Search

Low-income and middle-income countries are struggling with a growing epidemic of non-communicable diseases. To achieve the Sustainable Development Goals, their healthcare systems need to be strengthened and redesigned. The Starfield 4Cs of primary care-first-contact access, care coordination, comprehensiveness and continuity-offer practical, high-quality design options for non-communicable disease care in low-income and middle-income countries. We describe an integrated non-communicable disease intervention in rural Nepal using the 4C principles. We present 18 months of retrospective assessment of implementation for patients with type II diabetes, hypertension and chronic obstructive pulmonary disease. We assessed feasibility using facility and community follow-up as proxy measures, and assessed effectiveness using singular 'at-goal' metrics for each condition. The median follow-up for diabetes, hypertension and chronic obstructive pulmonary disease was 6, 6 and 7 facility visits, and 10, 10 and 11 community visits, respectively (0.9 monthly patient touch-points). Loss-to-follow-up rates were 16%, 19% and 22%, respectively. The median time between visits was approximately 2 months for facility visits and 1 month for community visits. 'At-goal' status for patients with chronic obstructive pulmonary disease improved from baseline to endline (p=0.01), but not for diabetes or hypertension. This is the first integrated non-communicable disease intervention, based on the 4C principles, in Nepal. Our experience demonstrates high rates of facility and community follow-up, with comparatively low lost-to-follow-up rates. The mixed effectiveness results suggest that while this intervention may be valuable, it may not be sufficient to impact outcomes. To achieve the Sustainable Development Goals, further implementation research is urgently needed to determine how to optimise non-communicable disease interventions.

An organocatalytic Markovnikov-type bromo-aminoxidation of styrene derivatives was achieved by using in-situ-generated bromine and N-oxyl radicals, and the desired products were obtained in moderate to good yields. The bromine radical was generated from phenyliodine(III) bis(3-bromopropionate) through consecutive decarboxylation and deethylenation reactions via an organocatalytic SET process. Phenyliodine(III) bis(3-bromopropionate) also served as the oxidant for the generation of the N-oxyl radicals, while the N-oxyl radical was also crucial for the generation of the key β-enaminyl radical intermediate that was required for the SET process. [ABSTRACT FROM AUTHOR]

An image caption generator is essential for social media enthusiasts or visually impaired individuals. It can be used as a plugin in popular social media platforms to recommend suitable captions or to assist visually impaired people in comprehending the image content on the web, thereby eliminating ambiguity in image meaning and ensuring accurate knowledge acquisition. This research describes an image caption generator that utilizes a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model to generate natural language descriptions of images. The CNN was employed to extract features from the input image, which were then fed into the LSTM to generate the corresponding caption. The model is trained on a large dataset of image-caption pairs, using a combination of supervised and reinforcement learning techniques. The model's performance is evaluated using several metrics, and the results demonstrate that the proposed CNN LSTM model outperforms existing state-of-the-art approaches in generating accurate and diverse image captions. This model has the potential to be used in various applications, including image retrieval, content-based image search, and assisting visually impaired individuals to understanding their surroundings. It also discusses about the structure and functions of the various neural networks involved. [ABSTRACT FROM AUTHOR]