Changes in Kawasaki disease incidence and phenotype during the COVID-19 pandemic

INTRODUCTION. The aetiology of Kawasaki disease (KD) remains unknown. Changes in infectious exposure during the COVID-19 pandemic owing to infection prevention measures may have affected the incidence of KD, supporting the pathogenic role of an infectious trigger. The purpose of this study was to evaluate the incidence, phenotype and outcome of KD before and during the COVID-19 pandemic in Denmark. METHODS. This was a retrospective cohort study based on patients diagnosed with KD at a Danish paediatric tertiary referral centre from 1 January 2008 to 1 September 2021. RESULTS. A total of 74 patients met the KD criteria of whom ten were observed during the COVID-19 pandemic in Denmark. Alof these patients were negative for SARS-CoV-2 DNA and antibodies. A high KD incidence was observed during the first six months of the pandemic, but no patients were diagnosed during the following 12 months. Clinical KD criteria were equally met in both groups. The fraction of intravenous immunoglobulin (IVIG) non-responders was higher in the pandemic group (60%) than in the in the pre-pandemic group (28.3%), although the rate of timely administered IVIG treatment was the same in both groups (>= 80%). Coronary artery dilation was observed in 21.9% in the pre-pandemic group compared with 0% in KD patients diagnosed during the pandemic. CONCLUSION. Changes in KD incidence and phenotype were seen during the COVID-19 pandemic. Patients diagnosed with KD during the pandemic had complete KD, higher liver transaminases and significant IVIG resistance but no coronary artery involvement.Copyright © 2023, Almindelige Danske Laegeforening. All rights reserved.

Realization of Coronavirus Testing Kit Using a Combination of a Triangular Photonic Crystal (PhC) Structure and a 412 nm Laser Beam

The design of a Covid-19 testing kit is proposed in this research using a photonic crystal structure (PhC) and a violet laser beam. The basic principle of this structure relies on the phenomenon of absorbance reflectance and transmission at the signal of a 412 nm laser beam. Finally, the transmitted light energy through the PhC structure is the conclusive factor to detect the types of virus which is the function of the reflectance and absorbance. The reflected light energy is computed by plane wave expansion (PWE) whereas the absorbance of light energy is obtained through numerical computation. The notable advantages of this technique are that the virus related to Covid-19 can be recognized by observing the colour of transmitted energy through a photo energy meter. Finally, the outcomes of the research affirm that the sample could be Covid-19 if the output energy would be infrared (IR). Similarly, the sample could be a normal coronavirus, if the output energy would lie within the visible regime.

Review on two-dimensional material-based field-effect transistor biosensors: accomplishments, mechanisms, and perspectives.

Field-effect transistor (FET) is regarded as the most promising candidate for the next-generation biosensor, benefiting from the advantages of label-free, easy operation, low cost, easy integration, and direct detection of biomarkers in liquid environments. With the burgeoning advances in nanotechnology and biotechnology, researchers are trying to improve the sensitivity of FET biosensors and broaden their application scenarios from multiple strategies. In order to enable researchers to understand and apply FET biosensors deeply, focusing on the multidisciplinary technical details, the iteration and evolution of FET biosensors are reviewed from exploring the sensing mechanism in detecting biomolecules (research direction 1), the response signal type (research direction 2), the sensing performance optimization (research direction 3), and the integration strategy (research direction 4). Aiming at each research direction, forward perspectives and dialectical evaluations are summarized to enlighten rewarding investigations.

Acute myeloid leukemia complicated by post COVID-19 multi-system inflammatory syndrome-children

Corona Virus disease 2019 (COVID-19) pandemic has presented a huge challenge to the health care system in terms of magnitude of cases and to pediatric oncology units with varied clinical presentations. Acute myeloid leukemia(AML) is a rare heterogenous cancer of childhood with an induction mortality around 15% in our country due to neutropenic sepsis. Multisystem inflammatory syndrome in children(MIS-C) is an hyperinflammatory syndrome seen 4–6 weeks after COVID-19 infection. COVID infection in some of these children would have gone unnoticed. Here we report a two year eight months old boy diagnosed with AML on induction chemotherapy developed post COVID MIS-C. © 2022

Forward-backward and period doubling bifurcations in a discrete epidemic model with vaccination and limited medical resources.

A discrete epidemic model with vaccination and limited medical resources is proposed to understand its underlying dynamics. The model induces a nonsmooth two dimensional map that exhibits a surprising array of dynamical behavior including the phenomena of the forward-backward bifurcation and period doubling route to chaos with feasible parameters in an invariant region. We demonstrate, among other things, that the model generates the above described phenomena as the transmission rate or the basic reproduction number of the disease gradually increases provided that the immunization rate is low, the vaccine failure rate is high and the medical resources are limited. Finally, the numerical simulations are provided to illustrate our main results.

Automated Covid-19 Disease Identification on Chest-Ct Images Using Cnn and Vgg16

The human respiratory system is most affected by COVID-19, a coronavirus illness that has been identified. Infectious disease COVID-19 was brought on by a virus that emerged in Wuhan, China, in December 2019. The key problem for healthcare professionals is early diagnosis. Medical organizations were confused in the early stages because there were no suitable medical tools or medications to detect COVID-19. Reverse Transcription Polymerase Chain Reaction, a novel diagnostic technique, was released. The COVID-19 virus congregates in the patient's nose or throat, thus swab samples from those areas are collected. There are various accuracy and testing time restrictions with this method. Medical professionals advise using a different method called CT (Computerized Tomography), which can rapidly identify the infected lung regions and detect COVID-19 at an earlier stage. With the help of chest CT images, computer scientists created a number of deep learning models to recognize the COVID-19 condition. In this paper, a model for automatic COVID-19 recognition on chest CT images is presented that is based on Convolutional Neural Networks (CNN) and VGG16. A public dataset of 14320 CT scans was used in the experiment, and the findings revealed classification accuracy for CNN and VGG16 of 96.34% and 96.99%, respectively.Copyright © 2022, Anka Publishers. All rights reserved.

COVID-19 Dataset Clustering based on K-Means and EM Algorithms

In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories: "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Resilience Regulation Strategy for Container Port Supply Chain under Disruptive Events

There are many inevitable disruptive events, such as the COVID-19 pandemic, natural disasters and geopolitical conflicts, during the operation of the container port supply chain (CPSC). These events bring ship delays, port congestion and turnover inefficiency. In order to enhance the resilience of the CPSC, a modified two-stage CPSC system containing a container pretreatment system (CPS) and a container handling system (CHS) is built. A two-dimensional resilience index is designed to measure its affordability and recovery. An adaptive fuzzy double-feedback adjustment (AFDA) strategy is proposed to mitigate the disruptive effects and regulate its dynamicity. The AFDA strategy consists of the first-level fuzzy logic control system and the second-level adaptive fuzzy adjustment system. Simulations show the AFDA strategy outperforms the original system, PID, and two pipelines for improved dynamic response and augmented resilience. This study effectively supports the operations manager in determining the proper control policies and resilience management with respect to indeterminate container waiting delay and allocation delay due to disruptive effects.

2d Qsar Approach to Develop Newer Generation Small Molecules Active against Sars-Covid

We are in the half past of 2022, but still, we are facing the coronavirus pandemic situation. When a patient is hospitalized, only some FDA-approved drugs were administered to cure the patient. In treating coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors, and various monoclonal antibodies are present. But all the molecules used in the treatment were not so effective in fully curing the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches played an essential role. 2D QSAR studies related to anti-SARS-CoV-2 molecule development, some QSAR models observed with good statistical parameters such as R2: 0.748, cross-validated Q2 (LOO): 0.628, external predicted R2: 0.723 and another model suggested with R2: 0.764, Q2: 0.627 and Rm2: 0.610, Q2 (F1): 0.727, Q2 (F1): 0.652, MAE score: 0.127. We developed a new 2D QSAR model with a higher number of molecules and greater statistical parameters. A dataset of 84 anti-SARS-CoV2 molecules was obtained from literature followed by descriptor calculation PADEL software;the QSAR model was generated using the Modelability index, dataset pretreatment, division, MLR equation, validation, and Y randomization test. The model was pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP with R2 and Q2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298. Atoms E-state and electronic features of the molecules directly related to anti-SARS-CoV-2 drug activity. It can be easily concluded that we want to develop a small molecule effective against SARS-CoV-2 disease in the near future.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.

Evaluation of cardiovascular autonomic dysfunction in symptomatic post COVID-19 patients

Background: The COVID-19 disease is known for its severe respiratory complications, however it was found to have some cardiovascular complication in post COVID-19 patients. The heart rate variability (HRV) is a non invasive, objective and reliable method for assessment of autonomic dysfunction in those recovered patients. Purpose(s): We aimed to evaluate the cardiac autonomic function by using valid HRV indices in subjects who recovered from mild to moderate acute COVID-19 but still symptomatic. Method(s): The study Group composed of 50 subjects with confirmed history of mild to moderate post COVID 19. All subjects underwent routine 2D echocardiography assessment in addition to 2D speckle tracking and 24 hours Holter monitoring for HRV analysis. Result(s): The mean age of the study population was 42+/-18 years, symptoms were reported as follows 27 (54%) had Dyspnea, 17 (34%) had palpitations, 7 (14%) had dizziness. Time domain parameters SDNN, SDANN and rMSSD were diminished with mean SDNN value being markedly impaired in 12 (24%) patient, while frequency domain parameters as assessed by LF/HF ratio with mean of 1.837 with 8% of patients being impaired. SDNN was significantly reduced in elderly patients (p=0.001), smokers (p=0.019) and hypertensive (p=0.016) and those complaining mainly of palpitation (p=0.006). SDNN was significantly reduced in patient with impaired LV diastolic function (p=0.009), in patients with reduced MAPSE (p=0.047), reduced TAPSE (p=0.00) and impaired Global longitudinal strain (0.000). Conclusion(s): Patients with post COVID-19 syndrome have abnormalities in the HRV which indicates some degree of dysfunction in the autonomic nervous system and consequently impaired parasympathetic function in this population, however this have been also correlating with subtle impairment of the left ventricular systolic function.We believe that this preliminary research can serve a starting point for future research in this direction.

A Novel Approach to the Two-Dimensional Cargo Load Problem

The transport and logistics sector, which include freight forwarders companies, constitutes a vast network of entities that are central to a good performance in services. With the COVID-19 pandemic and its effects on the global economy, there was a huge shortage in the number of containers available, thus creating the need to optimize the loading of available equipment to avoid waste and maximize profits from each export. The present work presents a novel approach where a set of restrictions were created that, applied in synergy with the Non-Linear GRG algorithm, aim to allocate the boxes in different consecutive lines until forming a wall, and, therefore, the walls complete the container, in order to maximize the occupancy on it. To validate the proposed approach a prototype was developed and studied in real-world problem where the solutions resulted in occupations around 80% to 90%. Thus, we can foresee the importance of the proposed approach in decision-making regarding container consolidation services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An optimal aggregation method for interval grey numbers using on Steiner-Weber point with application

Aiming at the problem of low accuracy of two-dimensional preference information aggregation, this paper takes two-dimensional interval grey numbers as an example to define its preference information mapping rules. This rule maps preference information to preference points on a two-dimensional plane. Based on the theory of plane Steiner-Weber point, we construct a two-dimensional optimal model, and prove the optimality of the model theoretically. Then, adopt plant growth simulation algorithm (PGSA) to solve the proposed model. The obtained optimal aggregation point that can represent the comprehensive opinions. Finally, by analyzing the selection problem of Fangcang shelter hospital and comparing it with the particle swarm optimization (PSO) method, we conclude that the sum of weighted Euclidean distance obtained by our method is minimal. The aggregation precision of our method is higher than that of other aggregation method to a certain extent. © 2023 Elsevier Ltd

Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

Abnormal echocardiographic findings after COVID-19 infection: A multicenter registry

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has transformed health systems worldwide. There is conflicting data regarding the degree of cardiovascular involvement following infection, generating uncertainty in patients and an additional healthcare burden with increased diagnostic testing. A registry was designed to evaluate the prevalence of echocardiographic abnormalities in Latin American adults recovered from COVID-19. Method(s): We prospectively evaluated 595 participants (mean age 45.5+/-14.9 years;50.8% female) from 10 institutions in Argentina and Brazil. Echocardiographic studies were conducted with General Electric equipment;2DE imaging and global longitudinal strain (GLS) of both ventricles were performed. Comparisons between groups were made with Chisquare, Fisher and Student's t-test. Logistic regression was performed to determine variables associated with abnormal echocardiogram findings. Result(s): A total of 61.7% of the participants denied any relevant cardiovascular medical history. Table 1 summarizes the comorbidities of the included patients. The majority of patients (82.5%) had the disease at home or in an out-of-hospital center. Of the patients who required hospitalization, 15.3% were in a general ward, 1.9% in intensive care and 0.3% required mechanical ventilation during the disease. The median time between infection and performance of the echocardiographic study was two months (IQR 1- 3 months). Among patients who reported symptoms following COVID-19 recovery (41.8%), the most frequently reported was dyspnea (47.4%), followed by mild symptoms such as asthenia, arterial hypertension or palpitations (32.9%), 12.9% referred chest pain, 6% of patients reported dyspnea and chest pain, and 0.8% reported various other symptoms. The mean left ventricular ejection fraction (LVEF) was 61.0+/-5.5% and the mean left atrial volume was 33.1+/-13.2 ml/m2. In patients without prior comorbidities, 8.2% had some echocardiographic abnormality (Figure 1). We found no significant differences in LVEF between symptomatic and asymptomatic patients (61.4% versus 60.6% respectively, p=0.104). Symptomatic patients showed slightly reduced GLS (-20.3% versus -20.9%, p=0.012) with a trend in the same direction in the RV free wall GLS (-25.6% versus -26.3%, p=0.103). Male patients were more likely to have any new echocardiographic abnormalities (OR 2.82, p=0.002). Time elapsed since infection resolution (p=0.245), the presence of symptoms (p=0.927), or history of hospitalization during infection (p=0.671) did not have any correlation with echocardiographic abnormalities. The difference between sexes remains unchanged after adjusting for left atrial volume, wall thicknesses, diastolic function and abnormal wall motion. Conclusion(s): Our results suggest that cardiovascular abnormalities after COVID-19 infection are rare and usually mild, especially in cases of mild disease. These abnormalities may be more frequent among males.

Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images

COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed;however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two-dimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texture-based Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square- support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non-COVID CT lung images. The results of the trial showed that our model outperformed cutting-edge methods for COVID classification. Using tenfold cross-validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning-based models like random forest, K-nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily. © 2023 Wiley Periodicals LLC.

Case series of acute pulmonary thrombo-embolism following endourological proceedures in COVID era: A serious threat to patients life and urologist!

Introduction and Objectives: In late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as the cause of a cluster of pneumonia cases in China, and the corresponding disease was designated as Coronavirus Disease 2019 (COVID-19), spreading quickly around the world resulting in a pandemic. COVID-19 is associated with a set of coagulation abnormalities that increase the risk of thromboembolic events. Material(s) and Method(s): We report series of five cases of acute pulmonary thromboembolism following endourological procedures, treated in our tertiary care center, which after an apparent clinical improvement, developed acute pulmonary thrombo-embolism between second and third post-op day. Results and Observations: Among five cases, three were post PCNL and two post URSL. All Patients presented with dyspnoea, tachycardia, desaturation and hypotension. Further investigated with E.C.G, D-dimer, 2D-echo and CT-pulmonary angiogram, all suggestive of PTE. Hence patients were managed sucessfully in CCU with cardiologist advice and timely intrevention. Among five, three were managed with IV thrombolytic and anticoagulant therapy and two managed with IV anticoagulation alone , dose monitored with periodic coagulation profile. All patients discharged with oral newer anticoagulants and periodic follow up for 6 months. All patients on follow up and doing well. Conclusion(s): Thromboembolic events are potential complication of COVID-19 and can manifest later. Although very rare after endourological procedures, it requires high index of suspicion so as not to be missed as diagnosis, especially in hemodynamically unstable patients with respiratory distress. Early diagnosis and proper therapeutic actions is crucial for patients.

Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences

The t-distributed stochastic neighbor embedding (t-SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE.In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS-CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k-ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization. © 2022 IEEE.

Effect of Two-Dimensional Geometry Learning on Geometric Thinking of Undergraduate Students During the COVID-19 Pandemic

This study aimed to analyze the effect of two-dimensional geometry learning on the geometric thinking of undergraduate students during the COVID-19 pandemic. This qualitative research involved students in the sixth semester. Data were collected using documentation, test descriptions, and interviews. The results showed there are some reasons why the students faced difficulty in mastering a higher level of geometric thinking: The lack of understanding of the concepts in geometry, the lack of knowledge of the definitions of terms and statements, and how to use them to prove. This study concludes that the variation in the geometric thinking level of undergraduate students shows that there are various students' abilities in two-dimensional geometry, starting from mastering concepts, definitions, and theorems to their use in proof. The suggestion is that several strategies are needed to serve these variations, starting from learning assistance and using diverse learning media. Stakeholders, teachers, and prospective teachers can use the results of this study to improve their understanding of geometry and how to teach it in schools. © 2023, North American Business Press. All rights reserved.

Prognostic Value Of Echocardiographic Evaluation Of Left Ventricular Function In Recovered Covid-19 Patients

Cardiovascular complications are frequently reported in COVID-19 patients and are associated with increased mortality during hospitalization. However, no data exists on cardiac involvement in patients recovered from COVID-19 infection. Our study suggests a need for closer follow-up among COVID-19 recovered subjects including echocardiographic assessment of left ventricular function to elucidate long-term cardiovascular outcomes by early detection of left ventricular dysfunction.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.