Although visual attention is one of the most thoroughly investigated topics in experimental psychology and vision science, most of this research tends to be restricted to the near periphery. Eccentricities used in attention studies usually do not exceed 20° to 30°, but most studies even make use of considerably smaller maximum eccentricities. Thus, empirical knowledge about attention beyond this range is sparse, probably due to a previous lack of suitable experimental devices to investigate attention in the far periphery. This is currently changing due to the development of temporal high-resolution projectors and head-mounted displays (HMDs) that allow displaying experimental stimuli at far eccentricities. In the present study, visual attention was investigated beyond the near periphery  and  in a peripheral Posner cueing paradigm using a discrimination task with placeholders. Interestingly, cueing effects were revealed for the whole range of eccentricities although the inhomogeneity of the visual field and its functional subdivisions might lead one to suspect otherwise. Visual attention has been—and still is—one of the most thoroughly investigated topics in experimental psychology and vision science. This is evident from a large bulk of empirical evidence  and higher visual cortical areas (cf. ). ImportEvidently, inhomogeneity represents a key challenge for visual information processing and basic cognitive functions like object recognition or visual search  by M by M11])To give possible exogeneous attention effects in the medium and far periphery the best chance to be revealed, the present study uses: (1) a peripheral spatial cue that is not predictive of the succeeding target location, but will occur at each possible target location with equal probability. (2) It combines this peripheral non-predictive cue with a discrimination task and placeholders  becauseHypothesis (1). A facilitative cueing effect  is expected at the near periphery eccentricity (15°). This hypothesis is derived from the vast number of near periphery visual attention studies showing facilitative cueing effects with similar experimental designs. Hypothesis (2). Due to restricted empirical knowledge on visual attention beyond the near periphery, two alternative predictions are made for the medium and far periphery: Hypothesis (2a). Either, cueing effects rise with eccentricity  cueing effects can only be found at the near (15°) and medium periphery (30° Exp. 1 or 35° Exp. 2) because 56° lies well beyond the EOMR ~45° and several studies using peripheral cues in combination with a detection task failed to reveal cueing effects beyond the EOMR [Hypothesis (3). Furthermore, if cueing effects are revealed for all periphery ranges, a positive correlation between the different cueing effects might be expected: A participant with a large cueing effect at one eccentricity should have large cueing effects at the other eccentricities and vice versa. A positive correlation would provide strong support for the assumption that the same attentional process is at work in different parts of the periphery. However, less emphasis lies on Hypothesis 3 because analyzing cognitive experimental psychology tasks such as Posner Cueing with an individual difference approach has inherent problems . Or . Or Hypothe EOMR ,45,46,47roblems ,49). Usu. UsuHyporoblems , median age 24 years (range 19–28 years), took part in Experiment 1. Before the experiment, they provided informed written consent. Participants received a compensation of 8 Euros per hour and had normal or corrected-to-normal visual acuity. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics-Committee of Bielefeld University (EUB-2015-094), approval date 21/07/2015.The experiments were conducted in a dark room. Stimulus presentation was controlled by a Dell computer using Ubuntu Linux (14.04 LTS) and presented by a Propixx Projector  on a screen with front projection, with the projector mounted on a frame below the room ceiling that would allow to shift the distance between projector and screen between experiments. The projected image had a size of 2.08 m width × 1.17 m height and a diagonal value of 2.38 m. The projected image resolution was set to 1920 × 1080 pixels. Participants sat at a distance of 88 cm to the screen, their head fixated by a combined chin and head rest. The refresh rate of the projector was set to 120 Hz. Stimuli were a black fixation cross , seven black rectangular placeholders (8.5° × 8.5°). The cue was an abrupt broadening of one of the placeholders from 4 px to 12 px. The target stimuli were the black letter E or a black Mirror-E (4.3° × 1.8°). Stimuli were presented on a medium gray background (23.6 cd/m²) only in a part of the projected image in a horizontal band of 1920 px × 360 px in the lower part of the screen. The rest of the projected image was set to black RGB (0.6 cd/m²). Participants’ responses were recorded by a ResponsePixx Button-Box with five buttons  arranged in the shape of a cross. The experiment was programmed using Matlab .E (360), in the other half Mirror-E (360). Target identity was equally distributed over all cueing conditions  and also over all eccentricities . However, due to a programming error, target identity was not equally distributed at each target side: 15Left; Ecc30Left;Ecc56Left;Ecc15Right;Ecc30Right;Ecc56Right. EccEach trial started with the presentation of the fixation cross and seven square-like placeholders in the middle of the lower horizontal band of the screen. The middle placeholder surrounded the fixation cross, the other six placeholders were displayed in pairs of three to the left and three to the right of fixation with a horizontal distance (center-to-center) of 15°, 30.2° (medium periphery), and 56.2° (far periphery) from fixation see . (Note tBut note that neither target identity (E/ME) nor target side (left/right) are theoretically relevant for the present study. Nevertheless, RT results separated by target side can be seen in For data analysis, median reaction times of correct responses were calculated for each participant and each condition of the within-subject factors Cueing  and Eccentricity . To exclude outliers, reaction times that were below or above three standard deviations (SD) of the mean were excluded from data analysis for each participant. For Experiment 1, a mean of (1.7%) percent of trials were excluded . Note that for comparability with other studies, analyses were also calculated for median reaction times without outlier exclusion and mean reaction times with outlier exclusion (+/−3 SD). However, the result pattern remained the same. In Experiment 1, cueing effects for mean reaction times with outlier exclusion were Ecc 15° = 49 ms, Ecc 30° = 16 ms, and Ecc 56° = 80 ms; and cueing effects for median reaction without outlier exclusion were Ecc 15° = 48 ms, Ecc 30° = 18 ms, and Ecc 56° = 80 ms. Repeated measures ANOVAs with post-hoc pairwise t-tests were conducted for effects of factors Cueing and Eccentricity. If violations of sphericity occurred, degrees of freedom were corrected using Greenhouse-Geisser-Epsilon. If the assumption of normal distribution was violated, a non-parametric Friedman test was performed but only reported if the results differed from the ANOVA. Post-hoc Bonferroni-Holm corrected Wilcoxen tests were also only reported if they differed from the pairwise t-tests.F = 34.86, p < 0.001, η²G = 0.05 and Eccentricity, F = 104.33 p < 0.001, Gη² = 0.22 as well as a significant interaction (Cueing × Eccentricity), F = 5.40, p < 0.001, Gη² = 0.01 Bonferroni-corrected pairwise t-tests showed that mean reaction times rise with eccentricity  < −7.33, all p’s < 0.001). Furthermore, valid and invalid RTs differed significantly, in the expected direction  = 9.11, p < 0.001). However, the neutral condition  and the invalid condition  did not differ from one another. Cueing effects  were only found for the smallest eccentricity  = −7.40, p < 0.001) and the largest eccentricity (t(19) = −9.08, p < 0.001, Ecc56 validM = 629 ms, SD = 109 vs. Ecc56 invalidM = 701 ms, SD = 115; see Ecc30 validM = 579 ms, SD = 96 vs. Ecc30 invalidM = 596 ms, SD = 91, t(19) = −2.42, p = 0.08).A repeated measures ANOVA of correct median reaction times showed a main effect of Cueing, F = 7.25, p <0.01, η²G = 0.03, a main effect of Eccentricity, F = 94.21, p < 0.001, η²G = 0.53, as well as a significant interaction Cueing × Eccentricity, F = 3.16, p < 0.05, Gη² = 0.04 Bonferroni-corrected post-hoc pairwise t-tests showed a difference between the invalid and the neutral condition, responses were slightly more accurate in the neutral condition than in the invalid condition  = 2.91, p < 0.05). Note that mean values and SD are shown as untransformed values for better comprehensibility. The far eccentricity differed significantly from both other eccentricities  = 12.77, p < 0.001) and  = 12.79, p < 0.001. Responses were less accurate for the far eccentricity . Furthermore, t-tests showed a significant difference at eccentricity 15° between the valid and the invalid condition  = 2.54, p < 0.05) and a significant difference between the valid and the neutral condition at eccentricity 30°,  = −2.86, p < 0.01).A repeated measures ANOVA of arcsine transformed probabilities of correct responses revealed a main effect of Cueing, F = 24.93, p < 0.001, Gη² = 0.09, a main effect of Eccentricity, F = 67.14, p < 0.001, Gη² = 0.21, as well as a significant interaction Cueing × Eccentricity, F = 3.72, p < 0.01, Gη² = 0.02  = −7.40, p < 0.001) and a significant difference between the valid condition and the neutral condition  = −6.27, p < 0.001). This reflects a cueing effect in the expected direction  of 39 ms. The main effect of Eccentricity is due to significant differences between all three eccentricity conditions  < −6.07, all p’s < 0.001); reflecting a rise in reaction times with eccentricity. At the eccentricity level, there was a cueing effect of 42 ms at eccentricity of 15°  = −6.40, p < 0.001), and a significant difference between the neutral and the valid condition  = −4.22,p < 0.01). At eccentricity of 56°, there was a cueing effect of 61 ms  = −5.39, p < 0.001) and a significant difference between the valid and the neutral condition at this eccentricity  = −4.28, p < 0.01). At eccentricity 30°, the cueing effect of 17 ms is only marginally significant  = −2.41, p = 0.08). However, the Bonferrroni-Holm corrected Wilcoxon signed rank test for the cueing effect at eccentricity 30° was not significant, p = 0.91.Furthermore, to separate perceptive and sensory processes from decision processes with respect to their contribution to reaction times, the reaction time distributions of each participant were fitted with an ex-Gauss function with 3 parameters  using the Matlab Toolbox by Lacouture and Cousineau . Paramet0.02 see . BonferrF = 11.91, p < 0.001, Gη² = 0.10. Bonferroni-corrected post-hoc pairwise t-tests revealed a significant difference between the near and far eccentricity  = −4.21, p < 0.001) and the medium and far eccentricity  = −3.49, p < 0.01). Thus, reaction times vary more strongly at the largest eccentricity.A repeated measures ANOVA of parameter σ revealed only a main effect of Eccentricity, F = 17.97, p < 0.001, η²G = 0.07. This main effect is due to significant differences between all eccentricities,  < −2.99, all p’s < 0.05). Thus, attentional lapses or other decision processes seem to rise with eccentricity.A repeated measures ANOVA of parameter τ revealed only a main effect of Eccentricity, r(18) = 0.18, p = 0.45, medium and far eccentricity, r(18) = 0.17, p = 0.46, near and medium eccentricity, r(18) = −0.16, p = 0.50).Furthermore, individual differences in cueing effect size (cost + benefit) are analyzed by correlating cueing effects between the three eccentricities. None of the correlations was significant , which is not ideal for individual difference analysis, individual cueing effects at each eccentricity probably do not provide stable estimates of individual cueing effects. Thus, the lack of positive correlations between cueing effect size at different eccentricities should not be interpreted as evidence for different attentional mechanisms in near, medium, and far periphery. Experiment 1 demonstrates a cueing effect by peripheral cues of 48 ms at the smallest eccentricity (15°) and 72 ms at the largest (56°), but only a marginally significant cueing effect of 17 ms at the medium eccentricity (30°). As predicted by Hypothesis 1exogenous as well as endogenous attention cannot be deployed outside the EOMR. However, based on their empirical findings, Smith and colleagues proposed a weaker version of the premotor hypothesis in which only exogeneous attention  fails to elicit cueing effects outside the EOMR, but endogenous attention  does elicit cueing effects also beyond the EOMR. They argued that the eye movement system is not necessarily involved in endogenous attention because the participants’ knowledge about the cue’s predictiveness biases the visual system towards the cued location by top-down cognitive processes and expectations without involvement of the oculomotor system. Nevertheless, the EOMR could provide an explanation for the reduced cueing effect at 30° eccentricity, if individual levels of the EOMR for many participants lay well below the mean EMOR of ~45°. Thus, no cueing effects would be found for these participants at the medium eccentricity because it would be already located beyond their individual EOMR range. Indeed, Casteau and Smith [On a theoretical level, Experiment 1 implies that the EOMR—eccentricity range that can be reached by an eye movement alone—is not necessarily a boundary for exogeneous attention because the 72 ms cueing effect at 56° lies well beyond the EOMR of ~45° in the far periphery. By contrast, due to the close link between visual attention and eye movements, Smith and colleagues assume that cueing effects cannot be elicited by non-predictive peripheral cues outside the EOMR. This assumption is derived from premotor-theory or hypothesis of attention , which and Smith  measurednd Smith , since tBasically, the reduced and marginally significant cueing effect at eccentricity 30° could be explained either due to inhomogeneity-related phenomena  or methodological factors . The first inhomogeneity related factor that might have contributed to the reduced cueing effect at the medium eccentricity of 30° is crowding. Crowding should impair the medium position most strongly because it is located between two placeholders at each side. However, the far position has only one placeholder to its side. Even though the near position is also located between two placeholders (fixation and medium placeholder), it probably experiences less crowding from the fixation placeholder. Furthermore, although strong crowding effects are unlikely with the chosen eccentricities, crowding cannot be ruled out completely. “Bouma’s rule” adapted by Strasburger et al.  would reThe third inhomogeneity-related factor that could explain the reduced cueing effect is eye movements in the direction of the peripheral cue. However, this does not seem very likely since eye movements in the direction of the cue should, if anything, rather increase reaction time differences between the valid and invalid condition, but probably not diminish it. A fourth inhomogeneity-related explanation might be provided by an IOR-related functional subdivision of the visual field beyond 15°, the smallest eccentricity used in Experiment 1. In a detection task Bao et al.  found a However, the reduced and only marginally significant cueing effect at 30° eccentricity could also be explained by methodological factors. Reduced power, reliability or effects of the specific sample might have led to a random finding of a non-existent or reduced cueing effect at the medium periphery and might not bear theoretical implications at all. This explanation would be excluded by replicating this finding in the medium periphery.Furthermore, Experiment 1 showed rising reaction times with eccentricity. This is in accordance with previous studies that demonstrated an increasing effect of eccentricity on reaction times for stimuli not scaled for cortical magnification, for a review see  (p. 17),Since empirical evidence on visual attention in the medium and far periphery is sparse, it is the aim of Experiment 2 to replicate the main results of Experiment 1—cueing effects in the near and far periphery, and a non-existent or a reduced cueing effect in the medium periphery—in a slightly changed experimental design. First, eye movements as well as other gaze deviations from the designated fixation area (fixation errors) are controlled for in Experiment 2 by discarding and repeating trials with fixation errors in the critical trial period. In addition to eye movements elicited by the peripheral cue, this fixation control will also eliminate unspecific eye movements and any other deviation from fixation. This should lead to a reduction of intra-individual noise and consequently an increase in reliability because it also eliminates trials in which participants did not comply with the fixation task instructions correctly due to various reasons . Moreover, fast eye movements might have enhanced cueing effects at the near and far periphery in Experiment 1. Furthermore, a different medium eccentricity of 35° degrees is used in Experiment 2, because 30°–35° constitutes the outer border of another performance plateau in the visual field, that of light sensitivity ,52,53. IExperiment 2Experiment 2 is a replication of Experiment 1 with control of possible eye movements and a slightly changed medium target location of 35.2°. Eye movements were monitored with an Eyelink 1000 Plus Desktop Mount  monocularly at a refresh rate of 2000 Hz. In all participants, the left eye was tracked. For offline analysis, eye tracking data were downsampled to 250 Hz. Materials and Methods of Experiment 2 were very similar to Experiment 1; hence, only deviations from Materials and Methods with respect to Experiment 1 are reported here.Seventeen participants recruited at Bielefeld University took part in Experiment 2 . One participant’s session had to be aborted because she made too many fixation errors. The data of one further participant had to be excluded because the median of reaction times in several conditions was larger than 1000 ms.In addition to apparatus and stimuli described in Experiment 1, Experiment 2 used an Eyelink 1000 Plus Desktop Mount  to monitor for possible eye movements.In addition to the procedure already described in Experiment 1, the position of the left eye was monitored at 2000 Hz. A trial was repeated if the participants did not fixate in a circular area of 2.5° around the fixation cross before the onset of the cue and after the offset of the target. If participants did not fixate correctly, the fixation cross became red after the trial. Furthermore, additional trials were excluded offline from the analysis, if eye positions outside an area of 2° around the fixation cross were registered in the critical trial interval between cue onset and target offset. For the offline analysis, data was downsampled to 250 Hz. On average, 6.6 percent of trials had to be repeated and 1.8 percent of trials were excluded offline because participants fixated outside the specified area. F = 21.03, p < 0.001, Gη² = 0.05 and Eccentricity, F = 72.99, p < 0.001, Gη² = 0.18 as well as a significant interaction (Cueing × Eccentricity), F = 2.99, p < 0.05, Gη² = 0.01  and the invalid condition  = −6.33, p < 0.001), and thus a cueing effect of 51 ms, as well as a significant difference between the valid and the neutral condition  = −5.91, p < 0.001). The main effect of Eccentricity is due to reaction times rising with eccentricity; significant differences were found between all three eccentricity levels , all t’s(44) < −6.75, all p’s < 0.001). Bonferroni-corrected pairwise t-tests at each eccentricity level showed cueing effects in the expected direction at all eccentricities  < −3.43, all p’s < 0.05), but the neutral condition differed only significantly from the following conditions  < −4.05, all p’s < 0.01). First, median reaction times of correct responses were calculated for each participant and each condition of the within-subjects factors Cueing  and Eccentricity . To exclude outliers, reaction times that were above or below 3 SD of the mean were excluded from data analysis. On average, 1.6 percent of trials were excluded as outliers. Note that as previously demonstrated in Experiment 1, analyses with median reaction times without outlier exclusion and mean reaction times with outlier exclusion lead to the same result pattern. A repeated measures ANOVA of correct median reaction times showed a main effect of Cueing 0.01 see . BonferrF = 57.45, p < 0.001, Gη² = 0.43. This main effect indicated decreasing accuracy with increasing eccentricities  > 2.89, all p’s < 0.05). Note that all post-hoc tests were performed on arcsine-transformed data, although untransformed means and standard deviations are reported here for better comprehensibility. As in Experiment 1, reaction time distributions were fitted with an ex-Gauss function using the Matlab Toolbox by [F = 17.88, p < 0.001, Gη² = 0.12 as well as a main effect of Eccentricity, F = 55.25, p < 0.001, Gη² = 0.21 Bonferroni corrected pairwise t-tests revealed that parameter μ differed for the valid and the invalid condition  = −4.79, p < 0.001) and the valid and the neutral condition  = −5.97, p < 0.001). Thus, parameter μ shows a cueing effect of 51 ms. The main effect of Eccentricity is due to rising reaction times with increasing eccentricity  < −3.53, all p’s < 0.01). A repeated measures ANOVA of parameter σ revealed only a significant main effect of Eccentricity, F = 22.25, p < 0.001, Gη² = 0.14 Bonferroni-corrected pairwise t-tests revealed that the main effect is due to significant differences between all eccentricities  < −2.60, all p’s < 0.05). This might be interpreted as an indicator that the perceptual part of reaction times is more variable at larger eccentricities. A repeated measures ANOVA of parameter τ revealed a main of Eccentricity, F = 9.87, p < 0.001, Gη² = 0.03 Bonferroni-corrected pairwise t-tests revealed that the main effect is due to a significant difference between the near and far eccentricity  = −4.36, p < 0.001) as well as a significant difference between the medium and far eccentricity  = −2.71, p < 0.05). A two-way ANOVA of arcsine-transformed percent correct data with factors Cueing × Eccentricity revealed only a main effect of Eccentricity, olbox by ), see Fir(13) = 0.62, p < 0.05 Neither the correlation between the medium and the far eccentricity, r(13) = 0.37, p = 0.18, nor the correlation between the near and medium eccentricity were significant, r(13) = 0.37, p = 0.17.Furthermore, as in Experiment 1, individual differences in cueing effect size (cost+benefit) are analyzed by correlating cueing size between the three eccentricities. There was a moderate positive correlation between cueing size of the near and far eccentricity, r = 0.61 between the near and far eccentricity; lower correlations with the medium eccentricity were not significant. Thus, it can be concluded that the same attentional mechanism is at work in the near and far periphery. Furthermore, as expected, intra-individual noise reduction due to eye movement and fixation control in Experiment 2 helped to reveal this effect, because the near and far experimental condition differed from Experiment 1 only in this respect. However, whether the same attentional mechanism also operates in the medium periphery is less clear and should be investigated in future research with an experimental design more tuned to an inter-individual difference approach.Overall, Experiment 2 replicated the results of Experiment 1 with additional eye position control and a slightly changed medium eccentricity position of 35°. Like Experiment 1, it revealed the expected cueing effect in the near periphery (Ecc 15° = 43) and a larger cueing effect in the far periphery (Ecc 56° = 66 ms). However, the non-existent or reduced cueing effect in the medium periphery was not replicated; instead, Experiment 2 revealed a substantial cueing effect (Ecc 35° = 45 ms). Replicating the main findings of Experiment 1 was especially important since, as already discussed broadly, few studies on spatial cueing have been conducted in medium and far periphery so far. As predicted in Hypothesis 2a, Experiment 2 shows at least numerically rising cueing effects  and no reduction of the cueing effect at the medium eccentricity as in Experiment 1. Furthermore, as predicted in Hypothesis 3, Experiment 2 revealed a positive moderate correlation Thus, Experiment 2 is in complete accordance with the quantitative Hypothesis 2a that predicts rising cueing effects with rising eccentricity. However, does this indicate that the reduced cueing effect at the medium position in Experiment 1 is due to deviations from fixation and small eye movements that could not be detected due to a lack of eye tracking control or fixation control? This does not seem likely for undetected eye movements elicited by the peripheral cue because they should be drawn primarily in the direction of the peripheral cue, that means in the direction of the target in the valid condition but away from the target in the invalid condition. Thus, eye movements towards the peripheral cue should enhance cueing effects rather than diminish them. However, eye movements and deviations from the fixation area induced by various other factors  might have randomly been incorporated in the medium position cueing effect in greater proportion, thereby reducing the cueing effect. The cueing effect of 45 ms might also be due to the shift of medium eccentricity from 30° to 35°. Reasons for this difference might be reduced cue effectiveness in Experiment 1, either due to crowding or to a location at the border of the often-found light sensitivity plateau or a different time course of IOR between the two locations. However, none of these options seems especially likely, since it was only a small shift of 5°. Thus, although Experiment 2 cannot finally answer the question of what caused the reduced cueing effect in the medium eccentricity, it indicates that the effect is not very robust or stable because it could not be replicated when the test location in the medium periphery was slightly shifted and eye movements and fixation errors are controlled for. It seems unlikely that a theoretically relevant effect could only be revealed at the specific location of 30°. Finally, as in Experiment 1, Experiment 2 shows rising reaction times with eccentricity, which is in accordance with previous findings, ,54,55 anThe present study explored visual attention beyond the near periphery to which most visual attention studies are restricted. More specifically, it investigated the question whether exogenous attention elicited by non-predictive peripheral cues shows qualitative or quantitative differences between near periphery attention and medium or far periphery attention. A qualitative difference would indicate either no cueing effect or a reversed inhibitory cueing effect  in the medium or far periphery since facilitative cueing effects are expectable  in the near periphery. A quantitative difference would indicate a difference in cueing effect size, but not in direction or even existence. Theoretically, a qualitative difference between near, medium, and far periphery might be predicted due to the visual field’s inhomogeneity because it leads to very different conditions under which attention must operate across the periphery. Furthermore, a qualitative difference between near/medium vs. far periphery is predicted by the premotor hypothesis of attention  and studquantitative differences between attention effects in different parts of the periphery: Somewhat larger cueing effects were found in the far periphery  than in the near periphery. What mechanisms might explain the rising size of cueing effects towards the far periphery? In consideration of the IOR increase beyond 15°–20°, Bao and colleagues [However, Experiment 1 and 2 revealed small-to-medium lleagues ,14 suggelleagues ,55), rea15 = 42 ms*, Ecc30 = 17 ms*, Ecc56 = 61 ms*; Experiment 2: Ecc15 = 45 ms*, Ecc35 = 32 ms*, Ecc56 = 77 ms). Additionally, reaction time reliability was higher at the far periphery eccentricity, reflected in a larger parameter σ of the fitted reaction time distributions . Furthermore, in both experiments, decision processes or attentional lapses seemed to be more variable in the far periphery. Parameter τ was larger for the largest eccentricity . However, some caution in interpreting these parameters might be in order due to the ongoing debate surrounding the question whether specific cognitive processes can be mapped to the different parameters of the ex-Gauss function or not [In addition to the already discussed main findings of the present study, ex-Gauss fitted reaction times distributions support the reported findings but allow further inferences about the nature of these processes: Cueing effects for all eccentricities were revealed in the measure of the central tendency parameter μ, except an only marginally significant cueing effect at 56° in Experiment 2  What are the time courses of facilitative and inhibitory cueing effects (IOR) beyond the near periphery? (2) How do these time courses vary across individuals and eccentric locations? (3) How do they interact with crowding? (4) How do detection and discrimination cueing paradigms differ beyond the near periphery?


A large amount of the current literature dedicated to solid states of active pharmaceutical ingredients (APIs) pays special attention to polymorphism of flavonoids. Taxifolin  is an example of a typical flavonoid. Some new forms of taxifolin have been reported previously, however it is still unclear whether they represent polymorphic modifications. In this paper, we tried to answer the question about the taxifolin polymorphism. Taxifolin microtubes and taxifolin microspheres were synthesized from raw taxifolin API using several methods of crystal engineering. All forms were described with the help of spectral methods, scanning electron microscopy (SEM), X-ray powder diffraction (XRPD), and thermal analysis (TA). SEM reveals that the morphology of the solid phase is very specific for each sample. Although XRPD patterns of raw taxifolin and microtubes look similar, their TA profiles differ significantly. At the same time, raw taxifolin and microspheres have nearly identical thermograms, while XRPD shows that the former is a crystalline and the latter is an amorphous substance. Only the use of complex analyses allowed us to put the puzzle together and to confirm the polymorphism of taxifolin. This article demonstrates that taxifolin microtubes are a pseudopolymorphic modification of raw taxifolin. Development of drugs is a long and expensive process. That is why pharmaceutical companies are poised to invest more money in research ,2. In suOne of the primary goals of crystal engineering in drug design consists in obtaining the polymorphic landscape of the active pharmaceutical ingredients (APIs). Vernadsky defined polymorphism as a general property of materials . This teA significant part of the current literature on polymorphism pays particular attention to flavonoids. From the point of view of chemical sciences, this group of compounds is a derivative of 1,3-diphenylpropane. They are secondary metabolites of plants . FlavonoLarix spp. wood. Taxifolin has reducing properties and is well known as a food ingredient thanks to its antioxidant activity [Taxifolin, also known as dihydroquercetin, is a commercially available flavonoid . Its maiactivity ,19,20. Iactivity ,22, neuractivity ,24, hepaactivity ,26, regeactivity , antitumactivity , anti-inactivity , antidiaactivity , and antactivity ,32. Due In order to obtain a standard sample, a complex investigation of taxifolin was performed. There exist several articles that focus on the X-ray analysis of such samples, produced by a different manufacturer ,34. PhysThis paper relays the analysis of different forms of taxifolin with the objective to try to categorize them as polymorphic modifications.For a detailed examination of the sample morphology, scanning electron microscopy (SEM) was performed. Microphotographs of different taxifolin forms are shown in In contrast to this sample, taxifolin microtubes c,d have The taxifolin microspheres, obtained by spray drying, are illustrated in X10, X50, and X90 were 4.18, 23.80, and 214.00 µm, respectively , which are chromophores. UV spectroscopy may be used to confirm the absence of structural degradations during the processing. UV spectra of taxifolin solutions, obtained from different modifications, show an absorption maximum at 7 nm see .m/z: 303, 417, and 607  and thermal gravimetric analysis (TG) of raw taxifolin, taxifolin microspheres, and taxifolin microtubes are presented in The melting point of taxifolin samples lies at 228 ± 1 °C, and the process is accompanied by decomposition, suggesting that all modifications eventually transform into high melting forms see . There aApparently, all samples contain moisture, physically sorbed water, which is completely released at 120 °C. Moreover, the DSC of taxifolin microtubes shows a pronounced endothermic effect at ca. 89 °C, most likely indicating the elimination of crystal solvent b. Simultm/z 14 (N+), 15 , 16 (NH2+), 17 , 28 , 29 (HCO+), 42 (NCO+), 43 (HNCO+), 44 (CO2+). Even though the amount of urea molecules was small, it could be considered as an inclusion either in microtubes’ cavity or in the crystal structure. Thus, X-ray powder diffraction (XRPD) was performed to elucidate it.For taxifolin microtubes, there is an endothermic effect starting from ca. 170 °C. This peak can be attributed to decomposition of urea contained in taxifolin tubes. This hypothesis is supported by STA-MS see : charactθ 7.16, 7.72, 14.28, 15.04, 15.48, 17.64, 20.96, 24.88, 25.60, 26.28, 27.40, 31.68, 34.56, 37.88, 39.32, and 46.28 , 110 (2θ = 33.8°), and 111 (2θ = 44.5°). Furthermore, with the help of the MOLSV program, it was ascertained that the van der Waals volumes of urea and water molecules were 40.4 Å3 and 13.1 Å3 respectively. According to taxifolin structural data, available in the Cambridge Structural Database (CCDC) [A theoretical calculation of urea XRPD patterns based upon X-ray crystallography data published previously  showed te (CCDC)  under idθ 15.3°, the new patterns of this sample are almost identical to the patterns shown in θ 10.9, 12.1, 18.2, 23.4, 24.8, 28.4, 29.8, 30.9, and 32.6 and disappearance of peaks at 2θ 11.6, 15.3, 23.0, 24.0, and 25.9°). To clarify the nature of the phase transition, we conducted in situ variable-temperature XRPD . Figure The objective of this research was to investigate the nature of the different solid states of taxifolin. Our results indicate that taxifolin microspheres are an amorphous substance. This conclusion was confirmed by the presence of an exothermic effect in the thermogram and by an amorphous halo in the XRPD pattern. This form can thus be considered as a polymorphic modification of raw taxifolin according to the definition of polymorphism as defined by the U.S. FDA. At the same time, we found that taxifolin microtubes represent a pseudopolymorphic modification of raw taxifolin. Although XRPD patterns of these forms look quite similar at room temperature, thermal analysis revealed phase transitions between taxifolin modifications. Only the use of comprehensive analysis allowed us to observe phase transitions from a hydrate to an anhydrous form. In this way, the puzzle was put together and we confirmed the pseudopolymorphism of taxifolin. Nowadays, the optimization of the API phase state has come to be seen as a new stage of drug development. For this reason, we need to have a clear analysis strategy for such objects. There is a wide range of methods that lend themselves in this respect, however all of them have a combination of advantages and disadvantages . A sole Overall, while the nature of taxifolin microtubes and taxifolin microspheres is still being explored, these modifications have the potential to be registered as new APIs. Thermal and microscopic analyses may be used in taxifolin quality control to identify the form of its solid state. Thanks to their morphology, taxifolin microtubes and taxifolin microspheres are promising objects for medical application due to expected unique physicochemical properties ,47,48.R,3R-taxifolin , throughout the paper referred to as raw taxifolin, and urea  were used in this study. Denatured ethanol  was used as a solvent.Solid pharmaceutical-grade 2Stock solution was obtained by mixing 1 g taxifolin with urea under 1:1 molar ratio. The mixture was dissolved in 50 mL of denatured ethanol, and deionized water was added dropwise to the stock solution. Liquid samples were stored at room temperature for 48 h, and then the solid phase was isolated from the surfactant via filtration. The precipitate was left to dry in the air for 24 h.The stock solution was prepared by dissolution of 1 kg taxifolin in 40 L of deionized water at 60 °C with continuous stirring. Microspheres were obtained by using GLP-60 centrifugal spray with a blade high-speed disk under the following conditions: inlet air temperature was 180 °C, the outlet temperature was 80 °C. Taxifolin remained in the chamber of the dryer for 1.5–2 s.SEM was carried out on JSM-6380LA  and involved various magnifications. It was operated at 20 kV accelerating voltage in SEI-mode (Secondary Electron Imaging). Each sample was fixed on an aluminum sample holder with double-sided carbon tape and coated with gold in Argon atmosphere at 0.1 Torr in an IB-3 ion coater . The gold coating was approximately 20 nm thick. As the maximum magnification during SEM analysis was 10,000×, gold layer thickness influence was negligible.A laser particle sizer Analysette 22  was employed to measure the particle size distribution of taxifolin solids. A small aliquot of each powder was dispersed in water. The measuring range was from 0.1 to 1250 µm. For calculations of particle size, we used Fritsch Analysette software.The UV spectra were obtained with a Cary 100 spectrophotometer . To perform the UV spectroscopy, each taxifolin sample was dissolved in denatured ethanol.m/z 10–1000.The mass spectrometry was carried out using an Advion LC-MS mass spectrometer  equipped with an electrospray ionization source operated in negative ion mode. Each taxifolin sample was dissolved in solvent methanol/water (4:1) with 0.1% of formic acid. Data were collected over a mass range of 1H spectra of taxifolin samples were recorded on a Varian VNMRS-400 spectrometer  operated at 399.82 MHz at 25 °C in DMSO-6d in 5 mm sample tubes. The chemical shift was externally referenced to tetramethylsilane.NMR ® differential scanning calorimeter, TG 209 F1 Iris® thermobalance, and STA 409 PC Luxx® simultaneous thermal analyzer coupled with QMS 403C Aëolos® quadrupole mass spectrometer . Measurements were taken in aluminum (DSC) and alumina  crucibles (lid with a hole) under dry nitrogen flow (20–70 mL·min−1) with a heating rate of 10 °C·min−1. All instruments were previously calibrated for temperatures and enthalpies of phase transitions of pure (99.999%) standard substances in compliance with ASTM Practices E967, E968, E1582, and E2253: cyclohexane, Hg, Ga, benzoic acid, In, Sn, Bi, Pb, Zn, CsCl—for DSC; In, Sn, Bi, Zn, Al, Ag, Au—for TG and STA. Calcium oxalate monohydrate was used for validation of thermobalances. Mean estimated temperature and mass determination errors were 0.3 °C and 0.2%. Experimental data were processed in NETZSCH Proteus® Software according to ASTM E794, E2550 and ISO 11357-1.Specimens with the weights of 2.00–10.00 mg  were tested with a DSC 204 F1 Phoenixθ–θ wide-angle goniometer and a Peltier solid-state detector with monochromatic Cu-Kα radiation (λ = 1.54 Å) operated at 25 mA and 45 kV was used. X-ray powder diffraction (XRPD) data were collected at 295 K. The range of the 2θ diffraction angle was 5°–50° with a step size of 0.04° and an integration time of 1 s. Each sample was placed similarly inside the plastic sample holder.An ARL X’TRA X-ray diffractometer  with a vertical θ range of 10–35° with a step size of 0.05° and 4.0 s counting per step. The copper block with the sample was heated at the rate of 300 °C/min. Temperature control was performed by Eurotherm 2604  with BP5\20 thermal element. Data recollection lasted 7 min. Diffraction data were collected at 25, 130, and 170 °C so that changes in structure during heating could be observed. The samples were vacuumed, the residual pressure ranged from 5 × 10−5 to 5 × 10−4 Pa.Variable-temperature powder X-ray diffraction measurements were carried out with an ARL X’TRA diffractometer  and HTK2000  using Cu-Kα radiation (λ = 1.54 Å) operated at 40 mA and 45 kV. A tungsten block acted as a sample holder. The thickness layer measured ca. 50 μm. Patterns were collected in the 2The array of data we obtained makes it possible to complete the puzzle. Herein, taxifolin API and microtubes were demonstrated to be crystal substances. Tubular modification was characterized by the presence of crystal water; hence it corresponds to a hydrate form. The solid phase of taxifolin API did not contain crystal water. As such, these forms represent pseudopolymorphic modifications. At the same time, microspheres are an amorphous substance. In general, according to the FDA guidelines, both forms of taxifolin may be considered polymorphic modifications of the API.In this paper, we have also highlighted the need for complex analysis while revealing an API’s polymorphism. Only a multifaceted approach to the problem of polymorphism can prevent biases in drug development.According to our data, this paper is the first to demonstrate the polymorphic nature of differences among physical and physicochemical properties of new taxifolin forms. It may have a significant impact on the development and registration of new taxifolin-based medicines.


Pain is a multidimensional experience of sensory-discriminative, cognitive, and affective processes; however, current basic research methods rely heavily on response to threshold stimuli, bypassing the supraspinal processing that ultimately gives rise to the pain experience. We developed the operant plantar thermal assay (OPTA), which utilizes a novel, conflict-based operant task requiring evaluation and active decision-making to obtain reward under thermally aversive conditions to quantify thermal pain tolerance. In baseline measures, male and female mice exhibited similar temperature preferences, however in the OPTA, female mice exhibited greater temperature-dependent tolerance, as defined by choice time spent in an adverse thermal condition to obtain reward. Increasing reward salience (4% vs 10% sucrose solution) led to increased thermal tolerance for males but not females. To determine whether neuropathic and inflammatory pain models alter thermal tolerance, animals with chronic constriction injury (CCI) or complete Freund’s adjuvant (CFA), respectively, were tested in the OPTA. Surprisingly, neuropathic animals exhibited increased thermal tolerance, as shown by greater time spent in the reward zone in an adverse thermal condition, compared with sham animals. There was no effect of inflammation on thermal tolerance. Administration of clonidine in the CCI model led to increased thermal tolerance in both injured and sham animals. In contrast, the non-steroidal anti-inflammatory meloxicam was anti-hyperalgesic in the CFA model, but reduced thermal pain tolerance. These data support the feasibility of using the OPTA to assess thermal pain tolerance to gain new insights into complex pain behaviors and to investigate novel aspects of analgesic efficacy. The translation of novel pain management techniques has been hindered, in part, by reliance on pre-clinical models that do not to measure the multidimensional experience of pain. Here we present a novel device and protocol to assess pain tolerance in the mouse. We show that pain tolerance is a dynamic behavior influenced by sex, that hypersensitivity does not necessarily predict pain tolerance, and that analgesics that reduce hypersensitivity may not enhance pain tolerance. This approach increases the capability to pursue new directions in basic pain research.Chronic pain, as a primary condition and sequela, is a leading cause of global morbidity and disability . CurrentPain is a multidimensional experience, comprised of sensory-discriminative, affective, motivational, and cognitive components, which are generated by the brain . Given tWe designed and constructed a novel, inexpensive device and developed a behavioral protocol to quantify pain tolerance in mouse models through an investigator-independent and un-biased operant task. The operant plantar thermal assay (OPTA) utilizes operant learning and decision-making within an approach-avoidance conflict paradigm to establish the duration and intensity of a noxious stimulus an animal will withstand to obtain a reward. Here we establish the parameters at which the OPTA can be used to determine baseline thermal pain tolerance. We test whether thermal pain tolerance is a dynamic behavior influenced by sex, motivation, and analgesics in neuropathic and inflammatory models of pain. We further demonstrate the effects of common analgesics on thermal pain tolerance behavior under this conflict paradigm. These experiments illustrate the utility of the OPTA as a practical tool to establish and modulate pain tolerance behavior, which can be used to complement standard threshold-level nociceptive testing.The floor of the OPTA is made of two 12” × 6” × ¼” aluminum plates , which are fixed to a cold plate peltier  using MX4 Thermal Conductive Paste (Arctic) and countersunk screws (M4 machine screws), then joined using non-conductive PL Premium Polyurethane construction adhesive (Loctite). Each Peltier is independently controlled by a power supply  and temperature controller . A thermistor delivers real-time feedback to the temperature controller, allowing precise thermal regulation of the floor. A ¼” thick acrylic (SourceOne) enclosure surmounts the apparatus. This enclosure is divided into two equally sized chambers (5.5” × 5.5” × 12”) by placing a wall (¼” × 4” × 12”) at the midpoint of the enclosure creating a narrow, 1.5” wide passageway to allow movement between the chambers. Two equidistant holes are drilled through the rear wall of the acrylic enclosure through which the spouts of water bottles are mounted. To circumvent the potential confound of object novelty, spouts matched those used in the home cage. One bottle was empty (null zone), the other contained a 4% sucrose solution (reward zone), which is innately rewarding to mice and widely used in appetitive operant conditioning paradigms . Zone arad libitum, except as described. Facilities were maintained at ∼22% humidity and 22°C on a 12/12 h light/dark cycle. Testing occurred during the light cycle. Animal testing procedures and handling complied with the ethical guidelines and standards established by the Institutional Animal Care and Use Committee in compliance with the Guide for Care and Use of Laboratory Animals [Adult (8- to 12-week-old) male and female C57BL/6J mice were housed four per cage. Food and water were available  Animals .Neuropathic pain was induced using a modified chronic constriction injury (CCI) of the left sciatic nerve performed under isoflurane anesthesia, wherein three ligations were applied to the nerve using 6–0 chromic gut, until a brief twitch of the surrounding muscle was seen . ControlFor all experiments, an equivalent volume of 0.9% biological saline (Hospira Inc) was injected as vehicle control. The α2-agonist, clonidine (Sigma-Aldrich) was administered intrathecally at a concentration of 0.1 μg/5 μl in a total volume of 5 μl to animals having received CCI or sham, as described previously . This doA modified Hargreaves test  was usedt tests (two-tailed), were conducted to assess differences in thermal preference, thermal threshold, reward seeking, learning and extinction, distance traveled, pooled comparisons, and time in reward zone. ANOVA was used to explore the effect of training day, impact of sex, sucrose concentration, temperature, pain, and pain with analgesia on time spent in the reward zone, thermal threshold, and distance traveled. Pearson correlation assessed time in reward zone with sucrose consumption. Where appropriate, Tukey’s or Sidak’s post hoc tests were conducted to determine significance. All analyses were done using GraphPad Prism 7; α = 0.05 was set as the determinant of significance. Data are reported as mean ± SEM.The principle measure of interest from the OPTA was time spent in the reward zone. Distance traveled was also recorded to assess mobility; t(30) = 0.6, p = 0.55, males and females combined, data not shown). To establish the thermal preference of wild-type, naive female and male mice in the OPTA, five free choice tests were performed, wherein animals were given 900 s to freely explore the OPTA with floor temperature pairings of 15/20°C, 20/25°C, 25/30°C, and 30/35°C. A reduced test time of 600 s was set for 35/40C° to prevent the possibility of undue discomfort to the animals. No bottles were present during these tests. Preference or aversion was measured as proportion of total test time spent in each chamber. The chamber in which significantly less time was spent was interpreted as thermally aversive.Side bias was assessed for female and male mice during a 10-min free exploration of the OPTA with two empty bottles present. Neither females nor males displayed a side preference (t(4) = 4.32, p = 0.01); a preference for 30°C in the 30/35°C condition (t(4) = 3.25, p = 0.03); and a preference for 35°C in the 35/40°C condition (t(4) = 3.05, p = 0.04). Overall, male and female mice displayed the same pattern of thermal preference, with no between-sex difference. As there was no difference between sexes at any given temperature, data were combined to assess overall thermal preference (t(9) = 2.36, p = 0.04); 25°C to 30°C (t(9) = 2.74, p = 0.02); 30°C to 35°C (t(9) = 2.88, p < 0.02); and 35°C to 40°C (t(9) = 3.82, p = 0.004). Based on these results, future experiments set the null side of the OPTA to 30°C, as the approximate midpoint between 43°C and 15°C, the thermal thresholds for acute activation of nociceptors (Within sex preferences (data not shown) indicated that female mice did not prefer any temperature significantly across the five experimental conditions. Male mice displayed a preference for 25°C in the 25/30°C condition  = 0.88, p = 0.457), nor did male mice  = 1.754, p = 0.215; F = 7.128, p = 0.006; F = 9.103, p < 0.005, respectively;  1.170) to day 2  = 2.616, p = 0.02) demonstrating a clear preference for location of sucrose reward. This indicates that mice successfully acquired and executed the operant paradigm with 2 d of training, thus future experiments included two training days.We next established the number of trials required for female and male naive mice to attain a stable baseline level of time in the reward zone . Mice weF = 14.62, p <0.001), with all mice displaying an aversion to the extreme temperatures as indicated by less time in reward zone. A significant main effect of sex  = 12.47, p = 0.003) indicated that, compared with males, female mice overall spent more time in the reward zone, displaying a significantly higher tolerance to 40°C and 45°C compared with males in the OPTA . Within group, females showed a specific aversion only to 50°C compared with all other temperatures (Tukey post hoc: p ≤ 0.05). Males tolerated 15°C significantly more than 40°C, 45°C, and 50°C (Tukey post hoc: p ≤ 0.005) and also tolerated 30°C significantly more than 45°C and 50°C (Tukey post hoc: p ≤ 0.01). Representative heat maps are shown.Next, we investigated thermal tolerance in naive female and male mice by testing in the OPTA for 20 min with the null side set to 30°C, and the reward side set to 10°C, 15°C, 30°C, 40°C, 45°C, or 50°C, with 4% sucrose available in the reward zone. Temperatures were presented 24 h apart, in a pseudo-random order, over 6 d. The values for each temperature were matched and means compared within and between sexes A. There F = 6.63, p = 0.02; F = 3.2, p = 0.012), indicating that increased reward led to increased thermal tolerance. Male mice specifically spent more time in the 10% reward zone compared with the 4% zone at 40°C . Representative heat maps of 4% and 10% sucrose conditions for males at 30/40°C are shown below.To assess the magnitude of reward on thermal tolerance, female and male mice were exposed to the protocol as above, however a 10% sucrose solution was used. Total time in reward zone was compared with that of the sex and age matched animals from the above experiment. Female mice exhibited less time in the reward zone under 10% sucrose conditions (t(18) = 2.06, p = 0.05; 18 = 0.83, p = 0.003; t(7) = 2.67, p = 0.03; We next established whether 4% sucrose was sufficiently motivating for male mice to spend time in the reward zone. Male mice spent more time in the reward zone when 4% sucrose was present relative to an empty bottle (t(14) = 5.40, p < 0.0001; F = 22.19, p = 0.0001), with CCI and Sham groups displaying an aversion to cold and hot temperatures. There was also a main effect of CCI  = 9.41, p = 0.01), surprisingly, CCI animals displayed an increase in time spent in the reward zone across all temperatures. Total time spent in the reward zone across all temperatures showed that animals with CCI spent more time overall in the reward zone versus sham (t(5) = 3.24, p = 0.02; F = 4.22, p = 0.06) and a main effect of temperature  = 5.84, p < 0.001). Total distance traveled across all temperatures showed that CCI animals exhibited increased distance traveled compared with sham (t(5) = 4.78, p = 0.01; The radiant heat withdrawal assay was performed 7 d after CCI or sham surgery to assess the effect of neuropathic injury on thermal nociception. Mice with CCI displayed thermal hyperalgesia as indicated by reduced withdrawal latency compared with sham animals  was tested to evaluate the effect of analgesia on time in reward zone in the CCI-induced model of neuropathic pain versus sham surgery. To mitigate a potential ceiling effect and enhance detection, 45°C was chosen as the challenge temperature for testing with clonidine.F = 0.68, p = 0.41). A main effect of clonidine  = 40.25, p < 0.0001) was found. Both sham and CCI animals administered clonidine exhibited an increased withdrawal latency compared with vehicle (t(30) = 6.72, p < 0.001, data not shown).Threshold for withdrawal to radiant heat A was tesF = 9.51, p = 0.005), with no effect of CCI on time in the reward zone  = 0.10, p  =  0.76). Representative heat maps are presented in F = 0.74, p = 0.40) or CCI  = 1.40, p = 0.25) compared with respective controls. Representative tracking plots are presented in When assessed for effects on thermal tolerance B, clonidt(15) = 3.46, p = 0.004; t(15) = 2.62, p = 0.02, respectively), but did not show reduced withdrawal latency 72 h after injection (t(15) = 1.6, p = 0.13). Subsequent OPTA testing was therefore limited to 2 d post-CFA administration.The radiant heat withdrawal assay was conducted 24, 48, and 72 h after CFA or vehicle injection A. The CFF = 14.53, p < 0.0001), but no main effect of CFA, indicating that CFA did not modify thermal pain tolerance  = 0.80, p = 0.37; heat maps at 30/40°C below; F = 7.53, p < 0.001), but no main effect of CFA on distance was found  = 0.20, p = 0.65; tracking plots at 30/30°C below; In temperature-dependent tolerance tests, there was a main effect of temperature on time spent in reward zone  = 12.03, p = 0.002), indicating that the effect of treatment on withdrawal depended on the type of injury. Specifically, CFA animals with vehicle treatment displayed decreased withdrawal time compared with control animals with vehicle treatment , control animals with meloxicam treatment , and CFA animals with meloxicam treatment . This indicates that the anti-hyperalgesic properties of meloxicam are apparent in the presence of inflammation only, and no additional reduction in hypersensitivity was seen as a result of meloxicam treatment in control animals as compared with vehicle treatment in control animals.The effects of meloxicam, or equivalent volume of vehicle , were tested to evaluate the effect of analgesia on thermal threshold and time in reward zone at 40°C in the CFA-induced model of inflammatory pain. This temperature was chosen as it resulted in the greatest difference in temperature dependent tolerance tests in CFA versus control animals B. ThreshF = 6.507, p = 0.02) as well as treatment  = 12.25, p = 0.002). Representative heat maps are presented in  32.86, SEM = 9.58) than control animals , and meloxicam-treated animals spent less time in the reward zone  than vehicle-treated animals . Specifically, control animals with vehicle treatment spent more time in the reward zone than both CFA animals given vehicle  and CFA animals given meloxicam . Analysis of distance traveled  = 11.05, p = 0.003), with CFA-injected mice traveling less  than controls . Specifically, vehicle-treated CFA animals traveled significantly less than meloxicam-treated control animals . Representative track plots are presented in When assessed for thermal tolerance using the OPTA B, there traveled D revealeUnsatisfactory translation of reflexive measures of nociception, in which the endpoint is withdrawal to a threshold stimulus, has led to a crisis and demand for new models to assess non-reflexive measures of pain in animals . Pain toOperant tests targeting supraspinally regulated affective, cognitive, and motivational processing of pain are increasingly used in pain research, including conditioned preference, place escape/avoidance, the mechanical conflict system , and theThe OPTA reward floor, set at temperatures above 35°C or below 25°C, generated avoidance behavior indicative of an unpleasant somatosensory, affective, and motivational experience. Cutaneous thermosensation at somatic contact points with the floor is mediated by a heterogeneous population of primary afferent Aδ and C fibers, including thermosensitive nociceptors which activate at ≤ 28°C for cold and ≥ 37°C for heat . The cenBoth male and female mice exhibited similar baseline preferences for temperature when no reward was present. Male and female mice also similarly acquired the operant paradigm during training in the OPTA. Interestingly, in the OPTA during challenge, female mice demonstrated increased heat pain tolerance compared with males. This previously unrecognized observation of sex differences on pain tolerance in mice does not match a human psychophysical study that showed added incentives to tolerate pain in the cold-pressor test increased tolerance similarly in both men and women . This diOverall, female mice tend to exhibit higher sensitivity and more severe and lengthened responses to pain, and this is generally consistent with humans . InflammTo determine whether ongoing pain modifies pain tolerance behavior, the OPTA was used to test mice with CCI-induced neuropathy. This unexpectedly revealed that neuropathic mice showed greater reward time and tolerance to aversive temperatures than sham. Previous animal studies of reward-seeking behavior under neuropathic conditions have shown mixed effects with some reporting a loss of reward-seeking behavior, or anhedonia , or no cWe hypothesized that analgesics may work differently on pain tolerance and nociceptive thresholds. Clonidine is often used in research models of neuropathic pain, due to its analgesic efficacy and lack of innate reward . We confConsistent with the literature, CFA produced thermal hyperalgesia, that was attenuated by the NSAID meloxicam, without affecting mobility . In lighTaken together, these results suggest that while some NSAIDs exhibit anti-hyperalgesic properties, they may also limit the capacity to tolerate pain in some cases. Psychophysical studies in humans have shown mixed results of NSAIDs on pain tolerance. The cold pressor test failed to show that NSAIDs enhance tolerance , althougIn addition to traditional analgesics, sucrose itself has been reported to elicit analgesic effects, which may have contributed to some of the results. However, these effects are largely found only in pediatric subjects with varying efficacy and would not be expected to interfere with the decision to cross the reward side floor which precedes sucrose consumption .Using the OPTA, we have identified previously uncharacterized, non-reflexive behavioral outputs of neuropathic and inflammatory pain models in male mice. Our observations indicate that hypersensitivity does not predict decreased pain tolerance, and that analgesics that reduce hypersensitivity may not necessarily enhance pain tolerance. Affective, cognitive, and motivational processing required for pain coping is thought to be mediated through higher-order neurons in regions such as the anterior cingulate, amygdala, and nucleus accumbens . These rPain tolerance is highly plastic within individuals and is altered by stress, exercise, drug use, age, race, and social situations . Longitu


Diabetic microvascular complications of retinopathy, nephropathy and neuropathy may occur at hemoglobin A1c levels (HbA1c) below the 6.5% (48 mmol/mol) diagnostic threshold. Our objective was to assess the validity of the HbA1c diagnostic cutpoint of 6.5% based upon published evidence of the prevalence of retinopathy, nephropathy and neuropathy as markers of diabetes.Data Sources PubMed, Embase, Cochrane, Scopus and CINAHL from 1990-March 2019, grey literature sources. Study Selection All studies reported after 1990  where HbA1c levels were presented in relation to prevalence of retinopathy, nephropathy or neuropathy in subjects not known to have diabetes. Data Extraction Studies were screened independently, data abstracted, and risk of bias appraised. Data Synthesis Data were synthesized using HbA1c categories of < 6.0% (< 42 mmol/mol), 6.0–6.4% (42–47 mmol/mol) and ≥ 6.5% (≥ 48 mmol/mol). Random-effects meta-analyses were conducted for retinopathy, nephropathy and neuropathy prevalence stratified by HbA1c categories. Random-effects multivariable meta-regression was conducted to identify predictors of retinopathy prevalence and sources of between-study heterogeneity.Q-statistic p < 0.0001).Pooled mean prevalence was: 4.0%(95% CI: 3.2–5.0%) for retinopathy, 10.5% (95% CI: 4.0–19.5%) for nephropathy, 2.5% (95% CI: 1.1–4.3%) for neuropathy. Mean prevalence when stratified for HbA1c < 6.0%, 6.0–6.4% and ≥ 6.5% was: retinopathy: 3.4% (95% CI: 1.8–5.4%), 2.3% (95% CI: 1.6–3.2%) and 7.8%(95% CI: 5.7–10.3%); nephropathy: 7.1% (95% CI: 1.7–15.9%), 9.6% (95% CI: 0.8–26.4%) and 17.1% (95% CI: 1.0–46.9%); neuropathy: 2.1% (95% CI: 0.0–6.8%), 3.4% (95% CI: 0.0–11.6%) and 2.8% (95% CI: 0.0–12.8%). Multivariable meta-regression showed HbA1c ≥ 6.5% , age > 55 , and African-American race , to be associated with higher retinopathy prevalence. Marked heterogeneity in prevalence estimates was found across all meta-analyses  in individuals below the diabetes diagnostic threshold.The online version of this article (10.1007/s00592-020-01606-5) contains supplementary material, which is available to authorized users. The prevalence of diabetes has reached epidemic proportions globally, with 424.9 million affected adults (20–79 y), representing 8.8% of the global adult population. Current projections indicate that this figure will rise to 628.6 million by the year 2045, affecting almost 10% of the worldwide adult population , 4; thisHemoglobin A1c (HbA1c) is considered key for assessing glycemic control in patients known to have diabetes, and several prospective studies using HbA1c have shown a strong, continuous association between HbA1c and the development of diabetes and complications –7. An InKey studies contributing to the IEC/ADA/WHO diagnostic threshold decision were the results of the cross-sectional Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) study  and threThe current HbA1c threshold was based on data available at the time of these groups’ reports and, even at the time, there was debate as to whether a level of 6.5% (48 mmol/mol) may be too high since, in many studies, it identified fewer patients as having diabetes than the traditional blood glucose criteria . Since tThe key aims of this study were to perform a contemporary systematic review and meta-analysis to verify the HbA1c cutpoint of 6.5% (48 mmol/mol) for the diagnosis of T2DM using currently available retinopathy data and, secondly, to extend the analysis to establish the prevalence of nephropathy and neuropathy at differing levels of HbA1c.This systematic review was guided by the Cochrane Collaboration Handbook , and fol). The search strategy developed in PubMed was replicated in all databases with eventual search terms or technical variations documented  and in MeSH/Subject Headings/Thesaurus when available. A publication year filter to include studies from 1990 up to the search date was applied to account for the predominant lack of standardized/harmonized HbA1c measurement before this time. In order to ensure literature saturation and inclusion of pre-indexed materials, no additional filters or limitations were included.In addition to the search in academic databases, Open Grey, Clinical Trials.gov, The New York Academy of Medicine-Grey Literature Report and ProQuest Dissertation and Theses were searched for grey literature. Hand screening of the references lists of all studies selected to be included in the review was also conducted.A full search log including search technical details, results and notes about search term variations/translations for all databases can be found in Supplementary Table https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=99410PROSPERO2018CRD42018099410.A review protocol for this study was registered in the Prospero international prospective register of systematic reviews: Search results were imported into the Covidence systematic review tool where duplicate publications were identified and duplicates excluded. Study eligibility was independently determined by two investigators (AEB and SLA). Where there was a discrepancy, both investigators revisited the publication in question, discussed the results with the group and re-entered their decision into the database.The criterion for inclusion was for a publication to report the prevalence of microvascular complications by levels of HbA1c. More specifically, studies were only considered if they provided a prevalence measure for a microvascular complication stratified by HbA1c of < 6% (< 42 mmol/mol), 6–6.4% (42–47 mmol/mol) and ≥ 6.5% (48 mmol/mol) in the same study. The reason for that is to ensure rigor in the association with microvascular complications. Exclusion criteria were studies that did not report microvascular complications by different HbA1c level and those published before 1990.For terminology, a “publication” is a document containing a relevant outcome measure, while a “study” indicates all details pertaining to a specific outcome measure—one publication may contribute multiple studies. A study, such as “prevalence of retinopathy,” could include multiple stratified “measures,” such as prevalence by age group.Following screening of records for eligibility, eligible studies had full-text screening . Extracted variables included: author(s), publication title, year(s) of data collection, publication year, country of origin, country of survey, study design, study sampling procedure, study population and its characteristics , sample size, HbA1c measurement method, HbA1c stratification outcome measures, complications, retinal photography method, number of eyes photographed, determination of albumin creatinine ratio, measure of peripheral neuropathy. Two investigators (AEB and SLA) independently assessed the full-text articles and determined the eligibility of studies for inclusion in the systematic review.Risk of bias (ROB) and precision assessments were performed for all studies included in the review. Guided by the Cochrane approach , studiesMeta-analyses were conducted to estimate the pooled mean of diabetes complications in relation to the stratified HbA1c values . Forest plots were constructed to visualize prevalence measures and 95% confidence intervals (CIs) associated with each of the three outcomes of interest . Estimates for the pooled mean prevalence and 95% CIs were then calculated using random-effects meta-analyses. Here, variances of prevalence measures were first stabilized using a Freeman–Tukey-type arcsine square-root transformation , 19. InvQ statistic to confirm existence of heterogeneity across studies and I2 to quantify magnitude of between-study variation that is due to true differences in effect size rather than chance [A heterogeneity assessment was further conducted using Cochran’s n chance , 23.Meta-analyses were implemented in R version 3.4.2 .p value ≤ 0.10 in univariable analysis were eligible for inclusion in the multivariable model. In the latter, a p value ≤ 0.10 but > 0.05 was considered as showing “good evidence” for an association with prevalence, while a p value ≤ 0.05 indicated strong evidence for an association with prevalence.Associations with prevalence and sources of between-study heterogeneity were identified using random-effects univariable and multivariable meta-regression analyses. Predictors considered a priori included: HbA1c levels, age and race. Factors associated with prevalence at The magnitude of the association between these predictors and prevalence was determined by calculating, in the univariable analysis, odds ratios (ORs) and their associated 95% CIs, and in the multivariable analysis, by calculating adjusted odds ratios (AORs) and their associated 95% CIs.Meta-regressions were conducted in Stata/SE version 13  using thA total of 12,845 references were identified through the literature search and uploaded to the systematic review software Covidence for de-duplication and screening. 9370 references (9324 from the database search and 46 from the grey literature search) qualified for title and abstract screening after de-duplication. The screening process in Covidence software was blinded. Of the 9370 references, 9206 records were excluded. 164 full-text articles were then assessed for eligibility; of those 135 were excluded because of insufficient details of the relationship of the microvascular complication to the HbA1c values less than 6.5% 48 mmol/mol). Twenty nine publications remained eligible for inclusion in the systematic review, 22 relating to retinopathy  and second-stage proliferative diabetic retinopathy  and fundal photography is required for diagnosis . HbA1c wThe forest plot for retinopathy is shown in Fig. The forest plot for nephropathy is shown in Fig. The forest plot for neuropathy is shown in Fig. n = 16 out of 22) of retinopathy studies, 50.0% (n = 2 out of 4) of nephropathy studies and 66.7% (n = 2 out of 3) of neuropathy studies included at least 100 participants and therefore were considered as having higher precision.Supplementary Tables Low risk of bias, assessed as consistency in measuring HbA1c across study participants, was found in 81.8% of studies assessing retinopathy, all studies assessing nephropathy, and 66.7% of studies assessing neuropathy. The majority of studies assessing retinopathy (95.5%) and all studies assessing nephropathy and neuropathy used probability-based sampling and hence were also classified as having low ROB on that quality domain.Overall, studies reporting the prevalence of microvascular complications of T2DM were of acceptable quality: 81.8% of retinopathy studies, all nephropathy studies and 66.7% of neuropathy studies had low ROB on both quality domains. High ROB on both domains was found in only 4.5% of retinopathy studies and none of nephropathy or neuropathy studies.The pooled mean prevalence was estimated at 4.1% 95% CI: 3.2–5.0%) for retinopathy, 10.5% (95% CI: 4.4–18.8%) for nephropathy and 2.5% (95% CI: 1.1–4.3%) for neuropathy , 6.0–6.4% (42–46 mmol/mol) and ≥ 6.5% (≥ 48 mmol/mol), there was a distinct increase in retinopathy prevalence from a pooled mean of 3.41% (95% CI: 1.84–5.42) in the < 6.0% (< 42 mmol/mol) stratum and a pooled mean of 2.40% (95% CI: 1.65–3.29) in the 6–6.4% 42–47 mmol/mol) stratum to a pooled mean of 7.97% (95% CI: 5.70–10.57) in the ≥ 6.5% (≥ 48 mmol/mol) stratum. The J-shaped curve describing this association can be found in Fig. The albumin/creatinine ratio  is a sensitive indicator of kidney disease, and patients are classified according to the 2012 guidelines developed by the Kidney Disease: Improving Global Outcomes organization (KDIGO)  as: normDiabetic neuropathy, defined as the signs and symptoms of neuropathy wherein diabetes is the underlying cause, most frequently manifests as a distal, symmetric deficit . For neuAs a higher prevalence of complications may be anticipated with increasing age, the meta-analysis data of studies reporting retinopathy were stratified by age using both two strata (age ≤ 55 years and > 55 years) and three strata  with levels of HbA1c  after controlling for the confounding effect of age and race. The model also showed that individuals > 55 years of age had threefold higher odds for retinopathy than younger individuals . African-Americans also had significantly higher odds for retinopathy prevalence than those of other race . This model explained 44.8% of the variation in retinopathy prevalence.The univariable analysis showed an association with prevalence for HbA1c levels, age and race; these were therefore included in the multivariable model. Here, HbA1c stratification showed a marked and significant increase of retinopathy prevalence at ≥ 6.5% with an AOR of 4.0  HbA1c. It should be also emphasized that the stratification of HbA1c and retinopathy was based on the detection of moderate retinopathy and it is unknown whether the diagnostic cutpoint for T2DM may be altered by prevalence studies on minimal diabetic retinopathy. These results differ from that reported in a previous meta-analysis that concluded that HbA1c thresholds could not be identified from the studies on microvascular complications, though the threshold of HbA1c of 6.5% was strong for severe retinopathy [The meta-analysis relating stratified HbA1c to the prevalence of retinopathy, nephropathy and neuropathy showed clearly that for moderate retinopathy, the recent data are in accord with a HbA1c diagnostic cutoff of 6.5% (48 mmol/mol and above) for T2DM, with the inflection point for the increase in diabetic retinopathy prevalence being at 6.5% (48 mmol/mol), as also shown by the stratified analysis using the inopathy .J-shaped curve showing accountable retinopathy prevalence at below 6.0%. The meta-regression also revealed the increase in retinopathy prevalence with those aged over 55 years, with an OR of 3.23 and a striking increase in retinopathy prevalence for African-Americans with an OR of 10.73 on multivariate analysis. While this is in accord with the previous data [The heterogeneity in the retinopathy data was controlled for in the meta-regression and showed, in both the univariate and multivariate analyses, the increased odds ratio of 3.2 and 4.05, respectively, with an HbA1c of 6.5% or greater; however, as noted the stratified HbA1c data revealed a ous data –11, retious data . It shouThe main limitations of the 22 studies of retinopathy included in the meta-analysis were that retinal photography was not standardized and that the degree of retinopathy was often reported as moderate or poorly specified , 38. As Four studies were evaluated which considered the onset of nephropathy, the data showing that the HbA1c cutoff of 6.5% (48 mmol/mol) was appropriate with a significant increase in the urine albumin/creatinine ratio. Again, it is evident that there were significant numbers of subjects with albuminuria in the range of “prediabetes” between 6.0–6.4% (42–47 mmol/mol); however, the main limitations were the small number of studies, heterogeneity of the population and lack of division of the HbA1c level into 0.1% ranges that prevented a more stringent determination. In addition, all of the studies were cross sectional with a need for longitudinal studies to be performed.There were only three studies with sufficient data that could be evaluated for the onset of neuropathy, and these were inconclusive with no inflection point seen for the HbA1c cutoff, with a prevalence of neuropathy at an HbA1c of 6.0–6.4 (42–46 mmol/mol) being the same as that of 6.5% (48 mmol/mol) and above. The main limitations here include the limited amount of data in too few studies, heterogeneity of the population and poor division of the HbA1c range into 0.1% ranges; however, given the limited data, it is unlikely that division of HbA1c into 0.1% ranges would have allowed a more stringent determination.In conclusion, based on the strongest data of moderate retinopathy, this systematic review and meta-analysis is in accord that the HbA1c diagnostic cutpoint of 6.5% (48 mmol/mol) is highly specific for diagnosing T2DM, though the increased prevalence of retinopathy in those aged 55 years or greater and in African-Americans may suggest a lower threshold is appropriate for those groups. However, this analysis highlights that diabetic microvascular complications commonly occur at lower HbA1c values, and the sensitivity of diagnosing T2DM may improve with a lower diagnostic cutpoint. There is a need for studies that standardize the definition of minimal diabetic retinopathy with more sensitive measures of nephropathy and neuropathy to determine if a lower threshold is appropriate.PRISMA Flow Diagram (PDF 215 kb)PRISMA checklist (DOCX 20 kb)Literature search (DOCX 32 kb)Summary of precision and risk of bias (ROB) assessment. Summary of precision and risk of bias (ROB) assessment for studies reporting the prevalence of microvascular complications associated with type 2 diabetes mellitus (DOCX 17 kb)Study-specific quality assessments (DOCX 19 kb)Below is the link to the electronic supplementary material.