How to read a paper about inequality in respiratory disease: Basic epidemiological principles and how inequalities are measured

Health inequalities in respiratory disease are widespread, and monitoring them is important for advocacy, the design and delivery of health services, and informing wider health policy. In this chapter, we introduce the different ways in which health inequalities can be quantified, including measures that quantify absolute and relative inequalities, and those that measure gaps between groups or differences across the entire social gradient. We consider the strengths and limitations of these different approaches and highlight things to look out for when reading a paper on health inequalities in respiratory health. These include how common the outcome is and whether other factors have been adjusted for, as both can have a crucial impact on interpretation and can lead to misleading conclusions.Copyright © ERS 2023.

COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images

To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly. © 2023 IEEE.

The impact of COVID-19 on access to mental health services and the use of teletherapy as an alternative form of treatment

The aim of this research study is to determine the impact of COVID-19 on access related to mental health services and also explain the use of teletherapy as an alternative form of treatment. This research study is based on secondary research data analysis to determine the research study data collected from websites related to the ratios of COVID-19 also that mental health services. Determine the research study used E-views software, and the generated result included descriptive statistics, correlations, the dickey fuller test analysis, the histogram, and state, also that explain the variance and test of equality between them. The overall result shows COVID-19 shows a direct impact on mental health services;teletherapy directly links with mental health services. Benefits make teletherapy the best online therapy session for overcoming various types of depression and mental illness in patients. Also, teletherapy is an alternative form of mental health service that is mostly provided to people affected due to the pandemic conditions of the coronavirus.Copyright © 2022, Universidade de Sao Paulo. Museu de Zoologia. All rights reserved.

COVID-19 Diagnostic System Using Medical Image Classification and Retrieval: A Novel Method for Image Analysis

With the rapid increase in the number of people infected with COVID-19 disease in the entire world, and with the limited medical equipment used to detect it (testing kit), it becomes necessary to provide another detection method that mainly relies on Artificial Intelligence and radiographic Image Analysis to determine the disease infection. In this study, we proposed a diagnosis system that detects the COVID-19 using chest X-ray or computed tomography (CT) scan images knowing that this system does not eliminate the reverse transcription-polymerase chain reaction test but rather complements it. The proposed system consists of the following steps, starting with extracting the image's features using Visual Words Fusion of ResNet-50 (deep neural network) and Histogram of Oriented Gradient descriptors based on Bag of Visual Word methodology. Then training the Adaptive Boosting classifier to classify the image to COVID-19 or NOTCOVID-19 and finally retrieving the most similar images. We implemented our work on X-ray and CT scan databases, and the experimental results demonstrate the effectiveness of the proposed system. The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and 97.84% for classifying CT scan to COVID-19 or NOTCOVID-19. © 2021 The British Computer Society.

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images.

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

Study of Deep Learning Approaches for Diagnosing Covid-19 Disease using Chest CT Images

Since March 2020, the World Health Organization (WHO) has declared COVID-19 a pandemic. An evolving viral infection with respiratory tropism causes atypical pneumonia. Experts believe that detecting COVID-19 early stage is crucial. Early diagnosis and tracking techniques have become increasingly important to ensure an accelerated treatment process and avoid virus spread. Images from Computed Tomography (CT) scans can provide quick and precise COVID-19 screening. A subdivision of Machine Learning (ML) called Deep Learning (DL) can improve diagnostic accuracy and speed by automating screening via medical imaging in collaborative efforts with radiologists and physicians This study aims to investigate the recently popularized and extensively discussed deep learning algorithms for COVID-19 diagnosis in connection to the sequence phases involved in image processing. Getting rid of the noise in these images requires some preprocessing. Histogram equalization, fuzzy histogram equalisation, Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the image quality and therefore increase the identification of the image. Afterwards, necessary features for disease detection are segmented using various deep models like U-Net, U-Net + FPN (Feature Pyramid Network), COVID-SegNet and Dense GAN. Once these distinct deep characteristics have been identified, they are extracted using a variety of different deep models. Finally, an illness is diagnosed using popular models such as SVM, ResNet-50, AlexNet, VGG16, DenseNet, and SqueezeNet. The deep learning models with a better optimization algorithm to be effective in the diagnosis of COVID-19 and also obtain a reduced and efficient feature set for image classification and feature extraction. © 2023 IEEE.

Identification of COVID-19 in CT Images using Deep Learning Model

Worldwide, COVID-19 has had a substantial impact on patients and hospital systems. Early identification and diagnosis are essential for regulating the growth of COVID-19. The input CT screening images are initially segmented into various regions using the Fuzzy C-means (FCM) clustering technique. Next, region-based image quality enhancement employs a histogram equalization method. Furthermore, certain necessary data is represented in a new image using the Local Directional Number technique. Lastly, the input images are portioned with the help of a traditional convolutional neural network model. The proposed convolutional neural network based system was able to give an accuracy of 98.60%, and the results revealed that methods for detecting COVID-19 impact from CT scan images must be developed significantly before considering it as a medical choice. Moreover, many diverse datasets are essential to assess the processes in a real-world setting. © 2022 IEEE.

Diagnosis of Pulmonary Diseases from Chest X-ray Using Deep Learning Approaches

In recent years, a severe pandemic has struck worldwide with the utmost shutter, enforcing a lot of stress in the medical industry. Moreover, the increasing population has brought to light that the work bestowed upon the healthcare specialists needs to be reduced. Medical images like chest X-rays are of utmost importance for the diagnosis of diseases such as pneumonia, COVID-19, thorax, and many more. Various manual image analysis techniques are time-consuming and not always efficient. Deep learning models for neural networks are capable of finding hidden patterns, assisting the experts in specified fields. Therefore, collaborating these medical images with deep learning techniques has paved the path for enormous applications leading to the reduction of pressure embarked upon the health industry. This paper demonstrates an approach for automatic lung diagnosing of COVID-19 (coronavirus) and thorax diseases from given CXR images, using deep learning techniques. The previously proposed model uses the concept of ResNet-18, ResNet-50, and Xception algorithms. This model gives the highest accuracy of 98% without segmentation and 95% with segmentation. Whereas, the proposed model uses CNN and CLAHE algorithms which achieves an accuracy of 99.22% without segmentation and 98.39% with segmentation. Therefore, this model will be able to provide assistance to health workforces and minimize manual errors precisely. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Implementation of LBPH Algorithm for Covid Patient Isolation Violation Detection

LBPH (Local Binary Pattern Histogram) is a Facial recognition algorithm used to monitor a COVID infected person using a non-contact method of isolation check. The algorithm is programmed using Python software and the results are analysed using visual studio code. The program extracts feature from an input test image and compares it with the system database. The major goal would be to send the message if the person has violated the isolation norms. This algorithm captures the image of an isolated COVID patient when he/she breaks the isolation norms by opening the door and trying to escape from isolation. © 2022 IEEE.

Smart Attendance System using Dlib Pre-Trained Neural Network

Recognizing and remembering various people is the most frequent task, which the human brain performs. With regard to this, the process of attendance becomes one of the hectic tasks, which requires subsequent modernization. The spread of COVID- 19 is also drastically increasing and are pushed to the situation of wearing mask the entire time. This brings in a situation of misidentifying the individuals and are also prone to impersonation in many official gatherings such as exams, meetings, etc. This cannot be decreased by unmasking their face in this pandemic situation just for the purpose of verification as it may lead to increase in COVID risk. Here, this research study implements a contactless face recognition system with a simple and smart database, which can take in any form of data as per the convenience. This system solves the above problem by making the face recognition smart using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) classifier. The main task of the system is to recognize the user's face (live) and automatically mark the time of recognition directly in the Google sheet along with the alphabets of P(Present), A(absent) or L(late) according to the given time range. This system makes effective use of google sheet for easy share ability, accessibility, and error free management. This can be used for number of purposes such as exam centers, schools, colleges, companies, hospitals and various other places in order to verify the people (contact less). © 2023 IEEE.

Is histogram analysis useful in the diagnosis of COVID-19 patients?

Aim: In this study, we aimed to show the contribution of the chest computed tomography (CT)-based histogram analysis method, which will enable us to make quick decisions for patients who are clinically suspected of having COVID-19 infection and whose diagnoses cannot be confirmed by polymerase chain reaction (PCR) tests. Material(s) and Method(s): A total of 84 patients, 40 in the PCR-positive group (age range: 17-90 years) and 44 in the PCR-negative group (age range: 15-75 years), were included in the study. A total of 154 lesions with ground-glass density, 78 in the PCR-positive group and 76 in the PCR-negative group, were detected in these patients' thorax CT scans. The region of interest was placed on the ground-glass opacities from the images and numerical data were obtained by histogram analysis. Numerical data were uploaded to the MATLAB program. Result(s): The localizations of ground-glass densities in the CT findings of patients with probable and definite COVID-19 diagnoses were similar;74.7% of the ground-glass areas in both groups showed peripheral distribution. Lesions were frequently observed in right lungs and lower lobes. In histogram analysis, standard deviation, variance, size %L, size %M, and kurtosis values were higher in the PCR-positive than the PCR-negative group. When receiver operating characteristic curve analysis was performed for standard deviation values, the area under the curve was 0.640, and when the threshold value was selected as 123.4821, the two groups could be differentiated with 62.8% sensitivity and 61.8% specificity. Discussion(s): The use of histogram-based tissue analysis, which is a subdivision of artificial intelligence, for clinically highly suspicious patients increases the diagnostic accuracy of CT. Therefore, performing CT analysis with the histogram method will significantly aid healthcare professionals, especially in clinics where rapid decisions are required, such as in emergency services.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Analysis of a Typical COVID-19 Outbreak Spread by Tourist Groups

Background: Novel coronavirus disease (SARS-COV-2 infection or COVID-19) is a respiratory tract infection that has been linked to severe acute respiratory syndrome transmitted particularly through touching and respiration. The purpose of this study is to understand the epidemiological characteristics of COVID-19 cases in a typical tourist-related outbreak and explore the possible route for its transmission. Method(s): All data and epidemiological survey reports of COVID-19 cases in the outbreak were reported by provincial and urban (county) Centers for Disease Control and Prevention and Health Commissions nationwide from October 16th to November 5th, 2021. The epidemiological survey reports included information on gender, age, source of infection (imported from other provinces or locally acquired), daily life track and itinerary, date of symptom onset, and date of diagnosis. The data were analyzed using descriptive statistical methods, one-way analysis of variance, independent t-test, and Chi-square tests. Histograms and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. Result(s): The COVID-19 outbreak associated with the tourist groups has involved 551 COVID-19 cases, with a median age of 44 years (interquartile range: 30-59 years), gradually spreading from the northwestern region to the national level across 15 provinces of China. One-fifth of the cases (16.0%) had traveled to Ejin Banner, resulting in 68 second-generation cases. We estimated an outbreak on 11 flights and 19 trains, accounting for a total of 27 confirmed cases. In addition, 42 clusters of outbreak cases were also reported to occur, 21 (50.0%) in households and 10 (23.81%) in restaurants. About 106 confirmed cases were related to the gatherings in restaurants. The median incubation period for this COVID-19 outbreak was 7 days (inter-quartile range: 5-10 days). Conclusion(s): The survey results indicated that this COVID-19 outbreak originated in Ejin Banner and was spread by tourist groups, which was a typical infection outbreak promoted by travel. Our results further confirmed that travel needs to be more strictly weighed in pandemics like COVID-19, and people need to pay more attention to the prevention against infectious diseases, particularly when traveling in a tourist group.Copyright © 2022 Zheng et al.

Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation

BackgroundChest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.ResultsThe Spearman's correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).ConclusionsThe automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.

Optimization of CNN for Diagnosis on Lung Disease by Lung Segmentation and Rib Suppression

The discrimination of lung diseases by chest X- ray images is a clinically important tool. How to use artificial intelligence to accurately and quickly help doctors to diagnose different lung diseases is very important in the context of the current COVID-19 global pandemic. In this paper, we propose a model structure, including two U-Net, which implement lung segmentation and rib suppression for chest X-ray images respectively, image enhancement techniques such as histogram equalization, which enhances images contrast, and a Xception- based CNN, which classifies the processed images finally. The model can effectively avoid the interference of regions outside the lung to CNN for feature recognition and the influence of environmental factors such as X-ray machines on the quality of X-ray images and thus on the classification. The experimental results show that the classification accuracy of the model is higher than that of the direct use of the Xception model for classification. © 2022 IEEE.

Face Mask Detection Using Viola-Jones and Cascade Classifier

Globally, the COVID-19 coronavirus outbreak is causing chaos in human health and therefore, the healthcare sector is in serious disarray. Many precautions have been taken to prevent the spread of this disease, including the usage of masks, which is strongly recommended by the World Health Organization (WHO). This research study has used the Viola-Jones algorithm for detecting face masks, where Histogram Equalization, Unsharp Filter and Gamma Correction are used as the preferred image pre-processing techniques to improve the overall accuracy. Haar Feature Selection is applied for creating integral images and AdaBoost training is performed on these images. Cascade classifier, a machine learning-based approach, is also integrated with the base algorithm where a cascade function assists Viola-Jones in accurately detecting objects in images. A total number of 1670 images is used in this work and our system is compared with four other machine learning algorithms, where Viola-Jones outperforms these ML-based classifiers and the overall accuracy obtained is 96%. © 2022 IEEE.

A Real-Time Crowd Detection and Monitoring System using Machine Learning

The COVID-19 pandemic has unquestionably warned all of us that, the outbreak of an infection can lead to a pandemic-like situation all over the world. In order to prevent outbreaks and provide better healthcare, appropriate crowd detection and monitoring systems must be deployed in public areas. By effectively implementing social distancing measures, the number of new infections can be greatly decreased. This idea served as the inspiration for the creation of a real-time Crowd Detection and Monitoring System (CDMS) for social distancing. This paper proposes a fully autonomous system for Real-Time Crowd Detection and Monitoring to help the educational institutions to monitor the students inside the premises more effectively. This system is developed using an OpenCV based Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) detector to detect and count the number of people gathered at an instance. The system raises an alarm to alert the people and adhere to the rules if the gathering is more than the threshold/permitted number of people in the cluster. © 2023 IEEE.

Chest X ray image enhancement using deep contrast diffusion learning

The chest x-ray (CXR) is a diagnostic imaging tool that aids in the early detection and diagnosis of lung abnormalities. Due to scattering radiation, the CXR would have poor contrast, and the diagnosis would be difficult. Although many methods exist, deep learning (DL)-based CXR image enhancement remains difficult due to the amount of contrast that needs to be enhanced and the locations where acceptable contrast must be extracted. In order to improve CXR images, a contrast diffusion network is introduced in this paper. The input image is initially placed through a multi-level contrast-limited adaptive histogram Equalization (CLAHE) process, from which the necessary contrast is extracted and sent into the convolutional neural network (CNN)-based residual learning network together with low contrast CXR. To create the enhanced CXR images, the learned contrast features were diffused over the input image. The amount of contrast to be diffused is determined by multiple levels of CLAHE. Various metrics are used to evaluate the enhanced image's quality. Additionally, the enhanced images are submitted to computer-assisted diagnosis, which improves overall classification efficiency. All of the results are based on the Shenzhen, COVID-CXR, and PadChest datasets. © 2023 Elsevier GmbH

A Single Centre Experience of Post Renal Transplant Infections in a Tertiary Care Centre in Eastern India

Introduction: In developing countries, Post renal-transplant infections is the leading cause of mortality, morbidity and decreased allograft survival. Our aims and objectives was to determine the incidence and prevalence patterns of clinically or microbiologically confirmed infection in the post renal transplant patients of our population and profiling of infections in relation to time period from the Transplant and the induction agent, also to develop strategies to counter risk of post transplant infection. Method(s): This was a retrospective observational study. Time period: January 2020- April 2022. Post renal transplant recipients presenting with infections (with informed consent) was enrolled in this study. Recurrent episodes of infection by different organisms in a same patient treated as a separate event. Data was tabulated using MS excel and all results projected in bar graphs, pie charts, histograms. Differences of quantitative parameters between groups were assessed using the t test(for data that were normally distributed) or nonparametric test (for data that were not normally distributed). Differences of qualitative results were compared using chi2 test. Kaplan-meier was used for survival analysis. P < 0.05 was considered significant. Result(s): 213 incidents of post renal transplant infections were documented in 148 patients between the study period. Of the 85 patients who underwent renal transplant(57 living donor and 28 cadaveric) in this time period 33(38.8%) patients presented with 42 incidents of infections. Majority (74.3%) : Males. Mean age: 36.3+/-5.6 years. Most common cause of native kidney disease was chronic glomerulonephritis(30%). 121 (81.7%) had living donor transplant and 27(8.3%) patients had cadaveric transplant. Induction agent was basiliximab in 97 patients (65.5%) had 133 infections (62.4%) and ATG was used in 51 patients (34.5%) had 80(37.6%) infections. In recent transplant (last 2 yrs) cases-In Basiliximab group: infection rate 4.1 in 100 patient months and in ATG group infection rate was 5.7 in 100 patient months. (p=0.28). 37.5%cases had infections with graft dysfunction most commonly AKI. Immediate post transplant infections (<1 month) were 34 (15.9%), most commonly UTI (44.11%) followed by pneumonia (15.9%). 48(%) infections occurred between 1-6 months, most commonly pneumonia(27.08%) followed by UTI(22.9%) and superficial fungal infection. Pulmonary tuberculosis was in 14 (6.6%) cases. 3 cases had disseminated TB. Infectious diarrhea was in 18(8.4%) cases, most common organism isolated was EAEC and EPEC. CMV colitis found in 3 cases. 27 (18.2%) patients had NODAT/PTDM. ParvoB19 was in 11(5.16%), CMV in 5 and BKVN in 3 cases. 41(19.2%) cases had severe sepsis requiring intensive care support. New baseline s.cr was achieved in 29.1% cases. Infection related death was 24(16.2%). COVID 19 infection was in 41 cases, 31.7% developed graft dysfunction and 18 (43.9%) required hospital admission due to moderate or severe disease. 2 patients had mucormycosis, one of them died after admission. [Formula presented] Conclusion(s): Profiling of infection in our centre is essential to formulate future strategies for infection control especially as the DDKT & ABOi KT is on the rise. Proper survillence, screening protocol, vaccination and patient education are essential to reduce the burden of post transplant infection and for better graft and patient survival. No conflict of interestCopyright © 2023

Outcome of Tunneled Cuffed Catheter as Hemodialysis Access;a Single Centre Experience in a Tertiary Care Centre in Eastern India

Introduction: Although AVFs are preferred vascular access for hemodialysis, tunneled cuffed catheters(TCC) are increasingly being used as dialysis access in certain clinical situations such as in AVF failure or lack of suitable vessels for AVF creation or bridge to living donor transplant. Aim and objective of this study was to study the characteristics of the population having benefited from tunneled cuffed catheters, to identify the different indications as well as the complications secondary to tunneled cuffed catheters in hemodialysis patients and to determine the catheter and patient survival rate and the factors associated with complications and survival. Method(s): This was an retrospective Observational study done after institutional ethics committee approval. All data was captured using standard proforma. The data was tabulated using MS excel and all results projected in form of bar graphs, pie charts, histograms or tables. Kaplan- meier analysis was used for survival. All patients included in the study consented for the procedure as well as collection of data. 527 TCC placement were done in 498 patients by nephrologists without fluoroscopy in a percutaneous fashion between jan 2021 to march 2022. Minimum follow up was 12 months. 37 patients lost to follow up. Result(s): 316 (68.5%) were males and mean age was 48.3+/-12.6 years. Staggered tip MAHURKAR MaxidTM Covidien, was used in every patient. Most common native kidney disease was cresentic GN 176(38.1%). Most common Site of TCC was right internal jugular 88.9%(441/496), followed by left internal jugular 10.48%(52/496), femoral TCC done in 0.6%. Mean blood flow achieved was 311+/- 32ml/min. Most common indication of TCC placement was starting of HD after 1/2 temporary access- 162(32.66%), followed by awaiting Maturation of autogenous AVF 66 (13.3%) and awaiting living-related transplantation 54(10.88%). Total catheter related infective episodes (CRBSI) were 229 (1.07 episodes/1000catheter days),Exit site infection was in 57 cases (0.26 /1000 catheter days), Tunnel infection was in 51(0.19/1000 catheter days), Infective endocarditis was seen in 3 cases. Catheter loss due to CRBSI was 23 (12.16%). Most common organism was Enterococci (29.7%), followed by s.aureus (24.32%). Most common immediate complication was tunnel bleeding (5.9% ), followed by improper tip position 4.68%. Late complications due to TCC thrombosis/ fibrin sheath was 74(15.07%). Recanalisation with urokinase was successful in 36.84%. Central venous stenosis was in 26 cases. successful recanalisation after central venoplasty was 16/19 (84.21%). Mean catheter survival was 201.9 +/- 114.9 days (3day to 12 months). Catheter survival at the end of 3 months was 75.76%, at 6 months 63.4%, at 12months 32.17%. Patient survival at 6 months was 86.7%, at 12 months- 77.5%. Most common cause of death was unrelated to TCC - cardiovascular cause (77.6%). Direct TCC related death was in 5 cases. Most common cause of catheter drop out was patient death (33.03%), followed by maturation of AVF (22.82%), catheter thrombosis/fibrin sheath (22.2%). [Formula presented] Conclusion(s): Though AVF is the best access, for late unplanned HD initiation in many CKD patients, TCC insertion becomes next best option. In access crisis patients, TCC may remain one feasible option for bridge to available live donor transplant. With strict asepsis protocol and technical aptitude TCC placement is safe with few side effects. No conflict of interestCopyright © 2023

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.