In recent years, the various health benefits of Cyanobacteria microalgae – such as Arthrospira platensis, commonly called Spirulina, an edible blue-green algae – have attracted scientific attention including micro-level examinations of its bioactive components. As a whole food and nutritional supplement, it serves as a plant protein source, which has shown positive effects across a wide range of human health concerns, from malnutrition to metabolic syndrome. Spirulina bioactives, such as essential amino acids, phycocyanin, polysaccharides, carotenoids, and chlorophyll, and essential vitamins and trace minerals, are responsible for its holistic actions against oxidative stress and inflammation, and its antiviral, antibacterial, and immune-modulating effects. Various in vitro, in vivo, and ex vivo experiments have established Spirulina's mechanism of action and its effect on immunity as a proof of concept. The phenolic compounds and extracellular metabolites released from Spirulina whole food after digestion are postulated to strengthen the epithelial lining with antibacterial effects against pathogenic bacteria, adding to its prebiotic effect on the gut microbiota (like Bifidobacterium and Lactobacillus) due to its fiber content. In this study, the digestibility of Spirulina was assessed by the determination of free amino acids and peptide release during the each phase of digestion in a simulated static digestive model system. The hypothesis bridging poor gut health to low-level inflammation and metabolic syndrome, and the potential to address those issues with nutritional supplementation, such as with Spirulina, could also be beneficial in the long run to reduce comorbid illnesses, such as those associated with the currently prevailing coronavirus disease 2019 pandemic.

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This white paper summarizes the key outcomes, topics, and recommendations from the Canada-India Healthcare Summit 2021 Conference, Biotechnology Session, held on May 20–21, 2021. In particular, the authors have focused their attention on topics ranging from research and development into the etiology and treatment of COVID-19 to novel approaches, such as ultraviolet-C disinfection and cell and gene therapy. The paper also deals with important topics around the effects of food distribution and nutrition on COVID-19 and vice versa, as well as key considerations around research and development, innovation, policy, grants, and incentives, and finally, summarizes the ways in which Canada and India, being close allies, have already begun to partner to fight the pandemic (as well as future strategies to continue this excellent progress). We also include key points raised during the summit and summarize them as part of this white paper.

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Artificial intelligence (AI) has a great impact on our daily living and makes our lives more efficient and productive. Especially during the coronavirus disease (COVID-19) pandemic, AI has played a key role in response to the global health crisis. There has been a boom in AI innovation and its use since the pandemic. However, despite its widespread adoption and great potential, most people have little knowledge of AI concepts and realization of its potential. The objective of this white paper is to communicate the importance of AI and its benefits to society. The report covers AI applications in six different topics from medicine (AI deployment in clinical settings, imaging and diagnostics, and acceleration of drug discovery) to more social aspects (support older adults in long-term care homes, and AI in supporting small and medium enterprises. The report ends with nine steps to consider for moving forward with AI implementation during and post pandemic period. These include legal and ethical data collection and storage, greater data access, multidisciplinary collaboration, and policy reform.

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Context: Efficiently diagnosing COVID-19-related pneumonia is of high clinical relevance. Point-of-care ultrasound allows detecting lung conditions via patterns of artifacts, such as clustered B-lines. Aims: The aim is to classify lung ultrasound videos into three categories: Normal (containing A-lines), interstitial abnormalities (B-lines), and confluent abnormalities (pleural effusion/consolidations) using a semi-automated approach. Settings and Design: This was a prospective observational study using 1530 videos in 300 patients presenting with clinical suspicion of COVID-19 pneumonia, where the data were collected and labeled by human experts versus machine learning. Subjects and Methods: Experts labeled each of the videos into one of the three categories. The labels were used to train a neural network to automatically perform the same classification. The proposed neural network uses a unique two-stream approach, one based on raw red-green-blue channel (RGB) input and the other consisting of velocity information. In this manner, both spatial and temporal ultrasound features can be captured. Statistical Analysis Used: A 5-fold cross-validation approach was utilized for the evaluation. Cohen's kappa and Gwet's AC1 metrics are calculated to measure the agreement with the human rater for the three categories. Cases are also divided into interstitial abnormalities (B-lines) and other (A-lines and confluent abnormalities) and precision-recall and receiver operating curve curves created. Results: This study demonstrated robustness in determining interstitial abnormalities, with a high F1 score of 0.86. For the human rater agreement for interstitial abnormalities versus the rest, the proposed method obtained a Gwet's AC1 metric of 0.88. Conclusions: The study demonstrates the use of a deep learning approach to classify artifacts contained in lung ultrasound videos in a robust manner.

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Coronavirus disease 2019 (COVID-19) pandemic has caused the millions of deaths worldwide. Much of the mortality has been associated with a cytokine storm syndrome in patients admitted to the hospital with acute respiratory distress syndrome. Vast arrays of anti-inflammatory therapies are being explored to decrease the cytokine storm to save the lives. None of these therapies have demonstrated efficacy at all stages of the disease thus underlining its complexity. The current vaccine approach is challenged by the emerging virus variants. A multi-target approaches have been used with success for human immunodeficiency virus and some types of cancer. It has been recently proposed to use the same strategy for COVID-19. With their polymolecular structure, botanical drugs may offer an option within that strategy. Thykamine™, a novel botanical drug, with demonstrated anti-inflammatory, antioxidant, and immunomodulatory effects may become the part of the therapeutic arsenal against COVID-19.

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The Canada India Health-care Summit 2021, (“CIHS 2021”), is the 3^rd Summit, focusing on healthcare, organized by Canada India Foundation, as part of an ongoing series of thematic Canada India Forums, to highlight opportunities for collaboration between Canada and India in key strategic sectors and make public policy recommendations to the respective governments. The Federation of Indian Chambers of Commerce and Industry, Toronto Rehabilitation Institute – University Health Network and the Consulate General of India in Toronto were co-organizers of the Summit. CIHS 2021 was focused on three themes: (1) artificial intelligence and its contribution to overcome COVID-19, (2) biotechnology and its contribution to overcome COVID-19, and (3) pandemic responses and initiatives. The Summit was held on May 20, 2021– May 21, 2021, and was preceded by three webinars. More than 60 healthcare experts and government leaders spoke at the Summit, to nearly 500 virtual attendees. A full report of the Summit with specific policy recommendations was made to the Canadian and Indian governments.

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Background: Breast cancer is one of the most frequent types of cancer among women and early identification can reduce the mortality rate drastically. Feature selection is one of the significant tasks in the breast cancer analysis process. Several types of feature selection algorithms have been implemented to select the most appropriate feature for breast cancer analysis. However, they have to take a longer time to converge, over-fitting problems and providing less accuracy. Hence, a hybrid bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed for feature selection. Aims and Objectives: An integrated optimized bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed to determine the relevant feature. Materials and Methods: Breast cancer mini-Mammographic Image Analysis Society database (MIAS) dataset is used for analysis. Further, median filters are used for preprocessing, Region of Interest (ROI) was utilized for segmentation, gray level co-occurrence matrix (GLCM), and texture analysis are utilized in the feature extraction process. A hybrid bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed for feature selection. K nearest neighbor (KNN) classifier is used for classification. Results: Performance of the proposed system is evaluated using standard measures and achieved the highest accuracy rate of (98.2%), specificity of (97.3%), and sensitivity of (98.3%) as compared to other relevant methods such as bat, chaotic bat, chaotic crow search, ant lion optimization, and chaotic ant lion optimization algorithm. Conclusion: The proposed BSCFC algorithm is designed to improve the performance of convergence speed and control balance between exploration and exploitation rate using five types of chaotic maps namely sinusoidal, sine, gauss, logistic, and tent maps. The results show that the BSCFC with sinusoidal maps can significantly boost the classification performance of the BSCFC algorithm in classifying the breast cancer images with reduced features, which in turn optimizes the radiologists' time for their interpretation.

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India, being the biggest producer of drugs including vaccines, emerged as a major supplier of the coronavirus vaccines for most of the countries across the world during the COVID-19 pandemic. Two vaccines, Covishield and Covaxin, were given emergency use authorization by India's drugs regulator during initial phases. Under the guidance of the National Expert Group on Vaccine Administration for COVID-19, India started its national vaccination program against SARS-CoV-2 on January 16, 2021. CoWIN (COVID-19 Vaccine Intelligence Network) is playing a vital role in real-time monitoring of COVID-19 vaccination. Having a robust Universal Immunization Program and experience of the previous immunization campaigns are advantages for India's COVID-19 vaccination program. Under Vaccine Maitri initiative, India is providing vaccines to nations across the world to ensure vaccine equity. In India, vaccination is being done in a phased manner where priority is given to the health and other frontline workers, people with age >50 years and people with comorbidities and above 18 years. As per the current policy, center government is responsible for buying 75% of all vaccines made for use in India and will distribute it to states based on their populations, disease burdens, and number of people to be vaccinated. Remaining 25% vaccines are available to be procured by private hospitals. However, existing facilities seem to be unable to meet the increased demand, and the government is considering approval for other vaccines to be imported. The world, including India, is still fighting against COVID-19 and vaccine equity is very important to win against this pandemic.

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Vaccination is the most effective approach in preventing and controlling the global public health threat of infectious diseases. Enveloped virus-like particles (eVLPs) offer advantages over other subunit vaccines because of their self-adjuvanting properties. Their optimal size and particulate structure activate antigen-presenting cells. The flexibility in manufacturing, applications, and advantages for preventing or treating disease are highlighted by the vaccine candidates described in this review. Previous preclinical and clinical studies demonstrated the immunogenicity of two eVLP vaccine candidates designed to protect against cytomegalovirus. The expression of viral envelope proteins in the eVLPs induces a robust neutralizing antibody response, which is considered a correlate of protection in many viral infections. VBI has developed two vaccine candidates against SARS CoV2, VBI-2902a, and VBI-2905a. Ongoing clinical development of these vaccine candidates will assess human safety and immunogenicity, after one or two doses in previously vaccinated and unvaccinated, individuals (NCT04773665).

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Vocal cord paralysis is a common problem faced by individuals, where the vocal cord fails to reverberate to produce sound waves. As a result, they are unable to speak out as they were speaking before. The proposed method is designed for aiding unilateral paralyzed peoples whose vocal cord fails to give the desired reverberations. The proposed system consists of voice-to-text and text-to-voice conversions. The voice of the paralyzed person is artificially reproduced by training a deep neural network with the unaffected voice of the patient. The confidence of the predicted output is improved by introducing voice-to-text conversion block along with the deep neural network. The performance metrics reveals the effectiveness of the proposed algorithm to reproduce natural sound. The similarity index is also high compared to that of other state-of-the-art techniques.

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Background: Cardiac arrhythmias are one of the leading causes of heart failure. In particular, atrial fibrillation (AFib) is a kind of arrhythmia that can lead to heart stroke and myocardial infarction. It is very important and crucial to predict AFib at an early stage to prevent heart disease. Electrocardiogram is one of the premium diagnostic tools which is used by most of the researchers for predicting irregular heartbeats. There are many works carried out in finding heart disease using machine learning classifiers. Aims and Objectives: Deep learning based hybrid Long Short Term Memory (LSTM) network is hybridized with Enhanced Whale Optimization (EWO) to minimize the network optimization and configuration issues faced in the existing models and proposed to increases the accuracy of predicting AFib. Materials and Methods: The proposed LSTM network is hybridized with a EWO technique for predicting AFib. This study uses a hybrid LSTM EWO network for classifying the various output labels of heart disease. EWO is used to predict the most relevant features from the raw dataset. Then, the LSTM model is used to predict the AFib of a patient from normal ECG data. Results: The DL based LSTM EWO achieves better results in all the performance metrics by analyzing the optimized features in feature space, training, and testing phase and successfully obtains better performance in an effective manner. LSTM improves the accuracy by reducing the number of units in the hidden layer which optimizes the network configuration. The proposed model achieves 96.12% accuracy which is 12.81% higher than RF, 15.01% higher than GB, 28.04% higher than CART, and 16.92% higher than SVM. Conclusion: The proposed model hybrid LSTM network integrated EWO for predicting the AFib. The EWO is applied for selecting the most appropriate features needed for the model to learn and produce improvised performance. The optimization and network configuration problems faced in the existing studies are avoided by choosing the suitable number of LSTM units and the size of the time window. This has been implemented through LSTM units and their window size. In addition, we made a statistical examination to prove the importance of proposed work against other models. It is observed that the experimental results attained with 96% of accuracy, better than conventional models.

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We present some recent activity in Ontario on the mathematical modeling of COVID-19 and the development of optimal strategies for vaccine distribution that take into account equity issues.

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This white paper will summarize the key topics, outcomes, and recommendations from the Canada-India Healthcare Summit 2021 COVID-19 Pandemic Response and Initiatives sessions held on May 20–21, 2021. In particular, the authors have focused their attention on topics on the effect of COVID-19 on noncommunicable diseases, depression, research on substance abuse, and post COVID-19 pain management. The authors have developed a better understanding of these conditions' interplay with COVID-19 infection. The paper also deals with important topics around the effects of NCD on COVID-19 and vice versa, as well as key considerations around research and development, innovation, policy, and finally, summarizes the ways forward in which Canada and India could collaborate strategically. We also include key points raised during the summit.

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