Adherence to healthy dietary patterns can prevent the development of non-communicable diseases and affect life expectancy. Here, using a prospective population-based cohort data from the UK Biobank, we show that sustained dietary change from unhealthy dietary patterns to the Eatwell Guide dietary recommendations is associated with 8.9 and 8.6 years gain in life expectancy for 40-year-old males and females, respectively. In the same population, sustained dietary change from unhealthy to longevity-associated dietary patterns is associated with 10.8 and 10.4 years gain in life expectancy in males and females, respectively. The largest gains are obtained from consuming more whole grains, nuts and fruits and less sugar-sweetened beverages and processed meats. Understanding the contribution of sustained dietary changes to life expectancy can provide guidance for the development of health policies. The UK Government aims to reduce premature deaths from non-communicable diseases by one-third by 2030 (SDG 3). Using life table methodology and data from the UK Biobank, this study indicates that sustained dietary changes—from median or unhealthy to a longevity-associated dietary pattern or to the Eatwell Guide recommendations—could lead to substantial life expectancy gains. Evidence on the mortality benefits of food choices is essential for the United Kingdom to achieve Sustainable Development Goal target 3.4, which is to reduce premature mortality from non-communicable diseases by one-third by 2030 (ref. 2) Internationally, the Global Burden of Diseases and Injuries consortium and the EAT–Lancet commission encourage healthy eating and quantify the population health that is associated with unhealthy eating5. Furthermore, Public Health England and the UK Government encourage the population to follow the diet pattern recommended in the Eatwell Guide to achieve a healthy and balanced diet6.In the United Kingdom, unhealthy diets are estimated to cause more than 75,000 premature deaths each year, including almost 17,000 deaths in the age group 15–70 years7, but it is not known how a sustained improvement in dietary patterns translates into gains in life expectancy at different stages of life. Estimating such gains in life expectancy would provide policymakers with a measure of the health gains that are possible in a population and provide guidance on which policies would be the most effective. Furthermore, health personnel would also benefit in identifying key risks related to unhealthy dietary patterns with the highest potential for gain when guiding people to prioritize relevant behaviour changes. In addition, most people do not adhere to healthy eating guidelines8, with research showing that less than 0.1% of the UK population adheres to all recommendations of the Eatwell Guide7. Therefore, it is important to estimate the gains in life expectancy that are expected from different types of dietary change and various degrees of adherence to the recommendations.Life expectancy is a measure of expected years an individual has left to live and is a commonly used metric for population health. Higher adherence to the recommendations of the Eatwell Guide is associated with reduced mortality in the United Kingdom9. In this paper, we use this model to estimate life expectancy gains from a sustained change from median or unhealthy dietary patterns in the United Kingdom to the longevity-associated dietary pattern, or to the recommendations of the Eatwell Guide.Recently, we developed a model for estimating age- and sex-specific gains or losses in life expectancy following sustained change  in the consumption of major food groups with measured intakesThe median dietary patterns in the United Kingdom were categorized as the intake level with the mid-quintile intake of each of the food groups from the UK Biobank data, from 467,354 participants. The longevity-associated dietary patterns were the quintiles for each food group with the lowest mortality risk estimates or second lowest if the confidence intervals were very wide Fig. . For theOur results showed that the longevity-associated dietary pattern had moderate intakes of whole grains, fruit, fish and white meat; a high intake of milk and dairy, vegetables, nuts and legumes; a relatively low intake of eggs, red meat and sugar-sweetened beverages; and a low intake of refined grains and processed meat Fig. . AnalyseWe present life expectancy estimates with uncertainty intervals  associated with various dietary patterns in Fig. Estimated gains from simulated sustained dietary changes from an unhealthy UK diet pattern to full adherence to the Eatwell Guide were 8.6 years (UI 6.8–10.2 years) for 40-year-old females and 8.9 years (UI 7.2–10.8 years) for 40-year-old males. Corresponding gains for 70-year-old females and males were 4.4 years (UI 3.6–5.4 years) and 4.0 years (UI 3.4–5.1 years), respectively.In sensitivity analyses, sex-stratified analyses of food groups and associations with mortality generally showed similar associations across the sexes except for white meat, which seemed to be more beneficial among females. A range of other sensitivity analyses are presented in In this paper, we present a method for estimating changes in life expectancy following changes in food choices, considering correlation between mortality and food group intakes, and effect delay. Such estimates may be useful particularly for policy purposes and for underpinning both guidance and interventions for improving public health. Our results indicate that UK adults aged 40 years with median dietary patterns can expect to gain approximately 3 years in life expectancy from sustained changes to the longevity-associated dietary patterns. Importantly, the estimated gain in life expectancy is approximately a decade for those shifting from the unhealthiest to the longevity-associated dietary patterns. Overall, the bigger the changes made towards healthier dietary patterns, the larger the expected gains in life expectancy are.15. Our estimates from the UK Biobank are also strengthened by meta-analyses of randomized trials on the consumption of various food groups and scoring of biomarkers for disease16, mirroring our estimates with nuts, legumes and whole grains performing most beneficially and sugar-sweetened beverages and red meat performing worst. We have also presented estimated health gains associated with adhering to the Eatwell Guide recommendations, showing that the Eatwell Guide does well on the longevity perspective. The life expectancy gains from changes from unhealthy eating to the Eatwell Guide achieve 82–83% of the potential compared with those from sustained change to longevity-associated patterns, which strengthens the evidence base for the promotion of the dietary targets in the Eatwell Guide in public health guidance and interventions. The Eatwell Guide might also be a more realistic target for dietary changes than the statistically set longevity-associated dietary patterns.Consuming less sugar-sweetened beverages and processed meats and eating more whole grains and nuts were estimated to result in the biggest improvements in life expectancy. Sensitivity analysis also adjusting for body mass index and energy consumption indicated that body mass index and energy consumption might partially mediate and/or confound a possible beneficial effect between life expectancy and whole grains, vegetables and fruits, and inversely for red meat and eggs. For white meat, associations were stronger when adjusting for energy intake and body mass index, while the situation was mixed for legumes. These estimates correspond well with meta-analyses on associations between intakes of food groups and mortality17). Life expectancies have steadily increased over time18, and the observed increase is parallel to the changes in life expectancy observed in the United Kingdom over the past 15 years17. A large shift towards healthy dietary patterns could contribute substantially to meeting Sustainable Development Goal target 3.4 that aims to cut premature mortality by one-third.Unsurprisingly, predicted gains in life expectancy are lower when the dietary change is initiated at older ages, but these remain substantial. For example, we estimated that people at the age of 70 years could expect to benefit from about half of the life expectancy gain predicted for adults at the age of 40 years, equivalent to a gain in 1.5 years when optimizing median dietary patterns and 4–5 years for those shifting from the unhealthiest dietary patterns. The UK population currently has a life expectancy at birth of 83.6 years for females and 79.9 years for males, and a 3 year gain in life expectancy associated with changes from median to longevity-optimized dietary patterns from the age of 40 years . Thus, our estimates could be useful also in clinical settings. We model sustained and prolonged dietary changes. However, maintaining lifestyle changes over time with dietary improvements can be challenging, and for many, dietary patterns fluctuate over time. We have not modelled potential changes in life expectancy of fluctuating changes. Furthermore, unhealthy and longevity-associated dietary patterns could be seen as dietary ‘constructs’ . This is not necessarily overlapping with typically presented dietary patterns such as the Mediterranean or the Nordic diets. Furthermore, the UK Biobank does not measure consumption of rice, which is particularly important for many migrant groups. Overall, the UK Biobank data under-represent non-white populations compared to the UK population. Even though the UK Biobank data contain about nearly half a million participants in our analyses, the size of the data is not sufficient to achieve precise estimates across all quintiles and there seems to be some random fluctuations between some of the quintiles. Thus, the exact quintile threshold should be interpreted with caution, and more emphasis should be placed on the general trends. There were limited and selective data in the dietary recall that could result in biases.In conclusion, for middle-aged adults in the United Kingdom, sustained dietary improvement is predicted to increase life expectancy by about 3 years for both females and males. Importantly, for those with the least healthy dietary patterns, change to the longevity-associated dietary pattern is predicted to translate into approximately 10 years gain in life expectancy. Changing from an unhealthy dietary pattern to eating in line with the Eatwell Guide was associated with an 8 year life expectancy gain. Gains in life expectancy are lower the longer the delay in the initiation of dietary improvements, but even for those initiating dietary change at age 70 years, the gain in life expectancy is about half of that achieved by 40-year-old adults. The biggest gains in life expectancy are associated with increased intake of whole grains and nuts, and with reduced intake of sugar-sweetened beverages and processed meats. Our findings suggest that these food groups should be specific targets for clinicians in the guidance of patients and policymakers in developing public health policy.The study complies with all relevant ethical regulations . All data included are from participants who provided written informed consent for use of their data. Detailed methods beyond the shortened description in this Brief Communication are provided in Further information on research design is available in the Supplementary InformationSupplementary text, code, Figs. 1–11, Tables 1–9 and TRIPOD checklist.Reporting Summary


Tooth wear causes irreversible cumulated surface loss and already occurs at a young age. Therefore, the objective of this clinical prospective observational study was to monitor the occlusal surface of a mandibular first molar in young adults for a period of 24 months. Furthermore, potential aetiological factors obtained by a questionnaire were considered.p < 0.05).The study teeth (FDI #36 or #46) of 81 participants (mean age 22.8 ± 2.2 years) were scanned with the intraoral scanner  at the second follow-up (T2) after an observation period of 24 months. Standard-tessellation-language datasets were superimposed with baseline (T0) and T2 scans in 3D analysis software (GOM Inspect). The maximum vertical substance loss was measured between T0 and T2 at 6/7 areas (4/5 cusps and 2 ridges) of each study tooth and data compared to the already published data of the first follow-up (T1) after 12-month observation period. The morphology of tooth wear was classified into three groups: cupping (C), facet (F) and combined cupping-facet (CF). The analysis of aetiological factors, such as acid impacts, was based on a questionnaire filled out by participants at time points T0, T1 and T2. Non-parametric tests were used for statistical analysis  were most affected by tooth wear. Loss values increased significantly at T2 compared to T1 for all areas, although significantly less than in the first 12 months (T0–T1). Areas that already exhibited F at T0 mostly displayed them also at T2 and only rarely developed further into C or CF. The only association between aetiological factors and loss values could be detected for sex as males had significantly higher loss values than females.Progression of tooth wear could be clearly shown with high interindividual variations in loss values among participants. This indicates the need for individual monitoring with IOS.IOSs show the potential for patient-specific monitoring to detect the progression of tooth wear. Thus, data of 24 months fills the gap of tooth wear data for young adults in literature. Further studies over a longer observation period are highly recommended to gain more information about the dynamic of tooth wear and aetiological factors. Tooth wear is defined as surface loss of mineralized tooth substance due to physical or chemophysical processes . The proThe data on the prevalence of tooth wear of different aetiologies in younger age groups varies. Little data has been published on the prevalence of dental hard tissue loss due to attrition though it seems to affect almost all individuals  and is aIn addition to the prevalence, the incidence and progression of tooth wear is of interest, in particular in the younger age groups. Already at this age, an increase of wear by attrition , 8 but a3 of dental hard tissue per square millimeter of tooth surface at the incisal edges of the upper anterior teeth and the occlusal surfaces of the mandibular first molars within 1 year. Latter are the most common teeth to exhibit these lesions and show the most pronounced progression rates [Today, it is possible to quantify and monitor the in vivo progression of tooth wear in the submillimeter range by superimposing serial digital models –14. Compon rates , 16–18. Previously published first data  from cliTherefore, a continuation of this study to collect further data on wear dynamics was sought. The question to be answered is to what extent tooth wear is progressing and whether its morphology is changing. Furthermore, the intention was to investigate whether the biological, nutrition-related and behavioural data, which could not associate with tooth wear during the first observation period, could be identified as influencing factors after a longer monitoring time.The present clinical prospective observational study assessed the wear process on the occlusal surfaces of mandibular first molars in young adults. The primary aim was to collect qualitative and quantitative data on the wear processes in the natural dentition of young subjects. The secondary aim was to use a specifically designed questionnaire to explore the impact of various aetiological factors, as well as to relate the data regarding quantitative loss to the assumed aetiological factors obtained from the questionnaires. The study explores the use of IOSs as an innovative tool for the monitoring of tooth wear after a 24-month observation period.The study was conducted at the Department of Prosthodontics and the Department of Conservative and Preventive Dentistry of the Justus Liebig University Giessen (Germany) in compliance with the ethical guidelines of the Declaration of Helsinki. The clinical trial was approved by the local ethics committee of the Justus Liebig University Giessen (ref. no. 148/18) and registered in the German Clinical Trial Register (DRKS00021279). All participants were informed about the procedure and background before the start of the study. Furthermore, a declaration of consent has been signed. The observation period was between the end of 2018 and 2021.The present study continues the monitoring of wear and follows up on the data from Schlenz et al. . For thiThe study tooth was a first lower molar (FDI #36 or #46). Inclusion criteria were age between 18 and 25 years at baseline (T0), a lower first molar without caries or visible plaque and with occluding antagonists. Fillings should not exceed one-third of the occlusal surface. Exclusion criteria were severe general disease , currentDetailed information about the clinical examination, measurement of tooth wear and questionnaire is available in 12-month publication . In brien = 109). The mean observation period for T0–T1 was 373 ± 19 days and for T0–T2 752 ± 51 days. Intraoral scans were captured with Trios 3  at T0, after 12-month follow-up (T1) and after 24-month follow-up (T2) by the same operator (M.A.S.) , all study participants were aged between 18 and 25 years . First, scan data was cropped to the area of interest, which was defined as the occlusal surface above the anatomical equator of the study tooth defined as the equatorial line that separates the tooth in retentive and non-retentive areas. Then, datasets of T2 were superimposed to the pre-existing datasets of T0 of the previous study using the established best-fit alignment with the iterative closest point technique , 25. In The measurement of maximum vertical tissue loss was conducted in six respectively seven areas of the occlusal surface Fig. : mesiobuAdditionally to the intraoral scanning procedure, all participants were asked to fill out a questionnaire, which was designed in cooperation with an experienced scientific nutritionist (A. J.) . PotentiStatistical analyses were performed using IBM SPSS Statistics version 27 . Loss values showed significant deviations from the Gaussian distribution (Kolmogorov-Smirnov test); therefore, non-parametric test procedures were generally used. Values are presented as median and 95% confidence intervals obtained by bootstrapping . The comparison of loss values at T1 and T2 was done using the Wilcoxon test and the wear rates from T0 to T1 and from T0 to T2 were correlated using Spearman’s rho. The morphology of wear at T0 and T2 is given descriptively with cross-tabulations. Kruskal-Wallis test and subsequent Mann-Whitney tests were used to investigate whether the loss values differed according to the wear morphology.U test and the relation of loss values to taste preferences with the Kruskal-Wallis test. Furthermore, the subjects were divided into those with C or CF and those with no morphological changes or F. The question of whether wearing a night guard, the consumption of chewing gum or taste preferences of these two groups differed was examined with cross-tabulations and Cramer’s phi, and whether the acid impacts of these two groups differed was examined with the Mann-Whitney-U test. Heartburn was reported rarely (n = 6); hence, it was not included in the statistical analysis. Loss values of the mesiobuccal area and acid impacts were correlated with Spearman’s rho. The level of significance was set to p < 0.05.In order to identify factors that might influence wear, the focus was on a region exhibiting the highest tissue loss values in the present study as well as the greatest morphological variability already at baseline; this was the mesiobuccal cusp area, which was therefore defined as a region of particular interest. The relation of loss values to sex, as well as to the questionnaire items “wearing a night guard” and “consumption of chewing gum” was examined with the Mann-Whitney-n = 24 dropped out due to relocation, and n = 4 were excluded because of restorative therapy on study tooth , data from 81 participants could be analysed, because out of the initially 109 participants, Table Overall, an increase in macroscopically visible wear was observed, whereby areas without wear at T0 mainly developed facets. Areas that already had F at T0 mostly displayed them also at T2 and only rarely developed further into combined cupping-facet.p-values ≤ 0.001), although significantly less than in the first 12 months (T0–T1) of monitoring . Loss values in the second observation period (T1–T2) were mb 15.0 , ml 8.0 , db 13.0 , dl 10.0 , d 3.0 , mr and dr: 0 . Loss values from T0–T1 did not correlate with those from T1–T2, except for d (r = 0.314: p ≤ 0.001) and dr .Loss values increased significantly at T2 compared to T1 for all areas . All other factors were not significantly associated with loss values.The relation of loss values to possible associated aetiological factors is presented in Table p = 0.654), consumption of chewing gum  or taste preferences .Also, when looking at the two groups (those who had cupping and combined cupping-facet compared to those who had no wear or facets), there was no significant difference in wearing a night guard  and 57 μm (mb) in the overall study period. This is less than data from an older study  on the wThe wear rate in the present study was not linear but decreased in the second year of follow-up. Similar was also shown in a longitudinal study on wear after the insertion of ceramic restorations . In thisA non-linear tissue loss was also found in the study mentioned above . In this2 for anterior teeth and premolars to 3.4 mm2 for molars, with a considerable interindividual range. The increase in facet area was associated with a few orthodontic classifications for anterior teeth and premolars, but not for molars. Self-reported grinding or clenching did not have a significant effect on the increase in facet size for any group of teeth supporting the present finding that subjects with facets in the mesiobuccal area did not differ from those with cuppings or combined cupping-facets regarding wearing a night guard.As in the first year, the wear rate of combined cupping-facets was higher than that of other wear morphologies, so this defect shape may perhaps be considered a risk indicator for increased wear rates. For facets, lower rates of progression were found, but little is known about how facets proceed. Similar to wear in general, facets have mostly been investigated with clinical indices, while quantitative measurements have rarely been carried out. A Dutch study  comparedCuppings are generally considered to be a sign of erosive tooth wear , but theThe present data could not show this association either, although different ways to characterize the nutrition behavior of study participants were used. Thus, instead of individual food- or beverage-related items combined overall acid impacts were analysed. Furthermore, not only the latest acid impacts at the end of the 2-year observation time but also the mean value from three survey points  with the loss values were evaluated. In addition, participants were asked for taste preferences expecting a more comprehensive and representative picture of nutrition behavior overall compared to one-time surveys and queries of individual items. Nevertheless, the findings did not show a correlation between loss values and acid impacts as well as taste preferences.One explanation could be that questionnaires are in general only partially suitable for recording nutrition behavior. For example, the analysis of data from the nutrition questionnaires from T0 and T1  showed tOn the other hand, nutrition habits may be highly variable, especially in younger adulthood , so therWhat has to be considered as well is that cuppings may not be a characteristic sign or result of erosion. Wear morphologies associated with concavities are common in historical and prehistoric individuals  and the Furthermore, it is not yet clear how such morphologies develop. Even though the observation period of 24 months is relatively short, the first indications that a facet evolves first before a cupping forms were shown. Until now, it was assumed that cuppings develop when dentin is exposed with increasing wear, which then wears out faster than the enamel due to its lower microhardness and thus forms a cupping . At leasHowever, other factors that have not been in the focus so far may also play a role. For example, in a study of the macrowear patterns of pre-industrial and industrial burial remains, it was reconstructed that food properties may influence chewing behavior and that there is a biomechanical feedback loop between food texture, chewing behavior and wear . Even thAnother factor could be the bite force, which seems to be higher in males than in females  and whicThere could also be a genetic disposition for wear –44. EpidTaken together, all this suggests that tooth wear appears to be an extremely complex process in which many more factors may play a role than have been investigated so far. It might therefore be worthwhile to rethink both the theoretical constructs of tooth wear as well as to improve the research methods for investigating wear and its triggering factors.Even though the association between tooth wear and the assumed aetiological factors is not entirely clear, a few principles can be derived that could be helpful for the prevention of tooth wear . Since aAs a limitation of the present study setup, the sole analysis of the occlusal surface of a single mandibular first molar has to be discussed. The authors are aware that tooth wear can affect all teeth in the dental arch and different surfaces. However, at the beginning of the study in 2018 transfer inaccuracies of full-arch impressions for IOS have been described and are even today challenging , 51. FurObservation data of tooth wear show a further progression after 24 months, which was, however, less than in the previous 12 months. Males had significantly higher wear rates than females, but neither nutrition habits nor the wearing of a night guard nor the consumption of chewing gum were related to loss values. Morphologically, there were mainly facets and only a small number of cuppings or combined facets/cuppings, which were located almost exclusively on the mesiobuccal cusp. The latter presented the highest loss values and could thus be a risk indicator for higher wear rates.


Quantum dots (QDs)are known for their size-dependent optical properties,narrow emission bands, and high photoluminescence quantum yield (PLQY),which make them interesting candidates for optoelectronic applications.In particular, InP QDs are receiving a lot of attention since theyare less toxic than other QD materials and are hence suitable forconsumer applications. Most of these applications, such as LEDs, photovoltaics,and lasing, involve charging QDs with electrons and/or holes. However,charging of QDs is not easy nor innocent, and the effect of chargingon the composition and properties of InP QDs is not yet well understood.This work provides theoretical insight into electron charging of theInP core and InP/ZnSe QDs. Density functional theory calculationsare used to show that charging of InP-based QDs with electrons leadsto the formation of trap states if the QD contains In atoms that areundercoordinated and thus have less than four bonds to neighboringatoms. InP core-only QDs have such atoms at the surface, which areresponsible for the formation of trap states upon charging with electrons.We show that InP/ZnSe core–shell models with all In atoms fullycoordinated can be charged with electrons without the formation oftrap states. These results show that undercoordinated In atoms shouldbe avoided at all times for QDs to be stably charged with electrons. Possiblypresent trap states in the bandgap would also simply be filled oremptied upon doping. However, it is well known that charging of semiconductorscan result in the formation of charge-compensating defects.28 For semiconductor nanocrystals such defectsmost likely appear on the surface in the form of local reduction/oxidationof surface atoms or the formation of dimers.29 For example, Du Fossé etal. showed in a computational study that Cd-based QDs without danglingbonds are stable up to a charge of one electron but form trap statesafter injection of more electrons.30 Additionallyit was shown that the local geometry of Cd atoms determines whetherthe reduction of a Cd atom is energetically favorable or not: absenceof ligands results in reduction, and the presence of L-type ligandsprevents reduction.30 Localized energy states in thebandgap are also observed for PbS QDs after the injection of threeor four electrons, originating from undercoordinated Pb atoms leadingto dimers on the surface.32 However, to the bestof our knowledge, there is no atomistic understanding of the effectsof charging InP QDs.InP-based QDs with high PLQY and narrow full width at half-maximum(fwhm) are nowadays synthesized and used in electronic devices.38 The structure and density of states (DOS) are first determined bydensity functional theory (DFT) calculations for the neutral QD, andconsecutively the QD is subjected to electron charging. The methodfor simulating electron charging is adapted from the work of Du Fossé etal. and consists of the placement ofone or more neutral K atoms on the surface of the QD.29 Per K atom, one electron is donated to the QD, resultingin a negatively charged QD and a positively charged K atom, whilethe overall system is charge neutral. DFT calculations are then performedto determine the effect of the additional electron in the QD.This work provides theoretical insightinto electron charging ofInP core-only and InP/ZnSe core/shell QDs. The influence of additionalelectrons on the structure and the electronic states of the QDs isstudied. In line with the current understanding of the surface ofInP QDs the QD models used in this study are all cation rich and containnegatively charged surface ligands, which compensate the positivecharge from the excess cations on the surface.Initially, DFT calculations, simulating electrochemical electroncharging, are performed on InP core-only QDs with either a sphericalor tetrahedral shape. The introduction of an electron into the QDsresults in the formation of a trap state for both shapes. This trapis associated with a single undercoordinated surface In atom, whichgets reduced upon electron addition, for both spherical and tetrahedralInP QDs.39 To study the role ofundercoordinated In atoms, two types of core/shell InP/ZnSe QDs aresimulated in this study. One type contains undercoordinated In atoms,and in the other type, all In atoms are in a 4-fold coordination.Only the core/shell QDs with all In atoms in a 4-fold coordinationhave a trap state free bandgap after electron charging, even up tothe addition of six electrons. Thus, the presence of undercoordinatedIn invariably leads to trap states when additional electrons are introducedin the QD. This shows that undercoordinated In atoms are not innocentbut guilty as charged and responsible for charge compensation andtrap state formation upon electron addition.A well-known solution to passivate the surface of QDsand thuscreating fully coordinated atoms at the surface is the growth of anepitaxial shell on the QD core.etal. predicts that both shapes are possible for small InP QDs but thatat larger sizes only tetrahedral-shaped InP QDs allow for completesurface passivation.35 The requirementof charge balance requires three negatively charged ligands per excessIn atom, and steric hindrance prevents this in large spherical QDs.In this work, both relatively small spherical and tetrahedral InPQDs are modeled and charged with electrons to determine and comparetheir stability upon electron charging. This section describes theresults of electron charging InP core-only spherical QDs. 68P55Cl39 nanocrystal with a diameter of 1.9 nm. 35InP QDs are reported withvarious shapes, but spherical and tetrahedral QDs are most commonlyobserved. The work of Dümbgen Figure S1. 29 The DOS in n = 0 and n = 1 is shownin Figure S2. The contour plots of theMO of the trap state, the HOMO of the α MOs, and the LUMO ofthe β MOs are shown in Supporting Information, respectively. These contour plots indicate thelocalization of the trap state MO on the ejected In atom. The LUMOof the α MOs remains delocalized, as shown in After establishing that the sphericalInP QD model correspondsto a trap-free system, we investigated what happens upon electroncharging. Figure S4 withthe undercoordinated atoms in red. In accordance with literature,no trap states are formed by these 3-fold-coordinated atoms beforethe addition of extra electrons, as has already been shown in theDOS in 35 However, the atom that is ejected from the QD lattice after electronaddition, as described above, is one of the undercoordinated In atoms.The fact that the trap state is localized on the undercoordinatedIn atom leads to the hypothesis that undercoordinated In atoms areresponsible for trap formation when additional electrons are provided.Further charging of the QD with n = 2 does not leadto the creation of more trap states but to filling of the energy levelof the trap state with two electrons, indicating that the same surfaceIn atom gets further reduced. However, charging the QD with n = 3 results in an additional trap state in the bandgap,as displayed in Figure S5 in the Supporting Information. The additional trap state for the QD with n =3 is localized on another undercoordinated In atom. Charging with n = 4 again does not lead to the formation of an additionaltrap state but rather to the filling of the second trap state witha second electron.Tounderstand why this particular In atom forms a trap state afterelectron addition, the coordination number of the In atoms in theQD is calculated. The geometry optimized model of the InP sphericalQD has In–P and In–Cl bond lengths of 2.53 to 2.65 Åand 2.34 to 2.73 Å, respectively. The coordination number forevery atom is therefore calculated by determining the number of atomswithin a radius of 2.75 Å. Following this definition, it wasfound that the QD has 16 In atoms with a 3-fold coordination, whereasthe preferred coordination number for InP in the zinc-blende latticeis four. These undercoordinated atoms are all located on the surfaceof the QD, as can be seen in 84P56Cl84 QD with an edge diameterof 2.6 nm. The calculated DOS is shown in Todetermine whetherthe formation of a trap state after the addition of an electron dependson the shape of the InP QD, tetrahedral QDs were also investigated. To simulate the electroncharging for the tetrahedral QD, one potassium atom is placed on thesurface of the model, as shown in 30 The more facile reductionof In can be explained by its higher Pauling electronegativity of1.78 compared to cadmium with an electronegativity of 1.69 and isin line with the more positive standard reduction potential for In3+ reduction  than forCd2+ reduction ,42 although it should be noted that these reductionpotentials depend on the solvation energy of the ions in water andrepresent the reduction of free metal ions not the semiconductor.The formation of a trap state after injection of one electron suggeststhat in the presence of excess electrons trap states form due to undercoordinatedatoms independent of the shape of the InP QD, suggesting that it ismostly the local coordination that determines the stability of Inatoms against reduction. The role of undercoordinated In also suggeststhat trap state formation can be prevented by ensuring full coordinationof every atom.Thus, for boththe spherical- and tetrahedral-shaped InP QD models,it is an undercoordinated In atom that has a large contribution tothe formation of a trap state after the injection of one electron.Interestingly, reduction of Cd in CdSe QDs only occurs after the additionof two electrons.44 The result of shelling with ZnSe or ZnSis that the QDs are terminated with Zn atoms instead of In atoms.The Pauling electronegativity of Zn is 1.65, which is lower than bothIn (1.78) and Cd (1.69). In line with this, the standard reductionpotential of Zn2+ is much more negative (E0 = −0.76 V vs NHE), so it is expected that Znions are more stable under reductive conditions. In both experimentaland computational studies it is indeed found that Zn-terminated particleshave higher electrochemical stability for both CdSe/CdS/ZnS and InP/ZnSe/ZnSQDs.29 With boththe complete coverage of the core In atoms and the increased stabilityin mind, InP/ZnSe core/shell QD models are developed for this study.Such full coordination can be achieved by growingan epitaxialshell around the core with a different material that has the samecrystal structure and a matching lattice constant. Currently, thebest InP-based QDs that are reported in literature are InP/ZnSe/ZnScore/shell/shell particles with PLQYs reaching unity.45 Next we addedan additional complete ZnSe monolayer to fully coordinate all In atomsand to simulate QDs with a thicker shell.The effect of passivating surfaceIn with the ZnSe shell is tested by first constructing a QD with anincomplete monolayer, leaving some surface In exposed and undercoordinated.Such QDs with thin/incomplete shells have recently also been describedin experimental literature.46 We are currently preparing a manuscript on this topic. Here thesurface reconstruction that we introduce is the removal of 25% ofthe surface Zn atoms from the (111) facets. Figure S6 shows a schematic example of the surface reconstruction.Charge balance of the model is achieved by adapting the number ofsurface Cl atoms. Such cation vacancies have earlier been shown tolead to delocalized HOMO and LUMO levels for CdSe QDs by Vozzny andSargent.47The exact compositionof the incomplete shell is not chosen randomly, but corresponds tothe introduction of specific surface vacancies which are known tooccur on bulk III–V and II–VI semiconductor surfacesand prevent the formation of surface bands.etal. shows that larger InP QDs prefer a tetrahedralshape to produce sufficient surface area for all required ligands.35 Cross-sections of the InP core-only and InP/ZnSecore–shell QDs are shown in 31P20Zn72Se72Cl33 QD which has one monolayer of theZnSe shell, including surface reconstructions, and is called InP/ZnSe(1ML) for simplicity. Note that for this QD the four corner In atomsof the core are removed, which is because they were found to be unstableduring the geometry optimization calculations. The surface reconstructionslead to an incomplete ZnSe shell, allowing to test our hypothesisthat the presence of undercoordinated In atoms will lead to the formationupon charging of the QD with electrons. 35P20Zn322Se328Cl33 QD with two monolayersof ZnSe shell named InP/ZnSe(2 ML) and has the surface reconstructionson the outermost ZnSe layer. Due to the second layer of ZnSe, allIn atoms have a 4-fold coordination, which should lead to a trap-freesystem upon charging with electrons, according to our hypothesis.The results of the addition of electrons to these QDs are discussedin the following sections.The two InP/ZnSe core/shellQDs developed for this study both includesurface reconstructions and are based on tetrahedral shapes. The tetrahedral-shapedQDs are chosen because the work of Dümbgen Figure S6. However, the result of the surface reconstructionis that not all In atoms have a 4-fold coordination.The InP/ZnSe(1 ML) QD is shown in 48 Therefore, it is difficult to draw conclusionsabout the energy offset between the core and the shell. The coordinationnumber for all In atoms is determined for this model, and it is foundthat four In atoms are undercoordinated. Similar to the InP core,these undercoordinated In atoms do not result in trap states for theQD without excess electrons.The DOS for the InP/ZnSe(1 ML) QD is shown in The addition of electrons is againsimulated by placing a potassiumatom on the surface of the InP/ZnSe(1 ML) QD. To prevent undercoordinated In atoms, a second layer of ZnSe isadded to the QD model, resulting in InP/ZnSe(2 ML). The cross-sectionin Supporting Information. Theseresults confirm that a 4-fold coordination due to two layers of aZnSe shell prevents formation of trap states when electrons are addedto the QD. The work of Park etal. shows that electrochemical charging of InP-based QDs is based onQDs with complete coverage of ZnSe/ZnS shells.23 However, there is no discussion on In reduction on thesurface of InP core-only QDs after electron charging. Passivationof the undercoordinated In atoms is here achieved by a complete ZnSeshell, but this might also be achieved by coordination to ligands.The exact effects of coordination of undercoordinated In atoms isoutside the scope of this work, but the work of Du Fossé etal. shows that L-type ligands stabilizeQDs against surface reduction, and it is likely that these resultsalso hold for InP QDs.30With all In atoms 4-fold coordinated, the InP/ZnSe(2 ML) QD issubjected to the addition of an extra electron. The QD with the extrapotassium atom on the surface is visible in It is concludedfrom the results in this work that trap state formationafter electron charging a QD can only be prevented by ensuring 4-foldpassivation of all the In atoms in the QD. Implementation of InP-basedQDs in electronic devices depending on electron charging of theseQDs therefore requires attention to surface and interface coordinationof the In atoms.In conclusion, this workdescribes the effect of adding electronsto InP core-only and InP/ZnSe core–shell QDs by DFT calculations.The results show that charging of InP QDs with electrons always resultsin the reduction of a surface or interface ion if undercoordinatedIn atoms are present. Only the formation of a complete ZnSe shellprevents this reduction. InP is thus intrinsically less stable againstreduction than Cd-based QDs, but it can be stabilized with Zn chalcogenideshells, provided that all In atoms become fully coordinated.38 The chloride anions are electronically similar to the carboxylicacid ligands used in experiments but are computationally less expensive.49 To calculate the required numberof chloride atoms and the number of excess electrons after charging,the charge-orbital model of Voznyy etal. is used, which is defined asn representingthe numberof excess electrons, Ni the number of atoms of type i, and qi the most common oxidation state ofatom type i.50 For n > 0, excess electrons are present in the QD and theQDis therefore negatively charged; for n < 0 theQD becomes positively charged. The oxidation states of the atom typesused in the QD models of this work are assumed to be 3+, 2+, 1+, 3–,2–, and 1– for In, Zn, K, P, Se, and Cl, respectively.In agreement with previouswork and the current understanding ofthe surface composition of InP QDs, the QD models in this work arecation-rich and have chloride anions on the surface to preserve chargebalance.52 A Perdew–Burke–Ernzerhof (PBE) exchange–correlationfunctional and a double-ζ valence polarization basis set areused for all atoms.54 Effective core potentials fromthe GTH pseudopotentials account for scalar relativistic effects.Simulations were all performed at 0 K in the gas phase. For QD systemswith an odd number of electrons, unrestricted spin calculations wereperformed. In unrestricted calculations, the spin-up (α) andspin-down (β) electrons are calculated independently from eachother, resulting in separate densities of states and MOs. For allcontour plots of MOs a value of 0.005 e/bohr3 was used.Calculations for structural relaxations, DOS, and MOs are all performedat the DFT level using the CP2K quantum chemistry software package.55 The absolute energies of the MOs may therefore differ from the experimentallyobtained values. The energy levels cannot directly be related to experiments,but the trends described in this work are expected to be valid.29It should be noted that the use of the PBE exchange–correlationleads to underestimation of the bandgap.


Patients with chronic low back pain (CLBP) and comorbid depression or anxiety disorders are highly prevalent. Negative affect (NA) refers to a combination of negative thoughts, emotions, and behaviors. Patients with CLBP with high NA have greater pain, worse treatment outcomes, and greater prescription opioid misuse. We present the protocol for SYNNAPTIC (SYNergizing Negative Affect & Pain Treatment In Chronic pain).A randomized comparative-effectiveness study of antidepressants, fear-avoidance rehabilitation, or their combination in 300 patients with CLBP with high NA. In the antidepressant- or rehabilitation-only arms, SYNNAPTIC includes an adaptive design of re-randomization after 4 months for nonresponders.A multisite trial conducted in routine pain clinical treatment settings: pain clinics and physical and occupational therapy treatment centers.Inclusion criteria include CLBP with elevated depression and anxiety symptoms. Antidepressant and rehabilitation treatments follow validated and effective protocols for musculoskeletal pain in patients with high NA. Power and sample size are based on superior outcomes of combination therapy with these same treatments in a 71-subject 4-arm pilot randomized controlled trial.SYNNAPTIC addresses the lack of evidence-based protocols for the treatment of the vulnerable subgroup of patients with CLBP and high NA. We hypothesize that combination therapy of antidepressants plus fear-avoidance rehabilitation will be more effective than each treatment alone.ClinicalTrials.gov ID: NCT04747314. In the present article, we share an overview of the clinical trial design and the supporting pilot study data. The fully detailed clinical trial protocol is available at www.ClinicalTrials.gov (ID: NCT04747314).There are numerous treatments with proven efficacy to improve both pain and NA in patients with CLBP and high NA, such as antidepressants (ADs), fear-avoidance rehabilitation, and cognitive behavioral therapy.,As noted, the evaluation of effective treatments in the subgroup of patients with CLBP with high NA is a central gap in the literature, and guidance for clinicians is lacking. Overall, although the efficacy of ADs or fear-avoidance / fear-of-movement therapy has been established in controlled trials, these treatments have not been directly compared in the high-NA subgroup of individuals with CLBP.application-enhanced fear-avoidance rehabilitation (AEFAR) because, in addition to the classical approach of graded exercise and exposure, we include a pain self-management/education tool delivered by a mobile application, which reinforces the treatment sessions . Studies have shown that fear-avoidance rehabilitation is effective in part through targeting multiple domains of NA, such as kinesiophobia, pain catastrophizing, and anxiety.,We chose to compare AD treatment and fear-avoidance / fear-of-movement rehabilitation because neither requires additional mental health services outside of what is commonly available in primary care to deliver the treatments, should they be effective . We describe the fear-avoidance rehabilitation in SYNNAPTIC as –To further improve the generalizability and scalability of our findings, we modified an AD treatment regimen from Kroenke et al. that was proved effective in primary care in the treatment of patients with musculoskeletal pain and depression .,A 3-arm design is ideal to evaluate the comparative effectiveness of combination therapy in pain trials.The primary outcome and endpoint in phase 1 is a composite measure of responder status that categorizes responders on the basis of improvements in pain, function, and depression. Responder status is based on Patient-Reported Outcomes Measurement Information System (PROMIS) measures at 4 months vs baseline, and responders are categorized as (1) combined responders who have both pain and function improvements and/or (2) depression responders. A participant could be a combined responder, a depression responder, both, or neither. We use accepted benchmarks for clinically meaningful improvement in CLBP and NA that have been validated as outcomes in chronic pain studies.Secondary outcomes in phase 1 focus on the clinical and mechanistic links among pain, NA, and prescription opioids. As patients with CLBP and high NA are more frequently prescribed opioids at higher doses and are more likely to misuse them, opioid dose and misuse are important secondary outcomes.Patients already prescribed opioids at enrollment will be sub-stratified during randomization so that we can more adequately test the secondary hypotheses that targeting high NA is effective to reduce opioid misuse and improve CPM. The final list of secondary outcomes includes changes in domains within the BACPAC minimum data set, neuropathic symptoms (measured with PainDetect), and opioid misuse .. We need as large a pool as possible of responders to each treatment to better identify such predictors of responses. Therefore, phase 2 will include the adaptive component for participants in the AD- or AEFAR-only arms in phase 1 not meeting the benchmarks for clinically meaningful improvement, who will then be re-randomized to continue or switch to the other treatment for 4 months (phase 2). We track outcomes for 8 months in all treatment arms to assess intervention durability . We hypothesize that participants with relatively elevated baseline scores on a measure of depressive symptoms will respond best to ADs and that participants with relatively lower levels of baseline physical functioning will respond best to AEFARrability .To minimize burden on the participants, we will assess outcomes with weekly ratings through emailed and texted RedCap surveys and monthly in-person visits (synchronized with AEFAR sessions when possible). In addition, the AD adjustment visits occur every 2 weeks via phone calls, and the 8 AEFAR sessions can be completed by 4 in-person and 4 telemedicine-delivered sessions if requested .,From our previous studies in CLBP,Ages 18–75 years. The risk of cognitive impairment significantly increases above age 75 years.Pain characteristics: (A) Duration >6 months, as this is unlikely to spontaneously improve,CLBP meeting Quebec Task Force Classification System categories I–III .A prior lumbar spine X-ray to screen out red flags such as tumor, infection, or fracture.,High NA at the first visit (with symptoms for at least 1 month), defined as >85th percentile on both the depression and anxiety modules of PROMIS. These scores correlate highly with having a comorbid major depression or generalized anxiety disorder.Receiving medical care from the University of Pittsburgh Medical Center, Brigham and Women’s Hospital, or Mayo Clinic, Rochester, so that monthly visits, usual care, other treatments, and health care utilization can be tracked within the electronic medical record.,Access to the Internet and a smartphone. Nationwide reports show that 85%–94% of low-income families have Internet access at home.For the opioid inclusion group who have sub-stratified randomization, those subjects must be prescribed opioids currently on a monthly basis for at least 3 consecutive months. This criterion will include those prescribed opioids daily, as well as those taking opioids intermittently during the week.Ability to understand English and comprehend the questionnaires.Back surgery within the prior 6 months, as this is within the window of normal healing.Psychiatric: (A) Acute suicidality, (B) history of bipolar disorder, or (C) history of psychosis. These conditions would require treatment other than the AD medication provided in the study. They will be assessed at study entry and through a review of history in the electronic medical record.Pregnancy or intent to become pregnant during study, as which AD to use in pregnancy is controversial. Child-bearing females will have to provide assurances that they use contraception if sexually active.Intent to add new pain or psychiatric treatments during phase 1 (except for what is part of the study).Intent to have back surgery during phase 1.We will use multiple pathways for recruitment depending on the preferred methods at each clinical site. These include patient volunteer registries, clinic advertising, electronic bulletin boards, and best practice alerts in the electronic medical records.See ,At their phone call, all interested volunteers will be asked to complete the Patient Health Questionnaire 4 (PHQ-4), which is a 4-item brief measure of depression and anxiety symptoms and is highly accurate for predicting a depression or anxiety disorder.At visit 2 in week 2, urine drug test results are reviewed to confirm study inclusion (if needed), surveys are completed, and opioid prescribing is coordinated with the current opioid prescriber, as needed. AD or AEFAR treatment begins, and medication is free. We track whether any new pain or psychiatric treatments are started during the study with a weekly self-completed log.The PROMIS-29 scores at visits 1 and 2 (1 week apart) are averaged to establish the baseline levels of pain, function, and NA, as recommended.Nonresponders are those not meeting the combined pain+ function metric and the depression response metric (at least 50% improvement in symptoms). They will be re-randomized in equal blocks of 4 to continue treatment or switch to the other treatment for 4 months  AD only, (B) AEFAR only, or (C) AD+AEFAR. The randomization will be sub-stratified by opioid status. Responders at 4 months in any arm will continue to be tracked for 8 months. 4 months .Subjects in the AEFAR and AD+AEFAR arms have 10 visits over 4 months . Because the key outcomes are collected remotely via RedCap, only the first- and second-week study visits are required in person in any of the arms. For the AD-only arm, there are 4 visits over 4 months for the non-opioid group, and there are 5 visits for opioid subjects because of the monthly weaning visits.ADs start at week 2, and AEFAR starts in week 4. As suggested by our preliminary data, when ADs are started 2 weeks before AEFAR, it is more likely that early improvements in pain and NA could translate into greater benefit from AEFAR to further improve pain, function, and NA. Responders and nonresponders to AD+AEFAR continue ADs for 8 months total. From months 5–8, we will do an abbreviated assessment every 2 weeks via an email and text message with a Web link to the surveys.CPM procedures require a conditioning stimulus to induce endogenous analgesic systems and alter pain perception and a test stimulus to evaluate the endogenous analgesic response to the conditioning stimulus. We will use the same CPM testing procedures as in our previous study in patients with CLBP and high NA who were prescribed opioids.2 rubber probe  over the upper trapezius muscle contralateral to the participant’s dominant hand. Pressure is manually increased at a rate of rise of 0.5 kgf/cm2/s  until participants first report that the pressure sensation becomes painful. Pressure intensity (in kgf/cm2) read from the algometer at that time is considered the pressure pain threshold. Measurements are conducted 3×/site with 60-second rest intervals between each pressure application. Probe placement is varied slightly from trial to trial to prevent sensitization from repeated testing of the same site. Mean pressure pain threshold is used for analysis.Pressure pain threshold is assessed with a hand-held algometer with a 1-cmConditioning stimulation will begin by immersing the hand to a level 10 cm above the wrist into the water bath. The hand will be immersed for a total of 30–60 seconds of hand immersion, and trapezius pressure pain threshold will re-measured 1–2 times while the hand is still immersed in the cold water. CPM magnitude will be calculated as the difference in mean pressure pain threshold measured before and during the conditioning stimulus, with increases in pressure pain threshold during conditioning interpreted as evidence of efficient endogenous pain inhibition.We will track AEFAR session attendance, AD adherence, use of self-management techniques, and any new pain or psychiatric treatments. We will also use audits of EPIC records to track these factors and the study withdrawal form in dropouts. Subjects will rate the usefulness of the interventions in all study arms at study visits with the Treatment Helpfulness Questionnaire (THQ).,,,,,,There are published protocol details for SCAMP, which we have modified for SYNNAPTIC.The prescriber maximizes the AD dose as tolerated up to a clinical endpoint of at least 50% improvement in depression or anxiety symptoms and at least 30% improvement in pain. There are 4 telephone contacts (one every 2 weeks) up until week 16, in which the study prescriber discusses the medication and makes adjustments. The phone call is structured with standardized assessment measures  to discuAEFAR includes graded exposure, activity, exercise, pain education, motivational interviewing techniques, and a smartphone app, which together drive behavioral change. Treatment is delivered by trained physical or occupational therapists. AEFAR starts at week 2 or 4 (depending on the study arm), is completed in 8–12 weeks, and includes: (1) A physical therapy examination (start and at end of 8 treatments), (2) Fear of Daily Activities Questionnaire (FDAQ) (baseline and monthly—tracked as an outcome),Subjects who were already prescribed opioids at enrollment are given the option to voluntarily reduce their opioids if desired, supervised by a pain specialist, at an individually tailored rate of 10%–25% per month. As in clinical practice, weaning is done individually, and participants are seen once per month if they are being weaned from chronic opioid therapy.The Mayo Clinic Guide to Pain Relief,To investigate feasibility of the protocol and to generate data for sample size calculations, we used the same inclusion/exclusion criteria to conduct a randomized trial in 71 subjects with CLBP and high NA (on or off opioids). We followed all approved institutional review board procedures of the University of Pittsburgh, including obtaining written informed consent from each participant. The HEAL/BACPAC program emphasizes the need for robust trial planning and pilot data, which are considered vital parts of protocol development. Subjects were randomized, in a 1:1:1:2 ratio, to ADs , fear-avoidance–based physical therapy (FABPT), pain education (which served as an active control), or the combination of AD+FABPT. FABPT did not include the pain education mobile app incorporated into AEFAR, but otherwise the content and delivery of the treatment sessions were the same. The pain education control condition used the content from Dr. Michael Hooten’s book, P values <.05 for the Fisher exact tests vs the other groups). The combination of AD+FABPT was the most effective across the key domains  in patients with CLBP and high NA, and these data formed the basis for our sample size calculations.There was an average 80% study completion rate in the 4 arms. Numbers of enrolled subjects were as follows: pain education, n = 16; AD, n = 11; FABPT, n = 12; and the combination group, n = 32. The primary comparisons of interest in SYNNAPTIC  are the proportion of pain + function (combined) responders and depression responders between study treatment arms at the end of 4 months of phase 1 (nonopioid + opioid subjects together). None of the phase 2 data will be used for the primary outcomes analysis. We hypothesize that the proportions of combined and depression responders will be significantly greater in the AD+AEFAR arm than in the other arms. Therefore, 2 primary pairwise comparisons will be made simultaneously: (1) AD+AEFAR vs AD alone and (2) AD+AEFAR vs AEFAR alone. In our pilot study, the combined responder rate was 40% in the AD+FABPT arm, 20% in the AD arm, and 25% in the FABPT arm. We will assume that in the proposed study, the rate of combined responders will be 20% in the AD-or AEFAR-only groups and 50% in the AD+AEFAR group. With equal sample size allocation among the 3 arms and with a 2-sided significance level of 0.025 for each comparison after Bonferroni adjustment, a sample size of 240 study completers (80 per arm) will provide about 95% power to detect a minimum difference of 0.30 from the baseline value of 0.20 in response rate for each hypothesis testing. After adjustment for a dropout rate of 20% at 4 months, the final sample size at enrollment will be about 300, ie, 100 per each arm. Subjects with missing data will be included in the data analysis after an intention-to-treat analysis, provided that they had treatment exposure. We will also perform an as-treated analysis and analyze the secondary outcomes similarly.For the secondary opioid outcomes in phase 1, we will compare the proportions of opioid misuse and dose changes between subjects by using simple descriptive statistics. Combining phase 1 and 2 data, we will use mediation analysis to investigate whether changes in CPM mediate changes in pain in those who were prescribed opioids, and we will perform a cross-lagged panel analysis to test whether changes in CPM precede changes in pain.In phase 2, the re-randomization of nonresponders to AD or AEFAR is an adaptive element to maximize an enriched pool of responders to any treatment in both phases. We anticipate in phase 1 to generate 50 AD+AEFAR responders, 20 AD responders, and 20 AEFAR responders, based on 100 enrolled subjects per arm. In phase 2, we anticipate 12 more AD and 12 more AEFAR responders, and thus, we anticipate having a total of 50 AD+AEFAR, 32 AD, and 32 AEFAR responder patients for analysis, vs 186 nonresponders . We will use logistic regression to build a prediction model including up to a maximum of 10 significant predictors via a variable selection method such as LASSO. The prediction error will be assessed and presented by use of the Brier scores.We will track subject attrition by treatment arm. We will inspect the Study Withdrawal Case Report Forms at least monthly to monitor and limit the amount and impact of missing data and to understand whether data is missing not at random. We will pay attention to dropout rates in substrata of subjects, such as those with higher psychiatric or pain symptoms and those on opioids. Analyses for handling missing data require assumptions about the type of “missingness,” and we will apply imputation methods accordingly.Missing data and treatment adherence might be particularly important issues during the COVID pandemic and even after it resolves. We anticipate that (1) recruitment to participate in the study could be challenging because there are some in-person required visits; (2) we will have to deliver the treatments as remotely as possible; (3) we will need to be flexible in application of the protocol so as to maximize study retention, treatment adherence, and data collection; and (4) staffing shortages that affect health care and medical schools could also impact recruitment, retention, and data acquisition quality.SYNNAPTIC addresses the lack of evidence-based guidelines for the optimal treatment approach in the prevalent subgroup of patients with CLBP and high NA. Our anticipated findings will provide high-quality evidence that can be incorporated into treatment guidelines. If successful, SYNNAPTIC will provide an effective pathway for treating psychiatric comorbidity in CLBP in routine medical settings that does not require additional mental health services. This suggests that SYNNAPTIC can be scaled up effectively and rapidly at the community level.pnad006_Supplementary_DataClick here for additional data file.