The impact of superphosphate and surface-applied lime on the profitability and sustainability of wool production on the tablelands of NSW

Soil acidification is one of the major forms of soil degradation in higher rainfall areas of the tablelands of NSW. A grazing experiment was conducted near Sutton, NSW, to assess the effect of various rates of superphosphate, lime, sewage ash and stocking rates on wool production and sustainability between 1999 and 2008. The results from the discounted cash flow analysis show that the net present value of the treatment without lime, the lower rate of superphosphate and the lowest stocking rate returned the highest net present value of $266.30/ha. Raising the application of superphosphate from 125kg/ha every two to three years to 250kg/ha every year on un-limed and limed soil reduced the net present value by$278.70/ha and $249.30/ha, respectively. The addition of lime at the rate of 4t/ha on un-limed soil at the low superphosphate level reduced the net present value by about$234.60/ha. The net present value fell by $205.24/ha when the level of superphosphate rate increased to 250kg/ha every year. The net present value decreased as the level of stocking rate increased. We conclude that wool producers will be unlikely to use lime to ameliorate acid soil, even though production will not be sustainable, unless there are more favourable input and commodity prices in the market and government intervention.economic, acid soil, lime, superphosphate, sewage ash, stocking rate, policy,

A comparison of emergency department utilization by elderly and younger adult patients presenting to three hospitals in Hong Kong

Background The elderly population is increasing in absolute and relative terms in most developed countries, and this is protected to have a major impact on the delivery of health care, particularly acute and emergency services. The aim of this study is to describe the pattern of utilization of emergency department (ED) services in Hong Kong by the elderly and to compare it to the utilization by younger adults. Methods Data on ED visits to three acute hospitals in the eastern New Territories were retrieved from a central computerized database of ED attendances. Data on all adult patients (aged ≥15 years) who attended the three EDs in 2006 were analyzed retrospectively. Patients aged 15 to 64 years were defined as younger adults; patients aged ≥65 years were defined as elderly. The attendance rate, ED consultation process, hospital admission rate and disease pattern of the two age groups were compared. Results Elderly patients required significantly more emergency care resources than younger adults. Elderly ED patients were brought to hospital more frequently by ambulance (42.8% vs. 14.8%, p < 0.0001) and required hospital admission more often (45.0% vs. 15.5%, p < 0.0001) than younger adults. A significantly higher proportion of elderly patients were triaged as being in the critical, emergency or urgent categories compared to younger adults (44.4% vs. 18.2%, p < 0.0001). Laboratory tests, radiography and CT scanning were performed on elderly patients more frequently than on younger adults (p < 0.0001), and their lengths of stay in EDs and emergency wards were significantly longer (p < 0.0001). Neurological symptoms and chest pain were the most common presenting symptoms in elderly ED patients. Conclusion With the foreseeable rapid growth of the elderly population, ED utilization by the elderly will increase. Health service delivery, including that in the ED, needs to take account of the specific features and requirements of the elderly population in each locale

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Filling the Gaps in Pharmacy Workforce in Post-Conflict Areas: The Case of 4 Countries in Sub-Saharan Africa

Background: While the pharmacy workforce is the third largest professional healthcare group worldwide, the pharmacy workforce landscape remains unclear in post-conflict areas in sub-Saharan Africa. Method: Key informants were selected for semi-structured interviews due to their role in providing pharmacy services in the selected country: the Central African Republic (CAR), the Democratic Republic of Congo (DRC), Ethiopia, and South Sudan. Transcripts from the interviews were anonymized, coded, and analyzed. Results: Nine participants were recruited (CAR: 2; DRC: 2; Ethiopia: 2; South Sudan: 3), and all except two were pharmacists. Conflict-specific challenges in pharmacy service delivery were identified as the following: unpredictable health needs and/or mismatched pharmaceutical supply, transport difficulties due to insecure roads, and shortage of pharmacy workforce due to brain drain or interrupted schooling. Barriers to health workforce retention and growth were identified to be brain drain as a result of suboptimal living and working conditions or remuneration, the perception of an unsafe work environment, and a career pathway or commitment duration that does not fit the diaspora or expatriate staff. Conclusion: To tackle the barriers of pharmacy health workforce retention and growth, policy solutions will be required and efforts that can bring about long-term improvement should be prioritized. This is essential to achieve universal health coverage and the targets of the sustainable development goals for conflict affected areas, as well as to “leave no one behind”

Expression of KOC, S100P, mesothelin and MUC1 in pancreatico-biliary adenocarcinomas: development and utility of a potential diagnostic immunohistochemistry panel

&lt;b&gt;Background&lt;/b&gt; Pancreatico-biliary adenocarcinomas (PBA) have a poor prognosis. Diagnosis is usually achieved by imaging and/or endoscopy with confirmatory cytology. Cytological interpretation can be difficult especially in the setting of chronic pancreatitis/cholangitis. Immunohistochemistry (IHC) biomarkers could act as an adjunct to cytology to improve the diagnosis. Thus, we performed a meta-analysis and selected KOC, S100P, mesothelin and MUC1 for further validation in PBA resection specimens.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods&lt;/b&gt; Tissue microarrays containing tumour and normal cores in a ratio of 3:2, from 99 surgically resected PBA patients, were used for IHC. IHC was performed on an automated platform using antibodies against KOC, S100P, mesothelin and MUC1. Tissue cores were scored for staining intensity and proportion of tissue stained using a Histoscore method (range, 0–300). Sensitivity and specificity for individual biomarkers, as well as biomarker panels, were determined with different cut-offs for positivity and compared by summary receiver operating characteristic (ROC) curve.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; The expression of all four biomarkers was high in PBA versus normal ducts, with a mean Histoscore of 150 vs. 0.4 for KOC, 165 vs. 0.3 for S100P, 115 vs. 0.5 for mesothelin and 200 vs. 14 for MUC1 (p &lt; .0001 for all comparisons). Five cut-offs were carefully chosen for sensitivity/specificity analysis. Four of these cut-offs, namely 5%, 10% or 20% positive cells and Histoscore 20 were identified using ROC curve analysis and the fifth cut-off was moderate-strong staining intensity. Using 20% positive cells as a cut-off achieved higher sensitivity/specificity values: KOC 84%/100%; S100P 83%/100%; mesothelin 88%/92%; and MUC1 89%/63%. Analysis of a panel of KOC, S100P and mesothelin achieved 100% sensitivity and 99% specificity if at least 2 biomarkers were positive for 10% cut-off; and 100% sensitivity and specificity for 20% cut-off.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusion&lt;/b&gt; A biomarker panel of KOC, S100P and mesothelin with at least 2 biomarkers positive was found to be an optimum panel with both 10% and 20% cut-offs in resection specimens from patients with PBA.&lt;p&gt;&lt;/p&gt