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With 50% of Australians having chronic disease, health consumer views are an important barometer of the "health" of the healthcare system for system improvement and sustainability.

To describe the views of Australian health consumers with and without chronic conditions when accessing healthcare.

A survey of a representative sample of 1,024 Australians aged over 18 years, distributed electronically and incorporating standardised questions and questions co-designed with consumers.

Respondents were aged 18-88 years (432 males, 592 females) representing all states and territories, and rural and urban locations. General practices (84.6%), pharmacies (62.1%) and public hospitals (32.9%) were the most frequently accessed services. Most care was received through face-to-face consultations; only 16.5% of respondents accessed care via telehealth. The 605 (59.0%) respondents with chronic conditions were less likely to have private health insurance (50.3% vs 57.9%), more likely to skip doses of prescribed medicines (53.6% vs 28.6%), and miss appointments with doctors (15.3% vs 10.1%) or dentists (52.8% vs 40.4%) because of cost. Among 480 respondents without private health insurance unaffordability (73.5%) or poor value for money (35.3%) were the most common reasons. Most respondents (87.7%) were confident that they would receive high-quality and safe care. However, only 57% of people with chronic conditions were confident that they could afford needed healthcare compared with 71.3% without.

Health consumers, especially those with chronic conditions, identified significant cost barriers to access of healthcare. Equitable access to healthcare must be at the centre of health reform. This article is protected by copyright. All rights reserved.

Health consumers, especially those with chronic conditions, identified significant cost barriers to access of healthcare. Equitable access to healthcare must be at the centre of health reform. This article is protected by copyright. All rights reserved.Systemic medications categorized as diphenylhydantoin, calcineurin inhibitor and calcium channel blocker may have effects on the oral cavity by modifying the inflammatory and immune response and causing undesired tissue proliferative reactions. Calcineurin inhibitors are medications commonly used for long periods in patients undergoing allogeneic hematopoietic stem cell transplant (HSCT) and solid organ transplantation. Medication-related fibrovascular hyperplasia (MRFH) is an extra gingival hyperplastic nodular growth associated with medications use. This study reports five cases of pediatric patients (6 to 12-years-old) diagnosed with Fanconi anemia (FA) after HSCT who presented similar oral mucosal lesions associated with the use of cyclosporine, phenobarbital and amlodipine. After excision of the lesions, histopathological analysis described them as pyogenic granuloma (PG). AZD9668 As the aetiology of the lesions manifested by the patients was associated with the use of medications, the final diagnosis was MRFH. Despite the clinical and histopathological similarity between PG and MRFH, it is fundamental to know the aetiological agent for achieving definitive diagnosis and correct management. Considering the etiologic agent (medication) and histopathological findings, it is suggested that the most appropriate term for this manifestation should be "medication-related fibrovascular hyperplasia". The correct nomenclature related to extra gingival hyperplastic lesions identified in patients on medications with potential to induce hyperplastic reactions should be adopted to facilitate scientific communication and improve the treatment.The development of metal-free syntheses toward 1,2,3-triazoles has been a burgeoning research area throughout the past decade. Despite the numerous advances, the scarceness of methods for the preparation of 1,5-disubstituted 1,2,3-triazoles from readily available substrates remained a challenge that was addressed by our group in 2016. A metal-free three-component reaction, which we have dubbed the triazolization reaction, was established for the rapid synthesis of 1,5-disubstituted, fully functionalized and NH-1,2,3-triazoles. This novel approach stands out because it utilizes widely available starting materials, namely primary amines and enolizable ketones. Furthermore, the broad substrate scope is a major advantage, and was further expanded by the number of modified protocols that have been reported. Triazolization products have successfully found utility as intermediates in various synthetic transformations, and were the subject of a few interesting biological activity studies.Automatic detection of maculopathy disease is a very important step to achieve high-accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.