We have developed a panel of rabbit polyclonal antipeptide antibodies against the five human somatostatin receptor subtypes (hSSTR1-5) and used them to analyze the pattern of expression of hSSTR1-5 in normal human islet cells by quantitative double-label confocal fluorescence immunocytochemistry. All five hSSTR subtypes were variably expressed in islets. The number of SSTR immunopositive cells showed a rank order of SSTR1 > SSTR5 > SSTR2 > SSTR3 > SSTR4. SSTR1 was strongly colocalized with insulin in all beta-cells. SSTR5 was also an abundant isotype, being colocalized in 87% of beta-cells. SSTR2 was found in 46% of beta-cells, whereas SSTR3 and SSTR4 were relatively poorly expressed. SSTR2 was strongly colocalized with glucagon in 89% of alpha-cells, whereas SSTR5 and SSTR1 colocalized with glucagon in 35 and 26% of alpha-cells, respectively. SSTR3 was detected in occasional alpha-cells, and SSTR4 was absent. SSTR5 was preferentially expressed in 75% of SST-positive cells and was the principal delta-cell SSTR subtype, whereas SSTR1-3 were colocalized in only a few delta-cells, and SSTR4 was absent. These studies reveal predominant expression of SSTR1, SSTR2, and SSTR5 in human islets. Beta-cells, alpha-cells, and delta-cells each express multiple SSTR isoforms, beta-cells being rich in SSTR1 and SSTR5, alpha-cells in SSTR2, and delta-cells in SSTR5. Although there is no absolute specificity of any SSTR for an islet cell type, SSTR1 is beta-cell selective, and SSTR2 is alpha-cell selective. SSTR5 is well expressed in beta-cells and delta-cells and moderately well expressed in alpha-cells, and thereby it lacks the islet cell selectivity displayed by SSTR1 and SSTR2. Subtype-selective SSTR expression in islet cells could be the basis for preferential insulin suppression by SSTR1-specific ligands and of glucagon inhibition by SSTR2-selective compounds.
Objective: We report the uptake, length of stay and vascular readmission rates of carotid endarterectomy (CEA) and CAS among patients with symptomatic or asymptomatic carotid artery disease in the English National Health Service (NHS).Methods: Retrospective cohort study based on routinely collected Hospital Episode Statistics (HES) inpatient data. We identified individual admissions for CEA (n = 15996) or CAS (n = 632) between 2006 and 2009. Summary data were used to describe procedure volumes between 2009 and 2012. We analysed trends in procedure use over time and used ordinary least squares regression to evaluate patient, clinical and organisational characteristics associated with longer length of stay for revascularisation.Results: CAS made up less than 5% of carotid revascularisation procedures; there was no trend for increasing use between 2006 and 2012. Patients treated with CAS were on average younger, lived in areas of higher deprivation and were more likely to have amaurosis fugax or a comorbidity of heart disease. CAS patients had a 19% (95% CI 14-24) shorter stay in hospital than CEA patients.Conclusion: Despite the early promise of CAS and numerous randomised controlled trials evaluating efficacy, it has not been rapidly adopted in England. Cautious adoption may be appropriate given the higher periprocedural risk of stroke or death after CAS, particularly in recently symptomatic patients.
In the dynamic distributed task assignment (DDTA) problem, a team of agents is required to accomplish a set of tasks while maximizing the overall team utility. An effective solution to this problem needs to address two closely related questions: first, how to find a near-optimal assignment from agents to tasks under resource constraints, and second, how to efficiently maintain the optimality of the assignment over time. We address the first problem by extending an existing forward/reverse auction algorithm which was designed for bipartite maximal matching to find an initial near-optimal assignment. A difficulty with such an assignment is that the dynamicity of the environment compromises the optimality of the initial solution. We address the dynamicity problem by using swapping to locally move agents between tasks. By linking these local swaps, the current assignment is morphed into one which is closer to what would have been obtained if we had re-executed the computationally more expensive auction algorithm. In this paper, we detail the application of this dynamic auctioning scheme in the context of a UAV (Unmanned Aerial Vehicle) search and rescue mission and present early experimentations using physical agents to show the feasibility of the proposed approach.
IMPORTANCE Disagreement in the presumed meaning of common medical terms may impair communication between patients and caregivers. OBJECTIVE To clarify the intended meaning of the term congestion among otolaryngology clinic patients and to identify discrepancies in definitions between patients and otolaryngologists. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional survey study from an otolaryngology clinic at an academic center, a semantics-based questionnaire was provided to consecutive new patients during intake for a clinical encounter from December 2016 through February 2017, and to 31 otolaryngologists and 28 nonotolaryngologist physicians in February 2018. Respondent definitions for congestion were selected from a list of 16 proposed terms covering 4 general categories. MAIN OUTCOME AND MEASURES Symptom categories for term used to describe congestion by patients and clinicians. RESULTS Among 226 patient respondents (133 female [58.8%]; mean [SD] age, 54 [15.6] years), the most commonly selected definitions for congestion were from the obstructive (199; 88.1%) and mucus-related (196; 86.7%) symptom categories. More than 1 general category was selected by 208 patients (92.0%), whereas 11 patients (4.9%) described congestion only in terms of mucus-related symptoms. Definitions were limited to upper respiratory tract symptoms by 83 (36.7%) patients and lower respiratory tract symptoms by 2 (0.9%) patients. Among 31 otolaryngologists, congestion was most frequently defined in terms of obstructive symptoms (difference, 11.9%; 95% CI, 7.4%-16.5%). In contrast, patients more often described congestion in terms of pressure-related (difference, 38.8%; 95% CI, 7.5%-70.1%) or mucus-related (difference, 51.2%; 95% CI, 22.6%-79.9%) symptoms. A total of 22 otolaryngologists (71.0%) defined congestion using 1 to 4 symptoms, compared with only 69 patients (30.5%). CONCLUSIONS AND RELEVANCE The definition of congestion appears to be highly variable and ambiguous for many patients. Moreover, the findings suggest that patients and otolaryngologists generally do not describe congestion using the same terms.
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