Digital Imaging System for the Detection of Diabetic Retinopathy

COVID-19 since its appearance caused serious problems to the health sector due to the increase in infected and deceased people by directly affecting their respiratory system, making it a primordial disease that led all countries to fight this virus, generating that other diseases go to the background such as diabetes mellitus, which is a disease caused by the neglect of people's lifestyles, that has been increasing over time and that has no cure but can be prevented by controlling your blood glucose level, this disease causes diabetic retinopathy in people that with the advance of it can cause loss of sight. In addition, to detect its stage the ophthalmologist relies on his experience, occupying a lot of time and being prone to make mistakes about the patient. In view of this problem, in this article a digital image processing system was performed for the detection of diabetic retinopathy and classified according to the characteristics obtained from the features by analyzing the fundus of the eye automatically and determining the stage in which the patient is. Through the development of this system, it was determined that it works in the best way, visualizing an efficiency of 95.78% in the detection of exudates, and an efficiency of 97.14% in the detection of hemorrhages and blood vessels, resulting in a reliable and safe system to detect diabetic retinopathy early in diabetic patients. © 2023 IEEE.

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient's body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible.

Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique

To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes. This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud. K-Means is used for segmenting images into pixels and auto-encoders to remove noise (de-noising);the Random Forest regressor, tree-method based ensemble model is used for classification. The study obtained CT scan-images as datasets from ‘Kaggle' and classifies the images into ‘Non-Covid' and ‘Covid' categories. The software utilized is Jupyter-Notebook, in Python. PSNR with MSE evaluation metrics is done using Python. Through testing-and-training datasets, lower MSE score (‘0') and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images.

COVID-19 and androgenic status: testosterone or dihydrotestosterone have a pivotal role?

The aim of our study is analysis of the androgenic status including testosterone (T) and dihydrotestosterone (DHT) in men hospitalized with coronavirus disease 2019 (COVID-19) and them relationship with the course of the disease. This is a monocentric prospective study performed on 125 male patients hospitalized for COVID-19. We conducted hematological examination, blood biochemical profile, hemostasis analysis and hormonal examination (T and DHT levels) lung and chest computed tomography and also assessed outcomes of hospitalization. Low DHT serum level was found only in 18 patients (14.4%). Subjects with low DHT were significantly older compare to subjects with normal DHT. At the same time in patients with normal DHT white blood cells (WBC) count, neutrophils at admission were higher than in patients with low DHT. No correlation was observed between T and DHT serum blood levels. C-reactive protein (CRP) has a weak positive correlation of DHT serum blood concentration (r = 0.22;p = 0.016). The inverse pattern was obtained for T serum blood concentration (r = -0.285;p = 0.001). After divided all males according to T concentrations we conducted next correlation analysis for DHT and CRP in two different groups: with normal T levels and with low T levels. We found that in males with normal T DHT levels are not correlated with CRP (r = 0.095;p = 0.462). However, in males with low T DHT and CRP had weak positive correlation with r = 0.317 (p = 0.012). Higher DHT concentrations are associated with higher CRP levels, however correlation is weak and in patients with normal T is absent, that may indicate anti-inflammatory effect of T and possible proinflammatory effect of DHT.Copyright © 2023 The Author(s).

Creating Accessible Digital Images for Vision Impaired Audiences and Researchers

In 1992, the UN has designated December 3rd each year as the International Day of Persons with Disabilities, an effort that aims to promote the rights and well-being of people with disabilities in all aspects of life. Under the UN Convention of the Rights of Persons with Disabilities, the rights and wellbeing of people with disabilities should be universally accepted and protected. While a laudable goal, thirty years later, we find that these protections are not being met in the museum sector or in the scholarly journals that focus on that work. Curator: The Museum Journal has acknowledged our failure to live up to the spirit of accessibility and continues to work to rectify these deficits in the coming year.Discussion of image accessibility is common in the discourses around social media, and the fields of Computer Science and Human-Computer Interaction, the same cannot be said for the cultural sector. The UK ‘Heritage Access' report (VocalEyes, 2022) demonstrated that digital access to cultural institutions for vision impaired, D/deaf, and neurodivergent users remains very low. They find that information continues to be communicated in ways that are inaccessible, despite the rapidly increasing digital presence of cultural institutions that emerged during the Covid-19 pandemic outbreak. Similarly, the ‘State of accessible publishing in the UK' report (PAAG, 2022) revealed that only a small minority of publishing institutions implemented and integrated accessibility into their workflow and their organizations.

Application of Three-Dimensional Printers for Production of Personal Orbital Implants during Pandemics

Objective: The production of personalized prosthesis depends on human resources and involves a manufacturing process in which patients are involved individually in. As the world is experiencing the COVID-19 pandemic, less contact with the manufacturer is needed to stay safe. 3D printed prosthesis has reduced the need for human resource in the process, while allowing the patient to be completely removed from the design and manufacturing process. In this study an approach in which the patient is kept out of the manufacturing process was investigated. Material(s) and Method(s): The prosthesis model was created by using the image data obtained from the medical imaging devices. The outer part of the prosthesis was shaped with a developed image sampling system. The model was produced using three-dimensional printer. A cytotoxic analysis of the raw material used in the manufacturing process was performed. Result(s): The total production cost of the orbital implants was approximately about 8$. The cytotoxic analysis showed that layered manufacturing strategies could be used to develop implants and prostheses applicable to patients. Conclusion(s): COVID-19 underlined the importance of social distancing which is hard to apply during manufacturing of an eye prosthesis. The manual method results in an eye prosthesis which suits well after numerous trials. On the contrary, Digital Imaging and Communications in Medicine (DICOM) based eye prosthesis designation and manufacturing is not only rapid but also flawlessly fitting due to precise measurement during the manufacturing. Copyright © 2022, Nobelmedicus. All rights reserved.

Association of Chest X- ray findings with SARS-CoV-2 severity

Background: The COVID-19 first surfaced when cluster of pneumonia patients arose in Wuhan, Hubei Province, China. Although the current gold standard for COVID-19 diagnosis is reverse transcriptase-polymerase chain reaction (RT-PCR), chest x-ray (CXR) and computed tomography (CT) play a vital role in sickness diagnosis due to their limited sensitivity and availability. Aim: To evaluate retrospectively the role of CXR, the main radiological findings in it and its diagnostic accuracy in COVID-19 pneumonia. Methods: This is a cross sectional study involving 264 PCR positive COVID-19 patients with their clinical-epidemiological findings admitted at Ziauddin Hospital from May-July 2020. CXRs were taken as digital radiographs in our emergency department's isolation wards using the same portable X-ray device, according to local norms. CXRs were taken in two directions: antero-posterior (AP) and postero-anterior (PA). The hospitals' database had all of the images. To determine the number of radiological findings, multiple radiologists on duty completed an independent and retrospective examination of each CXR. In the event of disagreement, a mutual agreement was reached. SPSS version 20 was used for statistical analysis. Results: We were able to find 264 patients who met our criteria. With a mean age of 56.4214.89, the majority of individuals were determined to be males 189(71.6%) and females 75(28.4%). (Range of 16 to 87 years). 127 patients (48.1%) had severe illness symptoms and were admitted to the ICU, while the remaining 102(38.6%) had mild to moderate disease 35(13.3%). Diffuse (29.2%) and middle and lower co-existing distribution (25.8%) whereas just lower lobe (13.3%) were the most common predominance in severity. Peripheral involvement was also seen in (8.7%) cases. Conclusion: Both lungs are equally affected with the disease having the consolidation and opacifications while the effusion is the major complication in the severe cases. Diffuse involvement of the lung lobes is seen in the study followed by the middle and lower lobe involvement.

Quantitative benefit of centralised quality assurance for the implementation of Lung SABR

Purpose or Objective SABR has become standard of care for early stage lung cancer where surgery is contraindicated. As a result of the COVID- 19 pandemic access to surgery was limited and demand for SABR as primary treatment has increased. A national program to implement lung SABR in all radiotherapy centres was commissioned and an associated QA program was developed. One of its components was a planning benchmark case to ensure optimal planning of target volumes whilst sparing organs at risk following the SABR Consortium Guidelines. Results of the benefits of the QA process are presented here. Materials and Methods A dual-lesion planning benchmark DICOM dataset was circulated amongst 24 participating centres, including a planning CT and a structure set. Centres had to plan the lesions to 55Gy in 5# and meet dose constraints, coverage and conformity criteria outlined in the Guidelines. All plans were reviewed on Velocity v4.1 (Varian Medical Systems) and PTV coverage, dose distribution, plan conformity and OAR dose constraints were assessed. Prescription Dose Spillage (PDS) was used to define conformity in the high dose area as Body V100(cc)/PTV V100(cc) and Modified Gradient Index (MGI) was defined for conformity in low dose area as Body V50(cc)/PTV V100(cc). The Mann Whitney test was used to evaluate differences in conformity across plans, with statistical significance set at 5%. Results Mean V100% for first submissions for lesions 1 and 2 were 97.26% (S.D. 1.86) and 98.19 % (S.D.1.61), respectively. All mandatory OARs were well within tolerance. The largest variation across centres was plan conformity, which is summarised in Table 1. (Table Presented) Ten plans failed their first attempt and centres were asked to resubmit following detailed feedback. Mean PDS for these plans changed from 1.19 (S.D. 0.09) to 1.13 (S.D. 0.05), although this was not statistically significant (p=0.12). Mean MGI was significantly improved on resubmission, decreasing from 7.08 (S.D. 0.8) to 6.16 (S.D.0.84), (p=0.03). Figure 1 shows increase in consistency and improvement in conformity across centres after resubmission. On completion of the QA process, the final set of accepted plans had improved conformity indices from initial PDS and MGI, however these were not statistically significant (p=0.31 and p=0.13, respectively). (Figure Presented) Conclusion A national QA program for lung SABR is critical for the safe implementation of this technique and to ensure standards are consistently high across multiple centres. The planning benchmark has highlighted differences in plan conformity and technique, in particular for MGI, however feedback within the QA process has allowed for increased consistency across departments through improved quality.

Precariousness and Hope: Digital Everyday Life of the Undocumented Migrants Explored Through Collaborative Photography

The article explores the digital everyday life of recently or currently undocumented migrants in times of Covid-19 in Finland. It is based on an empirical case study on a collaborative photographic exhibition and workshop including visual images, diaries, interviews, and discussions. The analysis explores the ways in which a photography exhibition and a workshop may depict meaningful moments in digital everyday life as well as open up an understanding of the various vulnerabilities that emerge in the life of the undocumented, as expressed by themselves. The study demonstrates the fundamental importance of communication rights for people in precarious life situations, expressed by themselves in visual images. The insight produced multidimensionally in images, discussions, and interviews illustrate how digital media environment exposes to coerced visibility and requires constant struggle for communicative rights. These struggles take place on the material infrastructural level of devices, chargers, and access, but also on the level of self-expression and connection on social media platforms. Finally, the article discusses the emancipatory potential of a collaborative exhibition and workshop as a way to encounter and deal with increasingly vulnerable life situations. It points out the relevance of collaborative work as a research method, in providing knowledge from experience as well as space of recognition.

Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images

Due to steady improvements in defensive systems, malware developers are turning their attention to mechanisms for cloaking attacks as long as possible. A recent trend exploits techniques like Invoke-PSImage, which allows embedding a malicious script within an innocent-looking image, for example, to smuggle data into compromised devices. To address such a class of emerging threats, new mechanisms are needed, since standard tools fail in their detection or offer poor performance. To this aim, this work introduces Mavis, an efficient and highly accurate method for detecting hidden payloads, retrieving the embedded information, and estimating its size. Experimental results collected by considering real-world malicious PowerShell scripts showcase that Mavis can detect attacks with a high accuracy (100%) while keeping the rate of false positives and false negatives very low (0.01% and 0%, respectively). The proposed approach outperforms other solutions available in the literature or commercially through “as a service” model.

Deep extracted features to support Content-Based Image Retrieval systems in the diagnosis of Covid-19 and Interstitial diseases

Purpose Coronavirus disease 2019 (Covid-19) may cause dyspnoea, whereas Interstitial Lung Diseases (ILD) may lead to the loss of breathing ability. In both cases, chest X-Ray is typically one of the initial studies to identify the diseases as they are simple and widely available scans, especially in under-development countries. However, the assessment of such images is subject to a high intraobserver variability because it depends on the reader's expertise, which may expose patients to unnecessary investigations and delay the diagnosis. Content-based Image Retrieval (CBIR) tools can bridge such a variability gap by recovering similar past cases to a given reference image from an annotated database and acting as a differential diagnosis CAD-IA system [1]. The main CBIR components are the feature extraction and the query formulation. The former represents the compared images into a space where a distance function can be applied, and the latter relies on the k-Nearest Neighbor (kNN) method to fetch the most similar cases by their distances to the query reference. In this study, we examine the quality of Covid-19 and ILD deep features extracted by a modified VGG-19 Convolutional Neural Network (CNN) [2] following the perspective of the Voronoi frontiers induced by kNN, which is at the core of the CBIR query formulation component. Methods We curated a dataset of annotated chest X-Rays from our PACS/HIS systems following a retrospective study approved by the institutional board. A set of 185 Covid-19 and 307 ILD cases from different patients was selected, being Covid-19 cases confirmed by RT-PCR tests and ILD images included after the analysis of two thoracic radiologists. We also added 381 images of ''Healthy'' lungs (without Covid-19 or ILD) to enrich the dataset. The resulting set includes 873 X-Rays (mean age 60.49 ± 15.21, and 52.58% females). We cast the DICOM images into PNG files by using the Hounsfield conversion and a 256 Gy-scale window. The files were scaled to 224 × 224 images and fed into a modified VGG-19 version we implemented [2]. Our version includes the stack of convolutional layers and five new layers after the block5-pool, namely: GlobalAveragePooling2D, BatchNormalization, a dense layer with 128 units and ReLU, a dropout layer with ratio 0.6, and a final dense layer with three neurons for classification. The Adam function was used to minimize cross-entropy, whereas batch size and epochs were set to 36 and 100, respectively. All layers start with ImageNet weights that were frozen until block4-pool so that only the remaining layers were updated. We fed the CNN with images and labels (i.e., {Covid-19, ILD, Healthy}) so that our feature extraction procedure was oriented towards those classes rather than autoencoders. The flattened outputs of the last max-pooling layer were collected as feature vectors of dimensionality d = 512. We clean and preprocess those vectors before applying the kNN-based search mechanism. First, we scaled the dimensions into the [0,1] interval. Then, we perform a reduction by using the Principal Component Analysis (PCA). The number of reduced dimensions was determined by the intrinsic dimensionality of the features, estimated by the mean (l) and standard deviation (r) of the pairwise distance distribution as the value μ2/2.σ2. Finally, the reduced vectors were also scaled into the [0,1] interval. The experiments were performed in a 3854 core 1.5 GHz GPU NVidia TitanX 12 GB RAM, and an Intel(R) Xeon(R) CPU 2.00 GHz, 96 GB RAM. The code was implemented under Tensorflow (v.2.1.0) and R (v4.1.2). Results We used two Principal Components to reduce the vectors according to the estimated intrinsic dimensionality. Figure 1 shows the Voronoi frontiers induced by kNN with a smooth separation between the three classes, which creates a search space in which CBIR searches are expected to be accurate. We quantify such behavior through a kNNbased classification on the two experimental settings (i.e., 10-folds and Holdout) by using the scaled features with and without dimensionality reductio . able 1 summarizes the results with the following findings: • The accuracy measures increased with the neighborhood (k = 1 vs. k = 5) in all experimental cases, • Covid-19 cases were more difficult to label than ILD according to F1 and RC, • The kNN hit-ratio (TP) for Covid-19 was comparable to the very first diagnosis stored into the PACS/HIS systems by readers on duty regarding the Holdout cases (readers' mean ∗ 63% vs. KNN ∼ 59%), • Searches over the reduced data were ∼ 4 9 faster, and • While dimensionality reduction was just as suitable as nonreduced data in the 10-folds evaluation, it expressively enhanced the kNN performance for the Holdout test (e.g., 0.68 vs. 0.82, k = 1 and F1). This result shows the side-effects of searching high-dimensional spaces with kNN (the ''curse of dimensionality''), which requires pre-processing the vectors or defining other query criteria to browse the data. Conclusion This study has discussed feature extraction for Covid-19 and ILD images from the perspective of kNN queries, the query formulation component within CBIR systems. Although we used cross-validation and one external batch to mitigate overfitting, a practical limitation was the size of the CNN training set. Still, our approach showed promising results in the extraction of suitable features for CBIR environments.

PREVALENCE OF MUSCULOSKELETAL PAIN AND DISCOMFORT DUE TO ONLINE TEACHING AND LEARNING METHODS DURING LOCKDOWN IN STUDENTS AND TEACHERS: OUTCOMES OF THE NEW NORMAL

Purpose: The sudden lockdown due to COVID-19 in India led to closure of schools and colleges. This resulted in an increased usage of online mode of study, with a more sedentary lifestyle. The survey study aimed to analyze the prevalence of any musculoskeletal problem in students and teachers due to the same. Methodology: A Google Form was distributed by a snowball sampling technique using various social media platforms. A total of 715 responses were received. Results: Maximum respondents were in the age range of 18-25 years. Eighty eight percent of participants in the survey were involved in the online mode of education, with 60.8% experiencing some form of musculoskeletal pain or discomfort;71% of people believed that the cause of pain was online working. Neck pain (51.3%) followed by low back pain (33.4%) and headaches (29.8%) were commonly reported. University teachers reported maximum pain followed by university students, school teachers and school students. Of all the respondents, 60.8% people admitted to adopting awkward postures while at work, whereas only 27.6% of them exercised to relieve pain and discomfort. Conclusion: There is an urgent need to develop appropriate intervention strategies for people involved in sedentary online work to prevent the occurrence of musculoskeletal pain and discomfort. Physical therapy can play a major role in managing this lifestyle hazard.

The Relationship between Digital Marketing, Customer Engagement, and Purchase Intention via OTT Platforms

The Internet and digital technologies have grown exponentially and have become a part of billions of people’s daily lives around the world. Over-the-top (OTT) services are delivered directly over the Internet, providing customers access to films and TV series. In India, there has been a tremendous surge in the consumption of video material on OTT platforms. The significant growth in the digital marketing application in online businesses and its ability to engage customers has enhanced its importance. The emergence of the Internet and digital technologies has provided a modernized setting and a fertile ground for business organizations to reach millions of customers at affordable prices with effective and customized promotion content. The present study aims to investigate the relationship between digital media marketing (DMM), consumer engagement, brand image, and OTT platform purchase intention in the Indian context. The researchers will also investigate the mediating role of consumer engagement and brand image in mediating the relationship between digital marketing practices and OTT platform purchase intention. In a survey of 417 Indian consumers, it was found that there was no direct effect of DMM on the purchase intention of OTT platforms. In addition, there is a strong indirect effect through brand image and consumer engagement, supporting the hypothesis that brand image and consumer engagement mediate the relationship between DMM practices and the purchase intention of OTT platforms. Some of the managerial implications, limitations, and scope of future research are also presented in the study.

Inspiring Technologies for Digital Inclusivity

The article entitled Enhancing Moon Crescent Visibility using Contrast-Limited Adaptive Histogram Equalization and Bilateral Filtering Techniques has automated the determination of the religious festivals date based on the analysis of digital images. The authors have used underwater images that contain coral reef images as their case study. Since underwater images are usually suffering from distortion and light attenuation, an efficient edge detection technique is very important. The paper entitled Detection of Monocrystalline Silicon Wafer Defects using Deep Transfer Learning uses deep learning technique to detect defects in monocrystalline silicon wafer for industrial production.

Editorial for the Special Issue on “Machine Learning in Healthcare and Biomedical Application”

The authors demonstrated the reliability of the use of cluster analysis in discovering intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonism patients, and, more importantly, showed how to transform a ML approach into a decision support tool for use in a clinical setting [5]. The proposed method [7] overcomes the problems of the time-consuming conventional approaches used for the identification and quantification of malaria parasitemia thanks to transfer learning, which is applied on digital images, with a Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD). [...]demand to increase the interpretability of ML findings has emerged [2,4,5], as the recent growing interest of the scientific community in Explainable Artificial Intelligence (XAI) demonstrates [8].

Secure Watermarking Technique for Color Images using Aadhar Number, DWT and SVD

In the current situation the use of electronic devices like mobile, laptop, computer, ear phone etc has been increased in the daily routine activities specially in COVID-19. The education sector and IT industries are almost fully working in online mode in the today environment by sharing of their content in the form of digital data or multimedia data. The digital data is increasing in large scale every day and due to this increasing large amount of data, new research areas has been come introduced like bigdata, data analytic, data science etc to manage the digital data in the better or proper way. But with this the other property of digital data like its security, copyright protection, Copy control, Content authentication, Integrity verification etc. are also major concerns. The digital data or multimedia data basically includes text, images, audio, video, software etc. of individuals / organizations. Each persons or organizations are sharing his/her digital data like videos, images, messages, etc. through social media (i.e., Say namaste, Telegram, Snapchat, Instagram, WhatsApp, Facebook, etc.) to other persons or organization without any authentications. This general activity of persons has been increased in the todays life and some persons are observing them and doing the fraud with them or misusing their digital data without his awareness. Sometimes it becomes major problems in term of legal issues. To ensure the authentication of digital data (photos), this paper proposed secure watermarking technique for color images using Aadhar number, DWT and SVD methodologies. The proposed methodology is best to protect from fraud or misuse from all type of color images shared in the public domain or globe. The experimental results are shown in different form which shows this technique is more secure and very useful for society when they are sharing their family photos in the globe.

EMF Comparison of the Effects of a Bromelain and Collagenase-based Enzymatic Agent on Inflammation and Eschar Debridement in a Partial Thickness Porcine Model

Study Objectives: Thousands of people are admitted to the hospital each year with burns. Many such burns are deep partial or full-thickness and require some form of debridement to allow burn depth assessment, enhance healing and reduce scarring. At present, most deep burns are treated surgically, with a minority of patients treated with the only commercially available enzymatic debridement agent in the US, collagenase. However, there are few if any studies that demonstrate the efficacy of a collagenase-based enzymatic agent. In contrast, based on extensive preclinical and clinical data, a bromelain based enzymatic agent is now approved in Europe for treating deep burns. This agent is derived from the stems of pineapples and enriched with bromelain. No study has directly compared the debriding efficacy of a collagenase-based versus bromelain-based enzymatic agent. In this study, we hypothesize that a bromelain-based enzymatic agent will be more effective at debriding partial thickness burns compared with the collagenase based agent. We further hypothesize that the bromelain based agent will completely debride the burns after a single 4-hour application while the collagenase-based agent will be less effective and will require multiple daily applications. Finally, we hypothesize that burns debrided with the bromelain-based agent will heal faster than those debrided with the collagenase-based agent. Supportive results may lead to a major advance in how we provide therapy to hundreds of thousands of burn victims each year. Methods: This study will be conducted in an accredited medical center with 2 female domestic pigs. Our method of creating burns results in standardized and reproducible partial thickness burns based on a previously validated model. Burn wounds will be followed for a period of 28 days. Digital imaging and full thickness skin biopsies will be obtained at 4 hours, and at 10, 14, 18 and 28 days. Biopsies will be subjected to blinded histomorphometric analysis. Results: None currently available. To date we are awaiting the bromelain and collagenase product which is being shipped internationally to our location (delayed due to COVID19). Conclusion: Although we cannot draw any conclusions presently, preliminary data from the mentor’s lab concluded that a single 4-hour application of a bromelain-based enzymatic agent completely debride deep partial thickness burns in a validated porcine model. If the bromelain-based enzymatic agent shows to be more efficacious than collagenase, it may replace both collagenase mediated and surgical debridement. Ultimately, this could lead to fewer consequences from the injury to the patient, better aesthetic outcomes at the site of injury, and potentially a decreased financial burden.

Crypto Collectibles, Museum Funding and OpenGLAM: Challenges, Opportunities and the Potential of Non-Fungible Tokens (NFTs)

Non-fungible tokens (NFTs) make it technically possible for digital assets to be owned and traded, introducing the concept of scarcity in the digital realm for the first time. Resulting from this technical development, this paper asks the question, do they provide an opportunity for fundraising for galleries, libraries, archives and museums (GLAM), by selling ownership of digital copies of their collections? Although NFTs in their current format were first invented in 2017 as a means for game players to trade virtual goods, they reached the mainstream in 2021, when the auction house Christie’s held their first-ever sale exclusively for an NFT of a digital image, that was eventually sold for a record 69 million USD. The potential of NFTs to generate significant revenue for artists and museums by selling effectively a cryptographically signed copy of a digital image (similar to real-world limited editions, which are signed and numbered copies of a given artwork), has sparked the interest of the financially deprived museum and heritage sector with world-renowned institutions such as the Uffizi Gallery and the Hermitage Museum, having already employed NFTs in order to raise funds. Concerns surrounding the environmental impact of blockchain technology and the rise of malicious projects, exploiting previously digitised heritage content made available through OpenGLAM licensing, have attracted criticism over the speculative use of the technology. In this paper, we present the current state of affairs in relation to NFTs and the cultural heritage sector, identifying challenges, whilst highlighting opportunities that they create for revenue generation, in order to help address the ever-increasing financial challenges of galleries and museums.

Diagnosing Covid-19 using Image Processing and Machine Learning

Image Processing has been a fond field for giving elaborated visual data to process the image data to simplify it for human for illustration for machine concept. With image processing you can have better solution for digital images. Training a machine to do something by providing it with certain training data is known as machine learning which may include here image processing. Machine learning have architectures, loss function, models and many other approaches that is used to determine and provide better image processing It is usually applied for image enhancement, restoration and morphing (inserting one's style of painting on an image). The objective is to provide a conceptual transfer learning framework by using image classification with the help of learning models, to support the detection of COVID-19 imaging modalities included CT scan and X-Ray. We will be going to make a custom Dataset and the Data Loader in the PyTorch. Then will train a ResNet-18 model for Image Classification performance. In end we will create a Convolutional Neural Networks and then we will be able to train it to analyze Chest X-Ray scans with honestly high accuracy. We will train the model using the ResNet-18 till the accuracy will be 0.95 or 95% in condition till then will stop the training where performance satisfied. So finally we given the 6 images and created a model and took 6 images from test set and put it in the training model and do the prediction and set the accuracy to the limit. Until the accuracy is not fulfilled the training will happen in the work.