scholarly journals Effect of Clonidine on Cigarette Cessation and in the Alleviation of Withdrawal Symptoms

Addiction ◽  
1988 ◽  
Vol 83 (12) ◽  
pp. 1467-1467 ◽  
Author(s):  
WEI HAO ◽  
DERSON YOUNG
2013 ◽  
Author(s):  
T. R. Schlam ◽  
M. E. Piper ◽  
J. W. Cook ◽  
M. C. Fiore ◽  
T. B. Baker

1970 ◽  
Vol 65 (4) ◽  
pp. 608-616 ◽  
Author(s):  
M. J. Levell ◽  
S. R. Stitch ◽  
M. J. Noronha

ABSTRACT Pituitary-adrenal function was tested in a group of 33 patients with multiple sclerosis who had been treated with corticotrophin for at least 1 year. Assessment was made by measuring the change in the plasma 11-hydroxycorticosteroid concentration following lysine vasopressin (LVP) administration. Ten patients showed abnormally small increases after LVP. Two of the 5 patients with the smallest increases still showed impairment 8 months later. The patients with no withdrawal symptoms had normal or nearly normal increases following LVP. There was an association between the concentration of 11-hydroxycorticosteroids immediately after withdrawal of ACTH and the subsequent response to LVP.


2020 ◽  
Vol 75 (6) ◽  
Author(s):  
Melissa Laus ◽  
Marianna Trignani ◽  
Domenico Crescenzi ◽  
Marco Radici ◽  
Adelchi Croce

2018 ◽  
Author(s):  
Jennifer Fillo ◽  
Kimberly E. Kamper-DeMarco ◽  
Whitney C. Brown ◽  
Paul R. Stasiewicz ◽  
Clara M. Bradizza

Approximately 15% of US women currently smoke during pregnancy. An important step toward providing effective smoking cessation interventions during pregnancy is to identify individuals who are more likely to encounter difficulty quitting. Pregnant smokers frequently report smoking in response to intrapersonal factors (e.g., negative emotions), but successful cessation attempts can also be influenced by interpersonal factors (i.e., influence from close others). This study examined the association between emotion regulation difficulties, positive and negative social control (e.g., encouragement, criticism), and smoking cessation-related variables (i.e., smoking quantity, withdrawal symptoms) among pregnant smokers. Data were drawn from the pretreatment wave of a smoking cessation trial enrolling low-income pregnant women who self-reported smoking in response to negative affect (N = 73). Greater emotion regulation difficulties were related to greater smoking urges (b = 0.295, p = .042) and withdrawal symptoms (b = 0.085, p = .003). Additionally, more negative social control from close others was related to fewer smoking days (b = -0.614, p = .042) and higher smoking abstinence self-efficacy (b = 0.017, p = .002). More positive social control from close others interacted with negative affect smoking (b = -0.052, p = .043); the association between negative affect smoking and nicotine dependence (b = 0.812, p < .001) only occurred at low levels of positive social control. Findings suggest that emotion regulation difficulties may contribute to smoking during pregnancy by exacerbating women's negative experiences related to smoking cessation attempts. Negative social control was related to lower smoking frequency and greater confidence in quitting smoking, suggesting that it may assist pregnant smokers' cessation efforts. Positive social control buffered women from the effects of negative affect smoking on nicotine dependence.


2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.


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