Abstract:
"nNeuroleptic Malignant Syndrome (NMS) is unusual but could be a lethal reaction associated with neuroleptic drugs. It occurs in almost 0.07-2.2% of patients under treatment with neuroleptics. There are some medical treatments that may also be helpful for its treatment, including dopamine agonists, muscle relaxants, and electroconvulsive therapy (ECT). We present this case to alert the clinicians to the potential for inducing afebrile NMS. Our case is a 41-year-old man with a history of schizophrenia showing signs and symptoms in accordance with NMS, 2 weeks after receiving one dose of 12.5 mg fluphenazine decanoate, abruptly following the 3rdsession of ECT. The patient presented with decreased level of consciousness, muscular rigidity, waxy flexibility, mutism ,generalized tremor, sever diaphoresis and tachycardia which progressed during the previous 24 h. Laboratory data indicated primarily leukocytosis, an increasing level of creatinine phosphokinase and hypokalemia during the next 72h. In patients receiving antipsychotics, any feature of NMS should carefully be evaluated whether it is usual or unusual particularly in patients receiving long acting neuroleptics.

Abstract:
Changes in the number and size of oocytes can lead to fertilization problems. The present study aimed to evaluate the number, volume, and surface area of oocytes in healthy as well as nandrolone decanoate-treated (ND) mice using stereological methods. Five control mice received vehicle, and five ND-treated mice received ND. Using the ‘isotropic Cavalieri’ design’, the ovary was sectioned. The volume of the ovary (cortex and medulla) was estimated. The oocytes’ volume and surface area were estimated using the invariator. The number of the oocytes was estimated using an optical disector. The volumes of the ovary, cortex, and medulla decreased ~50% in the ND-treated mice. The mean number (coefficient of variation) of preantral, antral, and atretic oocytes in the control ovary were 1,690 (0.29), 2,100 (0.52), and 3,900 (0.2), respectively, which decreased ~54%, ~87%, and ~91%, respectively in the ND-treated animals. The mean volume (coefficient of variation) of the preantral, antral, and atretic oocytes were 86,000 (0.27), 110,000 (0.48), and 27,000 (0.33) μm3, respectively. The mean surface area (coefficient of variation) of the three types of oocytes were 9,000 (0.24), 9,900 (0.28), and 4,700 (0.21) μm2, respectively. These parameters remained unchanged in the ND-treated mice. ND induces reduction in the number of oocytes, but not in the volume or the surface area.

Abstract:
The practising ophthalmologist is frequently confronted with treatment options shown to be "statistically significantly better" than those currently in use. Unfortunately what is statistically significant may not necessarily be clinically significant enough for the practitioner to change from the currently preferred method of treatment. In this article we use common ophthalmic examples to introduce the "number needed to treat" (NNT), as a simple clinical approach for the practising ophthalmologist wishing to assess the clinical significance of treatment options.

Abstract:
A review of nursing interventions for smoking cessation from the Cochrane Library provided different values for NNT depending on how NNTs were calculated. The Cochrane review was evaluated for clinical heterogeneity using L'Abbé plot and subsequent analysis by secondary and primary care settings.Three studies in primary care had low (4%) baseline quit rates, and nursing interventions were without effect. Seven trials in hospital settings with patients after cardiac surgery, or heart attack, or even with cancer, had high baseline quit rates (25%). Nursing intervention to stop smoking in the hospital setting was effective, with an NNT of 14 (95% confidence interval 9 to 26). The assumptions involved in using risk difference and odds ratio scales for calculating NNTs are discussed.Clinical common sense and concentration on raw data helps to detect clinical heterogeneity. Once robust statistical tests have told us that an intervention works, we then need to know how well it works. The number needed to treat or harm is just one way of showing that, and when used sensibly can be a useful tool.Cates [1] concentrates on Simpson's paradox, which relates to problems that can arise when there is an imbalance between treatment and placebo arms in controlled trials. This "paradox" is hardly new, having first been discussed by E.H. Simpson 50 years ago [2], and is now a staple of any undergraduate statistics course. Cates further contends that NNTs should be calculated from weighted risk differences (or odds ratios) rather than pooled raw events, although this is relevant to Simpson's paradox only if inappropriate statistical methods are being used in inappropriate circumstances.It all comes down to the old problem of meta-analysis, of whether you are comparing apples with something else, and how you count the apples when you've got them.All of this is based on a numerical analysis of a Cochrane review of nursing interventions for smoking cessation [3]. The pooled raw data show t

Abstract:
background: fluphenazine, one of only three antipsychotics on who′s list of essential drugs, has been widely available for five decades. quantitative reviews of its effects compared with placebo are rare and out of date. methods: we searched for all relevant randomised controlled trials comparing oral administration of fluphenazine with placebo on the cochrane schizophrenia group′s register of trials (october 2006) and in reference lists of included studies. data were extracted from reliably selected trials. where possible, we calculated fixed effects relative risk (rr), the number needed to treat (nnt), and their 95% confidence intervals (ci). results: we found over 1200 electronic records for 415 studies. ninety papers were acquired; 59 were excluded and the remainder were reports of the seven trials we could include (total participants=349). compared with placebo, in the short-term, global state outcomes for ‘not improved’ were not significantly different (n=75, 2 rcts, rr 0.71 ci 0.5 to 1.1). there is evidence that oral fluphenazine, in the short term, increases a person′s chances of experiencing extrapyramidal effects such as akathisia (n=227, 2 rcts, rr 3.43 ci 1.2 to 9.6, nnh 13 ci 4 to 128) and rigidity (n=227, 2 rcts, rr 3.54 ci 1.8 to 7.1, nnh 6 ci 3 to 17). we found study attrition to be lower in the oral fluphenazine group, but data were not statistically significant (n=227, 2 rcts, rr 0.70 ci 0.4 to 1.1). conclusions: fluphenazine is an imperfect treatment with surprisingly few data from trials to support its use. if accessible, other inexpensive drugs, less associated with adverse effects, may be a better choice for people with schizophrenia. it is time for the world health organisation to revise their list of essential antipsychotic drugs.

Abstract:
Bayesian Networks (BNs) are useful tools giving a natural and compact representation of joint probability distributions. In many applications one needs to learn a Bayesian Network (BN) from data. In this context, it is important to understand the number of samples needed in order to guarantee a successful learning. Previous work have studied BNs sample complexity, yet it mainly focused on the requirement that the learned distribution will be close to the original distribution which generated the data. In this work, we study a different aspect of the learning, namely the number of samples needed in order to learn the correct structure of the network. We give both asymptotic results, valid in the large sample limit, and experimental results, demonstrating the learning behavior for feasible sample sizes. We show that structure learning is a more difficult task, compared to approximating the correct distribution, in the sense that it requires a much larger number of samples, regardless of the computational power available for the learner.

Abstract:
Given a closed polygon P having n edges, embedded in R^d, we give upper and lower bounds for the minimal number of triangles t needed to form a triangulated PL surface in R^d having P as its geometric boundary. The most interesting case is dimension 3, where the polygon may be knotted. We use the Seifert suface construction to show there always exists an embedded surface requiring at most 7n^2 triangles. We complement this result by showing there are polygons in R^3 for which any embedded surface requires at least 1/2n^2 - O(n) triangles. In dimension 2 only n-2 triangles are needed, and in dimensions 5 or more there exists an embedded surface requiring at most n triangles. In dimension 4 we obtain a partial answer, with an O(n^2) upper bound for embedded surfaces, and a construction of an immersed disk requiring at most 3n triangles. These results can be interpreted as giving qualitiative discrete analogues of the isoperimetric inequality for piecewise linear manifolds.

Abstract:
We investigate the susceptibility to bias for alternative methods for calculating NNTs through illustrative examples and mathematical theory.Two competing methods have been recommended: one method involves calculating the NNT from meta-analytical estimates, the other by treating the data as if it all arose from a single trial. The 'treat-as-one-trial' method was found to be susceptible to bias when there were imbalances between groups within one or more trials in the meta-analysis (Simpson's paradox). Calculation of NNTs from meta-analytical estimates is not prone to the same bias. The method of calculating the NNT from a meta-analysis depends on the treatment effect used. When relative measures of treatment effect are used the estimates of NNTs can be tailored to the level of baseline risk.The treat-as-one-trial method of calculating numbers needed to treat should not be used as it is prone to bias. Analysts should always report the method they use to compute estimates to enable readers to judge whether it is appropriate.Cates [1] considers "how should we pool data?", focusing in particular on the calculation of the number needed to treat (NNT). He explains how Simpson's paradox may lead to the wrong answer when the NNT is calculated in a particular way. Moore and colleagues [2], responding to Cates, focus on the specific example used by Cates and address the question "which data should be pooled?". Unfortunately, they largely ignore Cates' methodological point. We suspect that many readers of these two articles will come away rather confused, so our aim is to try to clarify some of these issues.First, we note that incorrect methods may give the right answer some or even most of the time, but that does not mean that they should be used or recommended. They might be copied by others and used in situations where they do not work. For example, in the fraction 16/64 we can cancel the two sixes to give the right answer of ？. This method will almost always give the wrong

Abstract:
The effects of an intervention is best measured in a randomized controlled trial (RCT) and can be expressed in various ways using the measures such as risk difference, number needed to treat (NNT), relative risk or odds ratio. Risk difference (RD) is the difference in risk of the outcome event between control and experimental group. Control group is not exposed to the intervention, whereas experimental group is the one that is exposed to intervention. The risk of outcome event in the control group is also called baseline risk. The NNT is the inverse of the risk difference and indicates the number of patients required to be treated to avoid one additional outcome event. Risk difference and NNT are absolute measures of effect. Relative risk (RR) is a relative measure and is the ratio of the risk in the exposed group to that in the unexposed group. Relative risk reduction (RRR) is one minus RR and indicates the fraction (or percent) of baseline risk that reduces with exposure to the intervention. Odds ratio (OR) is ratio of odds of having the event in the exposed group to that in the unexposed group. These measures are suitable for different purposes and appeal to different constituencies. Odds ratio is the only measure suitable for use in logistic regression and case control studies.

Abstract:
brain and serum lipid peroxidation was studied in rats treated with vincristine sulphate and different doses of nandrolone decanoate. thirty rats were distributed into six groups (n=5). the treatments were applied once a week for two weeks. sample collection was performed in the third week. treatments during the first week were: g1 (control) - physiologic solution, g2 - vincristine sulphate (4mg/m2), g3 - physiologic solution, g4 - physiologic solution, g5- vincristine sulphate (4mg/m2), and g6 - vincristine sulphate (4mg/m2). in the second week, they were: g1 (control) - physiologic solution, g2- physiologic solution, g3 - nandrolone decanoate (1.8mg/kg-1), g4 - nandrolone decanoate (10mg/kg-1), g5 - nandrolone decanoate (1.8mg/kg-1), and g6 - nandrolone decanoate (10mg/kg-1). lipid peroxidation increased with the isolated use of vincristine and nandrolone decanoate, and with vincristine associated to the highest dose of the ester as well. these results suggest that vincristine sulphate and nandrolone decanoate increase free radical production. therapeutic dose of nandrolone decanoate when associated with vincristine sulphate proved to be beneficial, as it was able to protect the organism from damaging processes involved in free radical production