Metabolic effects of parasitization by the barnacle Polyascus plana (Cirripedia: Rhizocephala: Sacculinidae) on a grapsid host, Metopograpsus thukuhar.

Pathophysiological studies of rhizocephalan infections are rare. We describe differences in the levels of tissue and hemolymph metabolites between Polyascus plana-parasitized and unparasitized individuals of Metopograpsus thukuhar. Crabs were assigned to either a parasitized (carrying at least 1 externa, i.e. a protruding reproductive body) or an unparasitized (not carrying externae and determined to be rootlet-free by a barnacle 18S rRNA-based polymerase chain reaction) group. Quantification of metabolites showed that muscle glycogen levels were significantly lower and hepatopancreas levels were significantly higher in parasitized crabs compared to unparasitized crabs; hepatopancreas triacylglycerol levels were significantly higher and hemolymph levels significantly lower in parasitized hosts, and there was no significant difference in muscle triacylglycerol levels between unparasitized and parasitized animals. Glucose levels in the hepatopancreas, muscle, and hemolymph were all significantly higher in parasitized hosts. Significant levels of glucose, triacylglycerol, and glycogen were present in the barnacle externae. In addition, levels of crustacean hyperglycemic hormone in the sinus glands were not significantly different between unparasitized and parasitized animals. Glucose mobilized from the muscle is likely converted to glycogen and triacylglycerol in the rootlet-infiltrated hepatopancreas of parasitized hosts, and the eyestalk neuroendocrine system appears not to be significantly impaired, in terms of hormone production and storage, by parasitization.

Prevalence, risk factors, and optimized management of moderate-to-severe thirst in the post-anesthesia care unit.

Post-operative thirst is common and may cause intense patient discomfort. The aims of this retrospective study conducted in a high-volume post-anesthesia care unit (PACU) were as follows: (1) to examine the prevalence of moderate-to-severe post-operative thirst-defined as a numerical rating scale (NRS) score of 4 or higher, (2) to identify the main risk factors for moderate-to-severe post-operative thirst, and (3) to maximize the efficacy and safety of thirst management through a quality improvement program. During a 1-month quality improvement program conducted in August 2018, a total of 1211 adult patients admitted to our PACU were examined. Moderate-to-severe thirst was identified in 675 cases (55.8%). The use of glycopyrrolate during anesthesia was associated with moderate-to-severe thirst (71.7% versus 66.4%, respectively, p = 0.047; adjusted odds ratio: 1.46, p = 0.013). Following a safety assessment, ice cubes, room temperature water, or an oral moisturizer were offered to patients. A generalized estimating equation model revealed that ice cubes were the most effective means for thirst management-resulting in an estimated thirst intensity reduction of 0.93 NRS points at each 15-min interval assessment (p < 0.001)-followed by room temperature water (- 0.92/time-point, p < 0.001) and the oral moisturizer (- 0.60/time-point; p < 0.001). Patient satisfaction (rated from 1 [definitely dissatisfied] to 5 [very satisfied]) followed a similar pattern (ice cubes: 4.22 ± 0.58; room temperature water: 4.08 ± 0.55; oral moisturizer: 3.90 ± 0.55, p < 0.001). The use of glycopyrrolate-an anticholinergic agent that reduces salivary secretion-was the main independent risk factor for moderate-to-severe post-operative thirst. Our findings may provide clues towards an optimized management of thirst in the immediate post-operative period.

A physiology-based seizure detection system for multichannel EEG.

BACKGROUND: Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable. METHODOLOGY: This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching. PRINCIPAL FINDINGS: We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection. CONCLUSION: We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.