APPARATUS AND METHOD FOR POINT OF CARE, RAPID, FIELD-DEPLOYABLE DIAGNOSTIC TESTING OF COVID-19, VIRUSES, ANTIBODIES AND MARKERS, AUTOLAB 20

Abstract

An automated system communicated to a remote server for diagnostically field testing a sample taken from a patient using an automated portable handheld instrument to determine the presence of Covid-19 and/or antibodies thereto includes microfluidic circuits defined in a rotatable disk for performing a bioassay using a microarray to generate an electrical signal indicative of a bioassay measurement; the microarray operationally positioned in the microfluidic circuit; one or more lasers; one or more positionable valves in the microfluidic circuit; and a backbone unit for rotating the disk according to a protocol to perform the bioassay, for controlling the lasers to selectively open the positionable valves in the microfluidic disk, for operating the microarray to generate a digital image as a bioassay measurement; for communicating the bioassay measurement to the remote server, and for associating the performed bioassay and its corresponding bioassay measurement to the patient.

Claims

1. An automated system communicated to a remote server for diagnostically field testing a sample taken from a subject using an automated portable handheld instrument to determine the presence of viral antigens and/or antibodies thereto comprising: one or more types of microfluidic circuits defined in a rotatable disk, each type of microfluidic disk for performing a bioassay using a predetermined type of biodetector to generate an electrical signal indicative of a bioassay measurement; a biodetector operationally positioned in the microfluidic circuit; one or more lasers; one or more positionable valves in the microfluidic circuit; and a backbone unit for rotating the disk according to a predetermined protocol to perform the bioassay, for controlling and powering the one or more lasers to selectively open one or more positionable valves in the microfluidic disk, for operating the biodetector to generate an electrical signal indicative of a bioassay measurement; for communicating the bioassay measurement to the remote server, and for associating the performed bioassay and its corresponding bioassay measurement to the subject.

2. The system of claim 1 where the biodetector comprises a microarray and where the bioassay is a serology test, including testing for IgG and/or IgM.

3. The system of claim 1 where the biodetector comprises a microarray and where the serology test provided by the microarray is a respiratory antibody and/or antigen test.

4. The system of claim 3 where the serology test tests for Covid-19.

5. The system of claim 1 where the biodetector comprises a microarray and where microfluidic disk has a center and comprises: a sample inlet; a blood-plasma separation chamber communicated with the sample inlet and positioned on the disk radially farther from the center of the disk than the sample inlet; a mixing chamber communicated to the blood-plasma separation chamber through a corresponding selectively openable valve and positioned on the disk radially farther from the center of the disk than the blood-plasma separation chamber; a first wash chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; a secondary antibody chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber, a second wash chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; a microarray chamber communicated to the mixing chamber, the microarray being disposed in the microarray chamber; and the microarray chamber positioned on the disk radially farther from the center of the disk than the mixing chamber; and a waste chamber communicated to the microarray chamber by a siphon and by a corresponding selectively openable spin-dry valve and positioned on the disk radially farther from the center of the disk than the microarray chamber.

6. The system of claim 3 where the signal indicative of a bioassay measurement is a digital image of microarray spots which have been fluoroscopically activated by the sample in the performance of the bioassay, where the remote server is a Cloud server, where the backbone unit includes network circuitry which communicates the digital image to the Cloud server and a corresponding schema file associating the subject to the performed bioassay and its corresponding bioassay measurement; where the Cloud server, operating in an automated and modular protocol, aligns the microarray spots of the digital image, detects each of the aligned spots of the microarray and analyzes each of the spots of the digital image to assign a scalar value to each microarray spot to produce a processed microarray measurement set of data; where the Cloud server, operating in an automated protocol, analyzes the processed microarray measurement set of data to produce a diagnosis of the biomeasurement; and where the Cloud server, operating in an automated protocol, reports the results to the subject as determined by the schema file.

7. The system of claim 6 where the Cloud server comprises a cloud-based module for automatically determining under automated control whether the corresponding Z-scores of the communicated data output of positive and/or negative indications are indicative of Covid-19 rather than the Z-scores of the plurality of viral infections sharing at least some of the Covid-19 antigens and/or antibodies

8. The system of claim 6 where the Cloud server comprises means for identifying positive and/or negative indications of the digital image of microarray spots for a plurality of acute respiratory infections selected from the group including SARS-CoV-2, SARS-CoV, MERS-CoV, common cold coronaviruses (HKU1, OC43, NL63, 229E), and multiple subtypes of influenza, adenovirus, metapneumovirus, parainfluenza, and/or respiratory syncytial virus.

9. The system of claim 6 where the Cloud server comprises a cloud-based module for automatically evaluating antigens to discriminate output data of a positive group of antigens from a negative group of antigens across a range of assay cutoff values using receiver-operating-characteristic (ROC) curves for which an area-under curve (AUC) is measured to determine high performing antigens to diagnose Covid-19.

10. The system of claim 6 where the Cloud server comprises a cloud based module for automatically determining under automated control an optimal sensitivity and specificity for Covid-19 from a combination of a plurality of high performing antigens based on a corresponding Youden Index calculated for the combination of plurality of high-performing antigens.

11. An automated system communicated to a cloud-based server for diagnostically field testing a sample taken from a subject using an automated portable handheld instrument to determine the presence of viral antigens and/or antibodies thereto in a serology test to detect Covid-19 comprising: a microfluidic circuit defined in a rotatable disk for performing a bioassay using a microarray to generate a digital image indicative of a bioassay measurement; a microassay operationally positioned in the microfluidic circuit; one or more positionable valves in the microfluidic circuit; one or more lasers; a fluoroscopic microassay reader; and a backbone unit for rotating the disk according to a predetermined protocol to perform the bioassay, for controlling and powering the one or more lasers to selectively open one or more positionable valves in the microfluidic disk, for operating the fluoroscopic microassay reader to generate the digital image indicative of a bioassay measurement; for communicating the digital image to the cloud-based server, and for associating the performed bioassay and its corresponding bioassay measurement to the subject. where the microfluidic disk has a center and comprises: a sample inlet; a blood-plasma separation chamber communicated with the sample inlet and positioned on the disk radially farther from the center of the disk than the sample inlet; a mixing chamber communicated to the blood-plasma separation chamber through a corresponding selectively openable valve and positioned on the disk radially farther from the center of the disk than the blood-plasma separation chamber; a first wash chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; a secondary antibody chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; a second wash chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; a microarray chamber communicated to the mixing chamber, the microarray being disposed in the microarray chamber; and the microarray chamber positioned on the disk radially farther from the center of the disk than the mixing chamber; and a waste chamber communicated to the microarray chamber by a siphon and by a corresponding selectively openable spin-dry valve and positioned on the disk radially farther from the center of the disk than the microarray chamber. where the backbone unit includes network circuitry which communicates the digital image to the Cloud server and a corresponding schema file associating the subject to the performed bioassay and its corresponding bioassay measurement; where the Cloud server, operating in an automated and modular protocol, aligns the microarray spots of the digital image, detects each of the aligned spots of the microarray and analyzes each of the spots of the digital image to assign a scalar value to each microarray spot to produce a processed microarray measurement set of data; where the Cloud server, operating in an automated protocol, analyzes the processed microarray measurement set of data to produce a diagnosis of the biomeasurment; and where the Cloud server, operating in an automated protocol, reports the results to the subject as determined by the schema file.

12. The system of claim 11 where the Cloud server comprises a cloud-based module for automatically determining under automated control whether the corresponding Z-scores of the communicated data output of positive and/or negative indications are indicative of Covid-19 rather than the Z-scores of the plurality of viral infections sharing at least some of the Covid-19 antigens and/or antibodies

13. The system of claim 11 where the Cloud server comprises means for identifying positive and/or negative indications of the digital image of microarray spots for a plurality of acute respiratory infections selected from the group including SARS-CoV-2, SARS-CoV, MERS-CoV, common cold coronaviruses (HKU1, OC43, NL63, 229E), and multiple subtypes of influenza, adenovirus, metapneumovirus, parainfluenza, and/or respiratory syncytial virus.

14. The system of claim 11 where the Cloud server comprises a cloud-based module for automatically evaluating antigens to discriminate output data of a positive group of antigens from a negative group antigens across a range of assay cutoff values using receiver-operating-characteristic (ROC) curves for which an area-under curve (AUC) is measured to determine high performing antigens to diagnose Covid-19.

15. The system of claim 11 where the Cloud server comprises a cloud based module for automatically determining under automated control an optimal sensitivity and specificity for Covid-19 from a combination of a plurality of high performing antigens based on a corresponding Youden Index calculated for the combination of plurality of high-performing antigens.

16. A method for operating an automated system communicated to a remote server for diagnostically field testing a sample taken from a subject using an automated portable handheld instrument to determine the presence of viral antigens and/or antibodies thereto comprising: introducing the sample into a sample inlet; transferring the sample to a blood-plasma separation chamber communicated with the sample inlet and positioned on the disk radially farther from the center of the disk than the sample inlet; separating the blood from the plasma by spinning the disk at 5500 rpm for 5 minutes; opening a first valve using a laser-meltable plug, the first valve being disposed in a conduit in the disk between the blood-plasma chamber and a mixing chamber communicated to the blood-plasma separation chamber through the selectively openable first valve and positioned on the disk radially farther from the center of the disk than the blood-plasma separation chamber; transferring the serum to the mixing chamber and to a microarray chamber communicated to the mixing chamber, the microarray being disposed in the microarray chamber; and the microarray chamber positioned on the disk radially farther from the center of the disk than the mixing chamber; reciprocating the sample in the microarray chamber for 40 cycles at 2700-5428 rpm, followed by prime at 170 rpm and evacuation at 1000 rpm for 5 minutes to a waste chamber communicated to the microarray chamber by a siphon and by a corresponding selectively openable spin-dry valve and positioned on the disk radially farther from the center of the disk than the microarray chamber; opening a second valve using a laser-meltable plug, the second valve being disposed in a conduit in the disk between the mixing chamber and a first wash chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; transferring a first wash from the first wash chamber through the mixing chamber to the microarray chamber; reciprocating the first wash in the microarray chamber for 20 cycles at 2700-5428 rpm, followed by prime at 170 rpm and evacuation at 1000 rpm for 2 minutes to the waste chamber; opening a third valve using a laser-meltable plug, the third valve being disposed in a conduit in the disk between the mixing chamber and a secondary antibody chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; transferring the secondary antibody from the secondary antibody chamber through the mixing chamber to the microarray chamber; reciprocating the secondary antibody in the microarray chamber for 20 cycles at 2700-5428 rpm, followed by prime at 170 rpm and evacuation at 1000 rpm for 2 minutes to the waste chamber; opening a fourth valve using a laser-meltable plug, the fourth valve being disposed in a conduit in the disk between the mixing chamber and a second wash chamber communicated to the mixing chamber through a corresponding selectively openable valve and positioned on the disk radially closer to the center of the disk than the mixing chamber; transferring a second wash from the second wash chamber through the mixing chamber to the microarray chamber; reciprocating the second wash in the microarray chamber for 20 cycles at 2700-5428 rpm, followed by prime at 170 rpm and evacuation at 1000 rpm for 2 minutes to the waste chamber; opening a fifth valve using a laser-meltable plug, the fifth valve being disposed in a conduit in the disk between the microarray chamber and the waste chamber; spin drying the microarray chamber by spinning the disk at 5500 rpm for one minute; moving the microarray chamber to a position wherein a fluoroscopically induced digital image can be taken of the microarray; and generating the fluoroscopically induced digital image of the microarray.

19. The method of claim 18 further comprising: communicating the digital image using a backbone unit including network circuitry which communicates the digital image to a Cloud server and communicates a corresponding schema file associating the subject to the performed bioassay and its corresponding bioassay measurement; aligning the microarray spots of the digital image in the Cloud server, operating in an automated and modular protocol; detecting each of the aligned spots of the microarray in the Cloud server, operating in an automated and modular protocol; analyzing each of the spots of the digital image the Cloud server, operating in an automated and modular protocol to assign a scalar value to each microarray spot to produce a processed microarray measurement set of data; analyzing the processed microarray measurement set of data to produce a diagnosis of the biomeasurement in the Cloud server, operating in an automated protocol; and reporting the results to the subject as determined by the schema file using the Cloud server, operating in an automated protocol.

20. The method of claim 19 where analyzing the processed microarray measurement set of data comprises identifying positive and/or negative indications of the digital image of microarray spots for a plurality of acute respiratory infections selected from the group including SARS-CoV-2, SARS-CoV, MERS-CoV, common cold coronaviruses (HKU1, OC43, NL63, 229E), and multiple subtypes of influenza, adenovirus, metapneumovirus, parainfluenza, and/or respiratory syncytial virus.

21. A method of data chain identification communicated to a remote Cloud-based server for diagnostically field testing a sample taken from a subject using an automated portable handheld instrument to determine the presence of viral antigens and/or antibodies thereto, the data chain identification included in an image file of an assay of the viral antigens and/or antibodies performed in a microfluidic disk including a microarray comprising: providing the data chain identification structured as a tree graph including recursively accessible nodes to a unique patient/test code, a unique machine ID, a unique cartridge code, a UTC timestamp of the assay, and a unique cartridge code, where the machine ID is uniquely defined by a camera serial number and on-board computer (pi raspberry) serial number, where the cartridge code is defined by a cartridge assembly batch, which details a date of assembly, microarray information, disc information, and reagent catalog and lot number. where the disc information defined by a disc design and disc injection batch, where the microarray information is defined by a printing date, a microarray layout, a glass slide etching batch, a printing protein catalog and lot number, and a nitrocellulose lot used in the microarray, and where the glass slide etching batch is defined by a glass slide lot.

22. A method of coordinating user flow of an automated system communicated to a remote server for diagnostically field testing a sample taken from a patient using an automated portable handheld instrument to determine the presence of viral antigens and/or antibodies in which one or more types of microfluidic circuits defined in a rotatable disk, each type of microfluidic disk for performing a bioassay using a predetermined type of biodetector disposed in the microfluidic disk to generate an electrical signal indicative of a bioassay measurement from a backbone unit for rotating the disk according to a predetermined protocol to perform the bioassay, for operating the biodetector to generate an electrical signal indicative of a bioassay measurement, for communicating the bioassay measurement to the remote server, and for associating the performed bioassay and its corresponding bioassay measurement to the patient, the method coordinating tasks between the patient, the portable handheld instrument, the Cloud-based server, and a test operator of the portable handheld instrument comprising: logging into a Cloud portal to schedule an automated diagnostic test at a location by the patient; automatically scheduling the test and generating a unique QR privacy and control code whereby the patient controls communication of all test results, the unique QR privacy and control code identifying the patient, the test time and location and the disk to be used in the bioassay; automatically communicating the unique QR privacy and control code to the patient; automatically communicating appointment information for the patient to the test operator; presenting the unique QR privacy and control code by the patient to the test operator at the test location and sending the unique QR privacy and control code to the Cloud-based server; automatically determining if the unique QR privacy and control code is valid in the Cloud-based server, and automatically contingently authorizing the test in a designated type of disk for a corresponding bioassay; loading the identified disk into the portable handheld instrument by the test operator with a verified scanning of a code on the disk to confirm the designated type of disk, and communication of the scanned code to the Cloud-based server; automatically checking the scanned code of the disk loaded into the portable handheld instrument in the Cloud-based server, and if correct, downloading metadata of the disk from the Cloud-based server to the portable handheld instrument; taking a specimen from the patient and loading the specimen into the disk by the test operator; Initiating the automated test by the test operator in the portable handheld instrument; automatically performing the bioassay using the disk in the portable handheld instrument to generate a digital data result of the bioassay; automatically communicating the digital data result of the bioassay to the Cloud-based server; automatically data processing the digital data result of the bioassay in the Cloud-based server to generate a predictive diagnostic analysis; and automatically communicating the predictive diagnostic analysis from the Cloud-based server to a patient-controlled device.

23. The method of claim 22 further comprising communicating the predictive diagnostic analysis from the patient-controlled device to others only with presentation of the unique QR privacy and control code.

24. The method of claim 22 where the bioassay is performed using a microarray as a detector in the portable handheld instrument, and where automatically performing the bioassay using the disk in the portable handheld instrument to generate a digital data result of the bioassay comprises performing a pre-test diagnostic of the microarray to determine that at least three fiducials are visible, that fiducial intensity is within 20% of original images, and that fiducials are in focus by a data camera in the portable handheld instrument.

25. The method of claim 22 where automatically communicating the predictive diagnostic analysis from the Cloud-based server to a patient-controlled device comprises automatically generating a prediction and a corresponding confidence interval.

26. A system for an automated diagnostic procedure in combination with a patient-controlled device comprising: a unique privacy code, capable of storage in a tangible medium, identifying a patient and a field portable medical assay performed on the patient, the use of which code controls access to any communication relating to the patient and a bioassay, and to the use and privacy of medical data relating to the patient and bioassay and to a related diagnosis; a mobile field device for performing a laboratory quality assay in a microfluidic disk of a specimen from the patient in which disk a surface acoustic wave (SAW) detector for direct measure of a virus, bacterium, fungus or biomarker, an antibody microarray for measure of human antibody immunological response, and/or a reverse transcription-polyclonal repetition (RT-PCR) photometric detector for direct RNA detection of a virus is employed. where the mobile field device is capable of use by an operator without necessary specialized medical training to perform the field portable bioassay, and where the mobile field device generates the medical data without diagnostic processing the medical data in the mobile field device; and a Cloud-based remote server to receive communications from the mobile field device to automatically store and automatically process and analyze the medical data from the mobile field device in association with the unique code identifying the patient to generate a predictive diagnosis without human intervention, the Cloud-based remote server automatically communicating to the patient-controlled device the predicative diagnosis and any related medical analysis information for further recommunication to patient-selected physicians, healthcare provides, governmental units and/or others selected by the patient.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0073] FIG. 1 is a front three-quarter perspective of the backbone unit.

[0074] FIG. 2 is the view of FIG. 1 with the disk lid opened.

[0075] FIG. 3 is an end plan view of the backbone unit showing the external connections.

[0076] FIG. 4 is a front three-quarter perspective of the backbone unit with the cover removed showing the major components included in the backbone unit.

[0077] FIG. 5 is a block diagram of the circuitry and elements in the backbone unit.

[0078] FIG. 6 is a diagram of an LED ring board to provide even IR activation illumination to the sample in the microarray chamber.

[0079] FIG. 7 is a top plan view of the microfluidic disk carrying a microarray.

[0080] FIG. 8 is a flow diagram of the operation of the disk of FIG. 7.

[0081] FIG. 9 is flow diagram of the workflow implemented by the backbone unit.

[0082] FIG. 10 is a block diagram of the software architecture used to implement the workflow of FIG. 9.

[0083] FIG. 11 is a screenshot of the operator interface.

[0084] FIG. 12 is a screenshot of the operator interface when Scan QR Code is chosen in FIG. 11.

[0085] FIG. 13 is a block diagram of the backend operation architecture of the software operating in the backbone unit.

[0086] FIG. 14 is a flow diagram of the digital image analysis performed by the Cloud server.

[0087] FIG. 15 is a block diagram of the software organization used to implement the flow diagram of FIG. 14.

[0088] FIGS. 16a and 16b are graphs showing the IgG seroreactivity as measured by means of the fluorescence intensity of serum specimens on the coronavirus antigen microarray. FIG. 16a is a graph of fluorescence values from antigen spots specific to a plurality of viruses, shown side by side. FIG. 16b is an enlargement of SARS-CoV-2, SARS-CoV and MERS-CoV antigen spots.

[0089] FIG. 17 is a graph of normalized IgG reactivity of positive and negative sera on coronavirus antigen microarray. The plot shows IgG reactivity against each antigen measured as mean fluorescence intensity (MFI) with full range (bars) and interquartile range (boxes) for convalescent sera from PCR-positive individuals (positive, red) and sera from nine individuals prior to pandemic (negative, blue). Below the plot, the heatmap shows average reactivity for each group (white::: low, black::: mid, red::: high). The antigen labels are color coded for respiratory virus group.

[0090] FIGS. 18a-18d are graphs of an individual patients fluorescence values for IgG and IgM detection in a sample using a microarray, and the corresponding Z-score statistics. The red line is an average positive result used to assess whether a measure is positive. The blue line is an average of negative results. The red corresponds to an average seropositive result which is additionally confirmed via PCR. The blue line corresponds to an average seronegative result which is confirmed via PCR. If a patient's IgG bar graph looks like the red line, they test positive, if it looks like the blue line, they test negative.

[0091] FIG. 18a is a graph of the fluorescence values for IgG for several viruses, namely SARS-CoV2, SARS, MERS, CommonCoV, Influenza, ADV, MPV, PIV and RSV as a function of the antigen spots on the microarray as seen as listed on the x-axis in FIG. 18c.

[0092] FIG. 18b is a graph of the fluorescence values for IgM for several viruses, namely SARS-CoV2, SARS, MERS, CommonCoV, Influenza, ADV, MPV, PIV and RSV as a function of the antigen spots on the microarray as seen as listed on the x-axis in FIG. 18d.

[0093] FIG. 18c is a bar graph of the Z-score statistics of the IgG readings for several viruses, namely SARS-CoV2, SARS, MERS, CommonCoV, Influenza, ADV, MPV, PIV and RSV as a function of the antigen spots on the microarray as listed on the x-axis.

[0094] FIG. 18d is a bar graph of the Z-score statistics of the IgM readings for several viruses, namely SARS-CoV2, SARS, MERS, CommonCoV, Influenza, ADV, MPV, PIV and RSV as a function of the antigen spots on the microarray as listed on the x-axis.

[0095] FIG. 19 is a diagrammatic depiction of the microarray of the embodiment for testing for Covid-19.

[0096] FIG. 20 is a bar graph which reports the value of each control spot, and mean value and standard deviation for each antigen.

[0097] FIGS. 21a-21e is a diagram of the user flow or interaction with the system.

[0098] FIG. 22 is a tree graph of the data chain identification used to maintain data accountability for the tests and all involved components.

[0099] FIG. 22a is a diagram of an image of the microarray where after potential fiducials are identified, the program compares the distance ratios between all sets (combinations) of three contours, looking for ratios that match the theoretical fiducial spacing ratios given in the schema file identified in FIG. 22a as dashed circles.

[0100] FIG. 22b is a diagram of an image of the microarray where after determining the fiducials, a minimum fit rectangle is drawn around the three fiducials identified in FIG. 22b as a dashed rectangle.

[0101] FIG. 22c is a diagram of an image of the microarray where the minimum fit rectangle is then cropped and rotated so that the fiducials are located in the top left, bottom left, and top right as depicted in FIG. 22c.

[0102] FIG. 23 is a diagram of an image of a spot of the microarray the foreground median intensity is calculated and subtracted from the background mean intensity. Each spot is individually masked, and the median of each spot is calculated. Likewise, the mean of each background annulus is calculated and subtracted from the spot median as depicted in FIG. 23.

[0103] The disclosure and its various embodiments can now be better understood by turning to the following detailed description of the preferred embodiments which are presented as illustrated examples of the embodiments defined in the claims. It is expressly understood that the embodiments as defined by the claims may be broader than the illustrated embodiments described below.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0104] The apparatus of the illustrated embodiments include a backbone unit which includes the electronics, camera, optics, digital data gathering and communication via the internet to Cloud-based expert diagnostic servers, and electromechanical elements needed to provide field portable diagnostic testing of Covid-19 and other viral or bacterial infections. The same backbone unit supports at least three different microfluidic compact disks 68 (CDs) used for diagnostic assays or testing, namely for virology detection using a surface acoustical wave (SAW) detector, for microarray serology detectors for antibodies like IgG and IgM, and RT-PCR assays for nucleic acid targets using fluorescence detectors, which are denoted by Autonomous Medical Devices Inc. as its A10, A20 and A30 CD's respectively. The unit and its corresponding CDs are measurement or assay devices and do not perform high level diagnosis analysis, but provide the data needed to do so to fully developed diagnostic databases and expert systems resident in the Cloud in internet communication with the backbone unit.

[0105] The Backbone Unit

[0106] The backbone unit 10 shown in FIG. 1 is a desktop rectangular chassis with a color touch screen 12 on its top surface with a closeable lid 14 under which the microfluidic compact disks 68 (CDs) are placed on a spindle 16 shown in FIG. 2 for operation. More will be described below about the corresponding microfluidic disks 68. As shown in FIG. 3 one end of unit 10 is provided with a plurality of data and power connectors, such as external AC power receptacle 24, power switch 22, external USB port 20 and external Ethernet port 18. The primary electrical circuits, digital circuits, photonic elements, and electromechanical elements are depicted in perspective view of FIG. 4 which shows the interior layout of unit 10. Included among the pictured elements are CD motor 26 which spins the CDs, a CD index 28, a camera illumination subsystem 30, a camera 32, a power supply 34 with cooling fan 38, a motor controller 36 and a control board 40. To the side of motor 26 is a laser 48 used in CD operations, e.g. for opening selected valves in the CD. Also included is a CPU or Raspberry PI 42, an electrical fuse 44 and a second cooling fan 46. On one of the long sides of unit 10, a quick response (QR) scanner (not shown) is also provided wherein patient information is integrated into the data output.

[0107] The operation of unit 10 is now better understood by referring to the block diagram of FIG. 5 which shows the supporting circuitry and photonics. A photonics control board 40, a CPU board 42 which is supported and coupled to power supply 34, provides a plurality of DC voltages (e.g. 5 and 24 VDC) and ground connections. CPU board 42 carries a raspberry pi 43 CPU, which is the main control circuit for unit 10 and handles all high-level programming commands, communications, and data handling. CPU 41 on photonics board 40 is a state machine and provides the needed drive and command signals to motor 26 and various LEDs 56 and lasers 48. CPU 41 controls the speed and rotation provided by motor 26 through direction-enable-break motor commands communicated to translated to brushless motor driver 52. Driver 52 also communicates a tachometer signal, TACHO to CPU 41, and receives a reference signal, VREF, from OpAMP 53 which in turn communicates to CPU 41 through an onboard digital-to-analog converter.

[0108] CPU 43 is an ARM-based (Advanced RISC machine) processor with a Linux operating system. CPU 43 is coupled to and drives camera 32 and provides raw image processing though a USB link to generate a transmissible digital data image through a wireless module ultimately to Cloud 134. CPU 43 is associated with a fan 55, clock 35, RAM memory 37 and an eMMC (embedded multimedia controller) flash memory 39, a micro-secure digital memory card (SD) 61, an audio amplifier 65 with headphone speakers 63, power management circuit 71 and a power connector 69. Memory card 61 is used to capture copies of the test results that are additionally transmitted on the cloud 134. The audio amplifier 65 is to be used with the speaker 63 which wig transmit the health or status of the device to the user (test status, errors, etc). CPU 43 is coupled to display 12, both through HDMI and USB connections. Display 12 optionally drives a pair of stereo speakers 13 for communication to the user. CPU 43 is optionally communicated through a seven port USB hub 91 with a 6-degrees of freedom inertial measurement unit (IMU) 93, microphone 95, global navigation satellite system (GNSS) 97 with antenna, mouse/keyboard 99, barcode reader 89 allowing for location tracking, handing history, and user interaction and developer programming in the field.

[0109] A microcontroller with CPU 41 with its memory 43 and external oscillator/clock 43 in photonics board 40 is coupled to CPU 42 and provides the controls for motor 26 according to the protocol shown in the flow diagram of FIG. 7 and various LEDs, lasers and sensors operationally associated with the RT-PCR process performed on disk 68. Using an on-chip digital-to-analog converter CPU 41 is coupled through an operational amplifier 53 to the reference and tachometer input/outputs of driver 52 as well as directly providing directional, enable and break motor commands to driver 52. CPU 41 commands brushless motor driver 52 to drive spindle or CD motor 26. Motor 26 includes an encoder whose signal is feedback to CPU 41 so that its speed, and direction of rotation is controlled in a closed loop servo mode. Driver 52 supplies 3-phase driving signals to motor 26, which includes a Hall Effect sensor returning an rpm feedback signal to buffer 52 indicative of the motor rpm. CPU 41 is also coupled to an encoder feedback signal from motor 26.

[0110] As shown in FIG. 5 photonic control board 40 is coupled to power supply 34 and includes a voltage boost circuit 80 increasing the 5V supply to 6V and a low dropout regulator (LDO) 82 (3.3V, 1A). CPU 41 is clocked by oscillator 43, and includes memory 45, a temperature/humidity sensor 47, an in circuit serial programming (ICSP) and debug interface 49, a source of a reference voltage VREF coupled to CPU 41 through an onboard analog-to-digital converter and a limit switch 51 built into unit 10's lid so that the motor 26 and all other photonics is shut down whenever the lid is lifted.

[0111] The operation of photonics board 40 with respect to disk 68 can now be understood. The movement and position of disk 68 is tracked by a disk mounted magnet 66 sensed by magnetic and optical index driver 64 coupled to CPU 41 by which the angular orientation or position of disk 68 is determined. The test sample is disposed into sample inlet 94 of FIG. 7 in step 93 of FIG. 8. The treated sample is transferred at step 95 to a blood-plasma separation chamber 72, served by sample inlet 94. After separation as described below in connection with FIGS. 7 and 8, the separated plasma is transferred to receiving chamber 98 and then to microarray chamber 74 in which microarray 92 is disposed, where it is activated by 593 nm LEDs 268 on an LED ring board 269 shown in FIG. 6, which are driven by LED driver 86 coupled to CPU 41. In the embodiment of FIGS. 5 and 6, 10 LEDs, each operating at 593 nm are providing in a ring surrounding the detection chamber 209 to provide a field of substantially even IR illumination to activate the fluorescent readout. Camera 32 takes a digital image of the fluorescently tagged sample through low pass filter 76 and lens 78, which image is communicated to CPU 42 from which it is transmitted to Cloud 134. The prepared sample can then be disposed in waste chamber 114. The bio-readout is the data the camera 32 captures fluoroscopically activated microarray 92. The fluorescence intensity corresponds to the concentration of the sample. The camera 32 detects the fluorescence of the microarray 92. Camera 32 is focused on the microarray chamber 72 through lens 76 and a low pass filter 78 for fluorescence imaging. An LED driver 86 is included in photonic controller 40 which drives the 593 nm LEDs to activate the fluorescence of the tags in chamber 74.

[0112] A20—Disk Operation

[0113] Before discussing diagnostic methods for Covid-19 on a microarray, turn now and consider the general operation of disk 68 when a microarray detector 92 is employed as depicted in the top plan view of FIG. 7. The elements described below on disk 68, which has a diameter of 70 mm and 4.5 mm thickness, are provided in duplicate to allow either redundant measurements to be made or two separate antibody tests using different microarrays 92 to be run simultaneously on the same patient. Disk 68 is made of clear plastic and has multiple chambers, channels and valves numerically machined therein as described in detail in the following. Disk 68 may be sealed on its top and bottom surfaces by a thin laminate layer of plastic. The method begins at step 193 with insertion of the sample taken from the patient at the point-of-care into the sample inlet 94. Disk 68 is spun at 5500 rpm for 1 min at step 193 as depicted in the flow diagram of FIG. 8 to drive the sample into a blood-plasma separation chamber 72 where the centrifuging action separates the heavier blood constituents from the plasma. A first laser valve 62 is opened by positioning disk 68 so that laser valve 96, which is a meltable plug, is aligned with an underlying laser 48 in unit 10. The laser 48 is fired and valve 96 is opened and in about 0.5 min the plasma or serum flows from separation chamber 72 through receiving chamber 98 to microarray chamber 74 wherein microarray 92 is disposed. The transferred serum is reciprocated in microarray chamber 74 to react with the antibody dots of microarray 92 for about 5 min at step 197 for 40 cycles at 2700-5428 rpm followed by priming chamber 74 at 170 rpm and then evacuating chamber 74 by rotation at 1000 rpm through the primed siphon 93 into waste chamber 114.

[0114] At 199 laser valve 106 is aligned with a laser 48 in unit 10 and opened with a 0.5 min exposure. Thereafter, a wash buffer #1 stored in chamber 100 is transferred to microarray chamber 74 by reciprocation at step 201 for about 5 min at step 197 for 20 cycles at 2700-5428 rpm followed by priming chamber 100 at 170 rpm and then evacuating chamber 74 by rotation at 1000 rpm for about 2 min.

[0115] At step 203 laser valve 108 is aligned with a laser 48 in unit 10 and opened with a 0.5 min exposure. A secondary antibody stored in chamber 102 is transferred to microarray chamber 74 by reciprocation for about 5 min for 20 cycles at 2700-5428 rpm followed by priming chamber 102 at 170 rpm and then evacuating chamber 74 by rotation at 1000 rpm at step 205 for about 2 min. The secondary antibody is an anti-antibody. The antibody in blood binds to the antigen. The secondary antibody is an antibody that specifically binds to the tail of the antibody in the blood sample. This secondary antibody carries the fluorescent tag.

[0116] At step 207 laser valve 110 is aligned with a laser 48 in unit 10 and opened with a 0.5 min exposure. Thereafter, a wash buffer #2 stored in chamber 104 is transferred to microarray chamber 74 by reciprocation at step 209 for about 5 min at step 197 for 20 cycles at 2700-5428 rpm followed by priming chamber 104 at 170 rpm and then evacuating chamber 74 by rotation at 1000 rpm for about 2 min.

[0117] At step 211 valve 112 is aligned with a laser 48 in unit 10 and opened with a 0.5 min exposure. At step 213 disk 68 is spun at 5500 rpm for about 1 min to spin dry chamber 74 with wash #2 being evacuated to waste chamber 114. Chamber 74 and microarray 92 are then moved to align with camera 32 in unit 10. One or more grayscale images using induced fluorescence are taken by camera 32, stored and transmitted at step 215 in about 1 min by CPU 42 to the Cloud for data processing and diagnostic analysis as described below.

[0118] The total time needed to run the assay is about 16.5 minutes.

[0119] Cloud Processing and Diagnosis

[0120] Unit 10 performs the physical assay test using disk 68 and the detector provided in disk 68. What results in raw data in some form. Unit 10 does not further process the data nor analyze it to derive a diagnosis of the patient, but transmits the raw data to the Cloud, where remote servers provide processing and diagnostic analysis of the data. Using information associated with or in the patient's scanned QR code, the test results are then stored in a database and transmitted back to the patient's computer, smartphone or other electronic address of a health provider associated with the patient without further involvement with unit 10.

[0121] FIG. 9 is a diagram of data processing in the A20 at a high level. The patient's QR scan assigned to him or her by the health providers is scanned at step 216 associating a person with a test and at step 218 the disk barcode is scanned to associate a disk with the same test. The assay is run at step 220 as described above in connection with FIGS. 7 and 8 ending with a captured fluorescently stimulated image of microarray 92 at step 222. Unit 10 then sends it captured grayscale image or images to the Cloud at step 224. At this point, unit 10's role in the test is ended.

[0122] Prior to transmission of the captured data, unit 10 operates under software control as depicted in FIG. 10. A quality assurance test of the harness or wiring assemblies of unit 10 can be initiated by activating a QA Test Harness button 116 on touchscreen display 12 or a menu for operator interface (human-machine interface HMI) 118 activated, both of which operate with Java Script Object Notation (JSON). JSON is a type of data file that contains a human readable element. JSON is used because it is operating system agnostic, secure, and lossless (no data loss from the original data that comes off the camera sensor). FIG. 11 depicts a screenshot of touchscreen 12 when operator interface 118 is activated by a power on switch activation to display a Scan QR Code button and a Run Test button to scan the patient's QR code to associate the patient with the test and then to run the assay as described above respectively. Upon activation of the Scan QR Code button the operator then sees the screen of FIG. 12 and has access to the gear icon to allow setting the WiFi via a QR code. The gear icon is an icon on the human interface (GUI) which when touched, allows the user to enter the WIFI information for the device either manually or via a QR code.

[0123] Unit 10 operates autonomously under client/Python module 122, which includes responsive action to exterior communications as well as operating according to the onboard stored Linux Oracle programming protocol. The operator interface 118 communicates with the autonomously running backend software 120, which controls all operations of unit 10 through device control module 124. Major functions include Cloud bidirectional communication by Cloud module 126 hardware control module 128 and database module 130.

[0124] FIG. 13 illustrated the backend operation architecture. Database 130 is a SQLite device database module. SQLite Is a widely used C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file. SQLite is a compact library. Python device control 124 bidirectionally communicates with database module 130 and includes as an operating submodule Python hardware control 128, which controls CD motor 26, camera 32, lasers 48 and the other electronic and electromechanical devices of unit 10. Device control 124 is communicated via JSON and first-in-first-out (FIFO) with light and versatile graphics library (LVGL/C-HMI) 118, which is an open-source graphics library providing the tools needed to create an embedded graphic user interface (GUI) with graphical elements, visual effects and a low memory footprint made available to touchscreen 12. Both device control 124 and LVGL/C-HMI 118 are supported by a library of C executables library 132, which bidirectionally communicates with QR reader 50. Oracle or Linux based Cloud communication through module 126 to Cloud 134 with a red hat package manager (RPM) protocol which is used to store installation packages on Linux operating systems. C executables library 132 communicates with the Cloud 134 using a hypertext transfer protocol secure (HTTPS) encryption of JSON code.

[0125] Image Processing in the Cloud

[0126] As described above unit 10 generates raw digital images taken by camera 32 and transmits them unprocessed to Cloud 134. The object is to convert the scanned microarray images into a scalar value for each microarray dot or site. The image data processing proceeds by the steps of alignment 136, spot detection 138 and spot analysis 140 as depicted in FIG. 14. Microarray spot image analysis is described in detail in Bell et. al. “An Integrated Digital Imaging System and Microarray Mapping Software for Rapid Multiplexed Quantitation of Protein Microarray Immunoassays,” Grace Bio Labs, Bend, Oreg. The program structure of FIG. 15 has been written to keep each phase of the analysis modular. Each phase is passed an image 142, and a JSON information file 144. Each phase performs its work, and hands off the results for downstream processing.

[0127] The primary goal of the alignment step 236 is to correct for image inconsistencies, including angle of rotation, scale, and background noise. The alignment algorithm filters through all the shapes in an image, looking for objects that would qualify for spots or fiducials. After finding any potential spot or fiducial, the program looks for spacing ratios between all the potential fiducials that match the fiducial pattern indicated in the JSON schema file. Once the fiducials have been found, the image is rotated and cropped at step 246 to include only the region of interest. All processing is done on grayscale images.

[0128] Original or raw grayscale images 142 are imported into the program. The image 142 contains background information or noise that is not relevant to the processing of the image 142. The alignment phase aims to remove this region of noninterest (nROI) information by identifying the three bright fiducial spots at the corners of the microarray. A bilateral filter is applied to the image to reduce noise, but to keep sharp edges for downstream processing. Next, the image 142 is processed through an adaptive threshold filter to obtain a binary image of contours. Each contour is then filtered for a range of sizes or pixel areas. The size ranges are known beforehand and scale with the dimension of the image. Contours that are too large or too small are ignored. The remaining contours have a minimum fit circle drawn around their perimeter; the area of this circle is compared to the area of the contour to determine how ‘circular’ the contour is. Contours that have an area similar to the area of the bounding circle are retained. After potential fiducials are identified, the program compares the distance ratios between all sets (combinations) of three contours, looking for ratios that match the theoretical fiducial spacing ratios given in the schema file (FIG. 22a, dashed circles). The matching set of three contours are defined as the fiducials. After determining the fiducials, a minimum fit rectangle is drawn around the three fiducials (FIG. 22b, dashed rectangle). The minimum fit rectangle is then cropped and rotated so that the fiducials are located in the top left, bottom left, and top right (FIG. 22c). The location of each fiducial, and general information about the alignment routine is added to the JSON schema returned with the cropped image to be used by the next downstream application.

[0129] In the spot detection step 238 the primary purpose is to determine where each microarray spot is located within the region of interest image. This will be used downstream to determine each spot value. Using the fiducial locations and known size of the microarray, the cropped image is subdivided into a grid, where each square should contain a spot. Adaptive thresholding is applied within each square of the grid. The adaptive threshold image of each square is used to calculate the image moment, which is used to determine centroids for spots:

[00001]$C=\frac{\underset{p=1}{\overset{N}{.Math.}}{I}_p\left(\stackrel{\_}{X_p}+\stackrel{\_}{Y_p}\right)}{\underset{p=1}{\overset{N}{.Math.}}{I}_p}$

[0130] Where I.sub.p is the pixel intensity at the pixel p, X.sub.p and Y.sub.p are the distance vectors to the pixel p relative to a reference point, and N is the total number of pixels in the grid region. Spot diameters are measured in each square if detected. If no visible spot is detected, each spot is assigned the average diameter of found spots.

[0131] The purpose of the spot analysis phase is to assign a single scalar value to each spot in the grid. Currently this is done by calculating the foreground median intensity and subtracting it from the background mean intensity. Each spot is individually masked, and the median of each spot is calculated. Likewise, the mean of each background annulus is calculated and subtracted from the spot median (FIG. 23). The analysis output value is packed into the JSON structure for each spot and returned as a final result.

[0132] Diagnostic Processing the Cloud

[0133] Before considering the details of diagnostic processing of the processed image data in the Cloud, turn first and consider the microarrays used in the illustrated embodiments. The “multiplexed antibody array” in disk 68 provides an individual's virus “exposure fingerprint”, the “legacy antibody profile” reflecting past exposure and vaccination history. This array analysis approach is significantly more data rich (e.g. 67 antigens with 4 replicates per array) and is more quantitative than lateral flow assays in current use for measuring antibodies against the virus. To appreciate this point turn to FIGS. 16a and 16b where we show both positive and negative 2019 nCOV Array Sensitivity IgG results obtained on blood samples from the COVID-19 Washington State 2020 outbreak

[0134] High throughput cloning and constructing microarrays have previously been developed that contain human and animal antibodies with antigens from more than 35 medically important pathogens, including bacteria, parasites, fungi and viruses such as vaccinia, monkey pox, Herpes 1 & 2, Varicella zoster, HPV, HIV, Dengue, Influenza, West Nile, Chikungunya, adenovirus, and coronaviruses. A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. DNA microarrays are used to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles (10.sup.−12 moles) of a specific DNA sequence, known as probes (or reporters or oligos). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA, also called anti-sense RNA, sample, called target, under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm×12 cm and the first computerized image-based analysis was published in 1981. We have probed over 25000 samples from humans and animals infected with pathogens and identified over 1000 immunodominant and candidate vaccine antigens against these pathogens. We have shown that the individual proteins/antibodies printed on these arrays 92 capture antibodies and/or antigens present in serum from infected individuals and the amount of captured antibody can be quantified using fluorescent secondary antibody.

[0135] In this way a comprehensive profile of antibodies that result after infection or exposure can be determined that is characteristic of the type of infection and the stage of diseases. Arrays 92 can be produced and probed in large numbers (>500 serum or plasma specimens per day) while consuming <2 μl of each sample. This microarray approach allows investigators to assess the antibody repertoire in large collections of samples not possible with other technologies.

[0136] A coronavirus antigen microarray 92 (COVAM) was constructed containing 67 antigens that are causes of acute respiratory infections. The viral antigens printed on this array 92 are from epidemic coronaviruses including SARS-CoV-2, SARS-CoV, MERS-CoV, common cold coronaviruses (HKU1, OC43, NL63, 229E), and multiple subtypes of influenza, adenovirus, metapneumovirus, parainfluenza, and respiratory syncytial virus. The SARS-CoV-2 antigens on this array 92 include the spike protein (S), the receptor-binding (RBD), S1, and S2 domains, the whole protein (S1+S2), and the nucleocapsid protein (NP) as shown in the graph of FIG. 17. There is a similar set of antigens represented on the array from SARS-CoV, MERS-CoV, and the four common cold corona viruses.

[0137] To determine the antibody profile of SARS-CoV-2 Infection, the differential reactivity to these antigens was evaluated for SARS-CoV-2 convalescent blood specimens from PCR-positive individuals (positive group) and sera collected prior to the COVID-19 pandemic from naïve individuals (negative control group). As shown in the heatmaps of FIGS. 17a and 17b, the positive group is highly reactive against SARS-CoV-2 antigens. This is more evident for the IgG than for IgA. The negative controls do not react to SARS-CoV-2, SARS-CoV or MERS-CoV antigens despite showing high reactivity to the common cold coronavirus antigens. Positive group displays high IgG reactivity to SARS-CoV-2 NP, S2, and S1+S2 antigens and to a lesser degree SARS-CoV-2 S1 shown in FIGS. 17a and 17b. The positive group also demonstrates high IgG cross-reactivity against SARS-CoV NP, MERS-CoV S2 and S1+S2 antigens, while the negative group demonstrates low cross-reactivity with S1+S2 and S2 antigens from SARS-CoV-2 and MERS-CoV and no cross-reactivity against other SARS-CoV-2 antigens.

[0138] Table 1 contains the fluorescence intensity results for IgG shown in FIG. 18a, the Z-score statistics for the fluorescence results in FIG. 18c, the fluorescence intensity results for IgM shown in FIG. 18b, and the Z-score statistics for the fluorescence results of FIG. 18d. The Z-score shows how many standard deviations above (positive) or below (negative) the mean negative results a confirmed positive IgG or IgM sample is. Statistically significant Z-scores (5 or greater) have shaded numerals.

[0139] Antigens were then evaluated to discriminate the positive group from the negative group across a full range of assay cutoff values using receiver-operating-characteristic (ROC) curves for which an area-under curve (AUC) was measured. High-performing antigens for detection of IgG are defined by ROC AUC>0.85 as shown in Table 1. Four antigens are ranked as high-performing antigens: SARS-CoV-2 NP, SARS-CoV NP, SARS-CoV-2S1+S2, and SARS-CoV-2_S2. Additional high-performing antigens included SARS-CoV-2 S1 (with mouse Fc tag) and RBD, and MERS-CoV S2. The optimal sensitivity and specificity were also estimated for the seven high-performing antigens based on the Youden Index. Youden's J statistic (also called Youden's index) is a single statistic that captures the performance of a dichotomous diagnostic test. Informedness is its generalization to the multiclass case and estimates the probability of an informed decision. The lowest sensitivity was seen for SARS-CoV-2 S1, which correlates with the relatively lower reactivity to this antigen in the positive group. The lowest specificity was seen for SARS-CoV-2 S2, which correlates with the cross-reactivity for this antigen seen in a subset of the negative group. To estimate the gain in performance by combining antigens, all possible combinations of up to four of the seven high-performing antigens were tested in silico for performance in discriminating the positive and negative groups. The ROC curve with AUC, sensitivity, and specificity was calculated for each combination. There is a clear gain in performance by combining two or three antigens. For IgG, the best discrimination was achieved with the two-antigen combination of SARS-CoV-2S2 and SARS-CoV NP, with similar performance upon the addition of SARS-CoV-2S1 with mouse Fc tag (AUC=0.994, specificity=1, sensitivity=0.944). The addition of a fourth antigen decreased the performance.

[0140] Table 2 shows the performance data for combinations of high-performing antigens. ROC, AUC values and sensitivity and specificity based on Youden index for discrimination of positive and negative sera were derived for each individual antigen ranked, and high-performing antigens with ROC AUC>0.86 are indicated above the lines.

[0141] FIGS. 18a-d show an example of a single confirmed positive patient results. FIG. 18a shows the normalized fluorescence intensity for various IgG antibodies in a serum, with the two lines showing the average results for a confirmed positive (top) and confirmed negative (bottom). FIG. 18b shows the normalized fluorescence intensity for various IgM antibodies in a serum, with the two lines showing the average results for a confirmed positive (top) and confirmed negative (bottom). FIG. 18c shows the plotted Z-scores for the IgG antibodies between a positive and negative result, with the three dotted lines representing the various Z-score thresholds for mild, moderate, and significant response. FIG. 18d shows the plotted Z-scores for the IgM antibodies between a positive and negative result, with the three dotted lines representing the various Z-scores.

[0142] More particularly, the A20 serology test is an optical microarray test that performs an indirect immunofluorescence assay for qualitative detection of IgM and IgG antibodies to SARS-CoV-2 in human blood. The serology test is intended for use as an aid in identifying individuals with an adaptive immune response to SARS-CoV-2, indicating recent or prior infection. The serology test currently produces an image of the microarray and a graph of the intensities of the spots on the array. To develop a diagnostic standard known RT-PCR positive and negative samples are tested on the apparatus described above. This establishes cutoff thresholds for reactivity to each of the three SARS-CoV-2 antigens in the microarray, which enables the apparatus to autonomously provide a qualitative “yes” (reactive) or “no” (non-reactive) result.

[0143] Microarray Description

[0144] The serology test contains two identical microarrays on disk 68, one for testing IgG presence and the other for IgM presence. The two classes of antibodies are probed separately by using IgG or IgM reporter antibodies. Each of the two microarrays has the form diagrammatically depicted in FIG. 19. The microarray spots are characterized as:

a. Negative Controls: BUFFER (10 spots): Phosphate-buffered saline (PBS) with 0.001% Tween-20 (Polyethylene glycol sorbitan monolaurate, Polyoxyethylenesorbitan monolaurate). These spots are printing buffers and serve as a negative control to determine the baseline fluorescence of the array.
b. Positive Controls 1: HuIgG (5 spots): Human IgG printed in concentrations of eight dilutions from 0.3 to 0.001 mg/ml for a total of 40 spots. These spots serve as a positive control to indicate that the reporter antibody for IgG is performing appropriately to accurately determine cutoff values of the array when testing on serum samples. The concentration ladder can serve as a rough guide to interpret the microarray's fluorescence.
c. HuIgM (5 spots): Human IgM printed in concentrations of eight dilutions from 0.3 to 0.001 mg/ml for a total of 40 spots. These spots serve as a positive control to indicate that the reporter antibody for IgM is performing appropriately to accurately determine cutoff values of the array when testing on serum samples. The concentration ladder can serve as a rough guide to interpret the microarray's fluorescence.
d. Positive Controls 2: a. HuIgG (3 spots): anti-Human IgG printed in concentrations of 0.3, 0.1, and 0.03 mg/ml. These spots serve as a positive control to indicate that there are human IgG antibodies in the sample. a. HuIgM (3 spots): anti-Human IgM printed in concentrations of 0.3, 0.1, and 0.03 mg/ml. These spots serve as a positive control to indicate that there are human IgM antibodies in the sample.
e. Antigens: SGC-SPIKE19200701 (8 spots): SARS-Cov-2 Spike Protein (University of Oxford). Printed at 0.2 mg/ml. SARS-CoV2.NP (8 spots): SARS-Cov-2 Nucleocapsid Protein (Sinobiological). Printed at 0.2 mg/ml. SARS-CoV2.RBD.mFc (8 spots): SARS-Cov-2 Spike Protein (RBD, mFc Tag) (Sinobiological). Printed at 0.2 mg/ml.
f. Fiducial (3 spots): Streptavidin, Alexa Fluor 647 conjugate. These spots are designed to be the brightest spots on the array and are used to locate and orient the array.
g. PBSTwash (21 spots): PBS+0.05% tween20 used for washing pins.
h. Blank (2 spots): Unused microarray locations.

[0145] Microarray Results

[0146] The images of each microarray in an A20 serology test are uploaded to a server on the Oracle Cloud for analysis. After the corner fiducials are used to locate and orient the microarrays, the images are analyzed to produce scalar values for each spot in the microarray. These measurements are the median fluorescence intensity of each spot, minus the mean fluorescence intensity of the surrounding annulus. These measurements will be available to the user online in a file in JSON format, along with a plot summarizing the values of the three SARS-CoV-2 antigens printed on the microarray. The JSON file is a hierarchical file with the following top-level structure:

TABLE-US-00001 Top-Level Overview of JSON { “diskTypeID”: “1234-02”, “spots”: [ {...}, {...} ], // information about the grid analysis “gridInfo”: { “info”: “Grid Detect”, “version”: “0.1”, “avg_spot_dia”: 83 } }

[0147] The measurements for each spot are contained in a list in the “spots” entry, with thorough details of each spot:

TABLE-US-00002 JSON Details { // The QR code on the disk. “diskTypeID”: “1234-02”, // Array of all ‘spots' on the microarray image “spots”: [ { // spot row “row”: “2”, // spot col “column”: “5”, // group name if multiple virus-specific antigens are used “group”: “ ”, // name of the spot “id”: “SGC-SPIKE19200701”, // (x,y) pixel position of the spot “position”: [ 329, 146 ], // Array of different types of analyses “analysis”: [ { // Name of the analysis method “name”: “Spot Mean - Donut Median”, // Version of this analysis method “version”: “0.0”, // Final value of this analysis method “value”: 1.2824578790882057 } ] }, // {...} many more spots // ] }

[0148] The accompanying summary figure of each microarray is a bar chart, which reports the value of each control spot, and mean value and standard deviation for each antigen such as shown in an example in FIG. 20. From these results a conventional statistical model to distinguish blood samples with and without anti-SARS-CoV-2 antibodies is established.

[0149] Overall System Usage

[0150] The overall user flow or user interaction with the system is illustrated in FIGS. 21a-21e. In FIG. 21a the action of the patient, unit 10, Cloud 134 and the test operator running unit 10 are each identified in four horizontal rows. At step 400 the patient logs into a portal on the internet to schedule a diagnostic test at an available test location. The remote server in Cloud 134 communicated to the portal schedules the patient's test at step 402 and generates a unique QR code which has: 1) the test time and location; and 2) which test to run, i.e. whether a disk 68 associated with A10, A20 or A30 is to be run. The QR code is how the patient controls the use of the testing information and its privacy. The QR code is sent to the patient at step 404, which the patient downloads into his or her smartphone, laptop, or computer. Meanwhile at step 406 the remote server in Cloud 134 transmits the appointment information for the patient and inserts it into the testing schedule. At this point the procedure there may be a pause of one or more days before further action is taken.

[0151] On the day of the appointment at step 408 in FIG. 21b the patient goes to the testing location and scan his or her QR code into unit 10 in response to a screen prompt at step 410. The validity of the QR code is checked at step 412 in Cloud 134 and the remote server communicates to the testing site which of the disks 68 is to be used for the test, authorizing unit 10 to use a specific type of disk 68. The test operator, after checking at step 414 the humidity and temperature levels on the disk packaging to verify the integrity of disk 68, scans the disk's QR code at step 416 in response to a screen prompt from unit 10 at step 418. Cloud 134 communicates the disk's metadata to unit 10 at step 422, which metadata includes the status of the disk batch, a JSON file for the spin protocol, and grayscale TIF files of the microarrays 92 generated during quality control testing for the disk 68. Unit 10 downloads the disk's metadata from Cloud 134 at step 420 and the authority to use disk 68 in the test is determined.

[0152] If it is determined at step 420 that the authority to use disk 68 is denied, the operator is advised to reject disk 68 and replace it with another at step 424, after which the procedure returns to step 414. If use of disk 68 is authorized, then a blood sample, such as a finger prick, is taken from the patient by the test operator at step 426, loaded by the test operator into disk 68 at step 428, and disk 68 then loaded into unit 10 at step 430. Unit 10 displays a screen prompt to the test operator to begin the test at step 432 in FIG. 21c. In response the test operator touches the start button on the screen display at step 434.

[0153] Pretest diagnostic data is gathered in step 436, this includes checking the optical system at step 438 with both microarrays 92 in disk 68 by verifying that: 1) the three fiducial spots in each array are visible; 2) the fiducial intensity is within 20% of the original images of the microarray; and 3) the fiducial spots are in focus. Similarly, a watchdog routine in COU 43 at step 440 outputs diagnostic data from camera 32, the LEDs 56, motor 26, and lasers 48. Thereafter, unit 10 runs a spin protocol on disk 68 at step 442 as described above and takes a grayscale image of each microarray 92 at the end of the assay. The watchdog routine in CPU 43 at step 444 continues to monitor unit 10 during the assay procedure and generates an error message display in the event of a fault and stops the test or assay if needed.

[0154] The grayscale TIF image taken by camera 32 of each microarray 92 is uploaded to Cloud 134 at step 446 in FIG. 21d both of each microarray before the test to provide background data, after the test to provide test data, and files including the diagnostic data taken before and after the test. At step 448 an image processing algorithm as described above processes the digital images of microarrays 92 to generate a JSON file listing each spot name, spot location and fluorescence intensity, from which a diagnosis is made.

[0155] From the JSON output file the test processing is determined as being passed or failed at step 452 in FIG. 21e. If the test passed, a predictive diagnosis is made of the diagnosis and a confidence interval calculated. The predictive diagnosis is provided from a statistical model such as logistic regression or random forest, using fluorescence intensity or calculated Z-scores. At the same time at step 454 a list of antigens with mean fluorescent intensity values and standard deviations is plotted. These results are communicated from Cloud 134 to the patient's smartphone, laptop, or computer at step 456, and depending on the access level granted, the patient can see the results, graphs and/or raw data. At step 458 the patient then has the choice to forward the test data to his or her doctor, healthcare technician, research facility, governmental authority or wherever the patient deems necessary.

[0156] Data Chain Identification

[0157] Control of the data sent to the remote server in Cloud 134 is realized utilizing the identification chain 300 of FIG. 22. This identification chain 300 can be viewed as a tree graph as shown in FIG. 22, where each box is a node in the tree that can be traversed in either direction, and where each node corresponds to a manufactured component. Each node in the chain can be queried recursively to yield information of its corresponding components, or its own details of manufacture catalog number, date, origin, and batch. Each test 302 is encoded in a transmitted image analysis data file 304, which includes a TIFF package 306, which in turn is tied to a unique patient/test code 308, a unique machine ID 310, a unique cartridge code 312, and a UTC timestamp of the performed test. Connecting the test code to the patient/test code 308, machine ID 310, and timestamp 314 guarantees that no two test results can be misidentified, as two tests cannot be performed on the same machine at the same time.

[0158] Attaching a unique cartridge code 312 further guarantees the uniqueness of each test and its results, but also creates a complete identification chain to connect a particular test 302 and its results to every relevant assembly component involved in that test 302. This provides full traceability, allowing one to identify all component lot numbers used in a particular disc 68, or all discs 68 utilizing a particular component lot number. This allows one to acquire data from compromised tests and determine a faulty component lot or recall all discs that utilize a faulty component lot.

[0159] The machine ID 310 is uniquely defined by its camera serial number 316 and on-board computer (pi raspberry) serial number 318. The machine ID 310 can then provide the hierarchy of al sub-assemblies of all its mechanical and electrical components.

[0160] The cartridge code 312 is traced to the cartridge assembly batch 320, which details the date of assembly 328, microarray information 322, disc information 324, and reagent catalog and lot number 326 stored on the cartridge. The disc information 324 contains details of the disc design 330 and disc injection batch 332. The microarray information 322 contains details of the printing date 334, the microarray layout 336, the glass slide etching batch 338, the printing protein catalog and lot number 340, the nitrocellulose lot 342 used in the microarray. The glass slide etching batch 338 refers in turn to the glass slide lot 344.

[0161] Many alterations and modifications may be made by those having ordinary skill in the art without departing from the spirit and scope of the embodiments. Therefore, it must be understood that the illustrated embodiment has been set forth only for the purposes of example and that it should not be taken as limiting the embodiments as defined by the following embodiments and its various embodiments.

[0162] Therefore, it must be understood that the illustrated embodiment has been set forth only for the purposes of example and that it should not be taken as limiting the embodiments as defined by the following claims. For example, notwithstanding the fact that the elements of a claim are set forth below in a certain combination, it must be expressly understood that the embodiments includes other combinations of fewer, more or different elements, which are disclosed in above even when not initially claimed in such combinations. A teaching that two elements are combined in a claimed combination is further to be understood as also allowing for a claimed combination in which the two elements are not combined with each other but may be used alone or combined in other combinations. The excision of any disclosed element of the embodiments is explicitly contemplated as within the scope of the embodiments.

[0163] The words used in this specification to describe the various embodiments are to be understood not only in the sense of their commonly defined meanings, but to include by special definition in this specification structure, material or acts beyond the scope of the commonly defined meanings. Thus if an element can be understood in the context of this specification as including more than one meaning, then its use in a claim must be understood as being generic to all possible meanings supported by the specification and by the word itself.

[0164] The definitions of the words or elements of the following claims are, therefore, defined in this specification to include not only the combination of elements which are literally set forth, but all equivalent structure, material or acts for performing substantially the same function in substantially the same way to obtain substantially the same result. In this sense it is therefore contemplated that an equivalent substitution of two or more elements may be made for any one of the elements in the claims below or that a single element may be substituted for two or more elements in a claim. Although elements may be described above as acting in certain combinations and even initially claimed as such, it is to be expressly understood that one or more elements from a claimed combination can in some cases be excised from the combination and that the claimed combination may be directed to a subcombination or variation of a subcombination.

[0165] Insubstantial changes from the claimed subject matter as viewed by a person with ordinary skill in the art, now known or later devised, are expressly contemplated as being equivalently within the scope of the claims. Therefore, obvious substitutions now or later known to one with ordinary skill in the art are defined to be within the scope of the defined elements.

[0166] The claims are thus to be understood to include what is specifically illustrated and described above, what is conceptionally equivalent, what can be obviously substituted and also what essentially incorporates the essential idea of the embodiments.

TABLE-US-00003 TABLE 1 value ref. z.score value ref. z.score name IgG IgG IgG IgM IgM IgM SARS.CoV.2.NP 14551.00 1374.91 11.19 1729.86 1420.89 0.17 SARS.CoV.2.PI.pro 403.50 685.28 −0.40 180.72 153.55 0.12 SARS.CoV.2.S1 2553.60 1695.89 0.61 1379.55 625.83 2.66 SARS.CoV.2.S1.HisTag 3673.05 250.83 15.86 1588.15 67.92 11.26 SARS.CoV.2.S1.mFcTag 7440.25 545.47 13.16 5656.20 659.51 9.57 SARS.CoV.2.S1.RBD 7467.90 570.75 12.01 6846.95 1273.64 10.58 SARS.CoV.2.S1+S2 7518.00 2233.85 2.86 3435.40 969.59 5.86 SARS.CoV.2.S2 2452.55 1278.24 0.97 1178.90 583.27 3.11 SARS.CoV.2.Spike.RBD.Bac 2254.20 888.46 3.34 2099.50 520.37 4.72 SARS.CoV.2.Spike.RBD.His.HEK 2609.60 278.67 9.42 2110.35 115.50 14.17 SARS.CoV.2.Spike.RBD:rFc 4981.15 839.19 9.79 2974.90 618.89 6.03 SARS.CoV_NP 14529.50 2759.59 10.41 3772.35 2415.02 0.86 SARS.CoV_PLpro 1063.70 583.84 1.49 292.55 423.26 −0.51 SARS.CoV_S1.HisTag 679.55 1418.36 −0.85 276.60 856.47 −1.67 SARS.CoV_S1.RBD.HisTag 502.40 873.32 −0.87 227.70 448.94 −0.94 SARS.CoV_S1.RBD:rFcTag 853.15 1669.10 −1.39 637.10 925.24 −0.59 MERS.CoV_NP 501.15 1878.35 −0.55 1208.60 1569.42 −0.13 MERS.CoV_S1.AA1.725.His.HEK 137.65 303.19 −0.85 21.85 111.41 −0.61 MERS.CoV_S1.RBD.367.606.rFcTag 1141.75 3405.61 −1.78 775.00 1034.58 −0.47 MERS.CoV_S1.RBD.383.502.mFcTag 373.45 1285.92 −0.99 1247.55 2310.08 −0.82 MERS.CoV_S2 4554.95 2780.55 0.83 455.75 946.69 −0.77 DcCoV.HKU23.NP 917.55 2571.47 −0.97 352.05 588.45 −0.51 hCoV.229E.S1 3155.40 5439.22 −1.01 167.12 372.38 −0.82 hCoV.229E.S1_S2 7544.50 10036.24 −1.03 615.45 1927.26 −0.53 hCoV.HKU1.HE 3360.50 6264.33 −0.88 1380.10 4284.79 −1.02 hCoV.HKU1.S1_AA1.760 1648.75 2920.27 −0.78 53.15 180.58 −1.23 hCoV.HKU1.S1_AA13.756 998.35 3012.07 −0.94 251.50 422.75 −1.05 hCoV.HKU1.S1_S2 6798.20 4890.55 0.95 538.55 1013.67 −1.37 hCoV.NL63.S1 1281.60 1659.04 −0.52 124.70 253.78 −0.85 hCoV.NL63.S1_S2 2030.60 3302.70 −1.17 394.60 1028.94 −0.77 hCoV.OC43.HE 1263.10 2992.68 −1.11 153.25 447.21 −1.01 hCoV.OC43.S1 212.90 383.06 −0.74 67.05 223.34 −1.17 hCoV.OC43.S1_S2 12497.90 7958.39 2.01 841.28 1169.18 −0.61 Flu.B_Mal.HA1 7884.25 8558.08 −0.23 184.90 438.99 −0.45 Flu.B_Mal.HA1+HA2 11362.85 11918.07 −0.24 446.90 515.63 −0.12 Flu.B_Phu.HA1 7333.90 6356.85 0.32 175.35 332.97 −0.52 Flu.B_Phu.HA1+HA2 10142.05 11923.48 −0.67 858.40 1997.51 −0.55 Flu.H1N1.HA1 1731.10 4421.82 −1.03 252.75 489.13 −1.12 Flu.H1N1.HA1+HA2 10957.75 10597.92 0.10 1187.55 576.35 0.86 Flu.H3N2.HA1 12223.65 8603.17 1.17 260.70 336.37 −0.24 Flu.H3N2.HA1+HA2 13491.35 11237.43 0.78 696.30 1184.50 −0.56 Flu.H5N1.HA1 1845.55 3725.37 −0.98 931.65 1504.46 −0.82 Flu.H5N1.HA1+HA2 7285.50 9349.40 −0.63 1855.95 1730.49 0.15 Flu.H7N9.HA1 654.45 1138.46 −0.59 82.65 117.95 −1.23 Flu.H7N9.HA1+HA2 838.35 1501.44 −0.59 16.00 103.03 −2.19 hAdV3.Fiber 3108.70 5882.67 −0.62 820.25 857.55 −0.10 hAdV3.Penton 2758.50 3406.34 −0.35 796.30 824.49 −0.13 hAdV4.Fiber 4197.65 3940.21 0.08 519.55 640.59 −0.33 hAdV4.Penton 1466.70 2241.25 −0.44 490.85 640.63 −0.45 hMPV.A_G.52N.228N 603.80 1741.45 −1.59 496.10 616.97 −0.39 hMPV.B_F.280D.490G 275.70 746.49 −0.91 309.00 452.87 −0.47 hMPV.B_G.52D.238S 560.40 1016.13 −0.44 197.10 474.71 −0.75 hPIV.1.12O3_F 5352.15 7393.48 −0.83 786.92 1315.02 −0.93 hPIV.1.12O3_H 4208.50 7259.00 −1.50 1081.15 2833.11 −1.15 hPIV.3.2010_H 8143.55 7617.24 −0.76 840.20 1376.41 −0.90 hPIV.4.b.2016_H 1839.00 4052.51 −1.16 811.40 1267.62 −0.90 RSV.A.F 6134.12 9708.27 −1.86 473.80 1159.34 −1.17 RSV.A.G 4016.20 9260.15 −1.93 855.55 829.59 0.57 RSV.B.F 10774.30 11656.42 −0.49 1036.20 1529.28 −0.39 R5V.B.G 8945.12 11369.46 −0.87 1356.35 762.25 0.89