Uremic hyperhomocysteinemia: a randomized trial of folate treatment for the prevention of cardiovascular events

Homocysteine is a risk factor for atherosclerosis in the general population, and serum homocysteine levels are almost universally elevated in chronic renal failure patients. When such patients are treated with dialysis, cardiovascular disease accounts for more than 50% of their mortality, which, in some proportion, may be pathophysiologically related to the elevated serum homocysteine levels. From April 2003 to March 2005, we conducted a 2-year, double-blind, randomized, placebo-controlled trial of 186 patients with end-stage kidney disease due to any cause, who were older than 18 years and stable on hemodialysis. Patients were assigned to receive either oral folic acid 10 mg 3 times a week immediately after every dialysis session under nurse supervision or an identical-appearing placebo for the entire study. On admission, plasma total homocysteine (tHcy) levels were above 13.9 micromol/L in 96.7% of patients (median 25.0 micromol/L, range 9.3-104.0 micromol/L). In the placebo group, tHcy levels remained elevated at 6, 12, and 24 months, while oral folate significantly decreased tHcy to a median value of 10.5 (2.8-20.3) micromol/L, (p<0.01). During the study, 38 patients (folic acid group 17 vs. placebo group 21; p=0.47) died from cardiovascular disease. Kaplan-Meier life table analysis dealing with the incidence of cardiovascular events, both fatal and nonfatal (myocardial infarction, arrhythmias, angina, heart failure, cerebrovascular accident), showed that 2 years of folic acid treatment and the lowering of the homocysteine blood levels had no effect on cardiovascular events (p=0.41; hazard ratio 1.24, 95% CI 0.74-2.10). However, the carotid artery intima-media wall thickness measured in a blinded fashion decreased from 1.94 +/- 0.59 mm to 1.67 +/- 0.38 mm (p<0.01) after 2 years of folate therapy. In this short-term study of uremic patients, 2 years of folic acid supplementation normalized the tHcy blood levels in 92.3% of patients but did not change the incidence of cardiovascular events compared with the control group. However, ultrasonography of the common carotid arteries performed at entry and 24 months later showed a significant decrease in intima-media thickness with folate supplementation. This suggests that early folate supplementation may benefit patients with chronic renal failure by preventing cardiovascular deterioration.     

Social networks applied

Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise. The authors investigate the following areas concerning social networks: how to exploit our unprecedented wealth of data and how we can mine social networks for purposes such as marketing campaigns; social networks as a particular form of influence, i.e.., the way that people agree on terminology and this phenomenon's implications for the way we build ontologies and the Semantic Web; social networks as something we can discover from data; the use of social network information to offer a wealth of new applications such as better recommendations for restaurants, trustworthy email senders, or (maybe) blind dates; investigation of the richness and difficulty of harvesting FOAF (friend-of-a-friend) information; and by looking at how information processing is bound to social context, the resulting ways that network topology's definition determines its outcomes.     

Tree Induction for Probability-Based Ranking

Tree induction is one of the most effective and widely used methods for building classification models. However, many applications require cases to be ranked by the probability of class membership. Probability estimation trees (PETs) have the same attractive features as classification trees (e.g., comprehensibility, accuracy and efficiency in high dimensions and on large data sets). Unfortunately, decision trees have been found to provide poor probability estimates. Several techniques have been proposed to build more accurate PETs, but, to our knowledge, there has not been a systematic experimental analysis of which techniques actually improve the probability-based rankings, and by how much. In this paper we first discuss why the decision-tree representation is not intrinsically inadequate for probability estimation. Inaccurate probabilities are partially the result of decision-tree induction algorithms that focus on maximizing classification accuracy and minimizing tree size (for example via reduced-error pruning). Larger trees can be better for probability estimation, even if the extra size is superfluous for accuracy maximization. We then present the results of a comprehensive set of experiments, testing some straightforward methods for improving probability-based rankings. We show that using a simple, common smoothing method—the Laplace correction—uniformly improves probability-based rankings. In addition, bagging substantially improves the rankings, and is even more effective for this purpose than for improving accuracy. We conclude that PETs, with these simple modifications, should be considered when rankings based on class-membership probability are required.     

Programming by Demonstration Using Version Space Algebra

Programming by demonstration enables users to easily personalize their applications, automating repetitive tasks simply by executing a few examples. We formalize programming by demonstration as a machine learning problem: given the changes in the application state that result from the user's demonstrated actions, learn the general program that maps from one application state to the next. We present a methodology for learning in this space of complex functions. First we extend version spaces to learn arbitrary functions, not just concepts. Then we introduce the version space algebra, a method for composing simpler version spaces to construct more complex spaces. Finally, we apply our version space algebra to the text-editing domain and describe an implemented system called SMARTedit that learns repetitive text-editing procedures by example. We evaluate our approach by measuring the number of examples required for the system to learn a procedure thatworks on the remainder of examples, and by an informal user study measuring the effort users spend using our system versus performing the task by hand. The results show that SMARTedit is capable of generalizing correctly from as few as one or two examples, and that users generally save a significant amount of effort when completing tasks with SMARTedit's help.     

生物医用钙磷酸盐微晶玻璃

钙磷酸盐微晶玻璃因其具有生物活性、生物相容性 ,而广泛应用于牙科、骨科的替代及骨组织工程等领域。本文就钙磷酸盐微晶玻璃材料的制备工艺、组成性能、invitro vivo实验及医学应用作了较详细的评述。     

Stenosis in hemodialysis arteriovenous fistula: evaluation and treatment

Vascular access complications are one of the main causes associated with an increase in morbidity and mortality in stage 5 chronic kidney disease patients. The arteriovenous fistula is regarded as the vascular access of choice for hemodialysis (HD) because of its superior patency and lower complication rates. Stenosis is considered the major cause of dysfunction of arteriovenous fistula. Despite the relatively low thrombosis rates of arteriovenous fistula, surveillance programs are necessary for detection of stenosis. We report a case of a HD patient who had never achieved an adequate Kt/V since the start of maintenance HD. During the investigation, abnormal findings were found on physical examination of the fistula, in addition to an alteration in intra-access pressure (IAP) measurements. A venous stenosis was diagnosed by Doppler ultrasound and then promptly treated with percutaneous transluminal angioplasty. The purpose of the discussion is to highlight the peculiarities of arteriovenous fistulae, methods of surveillance, including physical examination, IAP, recirculation, and measurements of blood flow, and the importance of the correction procedures for the stenosis.     

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article's results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.     

Response of stress indicators and growth parameters of Tibouchina pulchra Cogn. exposed to air and soil pollution near the industrial complex of Cubatão, Brazil

The present study was performed in the vicinity of the industrial complex of Cubat?o, S?o Paulo, Brazil, in order to evaluate the response of 'manaca da serra' Tibouchina pulchra Cogn. (Melastomataceae), a common species of secondary Atlantic Rain Forest vegetation, to the impact of complex air pollution. Emphasis was given to changes of biochemical parameters such as ascorbic acid concentration, peroxidase activity, contents of water-soluble thiols, pH of leaf extract and buffering capacity. These plant factors are often used as early indicators of air pollution stress. Field experiments included sampling of leaves from mature trees in areas with different air pollution load (passive monitoring), exposure of saplings cultivated in uniform soil at these areas (active monitoring) and a study on the combined effects of contaminated soil and air pollution. In general, metabolic response of saplings was more accentuated than that of mature trees. Leaf extract pH and buffering capacity showed no or only small alterations in plants exposed to industrial emissions. In contrast, air pollution resulted in a distinct decrease in ascorbic acid contents and an increase in peroxidase activity and thiol concentrations in leaves. Cultivation of saplings in soil types from contaminated regions frequently caused the same modifications or enhanced the effects produced by air pollution. Growth analysis of exposed saplings demonstrated that a change of the relationship between above-ground and below-ground plant parts was the most obvious effect of air pollution and soil contamination. The experiments showed that even T. pulchra, a species considered resistant to air pollution, suffers metabolic disturbances by the present ambient air and soil quality. Although biochemical and physiological alterations were not related to a certain air pollution type, they could be used to estimate the overall pollution load and to map zones with different air quality.