Decrease in selenium status in relation to coal dust exposure

Selenium (Se) plasma levels were studied 222 coal miners to assess whether selenium is decreased in relation to coal dust exposure, taking age, alcohol, and tobacco consumption into account. Selenium levels decreased significantly with age and current tobacco consumption, among miners aged 34–50. Long-term and current exposure to coal dust were studied. The lowest Se values were observed for those with both long-term and current exposure (60.2 ng/ml), the highest for those never or slightly exposed (64.1 ng/ml); those with long-term exposure not currently exposed fell in an intermediate position (61.3 ng/ml). No relation was observed with alcohol consumption. The association of coal dust with low selenium remained significant after adjustment for age and smoking. © 1996 Wiley-Liss, Inc.     

Failings of the disease model of addiction

Colloquially, addiction ranges in meaning from addiction to good deeds to addiction to substances of abuse. Especially during the last four decades, ‘addiction’ in this extreme pejorative meaning has been portrayed alternatively as a disease or a sin, and has been subject to social and moral sanctions. In an open society of free individuals such a coercion cannot be justified unless the condition is defined precisely by the simultaneous attributes of severe psychotoxicity, severe withdrawal symptoms, and recurrence tied to the loss of self-control and individual volition. Still, these attributes are open-ended, and an explicit metric of severity at which they may trigger social objection has not been clarified. As a consequence, ‘addiction’ allegations are left to elicit emotional, subjective, and value-laden responses ready to be exploited. A clamorous example is the claim by US officials that cigarette smoking is equal to the abuse of heroin or cocaine. An unequivocal definition of ‘addiction’ may restore some sense of proportion to official normative intervention. © 1996 John Wiley & Sons, Ltd.     

Smoking motivation in normal subjects using event-related potentials

The goal of the present study was to evaluate the effects of acute nicotine administration following 18 h of abstinence from cigarette smoking. Event-related potentials (ERPs) were measured in 13 male volunteers for five successive sessions. The peak amplitude and the area of the P300 significantly increased during acute withdrawal. An increase in P300 values also was observed following resumption of smoking. However, in subjects pretreated with nicotine gum, no increase in P300 values was observed following resumption of smoking. The increase in P300 values persisted for several weeks and returned to control values following 1 month of routine daily smoking. The P300 amplitude was negatively correlated with the daily dose of nicotine. These results suggest that attention and/or arousal may be enhanced by the withdrawal and the resumption of smoking. The mechanisms involved in CNS hypersensitivity to motivate the subject to smoke may persist in the presence of an increased P300 value, even following resumption of routine daily smoking.     

The Effects of Text Message and Infographic on Reducing the Number Cigarettes Consumption: A Randomized Controlled Trial

Objective: To test the effect of a text-message and infographic to promote smokers quit smoking. Methods: A randomized control trial was conducted in two provinces of northeast Thailand. Three hundred and ninety-six participants were allocated to either a text-message and infographic group or a control group. We assessed the primary outcome by self-reported 7-day point prevalence smoking abstinence. Multiple logistic regression was used to test the effect of quitting smoking. Results: At 3-month follow-up, lost to follow-up 16 participants, 380 participants were included for analysis. The difference in the rate of quitting smoking between the intervention and control groups was not found a statistical significance (17.8% versus 11.6%). However, we found a statistically significant difference in the number of cigarettes smokes (the difference: -1.74; 95%CI: -2.63, -0.84).Conclusion: No effect of text message and infographic for help smokers to quit smoking. However, the intervention showed a decrease in the number of cigarettes smoked.     

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.