An intelligent Biochip System for Diagnostic Process Flow based Integration of Combined Detection Analyzer

Pranab Roy1,Mahua Raha Patra2,Hafizur Rahaman3,Parthasarathi Dasgupta4 1,2,3School of VLSI Technology,Bengal Engineering and Science University,Shibpur,India

4Indian Institute of Management,Calcutta,India 1ronmarine14@yahoo.co.in, 2mahua_75@yahoo.co.in, 3rahaman_h@yahoo.co.in, 4partha@iimcal.ac.in

Abstract- Digital microfluidic biochip (DMFB) has received a major attention as a second generation Lab-on-a-Chip (LOC) device in the area of biomedical and biochemical applications and analysis. Optical detection and analysis in DMFBs is of major importance nowadays as it provides the major diagnostic conclusions or final analysis results that may lead to future course of medication or procedure for next level of analysis. In this paper, we have used a combined dual mode automated detection based analyzer to design a multilevel intelligent system based on a predefined diagnostic process flow. The analyzer in the system automatically analyzes the results and decides next level of action for execution of a predefined bioassay protocol on a DMFB connected for analysis at the next level and the process is continued for all the following levels in a similar manner. The system works for any given predefined process flow with optimized number of analyzers and DMFBs (in case of selection of any test that may be incorporated in different levels).The tests has been carried out for determination of different metabolite parameters in human blood. The design is implemented in FPGA platform and the simulation as well as detection results are found to be encouraging. Keywords—Digital microfluidics Biochips, Optical detection, Automated analysis, Process flow, circuits, dual mode analyzer.

I. INTRODUCTION

Current researches on lab-on-chip devices are largely influenced by the recent advancement of microfluidic and microfabrication technology. This in turn enforces the development of miniaturized microfluidic devices being capable of executing complex laboratory functions and biochemical analysis within a single planar substrate. These devices termed as microfluidic biochips are comprised of microfluidic arrays based on rigid substrates through which, movements of fluids are tightly controlled to facilitate prescheduled biological reactions. Their functions include fluid handling, mixing, filtering, analysis, detection, separation and monitoring. As compared with the conventional and tedious laboratory procedures, the advantages of LOC devices include higher sensitivity, minimum cost due to smaller sample and reagent volumes, higher scalability in terms of system integration, better precision and accuracy [1]. Such devices are found to be widely used in chemical, biomedical and environmental applications. Earlier designs were based on the manipulation of continuous liquid

flow through micro-fabricated channels and the actuation was implemented with external application of micro-pump and micro-valve and other actuators. Moreover, permanently-etched channels largely restrict the feasibility and versatility of use in terms of scalable applications involving multiple bioassays simultaneously. Hence microfluidic research has gone through a paradigm shift from the continuous-flow-based architecture to droplet-based architecture specifically termed as Digital Microfluidic Biochip (DMFB) [2]. Digital microfluidic biochips manipulate one or multiple liquids in the format of droplet(s) on a single device at the same time. Each droplet can be considered as an individual reaction chambers for different bioassay applications [3]. Generally, a DMFB consists of a two-dimensional (2D) electrode array and peripheral devices such as optical detector and dispensing port. The sample carriers, droplets, being miniaturized and discretized liquids, are controlled by underlying electrodes using electrical actuation, which is a principle called electrowetting-on-dielectric (EWOD) [4]. By assigning time-varying voltage values to turn on/off electrodes, the droplets can be moved around the entire 2D array to perform fundamental operations (e.g., dispensing, merging, mixing, splitting and transportation) [5]. An inherent re-configurability becomes evident in DMFBs in terms of

area and time domain. For an EWOD based device, the most general design for a 2-D electrode array would require a multilayer arrangement of electrical connections, where each of its M × N electrodes is accessed directly and independently through the underlying layers of wires.The microfluidic array contains a set of basic cells made up of two parallel glass plates (Figure 1). The bottom plate contains a patterned array of individually controllable electrodes, and the top plate is coated with a continuous ground electrode. The filler medium, such as silicone oil, along with the sample droplet is sandwiched between the two plates.This channel medium is often necessary to reduce actuation voltage, prevent droplet evaporation and surface fouling [6]. If the ground plane is located on the top plate, typically a transparent conductor viz. indium-tin oxide (ITO) is utilized. Surfaces are typically rendered hydrophobic by coating the bottom plate and the top plate with a fluoropolymer, such as Teflon AF or Cytop [7]. Two different EWOD based designs are widely found to be employed for major applications. The designs differ mainly in the arrangement of the ground electrode planes [8]. In the parallel plate design, the ground plane is located on a top plate. In the co-planar design, the ground plane is located on the bottom plate along with the control electrodes. The co-planar system requires a two-level metal structure on the bottom plate that calls for more sophisticated fabrication techniques [9]. Optical detection in DMFBs is of major significance in terms of accuracy, final analysis and detection based clinical diagnosis. Few works have been reported so far on integrating the backend function of optical detection, in part because of the difficult requirement that detector integration must not interfere with electrowetting-based droplet transport, and in part because the heterogeneous integration of thin-film (microns thick) photonic components is an emerging technology that is just beginning to be applied to microfluidic systems [10]. However, with current advancement in photonic component integration technologies and development of chip scale sensing system, the integrated optical detection based microfluidic system has been accomplished in recent years. Integrated optical detection in EWOD based DMFB devices remains a major challenge in scalable microfluidic biochip design. On-chip colorimetric assays for measurement of the concentrations of target analytes involve an effective application for digital microfluidic biochips [12]. A colorimetric enzyme-kinetic method based on the Trinder’s reaction used for the determination of glucose concentration has been demonstrated in [13]. At the end of the mixing phase, the absorbance is measured for at least 30 seconds, using a 545nm LED-photodiode setup. The mixed droplet is held stationary by electrowetting forces during the absorbance measurement step. This device makes detection of glucose concentrations possible in the range of 25 mg/dl to 300 mg/dl, using dilution factors as low as 2 and 3, in less than 60 seconds. But optical absorbance detection scales poorly with miniaturization, because Beer’s law incorporates path length dependence [14]. Small path length poses major sensitivity problems, and limits the use of absorbance to assays with higher levels of analyte concentrations [8]. Hence, alternative optical sensing and integration schemes were designed for electrowetting based microfluidic systems. A point of care testing (POCT) device has been proposed in [15] for PCR manipulation. The system containing a miniature fluorimeter, consisting of an LED and a photodiode was made in-house and aligned to specific areas on the chip to enable real-time detection for the PCR reaction. The excitation wavelength was 475 nm and the emission filter’s wavelength was centered on 525 nm. System was

2013 International Symposium on Electronic System Design

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used with a long-pass dichroic mirror. Chemoluminescence based detection has been found to be highly compatible with digital microfluidic platform [16] together with diagnostic application as well as sequencing DNA by synthesis [17]. The technique proposed in [10] uses an integrated optical sensor based upon the heterogeneous integration of an InGaAs based thin film photodetector with a digital microfluidic system [19].

Fig. 1.Schematic for coplanar DMFB with integrated photodetectors

Fig. 2 (a) Top view of a 2D planar electrode array for DMFBs.

Fig. 2(b) Transportation and merging operation in a DMFB

A modification of a standard digital microfluidic system resulted in the coplanar digital microfluidic platform [18, 20-21]. The design employs an additional electrode that is coplanar to the dielectric and remains in contact with the droplets, enabling the voltage to be applied to the droplet eliminating the use of a conductive top plate. However the system still uses a top plate to contain the droplet as well as the silicone oil. The top plate is made up of glass and is significantly modified for integrating the photodetector and the corresponding leads and the contact pads (Fig. 2). For integration of a compound semiconductor photodetector with an electrowetting microfluidics system, Advanced Liquid Logic fabricated a coplanar digital microfluidic chip fabricated in printed circuit board (PCB) technology. Electrode voltage of 220V is applied to the chip for EWOD based actuation [16]. This integrated design was a significant step toward more sophisticated optical system integration with a digital microfluidics platform. A cyber physical DMFB design based on integration of optical detector with corresponding sensor data has been proposed in [22]. This method develops a physical-aware system reconfigurable technique that uses sensor data at checkpoints to reconfigure the biochip dynamically. In [23], a chip scale sensing system, made up of a planar integrated optical micro-disk resonator and a thin film InGaAs photodetector, is integrated with a digital microfluidic system. This system was able to detect changes in index of refraction as presented by glucose solutions in small droplets with different concentrations that were delivered by the DFMS. Automated detection based analysis have been reported in [11, 24]. In [24], using the integrated device proposed in [13, 16], a digital microfluidic system consisting of multiple DMFB has been designed. The system implements result based sequential execution of multiple bioassay protocols on different DMFBs controlled through a centralized timer and controller circuit. In [11], the design of a dual mode detection based analyzer has been proposed. The device operates for both homogeneous as well as heterogeneous sample based detection. Analysis and detection of the results from optical detection site is highly dependent on human intervention – may tend to be erroneous at times. Hence this may require precision analysis which can be incorporated by employing automated detection and analysis techniques using an intelligent knowledge based system.

In this work, we propose an intelligent system for integration of multiple biochips with respective automated analyzers for execution of a predefined decision based diagnostic process flow. The goal is to achieve a conclusive and automated diagnostic solution towards a predictive patient-specific disease profile. We have used a dual mode detection based analyzer as mentioned in [11] for decision based integration. The mode of operation for each analyzer is user defined and is applied with enable signals for each individual biochip. Rest of the work is organized as follows. Section II describes briefly the proposed design of the dual mode detection analyser to be embedded in the integrated system. Data acquisition and the diagnostic process flow are presented in section III. The architecture for integrated system is proposed in IV. Section V presents experimental results. Section VI concludes the discussion.

II. PROPOSED DUAL DETECTION ANALYZER

In this work, we have used a dual mode detection analyzer proposed in [11]. The purpose of this device is to measure the existence of any specified parameter within human physiological fluid. On execution of a prescheduled bioassay protocol within a DMFB, the optical detection using integrated sensor transmits an output (using a Transimpedance amplifier and an ADC) to the output register incorporated to the analyzer. A pre-characterized reference data is loaded to the reference register and two levels of comparison are carried out to ascertain the existence and the intensity of the pre-specified parameter targeted for detection and diagnosis towards biomedical applications. The analyzer operates on two modes. In the first mode it operates on a set of droplet samples of the same type .Based on the majority detection using a comparator, adder and a threshold register the analysis of the detection results are being carried out. The mode is termed as homogeneous mode. In second mode, a group of different samples used for multiple bioassay protocols to be executed simultaneously are being used. For each bioassay, the results are compared with standardized references loaded in parallel to respective reference registers (each reference being dedicated for one separate bioassay protocol). The comparison has been carried out in two levels. If first level output is found to be positive, a second level of comparison is initiated with a higher level of reference threshold for determination of the intensity or severity of the existing abnormality in the targeted parameter. However, in this proposed work, we have used only a single level comparison for the second design using optimized analysers. The reason of loading reference data for second level comparison may complicate the overall process flow. The second mode is termed as heterogeneous mode and a mode selection line is used for selection of the mode supplied initially with the biochip enable signal. The functional block diagram for operation of the dual mode combined detection analyzer is shown in Fig. 3.The circuit diagram for the detection analyzer [11] has been displayed in Fig. 4. The inputs being the mode selection signal, biochip enables and the preloads for homogeneous reference, heterogeneous reference Low and heterogeneous reference High and the threshold register value K. On completion of detection and analysis for all samples used for execution of the respective bioassay protocols, an EOC (End of comparison) is generated from the given analyzer (Fig.4).

III. DATA ACQUISITION AND DIAGNOSTIC PROCESS

FLOW

A. Absorption spectra for human samples Knowledge of micro-optical parameters of human blood (e.g. absorption coefficient (�a), scattering coefficient (�s) and anisotropy factor (g)) plays an important role in many diagnostic and therapeutic applications in laser medicine and medical diagnostics. These optical parameters can be used for the quality control of blood products and the monitoring of extracorporeal circulation of blood in cardiopulmonary systems. The optical parameters can be determined in the wavelength region 400-1000 nm for circulating blood for different concentrations of red blood cells (0.2-5.0 mill/�l), various degrees of oxygen saturation (60-100%), osmolarity (225-400 osmol) ,haemolysis(0-100%).Colorimetric analysis used for determination of blood parameters in DMFBs is a method of determining the

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concentration of a chemical element or chemical compound in a solution with the aid of a color reagent. It is applicable to both organic and inorganic compounds and may be used with or without an enzymatic stage. The in-vitro measurement of glucose and other metabolites,e.g. lactate, urea, glutamate, cholesterol, Ketone, pyruvate and others in human physiological fluids is of great importance in clinical diagnosis of metabolic and pulmonary disorders [22].

Fig.3. Functional Block diagram for dual mode analyzer operation

We used data acquisition techniques as proposed in [12,13, 25]. The setup consists of a light emitting diode (LED) and a photodiode (TSL257). The Photodiode provides a light to voltage conversion which is obtained through a trans-impedance amplifier on the same monolithic device. A 12-bit analog data acquisition board (PCI-DAS08) has been used for ADC conversion. The analog data acquisition board has a measurable range of −5 to 5V and an analog-to-digital (A/D) resolution of 2.44mV. Every chemical compound absorbs, transmits, or reflects light (electromagnetic radiation) over a certain range of wavelength. In biochemistry, this principle is used to determine enzyme-catalyzed reactions. In clinical applications, it is used to examine blood or tissues for clinical diagnosis. The absorbance A� at wavelength � is defined as:

Aλ= ε(λ)cd (Beer-Lambert’s law) where �(�) is the molar extinction coefficient or molar absorptivity (absorption coefficient) at wavelength �, d is the path length, and c is concentration. The molar extinction coefficient is given as a constant and varies for each molecule. �(�) has unit L·mol-1·cm-1. The path length is measured in centimeters [26-27]. The voltage V(t) is directly proportional to the light intensity incident on the photodiode and is related to the absorbance by the following equation.

v0 - vdarkA(t) = ln( )v(t) - vdark… (1) [23]

1 / cm gm�( ).c( ).l(cm)

moles / litre litreA =gm

gm - molecular - weight( )mole

… (2) [23]

Where A = molar extinction co-efficient ,C= concentration of substance. V0 corresponds to zero absorbance (or 100% transmittance) and Vdark corresponds to the voltage output of the photodiode under dark conditions. The absorbance is measured for 45s, using the LED-photodiode setup. The output is connected to the proposed analyzer for detection analysis. B. Test cases and the process flow In this work, we have used colorimetric tests for measurement of parameters, namely, Blood Glucose, Plasma Glucose, Total Cholesterol, HDL, LDL, Creatinine, Blood Urea, Lactate and

Pyruvate. The details for the specified Bioassays are omitted for brevity. The process flow for the test sequence is shown in Fig. 5. The process starts with two homogeneous detection for whole blood glucose and plasma glucose. Result High is represented as 1 and Low as 0. Based on the combination (11 or 10), the choice of the next level is executed. In the process flow, the decision on the next level execution is based on execution of homogeneous sample based detection in the previous level. The heterogeneous sample based detections are carried out at the terminal level (shown as leaf node). The reason being combination of multiple sample based output from heterogenous execution may require a complex decision table and the implementation needs more complex hardware utilization.

Fig. 4. Block level circuit diagram for dual mode detection analyzer [11]

IV.PROPOSED ARCHITECTURE FOR THE INTEGRATED

SYSTEM

Based on the process flow diagram as shown in Fig. 5, we have initially proposed an intelligent system that integrates multiple biochips being connected with a combined detection analyzer and separate register stacks. The design uses five separate analyzers with two decoders for decisions at two levels. The schematic for the first design is shown in Fig. 6. We used two different colors to denote homogeneous and heterogeneous sample based Biochips. It has been observed that at each level, at most two Biochips are being used in parallel .So at first, we partition the biochips into two separate groups namely A and B (denoted by two different colors). The corresponding analyzers used in each group are named as Ai and Bi respectively for the corresponding execution sequence i in the process flow. Hence, it is possible to optimize the design with only two combined detection analyzers each being logically connected with Biochip clusters A and B respectively.

So we can conclude that for any given flow for decision based bioassay execution, if there are n levels and at most m number of biochips is being used simultaneously for any specified level, the maximum number of analyzer can be incorporated equal to m. If number of biochips to be used is given by b, then n � b � (m × n) and the biochips are partitioned into m groups with each group being assigned to one specified detection analyzer. The circuit diagram for the optimized design is shown in Fig. 7. According to the observation stated above, we optimized the design using the aforesaid partitioning strategy and the biochips are grouped into two clusters namely A and B. Biochip B1, B4 and B6 are assigned with cluster A and are connected with combined detection analyzer A, whereas biochip B2, B3 and B5 are assigned with cluster B and are connected with combined detection analyzer B. The sequence of operation is given as follows. • Enable for biochip B1 and B2 is activated to execute the two

homogeneous bioassay protocols. The mode of execution is transmitted as 1 for homogeneous and 0 for heterogeneous selection, which are set together with the enable signal.

• Corresponding reference data is supplied from reference register

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stack A and B to the respective analyzers through (4:1) Mux 5 and Mux 6.

• ADC output from B1 and B2 are selected and supplied to the analyzer A and B respectively using Mux 1 and Mux 2.

• On completion of 1st level execution, an EOC is generated from each analyzer. This, in turn triggers two 2 bit counters simultaneously to generate the next address from each group.

• This address transmits output from first level to respective output register stack through demux A and B respectively.

• Combined output from simultaneous execution of two bioassays at B1 and B2 are fed to a (4:1) decoder (1st level decoder).

• Depending on the combination generated (11 or 10), the decoder sends enable either to biochip B3 and B4 or to Biochip B5. The mode of operation is determined together with the enable signals.

• On completion of 2nd level of bioassay execution, ADC output as well as reference data is supplied to respective analyzers being selected in a similar manner.

• Respective EOCs are generated to trigger the next address using 2 Bit counters.

• If B4 is selected for execution in 2nd level (together with B3), the output in turn is fed to a 2:1 decoder.

• Based on output, decoder decides on 3rd level execution of biochip B6 (output – 0) or B5 (output – 1) and respective outputs are stored in corresponding output register stack for final display.

• Mux 3 and Mux 4 are being used for selection of respective output registers connected with demux A and B respectively for final storage of outputs after analysis.

Whole system uses 6 Biochips, 2 analyzers, 2 decoders (one for each level), 2 demux, 6 mux, 2 Two-bit counters and 4 groups of register stacks. 1-bit registers are used for output and reference for homogeneous biochips, whereas 4-bit registers are used for output and reference for heterogeneous biochips as it stores data for four separate samples. In heterogeneous operations, we have chosen a single level comparison based detection for reducing complexity in the design. But this can be accomplished using a second group of reference register stack for next level of comparison with reference High. A device level block diagram with two biochips for two levels of conditional execution of multiple Bioassays has been shown in Fig.8.

Fig. 5. Diagnostic process flow diagram for the detection analysis

V. EXPERIMENTAL RESULTS The tests involved in process flow diagram as shown in Fig. 5 have been executed in the defined sequence with two different sets of samples and the results obtained are shown in table 4 and 5. Table 3 displays the standard values pre-characterized for each Bioassay protocols to be executed in course of the process flow. The optimized circuit is implemented in FPGA platform using Spartan 3E architecture. The simulation results (using Xilinx ISim 9.1) for execution with two separate sets of samples are given in Fig. 9 and 10. Device utilization and the timing summary for the optimized design have been displayed in table 1 and 2. For heterogeneous integration, we use clock signal (signal clk) of freq. of 10MHZ. Enable and Reset signal are used for initiation of enable and reset for first level of execution. At first biochip, address

001 (B1) in block A and biochip 010(B2) in block B are executed. Signal bioout1 [6:0] represents ADC output for biochip1 and so on. Signal reference1 [6:0] indicates reference preloaded in reference register stack A for execution of bioassay B1. The digitized outputs for homogeneous mode of execution for B1 are 14,15,12,13 and corresponding reference is taken as 64 (binary decoded value). Hence in terms of absorption, output is high and is indicated with signal result1 representing an output register dedicated for B1. The combined output from B1 and B2 (subjected to analyzer A and B respectively) is stored in signal pattern [1:0] to be transmitted to decoder1. Next level output from decoder1 as signal next_add1 [2:0] and next_add4 [2:0] are applied for enabling B3 and B4 respectively. The process is continued according to defined flow and final results are displayed in signals result1, result2, result3 [2:0], result4, result5[2:0] and result6. For intermediate storage of 2nd level comparator output in analyzers A and B signal display1 [1:0] and display2 [1:0] is being used.

Fig.6. A block level schematic for the initial proposed design using separate analyzers for each biochips. Analyzers marked for partition A are displayed in pink and those for partition B are marked in grey.

VI. CONCLUSIONS Here we proposed a diagnostic process flow based design of an intelligent system of multiple biochips operating in combined mode. The system is capable of decision based execution of test sequences selecting tests for next level on the basis of earlier level test results. The results obtained are found to be in conformance with conventional laboratory methods. The system is integrated for a predefined process flow and any change in test schedule may require a reconnection of components of the overall system. But it may be possible to design a preprocessor that can be programmed with specific instruction sets to incorporate any process flow dynamically within the intelligent system.

Fig.7. Block level circuit diagram for the Optimized design using two analyzers with separate groups of reference and output register stacks Table 1 – Device utilization summary for the optimized integrated system

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Fig.8. Device level block diagram involving sequential execution of a two

level diagnostic process

Fig.9. Simulation result with 1st group of samples giving 1st level output of 11.

Fig.10. Simulation result with 2nd group of samples giving 1st level output of 10.

Table 2 – Timing summary for the optimized integrated system

Table 3 – Standard threshold values for the specified protocols involved in the

diagnostic process flow

Table 4 –Test results with 1st group of samples giving 1st level output 11

Table 5 –Test results with 2nd group of samples giving 1st level output 10

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