The Food and Drug Administration (FDA), Substance Abuse and Mental Health Services Administration (SAMHSA), and the National Institute for Drug Abuse (NIDA) report that no sound scientific studies support the medicinal use of cannabis. Despite this lack of scientific validation, many patients routinely use "medical marijuana," and in many cases this use is for pain related to nerve injury. We conducted a double-blinded, placebo-controlled, crossover study evaluating the analgesic efficacy of smoking cannabis for neuropathic pain. Thirty-eight patients with central and peripheral neuropathic pain underwent a standardized procedure for smoking either high-dose (7%), low-dose (3.5%), or placebo cannabis. In addition to the primary outcome of pain intensity, secondary outcome measures included evoked pain using heat-pain threshold, sensitivity to light touch, psychoactive side effects, and neuropsychological performance. A mixed linear model demonstrated an analgesic response to smoking cannabis. No effect on evoked pain was seen. Psychoactive effects were minimal and well-tolerated, with some acute cognitive effects, particularly with memory, at higher doses. KeywordsNeuropathic pain; analgesia; cannabis; clinical trial; neuropsychological testingThe case for the clinical utility of cannabis as an analgesic derives from experimental studies as well as anecdotal reports. Activation of the endocannabinoid system suppresses HHS Public AccessAuthor manuscript J Pain. Author manuscript; available in PMC 2016 August 01. Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript behavioral responses to acute and persistent noxious stimulation through both central 71 and peripheral 45 mechanisms. Cannabinoid receptors are localized in neuroanatomic regions intimately involved with transmission and modulation of pain signals: The periaqueductal gray (PAG), the rostral ventromedial medulla (RVM), 40,66 and the dorsal horn of the spinal cord. 66 Animal experimentation has clearly demonstrated that synthetic and endogenous cannabinoids not only produce analgesia but also interact in some manner to potentiate opioids, 18,70 particularly in neuropathic pain. 41 Surveys involving the use of medicinal marijuana reveal that pain, sleep, and mood improve with only modest side effects. 72,73 In one human pain experiment, subjects had a significant dose-dependent antinociception (increased finger withdrawal latency) effect that was not reversed by opioid antagonism. 31 In a somewhat contradictory manner, hyperalgesic activity and enhancement of the perception of pain on acute exposure in chronic users of marijuana was reported. 20 Experience with cancer pain revealed that 120 mg codeine and 20 mg delta-9-tetrahydrocannabinol (9-THC) were similar to each other and significantly superior to placebo for the sum of the pain intensity differences and total pain relief. 55,56 However, there was a clear dose-response relationship for sedation, mental clouding, and other central nervous system (CNS) related side effects f...

Printed in Singapore. PrefaceThe role of statistical methods in general, and the statistical analysis of survival data in particular, in cancer research cannot be emphasized enough. They have penetrated deeply into clinical and experimental oncology, especially into such important areas as cancer epidemiology and clinical trials. It would be difficult to find a paper in the current literature which does not contain a reference to the Kaplan-Meier esti mator or Cox's regression model. Despite the fact that a great variety of parametric survival models are available (Johnson and Kotz, 1970; Cohen and Whitten, 1988), nonparametric methods of estimation and hypotheses testing are used the most. This is because nonparametric estimators and tests are quite general and easy to compute. At the same time, they provide less information than would be desirable. Using twosample significance tests, one can give the answer to the question as to whether there is a statistically significant difference between the two groups of patients under study. What is perhaps the more fundamental question, though, still remains: how can the observed difference be explained in terms of biological mechanisms, the simpler the explanation the better. Yet another question is how to make extrapolations beyond the follow-up period. These are questions any investigator would be likely to raise; to answer them would require the parametric techiques and substantive models that underlie them. Many other practical problems call for parametric methods, the design of optimal surveillance strategies is among them. There are some statistical problems of survival analysis for which no satisfactory nonparametric solutions are currently available. Some examples of this sort are given in this book. Needless to say that parametric methods are more difficult to handle in practice. They create psychological problems, casting doubts upon the basic assumptions used for the derivation of a given para metric model, since their validation is an arduous task for each specific application.There is no conflict between the two methodologies, each of them having merits and demerits as is usual in real life. Moreover, they supplement and enrich each other when used in combination. There are examples illustrating this point in the book by Yakovlev and Tsodikov. The most challenging problem is to find a "good" mathematical model, as applied to the analysis of biomedical data. There are no perfectly objective criteria for distinguishing a "good" model from a "bad" one.

EBV-naïve patients who receive a donor organ from an EBV-infected donor are in the highest-risk situation for PTLD development. Most of these lymphomas are CD20 positive. Follicular lymphoma is unusual. With treatment, survival of patients with PTLD was indistinguishable from that of the SEER population sample.

Hepatic steatosis is a recognized problem in patients after orthotopic liver transplant (OLT). However, de novo development of nonalcoholic fatty liver disease (NAFLD) has not been well described. The aim of this study was to determine the prevalence and predictors of de novo NAFLD after OLT. A retrospective analysis was performed on 68 OLT patients with donor liver biopsies and posttransplantation liver biopsies. Individual medical charts were reviewed for demographics, indication for OLT, serial histology reports, genotypes for hepatitis C, comorbid conditions, and medications. Liver biopsies were reviewed blindly and graded according to the Brunt Scoring System. Multivariate logistic regression analysis was used to study the risk factors for developing NAFLD. The interval time from OLT to subsequent follow-up liver biopsy was 28 +/- 18 months. A total of 12 patients (18%) developed de novo NAFLD, and 6 (9%) developed de novo NASH. The regression model indicated that the use of angiotensin-converting enzyme inhibitors (ACE-I) was associated with a reduced risk of developing NAFLD after OLT (odds ratio, 0.09; 95% confidence interval, 0.010-0.92; P = 0.042). Increase in body mass index (BMI) of greater than 10% after OLT was associated with a higher risk of developing NAFLD (odds ratio, 19.38; 95% confidence interval, 3.50-107.40; P = 0.001). In conclusion, de novo NAFLD is common in the post-OLT setting, with a significant association with weight gain after transplant. The use of an ACE-I may reduce the risk of developing post-OLT NAFLD.