Headache characteristics are described in 139 patients with chronic daily or almost daily headaches due to regular intake of analgesics and the short- and long-term results of drug withdrawal. Drug-induced headache was described as dull, diffuse, and band-like, and usually started in the early morning. The mean duration of the original headache (migraine or tension headache) was 25 years; regular intake of drugs and chronic daily headache had started 10 and 6 years prior to withdrawal therapy, respectively. Patients took an average of 34.6 tablets or analgesic suppositories or antimigraine drugs per week containing 5.8 different substances. The drugs most often used were caffeine (95%), ergotalkaloids (89%), barbiturates (64%), and spasmolytics, paracetamol, and pyrazolone derivates (45%-46%). A total of 103 patients (68 migraine, 35 tension or combination headache) were available for interviews at a mean time interval of 2.9 years after an inpatient drug withdrawal programme. Chronic headache had disappeared or was reduced by more than 50% in two-thirds of the patients. Positive predictors for successful treatment were migraine as primary headache, chronic headache lasting less than 10 years, and regular intake of ergotamine. Drug intake was significantly reduced and patients used single substances more often. Patients who originally suffered from migraine, superimposed on the daily headache, also experienced a significant improvement in the frequency of the migraines and their intensity. Migraine prophylaxis through beta-blocking agents and calcium channel antagonists was more efficient after drug-withdrawal therapy.
The present study used recordings of visual potentials evoked by pattern reversal (VEPs) to investigate the central effects of three drugs used in migraine prophylaxis: the calcium channel blocker nifedipine, the beta-1-selective blocker metoprolol, and the nonselective beta adrenoreceptor blocker propranolol. The study involved 58 patients with common or classical migraine who were treated in a double-blind randomized study over a period of 7 months, while the effectiveness of prophylactic treatment was recorded in headache diaries that were subjected to time series analysis. VEPs were recorded at the beginning of a 2-month baseline period without treatment, after 4 months of treatment, and at the end of a 3-month washout period. At baseline, migraine patients had significantly higher VEP amplitudes and longer latencies than did a group of 87 healthy control subjects. Patients were separated by statistical analysis into responders and nonresponders to each prophylactic treatment. Nifedipine had no effects on the frequency, intensity, and duration of migraine attacks, nor on amplitude and latency of the VEPs. In contrast, the use of beta blockers resulted in a significant decrease in VEP amplitude, both in responders and nonresponders, whereas VEP latency remained unchanged. VEP amplitudes returned to the initial values at follow-up in the nonresponders, but stayed at lower levels in responders. Beta blockers thus appear to have a significant effect on the increased excitability of the visual system in patients with migraine, although their action is not directly related to their reduction of migraine frequency.
For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.
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