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OBJECTIVE: To determine the cause of an outbreak of Escherichia coli 0157:H7 related to animal exposures so that further transmission could be prevented.

BACKGROUND: Verotoxigenic Escherichia coli (VTEC) was first described in Canada during the 1980s as an emerging foodborne disease in association with morbidity and mortality in outbreaks of hemorrhagic colitis caused by E coli O157:H7.

Aluminum - Magnesium - Silicon (Al-Mg-Si) 6063 alloy was heat-treated using under aged, peak aged and overage temperatures. The numbers of cycles required to cause the fatigue fracture, at constant stress, was considered as criteria for the fatigue resistance. Moreover, the fractured surface of the alloy at different aging conditions was evaluated by optical microscopy and the Scanning Electron Microscopy (SEM). The SEM micrographs confirmed the cleavage surfaces with well-defined fatigue striations. It has been observed that the various aging time and temperature of the 6063 Al-alloy, produces different modes of fractures. The most suitable age hardening time and temperature was found to be between 4 to 5 hours and to occur at 460 K. The increase in fatigue fracture property of the alloy due to aging could be attributed to a vacancy assisted diffusion mechanism or due to pinning of dislocations movement by the precipitates produced during aging. However, the decrease in the fatigue resistance, for the over aged alloys, might be due to the coalescence of precipitates into larger grains.

Background Identifying patients presenting with nonspecific abdominal symptoms who have underlying cancer is a challenge. Common blood tests are widely used to investigate these symptoms in primary care, but their predictive value for detecting cancer in this context is unknown. We quantify the predictive value of 19 abnormal blood test results for detecting underlying cancer in patients presenting with 2 nonspecific abdominal symptoms. Methods and findings Using data from the UK Clinical Practice Research Datalink (CPRD) linked to the National Cancer Registry, Hospital Episode Statistics and Index of Multiple Deprivation, we conducted a population-based cohort study of patients aged [greater than or equal to]30 presenting to English general practice with abdominal pain or bloating between January 2007 and October 2016. Positive and negative predictive values (PPV and NPV), sensitivity, and specificity for cancer diagnosis (overall and by cancer site) were calculated for 19 abnormal blood test results co-occurring in primary care within 3 months of abdominal pain or bloating presentations. A total of 9,427/425,549 (2.2%) patients with abdominal pain and 1,148/52,321 (2.2%) with abdominal bloating were diagnosed with cancer within 12 months post-presentation. For both symptoms, in both males and females aged [greater than or equal to]60, the PPV for cancer exceeded the 3% risk threshold used by the UK National Institute for Health and Care Excellence for recommending urgent specialist cancer referral. Concurrent blood tests were performed in two thirds of all patients (64% with abdominal pain and 70% with bloating). In patients aged 30 to 59, several blood abnormalities updated a patient's cancer risk to above the 3% threshold: For example, in females aged 50 to 59 with abdominal bloating, pre-blood test cancer risk of 1.6% increased to: 10% with raised ferritin, 9% with low albumin, 8% with raised platelets, 6% with raised inflammatory markers, and 4% with anaemia. Compared to risk assessment solely based on presenting symptom, age and sex, for every 1,000 patients with abdominal bloating, assessment incorporating information from blood test results would result in 63 additional urgent suspected cancer referrals and would identify 3 extra cancer patients through this route (a 16% relative increase in cancer diagnosis yield). Study limitations include reliance on completeness of coding of symptoms in primary care records and possible variation in PPVs if extrapolated to healthcare settings with higher or lower rates of blood test use. Conclusions In patients consulting with nonspecific abdominal symptoms, the assessment of cancer risk based on symptoms, age and sex alone can be substantially enhanced by considering additional information from common blood test results. Male and female patients aged [greater than or equal to]60 presenting to primary care with abdominal pain or bloating warrant consideration for urgent cancer referral or investigation. Further cancer assessment should also be considered in patients aged 30 to 59 with concurrent blood test abnormalities. This approach can detect additional patients with underlying cancer through expedited referral routes and can guide decisions on specialist referrals and investigation strategies for different cancer sites., Author(s): Meena Rafiq 1,2,*, Cristina Renzi 1,3, Becky White 1, Nadine Zakkak 1, Brian Nicholson 4, Georgios Lyratzopoulos 1, Matthew Barclay 1 Introduction Abdominal symptoms account for 10% of all [...]

This study aims to investigate the experiences of Solomon Islands students during the shift to online, remote teaching during the coronavirus (COVID-19) pandemic. It utilises the "tokstori" to collect data from 16 Solomon Islands students of the University of the South Pacific's (USP) Suva and Honiara campuses. It is framed and analysed data using Khan's eight dimensions of gauging successful e-learning experiences. Prior to COVID-19, many of the courses at the USP were offered in blended mode, which encompasses both online and face-to-face delivery. However, with a sudden move to fully online offerings, and the associated fears resulting from the unknowns of COVID-19, this was disruptive and traumatic for many students. However, analysis identified several key factors aiding student success including many Solomon Islands students being able to eventually cope with the disruption and displayed independence and resilience. It is also found that Solomon Island students got to experience new applications and tools such as, for example, having to be in Zoom or having to sit an online exam. This study has potential to inform higher education institutes (HEIs) in the Pacific Islands and beyond, and the need to carefully navigate the practical realities of the islands in the interaction between digital technologies, instructors and students to facilitate online learning post-pandemic.

The spread of the COVID-19 pandemic is observed to follow the shape of "waves" (i.e., the rise and fall of population-adjusted daily new infection cases with time). Different geographic regions of the world have experienced different position and span of these waves over time. The presence and strength of these waves broadly characterize the dynamics of the pandemic spread in a given area, so their characterization is important to draw meaningful intervention and mitigation plans tailored for that area. In this paper, we propose a novel technique to represent the trend of COVID-19 spread as a sequence of a fixed-length text string defined on three symbols: R (rise), S (Steady), and F (fall). These strings, termed as trend strings, enabled us searching for specific patterns in them (such as for waves). After analyzing county-level infection data, we observe that, US counties-despite their wide variation in trend strings-can be grouped into a number of heterogeneous classes each of which might have a representative COVID spread pattern over time (in terms of presence and propensity of waves). To this end, we conduct a latent class analysis to cluster 3142 US counties into four distinct classes based on their wave characteristics for one year pandemic data (January 2020 to January 2021). We observe that counties in each class have distinct socio-demographics, location, and human mobility characteristics. In short summary, counties have differing number of waves (class 1 counties have only one wave and class 3 counties have three) and their positions also vary (class 1 had the wave later in the year whereas class 3 had waves throughout the year). We believe that this way of characterizing pandemic waves would provide better insights in understanding the complex dynamics of COVID-19 spread and its future evolution, and would, therefore, help in taking class-specific policy interventions.