Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Anomaly detection in complex trading systems

System availability is one of the major requirements expected from systems in the trading domain. In order to prevent system outages that can deteriorate system availability, anomaly detection must be able to assess the status of the system and detect anomalies that can lead to failures on a real-time basis. This paper presents a framework for anomaly detection for complex trading systems based on supervised learning approaches. Multiple feature reduction techniques were experimented with, in order to eliminate the noisy features that were initially derived from the system parameters. A classification technique based on Radial Basis Function (RBF) kernel Support Vector Machine (SVM) along with a feature selection technique built on a tree-based ensemble displayed the most promising results

Extensive Bone Marrow Necrosis: A Rare Presentation of Acute Lymphoblastic Leukaemia

Background. Bone marrow necrosis (BMN) is a rare entity which presents with bone pain, fever, and peripheral cytopenia. Acute lymphoblastic leukaemia (ALL) is characterized by malignant proliferation of immature lymphocytes, and patients usually present with fatigue and bleeding manifestations. Presentation with BMN is an extremely rare finding and only few cases had been reported in the literature. Case Presentation. A 22-year-old male presented with nocturnal lower back ache, pleuritic central chest pain, and fever for two weeks. He was extensively investigated for a cause. His investigations revealed pancytopenia with severe neutropenia. Initial bone marrow aspiration and biopsy did not provide a positive result due to extensive necrosis. However, immunohistochemical analysis of few immature lymphoid cells on repeated BM biopsy showed evidence of acute lymphoblastic leukaemia. Conclusions. ALL usually presents with fatigue and bleeding manifestations. Presentation with BMN is extremely rare. The diagnosis was extremely challenging as this patient had only occasional atypical cells in the peripheral blood film and the repeat bone marrow (BM) biopsy showed extensive necrosis

Infusion of butyrate affects plasma glucose, butyrate, and β-hydroxybutyrate but not plasma insulin in lactating dairy cows

The objective of this study was to investigate the effects on plasma metabolites and rumen traits when butyrate was infused into the rumen or abomasum of lactating cows. Jugular catheters were inserted into 5 ruminally fistulated Holstein cows [94.2 ± 26.3 DIM; 717 ± 45 kg of body weight (BW); mean ± SD] in a 5 × 5 Latin square with 3-d periods. Cows were infused for 24 h with 1 of 5 treatments: water (CON), 1 g/kg of BW of butyrate infused into either the abomasum (A1) or rumen (R1), or 2 g/kg of BW of butyrate infused into either the abomasum or rumen. Sodium butyrate was the source of butyrate and NaCl was added to the CON, A1, and R1 treatments to provide the same amount of sodium as supplied by the sodium butyrate treatment in the 2-g treatments. Plastisol flanges were inserted into the abomasum to allow infusion to the abomasum and peristaltic pumps provided continuous infusion at 9.3 mL/min for all treatments. The concentration of NaCl and sodium butyrate was varied in the infusate to provide the correct infusion amount. Rumen fluid samples were collected at −2, −1, 0, 1, 2, 3, 4, 6, 8, 12, 18, 24, 28, and 32 h relative to start of infusion. Serial blood samples were collected at −2, −1, 0, 0.5, 1, 2, 3, 4, 6, 8, 12, 18, 24, 26, 28, and 32 h relative to start of infusion. Compared with CON, infusing butyrate increased both plasma butyrate and plasma β-hydroxybutyrate (BHB), whereas plasma glucose decreased. Increasing butyrate infusion from 1 to 2 g increased plasma butyrate, tended to decrease plasma glucose, and tended to increase plasma BHB. Compared with abomasal infusion, rumen infusion of butyrate increased rumen butyrate, did not affect plasma glucose, and tended to increase plasma BHB. Treatment had no effect on plasma insulin. Results demonstrated that site of infusion and amount of butyrate affected several plasma metabolites when butyrate was infused in lactating dairy cows over a period of 24 h

A framework for organization-aware agents

Open systems are characterized by the presence of a diversity of heterogeneous and autonomous agents that act according to private goals. Organizations, such as those used in real-life to structure human activities such as task allocation, coordination and supervision, can regulate the agents’ behavior space and describe the expected behavior of the agents. Assuming an open environment, where agents are developed independently of the Organizational structures, agents need to be able to reason about the structure, so that they can deliberate about their actions and act within the expected boundaries and work towards the objectives of the organization. In this paper, we present the AORTA reasoning framework and show how it can be integrated into typical BDI-agents. We provide operational semantics that enables agents to make organizational decisions in order to coordinate and cooperate without explicit coordination mechanisms within the agents. The organizational model is independent of that of the agents, and the approach is not tied to a specific organizational model, but uses an organizational metamodel. We show how AORTA helps agents work together in a system with an organization for choosing the best tender for a building project