Continuous glucose monitors (CGM) can provide detailed information on glucose excursions. There is little information on safe transitioning from hospital back to the community for patients who have had diabetes therapies adjusted in hospital and it is unclear whether newer technologies may facilitate this process.Our aim was to determine whether offering CGM on discharge would be acceptable and if CGM initiated on hospital discharge in people with type 2 diabetes (T2DM) would reduce hospital re-presentations at 1 month.This was an open-label study. Adult inpatients with T2DM, who were to be discharged home and required postdischarge glycemic stabilization, were offered usual care consisting of clinic review at 2 weeks and at 3 months. In addition to usual care, participants in the intervention arm were provided with a Libre flash glucose monitoring system . An initial run-in phase for the first 20 participants was planned, where all consenting participants were enrolled in an active arm. Subsequently, all participants were to be randomized to the active arm or usual care control group.Of 237 patients screened during their hospital admission, 34 had comorbidities affecting cognition that prevented informed consent and affected their ability to learn to use the CGM device. In addition, 21 were not able to be approached as the material was only in English. Of 101 potential participants who fulfilled eligibility criteria, 19 provided consent and were enrolled. Of the 82 patients who declined to participate, 31 advised that the learning of a new task toward discharge was overwhelming or too stressful and 26 were not interested, with no other details. Due to poor recruitment, the study was terminated without entering the randomization phase to determine whether CGM could reduce readmission rate.These results suggest successful and equitable implementation of telemedicine programs requires that any human factors such as language, cognition, and possible disengagement be addressed. Recovery from acute illness may not be the ideal time for introduction of newer technologies or may require more novel implementation frameworks. The COVID-19 pandemic has accelerated the incorporation of telemedicine including remote monitoring into patient care, including the outpatient setting . StudiesContinuous glucose monitors (CGMs) are minimally invasive, recording interstitial glucose levels every 5-15 minutes . Since CThe aim of our study was to determine whether the use of continuous subcutaneous glucose monitoring in patients with T2DM being discharged from hospital would reduce unplanned hospital re-presentations at 1 month as compared to a control group using capillary blood glucose meters. The secondary aim was to determine whether continuous glucose monitoring would be acceptable in this cohort.The study was conducted at Prince of Wales Hospital, a tertiary referral teaching hospital in an urban area of Australia. There are approximately 50,000 admissions annually. Although servicing a broader area, the hospital is located in the Randwick local government area, where 18% of residents are aged ≥60 years and 29% of the residents are from countries where English is not their first language. Inpatient care of diabetes is primarily the responsibility of the admitting team. Consultations to the diabetes service are made on an ad hoc basis by formal referral from the admitting team. Potential participants were identified from consultations to the diabetes ward service, and so could be recruited from any medical or surgical wards. Inclusion criteria were adult inpatients with T2DM as primary or secondary diagnosis, who were to be discharged home and required postdischarge glycemic stabilization. Exclusion criteria were patients with other forms of diabetes, or who were unable to provide consent. Potential participants were approached between October 1, 2019, and March 20, 2020.This was an open-label, prospective, controlled study. Potential participants were identified by the endocrinologist or fellow providing ward consultation service and recruitment was undertaken by a clinician not involved in the care of the potential participant. An initial run-in phase for the first 20 participants was planned, where all consenting participants were enrolled in an active arm. The run-in period enabled the establishment of streamlined referral pathways and familiarization with technology platforms. Subsequently, all participants were to be randomized to the active arm or usual care control group.Usual care consisted of clinic review at 2 weeks  and at 3-4 months . In addition to usual care, participants in the intervention arm were provided with Libre flash glucose monitoring system . Education on the use of the glucose monitoring system was provided by a credentialled diabetes educator. A disposable sensor was applied to the back of the upper arm on the day of discharge. No capillary calibration is required. Participants were provided with a handheld reader and encouraged to pass the reader over the sensor at least 3 times per day. Glucose results are available in real time to the participant and glucose data can be downloaded and reviewed with the participant at the 2-week visit. The manufacturer did not supply the device and was not involved with the study in any way.Ethics approval was granted by the South Eastern Sydney Local Health District Human Ethics committee (18/263 HREC/19/POWH/102).The primary outcome was to determine whether the addition of subcutaneous continuous glucose monitoring to usual care during glycemic therapy stabilization after hospital admission can reduce the number of unplanned hospital re-presentations in the first month following discharge. The secondary outcome was assessment of the acceptability of continuous glucose monitoring after hospital discharge.1c) of 8% or higher, intensification of glycemic management during admission was associated with reduced 30-day readmission  [We planned to recruit 440 patients. Early unplanned hospital re-presentation for patients with type 2 diabetes are up to 20% . In medi12-0.88) . We plan1c improved (or was stable when to target) in 17 of 18 participants who had a 3-month HbA1c result available. Of the 19 participants, 16 were very satisfied and 3 fairly satisfied with the medical devices available to monitor glucose levels at 2 weeks postdischarge. Of the 82 patients who declined to participate, 31 advised that the learning of a new task toward discharge was overwhelming or too stressful, 26 were not interested, 15 did not wish to attend for follow-up, 6 were approached but were discharged after trial closure, and 3 elected to self-fund CGM.A total of 237 patients were screened for eligibility from October 1, 2019, prior to study suspension on March 20, 2020, due to poor recruitment and restrictions on non–COVID-19 research. Overall, 136 patients were not eligible . Of the 101 potential participants who were approached over 5 months, 19 were recruited and completed the study. Participant characteristics are given in Only 19 of 101 potential participants were recruited to the study. Of the 19 participants in the intervention arm, only 2 (10%) had an unplanned hospital re-presentation in the first month after discharge, versus a published rate of 17% . ParticiThe underpowered sample size and early termination were significant limitations to our study, and we were not able to address our primary or secondary aims. A further limitation is that we were unable to ascertain whether our low recruitment rate, particularly for those not wishing to attend a follow-up clinic visit, may reflect reticence due to recovery from acute illness or chronic disease burnout. It is unclear whether our intervention would have been more acceptable if offered at a different time point in the provision of diabetes care . Age mayDiabetes therapy frequently requires adjustment on discharge from hospital. Newer technologies such as CGM provide a more comprehensive glucose profile that can be incorporated into remote patient monitoring. Where used, satisfaction with such newer technologies to facilitate telemedicine for diabetes management is high. The limited recruitment to our study suggests hospital discharge may not be the optimal time to introduce complex new technologies to patients. In embracing the promise of telemedicine including remote monitoring, further research on addressing human factors, to ensure equity, is required.


Population dynamics set the framework for human genetic and cultural evolution. For foragers, demographic and environmental changes correlate strongly, although the causal relations between different environmental variables and human responses through time and space likely varied. Building on the notion of limiting factors, namely that at any one time, the scarcest resource caps population size, we present a statistical approach to identify the dominant climatic constraints for hunter-gatherer population densities and then hindcast their changing dynamics in Europe for the period between 21,000 to 8000 years ago. Limiting factors shifted from temperature-related variables (effective temperature) during the Pleistocene to a regional mosaic of limiting factors in the Holocene dominated by temperature seasonality and annual precipitation. This spatiotemporal variation suggests that hunter-gatherers needed to overcome very different adaptive challenges in different parts of Europe and that these challenges varied over time. The signatures of these changing adaptations may be visible archaeologically. In addition, the spatial disaggregation of limiting factors from the Pleistocene to the Holocene coincided with and may partly explain the diversification of the cultural geography at this time. Here, the authors use climate and resource availability, to statistically model the limiting factors in the dynamics of hunter-gatherer population densities in Europe between 21,000 and 8,000 years ago. They find that limiting factors varied spatiotemporally and the effects of these may be visible in the archaeological record. The relevance of past demography for understanding culture change in prehistory has long been recognised3. Demographic conditions impinge on cultural transmission6 but are also clearly implicated in the boom-and-bust patterns of population fluctuations—including periodic extirpations-—suggested to have characterised the demographic histories of prehistoric foragers and incipient farmers in many regions10. Numerous recent studies have focused on the drivers of population expansion to explain the pattern and timing of human colonisation using a variety of ecological comparative approaches12  to 8000 years before present (kyBP), a climatically volatile period also known as the Last Glacial-Interglacial Transition27. We couple this to a suite of quantile generalised additive models (qGAMs) that describe changes in the maximum (90th percentile), mean (50th percentile), and minimum (10th percentile) population density as a univariate function of environmental variables related to the effect of energy or water availability, productivity, and annual limits and variability  that acted as a limiting constraint . We only predict population densities for those climatic variables associated with the best-performing univariate qGAM models where performance was defined using a cross-validation approach (see Methods). Population density for each cell within each 500-years interval is then determined as the minimum predicted density for that cell. Population size for each period was determined by summing the products between population size and the area of each grid over the ice-free areas. We define the limiting environmental factor as the variable predicting the lowest mean population density at a given place and time and explore the dynamic changes in these factors as a function of the quantile used to make our predictions. This approach allows us to query the spatial dynamics of forager limiting factors across the Last Glacial-Interglacial Transition and derive specific hypotheses as to which selection pressures acted most strongly on different forager communities in Late Pleistocene and Early Holocene Europe.Based on this analysis, we hindcast hunter-gatherer population densities between GS2 and 8kyBP in 500-year intervals using the downscaled centennial average conditions of each predictor derived from a transient climatic simulation  relate to spatiotemporal energy variation34. Lastly, net primary productivity (NPP) reflects environmental productivity in the form of the availability of plant resources, where high values lead to larger population densities, as already suggested by a plethora of earlier studies38. These variables display high correlations (Pearson correlations range between 0.83 and 0.96), supporting the notion that they broadly reflect energy availability and variability effects on forager demography. Other variables related to environmental productivity (PET) have lower yet somewhat similar predictive accuracy  time points. Therefore, MWM was not considered in our population density estimation and limiting factors. For the other variables, we do not see either truncation or severe non-analogy: the climate space observed at different moments during the 21-8kyBP period shows broad overlaps with the climate space used to develop our qGAMs  and largest at 8kyBP . Based on the 90th and 10th percentile models, these climate-limited human population sizes could have been as high as ~625,000 individuals and as low as ~28,300 individuals at 21kyBP. At 8kyBP, a climatically limited population size could have been as high as ~1,111,000 individuals and as low as ~52,000 individuals. These estimates represent the overall continental-scale human population size to be expected if climatic conditions were the only factor affecting the number of individuals and under conditions where all available space was, in fact, occupied.2, we show that at the warmest point of Greenland Interstadial 1 , Europe’s human population size estimated by our model was ~155,000 individuals; a number that decreased to ~143,000 individuals at the coldest point of Greenland Stadial 1 . The overall occupied area (number of inhabited cells), based on our 50th percentile qGAM model, was 62% of the ice-free region at the end of the GS2 (~21kyBP), increasing to ~74% during GI1 and reaching its nadir (~93%) by 8kyBP. Forager land-use was evidently extensive, however, including many largely empty spaces46. Still, at continental and centennial-to-millennial scales, overall population growth is suggested. As indicated by a strong correlation (Pearson’s rho = 0.7 p = 0.007)-—and despite the differences in the temporal aggregation and types of data utilised—there is a robust alignment in trends between our estimates of population size and independently derived archaeological occupation proxies  as well as cooling (GS1) events between 14.7kyBP to 11.7kyBP. During this period, the overall importance of ET steadily decreased, with both TS (~32% of cells) and TAP becoming the main factors limiting population density Figs.  and 6A. 61. Furthermore, this shift also aligns with the idea that decreasing territory sizes and more marked boundary formation directly relate to the spatiotemporal dynamics of resource availability62.The last period encompasses the early part of the Holocene from its onset at 11.7ky to 8kyBP. Here, TS and TAP were the main limiting factors  in the Mediterranean and are predicted to relate to temperature (= clothing or pyrotechnology) in higher latitudes. Certainly, for the latter two technologies, recent analyses suggest that the diversity and complexity of sewing technologies55, shelters67 and fireplace construction56 and use69 dates to the periods in which temperature-related variables acted as limiting factors. Conversely, where such technologies are absent in the archaeological record, we can also begin to think about population vulnerability to climatic factors at regional levels. Especially in higher latitudes, population fluctuations may have been pronounced at the sub-centennial scale, to the point of local population extirpations70.Our analyses show that the main limiting factors constraining forager population densities across the Last Glacial-Interglacial Transition in Europe changed markedly over space Fig.  and time71. In line with these expectations, regional cultural signatures became more pronounced in the Holocene, and borders between different cultural zones were eventually more strongly articulated. These patterns could be seen as a response to the fundamental shift in limiting factors we have identified in our models.Finally, the marked shift in limiting factors at the onset of the Holocene may be reflected in the observed shift away from energy and heat conservation technologies towards resource access and harvesting behaviours and technologies via processing, and that the increased spatial heterogeneity of limiting factors also engendered stronger cultural differentiation at a regional scale. The spatiotemporal dynamics of resource availability directly impact land-use, mobility, territoriality and the formation of information networks in foragers13, we highlight that while the environment can be said to strongly constrain forager lifeways, precisely which aspects of the environment do so at any one place and time must be expected to vary. Our approach offers a way to infer the hierarchy of limiting factors and hence provides a spatiotemporal hypothesis for major selection pressures acting on forager populations in the past.Seeking correlations between environmental variables and past human population densities is not a new endeavour. Following recent calls for more theoretically-informed rather than mere statistical explorations of this relationshipIndependent palaeodemographic estimates broadly support our models, but many questions remain. Climate models, for instance, only indirectly capture the interaction of human population dynamics with changes in biodiversity and ecosystem compositions. In addition, the match between modelled population densities and the field-validated presence of Late Pleistocene/Early Holocene populations is not equally robust everywhere. These deviations may stimulate targeted field-testing that assesses whether and why population densities periodically fell short of or exceeded modelled values. In conjunction with legacy data derived from archives and the literature, such fieldwork can also shed light on the specific strategies these past foragers employed to mitigate the risks posed by specific limiting factors.75. Our limiting factor model provides an explicit spatiotemporal hypothesis as to which risk mitigation measures could have been in use at any given time and place. By comparing our climate-driven estimates of population density to those estimated using the archaeological record we can identify potential mismatches suggestive of declines or increases not predicted by our models. By then identifying if shelters, fire or projectile technology, among other technologies, provided a given group with the capacity to buffer and perhaps even overcome climatically determined demographic limitations, we can better assess the adaptive role of specific cultural adaptations. The successful testing of such hypotheses would then shed light on these populations’ resilience and adaptation—or lack of it—during this climatically and environmentally tumultuous time. Finally, the marked shifts in dominant limiting factors identified in our models map onto results of Late Pleistocene/Early Holocene Earth System tipping points recently discussed by Brovkin and colleagues76. Analogous to anthropogenic warming in the present, these periods of rapid and substantive climatic change are likely to have created challenges for contemporaneous forager populations. In an effort to align archaeological perspectives on climate change with the quandaries of our time . Equally importantly, this approach prevents any estimation biases due to differences between the data used to define climate-density relations and paleoclimatic surfaces used to estimate population density changes and limiting factors over time.We used climate data on historical averages 1970–2000) for 19 climate variables . Modelling such dynamics using qGAMs offers a transparent way to determine the nonlinear changes in different percentiles of a response variable (= population densities) to one or multiple environmental variables and to understand the response-predictor dynamics outside of the mean of the data, making it useful in understanding outcomes that are non-normally distributed and that have nonlinear relationships with predictor variablesIn contrast to previous studies evaluating past human population density changes, we do not consider the synergies between multiple climatic variables when describing the relation between population densities and climate. We reasoned that using multiple predictors simultaneously in one model results in the evaluation of relative effects, and that these are contingent on the variables used in the model. Furthermore, these relative effects do not capture the limiting effects exerted by a given variable, but the partial contributions of each evaluated variable to the deviation from the regional mean population density. We therefore instead focus on the individual effects of evaluated variables on different percentiles of population densities  to (i) identify the most pronounced limiting factor determining palaeodemographic patterns and (ii) assess how the climatically possible maximum, average and minimum population densities changed in space and time. The shapes of the relation between population densities as a function of environmental variables were consistent for different percentiles  to be included in our training dataset. All models and prediction accuracy estimates were implemented in R (version 3.698) using the mgcv (version 1.8.2499) and qgam (version 1.3.2100) packages. Code to replicate these analyses is available from101.The population density estimates derived from the ethnographic data followed a log-normal distribution, so these were log transformed for subsequent analyses, and a gaussian response distribution was used in our qGAMs models. Annual and yearly limit precipitation variables were similarly transformed. The ability of each of the evaluated variables to predict hunter-gatherer population densities was determined using the mean deviance explained ), which in the context of this study is identical to a GLM unadjusted 102. These were bias-corrected and downscaled to 0.5° × 0.5° following the methods described by ref. 33. Source climate surfaces were aggregated to 500-years intervals from the original decadal averages of monthly values. We only considered locations not covered by ice, using a mask from the ICE-6G gridded data product104. As CCSM3 SynTrace paleoclimate simulations do not contain estimates of uncertainty, we cannot assess how variability in paleoclimatic model outcomes would propagate to our estimates of population density and limiting factors.The monthly average temperature and annual precipitation values for Europe for the 21ky to 8kyBP period come from the CCSM3 SynTrace paleoclimate simulations2 (the lowest densities recorded in the ethnographic dataset) by the land area of the corresponding cell to arrive at per cell population size. We then summed these values to arrive at the total population size for each century. As our objective was to establish the climatic variable that imposed the strongest constraints on hunter-gatherer population density at any one time, we determined for each of the three evaluated percentiles the variable estimating the lowest population density for a given cell at each evaluated time-period to be the limiting factor  of the ethnographic and climate data at each time step. Each model was used to hindcast population densities, estimate the percentage of inhabited land area and human population size, and define the relevant limiting factor. Uncertainty in continental-scale estimates of population densities, occupied area and population size was determined using 95% confidence intervals. The variable selected as the limiting factor in most cross-validation folds was selected as the limiting factor.105. Changes in the density of records are a useful continental-scale proxy measurement of prehistoric population size changes are ommonly used to describe prehistoric human population dynamics trends111. We extracted 14C dates from the INQUA Radiocarbon Palaeolithic Europe Database, aggregating these to the closest 1000 years in order to determine the match between our qGAM-derived populations' density estimates and those derived from the frequencies of radiocarbon dates between 21kyBP and 10kyBP, closely following ref. To assess the validity of our population density estimations, we use the International Union for Quaternary Science (INQUA) Radiocarbon Palaeolithic Europe Database v2826, these estimates present independently derived estimates of population density for Late Pleistocene populations in Europe.We also used site-based estimates of population density as derived using the Cologne Protocol by ref. 112 (version 3.4-13) and maptools113 (version 1.1-2). Code to replicate these is available from ref. All maps were generated using R (version 3.6; ref. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Data quality of phenotypic traits (repeatability) in the controlled HTP experiment was above 85% throughout the life cycle and at maturity. Drought stress had a strong effect on growth in all wheat genotypes causing biomass reduction from 2% up to 70% at early and late points in the drought period, respectively. At maturity, the drought caused 47–55% decreases in yield-related traits grain weight, straw weight and total biomass and reduced TKW by 10%, while water use efficiency (WUE) increased under drought by 29%. The yield-enhancing effect of the introgressed QTLs under drought conditions that were previously demonstrated under field/screenhouse conditions in Israel, could be mostly confirmed in a greenhouse pot experiment using HTP. Daily precision phenotyping enabled to decipher the mode of action of the QTLs in the different genetic backgrounds throughout the entire wheat life cycle. Daily phenotyping allowed a precise determination of the timing and size of the QTLs effect (s) and further yielded information about which image-derived traits are informative at which developmental stage of wheat during the entire life cycle. Maximum height and estimated biovolume were reached about a week after heading, so experiments that only aim at exploring these traits would not need a longer observation period. To obtain information on different onset and progress of senescence, the CVa curves represented best the ongoing senescence of plants. The QTL on 7A in the BarNir background was found to improve yield under drought by increased biomass growth, a higher photosynthetic performance, a higher WUE and a “stay green effect.”Drought events or the combination of drought and heat conditions are expected to become more frequent due to global warming, and wheat yields may fall below their long-term average. One way to increase climate-resilience of modern high-yielding varieties is by their genetic improvement with beneficial alleles from crop wild relatives. In the present study, the effect of two beneficial QTLs introgressed from wild emmer wheat and incorporated in the three wheat varieties BarNir, Zahir and Uzan was studied under well-watered conditions and under drought stress using non-destructive High-throughput Phenotyping (HTP) throughout the life cycle in a single pot-experiment. Plants were daily imaged with RGB top and side view cameras and watered automatically. Further, at two time points, the quantum yield of photosystem II was measured with a top view FluorCam. The QTL carrying near isogenic lines (NILs) were compared with their corresponding parents by  Wheat is one of the three most important staple foods worldwide and is consumed daily in the form of baked goods or pasta. Baked goods such as bread and flat bread are an important source of carbohydrates and protein for a large part of the human population . The ong2 as well as water and light. CO2 flows through the stomata into the mesophyll at a rate described by stomatal conductance, which is related to turgor pressure and osmotic potential  offers a precise and rapid way to study genotypes in an objective and standardized manner . It can However, up to now, HTP experiments have only been conducted until about flowering time. Insight into the senescence phase could previously be obtained by mobile field phenotyping from flowering to final maturity  during sWild species represent an important genetic resource to identify beneficial alleles from landraces and wild relatives and incorporate these into modern varieties . Wild emIn a recombinant inbred line (RIL) population derived from a cross between durum wheat (cv. Langdon) and wild emmer wheat (acc. G18-16), two beneficial QTLs originating from wild emmer wheat were identified . A QTL oIn the current study, we investigated the NILs carrying the two wild emmer QTLs along with the parental cultivars in an HTP experiment using the previously established protocol to simulate drought stress . For theT. turgidum ssp. dicoccoides, acc. G18-16) alleles in selected QTL regions into durum and bread wheat cultivar., as described previously by 3F7 and their recurrent parental cultivars: NIL-B-7A-2 and NIL-Z-7A-5 both carrying the 7A QTL with a size of 46 cM in the background of bread wheat cultivars BarNir and Zahir, respectively, and NIL-U-2B-3 carrying the 2B QTL comprises 43,5 cM the background of durum wheat cultivar Uzan  system  used in the current study is installed in an environmentally controlled greenhouse at IPK Gatersleben . On this system, each plant is transported by conveyor belts to the imaging chambers equipped with top and side view RGB and fluorescence cameras, where the lifter allows imaging from different angles in side view. The balance-watering station enables controlled watering and thereby defined drought setups. The system has been upgraded with a chlorophyll fluorescence camera (FluorCam) from PSI to measure photosynthetic performance from top view .2O5, and 10% K2O. Below each pot is a container to collect water in case not all water during automated watering can be fully absorbed immediately. Since the growing conditions are not homogenous within a greenhouse, all pots were randomized by the modus “Random 208,” which exchanges randomly positions of 208 plants with the position of another plant, included in the operating software of the commercial system, several times a week. Plants were imaged and watered daily, imaging was performed from three side view angles  and top view. In the well-watered treatment, plants were always watered to 90% plant available water content according to All wheat lines were phenotyped on the HTP platform under contrasting water supply with 10 biological replicates per line in each treatment. The HTP experiment took place from July 2019 to November 2019 and covered the entire life cycle of the wheat plants from sowing until maturity. Seeds were provided by Prof. Y. Saranga, the Hebrew University of Jerusalem, Israel. To ensure the presence of one plant in each pot even, two seeds per pot were sown and thinned after germination to leave one seedling per pot. Each pot (18.5 cm height x 14.9 cm diameter) was filled in with Klasman substrate no. 2 and supplemented with 7 g fertilizer, containing 19% total nitrogen, 9% P2/s) is used to measure the working efficiency of photosystem two under strong light influence (800μmo/m2/s), shortly followed by a second light flash to measure the quantum yield after a lower light intensity (80 μmol/m2/s) (Measurements of chlorophyll fluorescence were made with the FluorCam at two time points during severe drought stress (DAS 69 and 74). Regular plant recordings and irrigation were carried out at night, starting at midnight. The first saturating light flash  . If the Several additional traits were measured during the HTP experiment. Number of tillers were counted at DAS 28 before the drought, at DAS 53 (about 3 weeks of mild drought) and at DAS 70 about one week after the onset of severe drought . HeadingAt the ripening phase, plants were inspected and taken off the system for harvest when having reached full maturity. The first plants were removed from the system at DAS 96, while the last mature plants were removed at DAS 121. The day of final maturity, called final estimated biovolume (EB), was considered to be the day when, for the last time, seven out of ten biological replicas were still on the platform. As plant appearance only slowly changed in the ripening period, images were only taken every 2 days from DAS 96 onwards, while watering was still continued daily. At plant maturity, several growth and yield parameters were recorded manually . We deteIn order to evaluate to what extent, the data from the HTP studies correspond to the results in the two field/screenhouse experiments, the comparable traits were used: Culm Length , Days Planting to Heading (here referred to BBCH55), Grains per Ear, Grain Yield (g), Harvest Index, Osmotic Potential (Mia), Number of Spikes per Plant, TKW and Plant Biomass g; . The absThe image analysis was carried out with the Integrated Analysis Platform IAP; . Due to Our focus in image data processing was on parameters from the RGB camera: the EB [voxel], plant height (PH) [mm] and the mean color value (CVa) [hue] from the side view. This average color value is part of the HSV color space, which is described by the hue, saturation and value of a color. To simplify the analysis of colors, different hue values are combined in a 20-bin model. Based on this model, an average hue of 0.23 corresponds to an image of a green plant. The EB is calculated based on images from top view and three different angles of side view.For the statistical analysis of the HTP experimental data, R Studio was used as described by Y = (μ + G + e), where Y is the vector of observed phenotypic values, μ is the intercept, G is the effect of genotype and e is the residual value for each plant. μ and G were treated as fixed effects.The outlier test was performed according to the Tukey method . BLUE vaY = G + e, where G is the effect of genotype and e is that of residual. There is also the assumption that all effects are random effects. Since this is a single experiment, repeatability is calculated from the variance of the genotype the error variance and ten biological replicates.The variance components were calculated using the mixed linear model R RepeatabilityGV genotypic varianceeV error variancenRep number of biological replicates.6 during all calculations. Further, due to a management error, the FluorCam was forgotten to pull back and therefore blocked the view of the RGB top view camera from DAS 28 to 32. Therefore, these days were excluded for EB that is calculated from side and top view areas but could be analyzed for PH (from side view images) and side view CVa. As FluorCam measurement on the first time point before the onset of drought was not successful, we did not consider this day in the analysis and only used the data from DAS 69 and 74.Values for the EB were analyzed with the downscaling factor of 10Data quality, represented by repeatability, was very high throughout the life cycle for PH and EB, i.e., above 85%. Except for a few days between DAS 60 and 80, repeatability was also higher than 80% for CVa .To determine how well EB predicts the true value of plant biomass and its components, final EB was correlated with straw weight, grain weight, and biomass parameters measured manually at maturity . With a Drought stress treatment had a strong effect on growth in all wheat lines. Regarding EB and PH significant differences between the control and the stress treatment were observed after one week of drought, from DAS 38 to harvest . The redThe mean time to reach BBCH55 under stress conditions was DAS 48, 2 days earlier compared to control , 5. BarNThe effect of drought stress was also evident for the trait “time to maturity.” The stressed plants matured on average 18 days earlier than control plants with larger variation in stress treatment compared to control .For the traits recorded before maturity, significant differences were found between the parents and the corresponding NILs in a few traits . AgainstThe treatment has a significant effect on the time of heading BBCH55; . For theThe 7A-QTL segment introduced in BarNir, NIL-B-7A-2, conferred significantly 25% higher plant biomass in the control and 33% higher plant biomass in the drought stress treatment at maturity . In addiThe wild emmer wheat QTL on chromosome 2B in NIL-U-2B-3 caused a more pronounced tiller number of 35% more tillers under stress compared to the recurrent parent Uzan . FurtherThe QTL effects for common traits between the HTP study and the field/screenhouse experiments from Significant differences between parents and NILs were found for several traits across the environments. For example, NIL-B-7A-5 was significantly larger than BarNir in all environments, as measured by culm length. NIL-U-2B-3 was significantly smaller in the HTP experiment and significantly larger in the field experiment. The increased grain yield and plant biomass for NIL-B-7A-5 was also significantly greater for all environments between 43 and 69%. For TKG, the difference between the first field/screenhouse experiment and the HTP experiment was equally significant. In both environments, NIL-U-2B-3 showed a number of spikes, but it was only significant under control for both environments.Significant differences were found for EB between BarNir and NIL-B-7A-2 for the period from DAS 45–102 in the control treatment and DAS 33–102 in stress treatment , 9. ThusConsistent with the observations at maturity, plants of NIL-B-7A-2 were significantly taller than those of the parent BarNir throughout the entire life cycle under both conditions –14. SimiThe QTL 7A affected the CVa. Until around DAS 60, the NIL in the BarNir background had a significantly slightly lower CVa in both treatments in the BarNir background. However, in the severe drought treatment during the late ripening phase, plants of the NIL had a significantly higher CVa from DAS 90 onwards, showing a slower ripening of the NIL .The differences between the treatments were significant from DAS 70 and 71 onwards for the NIL-B-7A-2 and BarNir, respectively –18.NIL-Z-7A-5 produced less EB in both treatments compared to Zahir, with this effect being more pronounced in the control treatment than in the stress treatment. However, the difference between NIL and parent was significant only for a few DAS, such as DAS 10 to 13 in the plant establishment phase and during the mild stress phase from DAS 33 to 43 , 11. HowIn the Zahir background in both treatments, Zahir and NIL-Z-7A-5 did not differ in PH throughout the life cycle, which is in accordance with the absent difference for PH at maturity –14. The In Zahir background, the 7A QTL showed no effect on CVa until the ripening stage and the treatment effect was significant from DAS 69 onwards for Zahir and from DAS 72 for NIL-Z-7A-5 –18. HoweRegarding the effect of the 2B-QTL in the genetic background of Uzan, NIL-U-2B-3 produced significantly more EB compared to Uzan in the stress treatment during DAS 33–79 , 9. In tIn accordance with reduced PH at maturity , the NILThe 2B QTL showed a minor effect on the CVa. Uzan had a higher CVa during the mild drought phase than NIL-U-2B-3 , 16, 18.In the present study, the detailed effects of two QTLs introduced from wild emmer wheat for higher productivity with respect to spike and total dry matter under drought (chromosome 7A) or productivity with respect to grain yield across drought and control environments (chromosome 2B) were examined under well-watered and drought stress conditions using non-destructive HTP, for the first time throughout the whole plant life cycle. Our daily phenotyping results confirm advantageous effects of yield or yield parameters of the QTLs which were identified under field/screenhouse conditions in Israel and enabled us to determine the QTL effects on growth and their timing.The phenotyping platform used in this study has been used previously and has proven useful for estimating plant biomass up to the flowering stage under well-watered and drought stress conditions in barley . As the With the application of an established standardized phenotyping protocol, high repeatability was achieved for EB and PH for each day throughout the life cycle under both, well-watered and drought stress conditions. Except for a few days, the data quality of the CVa was also high. The results of this study are in line with the high data quality throughout the vegetative growth period in well-watered and drought stress conditions on the same HTP system . The suiFurther, the data set allows to make statements about which imaging traits provide relevant information during or until which cereal developmental stage for HTP experiments. The EB increased dynamically until the milk ripening stage, while final PH was reached already shortly after flowering. After that, PH remained constant, while EB decreased with the ripening process due to the increase in mature plant parts, as these contain less water and thus the visible area is smaller. The CVa can be used to visualize and quantify the process of senescence of the plant by the changing plant color from green to yellow . If therThe timing of drought significantly affects plant development. If drought stress occurs during the tillering stage, biomass, and the number of tillers per plant are reduced . PH is aIn this HTP experiment, the plants of control and stress treatment started to show significant differences for EB and PH 7 days after the onset of drought. This is in accordance with previous observations in barley on the same HTP system, where biomass in control and drought treatment differed after 5 to 7 days . The sloHeading time occurred much earlier in the greenhouse compared to the field. A significantly earlier flowering of up to 15 days in controlled environments compared to the field has recently been described in wheat . Still, Besides the obvious differences of single plant growth in pots to growth in plant stands in a field soil, the growing conditions in terms of temperature, light quantity and quality, humidity and water availability are not the same. Though the HTP study incorporated a temperature gradient over the growing period, temperatures during day and night are stable in the climate-controlled greenhouse and do not show fluctuations as in the field and also the maxima in temperature reached in the two field years are technically not possible to reach in our greenhouse. Water availability is also difficult to compare between pot and field, due to the different ways of measuring in liters per pot in HTP and mm of precipitation in the field, which are two entirely different systems. According to Strikingly, NIL-U2-B-3 in the Uzan background had an increasing effect on plant height, while having a decreasing effect in the HTP study in contrast. In general, the plant density in a pot experiment is much lower than in a field experiment. via gas exchange and with a portable device via photochemical quenching during sprouting and grain filling, and inferences were also made about WUE via carbon isotopes. In addition, the higher grain yield, TKW, and total dry matter of NIL-B-7A-2  by measuring the leaf reflectance of green and glaucous leaves with a spectroradiometer  and the German Federal Ministry of Food and Agriculture (FKZ: 2813IL03). YS is the incumbent of the Haim Gvati Chair in Agriculture. This work was conducted as part of the STARGATE project funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement number 952339.CK was employed by company BASF SE.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance. Changes in sleep spindle characteristics have been reported for various sleep disorders6, mental illnesses such as schizophrenia9, autism11 and bipolar disorder12, and neurodegenerative diseases such as Alzheimer’s disease14. These findings suggest that sleep spindles may serve as prognostic and diagnostic biomarkers for various diseases in the future1.Sleep spindles are phenomena occurring in the brain of sleeping mammals15. Sleep spindles are generated in the thalamic reticular nucleus and relayed to the cortex by neuronal feedback loops1. The gold standard for spindle detection is spindle identification by trained experts who visually inspect and annotate EEG signals. This manual process is known to be time-consuming and therefore expensive, and is thus a limiting factor for large-scale spindle studies. Moreover, inconsistencies and disagreements among experts introduce noise in spindle annotations (label noise). Indeed, only substantial agreement was reported when an expert inspected the same data multiple times  was only moderate  with frequencies in the range of 11–16 Hz (sigma-band) and a duration of at least 0.5 s, according to the American Academy of Sleep Medicine (AASM)expert consensus)16. By increasing the number of experts, such a consensus converges to generalizable annotations17. For sleep spindle identification, it was estimated that 2–3 experts were required to achieve a consensus of ‘substantial’ reliability, and at least 4 or more experts were needed to reach ‘almost perfect’ reliability16. Publicly available datasets of spindle annotations  contain recordings of several subjects that are annotated by only one or two expert scorers. Notably, the DREAMS dataset19 was scored by five expert scorers but only comes from eight participants that were about 45.9 years of age20, thus limiting the generalization of systems trained thereon. Recently, in a remarkable crowdsourcing effort, the MODA (Massive Online Data Annotation) dataset was created in which EEG recordings from 180 subjects were scored for spindles by a median number of 5 experts (more than 95% of the data was annotated by at least 3 experts)21. Among the investigated methods for automated spindle identification, the algorithm A722 achieved the highest level agreement with the expert consensus with a performance similar to an average expert scorer. However, as the consensus reduces the label noise substantially, we entertain the hypothesis of an automated system that exceeds individual expert scorers and replaces the manual annotation process, thereby enabling large-scale spindle studies.An automated system for the detection of spindles that mimics an expert scorer would determine spindles consistently, but it would be limited by the level of disagreement among experts. This challenge can be addressed by having a group of experts separately identify sleep spindles in order to form a group consensus  that approximates the expert consensus derived from the MODA dataset to identify sleep spindles. The network architecture is inspired by U-Net34, a deep neural network that has been used with great success in the field of image segmentation35. The model complexity of SUMO is low compared to aforementioned deep learning models, which allows for data-efficient training on the MODA dataset. We demonstrate that our model surpasses the accurateness of the A7 algorithm and the average expert to identify spindles compared to the group consensus. Downstream measures (spindle duration and density) derived from the model’s predictions correlated stronger with the expert consensus compared to A7. We consider approaches such as ours to be promising for the future of automated sleep spindle identification.In this contribution, we introduce a deep neural network model called 18, which are publicly available36. Retrieval and analysis was approved by the local ethics committee at Technische Universität Dresden, Germany (permit number BO-EK-50012021). All analytical methods were carried out according to the ethical principles of the Declaration of Helsinki37 and the recommendations on good clinical practice38. The second dataset consisted of freely available crowdsourced spindle annotations from the Massive Online Data Annotation (MODA) study21. Of the 200 individual recordings contained in MASS, the MODA study selected 180 recordings of C3-LE or C3-M2 EEG channels. Recordings were split into a younger (24 years mean age) and an older (62 years mean age) cohort to make annotations. Each recording was divided into several 115 s long segments containing N2 sleep without artifacts. Ten segments were randomly sampled from each recording in a subset of 30 recordings (from 15 younger and 15 older subjects), and three segments were randomly sampled from each of the remaining 150 recordings. After band-pass filtering and downsampling, these segments were exhaustively scored by 47 certified EEG technicians. A median of five EEG technicians reviewed any one segment. Available to use was an expert consensus of spindle annotations formed from the annotations of the EEG technicians. The method by which the expert consensus was formed as well as other details can be found in Lacourse et al. (2020)21.We obtained two datasets to train and study the model. The first dataset contained EEG recordings from the Montreal Archive of Sleep Studies (MASS)We downsampled each EEG segment to 100 Hz using a phase-preserving, 10-th order Butterworth filter (passband 0.3–30 Hz) according to the MODA study. In addition, we z-transformed each segment  before further processing to decrease inter-individual differences in the recording modalities.We divided the dataset into a training and a test set  was achieved by nearest-neighbor upsampling followed by a convolution mapping to half the number of input filters. This half was then supplemented by concatenating the output activations from the matching contracting step in the left side of the U.Each level of compression and expansion consisted of two composite layers of convolutionWhen predicting spindle annotations, the raw output probabilities in the spindle and no-spindle channel were temporally smoothed with a moving-average filter and converted to spindle indicator values by taking the point-wise maximum. Consecutive positive indications were then joined to form spindle annotations constituted by a starting sample and a duration. The width of the moving-average filter was a hyperparameter that we optimized see “” section42. Feeding the network with minibatches of training examples (12 segments), we evaluated the output and the expert-consensus annotations using a generalized dice loss function43. The resulting error signals were backpropagated and parameters were updated using Adam stochastic gradient descent with parameters 44. After processing each sample in the training data once , we computed the network’s F1 score (see next section) for the validation set which was one of the six folds. If the F1 score did not improve over the course of 300 training epochs, or if the 800-th training epoch was reached, the training was stopped. We then stored the parameters of the best performing models  from the training session for further processing.Before training, we divided all training segments into training and validation sets using a six-fold cross validation procedure for hyperparameter optimization and early stopping. Similar to the test-training splits, the six folds were split on a by-individual basis wherein the 10-segment individuals were equally divided across the folds. This means that the 144 subjects of the training set  on a by-spindle (by-event) basis. For each EC spindle, we determined the temporal overlap of the closest detected spindle relative to the joined duration of both. If that relative overlap exceeded a threshold, we counted a true positive (TP). Else, we counted a false negative (FN). Each detection that did not sufficiently overlap with an EC spindle was counted as a false positive (FP). We note that an alternative to this by-event evaluation would be to evaluate each data point in a spindle separately. However, such an approach would put additional weight on long spindles because of temporal correlations, which we wanted to avoid.We evaluated the performance of our model in an by-event analysis, we studied SUMO’s performance stratified by age cohorts.From the evaluations, we computed the recall, In our analysis by subject, we investigated the usefulness of the predictions for downstream measures such as average spindle characteristics which are of interest to sleep researchers. From the predicted spindles, we determined average spindle duration and spindle density per subject. We studied how much these characteristics were correlated with those derived from the expert consensus.We explored how accurate SUMO could predict the expert consensus depending on the architecture of the model, i.e. the number of levels, the maximal receptive field, the number of channels, and the width of the moving-average filter applied to the net’s raw output. We focussed on these parameters hypothesizing their importance to accurately predict spindles, which show characteristic time scales in their occurrence, duration, and rhythm. For a given configuration, we carried out a six-fold cross validation by training six SUMO models that were independently initialized. After one training epoch of 45 training steps , we applied the model in its current state to the validation set to determine the F1 score dependent on the overlap threshold. From this dependence, we computed entclass1pt{minimaAfter all six training sessions were concluded, we compared the average across the best mentclass2pt{minimIn total, we trained about 100 such configurations and found their F1 scores to be remarkably stable across folds. We thus determined the optimal model configuration within our search space to consist of three levels, each one pooling across 4 time points. The raw output was further processed with a 0.42-s wide moving-average filter before thresholding in order to determine spindle predictions.21, supplementary Table 2. We also computed these metrics for A7 on the hold-out test set. Comparing A7 performance on the test and the whole dataset, we found that all test set metrics were equal to, or numerically exceeded the metrics obtained on the whole dataset.We then went on to compare the performance of the SUMO model to algorithm A7 and the expert consensus , we found no signs of overfitting across all three sets.21 and overlaid the F1 scores from our best model on the test set. As shown in Fig. We analyzed how our best model compared with average-expert and A7 detections for overlap thresholds different from 20%. We extracted the comparative performance values from Figure 2 in Lacourse et al. (2020)Many sleep studies identify sleep spindles to derive characteristics such as the number of spindles per minute (spindle density), or the average spindle duration. We tested how these characteristics derived from automatic spindle annotations compared to the expert consensus.m. This slope should be 1 to accurately indicate changes of the given spindle characteristic caused by an intervention, for example. As a measure of precision of the predicted spindle characteristic, we determined the level of correlation using Pearson’s r. Resultant r-values were compared across the two algorithms after applying Fisher’s z-transform47. The results of our analyses are shown in Fig. We determined spindle density and duration for each individual in the test set from the expert consensus, A7, and the SUMO model presented in the previous section. We then analyzed their linear dependency by fitting a linear model to the relationship of algorithm-derived and consensus characteristics and noted the slope The expert consensus showed a bimodal distribution of spindle densities with modes at about 1 and 5.25 spindles per minute. Both algorithm-derived spindle densities showed a high level of correlation with the expert consensus, with A7 showing The slope of A7-derived spindle densities deviated numerically more from 1 than SUMO-derived densities, especially for younger individuals with In the analysis of spindle duration, we found much weaker correlations compared to the density results presented above. A7 showed When comparing the slope, we found that the A7 fit deviated markedly from an optimal slope showing 21. Note that we did not evaluate the individual expert performance on the test set. Furthermore, we compared by-subject estimates of clinically-important spindle metrics (spindle density and average duration) derived by SUMO and A7 to those determined from the expert-consensus spindles using correlation analysis.We trained the U-Net-type neural network SUMO on the MODA dataset to reliably identify sleep spindles in the electroencephalographic recordings in a test set stratified into younger (24 years mean age) and older (62 years mean age) individuals. Our hyperparameter tuning optimized 45, we evaluated the performance of our model for an overlap threshold of 31, 0.83 (SpindleU-Net)32, 0.83 (SpindleNet)31, 0.80 (DOSED)31). These observations may suggest that the accurateness of automated spindle detection has approached a limit imposed by non-negligible inter- and intra-rater reliability of experts who annotated the data. They also provide evidence of the superiority of deep learning methods compared to classical feature-based approaches such as A7, which achieved an F1 score of 0.74 for the younger cohort . We note, however, that datasets of older individuals often include patients with various sleep pathologies, introducing additional factors that may limit comparative assessment of results. Nevertheless, the trend towards decreased detection performance was also visible for A7  between consensus and automated estimates by A7 and SUMO within age groups. Spindle densities computed from the expert consensus showed high levels of correlation with those estimated from A7 . Indeed, we did not find evidence of overfitting since the F1 score obtained on the training set did not exceed the ones obtained on the validation and test sets , a task desirable when studying the topographical distribution of spindles. While in principle our model can be used repeatedly to identify spindles in other channels, it remains to be investigated how accurate such detections are. Accurate localization of spindles could be achieved by multivariate models that would base their detections on all EEG channels. To develop and assess such models, we consider the creation of datasets with expert-consensus annotations for all EEG channels as the most important prerequisite. Another limitation shared with previous work31 is also related to the data used to train our model. Data were selected from N2 sleep stages of healthy individuals and were free from EEG artifacts. Ordinary EEG recordings will not meet these criteria, and spindles are also studied in N3 sleep. Models that accurately identify sleep spindles in N2 and N3 sleep and account for artifacts could be developed as datasets with corresponding expert-consensus annotations become available. Another limitation is related to the interpretation of expert-consensus spindles as objective truth. It is not clear whether spindles detected by models but not identified by the expert consensus nevertheless are mechanistically identical or may even reflect other biologically meaningful processes21. Furthermore, if an individual expert identified “true” spindles much more accurately than other experts, the expert consensus would be of lesser quality compared to this best individual expert. Since our model approximates an expert consensus, detected spindles are only objective in the sense that many experts would agree on them. Finally, we consider comparisons with previously published deep learning models on the MODA dataset as informative for future model development. To reduce difficulties for such comparative assessments in future studies, we publish our model together with detailed instructions33.The present study is limited in several respects. As previous methods for automated spindle detection, our model was trained to detect spindles in univariate time series (channel C3). Such a univariate approach does not localize sleep spindles (scalp localizationOur results underline the potential of deep learning models to automate the cumbersome task of spindle detection with super-human performance. We consider the availability of large datasets with expert-consensus annotations as key driver for the progress of methods towards objective and reliable spindle detection. We are confident that automatic spindle detection will continue to improve, saving staff and sleep researchers time and facilitating large-scale sleep studies.


This study aims to evaluate the efficacy and safety of dotinurad in hyperuricemic patients with type 2 diabetic kidney disease by comparing serum levels of urate and plasma and urinary levels of indoxyl sulfate excreted This single-center, single-arm, open-label, prospective, exploratory study aims to evaluate the effect of switching from febuxostat to dotinurad on serum urate levels and its background factors. The study will include 50 hyperuricemic patients with type 2 diabetic kidney disease and urate levels exceeding 6 mg/dL despite administration of febuxostat 20 mg/day for at least 3 months. The primary outcome is the achievement rate of serum urate levels of ≤6 mg/dL after 24 weeks of treatment with dotinurad at 0.5 mg to a maximum of 4 mg once daily. Secondary outcomes include the changes in serum urate levels, plasma and urinary indoxyl sulfate levels, and renal injury-related markers from baseline to observation points at weeks 4, 12, and 24.The study hypothesizes that switching to dotinurad may reduce the plasma levels of indoxyl sulfate and increase its urinary levels in patients with hyperuricemia. These suggest that dotinurad can potently lower the serum urate level by inhibiting URAT1 without adversely affecting ABCG2. Thus, findings of this study are expected to provide useful insights into the treatment of hyperuricemia associated with type 2 diabetic kidney disease and the discovery of new possibilities for dotinurad.Prior to the study, its study protocol was scientifically and ethically reviewed and approved by the Japan Physicians Association Clinical Research Review Board . In addition, patients who provide written informed consent will participate in the study. The results of this study will be published through submission to a peer-reviewed scientific journal.https://jrct.niph.go.jp/en-latest-detail/jRCTs031220080, identifier jRCTs031220080. The incidence of gout is increasing every year in Japan, with an estimated 1.1 million patients according to the Comprehensive Survey of Living Conditions, while the number of patients with hyperuricemia is approximately 10 million or more –4. In paHyperuricemia is classified into reduced urate excretion, renal overload , and mixed types. The prevalence of hyperuricemia is approximately 60% for the reduced excretion type, 30% for the mixed type, and 10% for the renal overload type; hyperuricemia with reduced excretion features accounts for the majority of cases  and OAT3, which are involved in excretion , 8. ABCGDotinurad is a novel selective urate reabsorption inhibitor (SURI) that selectively and potently inhibits URAT1 but with little effect on ABCG2, OAT1, or OAT3 . HoweverThe SWITCH SURI Study  is a single-center, single-arm, open-label, prospective trial. This is an exploratory study aimed at evaluating the effect of switching from febuxostat to dotinurad on the serum urate levels and renal impairment parameters, as well as the underlying factors and to evaluate the incidence of adverse events and changes in safety measures, such as liver enzymes, glucose, and metabolism Table 1.® 0.5 mg tablet orally once daily within 1 week after the end of the baseline examination, and if the urate level remains above 6 mg/dL after ≥2 weeks, the dose of Urece® tablets will be gradually increased (up to 4 mg), at observation points and at other scheduled visits, and continued until week 24 of the observation period.Dotinurad will be started with UreceBecause of the potential to affect the evaluation of this study, the concomitant use of urate-lowering drugs (other than dotinurad), drugs that act on urate transporters , or drugs that affect indoxyl sulfate levels  is prohibited until the patient visit at 24 weeks after the start of dotinurad administration (except in case of discontinuation). However, in principle, drugs that patients already take before they provide consent may be continued from the date of consent until week 24, and the dosage and administration should be increased or decreased as appropriate according to medical conditions. Moreover, drugs deemed medically necessary may be added, except for the above prohibited concomitant medications.Patients with type 2 diabetic kidney disease and hyperuricemia with urate levels exceeding 6 mg/dL even after receiving febuxostat 20 mg/day for at least 3 months who meet inclusion criteria and do not conflict with exclusion criteria will be eligible.Patients with serum urate levels greater than 6.0 mg/dL and less than 10 mg/dL within 8 weeks before the day of consentPatients with type 2 diabetes mellitusPatients on febuxostat 20 mg/day for at least 3 months who have not changed their diabetes medication dosage from at least 8 weeks before the day of consent until the day of consent2)Patients with renal impairment within 8 weeks before the date of consent and renal function between 30 and 60 by eGFR (mL/min/1.73 mPatients with eGFR measurement data available from 6 months ( ± 2 months) before the day of consentPatients aged ≥20 years on the day of informed consent (either sex)Patients who have provided their written informed consent to participate in this study.2) or end-stage renal failure on dialysisPatients with severe renal impairment Patients with a diagnosis or history of urinary tract stonesPatients diagnosed with malignancy or suspected or suspected of having malignancy Female patients who are pregnant, lactating, or may be pregnantPatients receiving uricosuric drugs, SGLT2 inhibitors, losartan, irbesartan, or spherical adsorptive carbon within 8 weeks before the day of consentPatients requiring a surrogatePatients with a history of hypersensitivity to any ingredients of the dotinurad productOther patients who are judged by the investigator to be inappropriate for this studyAfter patient selection, the principal investigator or the subinvestigator will explain to the patients the details of the study using a consent explanation document approved by an accredited clinical research review committee and obtain their written consent. The principal investigator or the subinvestigator will also explain that consent is given freely by the study participant and that the subject will not be treated unfavorably even if he/she does not consent, and that the participant may withdraw consent at any time after consent is given if he/she changes his/her mind, and that the subject will not be treated unfavorably in such cases.The primary outcome of this study will be the rate of achievement of serum urate levels of ≤6 mg/dL at week 24 of dotinurad treatment.The secondary outcomes of this study will include the following from baseline to observation points at weeks 4, 12, and 24.Change in serum urate levelsChanges in plasma indoxyl sulfateRenal impairment-related parameters: changes in eGFR, eGFR slope, serum creatinine, and urinary albumin and creatine (urine will be collected at any time and levels corrected for urinary creatine will be used)Changes in doses of the study drug used in the studyThe safety outcomes will include the following items.Incidence of adverse events and diseasesLiver function markers: changes in AST, ALT, and γ-glutamyl transpeptidase (γ-GTP)Glucose and metabolic markers: changes in hemoglobin A1c (HbA1c) and body mass index (BMI)Blood pressure and pulse: changes in systolic and diastolic blood pressure and pulse rateElectrolytes: changes in serum sodium and urinary sodiumThe exploratory outcomes will include the following items.Changes in urinary levels of urate and indoxyl sulfateInflammation oxidative stress markets: changes in serum high sensitive C-reactive protein (hs-CRP) and in urinary 8-hydroxydeoxyguanosine (8-OHdG)Comparison between groups of patients who achieved and did not achieve serum urate levels of ≤6 mg/dL (baseline levels and changes)Two analysis groups will be set for the efficacy of this study: Full analysis set (FAS) and per-protocol set (PPS), with FAS being the primary analysis. The FAS will be the population of eligible patients who have received the study drug and have serum urate readings at baseline and at least one time point during the observation period. The PPS will be the population from the FAS excluding the participants who deviated from the study protocol . The Safety Analysis Set (SAS) analysis population will be established for the safety of the study, and the SAS will be the population in which the study drug is administered and data on any of the safety outcomes are obtained.The target sample size will be set at 50. To evaluate the efficacy of switching to dotinurad in the participants in the study, a threshold value of 30% was set based on the achievement rate of serum urate levels of ≤6 mg/dL (20%) with 40 mg of febuxostat. To demonstrate that the lower limit of the two-sided 95% confidence interval exceeds the threshold of 30%, a significance level of α = 0.05 (two-sided) and a power of 1-β 90%, the required sample size was calculated by a binomial probability hypothesis test, resulting in 44. Considering dropouts, the target sample size will be set at 50.Summary statistics of the background data of the study participants will be calculated. For nominal variables, the numbers in each category and their percentages will be shown; for continuous variables, the numbers, mean, standard deviation, minimum, median, and maximum values will be calculated.The two-sided 95% confidence interval for the primary efficacy outcome of achieving a serum urate level of ≤6 mg/dL at week 24 of dotinurad treatment will be calculated, and a one-sample population proportion test will be performed for the null hypothesis of an achievement rate equal to 30%. Subgroup analysis will be performed by other candidate factors, such as subject background, to examine their impact on the primary outcome. For the secondary outcomes obtained from the metric data, summary statistics will be calculated at each observation point, a one-sample t-test or Wilcoxon’s signed rank test will be performed to evaluate their statistical significance, and if necessary, analysis of covariance and multivariate analysis will be performed. If the data are non-normally distributed, appropriate data transformations will be applied. When statistical tests are conducted, the significance level will be set at 0.05 two-sided, with the confidence coefficient for statistical estimation being 95% two-sided. Missing data in efficacy will be imputed using the last observation carried forward method.The efficacy of switching from febuxostat to dotinurad in patients with hyperuricemia or dotinurad in patients with type 2 diabetic kidney disease has not yet been studied in clinical setting. Furthermore, switching to dotinurad may affect plasma and urinary indoxyl sulfate levels in hyperuricemic patients, suggesting that dotinurad can potently lower the serum urate level by inhibiting URAT1 without adversely affecting ABCG2. Thus, the results of this investigation in this study may provide new insights into strategies for treating hyperuricemia with dotinurad.It is known that URAT1 inhibitors may be associated with high incidence of serum creatinine elevations and renal-related adverse events . The mecThe dose of febuxostat was selected based on the highest percentage of the dose (20 mg/day) prescribed in real-world clinical practice. In other words, the maintenance dose of febuxostat for hyperuricemia is usually 40 mg/day, but in a report  using thNonclinical studies reported that febuxostat inhibits the urate excretion transporters ABCG2. OAT1, and OAT3 , 13. TheIn this study, indoxyl sulfate will be used as an indicator of effects on ABCG2, OAT1, and OAT3. In patients with impaired renal impairment, the level of indoxyl sulfate in the circulating blood increases, thus a risk factor for CKD progression and cardiovascular complications due to its accumulation in the blood and various organs . Thus, lvia ABCG2, as does febuxostat . The study protocol was registered in the Japan Registry of Clinical Trials (jRCT) in May 2022 (jRCTs031220080) and is now available. This study will be conducted at the Naka Kinen Clinic in accordance with the ethical principles of the Declaration of Helsinki and in compliance with the “Clinical Research Act.” The physician in charge of the study will provide sufficient explanation to the study participants using the consent and explanation documents and obtain their free and voluntary consent in writing. The results of this study will be published through submission to a peer-reviewed scientific journal.https://jrct.niph.go.jp/en-latest-detail/jRCTs031220080.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: The studies involving human participants were reviewed and approved by Japan Physicians Association Clinical Research Review Board . The patients/participants provided their written informed consent to participate in this study.TO and MK are involved in the planning of the study. All authors are involved in the study design. TO and MK are involved in drafting this article. All authors read and critically reviewed this manuscript. All authors contributed to the article and approved the submitted version.


Chronic pain is a significant health problem and is particularly prevalent amongst the elderly. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has been proposed to reduce chronic pain. The aim of this study was to evaluate and compare the efficacy of active and sham tDCS in reducing pain in older individuals living with chronic musculoskeletal pain.n = 12) or sham tDCS  for five consecutive days. Pain logbooks were used to measure pain intensity. Questionnaires  were also used to assess pain in its globality.Twenty-four older individuals (mean age: 68 ± 7 years) suffering from chronic musculoskeletal pain were randomized to receive either anodal tDCS over the contralateral motor cortex , while sham tDCS did not produce any change (p = 0.15). Between-group comparisons for change in pain intensity reduction between active and sham tDCS showed a trend during treatment (p = 0.08) which was significant at the follow-up period (p = 0.02). Active tDCS also improved scores of all questionnaires , while sham tDCS only reduced MPDSS scores (p = 0.04). Between-group comparisons for the pain-related outcomes showed significant differences for BDI et PCS after the last tDCS session.Analysis of pain logbooks revealed that active tDCS led to a reduction in daily average pain intensity (all These results suggest that anodal tDCS applied over the primary motor cortex is an effective modality to decrease pain in older individuals. tDCS can also improve other key outcomes, such as physical and emotional functioning, and catastrophic thinking. Chronic musculoskeletal pain is the leading cause of disability among older adults, exceeding heart diseases, strokes, respiratory conditions, and injuries , 2. It iConservative treatments, including the use of pharmacological agents, remain one of the first line of treatment to alleviate chronic pain in late adulthood –19. DespNon-invasive brain stimulation techniques, in particular transcranial direct current stimulation (tDCS), have been extensively studied in the past years as a potential nonpharmacologic approach for reducing chronic pain , 27. AltTwenty-four elderly individuals were included in the study of whom 14 were part of a previously published pilot study . The floA randomized, parallel-arm, double-blind, sham-controlled design was used. The study lasted 19 days and was divided into 3 phases: a 7-day baseline evaluation (T1); a 1-week treatment period, which consisted of five consecutive daily treatment sessions of sham or active tDCS (T2); and a 7-day follow-up period (T3). Throughout the 19 days of the study, daily measures of pain were recorded with a pain logbook. Randomization to sham or active tDCS was performed using a random numbers table with a ratio of 1:1 based on the order of entry of the participants in the study created by the local institutional statistician. The randomized table design for our previous pilot study was extended to assign a total of 24 individuals (12 in each group); this new sample size was based on the calculations following the results in pain reduction obtained from the pilot study .Pain intensity (primary outcome measure) was evaluated with a daily pain logbook containing two numerical rating scales (NRSs) ranging from 0 to 10 . These two NRSs were used to evaluate the pain felt by the participant (i) on average in the past 24 h (average pain), and (ii) at its worst during the past 24 h . The pain assessment logbook was filled out by the participant at the end of each day throughout the duration of the study. The NRS have been shown to be reliable and valid to measure pain intensity in elderly patients with persistent pain . In addiPain intensity and quality measurements are essential, but they only capture a part of the pain experience and should be supplemented by other pain-related measures , 42–44. Participants were seated comfortably in an armchair during the 5 tDCS treatment sessions. The treatments with regards to one given participant were always given by the same investigator who was different from the evaluator. The investigator was responsible for the assignment of participants into the active or sham tDCS group, keeping the evaluator and the participants blinded. During the stimulation, the investigator hid the device from the participant with a towel. For each participant, the stimulations were given at the same time of the day to get as close as possible to a spacing of 24 h between tDCS sessions. Direct current was transferred to the participant by a saline-soaked pair of surface sponge electrodes (5 x 7 cm) and delivered by a constant, battery-driven, portable tDCS device . Participants received either anodal or sham stimulation of the primary motor cortex (M1). The anode was placed over M1, contralateral to the most painful site , and the cathode was placed on the supraorbital area contralateral to the anode. During active tDCS, a constant anodal current of 2 mA was delivered for 20 min. This anodal tDCS procedure has been shown to increase cortical excitability and reduce various types of chronic pain –64. For Pain intensity, as evaluated by the two NRS of the daily pain logbook  were averaged into 3 scores, reflecting the three phases of the study . As mentioned above, T1 represents the 7 days of baseline, T2 corresponds to the 5 days of tDCS treatments, and T3 represents the 7 days of follow-up. The mean values were used for all analyses. Percentages of hypoalgesia were also calculated to directly compare the efficacy of active and sham tDCS on pain, based on the following formula: hypoalgesia ={[pain score before treatment (T1)—pain score during or after treatment (T2 or T3)] / pain score before treatment (T1)} x 100. For pain intensity, a modified intention-to-treat analysis was used to handle missing data in the pain logbook. Specifically, participants with >10% of missing data were excluded from the analyses, whereas an average of the results for each time point was used for the participants who had <10% of missing data. There was no possibility of missing data for all the pain-related outcomes since participants completed the questionnaires in the presence of the evaluator.U Mann–Whitney tests were used to compare the two groups . This allowed us to evaluate if the outcome measures were different between the active and sham tDCS groups. Friedman tests and Wilcoxon Signed-Rank tests were also used to compare if the intervention affected the outcome measures in each group . All tests were performed using SPSS , and the differences were considered significant if p ≤ 0.05 was obtained.The study was designed to detect a clinically important difference of 2 points on an 11-point NRS , 70. To Twenty-four older individuals aged between 60 and 84 years  were included in the study. Two participants from the active tDCS group dropped out of the study during the pilot study, one because of a family event (death of a loved one–did not receive any tDCS session), and one because of a personal matter . These 2 dropouts were considered in the new randomized table generated by the statistician to include a total of 24 participants (12 by group). Data from the two participants who dropped out were excluded from all analyses. The demographic and general clinical characteristics of the 24 participants are summarized in B . Thus, we cannot conclude from participants' responses that they did better than randomly selecting their assignment group. The performance of the evaluator was similar; for the active and the sham tDCS groups; the evaluator guessed correctly for eight out of 12 participants and for six out of 12 participants, respectively. The probability of obtaining at least 14 correct answers out of 24 is estimated at 0.27 under a binomial distribution B , indicating that the evaluator, like the participants, did not do better than chance.After the end of the last tDCS session, all participants were asked to guess in which treatment group they thought they belonged to (active or sham tDCS). Of the 24 participants who completed the study, 18 had no clue and 6 had an idea about the treatment they had received. From these six participants, two assumed correctly that they received active tDCS, two assumed correctly that they had sham tDCS, and 2 (1 active and 1 sham) were incorrect. Considering all 24 participants , 13 correctly identified their assignment group. The probability of obtaining equivalent or higher success rates is 0.42 under a binomial distribution All participants tolerated the tDCS treatments well without experiencing any serious adverse effects. Fourteen participants out of 24 reported minor and transient side effects during or after the tDCS session. Of the 120 tDCS sessions delivered, 24 sessions (20%) were associated with minor side effects, namely mild tingling under the electrode (79%), mild headache (8%), mild sensation of bruise under the anode (4%), and moderate heat under the electrode (8%). The majority of the side effects (55%) were reported in the first tDCS session, and more than one-third of them occurred in the sham tDCS group.via the pain logbook filled daily by the participants during the 3 phases of the study. Three participants (one in the active tDCS group and two in the sham tDCS group) were excluded because >30% of missing data. For the remaining 21 participants included in the analyses, none had missing data except for one in the sham tDCS group (5% of missing data). For this participant, one of the 7 days of the follow-up period was not completed in the pain logbook; for this participant, pain intensity was calculated by averaging the scores over 6 days, rather than 7.Pain intensity measures (primary outcome) were obtained p = 0.006), but not in the sham tDCS group (p = 0.15). Post-hoc Wilcoxon signed-rank tests for the active tDCS group revealed that there was a significant reduction in the daily average pain during  and after  tDCS treatments, when compared to baseline (T1). For daily maximal pain scores (p = 0.05). No changes were observed for the sham tDCS group (p = 0.93).Pain intensity outcomes are presented in n scores , they alU Mann–Whitney tests comparing the active tDCS and sham tDCS groups revealed a statistically significant difference between the two groups at T3 (p = 0.02), but not at T2 (p = 0.08). For daily maximal pain, active tDCS reduced the pain scores by 19% at T2 and 21% at T3, whereas sham tDCS slightly increased daily maximal pain scores by 2% at T2 and 5% at T3. U Mann–Whitney tests comparing daily maximal pain between both groups failed to reach statistical significance, both at T2 (p = 0.09) and T3 (p = 0.06).To better delineate the effect of active and sham tDCS, percentages of hypoalgesia were calculated and compared between the 2 groups . Active p ≤ 0.02). Post-hoc Wilcoxon signed-rank tests revealed that active tDCS reduced MPQ, SF-BPI, BDI, BAI, PCS, and MPDSS scores at T2 compared to T1 , suggesting that tDCS had positive effects on the qualitative aspects of pain, physical functioning, emotional functioning (depressive symptoms and anxiety), catastrophic thinking, and the body surface covered by pain. Post-hoc Wilcoxon signed-rank tests also revealed significant improvements at T3 when compared to T1 , except for physical functioning (p = 0.31) and depressive symptoms (p = 0.09), suggesting that active tDCS produced a more persistent effect on the qualitative aspect of pain, anxiety symptoms, catastrophic thinking, and the body surface covered by pain. On the contrary, sham tDCS generated no change overtime for all pain-related outcomes , except for the painful body surface that was significantly reduced at T2 and T3 when compared to T1 (both p ≤ 0.02). U Mann–Whitney tests revealed between-group differences for depressive symptoms and catastrophic thinking at T2 (both p ≤ 0.03).Results from the questionnaires evaluating pain quality (MPQ) and depicting all other pain-related outcomes are presented in The present study aimed to evaluate the efficacy of tDCS on pain in older individuals living with chronic musculoskeletal pain. Overall, the results of this randomized, double-blind, sham-controlled trial indicate that tDCS is an effective approach for reducing chronic musculoskeletal pain and improving pain-related outcomes in elderly individuals. It is important to reiterate that the present study includes and builds on data coming from a previous pilot study ; the firWe observed a significant reduction in the daily average pain ratings among the active tDCS group during treatment (T2) and in the following week (T3) when compared to baseline (T1). Importantly, the percentages of hypoalgesia measured at T2 and T3 were found to be clinically significant, suggesting that patients with chronic pain may actually benefit from pain reduction achieved with tDCS , 70. OurOur results also showed that active tDCS improved all pain-related measures during the week of treatment (T2) when compared to baseline (T1). Physical functioning and pain quality are two of the most studied pain-related outcomes in trials looking into the effect of tDCS in older adults , 74, 78.In the present study, the improvements observed in all pain-related outcomes persisted over time and remained statistically significant 1 week after the last tDCS intervention (T3) when compared to baseline (T1), except for physical functioning and depressive symptoms. This follow-up period, although relatively short, is one of the strengths of this study, as most of the other studies looking into the effect of tDCS in older adults did not plan any follow-up assessments , 75–77. All participants included in the analyses completed all 5 tDCS sessions. Only 2 participants dropped out from the study: one before starting the first tDCS intervention (death of a loved one) and the other after attending 3 tDCS sessions . Like most studies of non-invasive brain stimulation in depression or chronic pain , no strTranscranial direct current stimulation was well tolerated in our participants (no important or severe side effects). Of all the tDCS sessions, only 20% were associated with benign side-effect (92% classified as mild and 8% as moderate). These observations correspond to the aggregation of tDCS experiences in humans, presented by Bikson et al., which revealed that tDCS did not produce any serious adverse effect or irreversible injury across over 33,200 sessions on 1,000 participants . TinglinBlinding was successful as only 13 out of 24 participants were able to correctly guess the type of stimulation they received, a ratio which is no better than chance or coincidence. Our results also suggest that our evaluator, who correctly guessed 14 times out of 24 in the group to which the participants belonged, was successfully blinded. Other tDCS studies conducted in the elderly also reported successful blinding of the participants, but did not discuss evaluators' blinding , 77, 78.post hoc tests. Given the relatively small number of participants and the fact that sample size calculation was done without considering such analyses, we refrained from applying corrections for the post hoc tests to reduce the risk of Type II errors. Finally, although many changes in pain intensity and pain-related outcomes are statistically significant, these results should be interpreted with caution as their clinical significance varies according to the thresholds set by different studies  reduced pain intensity and quality and improved many pain-related outcomes, such as physical functioning, emotional functioning (symptoms of anxiety and depression), catastrophic thinking, and body surface covered by pain. At this stage, and after the accumulation of evidence from all these small sample studies confirming the beneficial effect of tDCS in elders suffering from chronic musculoskeletal pain, we believe that the field is ready for the implementation of a large pragmatic multicenter study to confirm these promising results and better define the role tDCS could have in the prise en charge of this population.The data that support the findings of this study are available from the corresponding author upon request.This study was reviewed and approved by the Comité d'éthique de la recherche du CIUSSS de l'Estrie-CHUS. The patients/participants provided their written informed consent to participate in this study.MPH wrote the research protocol and the present manuscript, collected most of the data, and realized statistical analysis. MM and FH helped in data collection and revised this manuscript. ID helped with recruitment and data collection and revised the manuscript. ER helped to develop the research project and revised the manuscript. GL developed the research project, supervised the trial and the data analysis, and revised the manuscript. All authors contributed to the article and approved the submitted version.GL is supported by the Fonds de Recherche en Santé . The project was partially supported by the Center d'excellence en neurosciences de l'Université de Sherbrooke  and an internal start-up fund from the Research Center on Aging .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.