Outcomes after urgent thyroidectomy following rapid control of thyrotoxicosis in Graves’ disease are similar to those after elective surgery in well-controlled disease

Background Surgery for Graves’ disease (GD) is usually performed after adequate control with medical treatment. Occasionally, rapid pre-operative optimization is required. The primary objective was to compare the outcomes of patients undergoing elective surgery for well-controlled GD with those undergoing rapid pre-operative treatment. We also propose a formal treatment protocol for future use. Methods A retrospective cohort study in a tertiary referral centre included 247 patients with well-controlled GD undergoing elective surgery and 19 patients with poorly controlled disease undergoing surgery after rapid optimization. The latter group did not respond well to thionamides (carbimazole and/or propylthiouracil) or had intolerance or side effects to thionamides and were treated with a range of non-thionamide drugs, including Lugol’s iodine, cholestyramine, beta blockers and steroids (with or without thionamides), and closely monitored for 1–2 weeks before surgery. Outcome measures included thyroid storm, hypoparathyroidism and recurrent laryngeal nerve palsy. Results In total, 266 patients with male-to-female ratio of 1:6 and median (interquartile range) age of 39 (31–51) were included. Overall, long-term recurrent laryngeal palsy and hypoparathyroidism occurred in 1 (0.38%) and 13 (4.9%) patients, respectively. No patient had thyroid storm. There was no significant difference in hypoparathyroidism (p = 1), vocal cord palsy (p = 0.803) and post-operative bleeding (p = 0.362), between elective surgery and rapid optimization groups. Conclusion Rapid pre-operative treatment is effective, safe and is associated with similar outcomes compared to usual treatment. A rapid pre-operative optimization protocol is proposed

Adherence to treatment to help quit smoking: effects of task performance and coping with withdrawal symptoms

Background: Currently the combined cognitive-behavioral and pharmacological treatment is the best option to quit smoking, although success rates remain moderate. This study aimed to identify predictors of continuous abstinence in an assisted smoking cessation program using combined treatment. In particular, we analyzed the effects of socio-demographic, smoking-, and treatment-related variables. In addition, we analyzed the effect of several risk factors on abstinence, and estimated a model of risk for smoking relapse.Methods: Participants were 125 workers at the University of Granada (50 males), with an average age of 46.91 years (SD = 8.15). They were recruited between 2009 and 2013 at an occupational health clinic providing smoking cessation treatment. Baseline measures included socio-demographic data, preferred brand of cigarettes, number of years smoking, use of alcohol and/or tranquilizers, past attempts to quit, Fargerström Test for Nicotine Dependence, Smoking Processes of Change Scale, and Coping with Withdrawal Symptoms Interview. Participants were invited to a face-to-face assessment of smoking abstinence using self-report and cooximetry hemoglobin measures at 3, 6, and 12 months follow-up. The main outcome was smoking status coded as “relapse” versus “abstinence” at each follow-up. Kaplan-Meier survival analysis was performed to estimate the probability of continued abstinence during 12 months and log-rank tests were used to analyze differences in continued abstinence as a function of socio-demographic, smoking-, and treatment-related variables. Cox regression was used to analyze the simultaneous effect of several risk factors on abstinence.Results: Using alcohol and/or tranquilizers was related to shorter abstinence. Physical exercise, the number of treatment sessions, performance of treatment tasks, and coping with withdrawal symptoms were related to prolonged abstinence. In particular, failure to perform the treatment tasks tripled the risk of relapse, while lack of coping doubled it.Conclusions: Our results show that physical exercise, performance of treatment-related tasks, and effective coping with withdrawal symptoms can prolong abstinence from smoking. Programs designed to help quit smoking can benefit from the inclusion of these factors.This research was supported by the Occupational Medicine Area (Prevention Service) of the University of Granada

Ceramides bind VDAC2 to trigger mitochondrial apoptosis

Ceramides draw wide attention as tumor suppressor lipids that act directly on mitochondria to trigger apoptotic cell death. However, molecular details of the underlying mechanism are largely unknown. Using a photoactivatable ceramide probe, we here identify the voltage-dependent anion channels VDAC1 and VDAC2 as mitochondrial ceramide binding proteins. Coarse-grain molecular dynamics simulations reveal that both channels harbor a ceramide binding site on one side of the barrel wall. This site includes a membrane-buried glutamate that mediates direct contact with the ceramide head group. Substitution or chemical modification of this residue abolishes photolabeling of both channels with the ceramide probe. Unlike VDAC1 removal, loss of VDAC2 or replacing its membrane-facing glutamate with glutamine renders human colon cancer cells largely resistant to ceramide-induced apoptosis. Collectively, our data support a role of VDAC2 as direct effector of ceramide-mediated cell death, providing a molecular framework for how ceramides exert their anti-neoplastic activity

A model-based PID controller for Hammerstein systems using B-spline neural networks

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches