Many genetic diseases that are responsible for muscular disorders have beendescribed to date. Gene replacement therapy is a state-of-the-art strategy usedto treat such diseases. In this approach, the functional copy of a gene isdelivered to the affected tissues using viral vectors. There is an urgent needfor the design of short, regulatory sequences that would drive a high androbust expression of a therapeutic transgene in skeletal muscles, thediaphragm, and the heart, while exhibiting limited activity in non-targettissues. This review focuses on the development and improvement ofmuscle-specific promoters based on skeletal muscle α-actin, musclecreatine kinase, and desmin genes, as well as other genes expressed in muscles.The current approaches used to engineer synthetic muscle-specific promoters aredescribed. Other elements of the viral vectors that contribute totissue-specific expression are also discussed. A special feature of this reviewis the presence of up-to-date information on the clinical and preclinicaltrials of gene therapy drug candidates that utilize muscle-specific promoters. Inherited muscle disorders are diagnosed in 4–5 people per 20,000. These dTable). A promisingstrategy for treating such disorders is gene replacement therapy,which delivers a genetic construct with a functional copy of a gene(transgene) into muscle tissues. Adeno-associated virus (AAV) vectors areconsidered to be the most promising and safe for in vivodelivery of therapeutic genes [Table.Many inherited muscle diseases are caused by a protein deficiency resultingfrom mutations in the corresponding gene .Strong This review focuses on strategies for designing and improving naturalmuscle-specific promoters. It highlights current approaches to the engineeringof synthetic promoters, and it discusses their application in gene therapyconstructs.Fig. 1)[Eukaryotic gene transcription is controlled by two classes of regulatoryelements: promoters with core and proximal regions and distal regulatoryelements .The core  promoter is a specialized DNA sequence whichdirects transcription initiation and is located -50 to +50 bp from thetranscription start site (TSS) .There aThe core promoter supports the assembly of the preinitiation complex consistingof RNA polymerase II and basal transcription factors (TFs). Core promotersdiffer widely in terms of the conserved motifs that define their properties.Initiator (Inr) is the most common element of core promoters. The Inr sequencesurrounds the TSS and is recognized by the multiprotein transcription factor IID (TFIID) . AnotherThe proximal promoter typically encompasses ~50–1,000 bp upstream of theTSS and contains many transcription factor binding sites (TFBSs) . The uniThe distal regulatory elements of the eukaryotic gene include enhancers,silencers, insulators, and the locus-control regions (LCRs). Enhancer elementsare of particular interest for promoter engineering. Enhancers are DNAsequences ~100- to 1,000-bp long that can increase the transcription of genesregardless of their orientation and distance to the target promoter . These eA straightforward way to design a muscle-specific promoter is to use thenaturally occurring promoter of the gene with high expression levels inmuscles. To reduce the size of the full-length natural promoter, only the corepromoter and some proximal elements are left and supplemented with distalenhancers . Poorly Human skeletal α-actin promotersIn their early attempts to create muscle-specific promoters, researchersfocused on the promoter regions of proteins abundant in myocytes. Actin is themain protein that constitutes the sarcomere, the basic contractile unit ofstriated muscle. In higher vertebrates, six major isoforms of actin aredistinguished, each encoded by a separate gene: skeletal and cardiac muscleα-actin, smooth muscle α-actin, smooth muscle γ-actin, and twoisoforms with ubiquitous expression, cytoplasmic β-actin, and cytoplasmicγ-actin . The humHSA gene, as well as the first exon and the fragment of thefirst intron, is necessary and sufficient for muscle-specific expression in acell culture [The first studies demonstrated that the region located 2,000 bp upstream of theFig. 2A).Three mFig. 2A).HSA gene extending from c -2,000 bp to +7,500bp was used togenerate a transgenic mouse line in [The fragment of  line in .It was  line in , mice wi line in , and a m line in , as wellFig. 2B).The transgene was activelyexpressed in skeletal muscles and the heart, but no transgene expression wasdetected in the liver.Another truncated variant of the human HSA promoter was used in the AAV vectorto treat Duchenne muscular dystrophy .A high Fig. 2C).In the myoblast cell culture, thetransgene expression level ensured by this promoter was higher than thetransgene expression levels induced by the CMV promoter and the full-length HSApromoter, while this promoter was as active as the CMV promoter in nonmusclecell cultures. It appears that, although the addition of the universallyexpressed CMV promoter increased the activity of the chimeric promoter, itbecame tissue non-specific.The chimeric HSA promoter was used to produce coagulation factor IX in musclesand treat hemophilia B . Thisprin vitro andin vivo experiments, as well as to generate transgenic mice.The regulatory regions of the homologous chicken, rat4343, andbIn general, the skeletal muscle α-actin promoters exhibited a highexpression level and specificity in muscle cells; however, they are used inmodern studies not so frequently, because of their large size.Muscle creatine kinase promotersMCK/CKM,creatine kinase, M-type) gene is the second-most abundant mRNA inskeletal muscles [ MCKgene is also highly active in the cardiac muscle and istranscriptionally activated during the differentiation of myoblasts intomyocytes [The transcript of the muscle creatine kinase ( muscles . MCK catmyocytes .MCK promoter has been characterized well both invitro and in vivo. One of the major regulatoryregions of the mouse Mck gene is the muscle-specific 206-bpenhancer located within the -1256...-1050 region[The 0 region.This en0 region.MCKgene [ACTA1/HSA gene. (A) –the full-length HSA promoter includes the distal region, the proximal region(PR), and the basal region, which consists of the noncoding exon (+1...+90) andthe first intron fragment (+91...+239); (B) – shortenedversion of the HSA promoter; (C) – the chimeric HSA/CMVpromoter consisting of a fragment of the HSA promoter and the CMV promoter inall types of muscles [The proximal promoter (358 bp) is the key regulatory element of the MCKgene . As such muscles . The dat muscles .construct CK6,consisting of an enhancer (206 bp) and a proximal promoter (358 bp),ensured high muscle specificity. However, theexpression level in the muscles was ~12% compared to that attained using asimilar construct with the CMV promoter; the expression level in the heartremained low [Based on the discussed-above regulatory sequences, several series of small MCKexpression cassettes for adenoviral vectors were developed and tested. Thus, tined low .MHCK7 was developed to achieve a highexpression level of the transgene in the cardiac muscle[MCK was added to the promoter. TheTSS-containing sequence was replaced with the Inr consensus sequence. The mostimportant modification was the following: a 188-bp enhancer from the mouseα-myosin heavy chain gene (α-Mhc), which ensures ahigh expression level in the heart, was added to the expression cassettedescribed above [The chimeric promoter . It incled above . The newdMCK and tMCK promoterswere developed in another laboratory, almost simultaneously with the MHCK7promoter [The promoter . In thesCK8were developed atthe very same laboratory where the CK6 and MHCK7 promoters had been previouslycreated. Thus, the CK8 promoter (MHCK7 with two copies of the MCK enhancerinstead of the α-myosin heavy-chain enhancer) was used for intramuscularAAV8-mediated delivery of the growth hormone gene[CK8e construct carried a number of deletions in theenhancer and proximal promoter of muscle-type creatine kinaseand was more active than the CMV promoter in a differentiated humanmyoblast culture [However, the search for efficient muscle-specific promoters continued. Theconstructs named one gene. In miceone gene.It was one gene.The 436 culture . Deletio culture .MCK gene are widely used in the gene therapy vectors thatare currently undergoing preclinical and clinical testing(Table).The MHCK7 promoter is included in a vector undergoingpreclinical studies for the treatment of the Pompe disease[Due to a high specificity and activity in muscle tissues, promoters based onthe  disease. Clinica disease. The MHC disease. In the  disease. The CK8 disease; another disease. AcliniDesmin gene promotersDES gene and is one of the earliest myogenic markers[Desmin is a muscle-specific cytoskeletal protein belonging to the intermediatefilament family . It is e markers. This pr markers.  A functional analysis of the 5’-flanking region of the human desmin generevealed an enhancer (-973...-693) .The 5’-Fig. 4A)[The full-length dystrophin gene under the control of the human desmin promoter in a plasmid vector was used for intraarterialdelivery in mice.This prFig. 4B)[A variant of the human desmin promoter (715 bp) with a deleted silencer wasused in a comparative study of muscle-specific promoters for intravenous AAV9-mediated transgene delivery.In thatFig. 4B).Fig. 4C). Theactivity of this promoter was comparable to that of the CMV promoter inexperiments in vitro and in vivo, being evenhigher than that of the human MCK promoter (-1061...+28).Another variant of the human desmin promoter was used in a study focused ontransgene delivery in mouse muscles using lentiviral vectors. The regTable)focused on the Pompe disease [Fig. 4D)was evaluated inpreclinical studies focused on the therapy of myotubular myopathy, a geneticdisorder caused by mutations in the MTM1 gene[Desmin promoters were used in a number of studies (-250...+13),and the cardiac troponin T promoter (cTnT) (-300...+1). All threepromoters were superior to the ubiquitous EF1α promoter in theirexpression levels of transgene in the heart. Besides the cardiac expression,the cTnT and MLC2v promoters also drove expression in the liver and spleen,while the transgene, under the control of αMHC, was active exclusively inthe heart [For instance, to treat the cardiac variant of the Fabry disease, lentiviralconstructs with cardiac-specific transgene expression were developed . Three dhe heart . Howeverhe heart . MLC was used for AAV9-mediateddelivery of microdystrophin in a mouse heart [A chimeric promoter comprising the CMV-IE enhancer ligated to the 1.5-kbpfragment of the rat promoterse heart . The carse heart .TNNI1) gene and consisted of three copies of the enhancer(-1036...-873) and the minimal promoter of the TNNI1 gene witha portion of the first exon (-95...+56) [in vivo and in vitroexperiments. Let us mention that the ΔUSEx3 promoter delivered byadenoviruses ensured a transgene expression level comparable to that induced bythe synthetic SPcΔ5-12 promoter [The ΔUSEx3 promoter was developed on the basis of the human troponin I(5...+56) . The ΔUSpromoter ; howeverThe unc45b gene encoding the muscle-specific myosin chaperone in fish was alsoused to develop a muscle-specific promoter . Thus, tTo summarize, the promoters discussed in this section proved capable of drivingmuscle-specific expression but were less potent than the promoters based on theactin, muscle creatine kinase, or desmin genes.Synthetic promoterssynthetic promoters. This strategy enables one to engineerpromoters with defined properties, such as size and the expression profile ofthe transgene.A groundbreaking approach in promoter design is the creation of novelin silico analysis can substantially simplify the detection ofregulatory regions.The development of synthetic promoters relies on computational algorithms,which are used to identify regulatory sequences and TFBSs within the genome, aswell as to predict the promoter regions , 74, 75.et al. analyzed the sequences ofstrong muscle-specific promoters and identified the common binding sites ofmyogenic TFs  within their structure[Fig. 5).As a result, a library consistingof more than 1,000 promoter variants was created. The synthetic promoterlibrary was screened in primary myoblasts, and, based on the results, theSPc5-12 promoter was selected. SPc5-12activity in muscle fibers was sixfold higher than that of the CMV promoter. Thein vivo experiments confirmed that the SPc5-12 promoter isinactive in undifferentiated myoblasts and in various nonmuscle cell lines.In their pioneering study, Li tructure. These TThe SPc5-12 promoter was used to drive transgene expression in animal models ofDuchenne muscular dystrophy , thePomet al. used a similar strategy to construct syntheticpromoters [cis-regulatory elements of eukaryotic promoters . These motifs were randomly assembled to construct a library consistingof 1,200 primary clones, which were tested in vitro andin vivo. The strongest transcriptional activity was achievedwith the SP-301 promoter; it was 6.6times more active than the CMV promoter 2 days after intramuscular delivery ofthe construct in mice and remained active for at least a month. Many promotersachieved a higher in vitro activity compared to the CMVpromoter, but they were less active in vivo. The tissuespecificity of the SP-301 promoter was confirmed in transgenic mice. This studyonce again demonstrated the advantage of the strategy of designing syntheticpromoters using a combination of TFBSs and also highlighted the the benefit ofincluding viral motifs besides muscle-specific TFBSs for enhanced expressionlevels.Liu romoters . The pro et al.conducted an in silico analysis of various tissuespecificgenes and identified four clusters that con- sisted of a combination of thebinding sites of myogenic transcription factors. The first cluster was thepreviously discussed enhancer of the human desmin gene (-970...-826) [Ckm gene. The promoterthat contained all four elements (the MH promoter) ensured thehighest expression level in the muscle cell culture, being superior to thedesmin and CMV promoters, as well as the remaining hybrid promoters.Interestingly, intron made the greatest contribution to the expression level,while deletion of one or the two enhancers or even the core promoter did notsignificantly alter the expression level. To test the in vivoactivity of the hybrid promoter, AAV2/9 carrying the reporter gene wasdelivered intravenously in mice under the control of this promoter. Theactivity of the MH promoter in the cardiac and skeletal muscles was higher thanthat of the desmin and CMV promoters; however, the MH promoter did not inducetransgene expression in the liver [Another efficient approach, consisting in designing hybrid promoters, hasalready been partially discussed for the MHCK7 promoter . In the ...-826) ; the remhe liver .cis-regulatory modules(Sk-CRM) was employed in the next study [Sk-CRM4 module was selected for further studies.Six weeks after systemic delivery using AAV9, the Sk-CRM4 chimericpromoter enhanced the activity of the desmin promoter by 200–400 times indifferent skeletal muscles, the diaphragm, and the heart, while remaininginactive in non-target tissues. Moreover, the SkCRM4/Des promoter attained a25–173 times higher expression in different muscles as compared to theCMV promoter and also outperformed the Sk-CRM4/ SPc5-12 and SPc5-12 promoters.Therefore, the computationally designed Sk-CRM4/Des chimeric promoterdemonstrated improved muscle-specific performance as compared to the otherpromoters commonly used for muscle gene therapy, with length  beingits only drawback.The strategy of using muscle-specific xt study . TFBSs wTo conclude, implication of synthetic promoters in gene therapy holds greatpromise. Success in synthetic promoter engineering largely depends on thequality of the bioinformatic tools and efficient screening of the proposedvariants. Expansion of the regulatory element databases, revealing new TFs, andimprovement in the software for promoter identification will undoubtedlycontribute to further development in this area of research , 74,75.When developing gene therapy drugs, one should comprehensively evaluate theexpression of the genes of interest at the proper position and time, as thisdepends not only on promoter activity, but on other factors as well. Manyfactors affect the transgene expression at the post-transcriptional level.Fig. 6).The presence of the intron increases RNAstability in the nucleus due to the incorporation of mRNA into the spliceosome[Ckm gene makes the greatestcontribution to the transgene expression level. The MVM intron enhancedtransgene expression during an AAV-mediated delivery of coagulation factor IXmore than 80-fold compared to the construct without intron [Expression of the target gene can be enhanced due to the presence of an intronin the vector, which is usually positioned between the promoter and the codingregion .Thus, the 600-bp post-transcriptionalregulatory element of the woodchuck hepatitis virus (WPRE) delivered using AAVled to a manifold enhancement of transgene expression in the liver, brain, andmuscles [Along with the promoters, other muscles . WPRE prmuscles .Fig. 6)[A different approach can also be used to achieve tissue specificity: not onlyinducing expression in the target tissues, but also suppressing it innon-target organs through RNA interference mechanisms. For thiFig. 6). If tranFig. 6).Table), capsids are also modifiedto design novel, genetically engineered vectors and improve the targeteddelivery [.) [A proper choice of viral vector also plays a significant role in the deliveryof the transgene into the target organs and tissues. Along with the naturallyoccurring serotypes of AAVs (delivery . There idelivery .Transgevery [.) . An elabThe efforts to develop optimal muscle-specific promoters started more than 30years ago and are still underway. Early versions of natural muscle-specificpromoters consisted of the regulatory regions of the actin, desmin, and musclecreatine kinase genes and exceeded 1 kbp in length. The latest generations ofsynthetic promoters contain combinations of TFBS from common muscle-specificgenes, are much shorter, but the expression efficiency achieved by thesepromoters is comparable to or higher than that of natural promoters .in vitro studies, one should keep in mind that promoteractivity in this case does not necessarily coincide with in vivoactivity [It has been demonstrated in many studies that transcription factors and theirbinding sites in vertebrates are appreciably conserved , 72. Thaactivity .. Differentmuscle groups and types of muscle fibers are affected in patients withdifferent disorders [Since the group of genetic muscular disorders is heterogeneous, there is nouniversal promoter that could be used to develop vectors intended for thetreatment of all diseases. This can be largely attributed to the features ofthe pathogenesis and the different functions of the proteins whose deficiencyor dysfunction causes a given disorder isorders . The gai


A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients. The novel coronavirus disease 19 (COVID-19), caused by the highly pathogenic SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) virus, has resulted in more than 234 million infections and about 4.8 million global deaths up until October 2021, according to the World Health Organization (WHO) [The outcome of critical SARS-CoV-2 infection during the on-going pandemic has been found to be highly destructive and life-threatening for the host organism. It has been established that SARS-CoV-2 primarily affects the respiratory tract and in severe circumstances this virus can induce pneumonia and acute respiratory distress syndrome (ARDS) as well as several extrapulmonary manifestations. ARDS is a severe form of lung injury characterized by noncardiogenic pulmonary edema, bilateral pulmonary infiltrates and profound hypoxemia leading to respiratory failure. In the case of an advanced stage of lung injury or delayed diagnosis, even antibiotic treatment can become insufficient to avoid development of acute lung injury . The popARDS has also been established as one of the major hallmarks of severe COVID-19 infections. In fact, around 40% of COVID-19 patients developing pneumonia also developed ARDS and around 70% of deaths in COVID-19 infected critically ill patients occurred as a result of ARDS . A cliniEven though the on-going SARS-CoV-2 pandemic has unprecedentedly and severely impacted our health-care system and economic growth, a conclusive cure for it still does not exist. Though the majority of COVID-19 cases are mild, around 5–8% of patients develop life-threatening critical illnesses. The vaccination drives for COVID-19 are in progress in most countries, and seemingly, it may require a considerable amount of time before the vaccine reaches all over the globe. Additionally, growing numbers of incidents have been reported worldwide, where patients who survived the illness of COVID-19 and tested negative clinically, still continue to struggle through the symptoms of the disease and post virus-clearance complications, including significant lung damage and cardiac arrhythmias ,11. SeveThe lung, despite its general quiescent state, has a remarkable regenerative capacity, in which resident progenitor cell populations proliferate and differentiate into a variety of cell types in response to injury . Recent + basal cells was found to be enriched in the trachea and larger airways. In addition, a population of extensively proliferating AT2 cells was observed in the intrapulmonary airways and alveoli of these patients. This suggests that distinct populations of proliferating progenitor cells become enriched at the area of lung damage in order to regenerate the damaged trachea and alveoli following SARS-CoV-2 infection [+ cells in critical COVID-19 patients, both of which could act together synergistically to restore epithelial barriers and regenerate alveolar cells [To date, rodent models have been the most effective for providing direct experimental evidence for in vivo alveolar regeneration. However, despite the conservancy in lung development among mammals, the structure, cellular composition, molecular mechanisms, and reactions of mouse and human lungs are vastly different ,25. In rnfection . Zhao etnfection . Their sar cells . One othar cells . It was ar cells . The regenerative process is often capable of restoring the function and structure of the lung after damage. In the case of prolonged and pervasive damage, the lung is healed by an accumulation of fibers at the site of injury. Although this fibrotic repair mechanism produces scar tissue, it gradually results in epithelial and endothelial damage causing a significant loss of lung function and an increase in morbidity . PulmonaIt has been established that interactions between different types of cells is very crucial for the onset of fibrosis. In pulmonary fibrosis, mesenchymal cells and fibroblasts are recognized as key cell types which partake in heavy ECM deposition, leading to fibrosis and decreased lung function . These cViral infections play a key role in fibrogenesis and increase the risk of fibrosis . These iThe level of inflammation in the lungs following tissue injury is a crucial factor to determine whether regeneration or fibrotic repair will take place . Both reDue to lack of post-injury lung samples, suitable animal models, and regeneration-specific molecular markers, alveolar regeneration in humans is still not well characterized. In this pursuit, single-cell RNA sequencing (scRNA-seq) technology has revealed the cellular architecture of the lung and the intermediate state of AT2 to AT1 transition known as Krt8+ cells . AT2 andGiven the increasing number of incidences of post-COVID lung fibrosis, it is paramount to understand the underlying molecular mechanisms and develop effective targeted strategies for its treatment. A potential approach in this regard is to design therapeutic approaches that can promote regeneration over repair mechanisms. For instance, use of corticosteroids such as dexamethasone indirectly affects this by delaying the repair rate and reducing inflammation . The sucmiRNAs (micro-RNAs) are short (~22 nucleotides) non-coding RNAs which perform regulatory functions in the cell by targeting mRNA. Specific miRNAs of a host organism are differentially expressed when infected to promote viral RNA destruction and to control the immune response. From the pathogen side, viral-derived miRNAs play a role in immune evasion and their regulation of the viral cycle, and may also contribute to tumorigenesis ,66. In tEpigenetics play a crucial role in host–pathogen interactions. All cells in a human organism essentially share the same genome, but they differ in expressing their genes by another wall of regulation which creates an effect on their functions and characteristics thereby leading to distinct cell lineages. Epigenetics causes changes in the phenotype which are actually the result of changes in chromosomes  rather than in the DNA sequence itself . EpigeneACE2) gene, which codes for the entry receptor protein for SARS-CoV-2, also undergoes epigenetic regulation such as DNA methylation [ACE2 gene are directly associated with clinical factors for the outcome of the SARS-CoV-2 infection including age and gender [ACE2 methylation, SARS-CoV-2 can also cause alteration in potent epigenetic systems such as replication behavior, antigen presentation, immune response, epigenetic signaling, and histone mimicry. However, detailed mechanisms behind these processes are yet to be investigated [ACE2 but also coreceptors) play a pivotal role in the pathophysiology and the severity of COVID-19 disease. Thus, targeting of epigenetic modifications induced by the SARS-CoV-2 in the patient  can help to design novel approaches for therapeutic intervention  while direct targeting of methylation patterns is far more challenging [It has recently been demonstrated that SARS-CoV-2 also possesses the capability to manipulate the human host epigenome in order to evade the host immune response. In fact, the genome of SARS-CoV-2 potentially contains at least 50 m6A (N6-methyladenosine) modification sites. The alterations in these m6A methylation patterns of the virus can substantially affect various stages of viral infection including viral entry, replication, and host immune evasion . At the hylation ,101. Anod gender . Other tstigated . Furtherstigated . Taken tllenging . A disease phenotype is not just a result of the defect in a single gene, but a consequence of perturbations in the biomolecular interactions at intra- or intercellular levels. Thus, to investigate the molecular complexity in a pathological process, it is crucial to understand the biomolecular interactions governing the whole biological system rather than focusing on a single genetic mutation. In this context, network-based systems biology techniques have emerged as effective tools for understanding the complex behavior of biological systems . BiologiThe network-based computational approach has also been employed recently to investigate the molecular mechanisms involved in the SARS-CoV-2 infection. For instance, in a recent study, a PPI network was constructed in order to analyze the molecular basis of the effect of comorbidities such as diabetes or cardiovascular diseases on severity of progression of infection in COVID-19 patients . In seveWe also highlight here how we can construct a cellular signaling network of AT2 cells to elucidate critical factors involved in the state transition from AT2 to AT1 cells. By integration of a prior knowledge network (PKN) with omics, a data condition-specific signaling network can be modelled ,131, andKeyPathwayMiner is a tool to integrate PKN with omics datasets  to extract connected subnetworks with a high number of differentially expressed genes . KeyPathWe hereby propose that KeyPathwayMiner  can be sThe authors in  treat thIn control theory, biological networks are often analyzed to identify the driver nodes, which should be actuated by control signals for acquiring control over the system under study ,147. On Similarly, this algorithm can accurately identify the crucial proteins which should be modulated for phenotype transitions of p53-mediated DNA damage response network into normal, arrest, and apoptosis states , and lung-on-chip (LOC) models . At the In recent years, network-based system biology approaches have been established as robust tools for studying complex biological systems. In this review, we support the view that reprogramming of progenitor AT2 cells is of central importance for triggering regeneration. Understanding and activating the AT2 transition through pharmaceutical or biological therapies can potentially accelerate the alveolar regeneration. The hijacking of the lung-protective action of ACE2 receptors boosted by interferon for virus entry is a particular challenge of the COVID-19 infection. Hence, preventing this virus entry is clearly a key handle to treat COVID-19 infection as supported by the most recent impressive and promising clinical trial results from AstraZeneca for a two-antibody entry prevention strategy (AZD7442). However, for treatment of the chronic cases (long COVID-19 syndrome) and to booster recovery from COVID-19 infection, we need and expect to uncover new regeneration-specific steering proteins and markers but, even more so, improved network control points such as specific miRNAs and epigenetic modifications as outlined here to use as improved handles that will enhance our ability to boost regeneration processes not only in COVID-19 infection but also in other chronic and acute lung diseases.


We examined the association between changes in physical activity and leisure screen time and mental health outcomes during the early stages of the recommended COVID-19 stay-at-home period in a national sample of Mexican adults aged 18 years or older.A cross-sectional online survey conducted from May 29 through July 31, 2020, among 1,148 participants, reported time spent in physical activity and leisure screen time during a typical week before (retrospectively) and a week during the COVID-19 stay-at-home period. Mental health outcomes during this period were measured with the Depression, Anxiety and Stress Scale (DASS-21). Linear regression models were used to estimate the associations between changes in physical activity and leisure screen time and mental health outcomes by socioeconomic status (SES), adjusting for potential confounders.Compared with maintaining high levels of physical activity or increasing them, decreasing physical activity was associated with higher stress scores overall, and among people of high SES, with higher scores for DASS-21, depression, and anxiety. Among participants of low and medium SES only, increasing screen time was associated with higher DASS-21, depression, anxiety, and stress scores compared with maintaining low or decreasing leisure screen time.Results highlight the potential protective effect of physical activity and limited leisure screen time on mental health in the context of COVID-19 stay-at-home restrictions. Changes in physical activity and leisure screen time during the COVID-19 pandemic have been related to mental disorders in high-income countries. Differences by socioeconomic status have not been reported for Latin American countries.During the recommended stay-at-home period, associations between changes in physical activity and leisure screen time with mental health outcomes were modified by socioeconomic status.Interventions to reduce sedentary behaviors and promote physical activity to improve adults’ mental well-being during the COVID-19 stay-at-home period should be prioritized among those living in disadvantaged socioeconomic conditions.Engaging in regular physical activity and limiting sedentary time can improve mental health . HoweverThe pandemic has had profound effects on the physical inactivity epidemic . Stay-atPhysical activity in Latin America is mainly driven by necessity  and by the need for social interaction (leisure-based activity), more than by health or fitness benefits ,10. In LOur study’s objective was to evaluate the association between changes in levels of physical activity and leisure screen time and symptoms of depression, anxiety, and stress among adults in Mexico during the early stages of the COVID-19 stay-at-home recommendation. We also explored how these associations differed by socioeconomic status (SES).We conducted an online survey to assess self-reported physical activity, leisure screen time, and mental health before and during the COVID-19 stay-at-home period in Mexico among a non-probabilistic sample of adult (≥18 y) internet users. In Mexico, 72% of the Mexican population has access to the internet, most users are aged 18 to 40 years, and most are in the middle and high socioeconomic levels . Data weParticipants were recruited through the web pages and social media sites of 2 organizations, the National Institute of Public Health and the Latin American Congress for Physical Activity and Health Research. Two Facebook advertisements were placed to increase the number of people reached across all regions in Mexico. Participants provided informed consent online before completing the survey. We used a questionnaire to assess self-reported physical activity among people we hypothesized to be affected by stay-at-home restrictions before and during the pandemic. The questionnaire was based on selected items from the self-administered past-week Modifiable Activity Questionnaire (SMAQ)  and was A similar survey approach was used to measure leisure screen time. Participants self-reported the frequency and duration of leisure screen time during a typical day, before and during the recommended stay-at-home period. On the basis of previous studies , particiWe used the Spanish version of the 21-item Depression, Anxiety and Stress Scale (DASS-21) , a set o2, 22–38 m2, ≥38 m2). We also explored the amount of time per day in home confinement (≤19 hr/d or >19 hr/d) and changes in the participant’s work during the pandemic.We collected information on age, sex, education completed, and marital status. Household SES was measured with a validated questionnaire, and participants were categorized as either high SES or middle or low SES . Other hThe study sample size was powered to examine differences in the proportion of adults meeting physical activity recommendations or spending less than 2 hours per day in leisure screen time. We estimated that with a sample size of 528 participants, our study had an estimated 80% power to detect a 5% difference in this proportion.Means and SDs and proportions were used to describe time spent in physical activity and leisure screen time before and during the stay-at-home period. The difference between these 2 times was calculated to estimate changes in physical activity and leisure screen time from before to during the stay-at-home period.P < .05.We performed linear regression models to analyze the associations between physical activity changes and mental health outcomes during the pandemic. Separate models were used for the DASS-21 score and its 3 subscales. Robust SE estimates were used because score distributions of DASS-21 and its subscales were right-skewed. All models were adjusted for sociodemographic characteristics and changes in leisure screen time or physical activity . Additionally, because exploratory analyses indicated differences by SES in the association between outcomes and exposures, overall sample as well as stratified models were analyzed by SES. All data preparation and analyses were conducted with Stata 15.0 (StataCorp LLC). Differences were considered significant at P < .05). In total, 2,096 adults accessed the questionnaire link and consented to participate, and 1,619 (77.0%) completed the survey. After eliminating participants with missing information for any of the analytical variables (n = 471), a total of 1,148 participants were included in the final analysis. Most were women (77.6%) with no children aged 6 years or younger living at home (87.3%). Most were confined to home (78.9%). About half were classified as low or middle SES (55.8%), single (51.3%), with an undergraduate degree (42.2%), and with no changes in their job during the COVID-19 pandemic (52.0%). A total of 35.3% were aged 18 to 29 . During P < .01 for both)  . Before We found evidence of effect modification by SES in relation to physical activity and mental health . Among pAmong participants of high SES, those in the group with decreasing physical activity had higher scores  for all mental health outcomes:  compared with those from the group that maintained active or increasing physical activity.We found evidence of effect modification by SES in relation to screen time and mental health . Among lOur findings support the inverse association between physical activity and symptoms of anxiety, stress, and depression during the early stages of the COVID-19 pandemic. Conversely, results indicate an association between increased leisure screen time and worse mental health outcomes. These associations were modified by SES level.Physical activity is a well-known behavior for supporting mental health . We founIn contrast, and opposite to our initial hypothesis, decreases in physical activity levels were consistently and strongly related to higher anxiety, stress, and depression scores among high-SES participants compared with low- and middle-SES participants. Given the more health-driven nature of participation in physical activity among people of high SES , those pConversely, results of our study showed that increased leisure screen time was associated with increased symptoms of stress, anxiety, and depression during the stay-at-home period, as suggested by a review study identifying excessive sedentary time as a risk factor for depression in adults . FindingOur study provides original data from adults across Mexico and to our knowledge is the first study focused on the association between changes in physical activity and leisure screen time on mental health during the COVID-19 pandemic stay-at-home period across a large sample of Latin American adults. Changes in these behaviors threaten the physical and mental health of Mexican adults, especially because of the uncertainty of the length of social isolation required to prevent the spread of COVID-19. Knowledge gained from our study may inform interventions aimed at improving the mental well-being of adults in Mexico or other countries that share similar socio-contextual conditions.Our study had several limitations. First, although participants were asked to retrospectively report their physical activity and screen time before and during the pandemic, the cross-sectional design of the survey precludes inferring causality. The reported associations are possibly partly explained by reverse causality. Furthermore, data were collected at the initial stages of the COVID-19 pandemic in Mexico. Future studies should address variations in the measured behaviors for longer periods of time or at different time points of the stay-at-home period, which might differ from findings reported here. The stay-at-home period itself limited the possibility of using device-based measures of physical activity, which provide more accurate estimates of both physical activity and sedentary behavior . We usedResults of our study highlight the favorable effects of physical activity and limited leisure screen time on mental health well-being in the context of COVID-19 stay-at-home restrictions. Customized interventions to reduce sedentary behaviors and promote physical activity could improve the mental health of adults during similar stay-at-home restrictions. Concerted efforts should be prioritized among people living in disadvantaged socioeconomic conditions with limited access to leisure physical activity opportunities.


Patterns, Du et al. show that applying these methods to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reveals potential infection targets of the virus and possible interactions between SARS-CoV-2 proteins and human proteins.Network-based learning enables the identification of possible undiscovered interactions in biological systems. In this issue of  Patterns, Du et al. show that applying these methods to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reveals potential infection targets of the virus and possible interactions between SARS-CoV-2 proteins and human proteins.Network-based learning enables the identification of possible undiscovered interactions in biological systems. In this issue of  Patterns, Du et al. used network-based methods to discover novel virus-host interactions underlying the coronavirus disease 2019 (COVID-19) pandemic.Biological data embody a significant amount of information that is not easily represented in traditional data structures such as tabular data. Network-based data structures and methods have been an effective way of incorporating relational information and describing complex biological systems. In this issue of Network-based learning is based on the premise that we can use our current experimentally discovered knowledge to predict novel interactions that have not yet been identified. In this case, our current biological knowledge is structured into a network containing aggregated data from multiple mammals and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The network was organized into two layers: an organism and a protein layer. Four types of interactions were defined: protein-protein (between virus and host protein groups), infection (between virus and host group), similarity (between homologs of proteins), and belonging (between organism layer and protein layer). The final virus-host interaction network comprised 246 nodes and 1,766 interactions.With the biological network constructed, the goal has been to predict possible undiscovered interactions that can be used to develop treatment and vaccine targets. The interaction prediction problem has been formulated as a classification task in machine learning. The Multi-layer Perceptron (MLP) classification method was used to classify non-studied protein interactions into three classes of (1) protein-protein interaction, (2) infections, and (3) no-interaction. In comparison with classic methods that rely on potentially sparse subject-level data that test various interactions individually, this machine-learning approach considers the comprehensive structure of the network and individual interactions.By integrating multiple host-virus datasets, this work brings forward an interesting capability to predict novel susceptible hosts, a meaningful application in the context of the ongoing COVID-19 pandemic, and potential new emergent variants. Results revealed five potential infection targets of SARS-CoV-2 and 19 possible interactions between SARS-CoV-2 proteins and human proteins, a useful hypothesis for further experimental validation.This work has incorporated multiple datasets, and although the network-based methods and results are promising, the reader needs to be mindful of their limitations. Results from network-based methods are possibly skewed based on the quality of input data, level of certainty, and missingness percentage. Our biological knowledge is a product of research discoveries that are driven by funding (funding bias). Publication bias can also be a major issue, inherent in many text-mining studies used for networks or as priors for weights. Therefore, any constructed biological network based on publications is inherently biased. If the network has more information about a particular organism, or knowledge quality was not uniform among subnetworks, the results could be biased toward our preconceptions. For instance, in this work, only 17 mammalian species were included due to data availability.https://github.com/hangyu98/IMSP) in a structured and easy-to-follow manner.Ultimately, network-based methods in biomedical research are helpful approaches for generating hypotheses; however, like any method work, these results require more in-depth experimental validation and should be treated with caution as a guiding tool for experiment prioritization. To ensure reproducibility and application to the broader scientific community, transparency in network building and analysis is paramount. In this work, to help an independent replication and extension of the results, the analysis code is made available . The approach can be extended to domains other than infectious diseases by appropriately redefining the network’s nodes and interactions. However, the results depend on the availability and quality of input data. Thorough investigation is necessary to assess the level of bias in the training data, which is consequently introduced into the model.There are still many open questions and possible research directions in network-based learning: (1) how to effectively and efficiently provide for user interaction with the network(s) so that they could feed their experiential parameters and results into the model and receive interactive feedback; (2) how to incorporate the demographic diversity in the age, gender, and racial/ethnic composition of the population; (3) how to predict rare interactions which we don’t have much training data for; (4) how to enhance the interpretability of the models and to make them more understandable for the biologists; and (5) other than experimental validation, how to evaluate the robustness and generalizability of these models in biological settings across varying domains.


Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers’ predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well. I.Coronavirus (COVID-19) is a virus infection, named Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), which appeared in Wuhan toward the end of 2019 Machine learning algorithms have been gaining momentum over the last decades for medical applications such as computer-aided diagnosis to help physicians for an early diagnosis, which can lead to better-personalized therapies and enhancement of the medical care offered to patients Optimization is the process by which the best possible solution is found for a particular problem from all the available solutions In this article, a framework for COVID-19 classification is proposed based on three cascaded phases. The first phase automatically extracts features from the training CT images by a CNN model named AlexNet. Then, a proposed feature selection algorithm, using Stochastic Fractal Search (SFS) and Guided Whale Optimization Algorithm (Guided WOA) techniques, is applied to properly select the valuable features. The LSH-SMOTE  is used in the second phase to balance the extracted features. The last phase classifies the selected features by a proposed voting classifier, using Particle Swarm Optimization (PSO) and Guided WOA techniques, by aggregating the Support Vector Machine (SVM) •A COVID-19 classification framework based on proposed algorithms for feature selection and classification is developed.•A novel feature selection algorithm based on SFS and Guided WOA techniques is proposed.•A novel voting classifier based on PSO and Guided WOA techniques is proposed.•The proposed framework can classify the input CT images to COVID-19 or non-COVID-19 effectively.•The proposed framework is evaluated using two datasets of COVID-19 CT images and non-COVID-19 CT images.•Statistical tests of Wilcoxon rank-sum, ANOVA, and T-test are carried out to ensure the quality of the proposed algorithms.•This framework can be generalized to the applications of biomedical imaging diagnoses.Two kinds of CT datasets are used in the experiments to test the proposed framework. The first dataset has COVID-19 CT images, while the second dataset has extra CT images with clinical cases that have no COVID-19. For feature selection, the proposed (SFS-Guided WOA) algorithm is compared in experiments with binary versions of the original WOA This article contains the following sections. Related work and the problem definition are discussed in II.In this section, the recent literature utilizing the CT scans for diagnosing COVID-19 patients will be summarized. Then, the recent evaluation of Artificial Intelligence (AI) against COVID-19 based on the CT scans will be discussed as well.A.et al. proposed a methodology to recognize the infection rate using the coronal and axial view of lung CT scans. The proposed work achieved a specificity of 100%, AUC of 0.918, and sensitivity of 82.6%. Another study by et al. proposed a scheme to evaluate the lung CT scans and implemented visual inspection-based detection. Their scheme could achieve Specificity of 94%., AUC of 0.892, and Sensitivity of 83.3%. In et al. investigated 90 patients’ lung CT scans. Their investigation managed to detect the severity based on the time since the patient got infected. In et al. investigated the patient’s information and considered the CT scans and RT-PCR for the examination. They achieved a specificity of 100% and a sensitivity of 93%. In a similar study Recent study proposed several COVID-19 detection paradigms. In B.et al. proposed a method to diagnose COVID-19 using an AI technique based on CT slices and ten convolutional neural network models to correctly diagnose COVID-19 from non-COVID-19 groups. The authors found that both ResNet-101 and Xception have achieved the best performance. Moreover, ResNet-101 managed to detect COVID-19 cases with a specificity of 99.02%, Sensitivity of 100%, AUC of 0.994, and Accuracy of 99.51%. On the other hand, Xception achieved a Specificity of 100%, Sensitivity of 98.04%, AUC of 0.994, and Accuracy of 99.02%. The authors recommended the use of ResNet-101 to characterize and diagnose COVID-19 infections due to its higher sensitivity.Recent works show that the CT scans are mainly utilized to offer fast diagnostic methods to prevent and control the spread of COVID-19 and assist physicians and radiologists to correctly manage patients in high workload. Authors in et al.Another study in et al.et al.Chua The importance of the AI techniques in the early evaluation of COVID-19 and the areas where AI can contribute to the battle against COVID-19 are discussed in Most of the above-discussed studies mainly applied statistical analysis and visual inspection techniques to correctly diagnose COVID-19 infection. A lesser number of applied researches used transfer learning and CNN with CT datasets of coronavirus pneumonia patients, non-corona virus pneumonia patients, and healthy subjects. Therefore, more study needs to be conducted that utilizes AI with properly optimized performance metrics. As per the literature review of this work, it is recommended to use the CT images as a fast method to diagnose patients with COVID-19. The proposed paradigms need to be both reproducible and easily validated to can be quickly integrated into the arsenal of battling the COVID-19 pandemic.III.This section discuss data sets and methodologies of this research. The datasets, dataset balancing, and the optimization methods of WOA, PSO, and SFS are discussed. The CNN models, classification methods, and ensemble learning techniques are also explained.A.Data collection is considered as the first and main step in COVID-19 applications. Recently, it has been reported that several data collection works were done on COVID-19. The authors have used two datasets to apply the proposed paradigm. The first is the COVID-19-dataset which has 334 CT images containing clinical findings of COVID-19. While the second is the non-COVID-19-dataset that has extra 794 CT images with clinical cases that have no COVID-19. B.The extracted features from the utilized datasets may suffer from a class imbalance problem. Therefore, several algorithms were investigated to solve that type of problems. Some of the recent algorithms are the SMOTE and the LSH-SMOTE C.CNN is of the most well-regarded machine learning methods in the literature. One of the reasons of its popularity is due to the automatic hierarchical feature representation in recognizing objects and patters in images D.In the WOA algorithm, the inspiration is from the foraging behaviour of whales, in which bubbles are used to trap the prey by forcing them to the surface in a spiral-shaped The second mechanism includes a shrinking encircling, which decreases the values of The last mechanism can be achieved based on the Algorithm 11:Initialize WOA population 2:Initialize WOA parameters 3:Initialize t as the iteration counter4:Calculate fitness function 5:Find best individual 6:whiledo7:for .23:end while24:return Thus, the exploitation and exploration are controlled by E.The Stochastic Fractal Search (SFS) technique was proposed by F.•Position , which can often achieve better predictions. It is recommended to use diverse, independent classifiers in such methods to get the best outcome To create a better classifier, the predictions of each classifier can be aggregated and then determine the class with the most votes. This is called the majority-vote classifier which is considered a hard-voting classifier. Using this approach will raise the chance that the individual classifiers will make very different types of errors to improve the ensemble’s accuracy. Another way is to use the same algorithms with different data subsets such as the Random forest. In that ensemble classifier, “forest” is an analogy that refers to creating decision trees that is trained by “bagging” method.In bagging, a similar learning algorithm is used for all the predictors. To get the most reliable income, however, it is recommended to train them on different random subsets of the training set while sampling is performed with replacement. The general idea of this method is to increase the overall result accuracy due to the soft-computing nature of all methods in this area. Another type of ensemble classification is AdaBoost IV.The proposed framework has three phases. The first phase has a feature engineering process which includes the CNN training techniques. The second phase represents the proposed SFS-Guided WOA for feature selection and then applying the LSH-SMOTE method for balancing the selected features. The last phase, phase three, applies the proposed voting classifier algorithm (PSO-Guided WOA) for the selected features from the second phase to classify the infected cases.A.In the first phase of the proposed framework, CNN is used. As dsicussed above, CNN reduce the parameters of a given problem using spatial relationships between them, which makes them a more practical classifier specially in image processing where we deal with a large number of parameters (pixels), rotation, translation, and scale of images.Several CNN models including AlexNet To understand the CT images in the datasets, a Radiology Registrar at the Typical Medical complex in Riyadh and a Fellow of The Royal College of Radiologists in UK help the authors. They guided the authors to deal with COVID-19 CT images of the infected cases to differentiate them from the non-infected cases. The preprocessing step makes the data ready for the machine learning models. Based on the problem of COVID-19 and the available dataset, some data processing tasks are required before feeding the images to the learning model.To feed the current dataset of images to the convolutional network, they must be resized to have the same size. All the CT images have been resized to Algorithm 21:Input Resized CT images 2:Initialize3:for  algorithm for feature selection. The numerical features that are extracted from the first phase of the CNN model are the input to the second phase for the proposed algorithm as shown in 1)Algorithm 31:Initialize WOA population 2:Initialize WOA parameters 3:Initialize Guided WOA parameters 4:Set t = 15:Convert solution to binary [0 or 1].6:Calculate fitness function 7:Find best individual 8:whiledo9:for Based on the diffusion procedure of the SFS algorithm, a series of random walks around the best solution can be created. This increases the exploration capability of the Guided WOA using this diffusion process for getting the best solution. The Gaussian random walks as a part of the diffusion process around the updated best position 3)For the feature selection, the solution is converted to a binary solution of 0 or 1. The following sigmoid function is applied to convert the continues solution to a binary one 4)1)LSH-SMOTE initialization,2)converting the minority class instances into vectors,3)creating Hash Codes by using Hash Functions then creating Hash Tables,4)creating the nearest Neighbors List,5)Synthetic instances generation using the SMOTE algorithm.The LSH-SMOTE technique is employed in this research to balance the selected features by the proposed SFS-Guided WOA algorithm to improve the performance of the classification algorithm. The LSH-SMOTE technique consists of the following steps:5)•Population initialization: •Parameters initialization: •Iteration counter initialization: •Binary conversion: •Objective function evaluation: •Finding the best individual: •Position updating: •Diffusion process calculation: •Updating •Updating parameters •Converting updated solution to binary: •Objective function evaluation: •Best individual update: •Iteration counter increment: The SFS-Guided WOA algorithm’ computational complexity according to Algorithm As per the above complexities, the overall complexity of the proposed SFS-Guided WOA algorithm is C.Algorithm 41:Initialize WOA population 2:Initialize WOA parameters 3:Initialize Guided WOA parameters 4:Set t = 15:Calculate fitness function 6:Find best individual 7:whiledo8:if •Population initialization: •Parameters initialization •Iteration counter initialization: •Objective function evaluation: •Determining the best solution: •Position updating: •Objective function evaluation for each individual from Guided WOA: •Fitness function calculation for each individual from PSO: •Updating parameters •Best solution update: •Iteration counter increament: Thus, the overal complexity PSO-Guided WOA algorithm is The proposed PSO-Guided WOA algorithm’ computational complexity will be discussed here according to Algorithm D.Objective functions are used to evaluate the solutions in an optimization algorithm. The function is depending on two parameters of the classification error rate and the number of selected features. The solution is good if the subset of features gives a lower number of selected features and a lower classification error rate. The following equation is used to get the quality of each solution V.The experiments section in this article is divided into three scenarios. The first scenario is based on the first phase of the proposed model. This experiment shows the effectiveness of different CNN models for classifying the COVID-19 cases and interns show the importance of extracting features for the next phase. In the second scenario, the proposed feature selection algorithm (SFS-Guided WOA) is tested and compared to other algorithms to show its performance. The third scenario is designed to investigate the ability of the proposed voting optimizer (PSO-Guided WOA) for improving the classification accuracy of the COVID-19 cases. Finally, Wilcoxon’s rank-sum test and t-test are performed to verify the superiority of the proposed algorithms in a statistical way. The CT images datasets, A.The first experiment is designed to investigate the classification accuracy of five CNN models namely AlexNet 1)•Accuracy: measures the model ability to identify the whole cases correctly, regardless the cases are being positive or negative and can be formed as •Sensitivity: called the true positive rate (TPR) or recall. It computes the capability of the positive case and is calculated as •Specificity: called the true negative rate (TNR) or selectivity. It gets the capability of finding negative cases and is calculated as •Precision: called positive predictive value (PPV). It directs the rate of true positives among all positive values. It is calculated as •Negative Predictive Value (NPV): It directs rate of true negatives among all negative values. It is calculated as •F-score: measures the harmonic mean of precision and sensitivity and is calculated as The performance metrics calculated for the first phase are accuracy, sensitivity, specificity, precision (PPV), Negative Predictive Value (NPV), and F-score. Let 2)This scenario results are shown in Based on this experiment, the highest accuracy that can be achieved for the CT images from the COVID-19 dataset tested in this research is 79% by the AlexNet model. Since this is not acceptable accuracy in this critical endeavor, the features are extracted from the earlier layers of the AlexNet model, according to its promising performance, to be used for the next scenario for features selection and balancing.B.In this scenario, the importance and performance of the proposed feature selection algorithm (SFS-Guided WOA) are investigated. The proposed algorithm in the second phase is compared to other algorithms of the original WOA 1)•Average Error is calculated to show the accuracy of the classifier in giving the selected feature set. It is calculated as •Average Fitness is the selected features average size to the total number of features in the dataset  is the obtained best solutions variation which can be found by running an optimizer several times For the evaluation of the proposed SFS-Guided WOA algorithm effectiveness, the following metrics are employed. Let 2)The results of the proposed SFS-Guided WOA algorithm in this experiment are shown in Based on this experiment, the selected features are then balanced using two methods named SMOTE and LSH-SMOTE to be ready for the classification scenario. For both algorithms, the nearest neighbors parameter 3)For getting the p-values between the proposed SFS-Guided WOA algorithm and other algorithms, Wilcoxon’s rank-sum test is employed. This statistical test can determine if the results of the proposed algorithm and other algorithms have a significant difference or not; p-value < 0.05 will demonstrate significant superiority. By contrast, a p-value >0.05 shows that the results have no significant difference. Hypothesis testing is formulated here in terms of two hypotheses; the null hypothesis  with other ensemble learning techniques. In the last experiment, the proposed algorithm is compared with other voting classifier algorithms to check its effectiveness. Statistical tests of ANOVA and T-test are performed between the compared algorithms to show the effectiveness of the proposed algorithm. 1)This scenario performance metrics are the Area Under the ROC Curve (AUC) and the Mean Square Error (MSE). AUC is a good indicator of classification performance due to being independent from the distribution of instances between classes which is also referred to as a balanced accuracy or macro-average The Mean Square Error (MSE) evaluates the classifiers performance, calculates the difference between the required and the actual output of the classifiers according to this equation:2)The first experiment results for the SVM, KNN, NN, and DT as a single classifiers are shown in The next experiment results for comparing the proposed algorithm with other ensemble learning methods of Bagging, AdaBoost, and Majority voting are shown in 3)To conclude whether there is any statistical difference between the MSE of the proposed (PSO-Guided WOA) algorithm and other compared algorithms, a one-way analysis of variance (ANOVA) test was applied. The hypothesis testing can be formulated here in terms of two hypotheses; the null hypothesis  is assessed. Results show that the proposed algorithm outperforms the compared algorithms, including the original WOA algorithm, and could find the lowest fitness value for the feature selection of the extracted features from the COVID-19 datasets. In addition, the proposed algorithm has the lowest standard deviation compared to other algorithms that prove the stability and robustness of the proposed technique. Based on the second scenario results, the selected features are then balanced using the SMOTE and LSH-SMOTE methods to be ready for the last stage which includes the final classification. The third scenario shows the performance of the proposed classification algorithm (PSO-Guided WOA). Results show that the proposed voting classifier (PSO-Guided WOA) with LSH-SMOTE preprocessing could achieve an AUC with binary predictions  result of 0.995 and a MSE of 2.49569E-05 which outperforms other state-of-the-art ensemble learning techniques. That shows the importance of balancing the selected features from the previous stage by the LSH-SMOTE algorithm. The experimental results for comparing the voting classifier with other voting classifiers using WOA, GWO, GA, and PSO show the superiority of the proposed framework to identify COVID-19 patients using CT images. Thus, the efficacy of diagnosis can be improved while avoiding the radiologists the heavy workload associated with the initial screening of COVID-19 pneumonia.VII.This article proposes a framework for COVID-19 classification with three cascaded phases. In the first phase, the hierarchical feature representation is automatically extracted from the training CT images by the CNN model of AlexNet. Afterward, the proposed feature selection algorithm, using SFS and Guided WOA techniques, is applied to select features in the second phase. The selected features are then balanced by the LSH-SMOTE algorithm to improve the classification results. In the last phase, a voting classifier, using PSO and Guided WOA techniques, is proposed to aggregate the predictions of four single classifiers, named SVM, NN, KNN, and DT, and predict the most voted class. This increases the chance that the individual classifiers will make very different types of errors to improve the ensemble’s accuracy. Two datasets are used to test the proposed model. The first is the COVID-19 dataset which has CT images containing clinical findings of COVID-19 and the second is the non-COVID-19 dataset that has extra CT images with clinical cases that have no COVID-19. For feature selection, the proposed SFS-Guided WOA algorithm is compared in experiments with the original WOA, GWO, GA, PSO, hybrid of PSO and GWO (GWO-PSO), hybrid of GA and GWO (GWO-GA), BA, BBO, MVO, SBO, and FA in terms of average error, average select size, average (mean) fitness, best fitness, worst fitness, and standard deviation fitness. Finally, the proposed voting classifier (PSO-Guided WOA) result is compared with voting WOA, voting GWO, voting GA, and Voting PSO in terms of AUC and MSE. The statistical analysis of Wilcoxon rank-sum, ANOVA, and T-Test shows the superiority of the proposed algorithms. The utilization of each successive phase is aimed to improve the overall accuracy to offer a viable and reliable paradigm in the battle against the spread of COVID-19. A future research direction will be to tune the CNN parameters to increase the overall classification accuracy in case of using other datasets that cannot achieve satisfactory performance. Moreover, the proposed algorithms can be applied to several medical image processing applications that use other imaging modalities.


Background: COVID-19 vaccination has been recommended for severe asthmatics. We aimed to evaluate the safety, tolerability, and impact on disease control and patient’s quality of life of the mRNA SARS-CoV-2/COVID-19 vaccine in severe asthma patients regarding biologic treatment. Methods: Severe asthmatic patients regularly managed by two big allergy and respiratory referral centers were offered to undergo Pfizer COVID 19 vaccination at the hospital site. Patients filled in an adverse events questionnaire after the first and second dose, as well as the Asthma Control Test (ACT) and Asthma Quality of Life Questionnaire (AQLQ). Results: Overall, 253 patients were vaccinated; only 16 patients refused. No serious events were detected. Less than 20% of patients reported side effects, most of which were classified as very common side effects. No differences were reported according to the ongoing biologic drug. A significant improvement in both ACT and AQLQ was observed between the first and the second dose administration. Conclusions: Our data confirm the optimal safety and tolerability profile of mRNA SARS- CoV-2/COVID-19 in severe asthma patients on biologic treatment, as well as their positive attitude towards COVID-19 vaccination. The negligible proportion of patients reporting side effects and the absence of asthma exacerbations are relevant to support the COVID-19 vaccination campaign in severe asthma patients worldwide. The available evidence suggests that bronchial asthma cannot be considered an independent risk factor for COVID-19 ,3; howevWhen talking about severe asthma, the relationship between the currently marketed monoclonal antibodies and SARS-Cov-2 infection is still under debate, although a protective role of biologic treatment had been postulated ,5.The recent availability of vaccines against COVID-19 has the potential to dramatically reduce the risk of the disease and the related exacerbations in patients with more severe respiratory diseases, including severe asthmatics. In fact, COVID-19 vaccination has been recommended for severe asthmatics, whether on biologic treatment or not, and under the pathophysiological perspective, no specific risk factors for adverse events have been identified for that population ,6. HowevReal word data on the safety, tolerability, and impact on disease control of the COVID-19 vaccination in patients with severe asthma on biologic treatment are still lacking. The aim of the present study is to evaluate the safety of the mRNA SARS- CoV-2/COVID-19 vaccine in a population of severe asthmatics, most of them treated with monoclonal antibodies, as well as to assess the impact of vaccination on the control of the disease and on patient’s quality of life.Severe asthmatic patients regularly managed by two allergy and respiratory referral centers for severe asthma located in the Northeast of Italy (Verona and Padua), whether or not on biologic treatment, were offered to receive the mRNA SARS-CoV-2/COVID-19 (Pfizer) vaccination at the hospital site. Allergy history was carefully re-assessed in order to exclude specific risk factors for vaccine related adverse reaction, or permanent/temporary contraindications according to the current recommendations . In partPatients filled in a vaccination-related adverse events questionnaire after the first and second dose see . In partFurthermore, the Asthma Control Test (ACT) and Asthma Quality of Life Questionnaire (AQLQ) were administered at the time of first and second dose injection, as well as 35 days after.p value of less than 0.05 was considered significant.A Wilcoxon test was used to compare the ACT and AQLQ values after the first and after the second vaccine dose. Side effects rates were compared using the exact Fisher test. A Overall, 273 patients were contacted. Among them, 16 patients (5.9%) refused the vaccination, while four subjects (1.5%) were excluded because of ongoing acute asthma exacerbation and/or oral steroid daily dose >10 mg prednisolone or equivalent. The vaccinations were finally administered to 253 severe asthmatic patients. As highlighted in Common side effects were described by 2.1% of patients after the first vaccine administration, and by 2.5% after the second. Following the first dose, 17.2% of patients recorded uncommon adverse events, while 2.5% reported the same side effect subtype after the second dose. No rare side effects were experienced. No premedication with antihistamine/paracetamol was recommended or referred by any patients.When clustering the vaccinated population by biologic drugs, no significant differences could be described in terms of the reported side effects . OverallPatient reported outcomes are described in Our real-life study investigated the safety and tolerability of mRNA SARS-CoV-2/COVID-19 vaccination in severe asthma patients, most of them undergoing biologic treatment. Its impact on asthma control was also evaluated. According to our findings, no serious adverse events were detected. Less than 20% of patients reported side effects, most of them classified as very common side effects. No differences were observed when clustering the study population a ongoing biologic drug use. Overall, the asthma patients not treated with biologics experienced less adverse events. In terms of patient reported outcomes, a significant improvement of both ACT and AQLQ was observed between the first and the second dose administration.When considering the mechanism of action of mRNA SARS-CoV-2/COVID-19 vaccination, a higher risk of COVID-19 vaccine related adverse events in severe asthma patients in comparison with the general population cannot be hypothesized . SimilarThe other relevant point is whether COVID-19 vaccination may act as an asthma exacerbation trigger, although not expected when considering the vaccine mechanism of action . In our AQLQ also significantly increased from the first to the second assessment, at the time of the second dose administration. This could be related to patients’ positive attitude towards the protective role of the COVID-19 vaccination, as they are aware that severe asthma potentially increases their individual susceptibility to COVID-19 or to a more severe related condition . AccordiTo the best of our knowledge, our data provide the first real-life description of the optimal safety and tolerability profile of mRNA SARS-CoV-2/COVID-19 vaccine in severe asthma patients in biologic treatment, without relevant differences according to the specific monoclonal antibody. Our findings need to be confirmed in larger studies and thorough post-marketing surveillance protocols. However, both the negligible proportion of patients reporting side effects and the absence of asthma exacerbation episodes are relevant to support and carry on the COVID-19 vaccination campaign in severe asthma patients worldwide.


In this paper, we describe a case series of four patients who were admitted with emergencies related to aortic aneurysms over a 3-day period and were treated with endovascular repair. The first patient was an 81-year-old female with a history of abdominal pain and a ruptured aortic aneurysm diagnosed by AngioCT-scan. The second patient was a 63-year-old male with a history of oral digestive bleeding and an AngioCT-scan showing an aortoenteric fistula. The third patient was a 77-year-old female with sudden-onset abdominal pain and ruptured right common iliac aneurysm. The fourth patient presented with abdominal pain and an AngioCT-scan showed aortic rupture. All four patients were discharged with no major complications or surgical mortality. These case series show that despite the Covid-19 pandemic situation, since elective surgeries decreased, vascular emergencies have increased. Since the outbreak of the SARS-Cov-2 infection pandemic, a considerable impact on elective surgeries has been observed, with a fall in the number of elective repairs performed. This is because most patients preferred to postpone elective surgery and also because surgical societies all round the world have recommended postponement of elective surgery.2One of the first reports regarding the Covid-19 outbreak and vascular surgeries was published by Ng et al.,The objectives of this paper are to report a case series of patients with consecutive aortoiliac aneurysm emergencies and their outcomes and to present a review of vascular surgery practice during the Covid-19 infection pandemic.The Research Ethics Committee approved this study (decision number 4.091.832).9 cells/L) associated with coughing. A chest CT showed left lung consolidation suggestive of bacterial pneumonia. A diagnosis of SARS-Cov-2 was ruled out with laboratory tests and chest CT. She was put on Piperacillin-Tazobactan for 7 days, without needing admission to the intensive care unit and recovered satisfactorily, before being discharged 9 days after the endovascular repair.An 81 year-old female patient, with arterial hypertension, diabetes, and a history of an aortic aneurysm that had been diagnosed in 2015, presented at dawn on a Saturday and was admitted with abdominal pain with onset during the preceding 12 days. In view of the known history of aortic aneurysm, she underwent an AngioCT-scan that showed a ruptured pararenal aortic aneurysm with maximum diameter of 72mm and retroperitoneal hematoma . AlthougA 63-year-old male patient with a history of alcoholic liver cirrhosis was admitted during the Saturday morning with a history of oral digestive bleeding with onset 3 days before admission. He underwent upper digestive endoscopy that detected stomach bleeding. His past medical history reveled previous spine surgery, with vertebral fixation due to discitis. Six months later, a huge aortic pseudoaneurysm was diagnosed and an aorto-aortic Dacron bypass had been performed at our hospital. An AngioCT-scan showed an aortoenteric fistula . He was The third patient was a 77-year-old female active smoker with hypertension and congestive heart failure who was admitted on the Saturday afternoon with sudden abdominal pain. Once more, an AngioCT-scan showed a ruptured right common iliac aneurysm, without hemorrhagic shock . Under gThe fourth consecutive patient was a 64-year-old male active smoker with preexisting right limb claudication who arrived on the Monday afternoon with an abdominal pain that had had onset 2 weeks before admission, with symptoms worsening 2 days before he presented. An AngioCT-scan showed a ruptured aortic aneurysm with 53mm maximum diameter , with riAbdallah et al.,Mousa and BroceAnother study by Pini et al.,-10,12,This paper reports four cases of emergency aortic aneurysms that were all treated with EVAR and all of the patients had satisfactory outcomes. The EVAR‐1, DREAM, and OVER trials all showed an early survival benefit for EVAR, when compared to open aortic surgery.7Another interesting finding in this case report was the endovascular repair in a patient with aortoenteric fistula (AEF). Endovascular repair of AEF has emerged as an evolving treatment option with potentially improved mortality outcomes. The patient in this present paper probably had a secondary AEF. A recent meta-analysis reported an in-hospital mortality rate of 7% for 98 well-documented cases of endovascular AEF repair from the literature. When suitable, endovascular treatment of AEF is a safe procedure, with fewer complications and better outcomes than open surgery repair, particularly during the COVID-19 pandemic crisis.15We are facing a pandemic crisis situation that is changing vascular surgery and other surgical specialties, increasing emergency procedures and challenging vascular surgery all over the world. That is the importance of reporting the changing surgical flow and numbers of surgeries, as well as the causes of 4 consecutive ruptured aortic aneurysms in 3 days, and highlighting the necessity of choosing the endovascular approach to avoid COVID-19 infection and contamination.The Covid-19 pandemic crisis is a challenging situation that has increased the number of urgent and emergency surgeries in the vascular world. The four patients reported in this paper had excellent outcomes with EVAR treatment, showing that vascular surgery departments must be prepared to promptly treat their patients’ urgent needs despite the SARS-Cov-2 situation. Furthermore, compared with the same period last year, May 2019, when there were only elective repairs and no cases of emergency aortic repair in our vascular department, this paper shows a significant increase in aortic emergencies during the COVID-19 pandemic crisis.


Our digitizing plasmonic nanoarray biosensor also showed 20–30 min earlier detection compared to conventional loop-mediated isothermal amplification sensors.Rapid detection for infectious diseases is highly demanded in diagnosis and infection prevention. In this work, we introduced a plasmonic enhanced digitizing biosensor for the rapid detection of nucleic acids. The sensor successfully achieved the detection of loop-mediated isothermal amplification for the hepatitis virus in this work. The sensor comprised a nanodisc array and Bst polymerases conjugated on the rough surface of a nanodisc. The rough surface of the nanodisc provided plasmonic hot spots to enhance the fluorescence signal. The virus DNA was detected by conducting a modified loop-mediated isothermal amplification with fluorescence resonance energy transfer reporter conjugated primers on the sensor. The modified isothermal amplification improved the signal contrast and detection time compared to the original assay. By integrating the modified amplification assay and plasmonic enhancement sensor, we achieved rapid detection of the hepatitis virus. Nucleic acid with a concentration of 10 Rapid and accurate pathogen screening technology is important for the early diagnosis and prevention of infectious diseases. Plasmonic resonance is one of the most important technologies in biosensor development , especiaBacillus stearothermophilus), rapid detection was achieved by skipping the precise thermal cycle for unwinding double-stranded DNA in conventional PCR [Loop-mediated isothermal amplification (LAMP) is one of the widely used isothermal technologies for rapid pathogen DNA detection . In the onal PCR . Due to onal PCR . Differeonal PCR , for exaonal PCR , dropletonal PCR ,16, micronal PCR ,18, optional PCR , surfaceonal PCR  and inteonal PCR .Real-time LAMP monitoring is usually based on fluorescent intercalation dye , solutioIn this article, we combined the FRET-LAMP with a nanoarray and digitizing method to achieve rapid isothermal nucleic acid detection. In our previous study, single-spot LAMP with signal enhancement achieved the rapid detection of virus DNA . HoweverThe etching solution for nanoarray manufacturing was composed of 0.076-g thiourea , 0.2686-g ferric nitrate  and 18-μL 1-octanol  in 50-mL water. The linker used for nanostructure surface functionalization was 16-mercaptohexadecanoic acid , which was activated by 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide  and N-hydroxysuccinimide . The LAMP reagent mixture included 0.4-mM dNTPs, 0.4-M Mg2SO4, 1-M betaine , 1×Bst buffer  and the primer mixture  in ultra-pure water. The primer mixture contained 10 pmol of the forward/backward primers (FP/BP), 5 pmol of the forward/backward loop primers (LF/LB), 20 pmol of the backward inner primer (BIP) and 20 pmol of the forward inner primer (FIP). All primer designs were based on our previous publications . For FREA metallic tip and nanodisc array were prepared for single-spot and array LAMP, respectively. For-single spot LAMP, a gold film of seven nm thick was deposited onto the Pt/Ir-coated tips  by sputtering . The diaFor array-LAMP, a gold nanodisc array was produced by the dip-pen nanolithography platform . Gold film was deposit on clean glass slides by sputtering as a substrate. The metallic tips were dipped into MHA solution and used to draw patterns on the gold film . The MHAThe distance between the metal structure and the fluorophore was estimated to assess the possible quenching caused by the metal surface . The lenIn the first stage of LAMP, the amplicon initiated with FIP and BIP formed dumbbell-like DNA loops, which was the new template for further amplification. The new template then replicated into a flower-like long-chain DNA strand by self-annealing . In addiA nanoarray functionalized with Bst polymerase was designed for fast detection LAMP with a fluorescence resonance energy transfer primer. By digitizing the reaction spot, we could monitor the process of DNA amplification and observe the early increase of the amplification signal compared to the conventional method. A gold disc nanoarray was fabricated by DPN and functionalized the array with Bst polymerases, as shown in Any replicon starting with a FIP primer always has one fluorescence reporter at the 5′ end, as shown in A nanobiosensor with plasmonic enhanced fluorescence was designed for digitizing sensing. To achieve signal enhancement, nanodiscs with rough surfaces were fabricated with dip-pen nanolithography and chemical etching. The tips for dip-pen nanolithography were soaked in MHA dissolved in pure acetonitrile for carrying writing inks. The ink was then transferred to the surface of a pre-sputtered Au film on a glass substrate by the water meniscus between the tip and surface during contacting. After etching, a nanodisc array with a 400-nm diameter and 40-nm height was then generated with the MHA ink on the gold surface. The center-to-center distance of the dots was one micrometer to prevent electrical field interference between structures, as shown in The concentration or enhancement of the electrical field on the metal surface is related to the material, shape and roughness of the metal structure. We simulated the intensity of the electrical field on the nanodisc with COMSOL Multiphysics to demonstrate the contribution of this roughness to the enhancement of the optical intensity C. An incTo demonstrate the enhancement of the fluorescence signal for the LAMP reaction, we compared the fluorescence intensity of the reactions in the solution and on the nanodisc structure D. The flConventional LAMP for HCV and HBV were performed by the real-time PCR detection system BIO-RAD CFX  for primer design verification. Before applying to the reaction, a synthesized FRET primer pair was investigated by the melting curve to ensure that the primer pair hybridized properly. The primer pair solutions were heated from 25 °C to 90 °C gradually, and the change of the fluorescence signal was recorded. During the heating process, the increase of the fluorescence intensity reflected the unwinding status of the primer pair. The peak of the fluorescence intensity slope in A multiplex detection for HCV and HBV was also demonstrated in Single-spot LAMP was performed to investigate the contrast improvement of FRET-LAMP compared to conventional LAMP. ® Green  primers. It showed capability in singleplex or multiplex nucleic acid detection. Although the gentle slope of the detection time calibration curve found in our research suggested that FRET-LAMP is more suitable for non-quantification screening, FRET-LAMP would still be very useful for fast and combination examinations for different viruses.While integrating the plasmonic enhancement technology, the single-spot LAMP results indicated the contrast of FRET-LAMP was up to 7.4-fold to reject the background fluorescence from randomly unwound primer pairs and increase the signal-to-noise ratio. Further integration with a nanodisc array improved the detection time more. A nanodisc with a rough surface has two to four times the enhancement in the electrical field compared to a structure with a perfect flat surface. The hot spot generated on the surface contributed to the sensitivity of this nanoarray biosensor.−3-μg/μL nucleic acid by FRET-LAMP, while the conventional LAMP control experiment with the same reaction conditions required 50–60 min. This result is comparable to the current work based on plasmonic enhanced Raman detection for the hybridization assay [According to the results, less than 40 min was needed for detecting 10on assay . Recentlon assay ,44,45. MOptimization of the reagent composition and reaction temperature has not been carried out yet in our experiment. Hence, a better performance would be expected with further optimization. In addition, variant active spot intensities were found in our FRET array-LAMP, which might imply that an uneven number of reactions happened at each spot. The optimization in controlling the polymerase number of each spot or in producing consistent nanostructures is important. A more precise fabrication technology like E-beam lithography, photolithography or nanoimprinting could be applied to ensure the sensor reproducibility in large-scale production .In summary, the method we demonstrated in this work has shown the potential of a LAMP nanodisc array in developing high-contrast, rapid and multiplex nucleic sensing. We believe that an amplification integrated signal enhancement biosensor could be a useful technology in infectious pathogen screening in the future.We demonstrated a new strategy for time lapse LAMP monitoring through the integration of plasmonic enhanced fluorescence, a nanodisc array, FRET-LAMP and a digitizing analysis. The main contribution of this work was to demonstrate a digitizing LAMP assay in a simple and high-signal contrasting manner without microfluidic isolation. Although the design did not show the same capability in counting the template number as other digitizing sensors, it proved to have rapid detection within 10 min, which is 20–30 min earlier than conventional LAMP under the same analyte concentrations. We expect this technology could be further applied to different pathogen DNA screenings in the future for rapid disease detection.