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Hotpoints mark punctual locations where emergencies occur repeatedly within a defined temporal interval and which show significantly strong event intensities in their neigbourhood [3] . In contrast to hotpoints, hotspots describe areas. This is often formulated imprecisely in the literature. Hotspots can be understood as concentration or clustering of events in space. In criminalistics, these spaces are regarded as high crime areas [5] . It is often assumed that there is a connection between aggregations of events and their spatial association. It is likely that events of this kind will occur again in the space identified as a hotspot. However, not every location in a hotspot becomes a scene of such an event [5] . These definitions have in common that they assume the totality of all events would describe a hotspot. This means that every event, even a statistically random one, is taken into account when determining the hotspot and thus contributes to the hotspot's profile (appearance, form and expression, cf. Fig. 1, 2) . These assumptions are factually unfounded and mathematically incorrect. |
Here the problem is described and a process is developed, which exclusively feeds the hotspot-characterizing events, namely hotpoints, into the analysis and thus allows more objective, because rule-based, visualizations of spaces of high event densities. Therefore it is defined: |
Hotspots mark areas based on the existence of hotpoints. Hotspots consist of a high density of hotpoints. Each hotspot has at least one density center, i.e. an area of highest event density [3] . |
Depending on predefined time intervals, such as time of day or month, hotspots can be determined and statements can be made about the behavior of hotspots in terms of permanence or periodicity, mobility or movement patterns and aggregation or dissimilation [6, 7] . The results can be used for the purpose of more detailed situation assessments and form a basis for prognostic and geovisual analyses. Long-term hotspots become visible and analyzable for strategical and operational tasks. This makes it possible to identify performance and weaknesses in previous implementation practice and subsequently adaption of identified needs. The aim is to support fire brigades and rescue services in the planning of their means required with data analyses that go beyond descriptive statistics and to make mission-relevant information visible and communicable. In this way, together with the longterm experience of the emergency forces, a set of instruments is created that strengthens the ability to act, and supports adaptation processes to changing hazard situations. |
Related work presents a large variety of different methods that are often presented one after the other, their respective advantages and disadvantages being pointed out. The connecting elements between the approaches are rarely pointed out. As a result, separate analyses and visualizations are produced, whose individual expressiveness is limited [1, 2, 5, [8] [9] [10] . On the other hand, methods are combined in so many ways, for example in the approach of software systemically linked views, that the multiple visual representations of one or more variables are not always easy to interpret and can sometimes lead to the viewer being overwhelmed [12] . |
A central element of most authors is the neighbourhood of the events to be investigated -usually reduced to their density. The understanding of neighbourhood underlying this work is based on the spatial and temporal proximity of events or hotpoints and hotspots. Tobler's first law of geography describes this connection in general terms: "Everything is related to everything else, but near things are more related than distant things." [13] In this field of application, emergencies that are several kilometres apart cannot be described as spatially close. This is a scale problem in cartographic representation. It should be noted that events that are spatially distant from each other are not adjacent. The spatial distance has to be taken into account and separate events should not be combined into one area. HENGL is convinced: "[Standard] grid resolutions (20-200 m in most cases) with which we work today, will soon shift towards finer and finer, which means that we need to consider grid resolution in time context also." [14] In the real world, 20 m street length, for example in real-estate areas, means one to three single-family or terraced houses, whereas in the city centre it means several business floors and numerous residential units in vertical construction. Therefore, even shorter distances are not recommended if the information is to be generated on a large scale. Furthermore, this is a not insignificant data protection issue: identity-exact data analyses are not permitted in Germany in the non-police use. Furthermore, they are not relevant for the questions and methods presented here. These values were verified in the course of our own unpublished calculations and discussed with the control services of the professional fire brigades of Cologne and Berlin. The resources, i.e. vehicles of the emergency services, report the geo-coordinate of the operation. This is usually not congruent with the actual location of the operation. A 20 to 40 m walk is quite common, in some cases even more. With a minimum mesh size, as proposed by HENGL, misinterpretations of the field cartograms can therefore occur. In order to counteract this problem, it is recommended here to set the minimum edge length of the grid cell at 50 m. |
Larger distances or cell sizes, as they are often found in US-American crime analyses, for example, should also be critically evaluated. Due to the method of representation, field cartograms with coarsely meshed grid cells give the impression that events are homogeneously distributed in this large area. In the real world this can include two to three street crossings, shopping arcades and high-rise commercial buildings. The importance of cell sizes and search radii is a considerable one for KDE, which is realized in field cartograms. It should be noted here that the mesh or cell size of 50 m  50 m to 200 m  200 m is recommended for this field of application as a sensible distance measure for neighbourhood, i.e. the distance between events. |
In addition, spatial distances must be delimited from each other in case of semantic proximity. This is possible in urban areas by abstract segmentation of space and is based on the functional selection of subspaces even before geo and statistical analyses are applied. In the transition from model space to representation space the scale is introduced. Each representation space is scaled, this effect does not exist in the data space. In addition, the treated facts are scaled spatially-semantically. The considered data have semantic information and spatial coordinates. The former can be classified into categories. These categories were taken and analysed with respect to their spatial distribution in order to identify patterns. Territorial units such as street sections, building blocks, statistical and administrative units are deliberately avoided. Spatial units consist of semantic categories. These are based on the function of space. Examples of this are public playgrounds, railways, airports, industrial areas, bridges and residential areas. This cannot be mapped onto territorial units without causing misinterpretations. |
The semantic facts are categorized and space is defined from them. This does not correspond to the conventional way of first defining the room reference and then mapping the data to it. The transformation from model to real space is data-driven by focusing on the point position in the surface. The common denominator is formed by field cartograms as a special form of area cartograms. In concrete terms, regular square grids are formed. Thus a common area reference is available, in which different semantic and spatial questions can be processed. |
After analysis, the segments formed are reassembled in the final field map. In this way it is possible to reduce the amount of data (segmentation by selection) and to increase the information content in the result (aggregation). |
Similar to the determination of hotpoints, various methods of cartography and statistics can be used to calculate and visualize hotspots. The aim of the approach presented here is to extend the spatial reference from single point to area information. Therefor a transformation of the point geometry into a surface geometry is necessary. |
Known methods among others are interpolation by isolines, cluster analyses like NNH and K-Means, creation of choropleth maps in which the point information is related to areas as densities. Kriging and Inverse Distance Weighting are able to estimate intensities between known points and output them as area information. |
However, if a geoprofile is needed, the methods mentioned are not suitable. When choosing a method, the objective is of central importance. This involves the development of hotspot maps in which the location, distribution, density values and movement patterns of deployment hotspots in urban space become visible. Based on the real distribution of events both in space and time, the basic assumption is valid: fire and rescue operations do not take place everywhere in the entire urban area and are often not homogeneously distributed -neither in space nor in time. Furthermore, the focus is not on estimating the probability of occurrence of events, but rather on the information about neighbourhoods contained in the data in the form of spatio-temporal event densities. One method that has proven to be suitable and has been accepted in criminology over the past 30 years is the KDE. With respect to Nadaraya, a sufficiently large sample and a correspondingly selected bandwidth allow an arbitrary good estimation of the unknown event distribution by kernel density estimation [15] . This is a non-parametric estimation method for density interpolation. A pioneer in this field is PARZEN, who worked continuously on a probability density function since the 1950s [16, 17] . The prerequisite for the application of the continuous estimator for the density of the distribution of the individual events are random variables independent of each other. It is generally true that the deployments of the fire brigade and the rescue service are not interdependent and that random samples from the deployment database are unrelated. |
How KDE works: first of all, in a GIS a uniform grid is laid above the study area. The spatial reference is provided by the georeferenced base map data of the study area (e.g. UTM). The grid has a maximum north-south and west-east extension according to the boundaries of the investigation area. Starting from the cell center x the kernel function K moves from cell to cell and searches for events X_i within the fixed bandwidth d. |
The events that lie within this window are weighted according to their distance from the cell center. The cell center is therefore the point at which the density is estimated. The following applies: Events that lie closer to the center point receive a greater weight than events that lie further away. Finally, the summed up and averaged density value is transferred to the raster cell. The set screws of the KDE are the parameters kernel function, cell size and bandwidth. |
Kernel functions, or kernels, are estimation functions that work like normal weighting functions. There are numerous functions that can be used for this purpose. SILVERMAN has shown in empirical studies that the differences in the estimation results are minor depending on the choice of the core function: "It is quite remarkable that the efficiencies obtained are so close to one […] ." [15] LEVINE confirms this in principle, but shows depth-differentiated effects for geospatial application: "Each method of interpolation will produce slightly different results. Triangular and negative exponential functions tend to produce and emphasize many small hot […] spots and thus produce a "molted" appearance on […] [the] map. Quartile, uniform, and normal distribution functions tend to smooth the data more." [10] The choice of cell size influences the calculation of density values and the visual granularity, i.e. the graphical output of density values into the field cartogram. If one compares different cell sizes with the same data basis, the cartographic results sometimes differ considerably. Smaller cell sizes cause a smooth surface. Larger cell sizes produce a more granular surface in which hotspots can disappear. For comparability between hotspots of different time intervals, uniform cell sizes should always be chosen [7, 15] . The discussion about a fitting cell size was already conducted above. The results are applied here. |
The third parameter is the bandwidth (syn.: smoothing parameter, search radius). The bandwidth is fixed during the entire process and must be determined before use. It is generally agreed that the choice of this parameter represents the greatest challenge in KDE. Figure 2 illustrates the effects of different cell size and bandwidth on KDE geoprofiles. Each geoprofile allows different conclusions. A well-founded decision support and planning basis for authorities and organisations with safety and security tasks has to be done differently, because this scope for interpretation is not scientifically acceptable [7] . |
Hotspots based on the conventional bandwidth selection for KDE are usually created by the mass of data, i.e. by quantity [1, 2, 5, 8, 9, 20, [22] [23] [24] . The aggregation of the mass data, its weighting and neighborhood analysis by the GETIS-ORD Gi* [18, 19] statistics for bandwidth selection, which is anchored in the concept presented here, defines a quality. This enables the classification of the geodata into non-significant events as well as weakly significant, significant, high and highly significant hotpoints. This smaller subset of the original sample is fed into the KDE. It follows that each hotspot identified by the KDE is based on at least one weakly significant hotpoint in the center. By applying the Gi* test statistics and calculating the mean distances between the hotpoints, the spatial proximity is taken into account directly at KDE. As a result, the hotspots are more sharply (mutually) distinguishable. |
The preceding discussion of methods and parameter problems points out interfaces from which a complete process for the rule-based generation of geoprofiles can be derived, which can be automated to a large extent. A new way to set parameters of the KDE process is defined here. This is done by means of a Gi* test statistic preceding the KDE, taking into account the adapted cell size approach of HENGL, in combination with an extension of the distance approach of Williamson et al. [20, 21] by masking the Fig. 2 . Influence of the kde-relevant parameters cell size and bandwidth on hotspot field cartograms and their effects on the interpretability of geoprofiles [3] . |
investigation area into segments. The analysis is described conceptually as follows. Figure 3 shows the whole process modelled in UML. |
a. Data pre-processing: a data set to be analysed is selected from the data set. The selection is based on the operation type or several operation types of one or several time intervals. This new dataset is used for the upcoming analysis. b. Spatial pre-processing: The expert in the control centre has a thorough knowledge of study areas. He is able to divide the total space for the analysis into abstract segments, which can be delimited from other/adjacent spaces, e.g. by spatial-structural elements (e.g. streets, fences, water bodies or by functions of spaces), and to generate masks. 2. Hotpoint analysis: For this purpose, a new data set must be generated. Based on 1. |
a., all operations that are located at the same location are summarized. The number is saved as new attribute weight. This data set is fed into the Gi* test statistics and the hotpoints determined are classified. 3. Distance analysis: the distances between the hotpoints determined in step 2 within their spatial segments (masks) are calculated and the mean value d is calculated. 4. Hotspot analysis: d is now set as bandwidth. The hotpoint data are fed into the KDE procedure and classified. 5. Map products: as a result, at least two visual products are available: a hotpoint map and a hotspot map (geoprofile). This information can be displayed in a map. The interpretation of hotspots combined with the localisation of hotpoints can be important in the analysis and planning of requirements. 6. Furthermore, it is useful to create temporal hotspot series in order to carry out a change analysis. This is realized by choosing several time intervals. |
For applications in civil security research, this KDE procedure is thus considerably enhanced in terms of the quality of the process and results. For the first time, it is not individual decisions or aesthetic aspects that determine the visual result, nor are there any rules of thumb or formulas without reference to geo space. When choosing the two parameters cell size and bandwidth, both the position of the data points in relation to each other and the real spatial and temporal anchoring of these events are directly taken into account: characteristics of neighbourhood relations are embedded into the KDE by hotpoint analysis, subsequent data preparation, and distance determination. Rule-based process for geovisual analyses of massive spatio-temporal data of emergency events to generate geoprofiles [3] . |
The functionality of the KDE process will be demonstrated by means of a case study in Berlin. For this purpose, Table 1 describes the process step-by-step in graphic and textual form on the following pages. Table 1 . The conception of the rule-based process for generating a geoprofile of massive spatiotemporal datasets using the case study (41,498 emergency alerts for rescue vehicles in an abstract space segment within one calendar year) [3] process steps explanation raw data Load the complete application data into a database program. It contains all alert information. The data set is sufficiently large. The quality check follows: incorrect and incomplete entries are not available. A uniform georeference is available. Steps to clean up or delete data are not necessary. |
Apply the Gi* test statistics to the data set in ArcGIS. The search radius is set to 50 m. The newly created data set is saved. |
selection |
Within the new record, hot-points are selected and all other data is removed. |
The applied selection rule: z-score ≥ 1,65 and p-value ≥ 0,95. |
A total of 379 hotpoints were identified. These can be displayed in the GIS (point map). The hotpoint data record is saved. |
classification |
The hotpoints of the hotpoint data record are classified. |
The applied classification rules: z-score ≥ 3,28 and p-value ≥ 0,9995 := highly significant z-score ≥ 2,58 and p-value ≥ 0,995 to < 0,9995 := high significant z-score ≥ 1,96 and p-value ≥ 0,975 to < 0,995 := significant z-score ≥ 1,65 and p-value ≥ 0,95 to < 0,975 := low significant |
The point map shows all calculated hotpoints (points in shades of red) and all non-significant data (points in black). |
An exemplary representation of the regular grid is shown, because at the chosen display scale the 50 m x 50 m cells would be too small to be visually detectable as such. |
The distances of the hotpoints to each other are determined and then the arithmetic mean is calculated. This value is fed into the core density estimation as bandwidth d. (d = 345 m) |
In ArcGIS the KDE is based on the hotpoint dataset with the cell size 50 m x 50 m and the bandwidth d = 345 m. The result is the hotspot field cartogram. |
The central research question of this dissertation project is: How does an automatable process have to be described in order to analyse point mass data of real emergencies with regard to their location in space and time geovisually and to make them interpretable? To answer this question, a concept was developed which enables rule-based geovisual analyses of the continuous and mass data stored in the control centres of authorities and organisations with safety and security tasks. The concept is characterized by the standardization of central process steps and can be implemented in GIS, for example, via program interfaces. The goal of transforming the point into the surface in order to increase the overall informative value is achieved. The KDE procedure is discussed for this purpose and a problem solution is offered by targeted modelling of a new method process. |
Neighbourhood and density are described in the geoanalytical context of space and time and the concept of horizontality and verticality of event neighbourhoods is introduced. Furthermore, methods for hotpoint and hotspot analysis are presented, their deficits were identified and a generic approach to their elimination is presented. Based on this, the experts of the control centres can formulate requirements and recommendations for strategic, operational and tactical action on deployment planning in more detail. |
Through the combination of methods and the definition of comprehensible spatial and temporal variables, it has been possible to develop a process of analysis for the calculation and visualization of hotspots that is capable of standardisation and largely automated. The necessary factor expert knowledge is part of the definition of the abstract segmentation of the space for mask creation. These masks are stored in the overall process. (They can be easily adapted in case of changing spatial structures.) |
The innovative, generic analytics process designed here to determine hotspots based on KDE is an extension of the optimised KDE-process. The proven benefit lies in the calculability, availability of expert knowledge in process form and in improving the quality of analysis. The concept is more comprehensive than the conventional KDE process. At the same time, the quality of the geovisual analyses increases. Not all emergency events of the entire urban space are included in the determination of hotspots. With the integration of the hotpoint determination into the overall process, a necessary selection of significant and actually significant emergency events into the hotspot calculation takes place. |
This also reduces the enormous data volume and increases the physical computing power. Practice shows that the reduced data volume due to hotpoint-based selection does not always provide a sufficiently large sample for the core density estimation to be carried out. This circumstance is not to be understood as a deficit of the concept developed here. In statistics, the functionality of numerous methods is based on minimum data requirements. Test and estimation methods must not be applied without the compliance with these requirements, which is subject to proof. If these indications are ignored, the procedures may lead to erroneous results as well as misinterpretations of the real situation. If there is a complete lack of hotpoints in a spatial segment, despite a comparatively high compression of individual events, methods other than KDE must be applied in order to generate a spatial, cartographic visualization. |
The result is geoprofiles in the form of maps with standardised class formation, signature and colour values. These also provide a starting point for further analyses and strategic, tactical and operational planning steps. |
An outbreak of highly infectious novel coronavirus disease-2019 (COVID-19) emerged in December 2019 in Wuhan city in the Hubei province of China which rapidly spread across worldwide and was later declared pandemic by WHO on March 11, 2020 [1] . The disease was caused by a novel coronavirus, severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) classified on the same day by International Committee on Taxonomy of Viruses [2] . Since then, disease is continuously spreading and as per the WHO report till 20 th November, it has nearly targeted 56,261,952 infected cases and cost over 1,349,506 human lives [3] . In India, first case of COVID-19 was reported on January 30, 2020, which originated from China. According to Indian Council of Medical Research (ICMR) and Ministry of Health and Family Welfare (MOHFW), total 9,004,365 confirmed cases were reported, whereas 132,162 deaths occurred till November 20, 2020 [4, 5] . |
Shanghai Public Health Clinical Center and School of Public Health researchers on January 7, 2020 jointly revealed the origin of this coronavirus from a seafood market in Wuhan city [6] . Number of cases started increasing since then, even in people not having any exposure to animal market, and hence human-to-human transmission was also reported to occur [7] . Coronavirus (CoVs) encompass a large virus family affecting mainly humans and different animal species including cats, cattles, camels and also bats [8] . Not so often, but animal coronaviruses have also infected human, recently introduced SARS-CoV2 has joined the list of middle east respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV). SARS-CoV2, a beta coronavirus, has probably originated from bats just like MERS-CoV and SARS-CoV [9] . Human coronaviruses in majority causes respiratory and enteric infections and SARS-CoV-2 infection causes flu like symptoms ranging from fever, cough, headache, asthenia etc. Once the virus reaches to respiratory epithelial cells, it enters by binding to angiotensin converting enzyme-2 through its spike proteins and is then endocytosed inside the host cells. Once inside the cells, the virus starts replicating and targets variety of host cells thereby causing severe pathologies. However, SARS-CoV-2 infection affects people of all ages, in some high-risk individuals, such as older people or those having co-morbidities, the virus might cause severe infections such as interstitial pneumonia, acute respiratory distress syndrome (ARDS), progressing to multi organ failure and ultimately causing respiratory failure leading to death [10] . Cytokine storm is one of the characteristics of COVID-19 infection majorly occurring in persons having dysfunctional immune responses leading to secretion of pro-inflammatory cytokines such IL-16, IL-1b, TNF-a, IL-2, IL-7, MCP1, G-CSF, IP-10, IL-10, MIP1a and others. This review, focusses on current incidence, etiopathogenesis, clinical characteristics with multiorgan involvements associated infections, diagnostic criteria's, its management and present and future prospects of clinical trials to prevent, manage and control COVID-19. |
Coronavirus (CoVs) are the largest group of viruses belongs to the Nidovirales order that comprises three families i.e. arteriviridae, roniviridae and coronaviridae [11] . On the basis of genome structure, coronaviridae comprises of four genera-alpha coronavirus, beta coronavirus, gamma coronavirus and delta coronavirus. Alpha coronavirus and beta coronavirus transmission are limited to mammals and in humans they cause respiratory illnesses such as SARS and MERS, whereas gamma coronaviruses and delta coronaviruses infect birds as well as mammals [12, 13] . Coronaviruses are 60 to 140 nm in diameter and have [26] [27] [28] [29] [30] [31] [32] kilobases positive sense single stranded RNA genome connected to a nucleoprotein surrounded by capsid [14] . A typical CoV has atleast six open reading frames (ORF) and all proteins including structural and accessory are encoded by a single guide RNA [15] . Two major overlapping open reading frames (ORFs) i.e. ORF1a and ORF1b comprise of two-third of the whole genome and the third ORF codes for four structural proteins i.e. spike (S), envelope (E), membrane (M), and nucleocapsid (N). SARS-CoV-2 genome encodes, a very large polyproteins pp1ab, four structural proteins and six accessory proteins (3a, 6, 7a, 7b, 8, and 10) which is equivalent to the structure of SARS-CoV and MERS-CoV [16, 17] . (Figs. 1 and 2). |
The Spike (S) proteins has been divided into functional subunits i.e. N-terminal S1 subunit and a C-terminal S2 subunit. The size of each monomer of S protein is 180 kDa and is involved in attachment and membrane fusion. Depending on the virus, both N and C terminal domain are involved in the membrane attachment and entry of viral genome inside the host cells. Apart from this, an additional furin like cleavage site is present at the junction of S1 and S2 subunits of S protein in SARS-CoV-2, which is absent in the genome of SARS-CoV. This additional subunit has been speculated to be involved in higher infectivity of SARS-CoV-2 than SARS-COV [18] . Further, using phylogenetic tree investigation that SARS-CoV-2 genome shares 79% genetic similarity with SARS-CoV [19] whereas it demonstrates 98% genetic similarities with bat coronavirus RaTG13 [20] and is 85.98% identical to pangolin-CoV genome [21] thereby suggesting its zoonotic origin. |
Further, whole genome sequencing analyses have revealed that mutations occurring in the spike protein are Ind J Clin Biochem important in the evolution of SARS-CoV-2. Phylogenetic and alignment study of 591 novel coronaviruses of different clades from Group I to Group V identified several mutations and related amino acid changes and majority were distributed over spike protein [22] . In a major study involving investigation of 3617 available whole genome sequence of SARS-COV-2 maintained in NCBI globally identified that E protein possess several non-synonymous mutations [23] . Additionally, from five different isolates identified in the state of West Bengal (Eastern state of India), India, two major mutations one at position 723 and other at 1124 were identified in the S2 domain of Spike (S) protein. Another mutation at 614 position in S1 domain of S protein was also identified which is similar to mutation found in the isolates of state of Gujarat (Western state of India). These mutations were crucial in modulating the affinity of receptor binding [24] . Thus, these whole genome study revealed that SARS-CoV-2 is continuously evolving virus with crucial mutation identified in the receptor binding regions. |
Virus spreads mainly from person to person through respiratory droplets or bits of liquid, mostly through sneezing or coughing [25] [26] [27] [28] . According to National Institutes of Health (NIH), SARS-CoV-2 virus is stable in aerosols and on surfaces for several hours to days depending upon the surface materials and also ambient environmental conditions. Virus has been reported to be detectable up to three hours in aerosols, up to four hours on copper, up to 24 h on cardboard and up to two to three days on plastic and stainless steel [29] . It has also been testified in stool and may contaminate water supply possibly and subsequently results in aerosol and/or feco-oral based route of transmission [30] . Another study reported the presence of SARS-CoV-2 virus in the tears and conjunctival secretions [31] . |
Virus can pass through nasal and larynx mucous membranes and enter into the lungs through respiratory tract. |
First step of any viral infection is the binding of some viral proteins with receptors expressed by host cells followed by fusion with host cell membrane. ACE2 has been speculated to be the ultimate target of spike protein of SARS-CoV-2. Initial target of virus include lung epithelial cells, where virus attach through its spikes to cellular angiotensin converting enzymes 2 (ACE2) receptor [32] . |
ACE2 is a membrane bound metalloproteinase and was discovered in 2000. It is a homolog of ACE, though its activity cannot be stopped by angiotensin-converting enzyme inhibitors (ACEIs) [33] . Further study reported that SARS-CoV-2 has nearly 10-20 folds higher affinity for ACE2 than SARS-CoV. ACE2 is also significantly expressed in other tissues like heart, renal, and gastrointestinal tract [34, 35] . It is an important regulator of reninangiotensin system (RAS) and renin angiotensin aldosterone system (RAAS). RAS is an indispensable system of human body which is regulated and counter-regulated by two main pathways: classical RAS axis ACE-angiotensin II-angiotensin I receptor pathway, and counter-regulatory RAS axis ACE2-angiotensin 1-7-MasR-based pathway, which plays a negative role in regulation [36, 37] . In classical RAS pathway, angiotensinogen is cleaved into angiotensin (Ang) by renin, which is then cleaved by ACE into angiotensin II. This Ang II after binding to type 1 angiotensin II receptor (AT1R) mediate functions like increases secretion of aldosterone and anti-diuretic hormone, increases blood pressure by decreasing sensitivity to baroreflex, increases vasoconstriction, decreases production of nitric oxide (NO), increases fibrosis, reactive oxygen species production and inflammation. Whereas ang II can also be cleaved by ACE2 in counter-regulated pathway into Ang-(1-9) and Ang-(1-7), Ang (1-9) which thenbinds to AT2R and triggers NO production thus mediating vasodilation and reducing blood pressure. In addition to that, it also reduces inflammation, fibrosis and cardiac hypertrophy. Further, Ang-(1-7) binds to proto-oncogene Mas receptor (MasR) and reduces the blood pressure and reverses all its function performed during classical RAS pathway. Ind J Clin Biochem SARS-CoV-2 attaches to the receptor of host cell through S1 domains of its spike glycoprotein (S), and this spike protein is proteolytically cleaved by transmembrane serine protease 2 (TMPRSS2) into S1 and S2 subunits and then S2 induces membrane fusion and virus internalization by endocytosis [38] . S1 regulates virus-host range and cellular tropism with the receptor binding domain (RBD), where S2 helps in virus cell membrane fusion with the help of two tandem domains, heptad repeats 1 and 2 (HR1 & HR2) [39, 40] . After the fusion it releases viral genome RNA into cytoplasm via endocytosis and replication and transcription of virus occur in cytoplasm. Positive RNA genome acts as a messenger RNA (mRNA). Once viral genome enters into host cell, it translates into pp1a and pp1ab, two large precursor polyproteins [41] and these polyproteins by ORF 1a-encoded viral proteinases, 3C-like proteinases (3CLpro) and papain-like proteinase (PLpro) get processed into 16 mature nonstructural proteins (nsp1-nsp16) [9, 42] . During viral RNA replication and transcription these nonstructural proteins (nsps) perform vital functions [43] (Fig. 3) . This viral replication and cell to cell transmission plays important role in the suppression of ACE2 expression. This suppression of ACE2 leads to decrease in Ang (1-7) synthesis and enhanced levels of Ang II which drives the Ang II-AT1R dependent inflammatory pathways in the lungs and causes parenchymal injury [44] . SARS-CoV-2 enhanced the apoptosis and p53 signaling pathways in lymphocytes thereby causing the lymphopenic situation in COVID-19 patients [45] . Additionally, coagulopathy has also been observed in COVID-19 infection, as elevated levels of plasmin(ogen) has been observed in patients. This plasmin along with other proteases might play an important role in the cleavage of furin site in the S protein of SARS-CoV-2, thus increasing its virulence and infectivity and is also associated with hyperfibrinolysis [46] . Among the existing coronaviruses, RNA recombination along with its potential proof-reading capacity has been involved in the development and occurrence of novel coronaviruses [47] . Organs presenting higher ACE2 expression are presented as possible targets Ind J Clin Biochem of SARS-CoV-2 virus infection such as lung being an important and primary infection target organ. Further, ACE2 expression and its organ wise distribution is significantly associated with COVID-19 clinical symptoms [25, 48] . (Fig. 4) . |
Nearly, 80% of the SARS-CoV-2 infected patients display mild symptoms, whereas 15% cases come in severe category and rest 5% fall under critical category requiring ventilation support [49] . The course of infection of SARS-CoV-2 spans from mild disease limiting itself to upper respiratory, non-severe pneumonia, could be severe pneumonia involving ARDS, multiple organ failure and ultimately death [50] . Common clinical symptoms include fever, dry cough, headache, sore throat, breathlessness, diarrhea, vomiting and abdominal pain [51, 52] . Olfactory and taste desensitization have also been reported in COVID-19 [53] . |
People who have some chronic medical comorbidities such as cardiovascular disease, any lung problems or diabetes or who are older and have higher risk of severe illness (charted out in Tables 1 and 2 ). Children who are infected with SARS-CoV-2 generally display mild symptoms which might be due to altered ACE2 activity and active innate immune response. Whereas adult patients display suppressed adaptive immunity and dysfunctional immune response. Further, it is also observed that women are less vulnerable to viral infection compared to men which may be due to incongruent immune system, steroid hormone or sex chromosome associated factors. In the next few paragraphs, we will observe that how infection with SARS-CoV-2 affects different organs of the body and also on the dysfunctional immune response. |
Immune system of an individual is highly responsible to depict asymptomatic or symptomatic clinico manifestations of COVID-19. Geriatric population with poor immune function along with comorbidities is at increased risk with more susceptibility to various viral and bacterial infections due to less efficient, less coordinated and slower immune response. Cytokine storm noted in critical cases of COVID-19 is a response of an uncontrolled immune Fig. 4 Schematic representation of SARS-CoV-2 binding to ACE2R decreases ACE2 levels that drives multiorgan pathogenesis through Ang II/ AT1R cross talk Ind J Clin Biochem mechanism with an uncontrolled release of cytokines. Multiple factors are involved in triggering cytokine storm which includes virus, bacterial components, sepsis, super antigens, toxins, chimeric antigen receptor T cells and others [60] . It is a life threatening condition leading to detrimental changes including capillaries leakage, edema, tissue toxicity, organ failure and even shock. Increased levels of IL-6 were significantly observed with clinical manifestation in critical COVID-19 cases [61] . A trend has been observed where, SARS-CoV-2 infection rapidly activates CD4 ? T lymphocytes thereby forming pathogenic T helper (Th1) cells and release various cytokines including GM-CSF. This cytokine environment, induces an inflammatory environment with production of CD14 ? , CD16 ? monocytes along with enhanced secretion of IL-6. Finally, pathogenic T-cells along with monocytes enter pulmonary circulation where monocytes induce to become macrophages [62] . Acute respiratory distress syndrome (ARDS) progression in SARS was generally manifested along with increased circulatory levels of pro-inflammatory cytokines including interleukin-1b (IL-1b), IL-6, CXC-chemokine ligand 2 (CCL2) and CXCL10. COVID-19 cases have been associated with increased plasma levels of pro-inflammatory mediators, including IL1-b, IL1RA, IL7, IL8, basic FGF2, IFNc, MCP1, MIP1a, MIP1b, TNFa, and others. Studies have documented levels of IL2, IL7, IL10, GCSF, IP10, MCP1, MIP1a, and TNFa were associated with disease severity in COVID-19 [32, 63] . (Fig. 5 ). |
Coronavirus infection frequently occurs in patients with pre-existing lung disease such as asthma and COPD [64, 65] . Lung tissue has high renin-angiotensin system (RAS) activity which is chief site of Ang II synthesis. Ang II is an effective pulmonary vasoconstrictor [66] . Ang II over activity also promotes pulmonary edema and impairs lung function [67] . Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury. ACE2 expression is very high in lung tissue and is associated with protection of lung tissue injury induced by sepsis [67] . Decreased expression of ACE2 is inversely correlated with angiotensin II levels thereby enhanced its production. Further, this angiotensin II stimulates its type 1a receptor, which then enhances lung vascular permeability and leads to its pathological phenotype [14] . |
Single-cell RNA sequencing data of 43,134 human lung cells reported that only 0.64% of total cells expressed ACE2 and out of which 83% of total ACE2 expression was present on type II apical surface of epithelial cells (AT2) [41] . Other cellular types including type I epithelial cells, airway epithelial cells, endothelial and others also expresses ACE2 but at levels considerably lower than AT2 cells [68] . Thus, these results indicated AT2 cells as primary targets of SARS-CoV-2 in lungs. Hypoxia is one of the clinical symptoms associated with various pulmonary diseases. During initial phase of hypoxia, enhanced expression of ACE2 was determine in pulmonary smooth muscle cells whereas during later phase. Hypoxia-Inducible Factor-1a (HIF-1a), induces ACE expression (one of the targets of HIF-1a) with concomitant decreased in the expression of ACE2 [69] . |
Kidneys are presented as another major targets of SARS-CoV-2 viral infection as virus was detected in the urine samples of few COVID-19 positive cases [25] . In these patients, acute kidney injury is presented as a critical Ind J Clin Biochem complication targeting renal intrinsic cells including proximal tubular epithelial cell thus leading to kidney dysfunction. Kidney expresses very high levels of ACE2, which is very high especially in the renal cortex [70] . |
During acute kidney disease [71] and other models of chronic kidney diseases, ACE2 expression levels are severely compromised which then deregulated the homeostasis of RAS and causes debilitating pathological changes in the kidneys [72] . [60] . In coronavirus infection, tropism of gastrointestinal may explain an incidence of diarrhea. SARS-CoV RNA might be detected in SARS patients stool specimens [73] . SARS-CoV-2 detected in fecal samples was most likely due to virus entering the blood from lungs and then traveling from blood to intestines [74] . This infected fecal sample can promote fomites-based transmission, especially because of generation of infectious aerosol from toilet plume. Literature has reported very high expression of ACE2 receptor in GIT, particularly in small and large intestines [75, 76] . Positive staining of viral nucleocapsid was detected in cytoplasm of gastric, duodenal and rectal epithelium cells [77] . These evidences provided indispensable information about mode of entry of virus into host cells and further unveiled the possible route of transmission. Some studies reported viral RNA presence in the stool of COVID-19 patients [78] [79] [80] . In another study of 73 COVID-19 patients, 39 (53.4%) were positive for SARS-CoV-2 RNA in stool, where duration of viral positivity ranged from 1 to 12 days. Even more, 17 (23.3%) patients that were negative for respiratory samples showed positive results with viral RNA in stool [75] . |
Patients with COVID-19 have been reported to have high levels of serum Alanine Transaminase (ALT) and Aspartate Transaminase (AST) [48, 77] . In a study, 56 COVID-19 patients, 54% patients have high levels of serum Gamma-Glutamyl Transferase (GGT) [81] . Chai et al. stated that ACE2 expressed in both liver cells and bile duct cells [82] . Nevertheless, expression of ACE2 in bile duct cells was higher as compared to liver cells. Epithelial cells of bile duct play important roles in liver regeneration and immune response [83] . According to these results in COVID-19, patient's liver injury occurred may be due to damage of bile duct cells, but not liver cells by virus infection. |
Respiratory failure is the main cause of death from COVID-19 patients, mostly in older adults and those who have weaker immune system [84, 85] . Most common cardiovascular (CV) complication is acute myocardial injury. SARS-CoV-2 enters human cells through ACE2 receptor. |
In normal healthy and in various disease conditions, ACE2 helps in neurohormonal regulation of CV system. When SARS-CoV-2 binds with ACE2R it alters the ACE2 signalling pathways that can lead to acute myocardial and lung injuries [86] . Roughly, 8-12% cases of acute myocardial injury reported significant elevation of cardiac troponin I (cTnI) [86] . In a meta-analysis from China [66, 87] , incidences of acute cardiac injury were reported to be 8%; however, in another study, they included only those patients who were discharged from hospital or dead and found 17% incidence of cTnI elevation [88] . Clinical investigations suggested that patients with heart diseases, hypertension or diabetes who were treated with ACE2 increasing drugs including inhibitors and blockers showed increased expression of ACE2 and these were at higher risk of getting SARS-CoV-2 infection [89] (Table 3) . |
ACE2 modifies neutral amino acid transporters expression on epithelial cells surface and growth of pancreatic islet cells along with insulin secretion by pancreatic b-cells [70] . |
A study involving a mice model of adenovirus mediated human ACE2 expression showed enhanced production of insulin along with decreased apoptosis of pancreatic islets [96] . In addition to that, ACE2 also improves the overall endothelial function of pancreatic islet microvascular unit [97] . Some studies stated that diabetes mellitus (DM) was associated with more severe disease, acute respiratory distress syndrome and increased mortality [25, 98] . In a study by Guan et al., patients with DM showed higher disease severity (16.2%) in contrast, patients without DM showed lesser disease severity (5.7%) [25] . Further, an obvious conclusion was made, where patients having DM higher in age was compared to non-Diabetes mellitus patients. Thus, as age advances it emerged as crucial prognostic factor in determining course of COVID-19 disease. Simultaneously, patients with DM showed lowered ACE2 expression possibly due to glycosylation, which explained increased comorbidity with severe lung injury and ARDS in COVID-19 patients [99, 100] . COVID-19 patients with diabetes as coexisting disorders had a worse clinical outcome. |
Currently, multiple approaches are efficiently being used for the diagnosis of COVID-19 infection [101] . Two broadly classified techniques include, Real time reverse transcriptase polymerase chain reaction (rRT-PCR) based detection and serology-based detection. RT-PCR technique involves conversion of viral RNA genome into DNA, which is further amplified using specific primers set against defined targeted regions of viral genome. Major targeted regions of SARS-CoV-2 genome include nucleocapsid Report that deficiency in murine angiotensin I converting enzyme (peptidyl-dipeptidase A) 2 (Ace2), which encodes a key regulatory enzyme of the renin-angiotensin system (RAS), results in highly increased susceptibility to intestinal inflammation induced by epithelial damage. The RAS is known to be involved in acute lung failure, cardiovascular functions and SARS infections |
Results identify ACE2 as a key regulator of dietary amino acid homeostasis, innate immunity, gut microbial ecology, and transmissible susceptibility to colitis. These results provide a molecular explanation for how amino acid malnutrition can cause intestinal inflammation and diarrhoea [91] 4 Andrew M. South 2020 |
Examines the evidence for ACE2 regulation by RAAS blockade and statins, the cardiovascular benefits of ACE2, and whether ACE2 blockade is a viable approach to attenuate COVID-19 |
Role of ACE2 in the progression of SARs-CoV-2 infection and, importantly, whether blockade of the peptidase is an appropriate step, at least acutely and targeted to the pulmonary system rather than systemically. Studies of the multi-faceted roles of the RAAS in the setting of infectious disease is warranted, which should not occur in isolation from the other well-known roles of the system in cardiovascular homeostasis, particularly given the recent focus on the RAAS and the balance of Ang II and Ang-in critical care medicine such as in septic shock [92] 5 Mahmoud Gheblawi 2020 |
Recombinant ACE2, gene-delivery of Ace2, Ang 1-7 analogs, and Mas receptor agonists enhance ACE2 action and serve as potential therapies for disease conditions associated with an activated RAS (reninangiotensin system). Recombinant human ACE2 hascompleted clinical trials and efficiently lowered or increased plasma angiotensin II and angiotensin 1-7 levels, respectively |
Highlighting the critical role of ACE2 as the novel SARS-CoV-2 receptor and as the negative regulator of the RAS, together with implications for the COVID-19 pandemic and associated cardiovascular diseases [93] 6 |
Yi-Ming Yuan 2015 |
To explore the role of the Renin-angiotensinaldosterone system (RAAS) in the pathogenesis of pulmonary arterial hypertension (PAH) induced by chronic exposure to cigarette smoke Chronic cigarette exposure may result in PAH and affect the protein expression of ACE2 and ACE in lung tissue, suggesting that ACE2 and ACE play an important role in the pathogenesis of smokinginduced PAH [94] 7 Yingxia Liu 2020 |
The viral load of 2019-nCoV detected from patient respiratory tracts was positively linked to lung disease severity. Blood biochemistry indexes, albumin (ALB), CRP, LDH, LYM (%), LYM, and NEU (%), may be predictors of disease severity Angiotensin II level in the plasma sample from 2019-nCoV infected patients was markedly elevated and linearly associated to viral load and lung injury. Results suggest a number of potential diagnosis biomarkers and angiotensin receptor blocker (ARB) drugs for potential repurposing treatment of 2019-nCoV infection [55] Ind J Clin Biochem (N) gene, envelope (E) gene and ORF1ab gene regions [102] . RT-PCR based method displays high sensitivity (85-90%) and high specificity for the COVID-19 diagnosis as it is dedicated to the direct amplification of viral genetic material and the turnaround time (TAT) is about 2.5-3.5 h. This technique is quantitative in nature. Sample types for RT-PCR include nasopharyngeal swabs and oropharyngeal swabs. The RT-PCR results generally become positive after 2-8 days of infection and it is able to process large batches of samples. However, there are some limitations of this technique apart from skilled manpower, mutation in the viral genome might give false negative results, and further possible limitation includes improper collection, transportation and handling contributing to false negative results [103] . Rapid PCR by cartridge system (CBNAAT) are also available which are equally efficient and has significantly short response time [104] . Additionally, loop mediated isothermal amplification (LAMP) is another version of CBNAAT, which is highly specific isothermal amplification technique. In this method, transcription of RNA to DNA is then followed by amplification done using 4-6 primers set against multiple targeted regions [105] . This method has got several advantages over RT-PCR in being specific, user friendly in detection, speed (turnaround time is 20 min) and less background. Recently, Sree Chitra Thirunal Medical Institute and Technology in collaboration with Agappe Diagnostics Ltd. developed Chitra Gene LAMP-N that detects two different regions of N gene. In addition to above, CDC has approved a one-step rRT-PCR test kit to quantitatively determine viral molecules in [ 90 samples within 45 min [106] . Additionally, Specific High Sensitivity Enzymatic Reporter Unlocking (SHER-LOCK) employs the CRISPR-Cas system for the detection of viral RNA genome with greater specificity [107] . Similar, technique was employed by Indian scientists who have developed FELUDA, a test kit for SARS-CoV-2 genome detection also employs CRISPR-Cas9 system [108] . |
Another, crucial diagnostic method is serology-based detection of SARS-CoV-2 infection among COVID-19 patients. This method employs detection of antigens/antibodies in the human blood. COVID-19 infection leads to the generation of IgM antibodies and then IgG antibodies inside the host body. Initially after infection, the titer of IgM increases within a week time. Later on, titer of IgG increases from day 4 post infection and reaches to its peak by day 14. Further, the levels of IgM degrade very rapidly whereas IgG antibodies titer persist in the body for longer duration thus helps in determination of active infection. Apart from antibodies detection, antigen detection (S and N protein) is also being explored for early detection of viral infection. Three major strategies are being employed for detection of Ags/Abs including lateral flow immunoassay, ELISA, and chemilumescence. |
Further apart from above two standardized techniques for detection of COVID-19 infection, other parameters such as high sensitivity CRP levels, lactate dehydrogenase, alanine transaminase, erythrocyte sedimentation rate have been observed [109] . Further, decreased lymphocyte count i.e. depletion of CD4 ? and CD8 ? cells and decreased IFN-c expression in CD4 ? T cells are linked with severe COVID-19, illustrating the cytokine storm. Another crucial observation is the presence of intravascular coagulation associated with increased D-dimer and fibrinogen levels in some COVID-19 infection. Lastly, chest X-ray (CXR) and computed tomography (CT) scan are considered as an essential tool in the detection of COVID-19 pneumonia during this pandemic. |
Despite significant efforts made towards development of vaccines and therapeutic drugs, no significant advances have been observed till today. Currently available drugs are generally categorized according to their targets, one acts on Performed an unbiased evaluation of cell type specific expression of ACE2 in healthy liver tissues using single cell RNA-seq data of two independent cohorts, and identified specific expression in cholangiocytes |
Finding suggested the liver abnormalities of SARS and 2019-nCoV patients may not be due to hepatocyte damage, but cholangiocyte dysfunction and other causes such as drug induced and systemic inflammatory response induced liver injury [82] 9 |
Mei Lin Calcitriol regulates angiotensin-converting enzyme and angiotensin converting-enzyme 2 in diabetic kidney disease |
Finding suggested that Calcitriol may be regulate the expression of tubular ACE and ACE2 expression [95] Ind J Clin Biochem mode of entry of virus and other acts on inhibition of enzymes responsible for viral genome replication. Combination of lisinopril and losartan treatment in normotensive Lewis rats abolished increase in ACE2 mRNA levels observed individually but retained losartan induced rise in ACE2 activity in heart [110] . Ang II can regulate ACE2 expression through AT1R. Healthy hearts and kidneys displayed high levels of ACE2 mRNA and protein expression, with moderate expression of ACE [82] . RAS over activation in CVD increases AT1R stimulation by Ang II, promoting ERK1/2 and p38 MAPK signalling pathways to downregulate ACE2 while upregulating ACE expression [111] . Promoting ACE2/Ang 1-7/Mas signalling by rhACE2 or Ang 1-7 receptors agonist AVE 0991 can have valuable therapeutic effects in CVD and lung disease from diverse aetiologies [112] . Ang 1-7 receptors agonist AVE 0991 has been shown to exert cardiorenal and pulmonary protective effects, [113] and treatment with rhACE2 improved symptoms of acute lung injury, CVD, and kidney injury in various preclinical models [67, 70, 114] . Maintaining ACE2 levels in patients with or predisposed to common CVD states such as diabetes, hypertension, and obesity wards off the advancement of these comorbidities in instances where, patient contracts SARS-CoV-2 by maintaining levels of ACE2/Ang1-7/ MasR negative counter-regulation. |
Vitamin D (calcitriol or vitamin D3) plays an important role in regulation of serum calcium concentration. Although less highlighted, several reviews have addressed role of vitamin D in prevention of viral infections [115, 116] . Multiple mechanisms are employed by vitamin D in reducing risk of microbial infection and diseases. In our previous papers, we have shown that various polymorphic variants of vitamin D receptor gene modulate the metabolism of blood lead levels along with circulatory levels of vitamin D [117, 118] . Such as in reducing risk of common cold, vitamin D employs three different categories of mechanisms: physical barrier, cellular natural immunity, and adaptive immunity [119, 120] . Some studies reported that administration of an oral dose of 50,000 IU of vitamin D reduces risks of influenza. Vitamin D adequacy also reduces severity of pneumonia, which is associated with coronavirus infection. Taking higher doses of vitamin D supplement or sun exposure is reported to boost immunity and reduce risk. Vitamin D helps in maintenance of various cellular junctions including tight junction, gap junction and adherence junction which are otherwise evaded by several viruses to get inside cells and thereby causing infections [121] . In response to the bacterial and viral infections, innate immune system produces both pro and anti-inflammatory cytokines. |
Vitamin D improves cellular immunity by minimizing these abrupt increases in cytokines produced mainly by innate immunity [7] . Vitamin D supplementation also stimulates antioxidant genes expression including glutathione reductase and glutamate-cysteine ligase modifier subunit [122] . This increased secretion of glutathione further spares ascorbic acid (vitamin C) which got significant antimicrobial properties [123] and is suggested to be used in prevention and treatment of COVID-19 [124] . SARS-CoV-2 attached to host cells through ACE2R and vitamin D might reduce ACE2R. Recent studies have established that RAS is a major target of vitamin D. Inverse relationship between circulating 25(OH)D levels (a physiological quantifiable form) and plasma renin activity in hypertensive subjects was reported more than two decades ago; however, significance of the same was recognized only after discovery that 1,25(OH)2 D3 is a negative endocrine regulator of renin production [125] . |
Further, a broad range of antiviral drugs have also been used in various clinical trials against COVID-19 [126] . RNA dependant RNA polymerase, a crucial protease required during viral RNA replication is also an important therapeutic target. Remdesivir, a broad-spectrum antiviral drug initially used for Ebola virus treatment is an analogue adenosine. It effectively blocks viral infection and replication in vitro and in animals and has shown some promising effect against SARS-CoV-2 [127] . Favipiravir, purine nucleic acid analogue initially used for the treatment of influenza is an effective RNA dependant RNA polymerase inhibitor [128] . In addition to above, combination of lopinavir and ritonavir has also been tested for the treatment of COVID-19 infected patients with little clinical significance [129] . Another potential drug is chloroquine, an anti-malarial and anti-parasitic drug, is a promising anti-viral known for neutralising the acidic endosomal pH and thereby blocking the endosomal mediated viral entry [130] . Chloroquine demonstrates potent anti-viral effect by inhibiting SARS-CoV-2 in-vitro with a 90% effective concentration of 6.9 lM. Hydroxychloroquine, an analogue of chloroquine is a less toxic and more potent drug. Chloroquine exhibits potent anti-inflammatory and immuno modulatory effects which might play an important role in reduction of COVID-19 infection probably by hindering cytokine storm [131] . Combination of chloroquine with azithromycin has provided some clinical benefits in COVID-19 infection [132] . |
Convalescent plasma therapy has long been given to patients infected with deadly viruses like SARS-CoV, H1N1, Spanish flu, Ebola and the MERS. The techniques involved identification of patients who have already infected with COVID-19 and have completely recovered 14 days prior to be successful donors. Further, they must have sufficient titer of neutralizing antibodies [133] . Various clinical trials have highlighted that the therapy significantly improved the patient's clinical outcomes and were able to successfully clear the infection and infected cells by activating complement system and phagocytosis [134, 135] . |
Cytokine storm is one of the characteristic phenomena of SARS-CoV-2 infected patients. Initial evidences revealed that targeting anti-inflammatory molecules such as IL-6, IL-1R and TNF-a might be important in reducing the inflammation and ultimately inflammatory response. Currently, clinical trials have shown that tocilizumab (anit-IL-6), a humanized antibody significantly improves the clinical outcome of COVID-19 cases [4] . Similarly, Anakinra (anti-IL-1R) is also important in decreasing the inflammation [136] . |
Corticosteroids are well known anti-inflammatory molecules are effective for the treatment of a variety of inflammatory diseases [137] . They behaved like a double edged sword as on one hand they reduce inflammation while on the other hand they inhibit immune response thereby delaying the infection clearance [137] . In COVID-19, various clinical trials have outlined the role of corticosteroids such as one retrospective observational study revealed that 72% of ICU patients with COVID-19 are receiving glucocorticoids treatment [48] . Further, another study revealed that treatment of COVID-19 patients having ARDS, with steroids significantly decreased the mortality rate compared to those that did not receive steroids (46% vs 68%) [100] . |
Mesenchymal stem cells (MSCs) are known to play important roles in immune modulation either by secreting cytokines or directly interacting with target cells. MSCs are available at multiple sources including blood, bone, adipose tissue, placenta, umbilical cord etc. [138] . A clinical trial in COVID-19 patients (n=17) revealed that the treatment cleared inflammatory cytokines secreting T cells population whereas dendritic cell percentage and IL-10 levels were increased. Further, absence of ACE2 and TMPRSS2 in the MSCs further highlights their importance in the COVID-19 treatment [139] . |
Recently, studies are focussing on curbing the complement system pathway, a component of innate immune system, in reducing hyperinflammatory and Darunavir Anti-retroviral protease inhibitor uses in combination drugs such as ritonavir or cobicistat [154] [155] |
Blocking Virus-Cell Membrane Fusion Recombinant Human Angiotensin-converting Enzyme 2 (rhACE2) |
Blocking the S protein of SARS-CoV2 from interacting with the cellular ACE2 rhACE2 could inhibit SARS-CoV-2 replication in cellular and embryonic stem decreased serum level of angiotensin II [156] Arbidol Hydrochloride (Umifenovir) Entry inhibitor against influenza viruses and arboviruses [157] [158] |
Ind J Clin Biochem hypercoagulation stage in severe COVID-19 patients [140] . Furthermore, Ibrahim et al. (2020) reported that intravenous injection of N-acetyl cysteine in small cases of COVID-19, demonstrated significantly decreased inflammation and clinical improvement along with markedly reduced CRP levels in all patients and ferritin in 9/10 patients [141] . Lastly, other options such as Imatinib a tyrosine kinase inhibitor and colchicine, an anti-inflammatory drug have also been explored in few COVID-19 cases [142, 143] . |
Currently, various clinical trials are ongoing to test specificity and efficacy of vaccines and antibodies precisely targeting SARS-CoV-2. Multiple pharmaceutical drugs that have kept therapeutic promises in the treatment of COVID-19 includes human immunoglobulin, chloroquine, hydroxychloroquine, remdesivir, favipiravir, ritonavir, lopinavir, arbidol etc. The role of these therapeutic drugs shown in Table 4 . |
Current review addressed relevant information of SARS-CoV-2, incidence, etiopathogenesis, clinical characteristics with multiorgan involvements in associated comorbid diseases, present and future prospects of clinical trials to prevent, manage and control COVID-19 pandemic infection. Information depicted in present review may serve to fill existing lacunae in knowledge on pathogenesis of COVID-19. Moreover, aspects of SARS-COV-2 etioclinio-pathogenesis and replication depicted inside infected cells might help in development of present and future prospects of ongoing clinical trials. New updates poured reticent on deadly virus from all over world are considered in present review to enhance knowledge and clear out concepts for present future prospects of clinico-trials to ascertain success in the possible drugs development for COVID-19 treatment. Extensive research to recognize novel pathways and their cross talk to combat this virus in precarious settings is our future positive hope. |
Conflict of Interests Authors declare that they have no conflict of interest to disclose. |
Since the onset of the COVID-19 pandemic there has been significant interest in the use of smartphone applications (apps) to facilitate contact discovery and tracing. Researchers have been pursuing methods using Bluetooth Low Energy (BLE) signals to estimate the duration and proximity of smartphones (and those carrying them) to one another. One such effort is the Private Automated Contact Tracing (PACT) Protocol [2] , which is a decentralized approach modeled after Apple's "Find My" protocol [3] . A smartphone using a PACT-based app DISTRIBUTION periodically emits BLE chirps derived from a secret seed known only to that phone. All smartphones log their seeds, the times the seeds were active, and any BLE chirps received with timestamps. If a user is diagnosed as COVID-19 positive, a record of all BLE chirps sent by the user's phone can be voluntarily uploaded to a database through a trusted public health authority. Other smartphones frequently check against this database to see if they have received any BLE chirps designated from a COVID-19 positive individual, and if so, the phone's owner can be notified to take the appropriate action (for instance, self-isolate or seek COVID-19 testing). |
In order for PACT and related efforts to be successful, a Too Close For Too Long (TCFTL) detector must be developed based on suitable signals (e.g., BLE) and associated measurements, such as the transmit power level, Received Signal Strength Indicator (RSSI), and received timestamp. There are three desired features of the TCFTL detector: 1) a high probability of detection when the two users (devices) are within a threshold distance (i.e., high recall), 2) low probability of false alarm (high precision), and 3) ability to be tuned based on guidelines from local health authorities (for example, based on an exposure of 15 or more minutes within 6 feet of an infected individual [4] ). |
Apple and Google (A|G) have released BLE-based exposure notification protocols and Application Programming Interfaces (APIs) for iOS and Android devices, a significant step forward for automated contact tracing efforts [5] . Unfortunately, the initial implementations of these protocols only log received BLE chirps when the device is awake, or no more often than every 5 minutes when the device is not awake. This limits the data available to a TCFTL detector, and it has been shown to have relatively poor performance (up to 50% false alarms and less than 25% of true contact events detected), with an effectiveness that varies across smartphones [1] , [6] - [9] . Increasing the BLE sample rate and estimating the carriage state of each smartphone has been shown to help [1] , although may not be possible due to battery life and privacy constraints. |
In this paper, we show that ultrasonic (US) 1 range mea- 1 Ultrasonic frequencies are typically defined as those exceeding 20 kHz. In the context of commodity smartphones and this paper, we use "ultrasonic" for frequencies around 20 kHz, but not necessarily exceeding that threshold. surement using the speakers and microphones built-in to all smartphones is a promising concept that could improve the accuracy of BLE-based estimates significantly. By using a BLE protocol augmented with US ranging (BLE+US), smartphones exchange time-tagged inaudible acoustic pulses in the ultrasonic frequency range to measure the time-of-flight between them. Because the speed of sound is known, the devices can then jointly solve for the range between them. By improving the ranging accuracy, this technique can be used together with BLE-based protocols to reduce the false alarm and miss rates of exposure notification services. For example, a BLE-based approach may flag neighbors in an apartment building as positive contacts, when they are actually separated by physical barriers (ceilings or walls) because the 2.4 GHz BLE signals will propagate through such barriers. A BLE+US approach would identify these BLE contacts as false alarms and eliminate them, since US frequencies are more significantly attenuated by the same barriers. This paper introduces SonicPACT, an ultrasonic ranging protocol developed by the PACT team to augment a BLEbased TCFTL detector for contact tracing. SonicPACT has the following key features: |
1) It can be implemented on both Android and iOS and allow interoperability between them. 2) It operates at inaudible near-ultrasonic frequencies to avoid disturbing users while measurements are made. 3) It uses BLE advertisements (rather than Wi-Fi, although it could also use Wi-Fi) to coordinate measurements and exchange information. This feature simplifies integration with BLE-based exposure notification protocols. 4) It uses pseudorandom noise (PN) waveforms generated by a random number generator with a seed based on the device's unique Bluetooth Universally Unique Identifier (UUID), allowing both devices to generate matched filters for the other's waveform without prior coordination. |
We have implemented SonicPACT as user-level apps on Android and iOS and have conducted several experiments with the initial implementation. Our results are promising: 1) In experiments done placing phones at distances between 2 feet (60 cm) and 12 feet (3,6 meters) of each other in multiples of 2 feet, and considering a threshold of ≤ 6 feet as the critical distance at which to flag a "contact", we find that the missed detection rate indoors is between 0% and 5.1% with no false alarms. 2) Indoors, the estimated distances are within 1 foot (30 cm) of the true distance between 56.7% and 70.9% of the time; outdoors, due to the absence of significant multipath, estimates are within 1 foot between 82.9% and 100% of the time. 3) These are proof-of-concept implementations with significant optimizations that can be done, but the low false alarm and miss rates compared to a BLE-only method when estimating if two devices are "too close" (within 6 feet or 1.8 meters) is an encouraging result for exposure notification and is therefore worth studying for implementation by A|G. Our goal in developing SonicPACT was to demonstrate that ultrasonic signaling, coupled with BLE technology widely available on A|G APIs, can provide a more robust implementation of a TCFTL detector. Ultimately, a power-efficient and ubiquitous implementation of an ultrasonic ranging protocol may (will?) need to be integrated within the iOS and Android operating systems, and may therefore need to be developed by A|G themselves. As such, SonicPACT as proposed here is not a turnkey app solution to contact discovery, but we view it as a promising technology and protocol to incorporate into Android and iOS. In addition to discussing our initial promising experimental results, we discuss at length the limitations of both our SonicPACT implementation and of ultrasonic ranging in general, along with directions for future work. |
Several research groups have sought to use the acoustic channel on smartphones or mobile devices for communications [10] - [13] , and indoor and outdoor localization [14] - [19] with some success. Most prior acoustic indoor localization techniques rely on external hardware to serve as beacons or receivers. Ranging between smartphones without any external hardware is more challenging, since depending on the method employed, accurate transmit and receive timestamps may be required to compute time-of-flight of the US signal between devices. Obtaining the timestamps with low or predictable latency from a user-space application is not easy. |
Peng et al.'s BeepBeep method [18] provides a ranging capability using only commercial off-the-shelf (COTS) hardware available on smartphones. BeepBeep ranging uses linear frequency modulated (LFM) chirp waveforms in the 1-6 kHz frequency range and a sample-counting technique to eliminate the latency and uncertainty introduced by time-stamping the transmit and receive pulses. The protocol uses Wi-Fi to exchange measurements between devices, enabling each device to compute the range to the other device. The BeepBeep paper reports that sub-centimeter ranging accuracy was achievable in many cases, and that performance could be achieved even in challenging indoor environments with multipath mitigation techniques. Fotouhi [20] extended this work by implementing the BeepBeep protocol on Android devices using spreadspectrum waveforms to differentiate between devices and maintain user privacy. |