Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation.

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

Changing Insect Catch in Viennese Museums during COVID-19

The COVID-19 pandemic led to significant changes in societies across the globe. In many countries in Europe, national lockdowns during the spring of 2020 meant that museums were closed, and maintenance and housekeeping were at a minimum. We compared the insect monitoring data of 15 museums in and around Vienna between the years 2018 and 2022 to see potential effects of the two lockdowns (spring 2020 and winter 2020/21) on insect populations. In Vienna, these changes altered the presence of pests, most notably an increase in silverfish by late spring (March–May 2020). We also found increased numbers of other pest species (notably Tineola bisselliella and Attagenus sp.), though these changes were seen later (June–October 2020). Thylodrias contractus, although found only in one museum, appeared to show decreased numbers during 2020. Storage areas in some of the museums revealed no significant increase in insect catch during the COVID-19 related closures. Since there are rarely visitors in such spaces, the situation did not change much during the closures. Silverfish are shy insects, but they were able to range more freely during the closures in the mostly darkened rooms. The increase of Tineola bisselliella and Attagenus sp. could be a result of reduced cleaning in the first lockdown. In the second lockdown, no significant changes were found. Human activity from staff was much higher compared to the first closure;a second reason could be the time of year, as in the winter period, it is mainly larvae that are active. Increased insect populations remind us that even when museums are unoccupied, they still need monitoring for possible risks from pests. No damage to the objects from the pests was observed in the museums investigated. © 2023 by the authors.

Newborns Born to Sars-Cov-2-Positive Mothers: A Single Institutional Experience

Introduction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the "coronavirus disease 2019" (COVID-19) pandemic, which started in late 2019 and has spread rapidly all over the world. Today it is the main global medical problem affecting the population of all ages. Data on the general condition and potential complications of neonates born to mothers with SARS-CoV-2 infection have been reported all around the world. Herein we present our observations derived from almost three months of work with infants born to SARS-CoV-2-infected mothers. Material and methods. Forty-two neonates born to women with positive SARS-CoV-2 antigen tests between December 2020 and February 2021 in the infective obstetric unit at Wroclaw's Regional Hospital were analyzed with respect to epidemiologic and clinical data obtained from the personal observations and medical records system. Results. Nineteen of the neonates were male (45%) and the median birth term was 39 weeks of gestation. All of the newborns had negative Antigen Rapid Test result for SARS-CoV-2 after birth. Most of them were asymptomatic, except 6 who presented with pneumonia. One of the symptomatic children had positive SARS-CoV-2 antigen and real-time reverse transcriptase-polymerase chain reaction (PCR) tests on the 9th day of life. It is likely that he acquired postpartum infection from his mother. None required invasive ventilation and no deaths were reported. Conclusions. Our study presented that post-birth, most babies of SARS-CoV-2-positive mothers are not infected by the virus and confirmed the fact that the risk of perinatal moth-er-to-child transmission is rather low.Copyright © 2022, Wydawnictwo Czelej Sp. z o.o.. All rights reserved.

Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

The growth of the "Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a "Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2. © 2022 Elsevier B.V.

Optimized face detector-based intelligent face mask detection model in IoT using deep learning approach

The growth of the "Internet of Medical Things (IoMT)” allows for the collection and processing of data in healthcare systems. At the same time, it is challenging to study the requirements of public health prevention. Here, mask-wearing is considered an efficient preventive measure for avoiding virus transfer. Hence, it is necessary to implement an automated mask identification model to prevent public epidemics. The main scope of the proposed method is to design a face mask detection model with IoT using a "Single Shot Multi-box Detector (SSD)” and a hybrid deep learning method. The novelty of the proposed model is that the enhancement made in the face detection and face classification with the developed ASMFO by optimizing the parameters like the threshold in SSD, steps per execution in ResNet, and learning rate in MobileNet, which makes it more efficient and to perform better the conventional models. Here, the parameter optimization is carried out using a hybrid optimization algorithm named Adaptive Sailfish Moth Flame Optimization (ASMFO). Then, the detected face images are given to the hybrid approach named Hybrid ResMobileNet (HResMobileNet)-based classification, where the parameters are tuned using the same ASMFO algorithm for achieving accurate mask detection results. However, the suggested mask identification model with IoT based on three standard datasets is compared with the conventional meta-heuristic algorithms and existing classifiers with various measures. Thus, the experimental analysis is conducted to analyze the effectiveness of the proposed framework over different meta-heuristic algorithms and existing classifiers. The implemented ASMFO-HResMobileNet provides 18.57%, 15.67%, 17.56%, 16.24%, and 19.2% elevated accuracy than SVM, CNN, VGG16-LSTM, ResNet 50, MobileNetv2, and ResNet 50-MobileNetv2.

Violence against children during the COVID-19 pandemic: Theory of Planned Behavior (TPB) analysis on Lowokwaru District, Indonesia

The high number of cases of violence against children has become a big concern, especially during the COVID-19 pandemic. Research purposes to analyze the Theory of Planned Behavior (TPB) in violence against children during the COVID-19 pandemic in Lowokwaru District, Malang City. Research design a quantitative analytic study with a cross-sectional study primary data. Research instrument used was a questionnaire with Google Form application which was online collecting by 100 moth-ers. Analysis technique uses Somers'd and Ordinal Logistic Regression. Variables related to the intention to commit violence against children are subjective norms (p=0.00<alpha=0.05) and behavioral control (p=0.002 <alpha=0.05), while attitudes are not related to the intention to commit violence against children (p=0.501 >alpha=0.05). Variables that have a significant effect on the intention of violence against children are subjective norms (p=0.001<alpha=0.05) and behavioral control (p=0.002<alpha=0.05). Subjective norms and behavioral control are related and have an effect on the intention to commit violence against children, while attitudes do not. Copyright © the Author(s),2022.

PULSERIDER-ASSISTED TREATMENT OF INTRACRANIAL ANEURYSMS IN THE STERLING REGISTRY

Introduction PulseRider (Cerenovus, Irvine, CA) is an adjunctive neck bridging device designed to aid in coiling of wide neck bifurcation intracranial aneurysms. We present outcomes of PulseRider assisted coil embolization of brain aneurysms in routine clinical practice included in the STERLING registry. Materials and Methods STERLING (NCT03642639) is a prospective, global registry of endovascular treatment of intracranial aneurysms with Galaxy and MicrusFrame coils (Cerenovus, Irvine, CA). PulseRider cases from STERLING were included in this interim analysis. Primary outcome measures were core-lab assessed modified Raymond-Roy (mRR) occlusion at final procedural angiogram, and where available, at 6 months (+/-3 months) or 1 year (COVID allowed window: -3 months/+1.5 years). Safety outcomes were procedureand device-related adverse events. Results Seventeen subjects (mean age 64.4 ± 8.69 years, 12 female) were treated with the PulseRider device. All cases were unruptured and two were retreatments of previously coiled aneurysms. All aneurysms had saccular morphology, 14/ 15 (93.3%) were wide neck and 13/15 (86.7%) were at a bifurcation. Target aneurysm locations included basilar artery (6/15, 40.0%), MCA bifurcation (4/15, 26.7%), ACA (3/15, 20%), ICA terminus (1/15, 6.7%), and M2 (distal to bifurcation, 1/15, 6.7%), with a mean parent vessel diameter of 2.65 ± 0.440mm. PulseRider was successfully implanted with the ability to retain the coil mass in all cases. Mean packing density was 29.7 ± 11.32%. Adequate occlusion (mRR I or II) was achieved in 86.7% (13/15) cases immediately post procedure, 100% (3/3) at 6 moths, and 75% (3/4) at 1 year. There were no intraprocedural ruptures, no symptomatic thromboembolic events, and no device related SAEs through the maximum follow up. 87.5% (7/8) subjects had mRS 0-2 at 1 year. There were no aneurysm retreatments. Conclusion In this interim analysis of the ongoing STERLING registry, treatment of intracranial aneurysms with the PulseRider device in conjunction with embolization using Galaxy and MicrusFrame coils showed excellent safety outcomes and high rates of adequate occlusion and good clinical outcome.

17th Symposium on Insect-Plant Relationships (SIP-17), Online, 25-30 July 2021

Originally, the 17th Symposium on Insect-Plant Relationships (SIP-17) was scheduled to take place in Leiden, The Netherlands, in July 2020. However, due to the COVID-19 pandemic, the symposium was postponed to July 2021 and held in an exclusively online format. This exceptional edition has resulted in four strong contributions to the journal. It is with great pleasure that we now present a themed issue including the proceedings of SIP-17, supplemented with eight regular articles within the subject of insect-plant relationships.

OUTCOMES OF PATIENTS IN ADVANCED CHRONIC KIDNEY DISEASE (ACKD) CONSULTATION WITH COVID-19 INFECTION

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) has affected our patients not only in renal replacement therapy (RRT), but also in advanced chronic kidney disease (ACKD) consultation. Our aim is to assess the impact of COVID-19 in a group of patients of our ACKD consultation. METHOD: Retrospective observational study in our centre of patients from ACKD consultation with hospitalization due to COVID-19 infection in the period from March to December 2020. We have studied demographic parameters, characteristics during hospitalization, analytics values (3 months before and 1, 3, 6 and 12 months after infection) and final status at the end of follow-up on 31 December 2021. A value comparison is made with the Wilcoxon test for paired data. RESULTS: In an ACKD consultation with 90 patients, 12 (13%) required hospitalization due to COVID-19. 75% were male, with a mean age of 77.6 years (SD 7) (range 59-89 years) and 25% due to Diabetic Kidney Disease. Mean time in consultation 28 months (SD 14) (range 12-58). Mean Comorbidity Charlson Index 8.2 (SD 1.2) (range 7-11), all hypertensive, 42% treatment with insulin, 25% ischemic heart disease and 42% chronic obstructive pulmonary disease. In COVID-19 hospitalizations, 83% they had pneumonia, 50% steroid treatment, 75% hydroxychloroquine, 92% several antibiotics, 33% low molecular weight heparin, only two required tocilizumab, and none required admission to the intensive care unit. During hospitalization, 3 (25%) patients died, and one died during follow-up, all were males. The consequences after infection: 42% pneumology (cough, varying degrees of shortness of breath), 8% neurological (headaches, varying degrees of memory loss) and 8% loss of smell (from 1 to 6 moths). At the follow-up, only one patient needs RRT with haemodialysis (at 19 months after COVID-19). Table 1 shows analytics comparison before and after COVID. From 6 months after COVID, the results are like 3 months after the disease. CONCLUSION: With the important limitation of few patients and without a control group, ACKD patients with hospitalization to COVID-19 show similar patterns to those with RRT: more frequent in males, advanced age, lung comorbidity and diabetics, elevation of inflammatory parameters, anemia and increase in creatinine during hospitalization. Recovery to values prior to admission occurs from the first month after infection. (Table Presented).

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation.

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.