Equine hepacivirus is the closest homologue of hepatitis C virus. Limited data on the clinical features of this infection are available. We report the identification of a horse with high-titre viremia by equine hepacivirus. Over a 15-month follow-up, the clinical signs and the viremic status persisted, suggesting a chronic evolution.

Objective: To compare the dental and skeletal effects of intermaxillary elastics on the correction of mild Angle's Class II division 1 malocclusion with clear aligner treatment (CA) versus fixed multibracket (FMB) in growing patients. Settings and sample population:The study sample consisted of 49 consecutively patients (mean age ± SD 12.9 ± 1.7 years), 32 females and 17 males referred from the School of Orthodontics of the University of Bratislava Comenius (Slovakia). All patients were treated with a non-extraction orthodontic treatment, 25 with FMB and 24 with CA. Methods:The cephalometric analysis was performed at the beginning (T0) and the end of the treatment (T1). The t test for unpaired data was carried out to compare cephalometric values at T0 and changes at T1-T0 between the two groups. The level of significance was set as P < .0035. Results:The two groups showed no statistically significant differences (ANPg = −0.1°; P = .762) in the correction of the sagittal intermaxillary relation. The analysis of vertical skeletal changes showed no statistically significant effects on mandibular inclination (SN/MP = 0.1°; P = .840). The two treatments had a statistically significant and clinically relevant difference in controlling the inclination of the lower incisors (L1/ GoGn = 4.8°, CAG = −0.5°± 3.9°; FMB = 4.3°± 5.8°; P < .001). Conclusions:Class II elastics combined with CA and FMB produce a similar correction on sagittal discrepancies in growing patients. CA presented a better control in the proclination of the lower incisors. CA and elastics might be a good alternative in the correction of mild Class II malocclusion in cases where a proclination of lower incisors is unwanted.

<p>Chronic pain is a prevalent condition where fear of movement and pain interferes with everyday functioning. Yet, there is no open body movement dataset for people with chronic pain in everyday settings. Our EmoPain@Home dataset addresses this with capture from people with and without chronic pain in their homes, while they performed their routine activities. The data includes labels for pain, worry, and movement confidence continuously recorded for activity instances for the people with chronic pain. We explored two-level pain detection based on this dataset and obtained 0.62 mean F1 score. However, extension of the dataset led to deterioration in performance confirming high variability in pain expressions for real world settings. We investigated activity recognition for this setting as a first step in exploring the use of the activity label as contextual information for improving pain level classification performance. We obtained mean F1 score of 0.43 for 9 activity types, highlighting its feasibility. Further exploration, however, showed that data from healthy people cannot be easily leveraged for improving performance because worry and low confidence alter activity strategies for people with chronic pain. Our dataset and findings lay critical groundwork for automatic assessment of pain experience and behaviour in the wild. </p>

No abstract

<p>Chronic pain is a prevalent condition where fear of movement and pain interferes with everyday functioning. Yet, there is no open body movement dataset for people with chronic pain in everyday settings. Our EmoPain@Home dataset addresses this with capture from people with and without chronic pain in their homes, while they performed their routine activities. The data includes labels for pain, worry, and movement confidence continuously recorded for activity instances for the people with chronic pain. We explored two-level pain detection based on this dataset and obtained 0.62 mean F1 score. However, extension of the dataset led to deterioration in performance confirming high variability in pain expressions for real world settings. We investigated activity recognition for this setting as a first step in exploring the use of the activity label as contextual information for improving pain level classification performance. We obtained mean F1 score of 0.43 for 9 activity types, highlighting its feasibility. Further exploration, however, showed that data from healthy people cannot be easily leveraged for improving performance because worry and low confidence alter activity strategies for people with chronic pain. Our dataset and findings lay critical groundwork for automatic assessment of pain experience and behaviour in the wild. </p>