The Cellular Genesis of Metabolic Syndrome and the Role of Anti-urate Drugs in Hyperuricemia Patients: A Systematic Review.

Hyperuricemia results due to the underexcretion of uric acid through kidneys or overproduction due to either intake of purine-rich foods, a high caloric diet, or a decreased activity of purine recycler hypoxanthine-guanine phosphoribosyl transferase (HGPRT). Increased xanthine oxidoreductase (XOR) enzyme activity may contribute to hyperuricemia. Literature provides growing evidence that an independent component that contributes to the development of metabolic syndrome (MetS) and associated comorbidities is hyperuricemia. Thus, precise cellular mechanisms involved during MetS and related comorbidities in hyperuricemia, and the role of anti-urate medicines in these mechanisms require further investigations. We searched online libraries PubMed and Google Scholar for data collection. We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines for literature identification, selection, screening, and determining eligibility to produce unbiased meaningful outcomes. We applied quality assessment tools for the quality appraisal of the studies. And, outcomes were extracted from the selected studies, which revealed the relationship between hyperuricemia and MetS components by causing inflammation, endothelial dysfunction, oxidative stress, and endoplasmic reticulum stress. The selected studies reflected the role of xanthine oxide (XO) inhibitors beyond inhibition. This systematic review concluded that hyperuricemia independently causes inflammation, oxidative stress, endothelial damage, and endoplasmic reticulum stress in patients with hyperuricemia. These mechanisms provide a cellular basis for metabolic syndrome and related comorbidities. In this context, XO inhibitors and their beneficial effects go beyond XOR inhibition to ameliorate these pathological mechanisms.

Significance and Amplification Methods of the Purine Salvage Pathway in Human Brain Cells.

Previous studies suggest that uric acid or reactive oxygen species, products of xanthine oxidoreductase (XOR), may associate with neurodegenerative diseases. However, neither relationship has ever been firmly established. Here, we analyzed human brain samples, obtained under protocols approved by research ethics committees, and found no expression of XOR and only low levels of uric acid in various regions of the brain. In the absence of XOR, hypoxanthine will be preserved and available for incorporation into the purine salvage pathway. To clarify the importance of salvage in the brain, we tested using human induced pluripotent stem cell-derived neuronal cells. Stable isotope analyses showed that the purine salvage pathway was more effective for ATP synthesis than purine de novo synthesis. Blood uric acid levels were related to the intracellular adenylate pool (ATP + ADP + AMP), and reduced levels of this pool result in lower uric acid levels. XOR inhibitors are related to extracellular hypoxanthine levels available for uptake into the purine salvage pathway by inhibiting the oxidation of hypoxanthine to xanthine and uric acid in various organs where XOR is present and can prevent further decreases in the intracellular adenylate pool under stress. Furthermore, adding precursors of the pentose phosphate pathway enhanced hypoxanthine uptake, indicating that purine salvage is activated by PRPP replenishment. These findings resolve previous contradictions regarding XOR products and provide new insights into clinical studies. It is suggested that therapeutic strategies maximizing maintenance of intracellular adenylate levels may effectively treat pathological conditions associated with ischemia and energy depletion.

Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis.

Background: Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system. Results: Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis. Conclusions: These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.

Emerging Roles of Xanthine Oxidoreductase in Chronic Kidney Disease.

Xanthine Oxidoreductase (XOR) is a ubiquitous, essential enzyme responsible for the terminal steps of purine catabolism, ultimately producing uric acid that is eliminated by the kidneys. XOR is also a physiological source of superoxide ion, hydrogen peroxide, and nitric oxide, which can function as second messengers in the activation of various physiological pathways, as well as contribute to the development and the progression of chronic conditions including kidney diseases, which are increasing in prevalence worldwide. XOR activity can promote oxidative distress, endothelial dysfunction, and inflammation through the biological effects of reactive oxygen species; nitric oxide and uric acid are the major products of XOR activity. However, the complex relationship of these reactions in disease settings has long been debated, and the environmental influences and genetics remain largely unknown. In this review, we give an overview of the biochemistry, biology, environmental, and current clinical impact of XOR in the kidney. Finally, we highlight recent genetic studies linking XOR and risk for kidney disease, igniting enthusiasm for future biomarker development and novel therapeutic approaches targeting XOR.

Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.

PURPOSE: Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable. METHODS: We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index. RESULTS: This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways. CONCLUSION: Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Mechanistically transparent models for predicting aqueous solubility of rigid, slightly flexible, and very flexible drugs (MW<2000) Accuracy near that of random forest regression.

Yalkowsky's General Solubility Equation (GSE), with its three fixed constants, is popular and easy to apply, but is not very accurate for polar, zwitterionic, or flexible molecules. This review examines the findings of a series of studies, where we have sought to come up with a better prediction model, by comparing the performances of the GSE to Abraham's Solvation Equation (ABSOLV), and Random Forest regression (RFR) machine-learning (ML) method. Large, well-curated aqueous intrinsic solubility databases are available. However, drugs may be sparsely distributed in chemical space, concentrated in clusters. Even a large database might overlook some regions. Test compounds from under-represented portions of space may be poorly predicted, as might be the case with the 'loose' set of 32 drugs in the Second Solubility Challenge (2020). There appears to be still a need for better coverage of drug space. Increasingly, current trends in predictions of solubility use calculated input descriptors, which may be an advantage for exploring properties of molecules yet to be synthesized. The risk may be that overall prediction approaches might be based on accumulated uncertainty. The increasing use of ML/AI methods can lead to accurate predictions, but such predictions may not readily suggest the strategies to pursue in selecting yet-to-be-synthesized compounds. Based on our latest findings, we recommend predictions based on both 'grouped' ABSOLV(GRP) and 'Flexible Acceptor' GSE(Φ,B) models with the provided best-fit parameters, where Φ is the Kier molecular flexibility index and B is the Abraham H-bond acceptor strength. For molecules with Φ < 11, the prudent choice is to pick the Consensus Model, the average of ABSOLV(GRP) and GSE(Φ,B). For more flexible molecules, GSE(Φ,B) is recommended.

A Tutorial Review on the Fluorescent Probes as a Molecular Logic Circuit-Digital Comparator.

The rapid progress in the field of fluorescent probes and fluorescent sensing material extended this research area toward more complex molecular logic gates capable of carrying out a variety of sensing functions simultaneously. These molecules are able to calculate a composite result in which the analysis is not performed by a man but by the molecular device itself. Since the first report by de Silva of AND molecular logic gate, all possible logic gates have been achieved at the molecular level, and currently, utilization of more complicated molecular logic circuits is a major task in this field. Comparison between two digits is the simplest logic operation, which could be realized with the simplest logic circuit. That is why the right understanding of the applied principles during the implementation of molecular digital comparators could play a critical role in obtaining logic circuits that are more complicated. Herein, all possible ways for the construction of comparators on the molecular level were discussed, and recent achievements connected with these devices were presented.

3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier.

The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.

Evaluation of Plasma Xanthine Oxidoreductase (XOR) Activity in Patients with Cardiopulmonary Arrest.

Plasma xanthine oxidoreductase (XOR) activity in patients with cardiopulmonary arrest (CPA) has not yet been studied.A total of 1,158 patients who required intensive care and 231 control patients who attended a cardiovascular outpatient clinic were prospectively analyzed. Blood samples were collected within 15 minutes of admission from patients in intensive care patients, which were divided into a CPA group (n = 1,053) and a no-CPA group (n = 105). Plasma XOR activity was compared between the 3 groups and factors independently associated with extremely elevated XOR activity were identified using a multivariate logistic regression model. Plasma XOR activity in the CPA group (median, 1,030.0 pmol/hour/mL; range, 233.0-4,240.0 pmol/hour/mL) was significantly higher than in the no-CPA group (median, 60.2 pmol/hour/mL; range, 22.5-205.0 pmol/hour/mL) and control group (median, 45.2 pmol/hour/mL; range, 19.3-98.8 pmol/hour/mL). The regression model showed that out-of-hospital cardiac arrest (OHCA) (yes, odds ratio [OR]: 2.548; 95% confidence interval [CI]: 1.098-5.914; P = 0.029) and lactate levels (per 1.0 mmol/L increase, OR: 1.127; 95% CI: 1.031-1.232; P = 0.009) were independently associated with high plasma XOR activity (≥ 1,000 pmol/hour/mL). Kaplan-Meier curve analysis indicated that the prognosis, including all-cause death within 30 days, was significantly poorer in high-XOR patients (XOR ≥ 6,670 pmol/hour/mL) than in the other patients.Plasma XOR activity was extremely high in patients with CPA, especially in OHCA. This would be associated with a high lactate value and expected to eventually lead to adverse outcome in patients with CPA.

XOR-analytic network process and assessing the impact of COVID-19 by sector.

The consequences of any extreme event can deteriorate any system at all levels: socially, economically, and operationally. The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), provides a good example of the tremendous impact that can be produced by such extreme events. To effectively measure and mitigate the impact of the COVID-19 pandemic and relaunch the Moroccan economy, policymakers need to determine which sectors have been most impacted. Due to the high level of uncertainty and complexity surrounding this health crisis, this study first develops a new technique for dealing with decision problems under uncertainty using exclusive-or (XOR) logic, called the XOR-analytic network process (XOR-ANP). Then, the proposed technique is adopted to assess the impact of COVID-19 on seven relevant sectors (tourism, transport, industrial, financial, agriculture, education, and healthcare) by considering social, operational, and economic dimensions. The key findings show that COVID-19 has a significant impact on Moroccan's tourism, healthcare, and transport sectors, with respect to social-economic and operational dimensions by 30.99%, 21.81%, and 17.88%, respectively. These results indicate that most of the United Nations Sustainable Development Goals for 2030, such as "Healthy Lives", "Decent Work" and "Economic Growth" have been severely impacted, thus, assistance and recovery are urgently needed.

The Xanthine Oxidase Inhibitor Febuxostat Suppresses Adipogenesis and Activates Nrf2.

Xanthine oxidoreductase (XOR) is a rate-limiting enzyme in purine catabolism that acts as a novel regulator of adipogenesis. In pathological states, xanthine oxidoreductase activity increases to produce excess reactive oxygen species (ROS). The nuclear factor erythroid 2-related factor 2 (Nrf2) is a critical inducer of antioxidants, which is bound and repressed by a kelch-like ECH-associated protein 1 (Keap1) in the cytoplasm. The Keap1-Nrf2 axis appears to be a major mechanism for robust inducible antioxidant defenses. Here, we demonstrate that febuxostat, a xanthine oxidase inhibitor, alleviates the increase in adipose tissue mass in obese mouse models with a high-fat diet or ovariectomy. Febuxostat disrupts in vitro adipocytic differentiation in adipogenic media. Adipocytes appeared at day 7 in absence or presence of febuxostat were 160.8 ± 21.2 vs. 52.5 ± 12.7 (p < 0.01) in 3T3−L1 cells, and 126.0 ± 18.7 vs. 55.3 ± 13.4 (p < 0.01) in 10T1/2 cells, respectively. Adipocyte differentiation was further enhanced by the addition of hydrogen peroxide, which was also suppressed by febuxostat. Interestingly, febuxostat, but not allopurinol (another xanthine oxidase inhibitor), rapidly induced the nuclear translocation of Nrf2 and facilitated the degradation of Keap1, similar to the electrophilic Nrf2 activator omaveloxolone. These results suggest that febuxostat alleviates adipogenesis under oxidative conditions, at least in part by suppressing ROS production and Nrf2 activation. Regulation of adipocytic differentiation by febuxostat is expected to inhibit obesity due to menopause or overeating.

Design, synthesis, and evaluation of tricyclic compounds containing phenyl-tetrazole as XOR inhibitors.

Based on analyses of the interaction between febuxostat and xanthine oxidoreductase (XOR), tetrazole was used to replace the carboxyl-thiazole fragment of febuxostat using a bioelectronic isosteric strategy. Three series of compounds were designed. The inhibitory activity against XOR of all compounds was evaluated and their structure-activity relationships determined. The inhibitory activity against XOR of compounds I was weak, with a half-maximal inhibitory concentration (IC50) value > 10 µmol, whereas the inhibitory activity of compounds II and III was increased significantly, among which compounds IIIa (IC50 = 26.3 ± 1.21 nM) and IIIc (IC50 = 29.3 ± 0.88 nM) were the best. Molecular docking showed that tetrazole could enter the active cavity instead of a carboxyl group and retain most of the interaction between febuxostat and XOR. For compounds III, the hydrogen bonds with Asn768 and Thr1010 of XOR were absent, but some new interactions were introduced to improve potency. A potassium oxazinate/hypoxanthine-induced model of acute hyperuricemia in mice also showed a significant hypouricemia effect of compounds IIIa, IIIc, and IIIe (P < 0.01), which was consistent with the results of inhibition in vitro. In conclusion, we identified a promising XOR inhibitor and provided new ideas for the design of XOR inhibitors.

Xanthine oxidoreductase mediates genotoxic drug-induced autophagy and apoptosis resistance by uric acid accumulation and TGF-ß-activated kinase 1 (TAK1) activation.

Autophagy is a highly conserved cellular process that profoundly impacts the efficacy of genotoxic chemotherapeutic drugs. TGF-ß-activated kinase 1 (TAK1) is a serine/threonine kinase that activates several signaling pathways involved in inducing autophagy and suppressing cell death. Xanthine oxidoreductase (XOR) is a rate-limiting enzyme that converts hypoxanthine to xanthine, and xanthine to uric acid and hydrogen peroxide in the purine catabolism pathway. Recent studies showed that uric acid can bind to TAK1 and prolong its activation. We hypothesized that genotoxic drugs may induce autophagy and apoptosis resistance by activating TAK1 through XOR-generated uric acid. Here, we report that gemcitabine and 5-fluorouracil (5-FU), two genotoxic drugs, induced autophagy in HeLa and HT-29 cells by activating TAK1 and its two downstream kinases, AMP-activated kinase (AMPK) and c-Jun terminal kinase (JNK). XOR knockdown and the XOR inhibitor allopurinol blocked gemcitabine-induced TAK1, JNK, AMPK, and Unc51-like kinase 1 (ULK1)S555 phosphorylation and gemcitabine-induced autophagy. Inhibition of the ATM-Chk pathway, which inhibits genotoxic drug-induced uric acid production, blocked gemcitabine-induced autophagy by inhibiting TAK1 activation. Exogenous uric acid in its salt form, monosodium urate (MSU), induced autophagy by activating TAK1 and its downstream kinases JNK and AMPK. Gene knockdown or the inhibitors of these kinases blocked gemcitabine- and MSU-induced autophagy. Inhibition of autophagy by allopurinol, chloroquine, and 5Z-7-oxozeaenol (5Z), a TAK1-specific inhibitor, enhanced gemcitabine-induced apoptosis. Our study uncovers a previously unrecognized role of XOR in regulating genotoxic drug-induced autophagy and apoptosis and has implications for designing novel therapeutic strategies for cancer treatment.

Improved affine encryption algorithm for color images using LFSR and XOR encryption.

In this study, a novel Improved Affine Algorithm (IAA) for color image encryption is proposed. Affine Algorithm (AA) is generally known as an algorithm used for plain text encryption. In the proposed IAA algorithm, Linear Feedback Shift Register (LFSR), XOR encryption, and the AA are combined for color images encryption. The plane image is firstly split into three channels: R, G, and B. The RGB channel image is encrypted using AA encryption with ten keys based on pixel locations and pixel values. The rows and columns of the image are encrypted with LFSR keys and XOR encryption procedures. Finally, the proposed algorithm is tested in Matlab environment to obtain the Histogram, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Unified Average Changing Intensity (UACI), Number of Pixel Change Rate (NPCR), and Entropy analyses. The values are compared with other algorithms. The results show that the proposed image encryption algorithm is secure and powerful, outperforming other algorithms.

Compact Image-Style Transfer: Channel Pruning on the Single Training of a Network.

Recent image-style transfer methods use the structure of a VGG feature network to encode and decode the feature map of the image. Since the network is designed for the general image-classification task, it has a number of channels and, accordingly, requires a huge amount of memory and high computational power, which is not mandatory for such a relatively simple task as image-style transfer. In this paper, we propose a new technique to size down the previously used style transfer network for eliminating the redundancy of the VGG feature network in memory consumption and computational cost. Our method automatically finds a number of consistently inactive convolution channels during the network training phase by using two new losses, i.e., channel loss and xor loss. The former maximizes the number of inactive channels and the latter fixes the positions of these inactive channels to be the same for the image. Our method improves the image generation speed to be up to 49% faster and reduces the number of parameters by 20% while maintaining style transferring performance. Additionally, our losses are also effective in pruning the VGG16 classifier network, i.e., parameter reduction by 26% and top-1 accuracy improvement by 0.16% on CIFAR-10.

Efficient Entropic Security with Joint Compression and Encryption Approach Based on Compressed Sensing with Multiple Chaotic Systems.

This paper puts forward a new algorithm that utilizes compressed sensing and two chaotic systems to complete image compression and encryption concurrently. First, the hash function was utilized to obtain the initial parameters of two chaotic maps, which were the 2D-SLIM and 2D-SCLMS maps, respectively. Second, a sparse coefficient matrix was transformed from the plain image through discrete wavelet transform. In addition, one of the chaotic sequences created by 2D-SCLMS system performed pixel transformation on the sparse coefficient matrix. The other chaotic sequences created by 2D-SLIM were utilized to generate a measurement matrix and perform compressed sensing operations. Subsequently, the matrix rotation was combined with row scrambling and column scrambling, respectively. Finally, the bit-cycle operation and the matrix double XOR were implemented to acquire the ciphertext image. Simulation experiment analysis showed that the compressed encryption scheme has advantages in compression performance, key space, and sensitivity, and is resistant to statistical attacks, violent attacks, and noise attacks.

Microring resonator-based all-optical parallel pseudo random binary sequence generator for rate multiplication.

In this paper, we design an all-optical Pseudo Random Binary Sequence (PRBS) generator in parallel configuration for operating rate multiplication purposes. The sequential circuit comprises of several clocked D flip-flops, XOR gates and multiplexers implemented using microring resonator (MRR)-based switches. The proposed design is demonstrated and validated through simulations for 500 Gb/s and 400 Gb/s rate doubling and quadrupling, respectively, of a 5-bit degree PRBS. The MRR critical operating parameters are also optimized against performance metrics through numerical investigation.

Pharmacodynamic evaluation of the XOR inhibitor WN1703 in a model of chronic hyperuricemia in rats induced by yeast extract combined with potassium oxonate.

Hyperuricemia is a common disease caused by a disorder of purine metabolism, which often causes hyperlipidemia and other metabolic diseases. WN1703 was demonstrated to be an effective xanthine oxidoreductase (XOR) inhibitor in our previous study. Here, we evaluated the pharmacodynamic effect of WN1703 on rats suffering from chronic hyperuricemia accompanied by disorders of lipid metabolism. We discovered that WN1703 was an efficacious uric acid (UA)-lowering compound. Simultaneously, it had effect on relieving renal injury, regulating lipid metabolism by reducing levels of triglycerides and low-density lipoprotein-cholesterol, increasing levels of high-density lipoprotein-cholesterol, and improving renal and liver lesions. WN1703 also exhibited anti-inflammatory and antioxidant activity by alleviating the increasing trend of levels of tumor necrosis factor-α, interleukin-1ß, monocyte chemoattractant protein-1, and malondialdehyde, and improving the activity of superoxide dismutase and glutathione peroxidase. WN1703 appeared to be more effective than febuxostat in inhibiting XOR and had higher antioxidant activity. In general, the pharmacologic action of WN1703 showed a clear dose-effect relationship.

Learning Higher-Order Transitional Probabilities in Nonhuman Primates.

The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second stimulus given the first (i.e., p(B|A)). In the present study, nonhuman primates (Guinea baboons, Papio papio) were exposed to a serial version of the XOR (i.e., exclusive-OR), in which they had to process sequences of three stimuli: A, B, and C. In this manipulation, first-order TPs (i.e., AB and BC) were uninformative due to their transitional probabilities being equal to .5 (i.e., p(B|A) = p(C|B) = .5), while second-order TPs were fully predictive of the upcoming stimulus (i.e., p(C|AB) = 1). In Experiment 1, we found that baboons were able to learn second-order TPs, while no learning occurred on first-order TPs. In Experiment 2, this pattern of results was replicated, and a final test ruled out an alternative interpretation in terms of proximity to the reward. These results indicate that a nonhuman primate species can learn a nonlinearly separable problem such as the XOR. They also provide fine-grained empirical data to test models of statistical learning on the interaction between the learning of different orders of TPs. Recent bioinspired models of associative learning are also introduced as promising alternatives to the modeling of statistical learning mechanisms.

U-shaped association of uric acid to overall-cause mortality and its impact on clinical management of hyperuricemia.

Serum uric acid (SUA) is significantly elevated in obesity, gout, type 2 diabetes mellitus, and the metabolic syndrome and appears to contribute to the renal, cardiovascular and pulmonary comorbidities that are associated with these disorders. Most previous studies have focused on the pathophysiologic effects of high levels of uric acid (hyperuricemia). More recently, research has also shifted to the impact of hypouricemia, with multiple studies showing the potentially damaging effects that can be caused by abnormally low levels of SUA. Along with these observations, recent inconclusive data from human studies evaluating the treatment of hyperuricemia with xanthine oxidoreductase (XOR) inhibitors have added to the debate about the causal role of UA in human disease processes. SUA, which is largely derived from hepatic degradation of purines, appears to exert both systemic pro-inflammatory effects that contribute to disease and protective antioxidant properties. XOR, which catalyzes the terminal two steps of purine degradation, is the major source of both reactive oxygen species (O2.-, H2O2) and UA. This review will summarize the evidence that both elevated and low SUA may be risk factors for renal, cardiovascular and pulmonary comorbidities. It will also discuss the mechanisms through which modulation of either XOR activity or SUA may contribute to vascular redox hemostasis. We will address future research studies to better account for the differential effects of high versus low SUA in the hope that this will identify new evidence-based approaches for the management of hyperuricemia.