Produce prescription programs represent a promising intervention strategy in the healthcare setting to address disparities in diet quality and diet-related chronic disease. The objective of this study was to understand adoption and implementation factors related to these programs that are common across contexts and those that are context-specific.In this qualitative case comparison study, we conducted qualitative interviews with eight clinic staff from five primary care “safety net” clinics, identified by a partnering non-profit organization that operated the programs, in April–July 2021.Across clinics, the ability to provide a tangible benefit to patients was a key factor in adoption. Flexibility in integrating into clinic workflows was a facilitator of implementation. Fit with usual operations varied across clinics. Common challenges were the need for changes to the workflow and extra staff time. Clinic staff were skeptical about the sustainability of both the benefits to patients and the ability to continue the program at their clinics.This study adds to a growing body of knowledge on the adoption and implementation of produce prescription programs. Future research will further this understanding, providing the evidence necessary to guide adopting clinics and to make informed policy decisions to best promote the growth and financial sustainability of these programs. ThesTypically in produce prescription programs, patients with a diet-related health condition or food insecurity are referred by healthcare providers and receive economic incentives, often in the form of electronic cards or paper vouchers, to redeem for fruits and vegetables at retail food outlets . Retail For these programs to achieve their potential in terms of public health impact, they will need to be widely and sustainably adopted within the healthcare system. It is therefore critical to understand factors that influence the decision to adopt these programs as well as barriers to and facilitators of their implementation and sustainability . It is aFew studies have examined factors related to the implementation of produce prescription programs. Most studies that examined implementation factors in-depth evaluated programs running at 3–4 clinics in a single state, and all partnered solely with farmers markets . These pWhile prior research identifies a number of barriers and facilitators to implementation, it does not fully address factors that may be common across contexts and those that may be context-specific. Furthermore, only one other study examines factors that influence clinics to adopt a program . We used2.2.1.Our academic team partnered with a non-profit organization to conduct an evaluation of their produce prescription programs, including this qualitative assessment of factors related to adoption and implementation. The non-profit partner is a leading national organization that has supported and developed nutrition incentive programs throughout the U.S. The non-profit partner operated the produce prescription programs by recruiting clinics and retailers as well as securing funding for the programs. Funding sources included a state department of public health, a food company, and healthcare organizations. All clinics served low-income populations. In all programs, enrolled participants received a financial incentive (paper voucher or electronic gift card), delivered once per month, to purchase produce at grocery stores. The programs otherwise varied by geography, monthly dollar amount, nutrition education component, size of the program, and the specific population served . The proWe interviewed staff from a convenience sample of five clinics that had partnered with the non-profit partner to implement a produce prescription program within the past 2 years. Interviews were conducted in April–July 2021. Clinic staff to be interviewed were identified by our partnering non-profit organization as the primary implementer at the respective clinics.The research team has expertise in qualitative methods and implementation science (SCF), economics (SBC), and nutritional epidemiology (FFZ). The team also included a project administrator (ZL) and a doctoral student whose dissertation research focused on food is medicine interventions (KH). The team was concurrently working on a quantitative analysis of the produce prescription programs.2.2.The interview guide was informed by Proctor’s framework  and coveInterviews were conducted online via the Zoom videoconferencing application either by SCF, ZL, KH, or by two graduate research assistants. SCF trained the other interviewers. All but one of the interviews were conducted one-on-one with the study team member and clinic staff. At Clinic 5, one of the interviews was conducted with both the medical administrative assistant and the business systems associate together. Interviews lasted 24–58 min. Participants received a $50 gift card for remuneration. The protocol was reviewed and deemed exempt by the Tufts University Health Sciences Institutional Review Board. Informed consent was obtained verbally from all participants.2.3.The interviews were recorded and transcribed, and then coded using NVivo software . SCF and ZL were primarily responsible for the analysis. We used a directed qualitative content analysis approach, which is fundamentally deductive . We draf3.We interviewed eight clinic staff from the five clinics in our sample. For clinics 1 through 4, we interviewed one staff member, who was the primary implementer. In clinic 5, because we had a strong research collaboration with the clinic, we were able to interview four clinic staff to obtain perspectives from multiple types of stakeholders. At that clinic, in addition to the primary implementer, we also interviewed the head of clinics, a medical administrative assistant, and the business systems associate. The multiple perspectives at this one clinic offered a well-rounded view, which enhanced but did not fundamentally change the results. While six clinics had implemented a produce prescription program in partnership with the non-profit partnering organization in the timeframe, one primary implementer failed to complete an interview due to leaving her position shortly after being identified.We present findings according to major topics covered in the interview guide: adoption, implementation, and impact on patients and sustainability.3.1.…it seems like hierarchy-wise it was a support at all levels…—Head of Clinics, Clinic 5). For willingness to adopt, there were similarities across sites that converged into two main themes: simplicity of the program and potential benefits for patients.Clinic staff at all clinics described enthusiasm about the program and a willingness to adopt it at all levels of decision-making .Clinic personnel were motivated to adopt the program because of the potential benefit to the patients, especially given the need in the populations that they served.So yeah, I'm dealing with an underserved population, whose—my mission is to support them, so that was a no-brainer. And many of our initiatives, again, like I said, come from partners from outside. So any partner outside willing to partner with us, with our patients, we tend to work on them with open arms, for the most part. That was something our dietitian was hearing left and right, was that just fruits and vegetables cost too much. And so she's trying to work with them on changing eating habits and they're saying I don't have the access to those things, so we thought it was a great way of removing that barrier and exposing people to some of the benefits of fruits and vegetables. Only one site described any pushback to program adoption. At Clinic 1, the primary implementer was enthusiastic about the program, but was initially concerned that the clinic did not have the patient base to meet the recruitment expectations of the non-profit partner. This concern was alleviated by opening the program up to community members who were not patients at the clinic.3.2.Because of the flexibility allowed by the non-profit partner, there was variation in how the program was implemented at each site and in the clinics’ experiences with the implementation.But [the non-profit partner] tried to stay pretty hands off, from the perspective of letting us develop something that was, you know, better for our own clinic, right. So they gave some guidance and they gave some ideas, but it's not like they handed us kind of like a turnkey program and just said go. They let us really develop what was—what we felt like was most needed for our patients and what worked within our workflow much, much better. Interviewees described five major tasks involved with program implementation: (1) recruiting/identifying patients and determining eligibility; (2) distributing and tracking the produce prescription incentives; (3) responding to patient inquiries related to the incentive; (4) obtaining and tracking outcome measurements ; and (5) providing nutrition education. These tasks fell to different staff members at the different clinics and for some, shifted over time.Physicians were minimally involved in implementation at all clinics. Their involvement ranged from attendance at several community recruitment events to initial identification of eligible program participants. The various tasks related to the program fell instead to front office staff, nurses, medical assistants, registered dietitians, the pharmacist, a health and wellness educator, and a volunteer.There was variation and no major theme identified around perceived fit with regular clinic operations. At Clinic 1, implementation was described as essentially a seamless fit with normal operations. At this clinic, a full-time health and wellness education professional was primarily responsible for implementing this and other special projects. In contrast, interviewees at the remaining clinics described challenges to implementation since the program required changes in standard workflows and put demands on staff time.The interviewee at Clinic 2 described putting in upfront effort to integrate the program into the EHR and other systems. At this clinic, physician involvement initially presented a challenge, and they shifted implementation to front office and nursing staff. With the upfront effort to create the protocols and the change in implementer, the program fit well within usual clinic practices thereafter.And so, at first we were running those through the provider and some of the nursing team. But that became a little problematic, in that they would forget or, you know, [the patients are] there for diabetes and hypertension and some of these other things, and so the provider’s thinking about prescriptions and those kinds of things and they're not thinking about the gift card as well, so we quickly removed it from the provider piece and put it on the front office and the nursing. And so between those two, the nurses would recommend based on what happened in the office, and then the front office could verify that they didn't have, you know, that they were kind of an underserved patient…So they implemented—once we moved to that piece it went pretty smoothly. At Clinics 3 and 4, the interviewees described major challenges in the first year of the program. Both sites included a substantial nutrition education component that required adaptations in the second year due to the COVID-19 pandemic. In both cases, they found that the modified model, which was remote, group-based nutrition education implemented by a registered dietitian, worked better.It was interesting that the pandemic provided us the opportunity to do something that worked really well. I think that that support group type setting was something that provided a lot of efficiency. Working with registered dietitians as opposed to staff members who are involved in direct care and had a lot of patient appointments anyways, this seemed to more naturally fit into the registered dieticians' workflow. They were able to handle a lot of the follow-up that typically would have been—or in the previous year, was very burdensome to the staff that was involved. Obviously, there's always room for improvement, but that second year was really a good model of what worked well and would be something that we would probably mimic if we were going to be doing that for another year. Clinic 5 experienced the most serious challenges to implementation. Clinic staff perceived the program as time- and resource-intensive. Here, the four staff interviewed described the program as a “collective responsibility” that required thoughtfulness and effective communication among many members of the clinic staff. For part of the program, they had a volunteer for 20 h per week, and many responsibilities fell to this person.…we also had a volunteer at the time from a program that we work with at the hospital that filled a lot of the data collection and updating spreadsheets and things like that, so we were very lucky to have someone that didn't have their daily tasks, that were being pulled from like their daily responsibilities, and so she kind of took on the little coordinator kind of role…. In this busy clinic, even with minimal nutrition education, implementation required multiple tasks and remained challenging.…so the only thing was like because we have to do it like during—when the patient was here. And usually we do have a workflow, like when a patient arrives, do the vital signs so—and to incorporate the [outcome measures requested by the non-profit organization], so we have to change the workflow to see how that we can place the order for the hemoglobin A1c and then how we can communicate a provider to place the order, and then after that we have to do—do the whole information on the tablet with that ID number and things like that, so it was a lot of——Lots of, yeah, lots of hands on—a lot of manual tracking, and yeah. 3.3.There was a high level of similarity across clinics in how interviewees described the impact on patients. A major theme was that they appreciated having something concrete to give to patients to help their social, nutritional, and economic struggles and improve their health.…again it was another component in supporting the social determinants infrastructure by dealing with their basic needs of eating and eat—and hopefully eating better with the fresh fruits and vegetables. So we've always kind of tried to help in some of those social determinants and some of those areas where our patients really struggle, but we've never been able to do it in a focus on being healthy as well, so that's where this really was an amazing benefit, is because now it was truly something that was specifically for people who we knew needed to be healthier and couldn't afford to do that. I think anytime that we have something that we can actually give them, that's concrete ways to change their eating behaviors is really helpful. Rather than just telling people to eat better, we can give them something to help them eat better. Additional benefits that were mentioned by some of the interviewees, but not consistently across clinics, included patients feeling supported by the clinic, benefiting from the nutrition education, and increasing engagement with the clinic, especially during the pandemic.I think the interaction too with the staff was nice for the patients because even during Covid it was like people were isolated and you still had the staff reaching out to say hey, don't forget your vouchers or hey, another mailing is coming in…there was still communication going back and forth, even during Covid when patients might have felt like isolated. So yeah, I think again the benefit outweighed the amount of work it took to implement it. Most clinic staff thought that patients were satisfied with the monthly produce prescription incentive amount, even though the dollar amount varied somewhat across clinics.A second theme related to impact on patients was that clinicians were skeptical about patients sustaining any changes that they had made without an ongoing incentive for free fruits and vegetables. Staff at clinics that had not provided much nutrition education felt that providing more in combination with incentives might be helpful in sustaining long-term dietary changes. Overall, though, the level of poverty among patients participating in the program was the basis for clinic staff skepticism of long-term program benefits. Staff expressed concern that without the additional dollars provided by the program, patients would have less ability to purchase the same amount of fruits and vegetables. The program manager at Clinic 4 suggested that financial education could possibly help the patients learn to fit produce into their budgets, an approach that had been tried at Clinic 3 in a small way at one of the final classes.I don't know if there is an amount of financial education or maybe working to get people other resources to show them how to do this. I think that maybe it's the potential of trying to find other ways to show patients or get them to acknowledge how improving their A1c made them feel, to the point where that would cause them to readjust some of their budget to include more fresh fruits and vegetables. Like I was saying before, just from some of the feedback, some people just have such deep poverty that that really is challenging. Only the wellness coordinator and coach at Clinic 1 was hopeful about the sustainability of changes. In this pediatric program, he felt that once parents saw that their children were willing to eat different types of produce, they would be more willing to purchase it after the conclusion of the program.And in that way, I think a lot of parents and participants were able to benefit from it, because they were able to experiment. So I think it really played a huge role, because even though they got off the program [because the program ended] they were using that $60 for six months, and they were able to say, okay, these were some of the things that work. And now they could—when they're investing their own money they can feel more confident knowing if I buy these grapes, my daughter's gonna eat them. In terms of sustainability of the program at the clinic level, there was consistency across clinics and a major theme was that the clinics would run the program again if given the opportunity, but they could not do so without external funding.And then funding, if you had a state that was willing to put some money into it to make it sustainable, or if you had a large, generous foundation or something like that that was willing to year over year really invest in to see, does this actually make a change over the course of, you know, 5 years of providing fresh fruits and vegetables to a family. And then see what the data would show, but without that kind of commitment, just like here, it was awesome while we had it. We’d definitely do it again in a heartbeat. But as soon as it went away, we look to the next kind of program to help our patients. And there might be push back [if I asked the healthcare corporation for money for the program] like I do not—I do not have enough leverage, I think, with my population to extol resources from the program from the corporation. However, for, let us say a department that brings in revenue to the system. Let us say, like the bariatric surgery program. They may exert leverage to say, “I need additional resources to administer this program.” See my point, they—whereas mine I go into the CEO or I have poor patients who I do not get money for, can you give me resources to administer this program. As opposed to, “I’m bariatric surgery and bringing in X millions, and this would be a great adjunct to what we do.” Right, they have greater leverage. 4.This study is one of the first to explore implementation factors related to produce prescription programs across multiple program types. In this qualitative study, clinic staff described simplicity, flexibility and the ability to provide something tangible to their patients as factors in the decision to adopt the program. There were challenges to implementation at most clinics, and the need for changes to the workflow and extra staff time were common ones. Physicians were minimally involved compared to other clinic staff. Clinic staff expressed skepticism about the sustainability of both the benefits to patients and the ability to continue to offer the program at their clinics.4.1.Participants described several key factors related to adoption. A major factor in program adoption was compatibility, or the degree to which the innovation fit within existing values, experiences, and systems . While t4.2.In addition to being a factor in the decision to adopt, across the sites in our study, flexibility was also described as a key facilitator of implementation. Other studies conducted have consistently described the need for flexibility and adaptability in the implementation of produce prescription programs , 12, 20.While we found that flexibility was a common facilitator of implementation, staff time was a common barrier across the clinics. A solution to staff time issues suggested by our results is assisting clinics with upfront changes that will alleviate workflow inefficiencies, such as adding fields to the EHR. Noting the differences among the clinics, our results also suggest that staff time issues might be alleviated if responsibilities for managing the program fall to staff whose job is well-aligned, such as health educators. Similarly, in Stotz et al.’s  intervieOur results indicate that produce prescription programs can be implemented, potentially more effectively, by clinic staff other than physicians. The study with hypertensive patients had similar findings . Both th4.3.While a major theme was that providing a tangible form of support had a positive impact on patients, all clinic staff expressed skepticism about sustainability at both the level of patients’ individual habits and at the clinic level. In a field scan of produce prescription programs, self-supported programs  lasted the longest, an average of 4.5 years, but only 4% were self-supported . In Sept4.4.This study had multiple strengths and limitations. It is one of the first studies to compare adoption and implementation factors across clinic sites. However, the programs were all associated with the non-profit organization that partnered with our research team and therefore this study only includes perceptions related to the specifics of produce prescription programs associated with that organization. Another limitation is the small number of clinics in the sample. While the overall sample was small, it allowed us to compare across clinics. We also achieved code saturation , suggest4.5.Communicating how the program fits with the values and practices of the clinic will help ensure that staff will persist in resolving barriers to implementation. The ability to provide a tangible support to patients was also a motivator for clinic staff.Ensuring that program logistics are simple and flexible may facilitate adoption and implementation.It is likely that up-front effort to build the program into workflows, including integrating it into the EHR system and possibly other forms of patient tracking, will pay off in terms of smooth implementation.Clinics may consider the involvement of multiple clinic staff to implement programs. Reliance primarily on physicians may impede implementation in some clinics.More intensive and targeted nutrition education and counseling  should be considered in future programming. Especially in areas of high poverty, some form of financial education could be considered as well.Clinics may be unable to bear the cost of sustaining the program. Other solutions should be explored to ensure the sustainability of the program.Based on our findings, the following points may be helpful to clinics considering the adoption of a produce prescription program:4.6.This study contributes information on factors related to adoption of produce prescription programs and corroborates previous research about barriers to implementation, including staff time. It also provides further evidence of common facilitators, such as the flexibility and adaptability of the programs. Our findings suggest the importance of policy solutions to ensure sustainability of produce prescription programs. This study described implementation in “safety net” clinics of programs that received resources from the same non-profit partner. Additional research is needed to understand implementation of produce prescription programs more fully across a range of settings, to further determine which barriers and facilitators are common and which are unique in different contexts. These studies will provide the evidence necessary to guide adopting clinics and to make informed policy decisions to best promote the growth and sustainability of these programs.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving humans were approved by Tufts University Health Sciences Institution Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin because the protocol was deemed exempt. Verbal consent was obtained.SF, SC, and FZ contributed to study conception. SF, ZL, and KH made substantial contributions to data acquisition. SF and ZL analyzed the data. All authors contributed to the article and approved the submitted version.


The solubility (25 °C) of syringic acid, ferulic acid, and milled wood lignin in SDESCO2 reached 230.57, 452.17, and 279.12 mg/g, respectively. Such SDES-dissolved lignin can be regenerated using acetone as an anti-solvent. The SDES-regenerated lignin exhibited a well-preserved structure with no noticeable chemical modifications. Furthermore, the SDESCO2 lignin possessed a higher molecular weight , improved uniformity (polydispersity index = 1.35), and a higher guaiacyl lignin unit content compared with the original milled wood lignin. The SDES system proposed in the present work could benefit the fractionation of lignin compounds and facilitate downstream industrial processes.Deep eutectic solvents (DESs) are promising for lignin dissolution and extraction. However, they usually possess high polarity and are difficult to recycle. To overcome this drawback, a variety of switchable ionic liquids (SILs) composed of 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) and alcohols was synthesized and screened. According to the thermodynamic modeling suggestions, the selected DBU–HexOH SIL was coupled with hydrogen-bond donors to form switchable-DES (SDES) systems with moderated viscosity, conductivity, and pH while maintaining switchability. The SDESs produced a well-improved lignin and lignin model compound solubility compared with those of SILs; charging CO Abundant reserves of lignocellulosic biomass make it an important renewable resource . As a maA deep eutectic solvent (DES) is a eutectic solvent formulated by hydrogen-bond donors (HBDs) and hydrogen-bond acceptors (HBAs) . ILs can2, generates a so-called switchable ionic liquid (SIL) .,45.2, geeveloped . Therefoeveloped . The proA variety of SILs composed of DBU and different alcohol compounds was synthesized and screened in this work. Optimized SIL was coupled with an HBD to form stable SDES systems; this was followed by using the systems to dissolve lignin and its model compounds. Lignin solubility was checked using ultraviolet spectroscopy, and characterizations such as Fourier-transform infrared (FTIR) spectrum, heteronuclear single-quantum coherence nuclear magnetic resonance (HSQC NMR), gel permeation chromatography (GPC), and thermogravimetric analysis (TGA) were carried out, to confirm the possibility of using the proposed SDES system for lignin dissolution and extraction processes.6, 99.9%), and potassium bromide  were purchased from Shanghai Macklin Biochemical Technology Co., Ltd. . Methanol , glycerol , isopropanol , tert-butanol , choline chloride , acetic anhydride , and urea  were purchased from Sinopharm Chemical Reagent Co., Ltd. . Ethanol , benzene , n-hexane , and butanol  were purchased from Tianjin Fuyu Fine Chemical Co., Ltd. . Hexanol , octanol , and pyridine  were purchased from the Tianjin Damao Chemical Reagent Factory . Acetone  was purchased from the Yantai Far East Fine Chemical Co., Ltd. . All chemicals were used as received, without further purification.Poplar wood chips were provided by a pulp mill in Shandong, China; 1,8-diazabicyclo[5.4.0]undec-7-ene , 99%), ethylene glycol , n-propanol , sec-butanol , ethanolamine , decane , 1,4-dioxane , tetrahydrofuran , syringic acid , vanillic acid , syringaldehyde , ferulic acid , alkaline lignin (Biopurity), ammonium sulfamate , imidazole , dimethyl sulfoxide-d6 (DMSO-d2. According to the method used by [CO2) can be obtained by bubbling CO2 into SIL under room temperature conditions for 30 min until the viscosity of the system increases significantly, whereas non-polar SIL (SIL′) can be restored by heating the SILCO2 at 80 °C for 10 h.Switchable ionic liquids (SILs) were synthesized according to the method described in the literature ,49, wher used by , polar S2O) as the hydrogen-bond donor (HBD). The HBA and HBD were mixed at a 1:10 to 10:1 weight ratio and stirred at 25 °C. A transparent and uniform SDES system was obtained, namely, DBU–HexOH/H2O. DBU–HexOH/H2O demonstrated switchable physical and chemical properties upon the absorption or release of CO2. Polar SDES (SDESCO2) can be obtained by bubbling CO2 into SDES at room temperature for 30 min until the viscosity of the system increases significantly, whereas non-polar SDES (SDES′) can be restored by heating the SDESCO2 at 80 °C for 10 h.The switchable deep eutectic solvents (SDES) were synthesized according to the method employed by , by usinThe pH of the testing sample was detected using a pH meter  at 25 °C. The conductivity of the testing sample was detected using a conductivity meter . The viscosity of the DES was measured according to the method described in , by placv/v). The extract was centrifuged, and the supernatant was evaporated, concentrated, and freeze-dried to obtain the MWL.The milled wood lignin (MWL) was prepared according to the method recorded in . A speciLignin solubility was detected according to the method described in . Lignin w/w) under continuous stirring at room temperature. The fully dissolved lignin solution was filtrated with a 0.45 μm organic filter. Acetone was added to the lignin solution  as an anti-solvent and then kept at 4 °C for 12 h to enable lignin regeneration. The precipitate was recovered using centrifugation  followed by filtration and then oven-dried at 105 °C until a constant weight was achieved.Lignin dissolution and regeneration were carried out according to the method employed in ,55. MWL The number average molecular weight (Mn), weight average molecular weight (Mw), and polydispersity index (PDI) were measured by gel permeation chromatography  equipped with an Agilent 1200 series high-performance liquid chromatograph (HPLC) and an ultraviolet detector (UV) ,56. Acet–1 at a resolution of 4 cm−1. Detailed structural characterization was performed using nuclear magnetic resonance (NMR) spectroscopy ; around 50 mg of lignin was dissolved in 0.5 mL of DMSO-d6 [1H-13C heteronuclear single-quantum coherence (HSQC) spectral standard pulse sequence was as follows: spectral width 1H of 11 ppm with 2048 sampling points; 13C spectral width of 190 ppm with 256 data points; 64 scans; and 1 s scan delay. Volume integration of the signals in the HSQC NMR spectra was performed in Bruker Top Spin 2.1 software.The structural characterization of the lignin samples was carried out using Fourier-transform infrared spectroscopy  . A total DMSO-d6 . NMR speThe molecular simulation was conducted using the conductor-like screening model for real solvents (COSMO-RS) model , in which quantum chemical calculations were combined with statistical mechanics to explore the thermodynamic behaviors of the chemicals used in this work.3 was drawn by Turbomole . The geometry optimizations were performed at the density functional theory (DFT) level and utilized the BP function with resolution of identity (RI) approximation; a triple-ξ valence polarized basis set (TZVP) was utilized [The structure of DBUH and HexCOutilized ,60. All utilized .2 into SIL caused the formation of its ionic state (SILCO2), leading to a significant increase in viscosity, and sometimes even the formation of solids . Taking the DBU–HexOH SIL system as an example, the DBU–HexOH is miscible with decane, whereas its ionic form (DBU–HexOHCO2) is not. As shown in 2 charging–discharging cycles. Pumping CO2 through the homogenous SIL–decane solution caused the formation of emulsion, while simply removing CO2 by heating restored miscibility. DBU–PrOH, DBU–BuOH, and DBU–OctOH SIL systems  both possess low electroconductivities. As expected, the SILs generated by mixing DBU and alcohols produced low conductivities. The conductivity of SIL, shown in 2 allowed the SIL to convert into its ionic form, producing a dramatic increase in conductivity in the SILCO2. Among all the tested SIL systems, DBU–HexOHCO2 presented the highest conductivity of 1.250 μS/cm. Discharging CO2 allowed the electroconductivity to be restored. However, the conductivity of SIL′ was generally higher than that of the SIL system; this could be attributed to the incomplete removal of CO2 during the heating process. In terms of their viscosities, SILs presented slightly higher viscosities than those of DBU (0.0005 Pa·s) and alcohols (~10−4 Pa·s). As shown in 2 caused a surge in SIL viscosity, especially for the DBU–BuOHCO2 (0.9127 Pa·s) and DBU–HexOHCO2 (0.8531 Pa·s) samples. Similarly, viscosity was restored by removing CO2 from the system. The significant increase in viscosity relates to the formation of an ionic compound: This created more and stronger ionic interactions, enhancing the internal friction in SILCO2. As shown in CO2  was much higher than that of DBU–BuOH (0.126 μS/cm), indicating the incomplete removal of CO2. Therefore, DBU–HexOH was deemed the preferred SIL system, as its solvent properties can easily be restored from 1.250 μS/cm to 0.240 μS/cm, demonstrating a preferred switching ability.The formation of ionic compounds was confirmed by analyzing the conductivity and viscosity of the SIL systems. Generally, SIL and SIL′ demonstrated significantly lower conductivity than that of SILn in CO2 c present2 caused the formation of SILCO2 and decreased its pH to around 12, while removing CO2 restored the SIL pH to about 14, which is close to that of SIL. Therefore, the SIL system pH was also regulated by charging and discharging CO2. For example, DBU–HexOH had a pH of 14.76; charging CO2 decreased the pH to 11.92 (DBU–HexOHCO2), and removing CO2 by heating restored the pH to 14.24 in DBU–HexOH’. However, it is proposed that a strong basicity environment may affect the aromatic structure of lignin, destroy the C–O single bond in lignin, and lead to a significant chemical modification of lignin macromolecules [CO2, which may prohibit lignin dissolution.DBU is a strong organosuperbase with a pH of 15.01; SILs composed of DBU and alcohols, therefore, also present strong basicity. As shown in olecules . It woul2 into SIL caused the formation of ionic SILCO2. The molecule structures and their charge distributions of the chemicals in the DBU–HexOH SIL system are shown in 2 caused the formation of a strong hydrogen-bond donor (HBD) center (blue-purple area) on the DBUH from DBU. In addition, a strong hydrogen-bond acceptor (HBA) zone (red area) was created on the HexCO3 from the HexOH. Therefore, in addition to the strong ionic interaction in SILCO2, possible hydrogen bonds could also exist in the DBU–HexOHCO2 mixtures. 2 are the HBD region, σ values between −0.0082 e/Å2 and 0.0082 e/Å2 represent the non-polar region, and values higher than 0.00821 e/Å2 are the HBA region. As shown in 2)/weak HBA (0.0082~0.015 e/Å2) regions. According to the reaction scheme  presenting a strong HBD affinity. Due to charging with CO2, the formation of ionic substances in SILCO2, therefore, mainly contributes to the internal ionic interactions and only produces a minor effect on the overall hydrogen-bonding ability of the mixture. In addition, based on the COSMO-RS modeling results, introducing a cosolvent such as an HBD is an effective method of enriching the hydrogen-bonding network inside the proposed SIL solvent system. Water normally acts as both an HBD and HBA, presenting characteristic peaks located at −0.016 and 0.017 e/Å2, respectively. The addition of water allowed it to perform as an ideal HBD in both the non-ionic (SIL) and ionic (SILCO2) states of the DBU–HexOH system. As shown in 2) and weak (−0.013 e/Å2) HBD regions, both of which overwhelmed the HBD assignments within the DBU–HexOH and DBU–HexOHCO2 (DBUH and HexCO3) systems. Therefore, the addition of an HBD as a cosolvent enriched the hydrogen-bond network within the DBU–HexOH SIL system. In this case, the SIL/SILCO2 worked as the HBA and water acted as the HBD within the mixture system.The use of cosolvent has been reported to enhance the solubility of lignin derivatives ,64. Howen scheme , chargin2O system with a 1:5 mass ratio, either with or without the presence of CO2 (2 into SDES caused the elevation of conductivity from 4.848 μS/cm (DBU–HexOH/H2O) to 23.030 μS/cm (DBU–HexOH/H2OCO2), and removing CO2 allowed the electroconductivity to be restored. However, the conductivity of DBU–HexOH/H2O′ (10.066 μS/cm) was higher than that of DBU–HexOH/H2O; this could be attributed to the incomplete removal of CO2 due to the presence of water. Charging with CO2 also increased the viscosity of the SDES system, from 0.0186 Pa·s (DBU–HexOH/H2O) to 0.0825 Pa·s (DBU–HexOH/H2OCO2). Moreover, releasing CO2 by heating allowed the viscosity of the DBU–HexOH/H2O to be restored to 0.0466 Pa·s. It should be noted that the addition of the HBD significantly reduced the viscosity in the ionic state to about 1/10 of that for the SIL system (0.8531 Pa·s for SILCO2 vs. 0.0825 for SDESCO2). In addition, the pH of the DBU–HexOH/H2O was 14.26: Charging with CO2 decreased the pH to 9.50 (DBU–HexOH/H2OCO2), which was much lower than that of SILCO2 . Therefore, the proposed SDES, composed of a mixture of DBU–HexOH and water, could moderate the viscosity and pH of the SIL system, while allowing it to maintain its switchable nature, making the SDES more suitable for the lignin dissolution process. Based on the COSMO-RS thermodynamic modeling results, a switchable deep eutectic solvent (SDES) system was proposed by adding HBDs as cosolvents into the SILs to improve their hydrogen-bonding networks. The addition of the HBD should be able to reduce the viscosity and cost of the solvent, expand the liquid range, and enhance the solubility ,37. In te of CO2 . It can CO2) did not demonstrate any lignin solubility. This phenomenon is very similar to that of DBU, which is a major component in SIL. DBU dissolved 22.79 mg/g syringic acid, 4.54 mg/g vanillic acid, 1.17 mg/g syringaldehyde, 2.26 mg/g ferulic acid, and 1.91 mg/g alkaline lignin at room temperature but did not demonstrate lignin solubility after being charged with CO2. As the other component in SIL, monohydric alcohol, however, demonstrated a different lignin solubility. Both HexOH and HexOHCO2 presented a very small but similar solubility to vanillic acid, syringaldehyde, and ferulic acid. However, charging with CO2 increased their solubility for syringic acid, and alkaline lignin increased from 34.9 mg/g and 9.72 mg/g to 40.24 and 16.30, respectively. Although water presented limited lignin solubility and no notable change after charging with CO2, the proposed DBU–HexOH/H2O SDES system, mixed with SIL and water, demonstrated a notable increase in lignin solubility. The solubility of syringic acid, vanillic acid, syringaldehyde, and ferulic acid in DBU–HexOH/H2O achieved 207.58 mg/g, 21.95 mg/g, 7.98 mg/g, and 58.12 mg/g, respectively. These could be further increased by charging CO2 into the system. The solubility of syringic acid, vanillic acid, syringaldehyde, and ferulic acid in the DBU–HexOH/H2OCO2 system was 230.57 mg/g, 78.43 mg/g, 11.64 mg/g, and 452.17 mg/g, respectively. Therefore, the lignin solubility in the SDES system could also be regulated by the addition of CO2, and this would vary depending on the type of lignin model compounds. Particularly, for vanillic acid and ferulic acid, after charging CO2 into SDES, their solubility increased by 357% and 778%, respectively. However, the solubility of alkaline lignin in SDES decreased from 5.67 mg/g to 4.44 mg/g after forming its ionic state. Therefore, the proposed SDES mixed with DBU–HexOH and H2O demonstrated improved lignin solubility, and this was also regulated by charging and discharging CO2.The solubility of lignin model compounds in the SIL and SDES systems is shown in 2O SDES dissolved up to 213.35 mg/g MWL. Charging CO2 into the SDES further increased MWL solubility to 279.12 mg/g. The SDES-dissolved lignin was easily regenerated using acetone as an anti-solvent, with a yield of 84.31% (SDESCO2–MWL). The variations in weight average molecular weight (Mw), number average molecular weight (Mn), and the polydispersity index (PDI) before and after SDES treatment were tested using gel permeation chromatography (GPC); the results are given in CO2 treatment resulted in a lignin stream with a much higher molecular weight  and with an improved uniformity (PDI = 1.35). Compared with the traditional DES–lignin . The DBU–HexOH SIL dissolved 9.05 mg/g MWL, while the DBU–HexOH/HS–lignin , the ligCO2–MWL samples demonstrated a more significant mass loss, which is possibly due to the partial breakage of connections between lignin macromolecules after regeneration. The third stage was the carbonization stage (400~600 °C), where methoxy groups and C–C bonds of lignin were disrupted, with the release of volatiles and production of bio-oils [CO2-treated MWL produced lower char residue rates: 28.3% and 1.3%, respectively. This indicates the lower thermal stability of SDES–MWL and SDESCO2–MWL compared with that of the MWL sample. The thermal oxidative degradation and stability of the MWL before and after SDES treatments were investigated using the thermogravimetric analysis (TGA) . As showbio-oils . The decbio-oils . This cobio-oils . Once th−1 [−1 and 1507 cm−1 can be seen in all testing samples; this indicates that no significant change in the aromatic ring structure of MWL occurred during the SDES treatments [−1 was attributed to the C–H vibration in CH3-/CH2 = groups. The characteristic peaks at the wave number of 1461 cm−1 were assigned to the C–H asymmetric vibration of CH2 = groups and C–H transformation in the aromatic rings [–1; this is where signals attributable to C=O bonds in unconjugated ketones, carbonyls, and ester groups are normally observed. Lower absorption energies (around 1700 cm–1) were reported for conjugated aldehydes and carboxyl acids [–1, while SDES–MWL and SDESCO2–MWL demonstrated lower intensities within this range, indicating lower carbonyl and carboxyl group content. The peak at 1327 cm−1 was attributed to the stretching vibration of the C–O in the syringyl (S) lignin unit, whereas the peaks at 1270 cm−1 represent the stretching vibration of the C–O in the guaiacyl (G) lignin unit [−1 demonstrates the presence of syringyl moieties in lignin because it represents the C–H deformation of the lignin S unit. Therefore, a feature of GS-type lignin was demonstrated in the FTIR spectra. Additionally, the signal at 1036 cm−1, corresponding to the C–O deformation of primary –Ohs, was detected in all lignin fragments [Structural characterization of lignin before and after SDES treatment was performed using Fourier-transform infrared (FTIR) spectroscopy; the assignments of the characterizing peaks are shown in −1 . The streatments ,71. The yl acids . MWL pronin unit . The pearagments . The FTICO2–MWL was characterized using 2D HSQC NMR spectroscopy [C/δH 50–90/2.5–6.0) and the aromatic (δC/δH 100–135/5.5–8.5) regions of the lignin samples are shown in α (δC/δH 71.8/4.86 ppm), Aγ (δC/δH 59.5–59.7/3.40–3.63 ppm), A′γ (δC/δH 63.2/4.33–4.49 ppm), Aβ(G/H) (δC/δH 83.9/4.29 ppm), and Aβ(S) (δC/δH 85.9/4.12 ppm). β-β connections are shown for Bα (δC/δH 84.8/4.65 ppm), Bβ (δC/δH 53.5/3.06 ppm), and Bγ (δC/δH 71.0/3.82 and 4.18 ppm). β-5 connections are shown for Cα (δC/δH 86.8/5.46 ppm), Cβ (δC/δH 53.3/3.46 ppm), and Cγ (δC/δH 62.5/3.73 ppm). Moreover, methoxyl connections were found at δC/δH 55.6/3.73 ppm. The signals for the Cα–Hα, Cβ–Hβ, and Cγ–Hγ correlations of substructures (I) were seen clearly at δC/δH 128.4/6.44, 128.2/6.25, and 61.4/4.10, respectively. It was found that all the lignin samples exhibited similar spectral patterns. Three main types of linkages, namely, aromatic ether bond β-O-4, β-β in resin, and β-5 in phenylcoumarane [CO2–MWL produced β-O-4 linkages of 38.27/100Ar, β-β linkages of 3.37/100Ar, and β-5 linkages of 3.63/100Ar. The main linkages within lignin macromolecules, therefore, remained unchanged after the SDES and SDESCO2 treatments, which agrees with the FTIR results. In addition, all the lignin samples exhibited strong signals for methoxy moieties, indicating that the –OCH3 groups also remained unchanged after the SDES and SDESCO2 treatments. Detailed structural analysis of MWL and SDES–MWL and SDEStroscopy ,76; the 2,6–H2,6 correlations from S-type units and the sum of C2–H2, C5–H5, and C6–H6 correlations from G-type units were used to estimate the S/G ratio [C/δH 103.8/6.71 belonged to the S unit at C2,6–H2,6, whereas the G units showing the cross-peak signals for C2–H2 (δC/δH 110.9/6.98), C5–H5 (δC/δH 114.9/6.77), and C6–H6 (δC/δH 119.0/6.80) can be found in all the lignin samples. It can, therefore, be confirmed that the MWL used in this work was predominantly comprised of S and G units. The S/G ratios of MWL, SDES–MWL, and SDESCO2–MWL were 0.88, 0.73, and 0.76, respectively, indicating that the SDES and SDESCO2 treatments caused slightly more S-type lignin loss than G-type lignin loss. Other signals observed in the aromatic regions could be assigned to p-hydroxycinnamyl alcohol end groups (I), cinnamaldehyde end groups (J), and p-hydroxybenzoate substructures (PB). The C–H correlated signals for I and J were found at δC/δH 128.2/6.44 and 126.1/6.76 ppm, respectively. The C2,6–H2,6 correlations for PB were observed as a strong signal at δC/δH 131.2/7.67. These characterization results clearly demonstrate, again, that the lignin macromolecule framework remained intact following both the SDES and SDESCO2 treatments. In the aromatic region, the C/G ratio . A stron2. According to the thermodynamic molecular modeling results, the selected SIL was coupled with a hydrogen-bond donor (HBD) to form a switchable deep eutectic solvent (SDES) system, providing moderate viscosity and pH while maintaining its switchable nature. The DBU–HexOH SIL system resulted in a switchable lignin dissolution performance, which was regulated by charging and discharging CO2. The addition of water as an HBD improved lignin solubility in the SDES, while preserving its switchable nature. The DBU–HexOH SIL dissolved 9.05 mg/g MWL, while the DBU–HexOH/H2O SDES dissolved up to 213.35 mg/g MWL. Charging CO2 into the SDES further increased MWL solubility to 279.12 mg/g. The lignin dissolved in SDES systems can be regenerated using acetone as an anti-solvent. Both FTIR and HSQC results indicate that the SDES- and SDESCO2-treated lignin did not produce any notable chemical modifications compared to the MWL sample. Interestingly, the regenerated lignin treated by SDESCO2 produced a high molecular weight  and improved uniformity (PDI = 1.35). On the other hand, the original MWL was only 7701 g/mol Mw and 2959 g/mol Mn, with a PDI of 2.60. Therefore, the presented work proposed a notable SDES system, whose solvent properties can be regulated by the charging and discharging of CO2. Moreover, it can be used for lignin dissolution and extraction to produce a lignin stream with a well-preserved structure, high molecular weight, and improved uniformity, all of which may facilitate the downstream process.In this work, switchable ionic liquids (SILs) composed of DBU and different alcohols were screened, based on their solvent properties. DBU–HexOH was selected as the optimized SIL for its stable liquid state and notable switchable nature in conductivity, viscosity, and pH, created by the addition and removal of CO


Nucleotide-binding and leucine-rich repeat receptors (NLRs) are a diverse family of intracellular immune receptors that play crucial roles in recognizing and responding to pathogen invasion in plants. This review discusses the overall model of NLR activation and provides an in-depth analysis of the different NLR domains, including N-terminal executioner domains, the nucleotide-binding oligomerization domain (NOD) module, and the leucine-rich repeat (LRR) domain. Understanding the structure-function relationship of these domains is essential for developing effective strategies to improve plant disease resistance and agricultural productivity. Plants have an innate immune system that can effectively defend them against pathogens. This system depends on the activation of various immune receptors that recognize pathogen molecules, such as pathogen-secreted proteins called effectors. Effectors manipulate the host by either suppressing its immune responses or by promoting its nutrient supply to increase the pathogen's fitness. Some effectors function in the extracellular apoplastic space, while others are secreted within the host nucleocytoplasm .To combat pathogens and their diverse effector repertoire, plants have evolved a large array of intracellular immune receptors, which primarily belong to the nucleotide-binding and leucine-rich repeat receptor (NLR) family . Upon paR gene products, and CED-4) involved in intramolecular regulation of the NLR, a C-terminal leucine-rich repeat (LRR) domain involved in ligand binding and intramolecular regulation, and an N-terminal domain responsible for initiating downstream signalling. The most common N-terminal domains are either Toll/Interleukin-1 Receptor (TIR) domains in TIR-NLRs or one of three sequence-unrelated but structurally and functionally conserved coiled-coil domains: the Rx-type coiled-coil (CC)-type NLRs (CC-NLRs), RESISTANCE TO POWDERY MILDEW 8 (RPW8)-type CC-NLRs (CCR-NLRs), and the G10-type CC-NLRs (CCG10-NLRs) .,12.+ cleomodimer  15,16].,16.+ cles active . Insteads active . These ns active . These Ts active ,22. The echanism . The exaechanism . Regardl 3′cADPR , while H 3′cADPR . Both ef 3′cADPR ,24. This 3′cADPR   domain, resembling domains found in the N-terminus of mixed lineage kinase domain-like (MLKL) proteins, Casitas B-lineage lymphoma (Cbl) proteins, heterokaryon incompatibility (HET) proteins, and the HeLo domain (for HET-s/loss-of-pathogenicity (LOP-B) protein) found in fungi . Be. Be28]. 2+) permeable cation channels [2+ channel activity of certain, but not all, CC-NLRs [Both the ZAR1 resistosome and the Sr35 resistosome function as non-selective calcium  of CC-NLRs contains a conserved and acidic EDVID motif, named after the most prominent amino acid residues found at each position in the motif, and which interacts with basic arginine residues in the LRR to stabilize both the active and inactive form of CC-NLRs via several salt bridges . CCR-NLR channel ,37. Simirization ,38.R-NLRs are important for immune signalling, proper localization, and specific phospholipid binding likely determined through their surface exposure in the inactive conformation of the NLR [G10 domain shares a similar four-helix bundle with CC/CCR-NLRs. Some CCG10 domains possess an N-terminal extension, which can be modified by the covalent attachment of fatty acids and which is required for localization to the plasma membrane and immune signalling [G10-NLRs is essential for cell death-inducing activity, and this cell death-inducing activity can be blocked by Ca2+-channel inhibitors, suggesting CCG10-NLRs like CC/CCR-NLRs also act as calcium-permeable channels [The second (α2) and fourth α-helix (α4) of both CC- and CC the NLR . In the  the NLR . However the NLR . Similar the NLR . Structugnalling . As is tchannels ,43.Finally, non-flowering plant NLRs exhibit a greater diversity of N-terminal domains, including protein kinase domains and α/β hydrolase domains ,45. WhetThe NB-ARC domain is a highly conserved feature in plant NLRs, providing a basis for their classification and function . The NB-R149 within the NBD thereby exposing the oligomerization interface. This configuration enables the NBD-HD1 surface of a protomer to interact with the NBD-WHD surface of its neighbour. The major interactions involve an HD1-WHD interface and an NBD-NBD interface. ATP/dATP, which is nested in the P-loop and a pocket in the NBD, stabilizes the active conformation of ZAR1 through direct interactions of the gamma phosphate group of ATP with ZAR1R149 [R149 and the MHD motif for instance, have facilitated mutation-based inactivation and autoactivation of NLRs, respectively –RESISTANT TO P. SYRINGAE 4 (RPS4) TIR-NLR pairs, interact prior to activation . HoweverCED4 NLR , transieThe mechanism of pathogen detection by NLRs can be either direct or indirect. In direct recognition, NLR proteins recognize effectors through direct physical interaction without the need for additional host proteins. Indirect detection follows the guard and decoy models, where NLRs monitor the status of a host component, known as a guardee or decoy .Most plant NLRs possess an LRR domain that has a distinct horseshoe shape, which differs from the elongated and sometimes twisted shape of LRR domains found in many plant cell surface receptors. This distinct architectural difference could be the result of evolutionary constraints arising from the principle of ‘form follows function’, and these constraints could be a direct consequence of the NLR activation mechanism. Both direct and indirect ligand recognition by plant NLRs appear to employ an allosteric mechanism involving a ‘steric clash’ with the NBD . In thisThe RPP1 and Roq1 structures revealed a previously unobserved domain directly following the LRR domain termed the C-terminal jelly roll/Ig-like domain (C-JID) ,12. The Finally, plant NLRs can also possess other ligand-binding domains. For instance, the N-terminal late-blight resistance protein R1 domain  occurs exclusively with the NB-ARC domain and might be involved in ligand-binding ,58. AcroIn summary, the N-terminal executioner domains, the NOD module responsible for the switch between inactive and active states, and the ligand-binding domain that facilitates NLR activation all contribute to the dynamic and versatile nature of these immune receptors. A deeper understanding of these molecular mechanisms and how they evolved will pave the way for the development of novel strategies to enhance plant resistance against pathogens and improve agricultural productivity.Understanding the structure-function relationship of plant NLRs is essential for devising efficient strategies to enhance plant disease resistance and boost agricultural productivity.R-NLRs to create similar pores at the plasma membrane.Current understanding indicates that ligand-binding by the C-terminal LRR domain of plant NLRs induces structural rearrangements in the nucleotide-binding oligomerization domain (NOD) module, which results in resistosome formation and activation of the N-terminal executioner domains. CC-NLRs create non-selective calcium-permeable pores at the plasma membrane, while TIR-NLRs form enzymatically active resistosome complexes that indirectly activate CCFuture directions in the field will likely focus on increasing our ability to predict the specific contributions of individual amino acids to NLR function, obtaining structural information on resistosome complexes within a membrane context, and investigating the activation mechanisms of paired and networked NLRs.


Prescription drug abuse is a growing epidemic in the United States, and opioids are among the most commonly abused and misused controlled substances. Managed care organizations can use pharmacy lock-in programs to limit patients’ access to opioids by requiring that they receive all scripts from 1 prescriber, potentially reducing inappropriate use.To evaluate opioid use patterns among patients in a Medicaid managed care lock-in program limiting opioid coverage to prescriptions written by assigned prescribers.This retrospective cohort study included all patients enrolled in the lock-in program at Blue Care Network (BCN) of Michigan Medicaid managed care from March 2008 through May 2013, with outcomes assessed through August 2013. BCN medical and pharmacy claims, the Michigan Automated Prescription System, and Blue Cross Complete Controlled Substance Committee reports were used to assess outcomes at 6, 12, 24, and 36 months after enrollment. Patients were defined as “stable” if they exclusively filled opioid prescriptions from assigned prescribers or received treatment for opioid dependence and “unstable” if they purchased prescription opioids with cash or submitted opioid claims not prescribed by assigned providers.Of the 59 patients enrolled in the program, over half (55.9%) dropped BCN coverage, and 1 died while enrolled. The proportion of patients who dropped coverage fell as time in the program increased, from 29% in the first 6 months to 11% semiannually after 24 months. Among those who remained enrolled, the proportion of stable patients increased from 31% at 6 months to 78% at 36 months. The small sample size did not permit formal statistical analysis.The finding that most patients exited the program by dropping coverage was an unintended consequence meriting further investigation. Conversely, the finding that patients who remained enrolled largely achieved desired outcomes indicates that this program played an important role in addressing opioid abuse. What is already known about this subjectMost previous evaluations of Medicaid lock-in programs focused on program costs, demonstrating reduced costs among patients enrolled in the programs, but they did not evaluate clinical outcomes.The Centers for Medicare & Medicaid Services recommends that all state Medicaid programs implement lock-in programs to curb opioid abuse and misuse.What this study addsThis study demonstrated that the majority of patients enrolled in a Medicaid managed care lock-in program dropped Medicaid managed care coverage, but those who remained enrolled were more likely to achieve stable outcomes with increased time in the program.The majority of patients who dropped coverage did so within 6 months of entering the lock-in program.As length of time in the program increased, a larger proportion of patients decreased or stopped use of prescription opioids, maintained steady use under physician supervision, or entered maintenance replacement therapy.1 From 1999 to 2008, the increased prevalence of opioid-related deaths and admissions paralleled the increase in prescribing opioids for pain management.1 Providers have been prescribing opioids more extensively for pain control since the 1990s in response to changing practice guidelines for pain management and rising concerns about untreated pain. The majority of opioid-related morbidity and mortality in the United States results from patients taking opioids as prescribed, rather than from abuse and misuse.2Prescription drug abuse is a growing epidemic in the United States, with prescription drugs driving the rise in fatal drug overdoses over the past 2 decades. Opioids are among the most commonly abused and misused controlled substances. Opioids are a class of drugs that relieve pain and include morphine, oxycodone, methadone, hydrocodone, and other narcotic drugs. Nationwide, deaths from prescription opioid overdoses now exceed deaths from heroin and cocaine combined.1 In 2011, opioids other than heroin represented approximately 10% of all substance abuse treatment admissions nationally, up from 2% in 2001. Among adolescents, opioids other than heroin rose from 29% of national admissions for substance abuse treatment in 2001 to 66% in 2011.3 Approximately 488,000 emergency department (ED) visits involved nonmedical use of opioids in 2011, accounting for 29% of all ED visits related to prescription drug abuse and misuse. ED visits related to opioid abuse or misuse rose 183% from 2004 to 2011, compared with 132%for nonmedical use of pharmaceuticals overall.4 In Michigan, opioids other than heroin were the drug of choice for over 7,000 people admitted for substance abuse treatment in 2013, representing nearly 14% of all admissions that year.5From 2001 to 2008, overdose deaths, drug abuse treatment, and sales of prescription opioids increased considerably. In 2008, 14,800 people died of opioid overdoses, representing 73.8% of all deaths from prescription drug overdoses that year, nearly 4 times higher than the 1999 death rate.6 Direct medical costs are also higher among opioid abusers because of higher use of health care services and prescription drugs, as well as higher prevalence of comorbid conditions.7Effective strategies to curb opioid abuse and misuse could defray the cost of this epidemic to the U.S. health care system. The most recent economic analysis of prescription opioid abuse and misuse found that nonmedical use had a total economic burden of $53.4 billion, driven primarily by lost productivity and criminal justice expenditures.13 Payer-based approaches include formulary controls; provider-directed interventions such as education; feedback from prescription monitoring programs; screening and intervention programs; and Medicaid lock-in programs.9 Lock-in programs are a way to limit use by patients who are “doctor shopping,” or seeking multiple opioid prescriptions from different providers within a short period of time, often filling these prescriptions at multiple pharmacies. Nationally, an estimated 0.3% to 0.7% of opioid purchasers are doctor shoppers.15In response to this crisis, payers, providers, regulatory agencies, and manufacturers have adopted a variety of approaches to curb opioid abuse and misuse.19 The Centers for Medicare and Medicaid Services (CMS) recommends that all state Medicaid programs implement lock-in programs.20 Little is known, however, about the clinical outcomes of payer-based lock-in programs. The purpose of this study was to examine the opioid use patterns of patients enrolled in the Medicaid lock-in program of a large managed care organization for up to 36 months, to determine whether enrollment in the program was correlated with reductions in inappropriate use, such as uncoordinated prescribing and potential doctor-shopping behaviors.Lock-in programs, also called patient review and restriction programs, limit payment for opioids to prescriptions from a single prescriber and/or pharmacy. They are designed to prevent dangerously uncoordinated care and eliminate reimbursement for inappropriate or fraudulent opioid prescriptions. Most states run lock-in programs for Medicaid beneficiaries, and internal evaluations have consistently demonstrated cost savings.Blue Care Network (BCN) is the Blue Cross Blue Shield of Michigan managed care organization, and its administrative arm for Medicaid patients was Blue Cross Complete (BCC) during the time of this study. This study used BCN medical and pharmacy claims data, supplemented by BCC Controlled Substance Committee reports, which identified patients eligible for the lock-in program and evaluated their behavior in the program quarterly. Data on cash purchases of prescription opioids were drawn from the Michigan Automated Prescription System (MAPS). MAPS is the state’s prescription drug monitoring program, a database of all controlled substance purchases in the state, which documents the patient’s name, date of purchase, drug name and quantity, prescriber and pharmacy identification numbers, and method of payment. MAPS was reviewed quarterly for each patient in the lock-in program to determine whether they had obtained controlled substances outside of BCN Medicaid coverage by paying cash. The University of Michigan Institutional Review Board and BCN Internal Review and Confidentiality Committee reviewed this study and determined that it was exempt from ongoing review.Patients were eligible for enrollment if they filled ≥ 10 controlled substance prescriptions from ≥ 4 prescribers within a 3-month period. BCN chose this threshold because it may signal potential “doctor shopping” or dangerously uncoordinated care. The criteria were designed to avoid a large number of false positives but not to miss severe cases of abuse.Patients meeting these criteria were flagged in BCC Controlled Substance reports on a quarterly basis and were reviewed by a BCN pharmacist to determine whether the BCC Controlled Substance Committee should evaluate the case . The BCCThis retrospective cohort study describes the outcomes of all patients enrolled in the BCN lock-in program from March 2008 through May 2013. Patient outcomes were evaluated at 6, 12, 24, and 36 months after the date of enrollment in the program. Patients were followed through August 31, 2013, or until 1 of the following events occurred: completion of 36 months in the program, termination of BCN Managed Care coverage, death (recorded in the quarterly BCC Controlled Substance Committee reports), or loss to follow-up . No comparable control group was available for this population because all patients who met the lock-in program criteria were enrolled.Stable use was defined as opioid use only as prescribed by the assigned PCP , were 72.9% female, and had an average of 1.2 chronic conditions . The mosEndpoints experienced by patients enrolled in the Medicaid managed care lock-in program were distributed as follows: over half (54%) of the 59 enrollees dropped BCN coverage, and 1 patient (2%) died 1 month after enrollment in the program . All theAs time in the program increased, the proportion of patients who dropped coverage decreased. Within 6 months of enrollment, 29% of the 59 patients who entered the lock-in program had terminated BCN coverage, while an additional 2% (1 patient) had died . BetweenHowever, the proportion of patients with stable outcomes increased as time in the program increased. Six months after enrollment, only 18 of the original 59 patients (31%) remained enrolled and were stable. By comparison, 22 (37%) patients remained enrolled but were unstable because of cash purchases of prescription opioids or attempts to bill BCN for opioid prescriptions not written by their assigned provider. At 12 months, the proportion of stable patients rose to 50% (19 of the 38 patients enrolled for that duration), while the proportion of unstable patients declined to 26% (10 patients). The proportion of stable patients continued to increase with time in the program, to 58% at 24 months (14 of 24 patients) and 78% at 36 months (7 of 9 patients). Within each time interval, most stable patients decreased their use of prescription opioids or stopped use entirely.Few patients protested their enrollment in the lock-in program after remaining in the program for over 6 months. At 6 months, 19% of the patients protested their enrollment in the program. This proportion fell to 3% at 12 months after enrollment, rose slightly to 4% at 24 months , and declined to zero at 36 months post-index.In this evaluation of a payer-based lock-in program to curb opioid abuse in a Medicaid managed care program, patient outcomes were strongly related to the amount of time spent in the program. In the early months after enrollment, the most common outcome was disenrollment from the managed care program. Patients may have switched to a different Medicaid managed care plan, entered Medicaid fee-for-service, acquired private coverage, or become uninsured.The attrition rate for the study population was higher than that of the general population in each time interval, particularly within 6 months of enrollment in the program. General attrition rates were calculated using a cross-section of all individuals enrolled in BCC on March 1, 2010. Approximately 20% of the total population dropped BCC coverage within 6 months of this date, as did an additional 12% between 6 to 11 months. From 12 to 23 months, 18% dropped BCC coverage , and 15% dropped coverage from 24 to 36 months .Although attrition rates were high for patients enrolled in the lock-in program, the patients who remained in the program were more likely to demonstrate improved patterns of opioid use—cessation, reduction, steady use, or a switch to maintenance replacement therapy. Cessation and decreased use were positively correlated with increased time in the program. Longer periods in the program also corresponded with decreased likelihood of leaving Medicaid managed care coverage or of maintaining coverage while protesting enrollment in the program. Among patients who remained in the program, the lock-in program may have succeeded in its goal of eliminating uncoordinated opioid prescribing among providers.21 For example, Oklahoma’s SoonerCare pharmacy lock-in program reported $31,500 in savings in reduced opioid prescriptions in 2009, averaging $600 per patient, without impacting patients’ use of maintenance medications.19 One previous article outlined payer-based interventions to address opioid abuse, and another assessed an intervention in which a payer supplied providers with information on specific patients with problematic opioid use.13 A third article evaluated pharmacists’ experiences in implementing the Medicaid lock-in program in North Carolina.22Most previous evaluations of lock-in programs have focused on program costs, demonstrating reduced costs among patients enrolled in the programs, and few of these evaluations were published in peer-reviewed journals.23To our knowledge, this is the first evaluation of opioid use patterns and participation over time among patients in a lockin program in the academic literature. The prevalence of back pain and headaches in our study population aligns with a 2010 study on opioid trends and risks, which found that individuals with these chronic conditions were more likely to abuse or become dependent on opioids, as were individuals with prior mental health and substance abuse diagnoses. The same study found that individuals aged between 31 and 40 years were at even higher risk for opioid abuse or misuse than those with prior mental health or substance abuse diagnoses.This study has several limitations, including the small numbers of lock-in program enrollees, the lack of a control group, and inclusion of patients from a single Medicaid managed care organization. For these reasons, the study’s findings may not be generalizable to other lock-in programs. While the lack of a control group prevents direct comparison of our patients’ propensity to leave the managed care program with other patients, the attrition rate after enrollment in the program exceeds that of the general population. Patients who were more likely to continue unstable opioid use may also have been more likely to terminate BCC coverage in order to leave the lock-in program. Conversely, patients who were more likely to adopt more appropriate opioid use may have been more likely to remain enrolled. This is a major confounding factor in the correlation between patients’ time in the program and decision to terminate coverage because patients who remained enrolled over time may have been those least likely to terminate coverage to begin with.Additional limitations include the lack of data on prescription drug use outside Michigan or illegal purchases of controlled substances, which could have resulted in misclassification of some individuals as having improved opioid use patterns. Additionally, our dataset did not include medications delivered during hospitalization or ED visits, which may also have undercounted some patients’ opioid use.20 In addition to this payer-based approach, other approaches to opioid abuse should continue, including provider-directed programs to improve physician prescribing and clinical practices,13 and regulatory approaches controlling prescribing practices and reporting requirements.12This study is the first to examine patient outcomes in a lock-in program for opioids, adding to the evidence on payer-based interventions to address prescription drug misuse and abuse. The apparent effect of promoting exit from managed care by patients early in enrollment in the lock-in program, if verified in future studies, is an unintended consequence of the program that merits investigation. Conversely, the apparent achievement of desired outcomes among patients who remained in the program suggests that lock-in programs play an important, albeit limited, role in addressing the opioid abuse epidemic. Our findings support the CMS recommendation that all state Medicaid programs implement lock-in programs.


Immunoglobulin M (IgM) is the largest antibody isotype with unique features like extensive glycosylation and oligomerization. Major hurdles in characterizing its properties are difficulties in the production of well-defined multimers. Here we report the expression of two SARS-CoV-2 neutralizing monoclonal antibodies in glycoengineered plants. Isotype switch from IgG1 to IgM resulted in the production of IgMs, composed of 21 human protein subunits correctly assembled into pentamers. All four recombinant monoclonal antibodies carried a highly reproducible human-type N-glycosylation profile, with a single dominant N-glycan species at each glycosite. Both pentameric IgMs exhibited increased antigen binding and virus neutralization potency, up to 390-fold, compared to the parental IgG1. Collectively, the results may impact on the future design of vaccines, diagnostics and antibody-based therapies and emphasize the versatile use of plants for the expression of highly complex human proteins with targeted posttranslational modifications. Immunoglobulin M (IgM) is the largest antibody (Ab) isotype that is produced by the immune system of vertebrates. It can bind to a wide variety of pathogens and antigens (Ag) and is an essential part of the immune response. Comprehensive serological profiling in the course of diverse viral infections, including SARS-CoV-2, revealed a discerning appearance of various Ab iso- and subtypes, with increased levels of IgM, IgA, IgG1, and IgG3 . The oveUnderstanding the isotype-dependent properties requires the characterization of monoclonal antibodies (mAbs) of various isotypes against a defined antigen/epitope. However, the isolation of pathogen-specific monoclonal IgM is challenging due to low serum abundance, rapid decline as disease progress and class switch to various isotypes . Early sWhile recombinant proteins have been expressed in whole plants for almost three decades this platform is increasingly being recognized for the expression of complex human proteins, including antibodies in higher molecular forms , 16. EspHere we report the plant-based recombinant expression of two anti-SARS-CoV-2 mAb isotypes that share the same antigen-binding fragments (Fab). The original IgG1 mAbs with substantially different Ag-binding and virus NT properties were isotype-switched to IgM and produced as multimers. Biochemical and functional features of the IgG1 monomers and respective IgM pentamers, consisting of ten heavy and light chains  plus one joining chain (JC), were investigated. We demonstrate the correct assembly of recombinant mAbs and reveal superior Ag-binding and NT activities for the pentameric IgM isotypes when compared with their IgG1 orthologues. In addition, we elucidate the detailed N-glycosylation status of IgG1 and IgM mAbs by MS-based glycosite-specific profiling.P5C3-HCFv (369 bp), H4-HCFv (379 bp); P5C3-LCFv (324 bp), H4-LCFv (339 bp)) were grafted onto magnICON® vectors containing constant domains of human IgG1 HC, IgM HC and κLC by using BsaI restriction sites (P5C3-IgG1-HC (1362 bp), H4-IgG1-HC (1371 bp) P5C3-IgM-HC (1731 bp), H4-IgM-HC (1741 bp) and the light chains P5C3-κLC (648 bp) and H4-κLC (663 bp). All vectors carry a barley α-amylase signal sequence for peptide secretion. Distribution among two compatible magnICON® plasmids was as follows: pICH26211: P5C3-IgG1, H4-IgG1, P5C3-IgM, H4-IgM; pICH31160: P5C3-IgGLC and H4-IgGLC  were grg the JC  was co-d2HPO4, 1.8 mM KH2PO4; pH 7.4). Washing was done with 20 CV PBS. Antibodies were eluted in 1 mL fractions with 0.1 M Glycine/HCl (pH 3.0), eluates were immediately neutralized with 1 M Tris (pH 9.0) and dialyzed overnight against PBS.Recombinant IgG1 was purified by affinity chromatography using protein A , IgM purification was performed by POROS™ CaptureSelect™ IgM Affinity Matrix . TSP extracts were loaded at a flow rate of 1.5 mL/min on a manually packed column which was pre-equilibrated with 10 column volumes (CV) PBS  detector RID 20A. MALS data was acquired using a miniDAWN treos detector . LabSolution Software (Shimadzu) and ASTRA V software were used for data collection. The samples were analyzed by using a dn/dc value of 0.185 mL/g as input for the MW calculation. MALS detector calibration was performed using BSA monomer (Merck).Monomeric and pentameric IgMs were separated by SEC. A Superose 6 Increase 10/300 GL, 10 mm i.d. × 300 mm column length  column was used for the SEC experiments. Mobile-phase flow rate was set at 0.75 mL/min. A 60 min isocratic analysis was performed using PBS containing 0.2 M sodium chloride as mobile phase. For analysis 210-240 µg protein at about 1 mg/ml was loaded. Column calibration was performed with a set of molecular mass standards ranging from 1.3 to 670 kDa (Bio‐RAD).The fractions corresponding to the monomeric IgG1 and mono- and pentameric IgM were collected and concentrated with Amicon centrifugal filters, MWCO 10,000 kDa . SDS-PAGE analyses were performed in 12% gels under conditions. Gels were stained with Coomassie Brilliant Blue R 250 staining (Carl Roth GmbH + Co. KG). Concentrations were determined by spectrophotometer . All purifications were performed at 4°C.http://www.functionalglycomics.org) was used.The N-glycosylation profiles of the purified Abs were determined by mass spectrometry (MS) as described previously , 26. BriFor peptide mapping the files were analysed using PEAKS , which is suitable for performing MS/MS ion searches.2SO4 after 5-7 minutes incubation. Absorbance was measured at 450 nm (reference 620 nm) using a Tecan Spark® spectrophotometer. All samples were analyzed at least in two technical replicates. EC50 values were calculated by non-linear regression of the blank-corrected data points based on a four-parametric log model with GraphPad Prism (version 9).Purified mAbs were diluted with PBS to 0.5 µg/mL (P5C3) and 2.0 µg/mL (H4). Certain mAbs were loaded with 50 µL/well to 96 well microplates  and incubated overnight After three washes with PBS-T (PBS with 0.05% Tween 20), the plates were blocked with 3% fat free milk powder, dissolved in PBS-T, for 1.5 h at RT. Recombinant RBD  was dil® spectrophotometer. All samples were analyzed at least in two technical replicates. EC50 values were calculated by non-linear regression of the blank-corrected data points based on a four-parametric log model (GraphPad Prism version 9).To perform avidity assay, 96 well Microplates  were coated with 50 µL/well of 2 µg/mL recombinant RBD , and in50), normalized data were used for nonlinear regression analysis with variable slopes (GraphPad PRISM Version 9). All experimental procedures involving SARS-CoV-2 were performed in a BSL-3 laboratory.Neutralization assays were performed according to . BrieflyNicotiana benthamiana line ΔXTFT  at the HC and one at the JC. Overall, N-glycans account for more than 10% of the mAb’s molecular mass, with significant functional impacts , 33. GS In order to determine the N-glycosylation status of ΔXTFT derived H4 and P5C3 mAbs, liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS) was performed. MS spectra of H4- and P5C3-IgG1 displayed a single dominant glycoform at the Fc GS, namely xylose and core fucose-free GlcNAc-terminated structures (predominantly GnGn), accompanied by ∼8% mannosidic structures, as typical for ΔXTFT-produced IgGs  Figure, 38. TheCollectively, multiple batches of P5C3 and H4 IgM were generated. Batch to batch variation of expression levels is between 10-15%. No differences in glycan profiles or pentamer formation were observed. However, not surprisingly both Abs have their specific expression profile, i.e. P5C3 expression level is higher than H4 IgM; pentamer formation of P5C3-IgM is higher than H4-IgM.50 106 pM for P5C3 and 1195 pM for H4). This feature also translated to the monomeric IgM-M . In accordance, similar antigen-binding activities were observed when comparing corresponding IgG1 and IgM-M forms, i.e., P5C3-IgG1 versus IgM-M; and H4-IgG1 versus IgM-M . Collectively, our results demonstrate the plant-based expression of glyco-engineered and isotype-switched functionally active P5C3 and H4 mAbs.Functional activity of anti-SARS-CoV-2 mAb variants were determined by antigen-binding assays. Direct sandwich ELISAs using SARS-CoV-2 spike protein RBD (Wuhan strain) as antigen and HRPO-labeled mAb CR3022 as secondary antibody was performed. Coating plates with target Abs directs the antigen to bind in a specific orientation, in contrast to direct coating of antigens that would result in random orientation, thereby reducing consistency. CR3022, initially developed against SARS-CoV, broadly detects SARS-related corona viruses  and doesELISA with RBD coating was done and the binding of the Abs at similar molar concentrations was compared. For the high affinity Ab P5C3, a four-fold increase of IgM versus IgG1 EC50 values was observed and a two-fold enhancement for the low affinity H4 Abs  and H4-IgM-P a ∼390 times increase (IC50 13 pM). Note, the remarkable high neutralization potency of P5C3-IgM-P that results in extremely low IC50 values might skew the results and it could well be that the activity difference of IgG1 to IgM-P is considerably higher for that mAb. We observed a remarkably high neutralization potency of P5C3 and obtained an IC50 concentration as low as 3 pM. Nevertheless, the difference of several orders in magnitude of the IC50 concentrations may limit a direct comparison of the calculated values.To determine neutralization activities of P5C3 and H4 Abs, Vero cell–based SARS-CoV-2 plaque reduction assays according to  were perAs higher eukaryotes, plants carry the intrinsic cellular machinery for the recombinant expression of authentic human proteins that is normally restricted to mammalian  cells. Moreover, the rapid expression, flexibility in modulation of post translational modification and simple handling has placed plants into a favorable position. Thus, this system is well suited to investigate IgM features.Collectively, the properties of IgM are only beginning to be explored in depth, which partly is due to the challenges in the generation of pentamers. Using a plant-based approach, we generated highly pure IgM pentamers. This was achieved by the simultaneous delivery of three expression constructs  to plants and a subsequent two-step purification procedure.While the production of IgG1 has been demonstrated in multiple cases using plant-based expression systems, only a few studies report the generation of more difficult to express Abs like IgG3 and dimeric IgA , 40–42. in vitro and in vivo, e.g. .The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GPST000323 (SK: conceptualization; data curation, formal analysis, investigation, supervision. RP: data curation, formal analysis, investigation. EF-H: data curation, formal analysis, investigation. LS: data curation, formal analysis. QC: data curation, provided material. FP: data curation, formal analysis. LE: data curation, formal analysis. RS: data curation, provided material. KZ: data curation, supervision, funding acquisition. HS: conceptualization, investigation, supervision, funding acquisition. All: writing–original draft; writing–review and editing. All authors contributed to the article and approved the submitted version.