extfieldsofstudy: Biology Medicine

text:
Subcellular Evidence for Biogenesis of Autophagosomal Membrane during Spermiogenesis In vivo

Although autophagosome formation has attracted substantial attention, the origin and the source of the autophagosomal membrane remains unresolved. The present study was designed to investigate in vivo subcellular evidence for the biogenesis of autophagosomal membrane during spermiogenesis using transmission-electron microscopy (TEM), Western blots and immunohistochemistry in samples from the Chinese soft-shelled turtle. The testis expressed LC3-II protein, which was located within spermatids at different stages of differentiation and indicated active autophagy. TEM showed that numerous autophagosomes were developed inside spermatids. Many endoplasmic reticulum (ER) were transferred into a special “Chrysanthemum flower center” (CFC) in which several double-layer isolation membranes (IM) were formed and extended. The elongated IM always engulfed some cytoplasm and various structures. Narrow tubules connected the ends of multiple ER and the CFC. The CFC was more developed in spermatids with compact nuclei than in spermatids with granular nuclei. An IM could also be transformed from a single ER. Sometimes an IM extended from a trans-Golgi network and wrapped different structures. The plasma membrane of the spermatid invaginated to form vesicles that were distributed among various endosomes around the CFC during spermiogenesis. All this cellular evidence suggests that, in vivo, IM was developed mainly by CFC produced from ER within differentiating spermatids during spermiogenesis. Vesicles from Golgi complexes, plasma membranes and endosomes might also be the sources of the autophagosome membrane.

INTRODUCTION
Autophagy is a unique membrane-trafficking process in which newly formed membranes, called phagophores, engulf parts of the cytoplasm and lead to the production of double-membrane autophagosomes that are delivered to lysosomes for degradation (Wild et al., 2011;Rubinsztein et al., 2012). Autophagy is the main cellular process responsible for degrading defective organelles and long-lived proteins. This catabolic pathway has been linked to numerous pathological and physiological conditions (Abada and Elazar, 2014). The physiological role of autophagy was deduced when it was discovered, but the origin of autophagosomal membranes remains unclear (Hamasaki et al., 2013a;Diao et al., 2015).
Autophagy initiates with the emergence of a doublemembraned isolation membrane, which encloses organelles and portions of the cytoplasm to form the autophagosome (Hayashi-Nishino et al., 2010;Carlsson and Simonsen, 2015;Sanchez-Wandelmer et al., 2015). Despite much progress in identifying the molecules responsible for autophagosome formation, the origin and the source of the autophagosomal membrane remain unsolved and have been the subject of long-standing debate. Autophagosome formation has recently attracted substantial attention (Abada and Elazar, 2014). Most of the seminal studies on the regulatory mechanisms of autophagosome biogenesis were conducted on Saccharomyces cerevisiae or culture cells in vitro (Hamasaki and Yoshimori, 2010). The membrane origins of autophagosomes may involve multiple sources (Hayashi-Nishino et al., 2009;Puri et al., 2013), including ER exit sites (ERES) (Zoppino et al., 2010;Graef et al., 2013), the ER-Golgi intermediate compartment (ERGIC) (Ge et al., 2013), the Golgi (Geng et al., 2010;Bodemann et al., 2011), the plasma membrane (Ravikumar et al., 2010) and recycling endosomes (Longatti et al., 2012;Knaevelsrud et al., 2013;Puri et al., 2013). However, there is little ultrastructural information from the initial stage of autophagosome formation. It is unclear if and where distinct membrane sources fuse during autophagosome biogenesis.
A spermatozoon is a highly differentiated cell developed from a spermatid through spermiogenesis in the convoluted seminiferous tubule of the testis. Spermiogenesis is the intracellular clearance process for male gametes. The decrease in cell volume occurs due to the shedding of unnecessary cytoplasm and organelles, but the process for reptilian gametes remains largely unknown (Zhang et al., 2007). Our previous studies showed that epididymal spermatozoa of Chinese softshelled turtles contain a huge cytoplasmic droplet with numerous lipid droplets that serve as energy and nutrition sources, which favors long-term sperm storage . There was not an obvious shedding process of unnecessary cytoplasm during spermiogenesis although, the cell size of the maturing spermatozoon was reduced. The cytoplasmic droplet always attached to the mid-piece of the spermatozoon without migrating down the sperm tail. In the present study, a subcellular mechanism for autophagosomal membrane biogenesis was examined in detail during in vivo turtle spermiogenesis. The turtle could be a potential animal model for long-term sperm storage at atmospheric temperature (Chen et al., 2015).

Animals
All procedures with turtles were conducted according to the Animal Research Institute Committee guidelines of Nanjing Agriculture University, China. Fifteen male adult Chinese softshelled turtles, aged 4-5 years, were purchased from a wild breeding base in Nanjing, Southeastern China (GPS coordinates N 32.050 E 118.783). The animals were collected in August, September and October. After breeding in the lab for 24 h, the turtles were rendered comatose using intraperitoneal sodium pentobarbital (20 mg/kg) and killed by cervical dislocation. One side of the testis was collected immediately after death and fixed for light and transmission electron microscopy. The other testis was stored at −70 • C for Western blot analysis. The sampling procedures were approved by the Nanjing Agricultural University Veterinary College. The protocol was approved by the Science and Technology Agency of Jiangsu Province. The approval ID is SYXK (SU) 2010-0005. All efforts were made to minimize the animal's suffering.

Immunohistochemistry
In brief, after deparaffinization and hydration, 3% H 2 O 2 was added to the paraffin sections to eliminate internal peroxidase activity. Then, slides were boiled in buffered citrate. Next, sections were blocked using 5% BSA and then incubated with anti-LC3B (1:200) antibody (Abcam, ab48394) at 4 • C overnight. Negative controls were prepared by exchanging the primary antibody with PBS. After washing in PBS, the sections were incubated with goat anti-rabbit IgG (Santa Cruz, sc2004) for 1 h at 37 • C. After a brief wash in PBS, peroxidase activity was determined using DAB (Sigma, D8001). Images were collected under a light microscope (Olympus DP73).

Transmission Electron Microscopy
The testis was immediately removed and cut into 1 mm 3 small blocks. After that, we immerse the samples into 2.5% glutaraldehyde fixing agent mixed with phosphate buffered saline at 4 • C (PBS, 0.1 M, pH 7.4) overnight. The tissues were rinsed in the same PBS and fixed in 1% buffered osmium tetroxide. Then we washed the samples in the buffer. The testis was dehydrated in ascendant concentrations of ethanol and permeated with a propylene oxide-Araldite compound before they were embedded in Araldite. Under an ultramicrotome, we sectioned the blocks. The sections (50 nm) were stained with 1% uranyl acetate and lead citrate. Finally, slices were examined and photographed using a Hitachi H-7650 TEM.

Statistical Analysis
All data were presented as the means ± SE. The TEM pictures were imported into Image-Pro Plus (IPP) 6.0 software for statistical data analysis. The statistical analysis was performed using SPSS software version 14.0 with one-way analysis of variance (ANOVA). The data were considered statistically significant when P < 0.05.

The Testis Expressed LC3-II Protein, Which was Located within Spermatids and Indicated Active Autophagy
Western blot tests showed that the turtle testis expressed LC3, which is a specific marker protein for autophagosomes, during spermiogenesis (Figure 1). LC3 immunohistochemistry of seminiferous tubules (ST) further showed that LC3 was distributed in different germ cells (Figure 2), including spermatocytes (Figure 2A), differentiating spermatids ( Figure 2B), spermatozoa (Figures 2A,C), and cytoplasmic droplets ( Figure 2C).

IM was Developed Mainly by CFC Produced from Endoplasmic Reticulum (ER)
Using TEM, numerous autophagosomes with different structures were visualized within germ cells at various developing stages (Figures 3-5, 7, 8). Some were autophagosomes, some  were small vesicles, and others were multivesicular bodies (MVB). A special structure 1-2 µm in diameter, named the "Chrysanthemum flower center" (CFC) in this study, was found inside differentiating spermatids (Figures 4-6). Numerous branched ER were distributed around the CFC (Figures 4, 5), and the ER ends inserted into the CFC through short narrow tubules that formed many daisy petal-like structures. These structures comprised the CFC from different directions (Figures 4C,D, 5A), which resembled a chrysanthemum flower. The density of the IM and the short tubular membranes within the CFC was higher than the original ER (Figure 4). Several double layer isolation membranes (IMs) were developed from the CFC (Figures 4, 5), which could elongate and enwrap some cytoplasm along with different structures (Figures 4A, 5A,B). The end of the extending IM expanded sometimes to form a vesicle ( Figure 5C) that was similar to some vacuoles inside the spermatid. From late spring to late autumn, the average diameter of the CFC increased from 0.48 ± 0.02 µm to 1.64 ± 0.05 µm (mean ± SE; n = 50). The CFC was more developed in spermatids with compact nuclei ( Figure 6B) than spermatids with granular nuclei (Figure 6A), which corresponds to late stage and early stage spermiogenesis, respectively. Some IM could also be transformed from a single ER ( Figure 5D).

Vesicles from Golgi Complexes, Plasma Membranes and Endosomes Might Also be the Sources of Autophagosome Membrane
On the trans face of the Golgi complex of differentiating spermatids, a large clear vesicle was formed that clung to lipid droplets and became flat ( Figure 7A). A doublelayer membrane from the trans-Golgi network wrapped some cytoplasm and its vesicles ( Figure 7B). Many vesicles in different sizes were distributed throughout the differentiating spermatids ( Figure 5C) and some spermatids nestled up against the lipid droplets and became flat ( Figure 7A). Plasma membrane invagination was frequently observed (Figure 8), which led to the formation of some different vesicles (Figures 8B,D).
A very large cytoplasmic droplet with lipid droplets was attached to the midpiece and sperm head of the mature spermatozoon after spermiation in the Chinese soft-shelled turtle (Figure 9). The CFC was no longer observed in mature spermatozoa.

DISCUSSION
Autophagy plays a vital role in maintaining cellular homeostasis through the continuous turnover of proteins as well as the elimination of defective organelles in lysosomes (Yang and Klionsky, 2010;Bejarano et al., 2014). Unlike other organelles that solidly present in the cell, it is considered that autophagosomes are formed on demand by dynamic membrane rearrangements (Shibutani and Yoshimori, 2014). It is difficult to confirm the membrane sources of autophagosomes because autophagosome formation proceeds rapidly once it is initiated. It is estimated that autophagosome formation takes several minutes in yeast and mammals (Mizushima et al., 2001;Fujita et al., 2008;Geng et al., 2008).Despite tremendous progress made in autophagy, the long-term issues about the source and origin of autophagic membranes remain undiscovered in the past few decades. Recognizing the autophagosome formation site will undoubtedly help to figure out another fascinating question: where does the membrane of autophagosome come from (Hamasaki et al., 2013b)? The testis of the soft-shelled turtle expressed LC3-II, which is the marker protein of the autophagosome, located within spermatids in different stages during spermiogenesis. Numerous autophagosomes were observed inside the spermatids under TEM. A special 1-2 µm diameter structure named the "Chrysanthemum flower center" (CFC) was also found within the spermatid and several double-membrane IM could be produced, elongate and enwrap cytoplasm to develop different autophagosomes from this structure. All the cellular evidence showed that the "Chrysanthemum flower center, " a newly formed structure in the spermatid, may be one important site for IM development and autophagosome formation during cell-size reduction in spermiogenesis. The CFC was more developed in spermatids with compact nuclei than in spermatids with granular nuclei, which corresponds to the late stage and early stage of spermiogenesis, respectively. In late spermiogenesis, the differentiation and remodeling of spermatids into spermatozoa is more active. As far as we know, this study has yielded the first subcellular evidence for an in vivo origin and source of IM and autophagosome formation in higher eukaryotes. In yeast, the membrane of the phagophore and early autophagic structures originate from a single source named the pre-autophagosomal structure (PAS) (Suzuki and Ohsumi, 2010). In higher eukaryotes, phagophore formationmight take place in different positions in the cell, but the current assumption is that phagophores formate at a complex membranous structure named the omegasome. Omegasomes were initially identified as endoplasmic reticulum (ER)-associated spots (Carlsson and Simonsen, 2015). The latest studies have shown that the ER exit sites (ERES), which are specialized ER regions where longlived proteins are classified into the secretory system, are critical elements in the formation of isolation membranes.Therefore, ERES are a new component that should be concluded into the depiction of autophagosome biogenesis (Sanchez-Wandelmer et al., 2015). It is hypothesized that further research on ERES function will provide provoking insights into eukaryotic cell biology. Electron tomography showed that endoplasmic reticulum associates with isolation membranes or phagophore in mammalian cells. The ER-IM complexs served as a subregion of ER by forming a cradle girding the IM, and showed that both IMs and ER are connected. However, there is little  ultrastructural information on the initial stage of autophagosome formation (Hayashi-Nishino et al., 2010). Through TEM, this study demonstrated that the developed Chrysanthemum flower center occurred in differentiating spermatids in vivo during turtle spermiogenesis. It was further revealed that CFC could produce long IM that enwrapped some cytoplasm to form the autophagosome. As a result, CFC in the spermatid might be equivalent to a phagophore nucleation site, or the omegasome, or the ER-IM complex in cultured mammalian cells.
Light microscopy experiments provide evidences to demonstrate the continuity between the ER and IMs. In addition, the experiments also found that DFCP1, an ER-resident protein, has access to IMs. (Axe et al., 2008). The mechanism by which the isolation membranes keep their unique identity while remaining connected to their donor membranes remains unclear (Kraft and Martens, 2012). Several pieces of evidence have shown a strong relationship between ER and autophagosome formation sites in mammalian culture cells (Hamasaki et al., 2013b). It was found that 70% of IMs were associated with ER (named ER-IM complexes) in Atg4B (Atg4BC74A) mutant cells, which suggests that the ER-IM complexes are an important early stage of AP formation (Hamasaki and Yoshimori, 2010). As a result, ER is probably the major donor of lipid bilayers for forming autophagosome and omegasome, which could represent the means to supply lipids from ER to a growing phagophore (Sanchez-Wandelmer et al., 2015). A large number of branched ER were distributed around the CFC, and their ends inserted into the CFC through short narrow tubules that formed many daisy petal-like structures that comprised the CFC from different orientations. The morphological connection of narrow tubules showed that the CFC unambiguously came from ER, which could develop IM and form autophagosome in the turtle spermatids. Our data could support the third hypothesis of autophagosome biogenesis that autophagosomes are formed neither by de novo membrane formation nor by direct utilization of ER cisternae (Hayashi-Nishino et al., 2009). Different biogenesis pathways might regulate autophagy concurrently in vivo. Membrane fusion events would ask several autophagic biogenesis complexes to work in a few locations at different stages and could be tied together with diverse membranes. Distinct membrane sources will conduce to biogenesis because with cell's third dimension, many membranes and organelles are close to the phagophore (Abada and Elazar, 2014). It is likely that the Golgi route contributes to autophagosome biogenesis in yeast, although it is not clear if this pathway is important in mammalian systems (Rubinsztein et al., 2012). By tracking Atg9 in Atg9 over-expressing mammalian cells using immune-electron microscopy(IEM), researchers noticed that upon induction of autophagy, a post-Golgi tubulo-vesicular compartment responsive for Atg9 transfers to the vacuole (Mari et al., 2010). Furthermore, the tubulo-vesicular compartment experiences homotypic fusion and reestablishment for purpose of producing isolation membranes (IMs) (Kraft and Martens, 2012). How vesicular precursors integrate into IMs is still unknown. In cultured mammalian cells, Atg9 partly colocalizes to trans-Golgi network and endosomes (Hamasaki et al., 2013b). Additionally, depriving of nutrition induces Atg9 vesicles exit from the trans-Golgi network (TGN) and instantaneous colocalization of Atg9 with LC3 (the autophagosome marker) in fluorescence microscopy experiments. (Young et al., 2006;Orsi et al., 2012). Hamasaki and Yoshimori revealed that Atg9 localization intensively suggests the trans-Golgi network (TGN) and endosome as possible origins of autophagosomal membranes (Hamasaki and Yoshimori, 2010). Using cytotoxic stressors, another study suggested the trans-Golgi network (TGN) as a potential membrane origin in Atg5/Atg7-independent alternative autophagy (Nishida et al., 2009). Double-layer membranes from the trans-Golgi network could wrap some cytoplasm and its vesicles to form autophagosomes in turtle spermatids, which suggests that Golgi complex might be another important autophagy biogenesis site in addition to the CFC. On the trans-face of the Golgi complex of the differentiating spermatids, a large clear vesicle was formed. These vesicles could became flat and cling to the lipid droplet, which bore the characteristics of lipophagy (Liu and Czaja, 2013;Dupont et al., 2014).
Experiments revealed that membrane traffic from early to recycling endosomes is crucial for autophagosome formation and that the recycling compartment is not merely responsible for recycling of plasma membrane receptors but also serves as a station in the early stages of autophagosome biogenesis (Puri et al., 2013). It has been proposed that isolation membranes FIGURE 10 | Model of IM biogenesis during spermatogenesis in Chinese soft-shelled turtle. Many endoplasmic reticula (ER) were transferred into a special "Chrysanthemum flower center" (CFC), from which several double-layer isolation membranes (IM) were formed and extended. Narrow tubules connected the ends of ER and the CFC. An IM could also be transformed from a single ER. Sometimes IM extended from a trans-Golgi network and wrapped different structures.
are formed by the fusion of vesicular carriers that are derived from the plasma membrane (Ravikumar et al., 2010;Moreau et al., 2011) or the Golgi apparatus (Ohashi and Munro, 2010;van der Vaart et al., 2010). Owing to autophagosome formation occurring away from donor membranes, diffusion barrier has no use for separating isolation membranes (IMs) from donor membranes in the vesicular fusion models (Kraft and Martens, 2012). Researchers revealed that the plasma membrane makes contributions to autophagosome formation and it is particularly significant in time of increased autophagy (Rubinsztein et al., 2012). Plasma membrane invagination was frequently observed on the spermatid membrane, which could lead to the formation of different vesicles and endosomes distributed around the CFC and autophagosomes or distribution close to the lipid droplet such as the Golgi vesicle in present study. The size reduced sharply from spermatids to spermatozoa. The plasma membrane's large surface area might act as a massive membrane store that allows cells to experiences cycles of autophagosome synthesis at much higher rates than under basic conditions. And it does not compromise other processes. Recent studies demonstrate the idea that these autophagy-related proteins and membranes that are necessary for autophagosome formation are originated from plasma membrane and that these components coalesce in recycling endosomes that are an early PAS or phagophore intermediate (Sanchez-Wandelmer et al., 2015).
In the present study, we determined that endoplasmic reticulum, vesicles from Golgi complexes, plasma membranes and endosomes might be the sources of autophagosomal membrane (Figure 10). The discovery of ER-derived "Chrysanthemum flower center" (CFC) supports the possibility of the cradle/omegasome model (Hamasaki and Yoshimori, 2010) or de novo membrane formation (Hayashi-Nishino et al., 2009). This finding brings us closer to understanding the origin of autophagosomal membranes. However, details in the formation or constituents of CFC still need further investigation.


extfieldsofstudy:
Computer Science
Medicine

text:
Evidence for an Optimal Algorithm Underlying Signal Combination in Human Visual Cortex

Abstract How does the cortex combine information from multiple sources? We tested several computational models against data from steady-state electroencephalography (EEG) experiments in humans, using periodic visual stimuli combined across either retinal location or eye-of-presentation. A model in which signals are raised to an exponent before being summed in both the numerator and the denominator of a gain control nonlinearity gave the best account of the data. This model also predicted the pattern of responses in a range of additional conditions accurately and with no free parameters, as well as predicting responses at harmonic and intermodulation frequencies between 1 and 30 Hz. We speculate that this model implements the optimal algorithm for combining multiple noisy inputs, in which responses are proportional to the weighted sum of both inputs. This suggests a novel purpose for cortical gain control: implementing optimal signal combination via mutual inhibition, perhaps explaining its ubiquity as a neural computation.

Introduction
Neuroscience lacks a generic system-level explanation of how information is combined in the brain. In the visual system, the early stages of processing are selective for features such as orientation, spatial frequency, and retinal location (Hubel and Wiesel 1959;Blakemore and Campbell 1969;Tootell et al. 1988). Yet, we have little understanding of the subsequent stages of cortical processing required to represent the textures, surfaces, and objects with which organisms must interact (Peirce 2015). A first step in addressing this problem is to identify general algorithms that describe how simple visual features are combined into a perceptual whole.
A desirable algorithm would describe signal combination within a range of different cues. For example, the early visual system must pool information across eye-of-origin to provide binocular single vision (Meese et al. 2006;Moradi and Heeger 2009), across retinal location to represent spatially extensive textures (Kay et al. 2013a), across spatial scale to represent edges , and across orientation to represent curvature (Gheorghiu and Kingdom 2008). Extrastriate areas appear to respond preferentially to textures containing combinations of such features (Freeman et al. 2013). Yet, despite the ubiquity of pooling at all stages of the visual hierarchy, explanations have typically been domain specific and are often inconsistent across neurophysiological and psychophysical approaches. A case in point is combination over area, which was long assumed to be nonlinear and physiological at a neural level (Derrington and Lennie 1984), but linear and probabilistic at a psychophysical level (Robson and Graham 1981). An efficient system should use the same process to combine information within each individual dimension, and this should generalize across different measurement techniques. But the form that such a general-purpose signal combination algorithm might take is not firmly established.
In this study, we first develop a family of models of signal combination. We then report the results of two experiments designed to test the predictions of these models directly for signal combination across both spatial (retinal) location and eye-of-presentation. We measured steady-state visual evoked potentials from cortex using electroencephalography (EEG) in normal human observers to provide a direct assay of neural population responses to a range of inputs (Busse et al. 2009;Tsai et al. 2012). The stimuli were designed to segregate across two dimensions of interest (space or eye), as shown in Figure 1a-d. We compared the pattern of contrast response functions elicited by the flickering stimuli with the model predictions. Only one model was able to predict the detailed form of the data. To verify its generality, we then tested the predictions of this successful model in several further conditions, and show that it is able to predict the harmonic and intermodulation responses across the entire frequency spectrum up to 30 Hz.

Model Development
We first derive a family of models of signal combination from basic principles. In the psychophysics literature, it is typical to assume that physical properties of a stimulus (i.e. contrast) are transduced into neural responses (perhaps involving nonlinearities), which are then combined somehow to produce a decision variable. We take this approach here, so it is the neural response to a stimulus that is of interest, which is assumed to relate in some straightforward (presumably monotonic) way to its physical properties. Consider two inputs (termed A and B) that the system wishes to combine (these could be signals from different eyes, or from adjacent locations in space, or across some arbitrary feature space, and are assumed to be monotonically related to the stimulus contrast of each input). The simplest combination rule is summation of the neural responses to the two stimuli that are linear transforms of their contrasts (resp = A + B, where resp is the overall neural response that might be measured using techniques such as magnetic resonance imaging [MRI] and EEG). Under this rule, the response to both inputs together is twice the response to either input alone (compare solid and dashed functions in Fig. 2a). A similar pattern is observed for energy summation (not shown), in which the component neural responses are square-law transforms of the stimulus contrasts (Adelson and Bergen 1985). The squaring (or any other pointwise) nonlinearity alters the steepness of the function relating input contrast to output, but does not affect the ordering of the functions.
Are these simple combination rules the ones used by the brain? For the domain of early contrast vision, this seems unlikely. Even in the case where the variances of the two signals are equal (as is typically assumed within a modality), it is well established that cortical responses follow a saturating transducer nonlinearity involving contrast gain control from nearby units Heeger 1994, 2012). This is modeled in both single-cell neurophysiology (Heeger 1992) and human psychophysics (Legge and Foley 1980) using a hyperbolic ratio function: resp = C p /(Z q + C q ). A nonlinearity of this type will distort the summation properties of the system, yet the equation contains only a single excitatory input, the contrast (C). In principle, there are five ways in which it could be extended to accommodate multiple signals, as we now outline.
Most straightforwardly, the system might sum the outputs of two individual independent transducer functions (one for each channel). This produces the pattern of responses shown in Figure 2b for canonical parameter values (p = 2.4, q = 2, and Z = 4). The response to two inputs (A + B, dashed curve) is exactly twice the response to a single input (A, solid curve). Thus, compared with the linear model in Figure 2a, the only effect of the transducer nonlinearity is to change the shape of the functions to be less bowed on the logarithmic contrast abscissa.
Alternatively, the responses to the two inputs could be summed before they pass through the nonlinearity. This computation describes a situation where two low-amplitude signals both fall within the receptive field of a single linear mechanism and has the equation: The predictions of this model (termed the early summation model) are shown in Figure 2c for the parameter values given above (A and B represent component contrasts). The relative increase in response when a second component is added (compare solid and dashed functions) is much smaller than for the linear and independent transducer models. This occurs because the denominator of the equation acts as a divisive "gain control" to normalize the two inputs (Carandini and Heeger 2012). In two further variants, either the numerator or denominator terms might be summed before exponentiation, and the others summed after exponentiation, giving rise to the models: with predictions shown in Figure 2d and e for equations (2) and (3), respectively. These models alter the relative slopes of the contrast response functions for one and two components. For the case in which inputs are summed linearly in the numerator (eq. 2, Fig. 2d), the response is doubled with two inputs, just as it is for the independent transducer model (Fig. 2b). For linear summation in the denominator (eq. 3, Fig. 2e), the gain is steeper for one component (solid curve) than for two components (dashed curve) because the denominator term causes exponentially greater inhibition with two inputs. These models are similar to those proposed by Foley (1994) to explain combination of suppressive signals across stimuli of different orientations, but here we additionally consider the effect of excitatory signal combination. A final arrangement is to sum on both the numerator and denominator after exponentiation: The predictions for this model are shown in Figure 2f. The model predicts almost equal responses for single (A, solid curve) and double (A + B, dashed curve) inputs, particularly at high contrasts. This happens because the saturation parameter (Z) becomes negligible for large input values, and the numerator and denominator terms balance, producing a similar output for one (A) or two (A + B) inputs. In the situation where the exponents are 2 (as is often approximately the case), this model is equivalent to that proposed by Busse et al. (2009) where the root mean square (RMS) energy of the untuned gain pool was used as a normalization factor across orientations.
The six models described above make distinct predictions about the neural response to stimuli comprising one or two components, as summarized in Figure 2. Further combinations of inputs are possible, such as fixing the contrast of one component (B) at a high level and varying the contrast of the other (A). This condition corresponds to a widely used experimental manipulation in which a signal is shown in the presence of a constant mask. This produces the dotted curves in each plot, which are also qualitatively distinct across the different models. We therefore have a set of predictions that can be empirically tested to determine the signal combination rule used by the early visual system. We now test these predictions for steady-state EEG responses measured from human visual cortex.

Materials and Methods
Observers Each experiment was completed by 12 observers of either sex, aged between 19 and 41. Five of the observers (including the first author) completed both experiments in separate sessions, the remaining observers completed only one experiment each. Observers had no history of abnormal binocular vision or epilepsy, and wore their prescribed optical correction if required. We obtained written informed consent from all observers, and the study obtained ethical approval from the Department of Psychology Research Ethics Committee of the University of York.

Apparatus and Stimuli
Experiments were run on an Apple computer using a Bits# device (Cambridge Research Systems Ltd, Kent, UK), driven by code written in Matlab using the Psychtoolbox routines (Brainard 1997;Pelli 1997). In the space experiment, stimuli were displayed on an Iiyama VisionMaster 510 CRT monitor running at 75 Hz. In the eye experiment, stimuli were displayed on a Clinton Monoray CRT monitor running at 120 Hz with stimuli presented independently to the left and right eyes using ferro-electric shutter goggles (CRS, FE-01). Both monitors were gamma corrected using a Minolta LS110 photometer.
Stimuli for the space experiment were micropattern textures made from single cycles of a 1 c/deg sine-wave grating modulated by an orthogonal full-wave rectified carrier at half the spatial frequency (see Meese 2010). The micropatterns were arranged in a square grid spanning 20 carrier cycles (20°). To are described in the text. All models are normalized to the largest response at 100% input contrast across the three conditions. create the 'A' stimulus, interdigitated checks of 2 × 2 micropatterns were set to 0% contrast (see Fig. 1a). This arrangement was reversed to create the complementary 'B' stimulus ( Fig. 1b) such that when both A and B components were combined they formed a continuous texture (Fig. 1c). The central four micropatterns were removed to make space for a small central fixation point (black, 7 arcmin wide). Stimuli flickered sinusoidally between 0% contrast and their nominal maximum contrast at combinations of 5 and 7 Hz as shown in Figure 1e, but did not reverse in phase. The orientation of the entire pattern was randomized on each trial to prevent retinal adaptation.
Stimuli for the binocular experiment were patches of horizontal sine-wave grating 2°in diameter, with a spatial frequency of 1 c/deg. They were spatially windowed by a raised cosine envelope. The stimuli were tiled in a 5 × 5 grid (14°in diameter), with the central grating patch omitted to make space for a cluster of dots (each 15 arcmin wide) of random luminance that was used as a binocular fusion lock and fixation marker (see Fig. 1d). The grid was rotated by a random amount on each trial, though the stimuli themselves remained horizontal to avoid exciting populations of neurons sensitive to horizontal carrier disparity. Stimuli flickered sinusoidally between 0% contrast and their nominal contrast at combinations of 5 and 7.5 Hz as shown in Figure 1f. Stimuli presented to the left eye were arbitrarily designated 'A' stimuli, and those presented to the right eye were 'B' stimuli.
EEG signals were recorded at 64 electrode sites on the scalp using a WaveGuard cap (ANT Neuro, the Netherlands). The EEG computer was synchronized with the display computer using an Arduino-based trigger device. Signals were amplified and digitized at 1000 Hz by a PC running the ASA software (ANT Neuro, the Netherlands), and stored for offline analysis.

Procedures
Participants were seated at a distance of 57 cm from the display, with their chin in a rigid headrest. In the eye experiment, the goggles were attached to the headrest (rather than mounted on the head) so that they did not interfere with the EEG equipment. Stimuli were presented for trials of 11 s duration, with gaps of 3 s between each trial. There was no task; participants were instructed to stare at the central fixation point and avoid blinking during stimulus presentation.
Each experiment consisted of 30 different conditions, which were different pairings of A and B stimuli at various contrasts (from 4% to 64% in logarithmic steps). In some conditions, A and B flickered at the same frequency, in other conditions they flickered at different frequencies. All of the conditions were interleaved within a block of 60 trials for a given experiment (space or eye). Each block lasted around 14 min and repeated each of 30 conditions twice in pseudo-random order. Participants completed five blocks in a single session, yielding 10 repetitions of each condition.
To analyze the data, we discarded the first 1 s of each 11-s trial (to eliminate onset transients), and took the Fourier transform of the remaining 10 s. The main dependent variables were the Fourier amplitudes at the stimulus frequencies (5 and 7 Hz or 5 and 7.5 Hz), calculated separately for each electrode. We performed coherent averaging across the 10 trials for each observer, and then averaged the absolute amplitudes across the 12 observers.

Results
The main dependent variable was the Fourier amplitude at the target frequency (5 Hz), which gave a robust signal-to-noise ratio (SNR; Fig. 3b) at the occipital pole (Fig. 3a). For a single component stimulus (e.g. a stimulus shown to one eye only or to a single set of spatial locations, as shown in Fig. 1a), the contrast response function was monotonic and showed evidence of saturation (circles in Fig. 3c,d). Note that the standard errors (shaded regions) are larger at higher response levels because of individual variation in the maximum amplitude of the steady-state visually evoked potential (SSVEP) response, as detailed elsewhere (Baker and Vilidaitė 2014), yet the pattern of contrast response functions we now detail was clear for individual observers.
To assess the summation properties of the system, we then flickered both components in phase at the same frequency (5 Hz). Regardless of whether the stimulus was shown binocularly, or to both sets of spatial locations, there was very little increase in the 5 Hz neural response (squares in Fig. 3c,d). This is a counterintuitive finding, as the input to the system has increased by a factor of 2 (presumably activating many more neurons), yet the population response remains approximately constant. Models that sum stimulus contrast linearly (Fig. 2a) or sum the outputs of two independent transducers (Fig. 2b) entirely fail to predict this result, as do models in which components are summed before any nonlinearities on either the numerator (Fig. 2d), the denominator (Fig. 2e), or both (Fig. 2c). However, the architecture of the late summation model predicts this precise pattern (Fig. 2f), which has been termed "ocularity invariance" in the binocular domain (Baker et al. 2007)-the observation that the world does not change in contrast when one eye is opened or closed.
A strong prediction of the late summation model is shown by the dotted curve in Figure 2f. This represents a condition where the B component is fixed at a high contrast (32%) and the contrast of the A component is increased. The model predicts that activity will remain constant over an intermediate range of A contrasts, despite the input to the system continually increasing. None of the other models makes this prediction, yet it is clear from the data in both experiments.
To assess which candidate model produced the best description of our results, we performed least-squares fits (downhill simplex algorithm, from 100 random starting vectors) for all six models (from Fig. 2) to the data from each experiment. The linear summation model had only a single free parameter (R max ) that multiplicatively scaled the maximum response. The other five models had four free parameters each: p, q, Z, and R max . For both the space and binocular experiments, the late summation model gave the best numerical fit, capturing 98% and 92% of the variance within each data set, respectively. The other models all produced poorer fits that explained a lower proportion of the variance, and had obvious qualitative failings. Summed squared errors (SSEs) and Akaike's Information Criteria (AIC) scores that account for the number of parameters (Akaike 1974) for the six models are summarized in Table 1.

Model Predictions for Suppression Conditions
Another manipulation available using the steady-state paradigm is to flicker the two components at different frequencies (Candy et al. 2001;Tsai et al. 2012). Responses to the two inputs can be measured independently through the early visual system (Regan and Regan 1988), permitting the isolation of suppressive processes. Figure 4a-d shows data and model predictions at the target frequency (5 Hz) for component A, when the second component (B) flickered at either 7 Hz (for the space stimuli) or 7.5 Hz (for the eye stimuli). Figure 4e,f shows the responses for the same conditions at the higher frequency. In each panel, the circles show the response for a single component (as shown in Fig. 3c,d). The gray triangles (in panels a, b, e, and f) show the response when both A and B components increase in contrast together. This is equivalent to the gray squares in Figure 3, but now flickering at different frequencies. Note that the gray triangle function in all panels is shallower than the single component (circle) function, showing the effect of suppression on the gain of the system. This demonstrates the action of the inhibitory terms in the denominator of equation (4).
The white inverted triangles in Figure 4c-f represent the condition in which a high contrast (32%) mask stimulus is shown at the higher frequency. The target contrast at the lower frequency (5 Hz) is increased along the abscissa. The white triangle contrast response function in Figure 4c,d is shifted to the right relative to the single component (circles) function. This is a classic contrast gain control effect, reported widely in previous human SSVEP (Candy et al. 2001;Busse et al. 2009;Tsai et al. 2012;Baker and Vilidaitė 2014), animal SSVEP (Afsari et al. 2014), and single-cell (Morrone et al. 1982;Carandini and Heeger 1994) studies. The white inverted triangles in Figure 4e,f show the complementary response at the mask frequency as target contrast increases. There is a reduction in response at higher target contrasts, showing the suppressive effect of the target on the mask.   We obtained predictions for the results of these additional conditions by assuming (following Foley 1994) that components at both frequencies continue to contribute to the gain pool (denominator). Responses to individual inputs can be identified by their frequency tags, so the numerator contains terms at only a single frequency (presumably involving the same populations of neurons as when a single component is presented at that frequency), giving, . 5 p q q q Importantly, we fixed the parameters at the fitted values from Figure 3. With no free parameters, the model correctly predicted the form of the data in Figure 4a-e. Because responses were reduced slightly at 7.5 Hz, we permitted R max to vary (it reduced from 0.71 to 0.53) in order to better fit the data in Figure 4f.
The model explained 89% (space) and 73% (eye) of the variance in these extra conditions. The poorer fit for the eye data is mostly due to a shallower-than-predicted contrast response function in Figure 4d (inverted triangles). This resembles a response gain effect (e.g. a change in R max ) rather than a contrast gain effect (see Li et al. 2005), and we are actively investigating this discrepancy in a further study. It is possible that a more sophisticated multi-stage model (Meese et al. 2006) would give a better description of the binocular data. However, despite this shortcoming, the model correctly predicts the differences in gradient of the contrast response functions in all panels of Figure 4. Overall, we can capture the pattern of eight contrast  response functions (40 data points) for two distinct stimulus domains with only four free parameters. This illustrates the generality and predictive power of the model.

Model Predictions for Harmonic and Intermodulation Frequencies
In addition to making analytic predictions about activity at a given frequency, gain control models can also be used to predict how stimuli of different frequencies interact (Tsai et al. 2012). This is achieved by passing the sinusoidal temporal waveforms for each stimulus through the model and inspecting the Fourier spectrum of the resulting output waveform. However, the noise in the Fourier spectrum of EEG data (e.g. Fig. 5a) declines as a function of frequency, and can have idiosyncratic properties unrelated to the stimulus frequencies (e.g. endogenous alpha activity around 10 Hz). We therefore calculated the SNR at each frequency to normalize out these differences, by dividing by the average amplitude in the neighboring frequency bins (the mean of the five bins on either side, with 0.1 Hz frequency resolution). This produced a flat spectrum aside from the substantial peaks evoked by the stimulus (Fig. 5b), with the salience of higher harmonics (integer multiples of the fundamental frequency) being enhanced because of the lower noise in that region of the spectrum. To produce model responses, we fed the temporal waveforms (scaled appropriately by contrast) into equation (4), and took the Fourier transform of the output. To convert to SNR, we added an arbitrary constant (0.01) to the model spectrum, and divided the sum by that same constant (so that in the absence of a signal the SNR was 1). Presentation of two frequencies simultaneously produced evoked responses at sums and differences of the fundamentals, as has been reported previously (Tsai et al. 2012). These can be seen in the empirical and model spectra shown in Figure 5c,d (highlighted green), and as a function of contrast for one condition in the surface plots of Figure 6. A novel observation is that responses are also evoked at specific additional frequencies that appear to be combinations of fundamental, harmonic, and intermodulation terms (e.g. 3, 4, 8, 9, 16, 18, and 19 Hz all had  Fig. 1c) at the highest contrast tested (64%). The spectrum shown in Figure 3b is a subset of these data (from 2 to 9 Hz). (b) The same data but expressed as SNR, with the amplitude at each frequency normalized by the average amplitude in the adjacent 10 bins (5 below and 5 above). (c) The spectrum for a condition in which one stimulus (A) was presented at 5 Hz and the other (B) at 7 Hz, both at 64% contrast. Responses at additional frequencies besides the sums, differences, and multiples of the fundamental frequencies are apparent. (d) Predictions of the late summation model for the conditions in (c), using the parameters from the fits described above. The model reproduces the additional responses reasonably well. In panels (a-c), gray shaded regions indicate the standard error across observers (N = 12). The colors highlight responses at the fundamental and integer harmonics of 5 Hz (blue) and 7 Hz (red), and the sums and differences of the fundamentals (green).
SNR > 2). The model also produces responses at most of these frequencies (Fig. 5d), though the signals are sometimes stronger (e.g. 12 Hz) and sometimes weaker (e.g. 14 Hz) than those found in the empirical data. We suspect that these discrepancies might indicate the presence of neurons that involve further nonlinear stages of processing. Examples include complex cells that code changes in contrast (which might account for the increased second harmonic responses) and conjunction detectors (AND gates). We intend to model these cell types explicitly in future, though doing so is beyond the scope of the present study.
We also attempted fitting the model to the entire spectrum from 1 to 30 Hz across all conditions. This produced similar parameter values to those described above, and comparable fits to the contrast response functions at fundamental frequencies, but additionally gave a good account of responses at harmonic, intermodulation, and other integer frequencies. The model was able to account for 69% of the variance of each of the space and eye data sets, with each data set consisting of 8730 data points (30 conditions by 291 frequencies in steps of 0.1 Hz). With only four free parameters, this seems an impressive performance.

Discussion
We tested the predictions of six models of cortical signal combination for two visual stimulus domains (space and eye-oforigin). We found that the late summation model (eq. 4) correctly captured the pattern of contrast response functions for three conditions (Fig. 3c,d) and predicted several other conditions with no free parameters (Figs 4a-c, 5c,d, and 6). Alternative models that sum individual channel responses before nonlinear transduction failed to correctly describe the pattern of results. We discuss the implications of these findings for our understanding of the relationship between perception and neural activity, and speculate that the preferred model of contrast normalization is a neural implementation of Bayesoptimal signal combination.

Perception and Neural Activity
A body of recent psychophysical work has converged on the same algorithm for signal combination that we are proposing in this study . These studies used a range of detection, discrimination, and matching paradigms to investigate the perception of stimuli summed across dimensions such as eye (Meese et al. 2006;Baker et al. 2007), space (Meese and Summers 2007), and time and orientation . Our results demonstrate that this model is consistent with the pattern of cortical responses at a population level, and other work has converged on a similar algorithm for binocular combination using functional MRI (fMRI) (Moradi and Heeger 2009) and for combination across orientation in neural populations (Busse et al. 2009). Such consistency across measurement techniques is extremely unusual, and implies that the model is an accurate reflection of the operations performed by the brain in combining signals.
The development of a general model will aid in our understanding of how signals are pooled across successive stages of processing. In the spatial domain, recent fMRI studies have characterized a compressive nonlinearity that grows more severe at later stages in the cortical hierarchy (Kay et al. 2013a(Kay et al. , 2013b. This is presumably closely related to the late summation model described here, which is effectively compressive at high contrasts with an overall exponent equivalent to the difference of the numerator and denominator exponents (p-q is typically around 0.4). However, the precise form of the suppressive interactions characterized here go far beyond a descriptive model and could presumably improve the accuracy of attempts to explain blood oxygen level-dependent (BOLD) responses to visual patterns of different spatial extents, and more generally to arbitrary broadband visual stimuli.
In general, there is no reason to think that the model proposed here applies only to vision, or to the specific visual dimensions explored (space and eye-of-origin). We have shown already that the same algorithm applies equally well to psychophysical summation across orientation and time Meese and Baker 2013). Furthermore, a strikingly similar model has been proposed for explaining neural population responses to stimuli of different orientations (Busse et al. 2009). That model involved subtle differences in the way that suppressive signals are pooled (computing the RMS contrast), but fitted the present data almost as well as the preferred model here (not shown). The models can be considered architecturally cate the mask frequency (7 Hz) and its harmonics. Green-highlighted functions indicate the intermodulation frequencies (7−5 = 2 Hz and 7 + 5 = 12 Hz). In the human data, the higher harmonics produce relatively larger amplitudes than in the model. Model parameters are given in the caption to Figure 3. equivalent, supporting the idea that common operations apply within distinct visual cues.
Given the ubiquity of canonical microcircuits and computations, such as gain control (Carandini and Heeger 2012), it is conceivable that the same algorithm will be implemented at higher levels of the visual system and in other senses. Likely candidates are binaural and cross-frequency combination in hearing (Treisman 1967), and spatial summation across the skin for vibration (Haggard and Giovagnoli 2011). In high-level vision, there is evidence that information is pooled across objects such as faces (Young et al. 1987;Boremanse et al. 2013). In principle, these operations might be achieved by the same computations that are used for combining simpler visual features.
At What Level is the Algorithm Implemented?
Steady-state EEG is believed to measure aggregate responses across large populations of neurons (most likely pyramidal cells in the superficial layers of cortex) that are responsive to a given stimulus (Norcia et al. 2015). This population response will likely encompass neurons with differential weightings across the two inputs (in the binocular case, different levels of ocular dominance, and in the spatial case, different receptive field positions or shapes), as well as a range of contrast sensitivities, that we do not model explicitly here (for further discussion, see Busse et al. 2009). As such, the model we propose can be thought of as representing an omnibus population response, rather than the activity of individual cells, in a manner similar to the fMRI BOLD response. From the perspective of David Marr's framework (Marr 1983), the models described here are therefore algorithm-level explanations of signal combination, rather than implementation-level explanations (i.e. circuit diagrams) that would make assumptions about synaptic connections between neurons, specific classes of cell involved, or numbers of cells responding to a particular stimulus. In principle, the basic algorithm could be implemented in any number of ways, and we expect that single-cell neurophysiology might reveal how this is achieved in cortex.
In psychophysical studies that have used similar stimuli (Meese et al. 2006;Meese and Summers 2007;Meese and Baker 2013), observers are assumed to base their responses on the activity of a small number of neurons most appropriate for the task at hand, and make discriminations within a signal detection theory framework (i.e. choosing the interval that produces the largest response after combination with additive internal noise). That the algorithm presented here is also able to give a good account of such data suggest that the combination rules are sufficiently generic that they apply across the whole population of neurons.

Optimal Signal Combination
Equation (4)  where P A is the response of the channel (or sensor) tuned to input A, and σ A 2 is the variance, with terms bearing the subscript B corresponding to a second channel (Einicke 2012). Hence, the filter weights each input by the inverse of its contribution to the total variance. This filter has numerous applications in engineering, including the fusion of image data from multiple sensors (e.g. Willner et al. 1976). For the situation where the two inputs have equal variance (i.e. the two eyes), the weight terms are immaterial, and the filter becomes similar to our model. In the hypothetical case where the numerator and denominator exponents are equal (as we found for binocular combination, see Fig. 3) and P represents contrast energy (A 2 and B 2 ), the two models become identical. We therefore speculate that combination within a cue might be statistically optimal as a consequence of divisive normalization. We also note that dynamic (time-dependent) Kalman filters include the history of recent inputs in calculating the weights, a computation that has obvious parallels with contrast adaptation (Carandini and Ferster 1997) and attention (Reynolds and Heeger 2009), both of which are closely related to gain control. Previous models that implement optimal cue combination have focused on cases where the two inputs are from different modalities (e.g. vision and touch, Ernst and Banks 2002) that have unequal variances. Indeed one recent study developed a normalization model with the same general form as that we propose here to account for several specific multisensory integration phenomena in neurons in the superior colliculus and area MSTd (Ohshiro et al. 2011). Yet so far a theoretical link between normalization and optimal signal combination has remained elusive: as Ohshiro et al. (2011) explicitly state, "It is currently unclear what roles divisive normalization may have in a theory of optimal cue integration and this is an important topic for additional investigation." We speculate here that gain control suppression may be the mechanism by which signals are weighted to permit their optimal combination. The exponent values in equation (6) are presumably a consequence of the neural implementation of this weighting principle.
To our knowledge, this is a novel account of the purpose of cortical gain control, which is a canonical neural operation observed throughout the brain (Carandini and Heeger 2012). In addition, it makes clear predictions for situations in which one input is noisier than the other. A natural example for binocular vision is amblyopia, in which the amblyopic eye's responses are both weaker (i.e. suppressed) (Baker et al. 2008(Baker et al. , 2015 and noisier (Levi and Klein 2003;Baker et al. 2008) than those of the fellow eye. Previously, the increased noise has been considered secondary to the suppression. But our account suggests that the amblyopic suppression might be a Bayes-optimal consequence of one input being noisier (perhaps because of erratic fixation due to strabismus) than the other during development. This might explain why attempts to reduce the suppression by increasing noise in the fellow eye appear to be successful (Hess et al. 2010).

Conclusions
A single, simple algorithm was shown to accurately predict a complex pattern of steady-state contrast response functions for signal combination across space and eye. This algorithm is a strong candidate for a canonical model of optimal neural signal combination, and may well be relevant in senses other than vision, and perhaps throughout the cerebral cortex more generally. Because the same model can explain both steady-state EEG and psychophysical data, it highlights the close link between perception and neural activity. By suggesting that gain control suppression implements optimal signal combination, we have shown how two of the most influential concepts in modern neuroscience (contrast gain control [Heeger 1992;Carandini and Heeger 2012] and Bayesian information theory [Ernst and Bülthoff 2004;Friston 2010]) might be unified.

Notes
We are grateful to Greta Vilidaitė and Kirstie Wailes-Newson for assistance in data collection. Conflict of Interest: None declared.


Selenium toxicity: A rare diagnosis

Sir, While trace elements are essential for normal body growth and regulation, excess intake of these elements can lead to toxic effects.[1] Selenium is an essential trace element which is a constituent of more than two dozen selenoproteins that play a critical role in reproduction, thyroid hormone metabolism, deoxyribonucleic acid synthesis and protection from oxidative damage and infection.[2] However, it is known to be toxic with a narrow interval separating deficiency and toxicity. According to the World Health Organization, selenium toxicity is prevalent in South Dakota (USA), Venezuela, and China.[1] In India, selenium toxicity has been reported in Hoshiarpur and Nawansahar districts of Punjab.

A 35-year-old man presented at SMS Medical College and Hospital, Jaipur with a four-month history of dystrophy of all nails and a one-month history of hair loss and fragility of hair all over the body. Initially, white spots and longitudinal red streaks appeared on the nail surface followed by increased brittleness and breaks in the nail plate. [ Figure 1a and b]. The new nails that grew subsequently were also dystrophic. The thumb nail was the first to be affected and later all twenty nails were involved [ Figure 2]. In some of the fingernails, fluid effused from the nail folds and there was pain at the fingertips. Within 2-3 months he noticed increased hair fragility and loss of scalp, beard, axillary, pubic and body hair [ Figure 3]. There was no significant history of any drug intake or chronic systemic disease. There were similar complaints in eleven members of his family and four members of a neighboring family [ Table 1]. On examination, there was involvement of all the finger and toe nails with varying severity in these individuals. There was patchy erythronychia with transverse, subtotal or distal leukonychia, onycholysis and roughening of the nail plate. Hair on the scalp, eyebrows, beard and mustache were dry, brittle and broke easily on slight pulling. Hair density was also decreased on the entire scalp, most pronounced on the occipital area. There was no mucosal involvement. General physical and systemic examinations were also unremarkable. Potassium hydroxide preparation and fungal culture of nail clippings were negative. Hair microscopy showed pigmented, shrunken anagen hair bulbs. Hair shafts were normal with no beading, twisting, nodes or fractures. Nail biopsy showed hyperkeratosis and irregular acanthosis with intra-corneal neutrophilic abscess. An intra-epidermal cleft was seen, filled with red blood cells and acute and chronic inflammatory cells. The dermis showed dense perivascular infiltration by acute and chronic inflammatory cells. All routine blood investigations were within normal limits.
On further evaluation, no abnormality was detected on electrocardiography, however, nerve conduction studies revealed signs of peripheral motor neuropathy affecting both the common peroneal nerves. Magnetic resonance angiography of the hand (to rule out microvascular involvement) was normal and computed tomography scan of the hand showed osteochondritis of the carpal bone.
Abrupt onset of hair loss and nail dystrophy and the presence of symptoms in the whole family along with a neighboring family made us suspect chronic poisoning due to some organic or inorganic material. Samples were taken from the hair and nails of the most severely affected patient and were sent to a laboratory for analysis. Inductively coupled plasma mass spectrometry revealed an excessive amount of selenium in the samples. The concentrations of selenium detected in the nail and hair were 27 µg/g and 35 µg/g respectively. The corresponding normal values are 0.809 µg/g and 0.36 µg/g, respectively for nail and hair. [3,4] Thus, the amount of selenium detected was almost 30 times higher in nails and almost 100 times higher in hair. Since the signs of poisoning were limited to only two families in the area, the potential sources were either polluted water supply or food material. It was found that both families were using wheat grain from the same source. Thus, the suspected wheat grain sample along with a control wheat grain sample (with no symptoms on consumption) were sent to the laboratory for analysis. Furthermore, a water sample was sent for analysis. The selenium concentration in the control wheat grain sample was 0.43 mg/kg, whereas it was 152.82 mg/kg in the suspected wheat grain sample. The reported normal value for selenium in wheat grains is 0.61 ± 0.006 mg/kg. [4] Thus, the level detected in the suspected wheat grains was almost 250 times higher than normal. Selenium was not detectable in the water sample.
The patients recovered remarkably, with growth of healthy nails and hair, after they stopped consumption of the toxic wheat.
Selenium is an essential element and its recommended daily allowance varies from 55 to 70 µg/day. The major source is dietary; it is present mostly bound to animal and plant proteins. The food sources are meats and seafood (0.3-0.5 mg/kg), cereals (0.1-10 mg/kg), vegetables and fruits (<0.01 mg/kg). The selenium content in crops depends on the water soluble  selenium present in soil and the amount in animals depends on their feed. Water per se, due to its low selenium content, is an unlikely source of selenium toxicity except in areas with an excess of selenium in the soil. [5] The level of selenium in most urban air ranges from 0.1 to 10 ng/m 3 , but higher levels may be found in certain areas such as near copper smelters. [1] Most water-soluble inorganic and organic selenium compounds present in food are absorbed across the gastrointestinal tract (80-95%). [6] After absorption, selenium is cleared by the liver and then transported by a specific transporter, selenoprotein P, to all organs with the highest concentrations occurring in the kidney, liver, spleen, testes and skeletal muscle. [7,8] The mechanisms of selenium toxicity at the cellular or molecular level are not yet fully understood. However, it is believed that it can interact with glutathione to form reactive selenotrisulfides and generate toxic superoxide and hydrogen peroxide species, oxidizing cell membranes and macromolecules, thereby damaging cell integrity and resulting in necrosis or apoptosis. [9] In a study in which human cells were treated with selenium, it was observed that selenium at higher levels has a cytotoxic effect with depletion of lipids and proteins and DNA damage. [10] Stewart et al. reported significant DNA-adduct formation in mouse keratinocytes (BALB/c MK-2) treated with 5 mg/L Se iv as a consequence of oxidation which is thought to directly arise from the generation of the hydroxyl (−OH) radicals. [11] Very low selenium status in humans has been associated with a juvenile, multifocal myocarditis called Keshan disease and a chondrodystrophy called Kaschin-Beck disease. [12][13][14] Chronic selenium toxicity leads to loss of hair and nails. Hair breaks at the level of the scalp with slight trauma, such as scratching, while the follicles remain intact. Hair can be lost from any body site. A rash on the scalp with severe itching has also been reported. Nails become brittle and are shed and the new nail is also dystrophic. Skin lesions have been reported, mainly on the limbs, i.e., dorsum of hands and feet, the outer side of legs and thighs, the forearms and the back of the neck. Affected skin becomes red and swollen and then blistered and ulcerated. Reddish pigmentation of the skin usually remains and gooseflesh may be left on the neck and thighs. Dental anomalies such as tooth decay and mottling might also be seen. Abnormalities of the nervous system such as peripheral anesthesia, "pins and needles," acroparesthesia, pain in the extremities, hyperreflexia of the tendons and motor disturbances are other features. Decreases in prothrombin time and in the concentration of glutathione have also been reported. [3,4,14] The only effective treatment is discontinuation of the source of selenium, i.e., either contaminated food material or water.
Financial support and sponsorship Nil.
Anonychia with absent phalanges and brachydactyly: A report of two unrelated cases Sir, Congenital anonychia or its milder phenotypic counterpart, hyponychia, is a relatively rare abnormality of nail development affecting all or some of the finger and toe nails, characterized by the complete absence of nails or the presence of only rudimentary nails, respectively. It could occur as an isolated abnormality, termed anonychia simplex or as part of a syndrome. [1] We report two cases of partial hyponychia/anonychia with underlying absent distal phalanges in both, and in addition hypoplastic/absent middle phalanges in one of them.
A 35-day-old female child, second born to non-consanguineous parents was brought with absent finger nails since birth. The mother denied any intake of medication or illness during the antepartum period. There was no similar history in the family. The child had short digits along with rudimentary nails of digits 2, 3, and 5 of the right hand and digit 5 of the right hand with absence of nails on digit 4 of the right hand and digits 3 and 4 of the left hand; the nails of the index finger of left hand, both thumbs and all toes were normal [ Figure 1a and b]. The head circumference was 35 cm (50 th centile for age) and there was no facial dysmorphism. The skin did not reveal any pigmentary abnormality. The plain radiograph of both the hands revealed the absence of distal phalanges of digits 2-5 [ Figure 1c and d]. The child's pelvis X-ray did not show iliac horns, the renal function was normal and the ultrasound of abdomen did not show any renal abnormality. The child's brainstem-evoked response audiometry (BERA) was also normal.
Our second case was a 11-year-old girl, second born of a non-consanguineous marriage, with congenital absence of nails of the right hand. There was neither a positive family history nor a history of maternal drug intake during pregnancy. Her right hand had shortened digits 2-4 with poorly defined finger creases. Digits 3 and 4 had anonychia, whereas the little finger and index finger nails were hypoplastic [ Figure 2a and b]. The nails of the right thumb, all digits of the left hand and all toes were normal. X-ray showed absence of middle and distal phalanges of digits 3-5 and absence of distal phalanx with hypoplasia and tapering of the middle phalanx of digit 2 of the right hand. There were rudimentary epiphyses seen in digits 3, 4 and 5 of the right hand [ Figure 2c]. She had no other cutaneous abnormalities.
Congenital anonychia/hyponychia is a spectrum of nail abnormalities which often occur concurrently in an individual. It is inherited as an autosomal dominant or recessive trait or can occur sporadically. [1] Simple anonychia has been identified to be due to mutation in R-spondin 4-gene and some cases of SOX9 duplication are reported as causes of brachydactyly-anonychia. Onychoatrophy, on the other hand, is distinct from hyponychia in that the nails are reduced in size and thickness. Table 1 lists the associations of anonychia/ hyponychia and causes of onychoatrophy.


Vitamin E supplementation fails to impact measures of oxidative stress or the anaemia of feline chronic kidney disease: a randomised, double‐blinded placebo control study

Abstract This study was designed to test the hypothesis that supplementation with vitamin E, an antioxidant, in cats with chronic kidney disease (CKD), would reduce oxidative stress and its impact on RBC membrane fragility, resulting in these cats maintaining a greater packed cell volume (PCV) compared with CKD cats not receiving supplementation. Thirty‐six cats with CKD were randomly assigned to receive either daily vitamin E or a placebo for 3 months in a double‐blinded study design. History and physical examination, blood pressure, complete blood count (CBC), PCV, biochemical profile and urinalysis (UA) were determined. Parameters of oxidative stress and osmotic fragility were measured. Cats were administered vitamin E or placebo once daily for 3 months. Cats were then reassessed and the diagnostics were repeated. Twenty‐four cats completed the study, 11 in the vitamin E group and 13 in the placebo group. There were no significant differences between the two groups at the start, or upon completion of the study with regard to biochemical parameters, oxidative stress, erythrocyte osmotic fragility or PCV. None of these parameters changed significantly in either group over the treatment period. Daily supplementation with 30 IU of vitamin E did not affect the measures of oxidative stress or the anaemia seen in cats with CKD.

Introduction
The supplementation of vitamin E (alpha-tocopherol) in cats with chronic kidney disease (CKD) may have beneficial effects on oxidative stress and therefore, a common clinical abnormality seen in these cats, anaemia (Keegan & Webb, 2010;Bargnoux et al., 2013;Brown, 2008;Peuchant et al., 1997). As an antioxidant, vitamin E prevents the oxidation of polyunsaturated fatty acids caused by oxidative stress acting on red blood cell membranes, thereby reducing erythrocyte membrane fragility and potentially decreasing haemolysis (Jilani & Iqbal, 2011, Traber & Stevens, 2011. Vitamin E deficiency can cause RBC membrane changes and haemolysis as osmotic fragility appears to be inversely related to alpha-tocopherol concentrations in certain circumstances (Jaja et al., 2005;Tomten & Høstmark, 2009). Vitamin E-coated dialyser therapy in human kidney failure patients is based on these potentially beneficial properties, although thus far the results are inconclusive (Usberti et al., 2002;Huang et al., 2015).
Oxidative stress may contribute significantly to the anaemia seen in human patients with CKD (Celik et al., 2011;Cruz et al., 2008;Usberti et al., 2002).
Antioxidant capacity is significantly decreased in cats with CKD (Keegan & Webb, 2010). One manifestation of oxidative imbalance is lipid peroxidation of erythrocyte (RBC) cell membranes, which would decrease RBC lifespan and could contribute to the anaemia of conditions such as CKD (Ansari et al., 2015;Gomez et al., 2013). Vitamin E has antioxidant properties uniquely suited to combating cell membrane lipid peroxidation with few known side-effects, making it a potentially beneficial therapy aimed at an alternative target contributing to the anaemia of CKD (Singh et al., 2005). Assays of RBC osmotic fragility have been used to characterise anaemia of unknown aetiology in cats, including the anaemia of Abyssinian and Somali cats with splenomegaly (Contreras et al., 2015;Tritschler et al. 2015). Erythrocyte osmotic fragility has also been used as a quantitative measure of the effect of nutraceutical supplementation on RBCs in healthy cats . The cupric iron reducing antioxidant capacity (CUPRAC) assay has been validated against atomic absorption spectrophotometry, and established as a reliable marker of total antioxidant capacity in erythrocytes under oxidative stress (Kondakc ßi et al. 2013, Soumya et al. 2015.
Although not yet used in cats, the fact that the CUPRAC assay measures total antioxidant capacity against a water-soluble vitamin E analogue as the standard made this an ideal parameter for this study.
To date the authors are aware of a single study looking at the effects of antioxidant supplementation (a combination of vitamins including vitamin E) in cats with CKD, and the results showed that the treatment reduced serum 8-OHdG levels and comet assay characteristics indicative of DNA damaged by oxidative stress (Yu & Paetau-Robinson, 2006). This study was designed to test the hypothesis that supplementation with vitamin E, an antioxidant, in cats with CKD, would reduce oxidative stress and its impact on RBC membrane fragility, resulting in these cats maintaining a greater packed cell volume (PCV) compared with CKD cats not receiving supplementation.

Selection of cases
Thirty-six adult cats diagnosed with CKD were recruited from the client-owned Colorado State University Veterinary Teaching Hospital population for this study. Inclusion criteria for study participation included a diagnosis of CKD of at least 6 months duration, based on a persistent serum creatinine >1.6 lg/dL and a urine specific gravity <1.030. Blood pressure and urine protein:creatinine ratio were obtained for all study cats for IRIS staging. Cats were excluded if they had evidence of a chronic disease other than CKD based on history, physical examination, CBC, biochemical profile, urinalysis (UA) with culture or total T4 levels. Cats were also excluded if they ever had, or were currently receiving, erythropoietin, darbepoetin, iron supplementation or antioxidants other than those already a part of their manufactured diet. Cats were given 30 IU of vitamin E (alpha-tocopherol) PO once daily for 3 months or an identical volume of a placebo (safflower oil) containing the same vehicle (tuna juice) but without vitamin E. [Stuart Products Inc., Emcelle â Tocopherol, 500 IU d-alpha-tocopherol/ 1 mL. Compounded: 1.2 mL into 8.8 mL tuna juice for 10 mL at 30 IU/0.5 mL in brown bottle, keep refrigerated. Administer 0.5 mL (measured syringe) per os once daily for 14 days, refill 6x]. Whether cats received the supplement or placebo was determined using a computer generated randomisation table, and the clinician, client and all involved in the running of all assays were blinded as to the group entry. Cats were excluded if unwilling to take the supplement/placebo per os or if the owner failed to administer the supplement/placebo per instructions for the duration of the study. Owners were instructed not to change their cat's diet or add any treatments or supplements during the course of the study, other than those given as a part of the study. A signed consent form was obtained and all sample acquisition and handling were in accordance with Colorado State University Animal Care and Use Committee guidelines. The cats were otherwise managed completely by the attending clinician.

Diagnostic work-up
During both the initial and final study appointments, a complete history and physical examination, including body weight, body condition score and systolic blood pressure, were performed. Samples for the CBC, PCV, biochemical profile, TT4, UA and UPC were obtained using standard venipuncture technique and cystocentesis.
EDTA-preserved whole blood was collected for the assay of RBC osmotic fragility and the 1,2-Dihexadecanoyl-sn-Glycero-3-Phosphoethanolamine, triethylammonium salt (DHPE) probe assay of lipid peroxidation, whereas serum was used for the CUPRAC assay of antioxidant capacity.

CUPRAC assay
The cupric ion reducing antioxidant capacity (CUPRAC) assay is a measure of total antioxidant capacity. A serial dilution of Trolox (water-soluble vitamin E analogue; .10, .08, .06, .05, .04, .02, .01, 0.0 mmol/L) was used to generate an external standard curve. For the standard curve, 50lL of a 7.5 mmol/L neocuproine hydrochloride monohydrate solution, and 50lL of a 1.0 mol/L ammonium acetate buffer, in that order (the CUPRAC solution), were added to 24 wells of a 96 well flat bottom plate. Each Trolox standard was run in triplicate. For the study samples, 200lL of each chemical, again in the specified order, was added to a 2.0 mL microcentrifuge tube. Both the plate and these tubes were incubated for 15 min at 37°C. Study samples were thawed at room temperature and vortexed before diluting 1:50 with 50% acetone (i.e. 10lL sample mixed with 490lL 50% acetone). These diluted study samples were vortexed and 400lL added to one microcentrifuge tube of CUPRAC solution immediately following incubation. The CUPRAC reaction occurs almost immediately. The tubes were then briefly vortexed and centrifuged at 2360g for 3 min at room temperature. Of the resulting supernatant, 250 lL was added to empty wells of the previously prepared 96 well plate. The study samples were run in triplicate. For each Trolox dilution, 100 lL was added to the previously prepared standard wells containing the CUPRAC solution. Again, the CUPRAC reaction in the Trolox standard wells occurs almost immediately, as seen by the production of a yellow colour.
The plate was read once at 450 nm on a Biotek Synergy HT plate reader. The triplicates of the Trolox standards and study samples were averaged and normalised against the 0 mmol/L Trolox control. The Trolox standard curve was created by plotting normalised absorbances vs. Trolox concentration (mmol/L). A linear equation was fitted to the data and used to interpolate the mmol/L concentration of Trolox in the study samples. The final concentration of antioxidant capacity as measured by mmol/L Trolox concentration in the study samples was determined by multiplying by the dilution factor.
Osmotic fragility assay EDTA-preserved blood samples were run within 24 h. From the EDTA blood 100lL was added to 2 mL microcentrifuge tubes (2 tubes per sample). Two microlitres (2 lL) of 0.80% cumene hydroperoxide (an oxidising agent) was then added to one of the two samples containing tubes and vortexed. The cumene-treated blood was incubated at room temperature for 15 min.
Two sets of serial dilutions of sodium chloride (NaCl) were created per study sample using distilled water (0.90%, 0.75%, 0.70%, 0.65%, 0.60%, 0.55%, 0.50%). For one set of serial NaCl dilutions, 10lL of cumene-treated blood was added to 1 mL of each dilution tube and mixed. For the second set of serial NaCl dilutions, 10 lL of non-treated blood was added to 1 mL of each dilution tube and mixed. Both sets were incubated at room temperature for 15 min. All tubes were centrifuged at 380g for 3 min. Two hundred microlitres of supernatant from each tube was added to a 96-well flat bottom plate in triplicate.
The plate was read immediately at 540 nm on a Biotek Synergy HT Plate Reader. The NaCl concentration at 50% haemolysis was calculated by taking the average of the highest and lowest absorbance reading and interpolating between these two absorbance readings.

DHPE Probe
The DHPE probe [DHPE probe, Fluorescein DHPE (N-(Fluorescein-5-Thiocarbamoyl)-1,2-Dihexadecanoylsn-Glycero-3-Phosphoethanolamine, Triethylammonium Salt), ThermoFischer Scientific, Waltham, MA.] was prepared with 0.5% ethanol to make a 5 mmol/L concentration and stored at À20°C. EDTA-preserved whole-blood samples were run within 48 h of acquisition. The PCV and RBC concentration (910 6 cells/lL) were recorded for each sample. From the study sample 50 lL EDTA blood was added to each of two 2 mL microcentrifuge tubes. One microlitre of 0.80% cumene hydroperoxide was added to one of the two tubes and vortexed. The cumene-treated blood was incubated at room temperature for 15 min.
All samples were washed three times with 1 mL of PBS and centrifuged at 2500 rpm for 3 min. Each sample was suspended in PBS such that the total cell count was 2 9 10 7 cells per mL PBS. One microlitre of this diluted blood was added to a new 2 mL microcentrifuge tube with 1 lL DHPE probe and mixed well. Samples were incubated at 37°C for 1 h. Excess diluted blood was saved and kept at room temperature for later use during flow cytometry.
The incubated samples were centrifuged at 4000 rpm for 3 min and the supernatant was discarded. The remaining pellet was washed once with 500 lL PBS and transferred to a flow cytometry tube. The saved diluted blood sample of 500 lL with no DHPE probe was used as a control.
All samples, including the cumene-treated blood, the cumene-treated blood with DHPE probe, the blood with DHPE probe and the untreated blood sample were read on the CyAn ADP Flow Cytometer using the FL1 fluorescence channel. The resulting scatter plot was gated using appropriate forwardand side-scatter coordinates to capture the RBC population and record the fluorescence produced by the DHPE probe. Fifty microlitres of each treated sample was added to the flow cytometer tube containing the untreated blood sample so that the signal from both treated and untreated RBCs could be quantified off the same scatter plot. Approxi-mately 200,000 events were collected for analysis, such that the standard deviation of the recorded signal became minimised and stable.

Statistical analysis
The CSU electronic medical record database was searched for cats with CKD over a period of 1 year (2011). From those cats for which the PCV was recorded on two separate occasions, the mean and standard deviation for the decrease in PCV was determined. These numbers were used in a two-sample one-sided power calculation to determine group size (n = 16) for a power of 0.8 and a Type I error rate of 5%. A computer software program [GraphPad Prism software, GraphPad Software, Inc. La Jolla, CA.] was used for statistical analysis. Gaussian distribution was confirmed with the Kolmogorov and Smirnov test, and the difference in the means between groups was tested using a two-tailed unpaired t-test assuming unequal variance with the Welsh correction. A paired t-test was used to compare means before and after supplementation with vitamin E or placebo. Statistical significance was set as P < 0.05. All data were expressed as the mean AE the standard deviation.

Cats
Thirty-six cats diagnosed with CKD and meeting the inclusion criteria were recruited into the study. Twenty-four cats completed the study, 11 in the vitamin E supplemented group and 13 in the placebo group. Nine cats were killed prior to completion of the study because of a progression of clinical signs and owner's perception of poor quality of life, six of those cats were from the vitamin E group and three were from the placebo group. Three cats were lost to follow-up with no explanation. None of the study participants reported any non-compliance in supplement/placebo administration, any change in diet, or any other changes to the treatment regimen for their cats during the 3-month study period, other than the administration of the study treatment. No owners reported any adverse side-effects with administra-tion. One cat initially refused per os administration, but became easily compliant following further instruction of the owner.
The vitamin E group included four neutered males and seven spayed females: eight domestic shorthaired cats, one domestic long-haired, one Siamese and one Persian cat. The placebo group contained seven neutered males and six spayed females: 11 domestic short-haired cats and two domestic longhaired cats. The mean age for the two groups was not significantly different, 13.5 AE 1.0 and 14.9 AE 1.0 years for the vitamin E and placebo groups respectively. The vitamin E group contained five IRIS Stage 2 cats and six IRIS Stage 3 cats; five of these cats were non-proteinuric and six were borderline; four cats were normotensive, one was borderline, two were hypertensive and four cats were considered severely hypertensive. The placebo group contained nine IRIS stage 2 cats and four IRIS stage 3 cats; four cats were non-proteinuric, seven were borderline and two cats were proteinuric; eight cats were normotensive, one was borderline, two were hypertensive and two cats were considered severely hypertensive. Table 1 compares the clinical parameters between groups at the start of the study. There were no statistically significant differences in any of these parameters between the two groups at study entry.
There was tremendous variability in the diets of both groups of study cats, and only one cat from each group was being fed a single diet, which in both cases was Hill's â k/d canned. All other study cats were reportedly fed multiple different foods. Four cats from the vitamin E group and five cats from the placebo group were not being fed any food specifically marketed or formulated for cats with CKD. Of these cats, one from each group was being fed Fancy Feast â canned, whereas all others were being fed "various [commercial] brands", both canned and dry. For all other study cats at least some portion of their diet was composed of a prescription feline CKD food. The distribution of brand and consistency was very similar between the two groups: three vitamin E and two placebo received Purina NF â canned, one vitamin E and two placebo received Purina NF dry, five vitamin E and six placebo received Hill's k/d canned, two vitamin E and three placebo received Hill's k/d dry and two vitamin E and two placebo received Royal Canin â Feline Renal Support canned.
Following 3 months of either vitamin E supplementation or placebo, the cats were reassessed using the same parameters as were measured upon study entry. Table 2 shows these parameters for each group. The only statistically significant difference was in total protein. There were no statistically significant changes in any of the other measured parameters for either group, comparing before and after  treatment values using a paired t-test (data not shown).

Antioxidant capacity
Antioxidant capacity was assessed using the DHPE and CUPRAC assays. Table 3 shows these parameters for each group before, and after the 3-month treatment period. There were no significant differences in these parameters between groups either before or following the 3-month treatment period, and there were no significant changes in either of these antioxidant parameters within either group, comparing values before and following the 3-month treatment period.

RBC Osmotic Fragility
RBC osmotic fragility was assessed using a series of NaCl concentrations and identifying the per cent NaCl concentration at which there was 50% haemolysis of the sample. Table 4 shows that there were no significant differences in the Vitamin E supplementation and placebo group prior to treatment, or following the 3-month treatment protocol. There was also no significant difference within groups between the pre-and post-treatment RBC osmotic fragility (data not shown).

Discussion
Oral supplementation with 30 IU of vitamin E (alpha-tocopherol) once daily for 3 months did not significantly change any of the measured parameters of oxidative stress or osmotic fragility in cats with CKD. This vitamin E protocol also failed to impact the PCV of the supplemented group compared with placebo control. The power of the study was decreased by the death of study participants, and although the group means appear similar, it cannot be ruled out that this represents a Type II error. Vitamin E is an antioxidant that is theoretically well suited to protect RBC membranes from lipid peroxidation. The study dose of 30 IU alpha-tocopherol per cat per day was taken from a standard veterinary formulary and appears consistent with the clinically recommended dose from a number of sources (Williams, 2000;Scherk & Center, 2005;Halliwell & Gutteridge, 2007). The absence of a significant increase in antioxidant capacity in these cats suggests that the study dose was insufficient, the supplement was not absorbed, or the supplement was not utilised by the patient. In one study, increasing the vitamin E content in a formulated diet resulted in an increase in the skin and serum levels of vitamin E in dogs and cats, but our study is the first to use a direct oral vitamin E supplement (Jewel et al., 2002).  -Robinson, 2006). In human dialysis patients the combination of vitamin E and alpha-lipoic acid supplementation failed to produce a significant decrease in biomarkers of lipid peroxidation, but did result in a significant decrease in interleukin-6 concentration (Ahmadi et al., 2013). The length of the study period may have been too short to capture progression of disease, although in preparation for this study a review of records suggested that a 3-month duration would be sufficient to see a significant drop in PCV in CKD cats. The progression of anaemia is likely different for different IRIS stages, with the cats in this study being predominantly IRIS stage 2 or 3. A limitation of the study was the non-uniformity of the diets consumed by the cats, and the potential impact diet may have had on the baseline plasma tocopherol concentrations between the two groups and within each individual cat. Qualitative comparison of the diets used in each group failed to reveal any systematic differences. Unfortunately a more quantitative comparison was impossible for a variety of reasons, including the fact that the vast majority of study cats were being fed multiple diets, each of an un-specified amount. Ideally the plasma tocopherol concentration would have been determined for each cat at the start and conclusion of the study, but the quantitative measures were oxidative stress and anaemia, not the pharmacokinetics of vitamin E supplementation. Plasma tocopherol levels would also have served as an independent measure of client compliance, an important factor that was only addressed through client communication at study entry and client reporting at study conclusion.
The oxidative stress in CKD cats may not be of the same magnitude or impact as seen in human kidney disease patients on dialysis (Kumar et al., 2014). Although an earlier study found a decrease in total antioxidant capacity of CKD cats, a more recent study failed to find significant differences in a variety of antioxidant parameters between CKD and normal cats (Keegan & Webb, 2010;Krofi c et al., 2014). The study population of CKD cats may also have been heterogeneous in ways that would only have been identified through imaging or histopathology. Both the degree of oxidative stress and the need for antioxidant supplementation in cats with CKD requires further study and clarification.