improvement possible, suchas reduction in the heterogeneity of bead metallization, the additionof probe-independent metal deposition catalysts, and fine-tuning ofthe detection system fluidics and electronics. Additionally, it hasnot escaped our attention that the unique bead impedance spectra basedon both bead size and metallization density could enable the “impedancebarcoding” of beads and fully electronic multiplexed bead-basedimmunoassays in the future.


Duck plague virus (DPV) is a member of the alphaherpesvirus subfamily, and its genome encodes a conserved envelope protein, protein UL10 (pUL10). pUL10 plays complex roles in viral fusion, assembly, cell-to-cell spread, and immune evasion, which are closely related to its protein characteristics and partners. Few studies have been conducted on DPV pUL10. In this study, we identified the characteristics of pUL10, such as the type of glycosylation modification and subcellular localization. The characteristic differences in pUL10 in transfection and infection suggest that there are other viral proteins that participate in pUL10 modification and localization. Therefore, pUL49.5, the interaction partner of pUL10, was explored. We found that pUL10 interacts with pUL49.5 during transfection and infection. Their interaction entailed multiple interaction sites, including noncovalent forces in the pUL49.5 N-terminal domains and C-terminal domains and a covalent disulfide bond between two conserved cysteines. pUL49.5 promoted pUL10 expression and mature N-linked glycosylation modification. Moreover, deletion of UL49.5 in DPV caused the molecular mass of pUL10 to decrease by approximately3 to 10 kDa, which suggested that pUL49.5 was the main factor affecting the N-linked glycosylation of DPV pUL10 during infection. This study provides a basis for future exploration of the effect of pUL10 glycosylation on virus proliferation.IMPORTANCE Duck plague is a disease with high morbidity and mortality rates, and it causes great losses for the duck breeding industry. Duck plague virus (DPV) is the causative agent of duck plague, and DPV UL10 protein (pUL10) is a homolog of glycoprotein M (gM), which is conserved in herpesviruses. pUL10 plays complex roles in viral fusion, assembly, cell-to-cell spread, and immune evasion, which are closely related to its protein characteristics and partners. In this study, we systematically explored whether pUL49.5 (a partner of pUL10) plays roles in the localization, modification, and expression of pUL10. Herpesviridae family contains multiple viruses that cause a variety of infections in diverse species, and the alphaherpesvirus subfamily can establish lifelong latent infection in neurons , bovine herpesvirus 1 (BHV-1), pseudorabies virus (PRV), and varicella-zoster virus (VZV)  10–. pUL10 p(VZV) 10–, 13–21. V) 10–13–. HSV-1 pV) 10–13–. MoreoveV) 10–13–. gH/gL wV) 10–13–. FurtherV) 10–13–. AdditioV) 10–13–. pUL10 iV) 10–13–. Two amiV) 10–13–, and theV) 10–13–.pUL10 has multiple partners at different stages in viral infection. Glycoprotein N partners with pUL10 to modulate its activity in membrane fusion  and has N-acetylglucosamine and asparagine residues of high-mannose, hybrid, and complex oligosaccharides. PNGase A differs from PNGase F in that it cleaves N-linked glycans with or without α--linked core fucose residues. Immature N-linked glycosylation was formed in the endoplasmic reticulum (ER), which was mainly cleaved by Endo H. When pUL10 was expressed alone in DEFs, it was partially digested by PNGase F and Endo H  in 12-well plates were transfected with pCAGGS-UL10-3HA and collected at 36 h posttransfection, and an indirect immunofluorescence assay (IFA) was performed. pUL10 was localized in the cytoplasm, but it was not uniformly distributed, and the red fluorescence was intense around the nucleus . To deted Endo H , which wd Endo H .50) assay, which is described in Materials and Methods. As shown in Next, the localization of pUL10 in DPV-infected cells was explored. Since there were no antibodies available for the IFA, a BAC-DPV-UL10-HA recombinant virus  was constructed. DEFs were transfected with BAC-DPV-UL10-HA recombinant plasmids, and the changes in fluorescent plaques and cells over 2 days are shown in The difference in pUL10 characteristics in transfection and infection suggested that other viral proteins participated in assisting in the modification and localization of pUL10 . Our labBased on the results shown in To explore the interaction between pUL49.5(30–60 aa) and pUL10, we compared their amino acid sites in 12 herpesviruses . There wThe expression and molecular mass of pUL10 in the pUL10/pUL49.5 coexpression group were higher than those in the pUL10 and pCAGGS vector cotransfection group, even when the number of pCAGGS-UL10-3HA plasmids in each group was the same  and E. TBased on the above-described experimental results, the effect of pUL49.5 on the N-linked glycosylation of pUL10 in viral infection was explored. Deletion constructs of UL49.5 of BAC-DPV (BAC-DPV-ΔUL49.5) and its revertant virus (BAC-DPV-ΔUL49.5 R) were prepared in our laboratory .DEFs were infected with equivalent titers of BAC-DPV-ΔUL49.5, BAC-DPV, and BAC-DPV-ΔUL49.5 R. Cell lysates were collected at 48 h postinfection and treated with or without PNGase F to detect the changes in pUL10 molecular mass. pUL10 was concentrated at approximately 36 kDa after treatment with PNGase F in the three strain virus infection groups. Differences were reflected in the untreated PNGase F group. Compared with the molecular weights of the other two virus strain infection groups, the pUL10 molecular weight was decreased by approximately 3 to 10 kDa in BAC-DPV-ΔUL49.5-infected cells, but it was still higher by approximately 2 to 3 kDa than that in the PNGase F-treated group . FollowiHerpesviridae family, such as pUL10 (glycoprotein M homologs), are included in the viral envelope. pUL10 homologs play complex roles in viral fusion, assembly, cell-to-cell spread, immune evasion, and vaccine development , is a harmful pathogen for waterfowl. DPV contains a genome, capsid, tegument, and envelope, which together form spherical virus particles. Many protein homologs of the elopment –21, 27. We detected the localization of pUL10 in the cytoplasm, while it was localized in the perinuclear region in DEFs that were infected with BAC-DPV-UL10-HA. In transfection, pUL10 is mainly digested by Endo H and PNGase F, but during infection, pUL10 is mainly digested by PNGase F , which sDPV pUL49.5 contains three or even more sites to build the connection with pUL10, which is crucial for the conformational stability, transportation, and modification of pUL10. The N-linked glycosylation process is very complicated, and many factors can influence it –38. Firs–41–DPV pUL10 contained two N-linked glycosylation sites according to bioinformatics analysis, and pUL10 was glycosylated during transfection and infection. Kaposi′s sarcoma-associated herpesvirus encodes a homolog of interleukin-6, and it contains two glycosylation sites, asparagine 78 and asparagine 89. The former is present in high-mannose-type N-linked sugars and is dispensable for cytokine function, while the latter affects protein conformation and is critical for receptor binding . The HSV2. HEK-293T cells were cultured with RPMI 1640 medium  supplemented with 10% fetal bovine serum  at 37°C with 5% CO2. The complete genomic sequence of the DPV CHv strain can be obtained from NCBI (GenBank accession no. JQ647509.1)  digestion and cultured in Dulbecco's modified Eagle medium  supplemented with 10% newborn calf serum  at 37°C with 5% COi GS1783 . BAC-DPVi GS1783 .E. coli GS1783 was used for BAC-DPV-UL10-HA construction based on the principle of two red markerless recombinations (inations . In brieAFC61832.1) and Flag tag were cloned into pCAGGS, and the construct was named pCAGGS-UL49.5-Flag. DPV UL10 (GenBank accession no. AFC61873.1) and the 3×HA tag were cloned into pCAGGS, and the construct was named pCAGGS-UL10-3HA. Moreover, eukaryotic plasmids of pCAGGS-UL10(C54A)-3HA, pCAGGS-UL10(186–409 aa)-HA, pCAGGS-UL49.5(C46A)-Flag, pEGFP-UL49.5(1–29 aa), pEGFP-UL49.5(30–60 aa), pEGFP-UL49.5(61–95 aa), pEGFP-UL49.5(1–60 aa), and pEGFP-UL49.5(1–95 aa) were constructed. The modified regions of the recombinant plasmid were checked by sequencing. pCAGGS, pDsRed2-ER, and pEGFP were prepared in our laboratory (DPV UL49.5 (GenBank accession no. boratory . All deshttp://www.nulenbio.com/). In some cases, DEFs in 6-well plates were infected with BAC-DPV-UL10-HA (MOI of 1). DEFs in 6-well plates were infected with 106 TCID50 of BAC-DPV-ΔUL49.5, BAC-DPV, and BAC-DPV-ΔUL49.5 R for detecting the changes in pUL10 glycosylation modification in the absence of pUL49.5. Cell debris was collected by NP-40 lysis buffer  at 48 h postinfection or posttransfection and clarified by centrifugation at 12,000 rpm for 15 min at 4°C. PNGase F , PNGase A , Endo H , and O-glycosidase  were used to explore the type of pUL10 glycosylation. According to the manufacturers′ protocol, enzymes and appropriate buffer were added to cell lysates, which were incubated at 37°C for 1.5 h  or 4 h (O-glycosidase). The samples were then subjected to Western blot analysis.To detect the type of pUL10 glycosylation modification in transfection and infection, DEFs in 6-well plates were transfected with 4,000 ng of pCAGGS-UL10-3HA or 2,000 ng of pCAGGS-UL10-3HA plus 2,000 ng of pCAGGS-UL49.5-Flag using 8 μL of PlusTrans transfection reagent -3HA plus 1,000 ng of pCAGGS-UL49.5-Flag, 1,000 ng of pCAGGS-UL10-3HA plus 1,000 ng of pCAGGS-UL49.5(C46A)-Flag, and 1,000 ng of pCAGGS-UL10(C54A)-3HA plus 1,000 ng of pCAGGS-UL49.5(C46A)-Flag. Cell lysates were collected by NP-40 lysis buffer  at 36 h posttransfection and clarified by centrifugation at 12,000 rpm for 15 min at 4°C. Each set of samples was divided into two parts: one was treated with β-ME, and the other was treated without β-ME. The samples were identified by WB analysis.To explore the interaction between pUL10 and pUL49.5 in infection and transfection, DEFs in 6-well plates were infected with BAC-DPV-UL10-HA (MOI of 1). In some cases, HEK-293T cells in 6-well plates were transfected with 2,000 ng of pCAGGS-UL49.5-Flag plus 2,000 ng of pCAGGS-UL10-3HA, 2,000 ng of pCAGGS-UL49.5-Flag plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA, 2,000 ng of pEGFP plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA, 2,000 ng of pEGFP-UL49.5(1–29 aa) plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA, 2,000 ng of pEGFP-UL49.5(1–60 aa) plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA, 2,000 ng of pEGFP-UL49.5(1–95 aa) plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA, 2,000 ng of pEGFP-UL49.5(30–60 aa) plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA, and 2,000 ng of pEGFP-UL49.5(61–95 aa) plus 2,000 ng of pCAGGS-UL10(186–409 aa)-HA using 8 μL of PlusTrans transfection reagent . Cell lysates were collected at 36 h postinfection or posttransfection by NP-40 lysis buffer , and they were clarified by centrifugation at 12,000 rpm for 15 min at 4°C. Mouse anti-HA tag monoclonal antibody (MAb)  or mouse anti-Flag tag MAb  was added to the supernatants to immunoprecipitate the bait protein, and they were incubated overnight in a rolling incubator at 4°C. The mouse IgG  control was analyzed in parallel. Protein A magnetic beads  were added to immunoprecipitate the antibody-bait protein complex. The samples were identified by WB analysis.50 of BAC-DPV-UL10-HA and BAC-DPV, and supernatants and cells were harvested from three parallel samples at 12, 24, 36, 48, 60, 72, and 96 h postinfection. Cell samples were resuspended in 1 mL of fresh medium and freeze-thawed three times. Viral titers were determined by TCID50 assay in DEFs. Subsequently, cytopathic effects were recorded and analyzed. Three independent experiments were performed. Statistical data are presented as the mean ± standard error of mean (SEM). Differences between two groups were analyzed using a t test and are presented as a P of >0.05 (nonsignificant [ns]), indicating no significant difference.DEFs in 12-well plates were infected with 100 TCIDDEFs in 12-well plates were infected with BAC-DPV-UL10-HA (MOI of 0.1). Infected cells were collected at 16, 24, and 36 h postinfection. DEFs in 12-well plates were transfected with eukaryotic expression plasmids using 3.2 μL of TransIntro EL transfection reagent  as follows: 800 ng pDsRed2-ER plus 800 ng of pCAGGS-UL10-3HA, 1,600 ng of pCAGGS-UL10-3HA, 800 ng of pDsRed2-ER plus 800 ng of pCAGGS-UL49.5-Flag, 800 ng of pCAGGS-UL10-3HA plus 800 ng of pCAGGS-UL49.5-Flag plus 800 ng of pDsRed2-ER, and 800 ng of pCAGGS-UL49.5-Flag plus 800 ng of pCAGGS-UL10(186–409 aa)-HA. The DMEM was removed at 36 h posttransfection. For the detailed experimental steps, please see a previous report . The rabDEFs in 12-well plates were cotransfected with 1,000 ng of pCAGGS-UL10-3HA plus 1,000 ng of pCAGGS-UL49.5-Flag using 4 μL PlusTrans transfection reagent . Then, 350 μM CHX  and isopycnic dimethyl sulfoxide (DMSO)  were added to the cells 24 h posttransfection. Cell lysates were collected at 6 and 12 h after drug treatment to assess the degradation of the target protein. Cells were treated with 350 μM CHX plus 50 μM MG132  and 350 μM CHX plus 200 nM bafilomycin A1 (BafA1)  for 6 h to identify the protein degradation pathway. Cell debris were subjected to WB analysis.To detect whether pUL49.5 promotes the expression of pUL10, DEFs in 12-well plates were cotransfected with 1,000 ng of pCAGGS-UL10-3HA plus 1,000 ng of pCAGGS-UL49.5-Flag using 4 μL of PlusTrans transfection reagent . To detect the expression of pUL10 by pUL49.5 in a dose-dependent manner, 1,000 ng of pCAGGS-UL10-3HA plasmid was transfected into DEFs, and the amount of pCAGGS-UL49.5-Flag plasmid was 0 to 1,000 ng with an increase every 250 ng in the first group. In another group, 1,000 ng of pCAGGS-UL49.5-Flag was transfected into DEFs, and the amount of pCAGGS-UL10-3HA plasmids was increased from 0 to 1,000 ng every 250 ng. The total amount of plasmids was supplemented to 2,000 ng with the pCAGGS vector plasmid. In the truncated pUL49.5-promoted pUL10 glycosylation assay, 1,000 ng of pEGFP plus 1,000 ng of pCAGGS-UL10-3HA, 1,000 ng of pEGFP-UL49.5(1–29 aa) plus 1,000 ng of pCAGGS-UL10-3HA, 1,000 ng of pEGFP-UL49.5(1–60 aa) plus 1,000 ng of pCAGGS-UL10-3HA, 1,000 ng of pEGFP-UL49.5(1–95 aa) plus 1,000 ng of pCAGGS-UL10-3HA, 1,000 ng of pEGFP-UL49.5(30–60 aa) plus 1,000 ng of pCAGGS-UL10-3HA, and 1,000 ng of pEGFP-UL49.5(61–95 aa) plus 1,000 ng of pCAGGS-UL10-3HA were transfected with the PlusTrans transfection reagent . Cell lysates were collected by radioimmunoprecipitation assay (RIPA) lysis buffer  and analyzed by WB. All samples were added to 5× SDS loading sample buffer at a ratio of 4:1. The samples were divided into two parts: one part was incubated at 30°C for 30 min to detect pUL10. The other needed to be boiled for detection of the expression of other proteins. The samples were separated using 15% or 12% separating gels based on the molecular mass of the target protein, and then they were transferred for 40 min or 90 min to a 0.22-μm polyvinylidene fluoride (PVDF) membrane . Then, PVDF membranes were blocked with 5% nonfat powdered milk  for 3 to 5 h at room temperature, followed by incubation with primary antibodies overnight and secondary antibodies for 1 h at room temperature. TBST  was used to wash PVDF membranes before antibody incubation. Commercial antibodies were used as follows: GFP rabbit monoclonal antibody , mouse anti-Flag tag MAb , mouse anti-HA tag MAb , mouse anti-GAPDH MAb , mouse anti-β-tubulin MAb , mouse anti-β-actin MAb , rabbit anti-HA tag polyclonal antibody , horseradish peroxidase (HRP)-conjugated goat anti-mouse IgG secondary antibody , HRP-conjugated goat anti-rabbit IgG secondary antibody , HRP-conjugated goat anti-mouse IgG light chain secondary (LCS) antibody , and HRP-conjugated goat anti-rabbit IgG heavy chain secondary (HCS) antibody . Some antibodies were prepared in our laboratory: rabbit anti-gE polyclonal antibody , rabbit anti-VP22 polyclonal antibody , rabbit anti-pUL10 polyclonal antibody , and rabbit anti-pUL49.5 polyclonal antibody . We used a prestained protein ladder  as an indicator of molecular mass.t test, and results are presented as follows: P < 0.05 (*), P < 0.01 (**), or P < 0.001 (***).Statistical data are presented as the mean ± SEM. Three independent experiments were performed. Differences between two groups were analyzed using a The data sets used during the present study are available from the corresponding author upon reasonable request.


Drosophila, the central brain is populated by neural stem cells derived from progenitors called neuroblasts (NBs). There are two types of NBs, type 1 and 2. T1NBs have a relatively simple lineage, whereas T2NBs expand and diversify the neural population with the generation of intermediate neural progenitors (INPs), contributing many neurons to the adult central complex, a brain region essential for navigation. However, it is not fully understood how neural diversity is created in T2NB and INP lineages. Imp, an RNA-binding protein, is expressed in T2NBs in a high-to-low temporal gradient, while the RNA-binding protein Syncrip forms an opposing gradient. It remains unknown if Imp expression is carried into INPs; whether it forms a gradient similar to NBs; and whether INP expression of Imp is required for generating neuronal identity or morphology. Here, we show that Imp/Syp are both present in INPs, but not always in opposing gradients. We find that newborn INPs adopt their Imp/Syp levels from their parental T2NBs; that Imp and Syp are expressed in stage-specific high-to-low gradients in INPs. In addition, there is a late INP pulse of Imp. We find that neurons born from old INPs (E-PG and PF-R neurons) have altered morphology following both Imp knock-down and Imp overexpression. We conclude that Imp functions in INPs and newborn neurons to determine proper neuronal morphology and central complex neuropil organization.The generation of neuronal diversity remains incompletely understood. In The online version contains supplementary material available at 10.1186/s13064-023-00177-9. Drosophila melanogaster as a model to understand how the neural diversity in the brain is generated. The Drosophila brain develops from neural stem cells, called neuroblasts (NBs )[. There ats (NBs ); there ats (NBs )–5; theres )[Fig. . Thus, eProgeny of T2NBs are major contributors to the intrinsic neurons of the central complex (CX), an evolutionarily conserved brain region in all insects assayed to date , and hasT2NBs are formed in the embryonic brain , 32, undDrosophila central complex (CX) [Newborn INPs (nINPs) will mature to become a young INP (yINPs) and continue to age to become a mid INP (mINPs), then old INPs (oINPs). As INPs age, they go through a series of 4–6 divisions, each division resulting in a pair of newborn neurons (nNeurons) or glial cells Fig. B 2–4, 2, 28. Thelex (CX) , 32. Thelex (CX) , 21, 28.lex (CX) . E-PGs alex (CX) . ImportaWe first wanted to know if Imp or Syp expression is present in INPs, and if their initial levels are equivalent to their the parental T2NB at time of INP birth. To target young cells within the INP lineage (nINPs and yINPs) and compare Imp/Syp expression to T2NBs we used 12E09-Gal4 > UAS-GFP. 12E09-Gal4 targets the entire INP lineage starting at yINPs in DM1–6 T2NBs but does not mark parental T2NBs or nINPs. We used Pointed (Pnt) antibody staining, which labels T2NBs, and found that Pnt expression carried over into nINPs, and thus could be used as a marker for those cell types   generated severe defects in PF-R and E-PG targeting to the CX  .Next, we wanted to confirm that 16B06-Gal6 > UAS-ImpRNAi or UAS-ImpOE to alter Imp levels and assayed two neuron populations that are derived from old INPs [To decipher the role of Imp in oINPs and nNeurons in regulating neuronal morphology, we used 16B06-Gal4 > UAS-Impold INPs : E-PG neRNAi resulted in an enlargement of all three neuropils . In controls, E-PG neurons innervate the EB, PB, and G neuropils, shown as a confocal image Fig. A and Imails Fig. P. ImpOE ils Fig. I-L, but ils Fig. I, M-O; tils Fig. P; the reRNAi resulted in varying alterations in the volume of each neuropil: the PB and RB were enlarged, while the FB was reduced  [. T2NB lineage-dependent gene expression of lnRNAs located close to Syp could be the cause the variation in Syp levels in each T2NB. Lastly, previous work has shown that co-expression of Imp and Syp in mushroom body NBs (MBNBs) results in a neuronal identity (α’β’ neurons) distinct from neurons produced with only high Imp (γ neurons) or high Syp levels (αβ neurons) [Previous research has documented the opposing Imp/Syp gradients in both type 1 and 2 NBs , 27, 28.ila Syp)  expressiila Syp) . LnRNA Nila Syp) . The lnRila Syp) . T2NB lineurons) . Thus, iAt 24 h some T2NBs are still in quiescence and T2NBs can only be differentiated at more medial or lateral instead of their specific lineages. At 24 h Syp expression is low in medial T2NBs with only a slight increase in lateral T2NBs. Syp levels don’t seem to become lineage-specific until 48 h into neurogenesis. This further supports the hypothesis of an independent mechanism for Imp and Syp overlap in specific T2NB lineages.Drosophila intestinal stem cells, (ISCs) Lin-28 and Imp are both expressed to promote ISC proliferation and regulation of each other [After confirming Imp/Syp expression patterns in T2NBs and validating that Imp/Syp levels are the same in newborn INPs, we found that at 48 h, INPs formed a high-to-low Imp gradient. In contrast, at 72 h and 96 h, Imp showed a peak of expression in old INPs Fig. A. This bch other . In factch other , 25, 28.ch other , 32, 34,ch other . The ImpRNAi was able to significantly reduce Imp levels in INPs across all timepoints, with the exception of newborn neurons at 48 h  let-7 inhibits Imp in the Drosophila testis stem cell niche [let-7 expression it could produce a negative feedback loop that keeps Imp levels low. Traditionally miRNAs bind the 3′ UTRs of mRNA targets, but the ImpOE used in this work lacked its normal 3′ UTR. Surprisingly, previous work shows that regulation of let-7 in the 5′ UTR does occur [let-7 were added to the 5′ UTR of its mRNA target lin-41 from C. elegans [let-7, it was sufficient for lin-41 repression [let-7 or another factor is activated by Imp and then represses Imp levels remains to be determined.Imp h  . Larvae were grown to specified time points, dissected, mounted on poly-D-lysine coated slips , and incubated for 30 minutes (adults incubated for 40 m) in 4% paraformaldehyde solution in Phosphate Buffered Saline (PBS) with 1% Triton-X (1% PBS-T) at room temperature. Larval brains were washed twice with 0.5% PBS-T (adults brains were washed twice with 1%PBS-T) and incubated for 2–4 days (adult brains were incubated for 4–10 days) at 4 °C in a blocking solution of 1% goat serum , 1% donkey serum , 2% dimethyl sulfoxide in organosulfur (DMSO), and 0.003% bovine serum albumin (BSA) . Larval brains were incubated for two nights at 4 °C in a solution of primary antibodies (see Table All larvae and adult https://imagej.net/software/fiji/). Imp/Syp pixel density in each cell type was calculated in FIJI. In FIJI, cells were manually selected in a 2D plane at the largest cross section of the cell with the polygon lasso tool, and the area and Raw Integrated Density (RID) was measured. The nucleus of each cell went through the same analysis steps. Imp is cytoplasmic and measuring fluorescence in the nucleus functioned as background subtraction. Imp levels were normalized to cell area using the equation: (Cell BodyRID – NucleusRID) / (Cell BodyArea – NucleusArea). ANOVA or two-tailed student t-tests were used to compare two sets of data. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. All graphs and statistical analysis were done in Prism . Note that we were unable to quantify Imp fluorescence in mitotic cells.All Imp data was collected with identical confocal settings; all Syp data were collected with identical confocal settings. Fluorescent images were collected on Zeiss LSM 800. Cells were counted using the cell counter plugin in FIJI . The surfaces tool was used to select individual neuropils.Images for figures were taken in FIJI. Figures were assembled in Adobe Illustrator . Any changes in brightness or contrast were applied to the entire image.Additional file 1: Supplementary Video 1. Imaris surface reconstruction of larval central brain at 60h. Magenta spheres represent T2NBs and their location within the larval brain. Scale bar 20 μm.Additional file 2 : Supplemental Fig. 1. INP staging criteria. Schematic showing markers that define different stages in INP lineage progression. T2NBs ; nINPs contact the parental NB ; yINPs  border nINPs; mINPs ; oINPs ; and nNeurons . GFP was driven in nINPs, yINPs, mINPs and oINPs with 12E09-Gal4, and in oINPs and nNeurons with 16B06-Gal4. Supplemental Fig. 2. At 24 h T2NB lineages can only be characterized as medial and lateral. (A) 12E09 > UAS-GFP at 24 h targets proliferative T2NBs . Scale bar 5 μm. (B) Quantification of Syp levels in medial and lateral T2NBs at 24 h. n = 5 brains. Student t-tests were used to compare medial cells to lateral cells. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. Supplemental Fig. 3. Lineage specific Syp levels in T2NBs and nINPs is equivalent except for DL2. (A) Quantification of Syp levels in T2NBs and nINPs in each specific lineage. n = 5 brains. Student t-tests were used to compare medial cells to lateral cells. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. Supplemental Fig. 4. 12E09-Gal4 is expressed in embryonic T2NBs and is required for PF-R and E-PG neuron morphology. (A) 12E09-Gal4 > UAS-GFP in embryonic T2NBs. T2NBs . Scale bar 5 μm. (B) Schematic of 12E09-Gal4 expression in embryonic and larval T2NBs and n/yINPs. (C) 12E09-Gal4 > UAS-ImpRNAi turns on earlier in development. T2NBs , nINPs , and yINPs  show a loss of Imp at 48 h in T2NBs. Scale bar 5 μm. (D-E) Confocal maximum intensity projections of control, ImpRNAi and ImpOE in PF-R and E-PG neurons. n = 5, scale bar 20 μm. Supplemental Fig. 5. 16B06 > ImpRNAi causes an increase in cell number at 48 h and 72 h. (A-B) Number of oINPs (A) and nNeurons (B) in control, ImpRNAi and ImpOE. Each point is an oINP (A) or nNeuron (B). n = 3–5 brains. Student t-tests were used to compare cell numbers to control. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.