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BackgroundStudies suggest that certain black and Asian minority ethnic groups experience poorer outcomes from COVID-19, but these studies have not provided insight into potential reasons for this. We hypothesised that outcomes would be poorer for those of South Asian ethnicity hospitalised from a confirmed SARS-CoV-2 infection, once confounding factors, health-seeking behaviours and community demographics were considered, and that this might reflect a more aggressive disease course in these patients.MethodsPatients with confirmed SARS-CoV-2 infection requiring admission to University Hospitals Birmingham NHS Foundation Trust (UHB) in Birmingham, UK between 10 March 2020 and 17 April 2020 were included. Standardised admission ratio (SAR) and standardised mortality ratio (SMR) were calculated using observed COVID-19 admissions/deaths and 2011 census data. Adjusted HR for mortality was estimated using Cox proportional hazard model adjusting and propensity score matching.ResultsAll patients admitted to UHB with COVID-19 during the study period were included (2217 in total). 58% were male, 69.5% were white and the majority (80.2%) had comorbidities. 18.5% were of South Asian ethnicity, and these patients were more likely to be younger and have no comorbidities, but twice the prevalence of diabetes than white patients. SAR and SMR suggested more admissions and deaths in South Asian patients than would be predicted and they were more likely to present with severe disease despite no delay in presentation since symptom onset. South Asian ethnicity was associated with an increased risk of death, both by Cox regression (HR 1.4, 95% CI 1.2 to 1.8), after adjusting for age, sex, deprivation and comorbidities, and by propensity score matching, matching for the same factors but categorising ethnicity into South Asian or not (HR 1.3, 95% CI 1.0 to 1.6).ConclusionsThose of South Asian ethnicity appear at risk of worse COVID-19 outcomes. Further studies need to establish the underlying mechanistic pathways.

Aims: To study the prevalence of microvascular complications and renal changes associated with cystic fibrosis-related diabetes (CFRD). Methods: This retrospective cohort study was conducted at the West Midlands Adult Cystic Fibrosis centre, United Kingdom. Data regarding age, sex, microalbuminuria, retinopathy neuropathy, and biochemical results were collected for all people with CFRD who had an annual review from 1 January 2018 to 31 December 2018 at the centre. Descriptive statistics were analysed using STATAv15.1. Results: A total of 189 patients were included, of which 56.6% were male and median age (interquartile range) was 33 (27–39) years; 79.4% (150/189) had their annual review in 2018. Those with a biochemically impaired renal function numbered 7.2% (13/180) and 22.7% (32/141) had microalbuminuria; 17.2% (10/58) had diabetes related retinopathy. No one in our cohort had diabetic ulcers; however, 10.3% (13/126) had absent foot pulses. Conclusion: We found a higher prevalence of microalbuminuria compared with retinopathy in a large cohort of cystic fibrosis adults. This study demonstrates the need for regular specialist follow-up to facilitate early identification of such complications and a long-term prospective cohort to understand underlying mechanisms.

Existing disease discovery in papaya leaves is most significant in achieving yield and profitability stability in the tropics but has proven difficult in the presence of deficiencies in manual exploration and tailored crop models in crop-AI systems. Therefore, this study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves. For real-world deployment in scarce-resource farming contexts, PapayaNet adopts batch norm and hierarchical attention steps in five convolution stages and accelerates both computational celerity and discriminability. Trained on 6618 manually annotated orchard images sourced from orchards in Bangladesh at a very high resolution, it has a 98.79% classification accuracy, all of which was realized using 483,926 parameters and an average infer time of 0.01 s, which is significantly better when evaluated using EfficientNetB6, DenseNet121, and VGG16. XAI methods, including Grad-CAM and LIME, showed model decisions towards the biologically informative parts of the leaf, thus boosting interpretability and user confidence. Systematic ablation analysis also confirmed the importance of distributed attention in ensuring robust generalization towards visually similar disease classes. An in-browser diagnostic portal deployed using Gradio provides intra-browser predictive deployment and interpretability overlay in real time, thus inviting field practicability. Given its low-latency inference and minimal computational footprint, PapayaNet is well-suited for integration into edge devices and drone platforms, offering a scalable solution for real-time in-situ crop health monitoring. This study advances the field of precision agriculture by delivering a crop-specialized, explainable, and deployable AI system for sustainable management of papaya diseases.

Documents are inevitably relevant in our day-to-day life. Forgery of document could have severe repercussions including financial losses, misjudgments, damage of respect, goodwill, etc. Hence, documents need to be secured from threats such as counterfeiting, falsification, tempering etc., and there should be an easy way of verification about the originality of documents. There are several existing methods for ensuring authenticity and integrity with modern technologies like the block chain, Digital Signature, etc. Most of the methods are not appropriate for public usage instantly due to their intricacy, excessive costing, and implementation problem for which the easy approach of verification is yet not available for mass people. In this situation, a method of document verification has been proposed in this paper which intends to provide (i) authenticity, (ii) integrity, (iii) availability, and (iv) non-repudiation. The proposed method will serve the purpose of mass people as it has no licensing fee, easily implementable and effortlessly useable for both electronic and printed documents. It is worth to mention that the proposed method will provide a mechanism to confirm the originality of the document using only a Smartphone in no time.

Background A. fumigatus has been associated with a wide spectrum of allergic disorders such as ABPA or SAFS. It is poorly understood what allergens in particular are being expressed during fungal invasion and which are responsible for stimulation of immune responses. Study of the dynamics of allergen production by fungi may lead to insights into how allergens are presented to the immune system. Methods Expression of 17 A. fumigatus allergen genes was examined in response to various culture conditions and stimuli as well as in the presence of macrophages in order to mimic conditions encountered in the lung. Results Expression of 14/17 allergen genes was strongly induced by oxidative stress caused by hydrogen peroxide (Asp f 1, -2, -4, -5, -6, -7, -8, -10, -13, -17 and -18, all >10-fold and Asp f 11, -12, and -22, 5-10-fold) and 16/17 allergen genes were repressed in the presence of cAMP. The 4 protease allergen genes (Asp f -5, -10, -13 and -18) were expressed at very low levels compared to the comparator ( β -tubulin) under all other conditions examined. Mild heat shock, anoxia, lipid and presence of macrophages did not result in coordinated changes in allergen gene expression. Growth on lipid as sole carbon source contributed to the moderate induction of most of the allergen genes. Heat shock (37°C > 42°C) caused moderate repression in 11/17 genes (Asp f 1, -2, -4, -5, -6, -9, -10, -13, -17, -18 and -23) (2- to 9-fold), which was mostly evident for Asp f 1 and -9 (~9-fold). Anaerobic stress led to moderate induction of 13/17 genes (1.1 to 4-fold) with one, Asp f 8 induced over 10-fold when grown under mineral oil. Complex changes were seen in gene expression during co-culture of A. fumigatus with macrophages. Conclusions Remarkable coordination of allergen gene expression in response to a specific condition (oxidative stress or the presence of cAMP) has been observed, implying that a single biological stimulus may play a role in allergen gene regulation. Interdiction of a putative allergen expression induction signalling pathway might provide a novel therapy for treatment of fungal allergy.

Covid-19 has been marked as a pandemic world-wide caused by the SARS-CoV-2 virus. Different studies are being conducted with a view to preventing and lessening the infections caused by covid-19. In future, many other wind-borne diseases may also appear and even emerge as “pandemic”. To prevent this, various measures should be an integral part of our daily life such as wearing face masks. It is tough to manually ensure individuals safety. The goal of this paper is to automate the process of contactless surveillance so that substantial prevention can be ensured against all kinds of wind-borne diseases. For automating the process, real time analysis and object detection is a must for which deep learning is the most efficient approach. In this paper, a deep learning model is used to check if a person takes any preventive measures. In our experimental analysis, we considered real time face mask detection as a preventive measure. We proposed a new face mask detection dataset. The accuracy of detecting a face mask along with the identity of a person achieved accuracy of 99.5%. The proposed model decreases time consumption as no human intervention is needed to check an individual person. This model helps to decrease infection risk by using a contactless automation system.

Cloud computing is readily being adopted by enterprises due to its following benefits: ability to provide better service to customers, improved flexibility, lower barrier to entry for an enterprise, lower maintenance cost on IT service, availability etc. However, the interaction between cloud service provider and customer is not well-defined yet. Understanding of the service offered while approaching cloud computing paradigm and also understanding of the required actions during the period of receiving a cloud service e.g. provision of new resources, scaling up/down, billing, etc. remains a concern for the enterprises. This paper proposes a segregated interaction model to manage the receiving of a cloud service in a hierarchical way.

BackgroundThe West Midlands Adult Cystic Fibrosis (CF) Centre based at Birmingham Heartlands Hospital provides care for adults with CF in the West Midlands. People with CF are prone to pulmonary exacerbations, which often require inpatient admission for intravenous antibiotics. We observed that the admission process was efficient during working hours (9:00–17:00, Monday–Friday) when the CF team are routinely available, but out-of-working hours, there were delays in these patients being clerked and receiving their first antibiotic dose. We were concerned that this was resulting in quality and potential safety issues by causing delays in starting treatment and prolonging hospital inpatient stays. We therefore undertook a quality improvement project (QIP) aimed at addressing these issues. An initial survey showed median time to clerk of 5 hours, with 60% of patients missing their first dose of antibiotics and mean length of stay of 16 days.MethodsWe applied the Plan-Do-Study-Act (PDSA) cycle approach, with the first PDSA cycle involving raising awareness of the issue through education to doctors, nurses and patients.ResultsThis led to a reduction of median time to clerk from 5 to 2 hours with 23% of patients missing their first antibiotic dose and mean length of stay reducing to 14 days. The second cycle involved introducing an admissions checklist and displaying education posters around the hospital, resulting in median time to clerk remaining at 2 hours but only 20% of patients missing their first antibiotic dose and the mean length of stay remaining at 14 days.ConclusionThis QIP has improved the out-of-hours admissions process for adults with CF in our centre. We plan to review the longer term effects of the project including sustainability, effects on clinical outcomes and patient satisfaction.

In this paper, a wavelet-based progressive image transmission (PIT) scheme is proposed. Here a combined method is proposed to reduce the image browsing time. The proposed scheme transforms a digital image from spatial domain into frequency domain by using discrete wavelet transformation. For wavelet transformation phase we have used Haar wavelet transformation. But it is computationally rigorous. Using concurrent computing we have significantly reduced computation time overhead as well as transmission time. According to the experimental results, the proposed scheme provides the accuracy of reconstructed image and the image browsing time reduces significantly.