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Analgesic, Anti-Inflammatory, Antipyretic, and Anesthetic Drugs: Dealing With Pain, Inflammation, and Fever
Published in Richard J. Sundberg, The Chemical Century, 2017
The structure of morphine was determined by Sir Robert Robinson at Oxford in the 1920s. This led to the synthesis of a number of analogs. Some were obtained by modification of morphine or other natural constituents of opium. Others were entirely synthetic, such as those derived from the benzomorphan structure and still others from simpler piperidine structures. The synthetic compounds that retain analgesic activity, however, also are highly addictive. A few of the compounds, such as nalorphine, naloxone and naltrexone are antagonists of morphine and can be used for treatment of addiction. Some of the synthetic analogs such as fentanyl and remifenatil are rapid-onset, short duration analgesics and are used during surgery and painful diagnostic procedures. The structures are shown in Scheme 13.3.
Substance use and related disorders among persons exposed to the 9/11 terrorist attacks: Essentials for screening and intervention
Published in Archives of Environmental & Occupational Health, 2023
Frank G. Dowling, Sandra M. Lowe
For relapse prevention, psychological and psychosocial strategies and medications for addiction treatment may be considered. CPGs recommend medications specifically for AUD (such as intramuscular (IM) or oral naltrexone, acamprosate, or topiramate) and for opioid use disorder (IM naltrexone, buprenorphine, or methadone).28,34,35 While the evidence base demonstrates that MAT is effective when abstinence is the treatment goal, reduced use and harm reduction may be more achievable for many patients and may still result in improvement of the SUD, co-morbid mental health conditions, and quality of life.28,36,38,39
Big data for cyber physical systems in industry 4.0: a survey
Published in Enterprise Information Systems, 2019
In the effectiveness direction, many researchers proposed different new methods, such as odds ratio (Mosteller 1968), relative risk (Sistrom and Garvan 2004), likelihood ratio (Neyman and Pearson 1992), lift (Brin et al. 1997), leverage (Piateski and Frawley 1991), BCPNN (Bate et al. 1998), two-way support (Tew et al. 2014), added value (Kannan and Bhaskaran 2009), and putative causal dependency (Huynh et al. 2007). Different methods will highlight different patterns as the random noise has different impacts on different methods for different patterns. For example, Leverage highlights correlated patterns which occur frequently in the dataset, while BCPNN highlights correlated patterns which occur infrequently (Duan et al. 2014). Besides handling random noise, another direction is related to causal analysis (Krämer et al. 2013). The results from correlation analysis are useful for prediction. For example, if events A and B are correlated, we can expect a higher chance for A to happen if B occurs. However, such a correlation relationship is not very useful for intervention. In other words, making efforts to reduce the probability of B can not necessarily reduce the probability of A. For example, it is very important to detect confounding factors to highlight more causal relationship from correlation. A confounding factor is an event C that is associated with both event A and B (Pearl 2000). The seemingly positive correlation between A and B is spurious when considering the confounding factor C. For example, in healthcare, the drug Naltrexone is positively correlated with the disease pancreatitis, because Naltrexone is used to treat alcoholism and alcoholism often leads to pancreatitis. The confounding factor alcoholism is a distortion of the genuine correlation between Naltrexone and pancreatitis. Popular methods for detecting confounding factors include the Cochran-Mantel-Haenszel method (Cochran 1954), logistic regression model (Li et al. 2014), and partial correlation (Baba, Shibata, and Sibuya 2004). In addition, timestamps of events are also useful for causal analysis, because causes always happen earlier than their effect (Kleinberg and Mishra 2009). Correlation and causal analysis is very useful for machine failure monitoring and maintenance in Industry 4.0. For example, machines could have many different failure types which require different interventions and maintenance. However, among numerous signals generated by machines and events associated with machines, a certain signal or event is associated with one type of machine failure but not the other. Correlation and causal analysis can help to associated signals/events with failure types, which is useful for machine failure prediction and maintenance plan improvement. Zaki, Lesh, and Ogihara (2001) utilized the correlation analysis to prune out unpredictable and redundant patterns to improve machine failure prediction performance. Sammouri (2014) utilized correlation analysis to connect severe railway operation failures with sensor data for vehicle, rail, high-voltage lines, track geometry, and other railway infrastructures, which allows the constant and daily diagnosis of both vehicle components and railway infrastructure.