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PurposeThis study aims to estimate the overall SARS-CoV-2 seroprevalence and evaluate the accuracy of an antibody rapid test compared to a reference serological assay during a COVID-19 outbreak in a prison complex housing over 13,000 prisoners in Brasília.Design/methodology/approachThe authors obtained a randomized, stratified representative sample of each prison unit and conducted a repeated serosurvey among prisoners between June and July 2020, using a lateral-flow immunochromatographic assay (LFIA). Samples were also retested using a chemiluminescence enzyme immunoassay (CLIA) to compare SARS-CoV-2 seroprevalence and 21-days incidence, as well as to estimate the overall infection fatality rate (IFR) and determine the diagnostic accuracy of the LFIA test.FindingsThis study identified 485 eligible individuals and enrolled 460 participants. Baseline and 21-days follow-up seroprevalence were estimated at 52.0% (95% CI 44.9–59.0) and 56.7% (95% CI 48.2–65.3) with LFIA;and 80.7% (95% CI 74.1–87.3) and 81.1% (95% CI 74.4–87.8) with CLIA, with an overall IFR of 0.02%. There were 78.2% (95% CI 66.7–89.7) symptomatic individuals among the positive cases. Sensitivity and specificity of LFIA were estimated at 43.4% and 83.3% for IgM;46.5% and 91.5% for IgG;and 59.1% and 77.3% for combined tests.Originality/valueThe authors found high seroprevalence of anti-SARS-CoV-2 antibodies within the prison complex. The occurrence of asymptomatic infection highlights the importance of periodic mass testing in addition to case-finding of symptomatic individuals;however, the field performance of LFIA tests should be validated. This study recommends that vaccination strategies consider the inclusion of prisoners and prison staff in priority groups.
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With modern medicine and healthcare services improving in leaps and bounds, the integration of telemedicine has helped in expanding these specialised healthcare services to remote locations. Healthcare telerobotic systems form a component of telemedicine, which allows medical intervention from a distance. It has been nearly 40 years since a robotic technology, PUMA 560, was introduced to perform a stereotaxic biopsy in the brain. The use of telemanipulators for remote surgical procedures began around 1995, with the Aesop, the Zeus, and the da Vinci robotic surgery systems. Since then, the utilisation of robots has steadily increased in diverse healthcare disciplines, from clinical diagnosis to telesurgery. The telemanipulator system functions in a master–slave protocol mode, with the doctor operating the master system, aided by audio-visual and haptic feedback. Based on the control commands from the master, the slave system, a remote manipulator, interacts directly with the patient. It eliminates the requirement for the doctor to be physically present in the spatial vicinity of the patient by virtually bringing expert-guided medical services to them. Post the Covid-19 pandemic, an exponential surge in the utilisation of telerobotic systems has been observed. This study aims to present an organised review of the state-of-the-art telemanipulators used for remote diagnostic procedures and surgeries, highlighting their challenges and scope for future research and development.
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An accurate, convenient, and rapid diagnostic platform, which can be applied in facility-limited or point-of-care (POC) settings, is essential to help prevent the spread of infectious diseases and enable the most effective treatment to be selected. In this study, we describe the development of a new isothermal molecular diagnostic system named multipurpose advanced split T7 promoter-based transcription amplification (MASTER) for the rapid and ultrasensitive detection of various pathogens containing single-stranded RNA and double-stranded DNA. MASTER produces a large number of RNA amplicons in the presence of target pathogens, which generate fluorescence or colorimetric signals based on light-up RNA aptamers or lateral flow assays. Implementing MASTER at 37 °C for<1 h achieved the detection of a single copy per reaction without cross-reactivity. Moreover, the testing of 40 clinical samples revealed that MASTER exhibited excellent accuracy with 100% sensitivity and specificity for SARS-CoV-2 diagnosis. Furthermore, a one-pot MASTER system capable of accelerating practical applications was demonstrated, indicating that the MASTER system is a promising platform for the effective surveillance of various pathogens. © 2023 Elsevier B.V.
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The emergence of the SARS-CoV-2 virus and the associated pandemic has affected the entire human population. Human susceptibility to the virus has highlighted a tremendous need for affordable diagnostic systems to manage the pandemic and monitor the effectiveness of vaccination. We have developed a simple and label-free electrochemical immunosensor for the detection of human anti-SARS-CoV-2 IgG antibodies, which consists of a supporting screen-printed carbon electrode (SPCE) modified with an electrodeposited polyaniline film and glutaraldehyde, allowing effective immobilization of the SARS-CoV-2 spike glycoprotein receptor-binding domain (RBD) as a biorecognition element. The impedimetric immunosensor showed a linear response over a wide concentration range of 0.01–10 μg mL−1, that is, 67 pM–6.7 nM, with a low detection limit of 25.9 pM. A dual working electrode configuration with a built-in negative control unit was demonstrated for practical field applications. The immunosensor was successfully used in a real serum sample from an infected patient and showed good reproducibility and fair agreement with ELISA. An optional amplification step with secondary goat anti-human IgG antibodies was demonstrated, resulting in an extended linear range and a detection limit as low as 0.93 pM.
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The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the "stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making. © 2022 Elsevier Inc. All rights reserved.
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Due to the rapid propagation characteristic of the Coronavirus (COV-ID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper, we propose a novel Convolutional Neural Network (CNN) model (COV2Net) that can detect COVID-19 virus by analyzing the X-ray images of suspected patients. This model is trained on a dataset containing thousands of X-ray images collected from different sources. The model was tested and evaluated on an independent dataset. In order to approve the performance of the proposed model, three CNN models namely MobileNet, Residential Energy Services Network (Res-Net), and Visual Geometry Group 16 (VGG-16) have been implemented using transfer learning technique. This experiment consists of a multi-label classification task based on X-ray images for normal patients, patients infected by COVID-19 virus and other patients infected with pneumonia. This proposed model is empowered with Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-Cam++ techniques for a visual explanation and methodology debugging goal. The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods. © 2023 CRL Publishing. All rights reserved.
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COVID-19 has caused a devastating effect in every aspect across the world. The pandemic brought life to a standstill. Frontline workers are working day and night to treat patients and save lives. As critical is the timely and quick detection of this communicable disease, it necessitates the need for a diagnostic system that is automatic and as accurate as possible. The number of false negatives and hysteresis must be as low as possible. CT scans of the lungs can help in quicker detection of the presence of the virus as opposed to RT-PCR test. The purpose of this article is to present a survey of current scientific work on CT scan classification techniques, outlining and structuring what is currently available. We conduct a systematic literature review in which we compile and categorize the latest papers from top conferences to present a synopsis of CT scan images data classification techniques and their issues. This review identifies the present state of CT image classification research and decides where further research is needed. A review paper discusses different classification methods for CT scan images, including a comparative study of major classification techniques. © 2022 Copyright for this paper by its authors.
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Background: The objective of this study was to study the benefit of the rapid antigenic detection of SARS-CoV-2 and to demonstrate the contribution of this technique compared to real-time RT-PCR. Methods: The SARS-CoV-2 nucleocapsid N antigen rapid diagnostic test (Standard Covid-19 Ag Test, SD Biosensor) was performed on 49 patients. Real-time RT-PCR testing was performed only in 12 patients. Results: Nasopharyngeal swabs were taken from subjects whose mean age was 35 years (range 23-68 years) and who presented one of the following symptoms: dry cough (30.61%), chest tightness (28 %), fever (28%), headache (24.48%), asthenia (22.44%) and diarrhea in only 14.28%. The time between the onset of symptoms and the completion of the test ranged from 0 to 2 days. Of all rapid tests performed, 35 (71.42%) were negative and 14 (28.57%) were positive. Of the samples tested, 44 came from different IMKO departments. RT-PCR was performed in 8 patients whose rapid tests were negative and gave a positive result in 2 cases. Conclusion: The detection of SARS-CoV-2 should be evaluated and compared to the standard RT-PCR technique, which often offers significantly better sensitivity. It is necessary to carry out large studies to better understand the issue of potential SARS-CoV-2 recurrence in COVID-19 patients.
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Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals' evolution of the disease.
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Automated sample-to-answer systems that promptly diagnose emerging infectious diseases, such as zoonotic diseases, are crucial to preventing the spread of infectious diseases and future global pandemics. However, automated, rapid, and sensitive diagnostic testing without professionals and sample capacity and type limitations remains unmet needs. Here, we developed an automated sample-to-answer diagnostic system for rapid and accurate detection of emerging infectious diseases from clinical specimens. This integrated system consists of a microfluidic platform for sample preparation and a bio-optical sensor for nucleic acid (NA) amplification/detection. The microfluidic platform concentrates pathogens and NAs in a large sample volume using adipic acid dihydrazide and a low-cost disposable chip. The bio-optical sensor allows label-free, isothermal one-step NA amplification/detection using a ball-lensed optical fiber-based silicon micro-ring resonator sensor. The system is integrated with software to automate testing and perform analysis rapidly and simply;it can distinguish infection status within 80 min. The detection limit of the system (0.96 × 101 PFU) is 10 times more sensitive than conventional methods (0.96 × 102 PFU). Furthermore, we validated the clinical utility of this automated system in various clinical specimens from emerging infectious diseases, including 20 plasma samples for Q fever and 13 (11 nasopharyngeal swabs and 2 saliva) samples for COVID-19. The system showed 100% sensitivity and specificity for detecting 33 samples of emerging infectious diseases, such as Q fever, other febrile diseases, COVID-19, human coronavirus OC43, influenza A, and respiratory syncytial virus A. Therefore, we envision that this automated sample-to-answer diagnostic system will show high potential for diagnosing emerging infectious diseases in various clinical applications. © 2023 Elsevier B.V.
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Globally, pneumonia is the leading cause of death for young people and children. An X-ray of the chest is usually used to diagnose pneumonia by a trained specialist. However, the process is tedious and can result in disagreements among radiologists. It is possible to improve diagnostic accuracy through the use of computer-aided diagnostic systems. In this work, the ResNet model was selected to work as the covid-19 and pneumonia detector based on X-ray image. Several experiments are conducted on to achieve an optimal results. © 2022 SPIE.
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IntroductionThe COVID-19 pandemic has highlighted the need for rapid assessment of potential health technologies that can improve health outcomes in COVID-19 patients, as well as helping pressurized health service provision. Medical technologies play a key role in the COVID-19 pandemic, especially diagnostic tests and respiratory technologies. This study evaluates the rapid response work that the medical technology evaluation programme (MTEP) at the National Institute for Health and Care Excellence (NICE) has done in response to the COVID-19 pandemic.MethodsCompanies routinely submit medical technologies for evaluation by NICE through HealthTech Connect, which is an online portal for devices, diagnostics and digital technologies intended for use in the NHS or wider United Kingdom health and care system. During the COVID-19 pandemic, companies were able to use a designated email address if they perceived their technology may benefit the healthcare system regarding the COVID-19 pandemic. This new system bypassed the usual full registration and data submission. All technologies were reviewed that were submitted via HealthTech connect and email between March 2020 and June 2021.ResultsDuring this period, 20 technologies were submitted to MTEP. Most of these technologies were submitted via email. These technologies consisted of a mix of digital, diagnostic, and respiratory technologies. Seven technologies were selected for a rapid COVID-19 MedTech innovation briefing (MIB), with one specifically addressing issues around waiting lists because of knock-on effects of COVID-19 restricting normal clinical work. A further six technologies were not selected because of limited evidence, while one was not selected because it was not perceived as innovative. The other five technologies were progressed as normal MIBs as there was not enough evidence of potential benefits related to COVID-19 to expedite to a rapid COVID-19 MIB. In total, two technologies were selected for medical technology guidance (myCOPD and Anaconda) and are currently in development.ConclusionsMTEP has responded to the COVID-19 pandemic by prioritising and producing rapid COVID-19 MIBs on technologies to improve health and social care.
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Background: Due to the extreme infectivity of SARS-CoV-2, sample-to-answer SARS-CoV-2 reverse transcription (RT) polymerase chain reaction (PCR) assays are urgently needed in order to facilitate infectious disease surveillance and control. The purpose of this study was to evaluate three sample-to-answer SARS-CoV-2 RT-PCR assaysBioFire COVID-19 Test, BioFire RP 2.1, and Cepheid Xpert Xpress SARS-CoV-2using clinical samples. Methods: A total of 77 leftover nasopharyngeal swab (NP) swabs (36 positives and 41 negatives) confirmed by reference SARS-CoV-2 RT real-time (q) PCR assay were collected. The clinical sample concordance, as specified by their respective emergency use authorizations (EUAs), in comparison to the reference SARS-CoV-2 RT-qPCR assay, was assessed. Results: The results showed that all three sample-to-answer SARS-CoV-2 RT-PCR assays provided perfectly concordant results consistent with the reference SARS-CoV-2 RT-qPCR assay. The BioFire COVID-19 Test exhibited the best turnaround time (TAT) compared to the other assays, regardless of the test results, using one-way analysis of variance followed by Scheffe's post hoc test (p < 0.001). The Xpert Xpress SARS-CoV-2 showed a shorter average TAT (mean ± standard deviation, 49.9 ± 3.1 min) in the positive samples compared to that (55.7 ± 2.5 min) of the negative samples. Conclusions: Our evaluation demonstrates that the BioFire COVID-19 Test, BioFire RP 2.1, and Cepheid Xpert Xpress SARS-CoV-2 assays compare favorably to the reference SARS-CoV-2 RT-qPCR assay, along with a 100% concordance in assay results for clinical samples and an acceptable analytical performance at their guaranteed limits of detection. The addition of a widely used simultaneous sample-to-answer SARS-CoV-2 RT-PCR assay will contribute to the number of medical laboratories able to test for COVID-19.
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COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , COVID-19/diagnosis , Clinical Laboratory Techniques/methods , COVID-19 Testing , Nasopharynx , Sensitivity and SpecificityABSTRACT
INTRODUCTION: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. METHODOLOGY: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. RESULTS: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. CONCLUSIONS: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. ADVANCES IN KNOWLEDGE: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.
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COVID-19 , Deep Learning , Humans , COVID-19/diagnosis , Research , RadiologistsABSTRACT
Introduction: In Lombardy and Piedmont (Northern Italy, about 14 million people) the GRESIF pharmacovigilance network project, aimed to collect, assess, treat and prevent severe systemic drug reactions was activated in 2021, supported by the Italian Medicines Agency (AIFA). GRESIF involves regional and hospital pharmacovigilance centers, and several hospital wards: burn, dermatology, allergology, internal medicine, infectivology and intensive care departments. The registry collects in the National Pharmacovigilance Network all reports of suspected adverse drug reactions (ADRs) concerning Toxic Epidermal Necrolysis (TEN), Stevens-Johnson Syndrome (SJS), Drug reaction with eosinophilia and systemic symptoms (DRESS) and Acute Generalized Exanthematous Pustulosis (AGEP). Objective: The specific objectives of the study are to early detect severe systemic ADRs, evaluate their incidence, morbidity and mortality rates, focus on new generation drugs such as RNA antivirals and oncological drugs, implement and optimize guidelines, manage long-term sequelae by follow-up and create a consultable web-based database. Methods: We have drawn up the guidelines [1,2], through a multidisciplinary approach in order to improve the management of very complex patients even in facilities that are not habitually involved in the treatment of these pathologies. This document aims to support professionals in standardizing diagnostic criteria and methods of therapeutic approach. Its useful to inform the general practitioner about responsible drugs and give some information about risk /benefit on the riexposure. Results: In 2021, 27 cases of SJS/TEN, 18 cases of DRESS and no cases of AGEP were collected. There is a female prevalence (25 cases out 44);the age range is from 20 to 93 years. The median age of patients in Lombardy and in Piedmont is respectively 55 and 66 for females, 47 and 63 for males. The total mortality for cases of SJS/ TEN is about 19% and for DRESS we have no deaths. More frequent suspected drugs are antibiotics, followed by allopurinol and anticonvulsants. Noteworthy is the presence of 4 cases of severe ADR related to anti Covid19 RNA vaccines. In all cases, according to the guidelines, the timely discontinuation of the responsible drug was fundamental as the general management. Furthermore we started a study for the HLA typing of these patients. We enrolled 18 cases and the results showed that 6 patients who received allopurinol were all positive to HLA B 58:01. Conclusion: Despite being extremely rare but serious reactions, the absolute need to implement shared diagnostic and therapeutic protocols to be applied promptly is highlighted, in order to reduce both patient mortality and long-term sequelae.
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Introduction: SFN is a relatively rare condition related to finer fibers of peripheral nervous system. A specific diagnostic procedure is necessary to reach a correct diagnosis.The most frequently reported symptoms are: pain (described as burning or a sensation of intense heat, as "aching cold", "pinpricks", "electric shocks),paraesthesia (spontaneous sensations of tingling,numbness,itching),dysesthesia and allodynia. Some published papers hypothesized a correlation between SFN and anticovid vaccine. Objective: Study target was to analyze the adverse events following immunization (AEFI) reported in our ASL (resident population 1,221,857, ¼ regional population) potentially linked to the SFN symptoms. Methods: Data relating to AEFI were extrapolated from the National Pharmacovigilance Network (NPN), while data referring to administered doses were extracted from the ASL QlinkView platform. Results: From 27 December 2020 to 26 April 2022, 624 reports, relating to vaccines anticovid AEFI, were received and recorded in the NPN. 2.109 AEFI were described in these reports. Administered vaccines: Comirnaty (346/624 sheets;1.164/2.109AEFI;2.092.042/ 3.028.781 total administered doses), the most reported AEFI were related to general pathologies: pain, wheal or erythema at the injection site, headache, fever, asthenia, nausea, malaise, tachycardia, muscle pain, fatigue, joint pain (25% of 1.164 AEFI). Other described symptoms: other pains, burning, itching, paraesthesia, tingling, numbness, allodynia, potentially linked to SFN (17.3% of 1.164 AEFI);Spikevax (112/624 sheets;392/2.109 AEFI;607.626/ 3.028.781 doses),the most reported AEFI, like Comirnaty, were related to the injection site (26% of 392 AEFI), while the potentially symptoms related to SFN were the 20.2% of 392 AEFI;Vaxzevria (146/624 sheets;512/2.109 AEFI;298.188/3.028.781 doses), the most reported AEFI, related to general pathologies, were the 33% of 512 AEFI, while potentially symptoms related to SFN were the 18.8%. Finally, as regards the Janssen vaccine (10/624 sheets;32/2.109 AEFI;30.702/3.028.781 doses), the most described events, related to general pathologies, are the 47% of the 32 AEFI, while 31% are potentially linked to the of SFN symptoms. No AEFI was reported related to the 223 doses about Novavax vaccine. The causality assessment was defined correlatable about 37 records (6% of 624 records). 12/37 records describe potentially linked to SFN symptoms (6 Vaxzevria (1%), 3 Comirnaty (0.5%) and 3 Spikevax (0.5%)). Conclusion: The analysis about AEFI reported in our ASL related to anticovid vaccines underlined the existence of symptoms potentially linked to SFN, although only in a few cases it was evaluated a causality assessment to vaccination. Just a specific diagnostic procedure can confirm the diagnosis and the correlation. Therefore, the correlation between SFN and vaccine needs larger-scale studies and insights for a correct evaluation.
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Introduction: During large-scale vaccination campaign against COVID-19, the Italian Medicines Agency (AIFA) in collaboration with the Regional Centres of Pharmacovigilance have carried out a closely monitoring of Individual Case Safety Reports (ICSRs) about Adverse Event Following Immunisation (AEFIs) related to COVID19 vaccines and have assured a constant communication through public monthly reports1. During the first months of the vaccination campaign, a signal of rare events of thrombosis associated with thrombocytopenia2, particularly in young women, was detected by health authorities associated with the viral vector vaccines ChAdOx1S S Ad26.COV2-S. Objective: To present a comprehensive assessment of thrombotic and thromboembolic events associated with thrombocytopenia following COVID-19 immunisation with viral vector vaccines recorded in the Italian National Pharmacovigilance Network database Methods: We selected all ICSRs reported from 27 December 2020 to 26 December 2021 containing Preferred Terms (PT) related to platelet count reduction associated with PT related to thrombotic and thromboembolic events (clinical symptoms and/or diagnostic tests). All cases of thrombotic and thromboembolic events reporting thrombocytopenia in the narrative description of the report were also reviewed. The selected ICRSs were submitted to the independent evaluation of three pharmacovigilance experts who blindly classified into 5 levels of diagnostic certainty, according to the definition provided by the Brighton Collaboration Group (BCG)3. Disagreement were resolved by plenary discussion. Results: 12,166,236 doses of ChAdOx1-S and 1,500,746 of Ad26.COV2-S have been administered in Italy during the considered interval with overall 23,358/117,947 ICSRs related to ChAdOx1-S (19.8 %) and 1,580/117,947 related to Ad26.COV2-S (1.3 %). A total of 134 reports after vaccination with adenoviral vaccines were identified according to the inclusion criteria, of which 107 cases were defined as thrombotic thrombocytopenia (95 following ChAdOx1-S and 12 after Ad26.COV2-S). 27 reports were defined as "not case" (level 5, Brighton) on the basis of clinical examination or investigation, or because of the presence of heparin as a concomitant drug. Furthermore, 3 reports were excluded because of a hereditary thrombophilia or a previous history of other thrombotic episodes. Seventy-seven cases were classifiable as BCG levels 1, 2, and 3 (definite, probable and possible cases, respectively) with an overall reporting rate at about 1 case per approximately 200,000 doses administered. Women aged 30 to 49 years showed the highest reporting rates. Conclusion: In Italy, the rates of thrombotic thrombocytopenia following COVID-19 immunisation with viral vector vaccines are in line with those reported in other Countries.
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Medical Image Segmentation has an imperative job in diagnostic systems on various applications. Ultrasounds, X-rays, MRI, CAT and PET (Positron Emission Tomography) are dynamic and developing domains for research especially in image-processing techniques and algorithms. This field has also attracted significant investments and developments in recent times. Deep Learning models, specifically the Convolutional Neural Network Models (CNN) are state-of-art technologies for identifying medical ailments through visual imagery. The objective of this research is to develop and implement a DepthWise Convolution model that provides high accuracy in detecting Covid 19 Pneumonia from lung x-rays. We also juxtapose it with other models which have great accuracy i.e Transfer Learning Models. © 2022 IEEE.
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Speed read Less than 20 per cent of people in developing countries have access to basic diagnostics for common diseases Diagnostic technologies unaffordable for many as rich nations dominate market Investment, training, research and development needed in lower-income countries [NEW DELHI] Less than a fifth of the population of developing countries have access to basic diagnostic tests for some of the most common diseases, amid a lack of trained staff and inequitable access to equipment, a report published in The Lancet shows. According to Horton, people worldwide are now more aware of the importance of testing, as a result of the pandemic. Fleming added: “We recommend that countries develop specific strategies for better provision of diagnostics particularly in primary care, based on drawing up a list of essential diagnostics that would ensure that every level in the health care system has the diagnostics appropriate for the disease burden in that country.
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A rapid diagnostic system is a primary role in the healthcare system exclusively during a pandemic situation to control contagious diseases like coronavirus disease-2019 (COVID-19). Many countries remain lacking to spot COVID cases by the reverse transcription-polymerase chain reaction (RT-PCR) test. On this stretch, deep learning algorithms have been strengthened the medical image processing system to analyze the infection, categorization, and further diagnosis. It is motivated to discover the alternate way to identify the disease using existing medical implications. Hence, this review narrated the character and attainment of deep learning algorithms at each juncture from origin to COVID-19. This literature highlights the importance of deep learning and further focused the medical image processing research on handling the data of magnetic resonance imaging (MRI), computed tomography (CT) scan, and electromagnetic radiation (X-ray) images. Additionally, this systematic review tabulates the popular deep learning networks with operational parameters, peer-reviewed research with their outcomes, popular nets, and prevalent datasets, and highlighted the facts to stimulate future research. The consequence of this literature ascertains convolutional neural network-based deep learning approaches work better in the medical image processing system, and especially it is very supportive of sorting out the COVID-19 complications.