Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method. IEEE

Molecular Modelling and Docking Simulation for Unravelling Medicinal Properties of Soor and Pakhoi, the Lesser-Known Traditional Drinks of Himalayas

With the spread of COVID-19, it has become a high need of finding out the drugs that can combat and prevent this deadly disease. Most of the researchers are finding suitable drugs by using coronavirus spike protein as a target. In this study, we have used alternate drug targets of the coronavirus and have used the phytochemicals of the alcoholic beverage of Tons Valley, Garhwal Himalayas called Soor and Pakhoi, to check whether the drink could act as an antiviral agent. For this purpose, modern computational techniques like molecular modelling and docking simulation with the help of iGEMDOCK software have been used in finding out the permissible results in the form of high docking interaction energy. Therefore, the specific binding of the phytochemicals with the viral protein could disrupt the natural targets of the virus with the viral proteins. Hence, Soor and Pakhoi can be a deterrent source for the coronavirus infection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diversity rhythm in pontellid copepods (Pontellidae: Copepoda) from the Covelong coast pre-and post-COVID-19 lockdown, Bay of Bengal

Copepods act as indicators of the aquatic ecosystem since they rapidly respond to changes in nutrient content of the environment. Plankton samples were collected for two years from the Covelong coast, India (January to December 2019 and January to December 2021). The diversity patterns of pontellid copepods before and after the COVID-19 lockdown were analyzed. Physicochemical parameters like temperature, salinity, pH, dissolved oxygen, calcium, magnesium, nitrite, phosphate, and ammonia level for both years were measured to compare and contrast the coastal health before and after the lockdown. Six species of pontellid copepods were reported before the lockdown period and 10 species were reported after the lockdown. Physicochemical parameters like ammonia, nitrite, and phosphate levels were reduced after the lockdown. Temperature and nitrite showed a considerable negative correlation with pontellid copepods (–0.749 and –0.782), whereas dissolved oxygen showed a high positive correlation (0.732). Regression analysis was carried out to emphasize the relationship between pontellid copepods with the environment. The regression (R2) coefficient with temperature, nitrite, and dissolved oxygen were 0.571, 0.682, and 0.636, respectively. However, high species diversity was observed in February during both pre-and postlockdown periods. Redundancy analysis was used to visualize the relationship between the pontellid copepods and physicochemical parameters. The density of pontellid copepods and the level of physicochemical parameters greatly fluctuated throughout the entire study period and showed variation in density and diversity. © TÜBÍTAK.

Flavonoids and other polyphenols against SARS-CoV-2

Phytonutrients (plant chemicals) called flavonoids may be detected in almost all fruits and vegetables. They are responsible for the brilliant colors of fruits and vegetables, together with carotenoids. Flavonoids, like other phytonutrients, are potent antioxidants with antiinflammatory and immune-enhancing characteristics. Polyphenols may diminish severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) viral infection by linking to the angiotensin-converting enzyme 2 (ACE-2) linking site and limiting viral entrance, as well as regulating the severity of COVID-19 lung destruction by controlling ACE-2 expression. A new potential relationship between SARS-CoV-2 and the co-receptor dipeptidyl peptidase 4 (DPP4) may induce the expansion of newer COVID-19 treatment methods, in addition to ACE-2. After glycosylation, flavonoids' solubility in water is significantly enhanced, which increases their pharmacological actions. Antioxidant and antiinflammatory effects have been discovered in resveratrol (RSV). Quercetin was discovered to have a possible repressing consequence against SARS-CoV-2 in a computer simulation. Main protease (Mpro) had a significant preference for quercetin. According to a computer study, the flavonoids icariin, myricitrin, naringin, quercitrin, and neohesperidin have a significant interaction potential for transmembrane protease serine 2 (TMPRSS2). The bioavailability improvement of quercetin has also been shown in vivo. The novel nanovesicles exhibited extended drug durability and significant therapeutic impact compared to uncoated ones due to chitosan resistance to stomach acid. This chapter aims to explain the use of flavonoids and other polyphenols against SARS-CoV-2. © 2023 Elsevier Inc. All rights reserved.

Quinone and SARS-CoV-2

Scientists provide initial biochemical screenings with recombinant pure severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) main protease (Mpro) to discover prospective lead compounds for future coronavirus disease-19 (COVID-19) therapies because viral proteases, after polymerases, are the most likely targets for antiviral drug development. Quinones attach to cysteine-rich proteins, and COVID-19 central protease contains a Cys145-rich active site. The antiviral action of five embelin-containing plant products from Bangladesh against influenza virus A/Puerto Rico/8/34 (H1N1) MDCK infected cells was examined. All the evidence pointed to scaffold simplification and changing the shikonin naphthazarin nucleus as appropriate approaches for reducing shikonin cytotoxicity as a natural SARS-CoV-2 Mpro inhibitor. As a part of an extensive investigation of the biological properties of naphthoquinones with shikonin as a lead, and to contribute to drug discovery against COVID-19, the present study led to the development of juglone and its enhanced version as potent and effective Mpro inhibitors of SARS-CoV-2, which are promising antiviral medication candidates awaiting further analysis. A fluorescently labeled short peptide carrying a Q-S carboxyl link was used to test the inhibitory activity of synthesized quinones on Mpro of SARS-CoV-2. In the first library of chemicals, the capacity of several natural naphthoquinones and synthetic vitamin K3 was determined to inhibit SARS-CoV-2 Mpro at 10mM. According to a process described in a recent study on the suppression of SARS-CoV-2 Mpro by a methide quinone Celastrol, while attacking the carbonyl carbon, the development of the S–C covalent bond results in a tetrahedral output where the bond develops at the same carbon to which the hydroxy group is connected. © 2023 Elsevier Inc. All rights reserved.

PSAC-PDB: Analysis and classification of protein structures.

This paper presents a novel framework, called PSAC-PDB, for analyzing and classifying protein structures from the Protein Data Bank (PDB). PSAC-PDB first finds, analyze and identifies protein structures in PDB that are similar to a protein structure of interest using a protein structure comparison tool. Second, the amino acids (AA) sequences of identified protein structures (obtained from PDB), their aligned amino acids (AAA) and aligned secondary structure elements (ASSE) (obtained by structural alignment), and frequent AA (FAA) patterns (discovered by sequential pattern mining), are used for the reliable detection/classification of protein structures. Eleven classifiers are used and their performance is compared using six evaluation metrics. Results show that three classifiers perform well on overall, and that FAA patterns can be used to efficiently classify protein structures in place of providing the whole AA sequences, AAA or ASSE. Furthermore, better classification results are obtained using AAA of protein structures rather than AA sequences. PSAC-PDB also performed better than state-of-the-art approaches for SARS-CoV-2 genome sequences classification.

Analysis of public reactions to the novel Coronavirus (COVID-19) outbreak on Twitter

PurposeThe novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter.Design/methodology/approachThis study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people's reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets.FindingsSeven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19's emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were "COVID-19,” "Coronavirus,” "Chinese virus” and the most frequent and high confidence sequential rules were related to "Coronavirus, testing, lockdown, China and Wuhan.”Research limitations/implicationsThe methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques.Social implicationsThis study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide.Originality/valueAccording to the authors' best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.

S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures

This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.

S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures

This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.

Analysis of rising trend of online streaming services during pandemic situation of COVID-19

The implementation of nationwide lockdown during this pandemic situation has resulted into huge impact on the living style of everyone and affected various industries and companies during this lockdown resulting into a boom for those who were already present in the market with their stronghold in the online market. Some of these were the digital payment method, streaming service providers, online study platform, etc. Some of these services even reported a boom in their profit and market shares. The goal of this study was to identify the major streaming service providers in India and assess whether they made a profit during the lockdown and increase in the number of subscriber, increase in the streaming time and increase in the customer satisfaction. The result of the study shows that there was increase in the number of subscribers not only in subscriber but also increase in the streaming time too. Copyright © 2022 Inderscience Enterprises Ltd.

Global overview of SARS-CoV-2 induced COVID-19 in 2020: biological characterization, epidemiology with social, economic and environmental implications

Background: COVID-19 is a global pandemic initiated in January 2020 that caused 79 million cases and more than 1.7 million deaths worldwide. The causative agent of COVID-19 is Severe Acute Respiratory Syndrome Coronavirus-2, a member of Betacoronvirus. COVID-19 patients are classified into asymptomatic, mild symptomatic, and severe symptomatic cases. Objectives: To review the prevalence, therapeutic interventions for the treatment, vaccination, and containment of COVID-19 in four quarters of 2020, emphasizing the advancements in biological studies, and the social, economic, and environmental impact of the pandemic. Methodology: Data of COVID-19 spread, identification, prevention, and control measures was analyzed. The impacts of pandemic on society, economy, and the environment were assessed.

CAPIRS: COVID-19-Based Application Programming Interface Recommendation System for the Developers

Context: From the past few years, Application Programming Interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs;however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19;a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system's output is validated by expert developers and evaluated on 120 expert developers' queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer's queries also to build Integrated Development Environment for the developers. © 2022 World Scientific Publishing Co.

Impact of Covid-19 Virus on Ophthalmology Residents and Fellows in a Tertiary Care Hospital

Purpose: To determine the impact of COVID-19 pandemic on the training of Ophthalmology residents and fellows in a tertiary care hospital. Study Design: Cross sectional survey. Place and Duration of Study: The study was conducted in a tertiary care hospital, Peshawar from august 1st to august 20th 2020. Methods: A self-designed questionnaire was distributed among 50 ophthalmology residents and fellows. Under-graduate students, house officers and post-graduate trainees from other specialties were excluded. Questions comprised of demographic data, and questions which were meant to investigate the changes experienced by the trainees during COVID-19. The data was analyzed by SPSS Software (Version 19). Results: Among 50 participants of this study, 16 (32%) were females and 34 (68%) were males. The age ranged from 27 to 35 years. There was no statistically significant difference in the perspectives of resident trainees and fellows regarding negative impact of COVID-19 on their training. Ninety-five percent of the residents and fifty five percent of the fellows had effect on their clinical skills with p values of less than 0.05. Hundred percent residents agreed that online case presentation could not replace the traditional long rounds and simulator based training could improve the surgical skills in pandemic. Twenty five (60.97%) trainees and 6 (66.66%) fellows mentioned that pandemic affected them psychologically and they felt fear while working. Conclusion: COVID-19 has adversely affected the training of post graduate trainees. Training directors should ensure to provide modern technological tools to improve trainees’ clinical and surgical skills until the crisis is over. © 2021, Ophthalmological Society of Pakistan. All rights reserved.

COVID-19 Lockdowns Afford the First Satellite-Based Confirmation That Vehicles Are an Under-recognized Source of Urban NH3 Pollution in Los Angeles

In situ measurements have suggested vehicle emissions may dominate agricultural sources of NH3 in many cities, which is alarming given the potential for urban NH3 to significantly increase human exposure to ambient particulate matter. However, confirmation of the prevalence of vehicle NH3 throughout a city has been challenging because of mixing with agricultural sources, and the latter are thus routinely assumed to dominate. Here we report vehicle NH3 emissions based on TROPOMI NO2 and CrIS NH3 (0.152 kg s(-1)) that are consistent with a model-based estimate (0.178 kg s(-1)) and show that COVID-19 lockdowns provide a unique opportunity for making the first satellite-based constraints on vehicle NH3 emissions for an entire urban region (western Los Angeles), which we find make up 60-95% of total NH3 emissions, substantially higher than the values of 13-22% in state and national inventories. This provides a new means of constraining a component of transportation emissions whose impacts may rival those of NOx yet which has been largely under-recognized and uncontrolled.

A MULTICENTER CASE SERIES OF COVID-19 INDUCED ACUTE PANCREATITIS AT 4 ACADEMIC CENTERS IN NEW YORK CITY

Background: SARS-Cov-2 infection (COVID-19) and associated gastrointestinal manifestations have been well documented during the pandemic. To date, several centers have reported isolated cases of COVID-19 and its effect on the pancreas. Here, we present a case series of 13 patients with acute pancreatitis (AP) due to COVID-19, which represents one of the larger case series to date. Methods: A retrospective review was performed from 3/1/2020 through 4/1/2020 at 4 NYC academic medical centers. Patients with a diagnosis of AP and COVID-19 were included. AP was diagnosed based on AGA criteria. COVID-19 infection was confirmed via nasopharyngeal viral PCR testing. All patients with a prior history of AP were excluded. Patients with apparent/suspected etiologies of AP (including gallstones, alcohol, hypertriglyceridemia, post ERCP, medication, and other viral etiologies) were excluded. 13 patients met our inclusion and exclusion criteria. Outcomes studied included mortality, ICU admission, length of stay, BISAP scores on admission and at 48 hours. Results: 7 of the 13 patients in this cohort were African American, 8 of 13 were men, and the median age was 51 years of age. The youngest patient was 18 years old and the oldest patient was 71 years old. Of the 13 patients, 5 patients died during their hospital course. Of those 5 who passed, 4 were African American, and all 5 were > 50 years of age. 6 of the 13 required ICU level of care. The mean length of stay for all patients was 23 days. On admission, 4 patients had BISAP scores > 3, at 48 hours 3 patients had BISAP scores > 3. Discussion: We report the characteristics of 13 patients with confirmed SARS-Cov-2 infection and AP without other common etiologies. We suspect that SARS-Cov-2 was a direct cause of AP in these patients. 5 patients died (38.5%) due to multiorgan failure from Acute Respiratory Distress Syndrome. Patients with COVID-19 and AP had a higher mortality rate than the overall mortality reported with COVID-19 during the same period. The mortality of patients in our series far exceeds the reported mortality in mild or moderate AP (less than 1%)1,2. Currently molecular theories suggest that viral attachment to ACE-2 receptors on pancreatic acinar cells leads to apoptosis, inhibition of nitric oxide production, and programmed cell death that ultimately leads to AP. Conclusion: This case series indicates a possible association between COVID-19 and AP and the increased mortality in this subset of patients. Further research is needed concerning the molecular mechanisms and clinical management of this entity. Larger studies are needed to confirm the worse outcomes with AP associated with COVID-19. Ref: 1. Russo MW et al. Digestive and liver diseases statistics, 2004. Gastroenterology. 2004;126:1448–53. 2. Triester SL et al. Prognostic factors in acute pancreatitis. J Clin Gastroenterol. 2002;34:167–76.

COVID-19 Genome Analysis Using Alignment-Free Methods

Examining the genome sequences of the novel coronavirus (COVID-19) strains is critical to properly understand this disease and its functionalities. In bioinformatics, alignment-free (AF) sequence analysis methods offer a natural framework to investigate and understand the patterns and inherent properties of biological sequences. Thus, AF methods are used in this paper for the analysis and comparison of COVID-19 genome sequences. First, frequent patterns of nucleotide base(s) in COVID-19 genome sequences are extracted. Second, the similarity/dissimilarity between COVID-19 genome sequences are measured with different AF methods. This allows to compare sequences and evaluate the performance of various distance measures employed in AF methods. Lastly, the phylogeny for the COVID-19 genome sequences are constructed with various AF methods as well as the consensus tree that shows the level of support (agreement) among phylogenetic trees built by various AF methods. Obtained results show that AF methods can be used efficiently for the analysis of COVID-19 genome sequences. © 2021, Springer Nature Switzerland AG.

Evaluation of serum procalcitonin level as a biomarker for disease severity in COVID-19 patients.

The pandemic of coronavirus disease 2019 (COVID-19) has infected millions of individuals around the globe. Forecasting the COVID-19 severity is essential, and various biomarkers could be used to evaluate it. The current study was therefore aimed to evaluate the serum pro-calcitonin (PCT) level as a biomarker for bacterial co-infection and disease severity in COVID-19 patients. A total of 430 COVID-19 positive individuals were examined, in which 332 (77.2%) were male individuals while 98 (22.8%) were female individuals. Among the examined samples, 281 were classified as moderate (PCT value 0.07 ± 0.06 ng/mL), 95 were severe (PCT value 0.5 ± 0.4 ng/mL), and 54 were classified as critical (PCT value > 1 ng/mL) individuals. The increase in the total serum level of PCT was observed with the severity of the disease (p < 0.05). The statistical analysis represented no association of PCT value with gender (p 0.9650) while revealed a significant association (p < 0.001) with the age and PCT value in COVID-19 patients. It can be concluded that the serial PCT measurement could determine the prognosis of the disease and the presence of bacterial co-infection in COVID-19 patients. Further exploration of the topic is needed to evaluate the effect of different therapies on the PCT level and to prescribe specific treatment options for coinfection.

MEMS Sensors for Diagnostics and Treatment in the Fight Against COVID-19 and Other Pandemics

As the world is going through an existential global health crisis, i.e., the outbreak of novel coronavirus-caused respiratory disease (Covid-19), the healthcare systems of all the countries require readily available, low cost and highly precise equipment for the rapid diagnostics, monitoring, and treatment of the disease. The performance and precision of this equipment are solely dependent on the sensors being used. The advancement in research and development of micro-electro-mechanical systems (MEMS) based sensors during recent years, has resulted in the improvement of the conventional equipment being used in biomedical and health care applications. Microfluidics (Lab-on-a-chip) and MEMS sensors are now being used extensively for quick and accurate detection, progression monitoring, and treatment of various diseases including Covid-19. The ongoing miniaturization and design improvements have resulted in more precise sensors and actuators for healthcare applications, even for micro and nanoscale measurements in drug delivery and other invasive applications. This article aims at reviewing the MEMS sensors being used or which can be used in the important equipment for the detection and treatment of Covid-19 or other pandemics. An insight into various designs and working principles of the research-based and commercially available MEMS sensors is presented. The study highlights the role and importance of MEMS sensors in the improvement of equipment with conventional sensors. MEMS sensors outperform the conventional sensors due to their small size (1μm-1mm), negligible weight, prompt response, precise measurements, portability, and ease of integration with electronic circuitry. CCBY

Using artificial intelligence techniques for COVID-19 genome analysis.

The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.