Portable blood glucose meter system for self-monitoring of diabetic patients

The appearance of COVID-19 changed the lifestyle of many people as it spread rapidly around the world, causing concern to the entire health system due to the high number of infected and leading to a general confinement, changing the lifestyle and eating habits of many people causing diabetes, which is a disease caused by the high level of glucose in the blood, which can generate serious problems in the health of the person since it has no cure, this progressive disease is controlled or monitored by conventional glucometer equipment that generates pain in patients because they require blood samples to measure glucose, worse for those diabetics who must have the measurement several times a day. In view of this problem, this article will make a portable blood glucose meter system for the self-monitoring of diabetic patients and determine the blood sugar level to visualize it by means of a screen, with this system the measurement will be made without pain and will show the value of the glucose level accurately, Helping diabetic patients who perform monitoring several times a day. Through the development of l system, it was observed that it works in the best way with an efficiency of 96.97% in the measurement of glucose, when comparing with others equipment glucometers obtained a relative error of 2.99%, being an error accepted to approach the real value. © 2023 IEEE.

Bacterial Opportunist Pathogens in ICU Patients and COVID-19 Pandemic

Introduction. The COVID-19 infection caused by SARS-CoV-2 is often severe and can lead to fatal outcomes. It is known that the main route of transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is airborne. Studies revealed that up to 25% of subjects from at-risk groups who have been affected by severe viral respiratory infections acquired secondary bacterial infections. These individuals have been reported to have superinfections and co-infections with SARS-CoV-2. Numerous opportunistic infections have been identified in patients with COVID-19, including the major bacterial pathogens responsible for healthcare-associated infections. The prevalence, frequency, and characteristics of bacterial agents isolated in patients during the COVID-19 pandemic are poorly understood and are considered to be a significant gap in knowledge. Therefore, it seemed appropriate to perform a comparative analysis of changes in the structure of bacterial etiological agents in patients hospitalized in intensive care units before and during the COVID-19 pandemic. Purpose. To review research and to study the structure of opportunistic bacterial agents isolated in hospitalized patients and to evaluate the impact of the COVID-19 pandemic impact on changes in incidence and characteristics of the main opportunistic infectious agents compared to the same period before the pandemic. Materials and methods. Bacteria were isolated and identified by conventional bacteriological methods using a variety of biochemical series, test systems for identification, as well as with the use of an automatic hemocultivator, a microbiological analyzer, and a mass spectrometer. The analysis includes data from the WHONET database of the centralized microbiological laboratory of the city of Minsk regarding the test results of blood and respiratory samples from adult patients hospitalized in intensive care units. A comparative analysis of the structure of etiological agents was carried out quarterly for two equal time periods. It's important to note that any bacteria detected from sources other than the respiratory tract or bloodstream was excluded from this data. In addition, all the data received from the intensive care units (ICUs) were analyzed apart from data obtained in other units. Results. The results of respiratory and blood samples of 52,530 patients have been analyzed. A comparative analysis of the structure of microorganisms isolated from 33,539 samples (63.8%, CI95% 63.6–64.0) was performed. In addition, isolates classified as of opportunistic bacterial pathogens were identified in samples of 20,053 patients (59.8%, CI95% 59.5–61.1). It was found that during the COVID-19 pandemic there was a 2.6–fold increase in the number of patients with blood samples tested and a 1.8–fold increase in the number of patients with respiratory specimens tested. The most frequent isolate from both blood samples (pre-pandemic 20.2% (CI95% 19.0–21.4) and 19.9% (CI95% 19.3–20.5) during the pandemic) and respiratory specimens (pre-pandemic 39.0% (CI95% 38.0–40.0) and 40.6% (CI95% 39.9–41.3) during the pandemic) was K. pneumoniae. At the same time, the structure of bacterial agents was modified due to increasing prevalence of non-fermenting gram-negative bacteria. Thus, the frequency of A. baumannii isolated from blood samples increased by 1.8 times, and those isolated from respiratory specimens by 1.3 times. For K. pneumoniae an increase in resistance frequency against penicillins, 3rd-and 4th-generation cephalosporins, carbapenems, fluoroquinolones, colistin, tetracycline, and tigecycline was established;for A. baumannii isolated from blood samples an increase in resistance frequency against gentamicin, tobramycin, and colistin was revealed, and for those from respiratory specimens an increase in resistance frequency against imipenem, meropenem, and tetracycline was revealed during the pandemic. Conclusion. An increase in frequency of isolation of gram-negative pathogens was established. It was indirectly confirmed that co-infection a d or superinfection with K. pneumoniae and A. baumannii and other opportunistic bacteria characterized by multiple resistance to antibacterial drugs, could increase the risks of severe course of underlying disease and lead to lethal outcomes. Prevention of secondary bacterial infections in ICU patients, including those diagnosed with COVID-19, requires an improvement in prevention programs, infection control practices and antibiotic therapy management systems. © 2022, Professionalnye Izdaniya. All rights reserved.

An Empirical study and assessment of minority oversampling with Dynamic Ensemble Selection on COVID-19 utilizing Blood Sample

The COVID-19 virus disease outbreak that erupted in China at the end of 2019 has had a tremendous and disastrous impact on the rest of the world. It has struck the globe to its core, and the destruction has substantially increased the diagnostic burden. In the pandemic zone, clinicians will be able to cut down on their workload and get the right diagnosis of the new disease great to the use of machine learning. A blood test has emerged as a critical tool for identifying false-positive or false-negative real-time rRT-PCR diagnostics. Notably, this is mostly because it is such a cost-effective and convenient method of detecting probable COVID-19 patients. Among the numerous hard consequences associated with COVID-19 illness has been established as one of the most prevalent among COVID-19 patients. The impetus for this research is the scarcity of post-COVID-19 dataset. Following pre-processing to manage address missing values, oversampling with SMOTE ENN is used to generate several instances and model training is carried out on these data sets. However, it has been demonstrated that normatively dynamic ensemble selection outperforms static selection and dynamic selection. The DI+SMOTEENN+DESKNU exceed existing benchmark Classification algorithms and obtain the best accuracy of 99.6%, according the results. © 2022 IEEE.

Detection of COVID-19 from X-Ray Images using Deep Neural Networks

In Dec 2019, Coronavirus has first shown in China and since then there is a big rise in the number of these cases. On March 28, 2020, WHO (World Health Organization) tweeted and proclaimed it's an epidemic. The test kits are relatively less likely to be checked by collecting blood samples because they are easily infectious, and collecting blood samples are very time taking. But it's important to get a quick and simpler way to validate the covid-19. Lungs are getting very badly affected by the Coronavirus and it increases in lungs gradually so we have to come up with the Convolutional Neural Network (CNN). This detects Corona virus utilising X-Rays of the Chest within about few seconds. To diagnose Coronavirus from X-ray Image dataset using different Convolutional Neural Network methodologies like Mobile Net, Inception, Exception, VGG. However, the findings obtained are based on the VGG16, VGG19 model. Apply the models to the X-ray dataset this was obtained from the Kaggle source. This dataset included 100 X-ray images of the lungs(chest) of the Patients with CORONA VIRUS, and 100 X-ray images of the lungs(chest) of People who are healthy. Python language is being used to execute the COVID19 dataset and Google Collaboratory is used for coding purposes. The focus of this research is to see how successful automatically detecting COVID-19 from chest X-rays using Convolutional Neural Networks. This study shows that to detecting COVID-19, VGG16 performs better than other method. The accuracy is 96.15% using VGG16 method. The excellent achievement of these models has the potential to rapidly better the COVID-19 diagnosis performance and speed. Although, A bigger dataset of chest X-ray pictures (COVID-19 positive) are necessary while using deep transfer learning to achieve consistent, accurate and better results to detecting COVID-19 diseases. © 2021 IEEE.

Investigation of SARS-CoV-2 on Ocular Surface of Coronavirus Disease 2019 Patients Using One-Step Reverse-Transcription Droplet Digital PCR.

PURPOSE: This study detects SARS-CoV-2 in the ocular surface through one-step reverse-transcription droplet digital PCR (one-step RT-ddPCR) and evaluates the possibility of the ocular surface as a possible transmission route. METHODS: A single-center prospective observational study was designed to investigate the viral loads in ocular surface. Specimens including the conjunctival swabs, nasopharyngeal swabs and blood were synchronously collected at a single time point for all COVID-19 patients. SARS-CoV-2 loads in nasopharyngeal swabs were tested by real-time polymerase chain reaction (PCR); the blood samples and conjunctival swabs were tested by real-time PCR and one-step RT-ddPCR. RESULTS: Sixty-eight COVID-19 patients confirmed by nasopharyngeal real-time PCR were recruited. In the single time point test, 40 cases showed positive SARS-CoV-2 detection in either the blood, tears, or nasopharynx, of which four cases were triple-positive, 10 were dual-positive, and 26 were single-positive. The positive rate of nasopharyngeal swab real-time PCR test was 22.1% (15/68). The positive rate of blood and conjunctival swabs by one-step RT-ddPCR was 38.2% (26/68) and 25% (17/68), respectively, whereas real-time PCR was all negative. Positive conjunctival swabs were significantly correlated with positive nasopharyngeal swabs (P = 0.028). The sampling lags from illness onset to sampling day in 3 out of 4 triple-positive patients and in 9 out of 10 dual-positive patients were respectively less than 9 days and less than 20 days. CONCLUSION: Our results indicate that the positive rate of SARS-CoV-2 on the ocular surface is much higher than expected. Transmission possibility through the ocular surface may be greatly underestimated.

Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.

BACKGROUND: The number of deaths from COVID-19 continues to surge worldwide. In particular, if a patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery. OBJECTIVE: The goal of our study was to analyze the factors related to COVID-19 severity in patients and to develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage. METHODS: We developed an AI model that predicts severity based on data from 5601 COVID-19 patients from all national and regional hospitals across South Korea as of April 2020. The clinical severity of COVID-19 was divided into two categories: low and high severity. The condition of patients in the low-severity group corresponded to no limit of activity, oxygen support with nasal prong or facial mask, and noninvasive ventilation. The condition of patients in the high-severity group corresponded to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 variables from the medical records, including basic patient information, a physical index, initial examination findings, clinical findings, comorbid diseases, and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest, and eXtreme Gradient Boosting (XGBoost); the AI model for predicting COVID-19 severity among patients was developed with a 5-layer deep neural network (DNN) with the 20 most important features, which were selected based on ranked feature importance analysis of 37 features from the comprehensive data set. The selection procedure was performed using sensitivity, specificity, accuracy, balanced accuracy, and area under the curve (AUC). RESULTS: We found that age was the most important factor for predicting disease severity, followed by lymphocyte level, platelet count, and shortness of breath or dyspnea. Our proposed 5-layer DNN with the 20 most important features provided high sensitivity (90.2%), specificity (90.4%), accuracy (90.4%), balanced accuracy (90.3%), and AUC (0.96). CONCLUSIONS: Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application so that anyone can access the model. We believe that sharing the AI model with the public will be helpful in validating and improving its performance.

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development.

BACKGROUND: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19-positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model.

BACKGROUND: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. OBJECTIVE: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. METHODS: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. RESULTS: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. CONCLUSIONS: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' outcomes.