Hematological parameters of viral infection

Abstract

Dynamic changes in volume parameters of circulating activated mononuclear cells in response to SARS-CoV-2 infection are quantitatively determined by an automated hematology analyzer. [Solution] Methods, devices, and computer-executable programs are provided for the early diagnosis of SARS-CoV-2 infection by using the volumetric and monocyte distribution width (MDW) biomarkers Lymphatic Index. The automated volumetric biomarkers Lymphatic Index and MDW can be used as viral biomarkers to help medical personnel in clinics or fever clinics quickly identify patients potentially infected with SARS-CoV-2 and provide valuable information for triage decisions.

Description

[Technical Field]

[0001] Field The present invention relates to the detection of viral infections, in particular the detection of coronavirus infections, and especially the early detection of coronavirus infections. [Background technology]

[0002] background In December 2019, a pneumonia outbreak of unknown cause occurred in Wuhan, People's Republic of China. Subsequent deep sequencing analysis of lower respiratory tract samples indicated that the pathogen was a novel coronavirus [1].

[0003] Coronaviruses, belonging to the Coronavirus genus in the family Nidovirales and family Coronaviridae, are a type of enveloped, linear, single-stranded, positive-sense RNA virus that are widespread in nature. Coronavirus genomes have a methylated cap structure at the 5' end and a poly(A) tail at the 3' end, and are approximately 27-32 kb in length, making them the largest known RNA virus genomes. Six coronavirus subtypes (HCoV-229E, HCoV-OC43, SARS-CoV, HCoV-NL63, HCoV-HKU1, and MERS-CoV) are known to infect humans. Among these, 229E, NL63, OC43, and HKU1 typically cause mild or moderate upper respiratory tract illnesses, similar to the common cold, while SARS-CoV and MERS-CoV can cause severe, potentially fatal illnesses. The pathogen causing the pneumonia outbreak in Wuhan is the seventh subtype of coronavirus identified to infect humans. Studies have shown that its genetic structure is 82% similar to SARS-CoV. [2] Based on this similarity, the International Committee on Taxonomy of Viruses (ICTV) named the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the World Health Organization (WHO) named the pneumonia disease coronavirus disease 2019 (COVID-19).

[0004] COVID-19 has now been declared a global pandemic by the WHO and has spread to over 100 countries. As of March 2020, the total number of cases worldwide has reached 900,000, with over 40,000 deaths, and these numbers are still rapidly increasing. Recent studies have revealed that SARS-CoV-2 can be transmitted between humans primarily through respiratory droplets. [3] The incubation period is generally 3–7 days, with a maximum of 14 days. The primary symptoms (low-grade fever, fatigue, and dry cough) are usually mild and nonspecific, making it difficult to identify infected individuals early or during the incubation period.

[0005] Several laboratory tests, such as lymphocyte count, C-reactive protein, chest imaging studies, or molecular testing [4], have been used to aid in the diagnosis of coronaviruses, but they are neither specific, sensitive, nor widely available. Currently, the diagnostic standard commonly used in various countries is the specific detection of nucleic acids in patient samples (e.g., nasal swabs), but such methods generally suffer from poor reproducibility, insufficient sensitivity (especially in the early stages of infection), inaccurate sampling, and high requirements for technicians. From an epidemiological perspective, it is important to identify infected individuals with high sensitivity early in the course of infection. Especially for individuals who are in close contact with known COVID-19 patients or who are in endemic areas, early screening and diagnosis can provide valuable information for timely and effective medical observation and intervention, triage decisions, and management decisions in disease-endemic areas. In the current diagnosis of COVID-19, patients with a contact history, clinical symptoms, CT scans resembling viral infection, and a positive molecular test are generally considered confirmed cases, whereas those with a contact history, clinical symptoms, CT scans resembling viral infection, and a negative molecular test upon admission are considered suspected cases. A drawback is the lag in the detection of nucleic acid molecules (a key marker for diagnosing COVID-19), which is undesirable for early disease screening. This is thought to be because the viral load is low early in the disease, making it difficult to obtain highly sensitive results through nucleic acid molecular detection. This makes it difficult to implement early identification, classification, and therapeutic intervention for "suspect" individuals and can lead to further spread of the disease due to a lack of timely isolation. Therefore, there is an urgent need to find ways to quickly identify patients who are likely infected, especially early in the infection, to provide valuable information for triage decisions. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Huang C et al. Lancet (2020) 395:497~506 [Non-patent document 2] Pan Y et al., European Radiology(2020)s00330-020-06731-x [Non-patent document 3] Chan JF et al., Lancet (2020) 395 (10223): 514–523 Summary of the Invention [Means for solving the problem]

[0007] overview State-of-the-art automated hematology analyzers using VCS (volume, conductivity, and light scattering) technology can determine the intrinsic biophysical properties of over 8,000 peripheral white blood cells in their "near in vivo" state. VCS technology uses laser light to measure direct current impedance for cell volume, radiofrequency opacity to assess conductivity for cytoplasmic chemical composition and nuclear volume, and light scattering at multiple angles for cell surface topography, cytoplasmic granularity, and nuclear structure. The degree of cell volume variation can also be measured. These morphometric measurements are known as cell population data (CPD). We have found that the dynamic changes in volume parameters of circulating activated mononuclear cells in response to SARS-CoV-2 infection can be quantitatively determined by an automated hematology analyzer and can serve as useful biomarkers for rapid screening of suspected individuals who have been in close contact with known COVID-19 patients or in areas of similar infection epidemics.

[0008] The present disclosure provides a method for measuring monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymph index, mean neutrophil volume (MNV), and The present invention relates to volumetric biomarkers for the early diagnosis of coronavirus infection, such as lymphatic index (LD) and neutrophil distribution width (NDW). The present invention also relates to methods, devices, and computer-executable programs for the early diagnosis of coronavirus infection using such biomarkers. According to some technical solutions of the present invention, the automated volumetric parameters lymphatic index and MDW can be used as viral biomarkers to help medical personnel in outpatient departments or fever clinics quickly identify people who may be infected with COVID-19 and provide valuable information for making triage decisions.

[0009] In this study, we unexpectedly found that dynamic changes in the volume parameters of circulating activated mononuclear cells in response to SARS-CoV-2 infection in patients suspected of having undiagnosed early COVID-19 could be quantitatively determined by an automated hematology analyzer and could serve as useful biomarkers for rapid screening. We demonstrated that some cell population data, particularly the lymphoid index and monocyte distribution width (MDW), are significantly increased in COVID-19 patients. With specified cutoff values ​​for the lymphoid index and MDW, we achieved sensitivities of 84.4% and 78.1%, respectively, in diagnosing COVID-19 infection. Furthermore, the lymphoid index combined with MDW demonstrated excellent diagnostic performance (AUC of 0.83, which was significantly higher than the AUC of MDW alone). (AUC of 0.77 and 0.79 for lymphatic index alone). This indicates that the combination of MDW and lymphatic index can more accurately reflect the likelihood that a subject has SARS-CoV-2 infection compared with MDW or lymphatic index alone. Furthermore, the inventors demonstrated that, like confirmed patients, the lymphatic index and MDW of suspected patients also increased significantly upon admission, suggesting a similar pathophysiological process. Notably, using this protocol, 89% of suspected patients with positive tests (along with negative nucleic acid molecular tests) were diagnosed with COVID-19 during the subsequent disease course. This demonstrates the clinical significance of using lymphatic index and MDW as sensitive screening biomarkers for rapidly identifying suspected individuals before nucleic acid confirmation and for developing appropriate management plans.

[0010] One aspect of the invention is a method for identifying a subject having a viral infection, comprising:

[0011] 1) flowing a body fluid sample obtained from a subject through a flow cell;

[0012] 2) measuring individual cells of multiple cells in a body fluid sample;

[0013] 3) determining one or more cell population parameter data values ​​in the body fluid sample based on the measurements, wherein the one or more cell population parameter data values ​​include population parameter data values ​​from one or more of lymphocytes, neutrophils, and monocytes; and

[0014] 4) determining whether at least one of the one or more cell population data values ​​exceeds a predetermined threshold; Including,

[0015] wherein viral infection in the subject is indicated if at least one of the one or more cell population parameter values ​​exceeds a predetermined threshold.

[0016] In an embodiment of this aspect, the viral infection is associated with an upper respiratory tract illness. In a preferred embodiment, the viral infection is a coronavirus infection. In a more preferred embodiment, the coronavirus is SARS-CoV-2. In an embodiment, the upper respiratory tract illness is COVID-19.

[0017] In embodiments, the cell population data includes one or more selected from the group consisting of monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymphoid index, mean neutrophil volume (MNV), neutrophil distribution width (NDW), or any combination thereof. In preferred embodiments, the cell population data includes MDW and one or more of lymphoid index and LV-SD. In preferred embodiments, the cell population data includes a combination of two or more selected from the group consisting of MDW, LV, LV-SD, LC, and lymphoid index. In preferred embodiments, the cell population data is a combination of MDW and one or more of LV, LV-SD, LC, and lymphoid index. In a more preferred embodiment, the cell population data consists of MDW and lymphoid index. In another embodiment, the cell population data includes or consists of MNV and NDW. The lymphoid index is calculated by lymphoid index = LV × (LV-SD) / LC.

[0018] In embodiments, viral infection in the subject is indicated if one or more of the cell population data exceeds a predetermined threshold.

[0019] In embodiments, a value for MDW greater than 20.27 indicates a viral infection in the subject. In preferred embodiments, a value for MDW greater than 20.42 indicates a viral infection in the subject. In more preferred embodiments, a value for MDW greater than 20.73 indicates a viral infection in the subject. In even more preferred embodiments, a value for MDW greater than 21.24 indicates a viral infection in the subject. In embodiments, a value for lymphatic index greater than 11.3 indicates a viral infection in the subject. In preferred embodiments, a value for lymphatic index greater than 11.63 indicates a viral infection in the subject. In more preferred embodiments, a value for lymphatic index greater than 11.8 indicates a viral infection in the subject. In even more preferred embodiments, a value for lymphatic index greater than 12.04 indicates a viral infection in the subject. In even more preferred embodiments, a value for lymphatic index greater than 12.35 indicates a viral infection in the subject. In embodiments, a value for LV-SD greater than 14.41 indicates a viral infection in the subject.

[0020] In an embodiment, the bodily fluid sample is whole blood.

[0021] In embodiments, the measuring comprises measuring one or more of a volume parameter, a conductivity parameter, and / or a light scattering parameter, hi embodiments, the measuring comprises measuring light scattering and DC impedance from individual cells of the plurality of cells.

[0022] In embodiments, the subject is an individual without confirmed viral infection. In preferred embodiments, the subject is an individual suspected of viral infection. In embodiments, the subject tests negative for viral nucleic acid. In embodiments, the subject is a human.

[0023] Another aspect of the present invention is a device for identifying a subject having a viral infection, comprising:

[0024] 1) a transducer for measuring cells passing through the flow cell;

[0025] 2):

[0026] receiving and processing measurement data from the transducer;

[0027] determining one or more cell population data values ​​based on the measurements, wherein the one or more cell population parameter data values ​​include population parameter data values ​​from one or more of lymphocytes, neutrophils, and monocytes; and

[0028] determining whether at least one of the one or more cell population data values ​​exceeds a predetermined threshold; one or more processors configured to perform operations including Including,

[0029] The device relates to a subject having a viral infection when at least one of the one or more cell population parameter values ​​exceeds a predetermined threshold.

[0030] In an embodiment of this aspect, the viral infection is associated with an upper respiratory tract illness. In a preferred embodiment, the viral infection is a coronavirus infection. In a more preferred embodiment, the coronavirus is SARS-CoV-2. In an embodiment, the upper respiratory tract illness is COVID-19.

[0031] In embodiments, the cell population data includes one or more selected from the group consisting of monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymphoid index, mean neutrophil volume (MNV), neutrophil distribution width (NDW), or any combination thereof. In preferred embodiments, the cell population data includes MDW and one or more of lymphoid index and LV-SD. In preferred embodiments, the cell population data includes a combination of two or more selected from the group consisting of MDW, LV, LV-SD, LC, and lymphoid index. In preferred embodiments, the cell population data is a combination of MDW and one or more of LV, LV-SD, LC, and lymphoid index. In a more preferred embodiment, the cell population data consists of MDW and lymphoid index. In another embodiment, the cell population data includes or consists of MNV and NDW. The lymphoid index is calculated according to the following formula: lymphoid index = LV × (LV-SD) / LC.

[0032] In embodiments, viral infection in a subject is indicated when the MDW and lymphatic index values ​​are both outside their respective reference ranges.

[0033] In embodiments, a value for MDW greater than 20.27 indicates a viral infection in the subject. In preferred embodiments, a value for MDW greater than 20.42 indicates a viral infection in the subject. In more preferred embodiments, a value for MDW greater than 20.73 indicates a viral infection in the subject. In even more preferred embodiments, a value for MDW greater than 21.24 indicates a viral infection in the subject. In embodiments, a value for lymphatic index greater than 11.3 indicates a viral infection in the subject. In preferred embodiments, a value for lymphatic index greater than 11.63 indicates a viral infection in the subject. In more preferred embodiments, a value for lymphatic index greater than 11.8 indicates a viral infection in the subject. In even more preferred embodiments, a value for lymphatic index greater than 12.04 indicates a viral infection in the subject. In even more preferred embodiments, a value for lymphatic index greater than 12.35 indicates a viral infection in the subject. In embodiments, a value for LV-SD greater than 14.41 indicates a viral infection in the subject.

[0034] In embodiments, the measuring comprises measuring one or more of a volume parameter, a conductivity parameter, and / or a light scattering parameter, hi embodiments, the measuring comprises measuring light scattering and DC impedance from the cells.

[0035] In embodiments, the subject is an individual without confirmed viral infection. In preferred embodiments, the subject is an individual with suspected viral infection. In embodiments, the subject tests negative for viral nucleic acid. In embodiments, the subject is a human.

[0036] Yet another aspect of the present invention is a computer storage medium having recorded thereon an executable program for identifying a subject having a viral infection, the executable program, when executed by a processor, performing the following steps:

[0037] 1) Measuring cells passing through a flow cell;

[0038] 2) determining one or more cell population parameter data values ​​in the body fluid sample based on the measurements, wherein the one or more cell population parameter data values ​​include population parameter data values ​​from one or more of lymphocytes, neutrophils, and monocytes; and

[0039] 3) determining whether at least one of the one or more cell population data values ​​exceeds a predetermined threshold; was carried out,

[0040] A computer storage medium, characterized in that viral infection in a subject is indicated if at least one of the one or more cell population parameter values ​​exceeds a predetermined threshold.

[0041] In an embodiment of this aspect, the viral infection is associated with an upper respiratory tract illness. In a preferred embodiment, the viral infection is a coronavirus infection. In a more preferred embodiment, the coronavirus is SARS-CoV-2. In an embodiment, the upper respiratory tract illness is COVID-19.

[0042] In embodiments, the cell population data includes one or more selected from the group consisting of monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymphoid index, mean neutrophil volume (MNV), neutrophil distribution width (NDW), or any combination thereof. In preferred embodiments, the cell population data includes MDW and one or more of lymphoid index and LV-SD. In preferred embodiments, the cell population data includes a combination of two or more selected from the group consisting of MDW, LV, LV-SD, LC, and lymphoid index. In preferred embodiments, the cell population data is a combination of MDW and one or more of LV, LV-SD, LC, and lymphoid index. In a more preferred embodiment, the cell population data consists of MDW and lymphoid index. In another embodiment, the cell population data includes or consists of MNV and NDW. The lymphoid index is calculated according to the following formula: lymphoid index = LV × (LV-SD) / LC.

[0043] In embodiments, viral infection in the subject is indicated if one or more of the cell population data exceeds a predetermined threshold.

[0044] In embodiments, a value for MDW greater than 20.27 indicates a viral infection in the subject. In preferred embodiments, a value for MDW greater than 20.42 indicates a viral infection in the subject. In more preferred embodiments, a value for MDW greater than 20.73 indicates a viral infection in the subject. In even more preferred embodiments, a value for MDW greater than 21.24 indicates a viral infection in the subject. In embodiments, a value for lymphatic index greater than 11.3 indicates a viral infection in the subject. In preferred embodiments, a value for lymphatic index greater than 11.63 indicates a viral infection in the subject. In more preferred embodiments, a value for lymphatic index greater than 11.8 indicates a viral infection in the subject. In even more preferred embodiments, a value for lymphatic index greater than 12.04 indicates a viral infection in the subject. In even more preferred embodiments, a value for lymphatic index greater than 12.35 indicates a viral infection in the subject. In embodiments, a value for LV-SD greater than 14.41 indicates a viral infection in the subject.

[0045] In embodiments, the measuring comprises measuring one or more of a volume parameter, a conductivity parameter, and / or a light scattering parameter, hi embodiments, the measuring comprises measuring light scattering and DC impedance from the cells.

[0046] In embodiments, the subject is an individual without confirmed viral infection. In preferred embodiments, the subject is an individual with suspected viral infection. In embodiments, the subject tests negative for viral nucleic acid. In embodiments, the subject is a human. definition

[0047] Some terms used herein are defined as follows: These parameters can be readily measured using commercially available equipment, such as the UniCel DxH 800 hematology analyzer (Beckman Coulter, Brea, Calif.). [Table 4] [Brief explanation of the drawings]

[0048] [Figure 1] Figure 1 shows the distribution of MDW and lymphatic index among the three groups (control, suspected, and confirmed). Figure 1a shows the distribution of lymphatic index, and Figure 2a shows the distribution of MDW.

[0049] [Figure 2] Figure 2 shows the results of using CPD parameters to predict the diagnosis of SARS-CoV-2 infection, among them controls (n = 32) versus confirmed diagnoses (n = 68). [Example]

[0050] Detailed Description material and method Case selection and data collection In this case-control study, clinical information including contact history, initial symptoms, routine hematological analysis, chest CT, and RT-PCT analysis was collected from the Wuhan Union Hospital in Wuhan, People's Republic of China. Data were collected from 128 hospitalized patients of Chinese ethnicity at the University of Tokyo Hospital between February 14 and 29, 2020. Based on criteria in accordance with the Guidelines for the Diagnosis and Treatment of 2019 Novel Coronavirus (COVID-19) Pneumonia (6th Edition), there were 96 patients (male:female ratio 40:56), including 68 confirmed cases (contact history, clinical symptoms, CT scans resembling viral infection, and positive molecular tests) and 28 suspected cases (contact history, clinical symptoms, CT scans resembling viral infection, and negative molecular tests upon admission). Thirty-two individuals without clinical or radiological evidence of viral infection served as controls.

[0051] Classification criteria According to the criteria in accordance with the Guidelines for the Diagnosis and Treatment of 2019 Novel Coronavirus (COVID-19) Pneumonia (6th Edition), the classification criteria are as follows:

[0052] (I) Suspected cases

[0053] A comprehensive analysis combining epidemiological history and clinical manifestations of:

[0054] 1. Epidemiological history

[0055] (1) Travel or stay history in Wuhan City and its surrounding areas, or in other communities with reported cases within 14 days before the onset of the disease;

[0056] (2) History of contact with a patient with COVID-19 infection (positive nucleic acid test) within 14 days before the onset of illness;

[0057] (3) History of contact with patients with fever or respiratory symptoms from Wuhan and surrounding areas or from communities with reported cases within 14 days prior to the onset of illness;

[0058] (4) Disease cluster outbreaks.

[0059] 2. Clinical manifestations

[0060] (1) fever and / or respiratory symptoms;

[0061] (2) Imaging characteristics of novel coronavirus pneumonia;

[0062] (3) Normal or decreased total white blood cell count and decreased lymphocyte count in the early stages of disease development.

[0063] Meets both any one of the epidemiological history and any two of the clinical manifestations.

[0064] Although there is no clear epidemiological history, the clinical manifestations are consistent with three criteria.

[0065] (II) Confirmed cases

[0066] Suspected cases with one of the following etiologic evidence:

[0067] 1. Positive detection of novel coronavirus nucleic acid by real-time fluorescent RT-PCR;

[0068] 2. Highly homologous to known novel coronaviruses based on viral gene sequencing.

[0069] Cell population data (CPD) analysis All blood samples were analyzed within 4 hours of collection on a UniCel DxH 800 hematology analyzer (Beckman Coulter, Brea, CA) with version 2.0 software. This instrument measures CBC with differential and cytomorphometric parameters, including specific cell volume and cell volume distribution within a group of cells, such as mean neutrophil volume (MNV), neutrophil distribution width (NDW), mean monocyte volume (MMV), monocyte distribution width (MDW), mean lymphocyte volume (LV), and lymphocyte distribution width (LV-SD). The simplified lymphocyte CPD, lymphoid index, was calculated as LV × LV-SD / LC (lymphocyte conductance). Additionally, five angular light scatter parameters were collected, including median-angle light scatter, upper median-angle light scatter, lower median-angle light scatter, low-angle light scatter, and axial light loss. Light scatter parameters quantitatively capture morphological variations reflected by cell complexity, granularity, and nuclear structure through measurements of AL2, which reflects cell size based on absorbed light.

[0070] statistical analysis All analyses, including ROC, are performed by SAS software, whose version is 9.4. Since there are a total of 13 variables for the three groups for analysis, if the variables are normally distributed, we use one-way ANOVA to test for differences among the three groups. If the variables are non-normally distributed, we use Kruskal-Wallis to test for differences among the three groups. If the P value is less than 0.05, we can conclude that there is a significant difference among the three groups. Then, we use the Dwass-Steel-Critchlow-Fligner test for two pairwise comparisons of non-normally distributed variables, and if the P value is less than 0.05, we can conclude that there is a significant difference between the two groups. Finally, we use Tukey's Studentized Range (Tukey's The Studentized Range (HSD) test was used for pairwise comparisons of normally distributed variables, and if the P value is less than 0.05, it can be concluded that there is a significant difference between the two groups.

[0071] result Demographic Data Epidemiological information, clinical data, laboratory tests, and radiological characteristics were reviewed from electronic medical records. Following the Guidelines for the Diagnosis and Treatment of 2019 Novel Coronavirus (COVID-19) Pneumonia (6th Edition), we prospectively collected and analyzed data from 128 patients (mean age 48.9 years, range 16-88 years; male:female ratio 61:67). There were 68 confirmed cases (common type), 28 suspected cases, and 32 controls without clinical or radiological evidence of viral infection. Among the 68 confirmed cases, the male:female ratio was 32:36, with 68% (46 / 68) having fever and 47% (32 / 68) having cough. Among the 28 suspected cases, the male:female ratio was 8:20, with 68% (19 / 28) having fever and 57% (16 / 28) having cough. Note: In follow-up of these suspected patients, 89% (25 / 28) were subsequently confirmed as having COVID-19 by positive nuclear tests; 11% (3 / 28) remained suspected of having COVID-19 infection, demonstrating that the disclosed method can accurately identify patients with SARS-CoV-2 infection at an early stage.

[0072] Comparison of conventional hematological parameters with CPD As shown in Table 1, no statistically significant differences in conventional hematological parameters regarding WBC, percent neutrophils, lymphocytes, monocytes, and the neutrophil / lymphocyte ratio were observed among all three groups. However, the monocyte distribution width (NDW), lymphocyte distribution width (LV-SD), and lymphatic index (LV × LV-SD / LC) were significantly increased in both the suspected and confirmed groups compared with those of the controls (Figure 1). No significant differences in NDW, LV-SD, and lymphatic index were found between the suspected and confirmed groups. An increase in mean lymphocyte volume (LV) and a decrease in mean lymphocyte conductance (LC) were also observed in both the suspected and confirmed groups, but the differences were not statistically significant compared with the controls. Table 1. CBC and CPD parameters in the three groups of patients [Table 1]

[0073] Sensitivity and specificity of CPD in diagnosing COVID-19 infection The sensitivity and specificity of CPD in predicting COVID-19 infection were then calculated at the indicated cutoff values. As shown in Table 2, MDW and lymphatic index demonstrated the best sensitivity (78.1% and 84.4%, respectively) and specificity (64.2% and 64.2%, respectively) for detecting COVID-19 infection compared with other parameters. ROC curve analysis revealed that MDW and lymphatic index had the largest area under the curve (AUC) of 0.77 and 0.79, respectively. When combined with MDW and lymphatic index, the AUC increased to 0.83 (Figure 2, Table 2), which was higher than the AUC of MDW alone and the AUC of lymphatic index alone. This indicates that the combination of MDW and lymphatic index can more accurately reflect the likelihood of a subject suffering from SARS-CoV-2 infection compared with MDW or lymphatic index alone. Furthermore, due to the different requirements for sensitivity and specificity in different application scenarios, for example, higher sensitivity is desired in early diagnosis and screening, and better specificity is required in diagnosis, the inventors also carried out a thorough study of the relationship between the cutoff point of MDW and lymphatic index and sensitivity and specificity (Table 3). The results show that the method of the present disclosure can be better adapted to different application scenarios by adjusting the cutoff value. Table 2. CPD parameters for predicting 2019-nCoV infection [Table 2] Table 3. Sensitivity and specificity of MDW and lymphatic index [Table 3]

[0074] Consideration Circulating monocytes and lymphocytes are the first to respond to viral infection. Several previous studies have shown that mononuclear cell volume parameters significantly increase during various viral infections [5-7]. Therefore, the volumetric increase of these immune cells has potential as a viral biomarker in humans. In this study, we demonstrated for the first time that lymphatic index and monocyte distribution width (MDW) significantly increase in COVID-19 patients. Using the indicated cutoff values ​​for lymphatic index and MDW, we achieved sensitivities of 84.4% and 78.1%, respectively, in diagnosing COVID-19 infection. Furthermore, the lymphatic index combined with MDW demonstrated superior diagnostic performance (AUC, 0.83), higher than the AUCs obtained with MDW alone and the lymphatic index alone. This indicates that the combination of MDW and lymphatic index can more accurately reflect the likelihood of a subject suffering from SARS-CoV-2 infection compared with MDW or lymphatic index alone. Furthermore, we demonstrated that the lymphatic index and MDW of suspected patients, like those of confirmed patients, significantly increased upon admission, suggesting a similar pathophysiological process. Note that all 28 suspected patients tested in this example had negative nucleic acid tests upon admission, but 25 of them (89%) were subsequently diagnosed with COVID-19 during the course of their disease. This demonstrates the clinical significance of using lymphatic index and MDW as highly sensitive screening biomarkers to rapidly identify these suspected individuals before nucleic acid confirmation and develop appropriate management plans. We did not observe any significant changes in neutrophil volume parameters, MNV, and NDW upon admission. This is consistent with recent human observations that neutrophils primarily function as first responders during the innate immune response to acute bacterial infection or sepsis [8-10].

[0075] Whole blood cell analysis plays an important role in healthcare decision-making, from diagnosis and prognosis through treatment. Currently, automated hematology analyzers can provide not only total white blood cell counts but also white blood cell volume parameters. However, changes in white blood cell numerical parameters, such as total white blood cell counts, tend to be highly variable and nonspecific. In COVID-19 cases, total white blood cells or lymphocytes may be normal or mildly decreased, providing no definitive information for differential diagnosis. Therefore, the clinical utility of volumetric parameters, lymphatic index, and MDW offers additional practical advantages. These parameters are generated during automated differential analysis without additional specimen requirements. They can be quantitative and are more accurate because significantly more white blood cells are simultaneously evaluated. Furthermore, they offer a more robust turnaround time and are more cost-effective. These volumetric parameters certainly have the potential to become useful viral biomarkers, helping healthcare professionals in outpatient departments or fever clinics quickly identify individuals potentially infected with COVID-19 and providing valuable information for triage decisions.

[0076] The scope of the present invention is not limited by the specific embodiments described herein. Indeed, various modifications of the present invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description and drawings. Such modifications are intended to be encompassed by the appended claims. Moreover, all embodiments described herein are deemed to be broadly applicable and can be appropriately combined with any and all other consistent embodiments. Furthermore, to the extent that prior art knowledge is not expressly incorporated by reference above, it is expressly incorporated herein in its entirety. Several publications are cited throughout this document, the entire disclosures of which are incorporated by reference in their entireties.

[0077] The disclosures of the following references are incorporated by reference in their entirety: References [ka]

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Claims

1. 1. A method for identifying a subject having a viral infection, comprising: 1) flowing a bodily fluid sample obtained from said subject through a flow cell; 2) measuring individual cells of a plurality of cells in the body fluid sample; 3) determining one or more cell population parameter data values ​​in the bodily fluid sample based on the measurements, wherein the one or more cell population parameter data values ​​include population parameter data values ​​from one or more of lymphocytes, neutrophils, and monocytes; and 4) determining whether at least one of the one or more cell population data values ​​exceeds a predetermined threshold; Including, wherein viral infection of the subject is indicated if at least one of the one or more cell population parameter values ​​exceeds a predetermined threshold.

2. 10. The method of claim 1, wherein the viral infection is associated with an upper respiratory tract illness.

3. 10. The method of claim 1, wherein the viral infection is a coronavirus infection.

4. 4. The method of claim 3, wherein the coronavirus is SARS-CoV-2.

5. 2. The method of claim 1, wherein the cell population data comprises one or more selected from the group consisting of monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymphoid index, mean neutrophil volume (MNV), neutrophil distribution width (NDW), or any combination thereof.

6. 6. The method of claim 5, wherein the cell population data comprises MDW and one or more of lymphatic index and LV-SD.

7. The method of claim 5 , wherein the cell population data comprises or consists of MNV and NDW.

8. 6. The method of claim 5, wherein viral infection in the subject is indicated when the values ​​of the MDW and the lymphatic index are both outside their respective reference ranges.

9. 6. The method of claim 5, wherein a value of the MDW greater than 20.27 indicates viral infection in the subject.

10. 6. The method of claim 5, wherein a value of the MDW greater than 20.42 indicates viral infection in the subject.

11. 6. The method of claim 5, wherein a value of the MDW greater than 20.73 indicates viral infection in the subject.

12. 6. The method of claim 5, wherein a value of the MDW greater than 21.24 indicates viral infection in the subject.

13. 6. The method of claim 5, wherein a value of the lymphatic index greater than 11.3 indicates a viral infection in the subject.

14. 6. The method of claim 5, wherein a value of the lymphatic index greater than 11.63 indicates a viral infection in the subject.

15. 6. The method of claim 5, wherein a value of the lymphatic index greater than 11.8 indicates a viral infection in the subject.

16. 6. The method of claim 5, wherein a value of the lymphatic index greater than 12.04 indicates a viral infection in the subject.

17. 6. The method of claim 5, wherein a value of the lymphatic index greater than 12.35 indicates a viral infection in the subject.

18. 6. The method of claim 5, wherein a value of LV-SD greater than 14.41 indicates viral infection in the subject.

19. The method of claim 1 , wherein the body fluid sample is whole blood.

20. The method of claim 1 , wherein the measuring comprises measuring one or more of a volume parameter, a conductivity parameter, and a light scattering parameter.

21. The method of claim 1 , wherein said measuring comprises measuring light scatter and DC impedance from individual cells of said plurality of cells.

22. The lymphatic index is Lymphatic index = LV x (LV - SD) / LC The method of claim 5, wherein the calculated value is calculated according to:

23. The method of claim 1 , wherein the subject is an individual without a confirmed viral infection.

24. 24. The method of claim 23, wherein the subject is an individual suspected of having a viral infection.

25. 24. The method of claim 23, wherein the subject tests negative for viral nucleic acid.

26. 24. The method of claim 23, wherein the subject is a human.

27. 1. A device for identifying a subject having a viral infection, the device comprising: 1) a transducer for measuring cells passing through the flow cell; 2) receiving and processing measurement data from the transducer; determining one or more cell population data values ​​based on the measurements, wherein the one or more cell population parameter data values ​​comprise population parameter data values ​​from one or more of lymphocytes, neutrophils, and monocytes; and determining whether at least one of the one or more cell population data values ​​exceeds a predetermined threshold. one or more processors configured to perform operations including Including, wherein viral infection of the subject is indicated if at least one of the one or more cell population parameter values ​​exceeds a predetermined threshold.

28. 28. The device of claim 27, wherein the viral infection is associated with an upper respiratory tract illness.

29. 28. The device of claim 27, wherein the viral infection is a coronavirus infection.

30. 30. The device of claim 29, wherein the coronavirus is SARS-CoV-2.

31. 28. The device of claim 27, wherein the cell population data comprises one or more selected from the group consisting of monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymphoid index, mean neutrophil volume (MNV), neutrophil distribution width (NDW), or any combination thereof.

32. 32. The device of claim 31, wherein the cell population data comprises MDW and one or more of lymphatic index and LV-SD.

33. 32. The device of claim 31, wherein the cell population data comprises or consists of MNV and NDW.

34. 32. The device of claim 31, wherein viral infection in the subject is indicated when the values ​​of the MDW and the lymphatic index are both outside their respective reference ranges.

35. 32. The device of claim 31, wherein a value of the MDW greater than 20.27 indicates a viral infection in the subject.

36. 32. The device of claim 31 , wherein a value of the MDW greater than 20.42 indicates a viral infection in the subject.

37. 32. The device of claim 31, wherein a value of the MDW greater than 20.73 indicates a viral infection in the subject.

38. 32. The device of claim 31, wherein a value of the MDW greater than 21.24 indicates a viral infection in the subject.

39. 32. The device of claim 31, wherein a value of the lymphatic index greater than 11.3 indicates a viral infection in the subject.

40. 32. The device of claim 31, wherein a value of the lymphatic index greater than 11.63 indicates a viral infection in the subject.

41. 32. The device of claim 31, wherein a value of the lymphatic index greater than 11.8 indicates a viral infection in the subject.

42. 32. The device of claim 31, wherein a value of the lymphatic index greater than 12.04 indicates a viral infection in the subject.

43. 32. The device of claim 31, wherein a value of the lymphatic index greater than 12.35 indicates a viral infection in the subject.

44. 32. The device of claim 31, wherein a value of LV-SD greater than 14.41 indicates a viral infection in the subject.

45. 28. The device of claim 27, wherein said measuring comprises measuring one or more of a volume parameter, a conductivity parameter, and a light scattering parameter.

46. 28. The device of claim 27, wherein said measuring comprises measuring light scattering and direct current impedance from said cells.

47. The lymphatic index is Lymphatic index = LV x (LV - SD) / LC 32. The device of claim 31, wherein the calculation is performed according to:

48. 28. The device of claim 27, wherein the subject is an individual without a confirmed viral infection.

49. 49. The device of claim 48, wherein the subject is an individual suspected of having a viral infection.

50. 49. The device of claim 48, wherein the subject has a negative viral nucleic acid test.

51. 49. The device of claim 48, wherein the subject is a human.

52. 1. A computer storage medium having recorded thereon an executable program for identifying a subject having a viral infection, the executable program performing the following steps when executed by a processor: 1) measuring cells passing through a flow cell; 2) determining one or more cell population parameter data values ​​in the body fluid sample based on the measurements, wherein the one or more cell population parameter data values ​​include population parameter data values ​​from one or more of lymphocytes, neutrophils, and monocytes; and 3) determining whether at least one of the one or more cell population data values ​​exceeds a predetermined threshold; was carried out, A computer storage medium, wherein a viral infection in the subject is indicated if at least one of the one or more cell population parameter values ​​exceeds a predetermined threshold.

53. 53. The computer storage medium of claim 52, wherein the viral infection is associated with an upper respiratory tract illness.

54. 53. The computer storage medium of claim 52, wherein the viral infection is a coronavirus infection.

55. 55. The computer storage medium of claim 54, wherein the coronavirus is SARS-CoV-2.

56. 53. The computer storage medium of claim 52, wherein the cell population data comprises one or more selected from the group consisting of monocyte distribution width (MDW), lymphocyte volume (LV), lymphocyte distribution width (LV-SD), lymphocyte conductance (LC), lymphoid index, mean neutrophil volume (MNV), neutrophil distribution width (NDW), or any combination thereof.

57. 57. The computer storage medium of claim 56, wherein the cell population data comprises MDW and one or more of a lymphatic index and LV-SD.

58. 57. The computer storage medium of claim 56, wherein the cell population data comprises or consists of MNV and NDW.

59. 57. The computer storage medium of claim 56, wherein a viral infection in the subject is indicated when the values ​​of the MDW and the lymphatic index are both outside their respective reference ranges.

60. 57. The computer storage medium of claim 56, wherein a value of the MDW greater than 20.27 indicates a viral infection in the subject.

61. 57. The computer storage medium of claim 56, wherein a value of the MDW greater than 20.42 indicates a viral infection in the subject.

62. 57. The computer storage medium of claim 56, wherein a value of the MDW greater than 20.73 indicates a viral infection in the subject.

63. 57. The computer storage medium of claim 56, wherein a value of the MDW greater than 21.24 indicates a viral infection in the subject.

64. 57. The computer storage medium of claim 56, wherein a value of the lymphatic index greater than 11.3 indicates a viral infection in the subject.

65. 57. The computer storage medium of claim 56, wherein a value of the lymphatic index greater than 11.63 indicates a viral infection in the subject.

66. 57. The computer storage medium of claim 56, wherein a value of the lymphatic index greater than 11.8 indicates a viral infection in the subject.

67. 57. The computer storage medium of claim 56, wherein a value of the lymphatic index greater than 12.04 indicates a viral infection in the subject.

68. 57. The computer storage medium of claim 56, wherein a value of the lymphatic index greater than 12.35 indicates a viral infection in the subject.

69. 57. The computer storage medium of claim 56, wherein a value of LV-SD greater than 14.41 indicates a viral infection in the subject.

70. 53. The computer storage medium of claim 52, wherein said measuring comprises measuring one or more of a volume parameter, a conductivity parameter, and a light scattering parameter.

71. 53. The computer storage medium of claim 52, wherein said measuring comprises measuring light scattering and DC impedance from said cells.

72. The lymphatic index is Lymphatic index = LV x (LV - SD) / LC 57. The computer storage medium of claim 56, wherein the computer storage medium is calculated according to:

73. 53. The computer storage medium of claim 52, wherein the subject is an individual without confirmed viral infection.

74. 74. The computer storage medium of claim 73, wherein the subject is an individual suspected of having a viral infection.

75. 74. The computer storage medium of claim 73, wherein the subject tests negative for viral nucleic acid.

76. 74. The computer storage medium of claim 73, wherein the subject is a human.

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