Moyamoya disease presenting as acute onset cortical blindness: A case report

We report a case where acute onset cortical blindness is the mode of presentation in Moyamoya disease. Cortical blindness is very rare presenting symptom of Moyamoya disease. Progressive visual loss and homonymous anopsia has been described previously, but this case had acute visual loss

The rs738409 (I148M) Variant of the PNPLA3 Gene and Type 2 Diabetes in Yakutia

The purpose of our research was to study the association of the PNPLA3 SNP rs738409 (C>G) with type 2 diabetes (T2D) in the Yakuts. The frequency distribution of alleles and genotypes of the PNPLA3 rs738409 SNP was in accordance with HWE. There were no statistically significant differences in the distribution of alleles and genotypes of the PNPLA3 SNP rs738409 (C>G) between T2D patients and non-T2D patients (P>0.05); the G allele and homozygous GG genotype prevailed in both groups. In T2D patients, a high frequency of the G allele (74.1%) was found, with a predominance of the GG genotype (58.5%). We also found that the mutant allele frequency is higher than in the studied populations of the world. Further studies with larger sample size are required to achieve sufficient statistical power to detect the association of the PNPLA3 SNP (rs738409 with the development of T2D in Yakut patients

Provider-Level Variation in Smoking Cessation Assistance Provided in the Cardiology Clinics: Insights From the NCDR PINNACLE Registry

Background: Studies show suboptimal provision of smoking cessation assistance (counseling or pharmacotherapy) for current smokers attempting to quit. We aimed to identify smoking cessation assistance patterns in US cardiology practices. Methods and Results: Among 328 749 current smokers seen between January 1, 2013, and March 31, 2016, in 348 NCDR (National Cardiovascular Data Registry) PINNACLE (Practice Innovation and Clinical Excellence)-affiliated cardiology practices, we measured the rates of cessation assistance. We used multivariable hierarchical logistic regression models to determine provider-, practice-, and patient-level predictors of cessation assistance. We measured provider variation in cessation assistance using median rate ratio (the likelihood that the same patient would receive the same assistance at by any given provider; \u3e 1.2 suggests significant variation). Smoking cessation assistance was documented in only 34% of encounters. Despite adjustment of provider, practice, and patient characteristics, there was large provider-level variation in cessation assistance (median rate ratio, 6 [95% CI , 5.76-6.32]). Practice location in the South region (odds ratio [OR], 0.48 [0.37-0.63] versus West region) and rural or suburban location (OR, 0.92 [0.88-0.95] for rural; OR, 0.94 [0.91-0.97] for suburban versus urban) were associated with lower rates of cessation assistance. Similarly, older age (OR, 0.88 [0.88-0.89] per 10-year increase), diabetes mellitus (OR, 0.84 [0.82-0.87]), and atrial fibrillation (OR, 0.93 [0.91-0.96]) were associated with lower odds of receiving cessation assistance. Conclusions: In a large contemporary US registry, only 1 in 3 smokers presenting for a cardiology visit received smoking cessation assistance. Our findings suggest the presence of a large deficit and largely idiosyncratic provider-level variation in the provision of smoking cessation assistance

Assessing long term impact of nutrient management and rainfall variability on the agroecological resilience of maize (Zea mays)- wheat (Triticum aestivum) system in NW India

A long-term (2000-2010) field experiment was carried out in the lower Shiwalik foothills of Punjab to study the carry over effect of organic manures and fertilizers on the productivity of maize (Zea mays L.)- wheat (Triticum aestivum L.) cropping system for efficient N management and resource use under rainfed conditions and to develop predictive models describing relationship between yields and seasonal rainfall. N management strategies involving combined application of 15 kg N/ha either through compost or leucaena loppings along with 20 kg N/ha through inorganic fertilizer for maize-wheat cropping sequence utilized growth resources most efficiently and maintained stable yield performance culminating in significantly higher system productivity, better resource use efficiencies and sustainable yield index, suggesting partial N substitution through compost or locally available plant material. The regression models developed to predict the effects of N sources on crop yields using monthly rainfall would be of interest to estimate the yield at a given level of rainfall with the likely fluctuation (as error) particularly under rainfed conditions

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

<p>Abstract</p> <p>Background</p> <p>There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.</p> <p>Methods</p> <p>Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.</p> <p>Results</p> <p>Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.</p> <p>Conclusions</p> <p>We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.</p

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity