Nalmefene, the 6-methylene derivative of naltrexone, was examined after subcutaneous (s.c.) (0.0001 to 8.0 mg/kg) and oral (10 to 80.0 mg/kg) administration in ethanol (EtOH)-preferring rats whose responding (i.e., lever pressing) was maintained by the presentation of EtOH. Naltrexone (0.01 to 40 mg/kg) was used as a reference opioid antagonist. EtOH (10% v/v) and saccharin (0.025 to 0.1% w/v) solutions were concurrently available for 1 hr each day under a two-lever, fixed-ratio schedule in which four responses on one lever produced the EtOH solution and four responses on the other lever produced the saccharin solution. When basal response rates for saccharin were 10% that of EtOH, all routes of nalmefene administration reduced control levels of responding maintained by EtOH by 38 to 84%. When basal response rates for saccharin-maintained responding were 60% or 82% that of EtOH, only lower s.c. naltrexone (e.g., 0.01 to 0.025 mg/kg) and nalmefene (e.g., 0.01 to 0.10 mg/kg) doses produced a selective dose-dependent suppression of EtOH-maintained responding. Higher nalmefene (0.25 to 8.0 mg/kg) and naltrexone (1.0 to 20.0 mg/kg) doses failed to produce a dose-dependent suppression on EtOH or saccharin maintained responding. Both antagonists suppressed responding maintained by EtOH primarily during the initial 10-min period, with little additional suppression occurring across the remainder of the 60-min period. Subcutaneous nalmefene was 3200- to 6400-fold more potent than oral nalmefene, suggesting bioavailability was optimized using the s.c. route. Nalmefene (0.5 mg/kg, s.c.) treatment for 10 consecutive days produced mild tolerance development, whose effects dissipated by day 8. Naltrexone (10 to 40 mg/kg) and nalmefene (1.5 to 3.0 mg/kg), given 8 to 24 hr before the test session, reduced control levels of responding maintained by EtOH by 82%. Thus, immediate opioid receptor occupancy was not required to observe antagonism. These data demonstrate that, under a variety of experimental conditions, nalmefene is an effective antagonist of responding maintained by EtOH and lend support to clinical reports that nalmefene may function as an alternative pharmacotherapy to naltrexone to reduce EtOH-motivated behavior and prevent relapse.

The pattern of illness in 60 consecutive children with homozygous sickle cell disease who attended the Paediatric Emergency Room of a busy Lagos hospital with acute illness was studied prospectively. Their ages ranged from 3 months to 13 years with a peak in the 2nd year. There were twice as many boys as girls. The commonest symptoms were fever, limb or abdominal pain and cough, and the commonest signs were pallor and hepatomegaly. Painful crises occurred in 27, anaemic crises in 11, and a combination of these in 12 children. Infection was detected in 76% of subjects in crises. Infection was found in 82% of all the children and was mainly bacterial. The commonest infections were pneumonia (35%), bacteraemia (32%), tonsillitis/pharyngitis (17%) and osteomyelitis (8%). The predominant bacteria isolated were Klebsiella spp (38%), E. coli (23%), Staph. aureus (23%), Staph. albus (23%) and Pseudomonas spp (23%). Some children had multiple isolates. Bacterial infection was a major cause of morbidity in very young children and merits appropriate control and preventive measures in this age group. The spectrum of bacteria isolated makes it unlikely that the specific anti-pneumococcal measures widely advocated in Europe and America for young children with SCA would be appropriate in Nigeria.

BackgroundSickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain.ObjectiveThe objective of this study was to show the feasibility of using objective, physiologic measurements obtained from a wearable device during an acute pain crisis to predict patient-reported pain scores (in an app and to nursing staff) using machine learning techniques.MethodsFor this feasibility study, we enrolled 27 adult patients presenting to the day hospital with acute pain. At the beginning of pain treatment, each participant was given a wearable device (Microsoft Band 2) that collected physiologic measurements. Pain scores from our mobile app, Technology Resources to Understand Pain Assessment in Patients with Pain, and those obtained by nursing staff were both used with wearable signals to complete time stamp matching and feature extraction and selection. Following this, we constructed regression and classification machine learning algorithms to build between-subject pain prediction models.ResultsPatients were monitored for an average of 3.79 (SD 2.23) hours, with an average of 5826 (SD 2667) objective data values per patient. As expected, we found that pain scores and heart rate decreased for most patients during the course of their stay. Using the wearable sensor data and pain scores, we were able to create a regression model to predict subjective pain scores with a root mean square error of 1.430 and correlation between observations and predictions of 0.706. Furthermore, we verified the hypothesis that the regression model outperformed the classification model by comparing the performances of the support vector machines (SVM) and the SVM for regression.ConclusionsThe Microsoft Band 2 allowed easy collection of objective, physiologic markers during an acute pain crisis in adults with SCD. Features can be extracted from these data signals and matched with pain scores. Machine learning models can then use these features to feasibly predict patient pain scores.

No abstract