PSSO: Political Squirrel Search Optimizer-Driven Deep Learning for Severity Level Detection and Classification of Lung Cancer

Lung cancer accounts for about 7.6 million deaths annually worldwide. Early identification of lung cancer is essential for reducing preventable deaths. In this paper, we developed a Political Squirrel Search Optimization (PSSO)-based deep learning scheme for efficacious lung cancer recognition and classification. We used Spine General Adversarial Network (Spine GAN) to segment lung lobe regions where a Deep Neuro Fuzzy Network (DNFN) classifier forecasts cancerous areas. A Deep Residual Network (DRN) is also used to determine the various cancer severity levels. The Political Optimizer (PO) and Squirrel Search Algorithm (SSA) were combined to create the newly announced PSSO method. Experimental outcomes are assessed using the dataset of images from the Lung Image Database Consortium.

Silver nitrate-induced skin inflammation mimicking surgical infection

Silver has been in medicine for hundreds of years and has proven antimicrobial properties. It was widely used until the Second World War, when antibiotics emerged. Silver nitrate (SN) sticks (75% silver nitrate and 25% potassium nitrate) are currently employed as a topical haemostatic agent for various cutaneous surgical procedures. In the initial phase of the COVID-19 pandemic, faced with a limited supply of personal protective equipment, we used SN stick haemostasis for several skin surgical procedures (including excisions). COVID-19-related guidance from the Trust recommended the avoidance of electrocautery owing to the generation of surgical plume;hence, SN stick haemostasis seemed a pragmatic option. Four female patients with a mean age of 67 years (range 48-75) presented with swelling, erythema and pain at the surgical site within a week of the procedure. Three had ellipse excisions for suspected melanoma and squamous cell carcinomas, and one had a shave excision for possible seborrhoeic keratosis. Postsurgical wound infection was suspected, but repeated microbiological swabs did not grow any pathogens. All patients failed to respond to broad-spectrum oral antibiotics, even after two courses. The inflammatory changes took up to 4 weeks to settle, with topical corticosteroids used for wound healing. On contact with moisture, SN sticks deliver free silver ions that form an eschar as they bind to the tissue and occlude vessels. The longer the tip contacts the tissue, the greater the degree of the resultant caustic action. It is widely used in clinical practice, especially wound care (overgranulation, epibole and delayed healing). A 2020 review found an increased incidence of postoperative pain along with pigmentary changes in surgical wounds treated with SN sticks vs. aluminium chloride hexahydrate and ferric subsulfate. In skin surgery, SN is used to cauterize superficial wounds after curettage and shave excision. It does not generate aerosol and, in a pandemic setting, this particular feature can be valuable. However, the potential to cause aseptic skin inflammation that mimics postoperative infection is noteworthy. There are no evidence-based guidelines for its use in dermatology. We believe that the SN is an effective haemostatic agent but can induce significant tissue inflammation in some patients, particularly if it is used in excisions when the cauterized tissue is closed. If SN-induced haemostasis for excision was to be adopted in clinical practice, our experience suggests that larger studies and guidelines are recommended.

Optimal Ensemble learning model for COVID-19 detection using chest X-ray images.

COVID-19 pandemic is the main outbreak in the world, which has shown a bad impact on people's lives in more than 150 countries. The major steps in fighting COVID-19 are identifying the affected patients as early as possible and locating them with special care. Images from radiology and radiography are among the most effective tools for determining a patient's ailment. Recent studies have shown detailed abnormalities of affected patients with COVID-19 in the chest radiograms. The purpose of this work is to present a COVID-19 detection system with three key steps: "(i) preprocessing, (ii) Feature extraction, (iii) Classification." Originally, the input image is given to the preprocessing step as its input, extracting the deep features and texture features from the preprocessed image. Particularly, it extracts the deep features by inceptionv3. Then, the features like proposed Local Vector Patterns (LVP) and Local Binary Pattern (LBP) are extracted from the preprocessed image. Moreover, the extracted features are subjected to the proposed ensemble model based classification phase, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), Optimized Neural Network (NN), and Random Forest (RF). A novel Self Adaptive Kill Herd Optimization (SAKHO) approach is used to properly tune the weight of NN to improve classification accuracy and precision. The performance of the proposed method is then compared to the performance of the conventional approaches using a variety of metrics, including recall, FNR, MCC, FDR, Thread score, FPR, precision, FOR, accuracy, specificity, NPV, FMS, and sensitivity, accordingly.

COVID-19 and Early Childhood Developmentin Rural India

Background: 'Stepping Stones' program is a cluster RCT aimed to improve parental competencies for early childhood development of children under 2 from rural India. Backed by Grand Challenges Canada, Saving Brains, this initiative focuses on multiple aspects of Early Childhood Development to enhance children's cognitive potential in their first 1000 days. This program aims to empower primary caregivers to promote overall child development. On March 22, 2020, India went into lockdown due to the Coronavirus Disease-19 (COVID-19) pandemic. The COVID-19 crisis has profound implications for the care and early childhood development intervention programs. In India, millions of children have limited access to nurturing care. Poverty and low education influence parent's ability to give their children the best start in early life. This further affects a child's development, health, well-being, and school readiness. Caregiver's knowledge about child development and their ability to gain and practice those skills is needed for stimulating a home environment conducive for early child development. This paper aims to study the effect of family-centered programs on caregivers and their children during COVID-19. Methods: This Cluster Randomized Trial was implemented in rural areas of India. Clusters were randomly divided into intervention and control arms. A total of 326 parents of children aged from 0 to 2 years were recruited through a phone survey. The intervention group received a family-centered parenting program for children aged 0-2 years for 6 months through e-platform. The control group received routine care. The baseline and end-line data on developmental scores of children were collected, and developmental scores were analyzed. Parents were assessed and analyzed for changes in knowledge and skills about child development. The effect of the intervention on the child development outcomes is analyzed. All analyses were performed using the STATA version 14. Results: At the endpoint, caregivers from the intervention group had significantly higher scores on knowledge for responsive parenting than those from their control arm. The statistically significant effect of the intervention was reflected on the home environment, mother-child interaction, motor, language, and socio-emotional development. Conclusion: During COVID-19, a family-centered parenting program has shown to be an effective approach for improving parents' competencies and confidence to improve their children's developmental scores.

Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud using Terahertz Signals

The recent COVID-19 pandemic has driven researchers from different spectrum to develop novel solutions that can improve detection and understanding of SARS-CoV- 2 virus. In this article we propose the use of Intelligent Reflector Surface (IRS) and terahertz communication systems to detect violent expiratory aerosol cloud that are secreted from people. Our proposed approach makes use of future IRS infrastructure to extend beyond communication functionality by adding environmental scanning for aerosol clouds. Simulations have also been conducted to analyze the accuracy of aerosol cloud detection based on a signal scanning and path optimization algorithm. Utilizing IRS for detecting violent expiratory aerosol cloud can lead to new added value of telecommunication infrastructures for sensor monitoring data that can be used for public health. IEEE

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks.

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator's machine in the DNA. Genetic analysis of the sample's DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment.

Ultrasound-based Control of Micro-Bubbles for Exosome Delivery in Treating COVID-19 Lung Damage

The recent COVID-19 pandemic has resulted in high fatality rates, especially for patients who suffer from underlying health issues. One of the more serious symptoms exhibited from patients suffering from an acute COVID-19 infection is breathing difficulties and shortness of breath, which is largely due to the excessive fluid (cellular leakage and cytokine storm) and mucoid debris that have filled lung alveoli, and reduced the surfactant tension resulting in heavy and stiff lungs. In this paper we propose the use of micro-bubbles filled with exosomes that can be released upon exposure to ultrasound signals as a possible rescue therapy in deteriorating COVID-19 patients. Recent studies have shown that exosomes can be used to repair and treat lung damage for patients who have suffered from the viral infection. We have conducted simulations to show the efficacy of the ultrasound signals that will penetrate through layers of tissues reaching the alveoli that contains the micro-bubbles. Our results have shown that ultrasound signals with low frequencies are required to oscillate and rupture the polymer-based micro-bubbles. Our proposed system can be used for patients who require immediate rescue treatments for lung damage, as well as for recovered patients who may suffer from viral relapse infection, where the micro-bubbles will remain dormant for a temporary therapeutic window until they are exposed to the ultrasound signals. © 2021 IEEE.

Evolving Intelligent Reflector Surface Toward 6G for Public Health: Application in Airborne Virus Detection

While metasurface-based intelligent reflecting surfaces (IRS) are an important emerging technology for future generations of wireless connectivity in its own right, plans for the mass deployment of these surfaces motivate the question of their integration with other new and emerging technologies that would require such widespread deployment. This question of integration and the vision of future communication systems as an invaluable component for public health motivated our new concept of Intelligent Reflector-Viral Detectors (IR-VD). In this novel scheme, we propose deployment of intelligent reflectors with strips of receptor-based viral detectors placed between the reflective surface tiles. Our proposed approach encodes information of the presence of the virus by flicking the angle of the reflected beams, using time variations between the beam deviations to represent the messages. This information includes the presence of the virus, its location and load size. The article presents simulations to demonstrate the encoding process that represents the number of virus particles that have bound to the IR-VD. IEEE

Editorial-Special Issue on Molecular Communications for Diagnostics and Therapeutic Development of Infectious Diseases

The COVID-19 pandemic in the last year has brought along numerous challenges on all fronts for humanity. Besides the high number of fatalities, economic and societal impact, the virus has also raised a question on the current approaches and methodologies for combatting pandemics. An important factor in this global fight against the pandemic, is the fact that key experts within the fields of biotechnology, virology and immunology have to return to the drawing board to develop new approaches to treat the virus. This has triggered a multi and inter-disciplinary research drive to bring in new concepts, techniques and methodologies that can be used to understand the virus in order to develop novel treatment techniques. The field of molecular communications, which is just over a decade old, is one area of research in the field of communications and networking that can contribute towards understanding of virus and their infection process. This is the aim of this special issue, where we have collected a number of publications from key researchers in the field of molecular communications that can contribute towards understanding the viral properties and behavior, which can be used to support the current as well as future pandemics.