In this page, we present how we envision OASYS in the real world - the proposed implementation, and most importantly, the proposed end users. Along each step of the project, we consulted professionals across various fields to ensure the conscientious and efficacious deployment of OASYS in practice.
In line with this goal, we also discuss how the device can be curated to be used by different sections under End User Analysis. We also briefly touch upon the opportunity of commercialization of the project, more details of which can be found under the entrepreneurship section, here.


MDD is a multifaceted disorder that shows different symptoms in different people, making its diagnosis very difficult. According to a recent study in Kerala, during the COVID-19 pandemic depression (75.2%) and anxiety (69.4%) were the major problems faced by the people in quarantine[1] . With around 280 million people around the world being affected by MDD [2] , having a reliable diagnostic method is a need.

What are the current methods of diagnosis and diagnostic aids in the market at this stage?
We asked this question to numerous experts in the field of psychiatry like Dr Arun B Nair (psychiatrist, GMC) and Dr Krishnan S (HOD of psychiatry, GMC TVM)(find out more here); and subsequently came up with a well-rounded analysis in order to get a better understanding of the gaps in diagnosis.

Figure 1 : Analysis of existing diagnostic methods of MDD

From this qualitative data, it is clear why clinical interviews using the DSM-5 or ICD11 criteria are considered the gold standard for diagnosis. One major advantage of this method is the fact that clinical interviews take into account the observations and experiences of the patient’s bystanders or close family. However, there are also obvious drawbacks to this solution since it is very time-consuming due to the requirement of multiple visits to the clinic; consequentially also increasing the costs of diagnosis. It might also be subject to personal bias on behalf of both the medical professional as is clear by the current stats for misdiagnosis.
In a study [3] , it was found that out of all the diagnosed MDD patients, 14.3% of them were misdiagnosed. These increasing rates might also be a result of the failure of proper communication on the patient’s end - as is observed with senior citizens, children, and patients afflicted by other neurological disorders (cases of co-morbidity of MDD)

All these point toward a need for a diagnostic aid that looks at the physiological markers in the body co-relating to MDD. In addition to speeding up as well as increasing the accuracy of the diagnosis, this method provides a better understanding of how the disease affects a person; thus accounting for the variability from person to person.
More importantly, a diagnostic aid like OASYS which relies on physiological markers, addresses an important issue. Interacting with the general public and professionals from multiple walks of life opened our eyes to the existence of unjustified misconceptions and stigma surrounding mental illnesses -especially MDD.

In an anonymous survey we circulated across India we asked them how a blood biomarker-based diagnostic tool can make a difference in their social and work life, and a lot of them brought up the concern that depression is not considered a "real illness", and is a topic of taboo. Having objective results not only makes it easier for them to explain their disease to others but also makes it easier for them to treat themselves better and discuss their mental health with more confidence.
Thus we understood that the idea behind OASYS, i.e. linking mental illnesses to their physiological basis, will help tackle these misconceptions and combat the stigma that is still pervading in the society.

End User Analysis

We realized the importance of having a human-centric approach to developing our product design so that it can benefit people who need it the most - MDD patients. We interacted with several people who had experienced depressive episodes or been diagnosed with MDD and other mental illnesses to gain insight into what a good diagnosis tool should look like throughout our project journey. However, OASYS is not a home-based tool. Instead, it is aimed at clinicians and other Mental Health Professionals. Having recognized various other applications for our tool (find our iHP story here!), in both research and pharmaceutical fields, we ensured to consider the potentials and the challenges of OASYS in the real world by discussing our project with various psychiatrists, psychologists, neuroscientists, and pharmacological experts.

What do the patients want?
Through our surveys and outreach activities, we connected with people who have been diagnosed with MDD to understand what they would want from a tool that analyses biomarker levels to screen for MDD. We reached out to MDD patients through our survey, which received 25 responses.

Figure 2 : Analysis of our patient surveys.

OASYS as a diagnostic tool

Our goal with OASYS is to make an objective and sensitive Point of Care Testing (PoCT) tool that can aid the process of MDD diagnosis. Its user-friendly and accessible design lends itself the potential to be an effective diagnostic aid. The current biomarkers chosen by us are putative and not validated by any clinical trials except for literature data. Once the reliability of the chosen biomarkers is established, the next step would be to find out the specific threshold blood levels for each biomarker through subsequent clinical trials, Thus, slowly but surely, OASYS can be integrated into current process of MDD diagnosis as an effective objective aid. Our tool is meant to benefit MDD patients. However, OASYS is not a home-based tool. Instead, it is aimed at clinicians and other Mental Health Professionals, intended to complement the current diagnostic methods. In no way is OASYS meant to be a self-diagnostic tool. Rather, the analysis of readings given by the chip can only be done by clinicians. This avoids any misuse of the chip considering the lack of awareness and sensitivity around mental illnesses.

However, OASYS is more than just a diagnostic tool.

Figure 3: We propose using the tool to generate biomarker data, enhancing our understanding of their correlation with Major Depressive Disorder (MDD). Thus the threshold levels of blood biomarkers can be set by the tool itself. Coming full circle, this allows the tool to be functional as a diagnostic aid for MDD.

OASYS as a research tool

As mentioned earlier, one of the primary problems we faced during the background research of our project was the severe lack of data regarding blood biomarkers related to mental health illnesses. Since more data needs to be collected to establish the correlation between the biomarkers and MDD, the initial users of our tool would be the scientific community. Talking to Dr. Srikumar from NIMHANS, we realized that OASYS can help build biomarker data that can help in scientific research advancements in the field of psychiatric illnesses. It can also help in novel drug development of antidepressants for MDD. Biomarker researchers, psychopharmacologists, clinical and psychiatry researchers can use our tool to help with research in their fields. Dr Varsha Singh, a cognitive psychologist from IIT Delhi, mentioned how “Biomarker research in mental illnesses needs to catch up”. In line with that goal, data collection through clinical trials and population studies can be made easier with the help of OASYS. Analysis of these could reveal the potential of each biomolecule as a biomarker for MDD. Our microfluidic device is not just a solution to misdiagnosis, but hopefully a catalyst for future groundbreaking research.

OASYS as a prognostic tool

A person's blood biomarker levels could be easily plotted using a tool like OASYS. Understanding and studying the biomarker profile of individuals diagnosed with Major Depressive Disorder (MDD) opens up the opportunity to tailor precise antidepressant treatment strategies for patients. This will considerably diminish reliance on trial-and-error approaches by the physician, as pointed out to us by Dr Srikumar, consequently aiding the paradigm shift toward personalized medicine.

OASYS for antidepressant tracking

The assessment of the response and efficacy of antidepressant treatment in Major Depressive Disorder (MDD) patients can be conducted by analyzing changes in biomarker levels within the blood. This would entail undergoing multiple microfluidic blood assays over a specific prescription period. This analytical approach shifts the focus from behavioral changes to analyzing biological alterations, providing a more nuanced and objective evaluation of treatment outcomes.

Optimising OASYS

As mentioned in the TRL analysis of our project, we are currently at TRL level 4. There are a number of things we planned out to do but weren't able to do due to time, financial, and competition constraints. However, talking to relevant stakeholders like Anvaya Biotech and Vidcare Innovations boosted our spirits to keep working on OASYS even after the iGEM competition since it would be a start of, or at least be a catalyst in the field of PoCT in mental healthcare. We realized that the preliminary design of the device proposed for the competition will have to undergo several optimizations to comply with the safety, qualitative and technical requirements.

Please click on the different buttons to read the plans in each domains


The primary challenge in attempting to devise a biomarker-based diagnosis for MDD lies not just in the absence of specific established markers but also in the stagnation observed in the research of biomarkers for mental health disorders. We were faced with an acute lack of biomarker data when it came to MDD and this was also affirmed by the words of Dr Krishnan S, the HoD of psychiatry at Govt Medical College and Lekha Dinesh Kumar, a scientist working on cancer research at thе Cеntrе for Cеllular and Molеcular Biology. (Find the detailed report of their inputs on our iHP timeline).

Currently, we have chosen 5 biomarkers that showed the most literature and experts’ backup. We have also compiled a preliminary list of the same kinds of biomarkers and biomarker functions related to MDD which shows almost similar degree of potential to be biological biomarkers of MDD. The exact pathways in which these work to co-relate with MDD is something that is yet to be explored and, again, currently facing a severe lack of data.

  1. Cytokines and inflammatory markers [4]
    • Inflammatory mediators have been found to affect many factors (monoamine and glutamate neurotransmission, GR resistance, and hippocampal neurogenesis) that are thought to be important in the etiopathogenesis of MDD. This suggests that inflammatory markers may be used as a marker for the diagnosis and treatment response of depression
    • IL-1, IL-6, and tumor necrosis factor α (TNFα) are known as pro-inflammatory cytokines, and IL-4, IL-10, and IL-13 are known as anti-inflammatory cytokines - they play a role in the maintenance of normal brain function by supporting neuronal integrity, neurogenesis, and synaptic remodeling
    • Cytokines released by peripheral immune cells and adipose tissue enter the central nervous system (CNS) through some regions of the blood-brain barrier or by the active reuptake mechanism and thereby affect behaviors.
    • Of the inflammatory markers, those which most confirm the changes in depression are proinflammatory cytokines such as IL-1, IL-6 and TNFα, and C reactive protein (CRP) Serum levels of IL-1, IL-6, and TNFα and the messenger ribonucleic acid (mRNA) expressions of these in peripheral cells are increased in patients with MDD ,elevated levels of IL-1, IL-6, and TNFα prior to treatment have been reported to be associated with non-response to antidepressant treatment
    • Pro-inflammatory cytokines stimulate IDO in glial cells. IDO converts the tryptophan to kynurenine, which is then converted to neurotoxic quinolinic acid in the brain. Quinolinic acid binds to N-methyl D-aspartate (NMDA) receptors. Thus, because of the pro-inflammatory cytokines inducing IDO, depression may develop as a result of the decrease in serotonin levels due to reduced tryptophan on the one hand and glutamatergic neurotoxicity on the other.
    • Serum levels of IL-1, IL-6, and TNFα and the messenger ribonucleic acid (mRNA) expressions of these in peripheral cells are increased in patients with MDD ,elevated levels of IL-1, IL-6, and TNFα prior to treatment have been reported to be associated with non-response to antidepressant treatment.
    • Cytokine levels differ for some subtypes of depression. increase in proinflammatory cytokines in those attempting suicide, in depression with suicidal ideas, and in geriatric depression whereas atypical and melancholic depressions show increased levels of CRP and IL-6 compared to typical depressions.
  2. miR-144-5p[5]
  3. In 178 patients with depression, anxiety, or stress and adjustment disorders, 36 inflammatory proteins with significantly different expression in peripheral blood of patients at baseline were seen, including 21 inflammatory proteins with increased levels and 15 with decreased levels, and all were associated with changes in miR-144-5p levels. In addition, the alteration in inflammatory proteins, which occurs after receiving treatment, was demonstrated to be associated with improvement in patients’ psychiatric symptoms.
  4. Oxidative stress markers [4]
    • Increased oxidative stress and reduced antioxidant defenses exist in MDD, and that specific components of oxidative stress play a role in the pathophysiology of depression.
    • Levels of lipid peroxidation products such as malondialdehyde (MDA) have been reported to be generally elevated in depressed patients. There are also studies reporting that MDA levels are higher in patients with recurrent depression than in those with a single episode. In depressed patients, several studies have reported that serum SOD is decreased or that erythrocyte SOD is increased .
    • A meta-analysis of 17 trials evaluating the majority of lipid peroxidation markers in MDD found that these markers were elevated in patients with depression and found an association between the levels of these and the severity of depression.
  5. Growth Factors [4]
    • A reduction in the hippocampal BDNF activity might be directly related to the pathophysiology of MDD, a stress-related disease.
    • BDNF changes are not specific to depression. For instance, the reduction is observed for both bipolar disorder and MDD, although serum BDNF levels are reported to be lower in bipolar depression compared to MDD


Temperature based assays for Aptasensors

Performing temperature-based assays for our aptasensor experiments can be crucial due to the change in aptamer binding affinity which can be temperature-dependent. This is because temperature could affect the secondary structure of the aptamer, which can in turn affect its ability to bind to its target, which in our case is the cDNA strand for the formation of the aptasensor and also the biomolecule once the cDNA has been displaced. For example, some aptamers may have a higher affinity for their target at lower temperatures, while others may have a higher affinity for their target at higher temperatures. Therefore, performing temperature-based assays can help to identify the optimal temperature for aptamer binding. Therefore running the hybridisation fluorescence based experiments for the formation and sensitivity of our aptasensors in varying temperatures will not only aim to hasten the formation of the quantifier molecules but could also in theory provide us with optimal temperature conditions for increased sensitivity of the aptasensor. In addition, temperature-based assays can also be used to study the thermodynamics of aptamer binding. This information can be used to design aptasensors with improved binding affinity and stability.

ITC experiments for nanoprobe
Isothermal Titration Calorimetry (ITC) is a powerful technique that directly measures the heat changes associated with molecular interactions, allowing the determination of thermodynamic parameters of binding events, including enthalpy (ΔH), entropy (ΔS), and the binding constant (Ka). When studying DNA-DNA binding, such as during the formation of DNA duplexes or the interaction between specific DNA sequences, ITC offers several advantages:

  • Direct Measurement: ITC provides direct measurement of the binding enthalpy. Unlike other methods where binding constants are the primary data obtained, and thermodynamics must be inferred or calculated, ITC directly measures heat, allowing for a direct determination of ΔH.
  • No Labeling Required: ITC doesn't require any labeling of the molecules involved. Many other techniques, like fluorescence or radioactivity-based assays, need tags that might interfere with the binding event or alter the properties of the molecules.
  • Comprehensive Thermodynamic Profile: ITC can provide a complete thermodynamic profile of the binding event in a single experiment. It allows determination of ΔH, ΔS, the Gibbs free energy (ΔG), and the binding constant (K_a or K_d).
  • Solution-based Measurement: ITC measurements are done in solution, which means that the natural three-dimensional conformation and dynamics of the molecules are retained, ensuring biologically relevant data.
  • Multiple Binding Sites: ITC can distinguish between multiple binding sites with different affinities on the same molecule, which can be useful when studying complex systems or interactions with multiple stages. Since it shows multiple binding sites, it’s advantageous since it can prove binding of three different hybridizing sequences together to form the probe.
  • No Need for Separation Steps: After binding occurs in an ITC experiment, there's no need to separate bound from unbound molecules. This is a contrast to techniques like gel-shift assays, where separation steps can introduce variability or inaccuracies.
  • Determination of Stoichiometry: ITC not only provides information about the strength and thermodynamics of binding but also about the stoichiometry of the interaction, revealing how many molecules of one type bind to the other.
  • Probing Molecular Details: By comparing ITC data from experiments conducted with modified DNA sequences or conditions, it's possible to probe the role of specific bases or structural elements in the binding interaction. This can help in understanding the molecular basis for specificity or the role of certain regions in DNA binding.

Protein Expression and Purification

We intended to complete Gsα protein expression and purification. However we were not able to follow through because of the time constraints of the competition. Future experiments include:
  • Bacterial expression involve plating experiments, preparation of secondary inoculum and IPTG induction for protein expression. The lac operon is induced by IPTG molecule for translation of the taget gene.
  • Sonication is the process of using sound energy to stir up particles in a sample for a variety of applications, including the extraction of several different chemicals.Any protein purification procedure must begin with the sonication of cells.
  • Ni-NTA chromatography, or Nickel-Nitrilotriacetic Acid chromatography, is a widely used technique in molecular biology for the purification and separation of proteins and other biomolecules.Ni-NTA chromatography is based on the principle of affinity chromatography, where a ligand with high affinity for the target molecule is used to selectively bind and separate it from a complex mixture.
  • SDS PAGE or sodium dodecyl sulfate-polyacrylamide gel electrophoresis is the most used approach in genetic, biotechnology, biochemistry, and molecular biology labs for separating proteins from a mixed sample and identifying and quantifying a single protein from a mixture.They separate proteins on the basis of their electrophoretic mobility in which the mobility of the molecule is inversely proportional to its size and directly proportional to its charge.
  • The Bradford protein assay is used to measure the concentration of total protein in a sample. The principle of this assay is that the binding of protein molecules to Coomassie dye under acidic conditions results in a color change from brown to blue.
  • Desalting and Ion Exchange Chromatography are two important steps performed to remove salts and other low molecular weight contaminants from a protein solution; and to separate proteins based on their net charge respectively.


Once the assay has been run, the individual fluorescence readouts need to be measured and analyzed. Commercial fluorimetry, due to its relatively high cost, might not be the optimal approach. Consequently, we propose an alternative, cost-effective fluorimetry device, informed by our expertise and insights garnered from available resources. This approach also allows for the readouts of each channel to be measured simultaneously.

Our current prototype uses basic components to showcase the concept, but we plan to use more efficient and affordable components in the future when our product is ready for market.

Towards the future, we are considering upgrading from a microcontroller, like the Arduino uno, to a microprocessor like the ESP32 series. There is a significant upgrade in the allowed memory ( 2 KB RAM to 520 KBRAM, 32 KB flash memory to 4 MB flash memory ) which allows us to expand our product to include more photo sensors and modules and also add more complex programming structures and functions. ESP32 boards have built-in WiFi and Bluetooth, allowing us to connect them to a user-friendly app or website. They can also run Python and TensorFlow, enabling machine learning on the device. This means that the product can measure data, collect it to a server, analyze it with machine learning, and optimize itself for any demographic. All these advantages come along with the point that currently the ESP32 board is ~20% cheaper than the Arduino, which is a key factor as our primary goal is humanitarian and to make the product affordable, even in developing or third-world countries.ESP32 boards are not ideal for beginners or intermediates and add complexity to the device. They require more voltage regulatory components, are more fragile and noise sensitive, and are not built for the simple but rough use that Arduino boards can’t handle.

For our proof of concept and demo, we have decided to forgo aesthetics and additional functionality that the ESP32 offers, and stick to the more simple yet robust Arduino Uno while not compromising on rigorous scientific thoroughness. We will also design a compact circuit board layout to minimize the size of the device. We are considering changing the measurement from voltage to capacitor discharge time, which would allow us to measure very low currents accurately.

We believe that our hardware has the potential to revolutionize the field of diagnostics. By making it easier and more affordable to develop and deploy new diagnostic tests, we can help people get the care they need sooner and start living healthier lives.


To ensure the collection of more biomarker data using more affordable and accessible means, we plan to go through multiple iterations of Design-Build-Test-Learn cycles to make our system the most efficient way to collect biomarker data.

Our current design that was constructed is only a two channel chip that contains one quantifier of each type to test out the validity of our quantifier-biomarker system. We are also in the process of designing the five-channeled microfluidic chip that can process all five of our biomarkers but requires much refining in order to be used for testing yet. We also plan to leave the domain of prototyping and entering the market as a tool that can be sold, for which we have a number of important steps that we would like to include in our microflidics chip-

  • Finger Actuated Pressure system - We want to reduce the hassle of using complicated pumps and syringes in the chip as much as possible, and therefore this is a technique we found that uses just a push of a finger on the device to pump in certain amounts of air pressure.[6]
  • We also wish to create a manufacturing mould for a scaled manufacturing of the chip so that it can be market ready. We were helped by CCAMP in Bangalore to plan this out for our future market endeavours


The initial design of the microfluidic chip is based on extensive research into the identification and quantification of specific biomarkers associated with depression. As the project progresses, we will continually refine the chip's design to enhance its sensitivity, accuracy, and efficiency.

Data Analysis

One of the primary functions of the microfluidic chip is to analyze biomarker data obtained from blood samples and provide a degree of risk for clinical depression. This risk assessment will be based on complex algorithms and statistical analysis of the biomarker data. We will continuously refine the algorithms used for risk assessment. This iterative process will involve incorporating newly discovered biomarker data, whether from our tool or independently, and improving the accuracy of of our quantification system.


To support our microfluidic chip's research and diagnostic capabilities, we hope to establish a secure, cloud-based database system. This database will serve as the repository for biomarker data collected from blood samples. The infrastructure of this will be designed to scale seamlessly with the growing volume of biomarker data generated by our chip and other research sources. This ensures that we can accommodate the expanding dataset efficiently.

The database will collect biomarker data, but it is important to stress that this data will be limited to relevant biomarkers and anonymized patient history. The focus is strictly on research and diagnostics, with no personal data involved. Robust encryption, access controls, and regular security audits will be implemented to protect the integrity and confidentiality of the biomarker data. Advanced anonymization techniques will be applied to the data to remove any identifying information, ensuring that patient privacy is preserved throughout the research process. Informed consent will be a fundamental aspect of data collection. Participants contributing their data to the database will be required to provide explicit consent, which will be documented and stored securely. We will establish a clear and user-friendly opt-out mechanism, allowing individuals to withdraw their data from the database at any time if they change their mind or wish to discontinue participation. Our project will adhere to ethical guidelines and data protection regulations. We will seek ethical oversight and approval from relevant authorities to ensure transparency and compliance with ethical standards.

A well-structured database is critical for efficient data retrieval and analysis. Our database will be organized to support various research inquiries related to depression biomarkers. Biomarker data will be categorized and tagged to facilitate easy sorting, filtering, and retrieval based on different parameters, such as sample source, date, or biomarker type.

The database will consist of multiple interconnected data tables that include, but not be limited to:

  • Patient Information - This table will store anonymized patient identifiers, such as unique study IDs, demographic data (age, gender), and relevant medical history.
  • Biomarker Data - Biomarker data will be stored in a structured format, associating each data point with a specific patient and timestamp. Attributes may include biomarker type, concentration, and any related metadata.
  • Consent Records - Consent records will document when and how consent was obtained from participants. It will link consent to specific patient data, ensuring transparency and compliance.
  • Researcher Information - If applicable, this table will contain information about authorized researchers with access to the database, including their credentials, roles, and research objectives.


We also pan to develop a mental health tracking app, designed to complement the microfluidic chip and enhance mental health monitoring. The app will prioritize accessibility, user-friendliness, and engagement. To maximize accessibility, the app will be developed to run on multiple platforms, including iOS and Android devices, making it available to a wide range of users.

The app's core functionality will revolve around tracking mental health and well-being, with a range of features to support users in monitoring and improving their mental health. Users will be able to record their daily moods and emotions through the app. This data will be graphically displayed, allowing users to visualize trends and identify patterns in their emotional states. The app will include the option to log daily activities and routines, helping users correlate their activities with changes in their mental well-being. A journaling feature will allow users to document their thoughts, feelings, and experiences. This serves as a therapeutic outlet and provides valuable insights into mental health fluctuations. Links to mental health resources, crisis helplines, and expert advice will be readily available to users.

Respecting user privacy is paramount in the development of the mental health tracking app. We will implement stringent privacy measures to ensure user data remains confidential and secure. Users will be required to provide informed consent before using the app. This consent will explicitly outline what data is collected, how it is used, and users' rights regarding their data. Similar to the database for biomarker data, the app will collect only anonymized data, with no personally identifiable information included.

The app's data can also be a valuable resource for research purposes, particularly in identifying correlations between mental health trends and new biomarkers relevant to clinical depression. Data collected from those undergoing antidepressant treatment along with biomarker tracking may lead to a deeper understanding of the relationship between medicines and biomarkers.

Safety and Ethics

In addressing the safety concerns of this tool, the diagnostic procedure does not endanger the patient since it only requires a blood sample. We strongly propose that this examination take place only in suitable facilities of medical supervision such as a hospital or a diagnostic lab- ensuring that the patient's examination will be conducted by qualified staff. Also, the proposed visualization-quantification software will automatically provide the degree of risk to MDD preventing any human errors in the result analysis

To continue working on the project, we need access to blood samples in order to improve the accuracy and specificity of our quantifiers in blood. To know more about the details of permissions and safety regarding the collection and use of blood samples, we contacted Dr Annapoorna, from GMC Cochin

The use of blood samples of MDD patients requires prior approval from the institute's ethics committee. We are also required to submit a proposal for the project to an investigator from the Department of Psychiatry, Thiruvananthapuram. Some additional measures we will have to take in order to keep in line with the ethical guidelines and ensure safety throughout the process of sample collection are:

  • Consent of the patient will be mandatory and privacy of data collected will be ensured.
  • The medical history of the person will also be collected to know if the blood sample should be treated with additional precautions.[7]
  • The experiments involving blood can proceed in the Bio Safety Level 1 (BSL 1) laboratory, as our research does not involve the study of any pathogens present in blood.
  • In our meeting with Mr Rohan Agarwal, it was brought up that the blood to be discarded and materials or surfaces that are exposed to blood should be disinfected using sodium hypochlorite.

We have also made a structured plan to collect the biomarker data from our microfluidic device to a common database while ensuring the privacy of the users. More details of this can be found in the Software section of Optimising OASYS.
For more information concerning the safety aspect of our project, visit our safety page.

Future Directions

Figure 4: Plan of action.

Certainly, for the implementation of our system in the manner we have envisioned, several tasks still ensue. After optimizing the different parts of our project, as mentioned in the previous section, we hope to have effectively showcased the efficacy of our system through molecular modeling. Consequently, practical tasks concerning safety validation, legal compliance, local registration, and the patenting of the microfluidic design cum hardware will take significance. Here are some of our future directions in mind.

Securing funds

Adequate financial resources are instrumental in advancing research and development efforts, facilitating the optimization of our diagnostic system, and ensuring its seamless integration into the sphere of healthcare and research. These funds are pivotal for conducting rigorous proof-of-concept studies, optimizing molecular modeling processes for human blood constituents, and addressing safety validation protocols

  • Applying for grants and approaching Incubtion centers - Our talks with various experts in the industry got us connected to multiple organizations that offer grants for scientific developments and accelerating startup ideas. We talked to our state authorities like KSUM and K-DISC regarding this based on which we have already applied for the Idea grant under Kerala Startup Mission. In near future we plan to apply for other grants provided by institutions like BIRAC, Nidhi Prayas, Nidhi-EIR, Biotech Ignition Grant etc. to secure funding for our project.
  • Collaborative Opportunities - We have also discussed future collaborative opportunities with up and coming biotech startups like Anvaya Biotech. We talked to Lavanya Bhagavatula, the founder and CEO, who is also part of the iGEM community. She expressed interest in supporting our project beyond iGEM.


Talking to Mamta Singla from Anvaya Biotech and Pallavi Kadam made us realize the importance of protecting our work vis-a-vis promoting it during the iGEM competition. In order to protect our innovation, we are in the process of filing an application for a provisional patent. A provisional patent will give us 12 months to file a proper patent while our innovation is protected, tagging our project with a ‘patent pending’ tag. This is a quick and cheap process, w If we decide to expand our business as described in Entrepreneurship, then we will be able to complete the patent to perfectly match our actual and future research.

Clinical Trials

Clinical studies refer to research studies that involve human participation to evaluate the safety, efficacy, and/or effectiveness of a medical intervention, which can include drugs, devices, treatments, or diagnostic tools. This would be imperative for a blood-based diagnostic kit like OASYS in order to standardize the readouts and sensor as well as learn about threshold levels of the biomarkers in the population. The set-up of clinical trials follows a standardized process in India when it comes to blood based diagnostic. An overview would look like [8] :

  1. Regulatory Approval: Before initiating a clinical trial, the diagnostic device must receive regulatory approval from the Central Drugs Standard Control Organization (CDSCO). The approval process involves submitting detailed information about the diagnostic kit's design, intended use, safety, and performance.
  2. Ethics Committee Approval: Ethics committee approval is a mandatory step for conducting any clinical trial involving human participants. The study protocol, including the ethical considerations and informed consent documents, must be submitted to and approved by an independent ethics committee.
  3. Study Design: A carefully devised study plan is formulated - taking into account factors such as the study population, sample size, inclusion and exclusion criteria, endpoints, and statistical methods. For blood-based diagnostics, the design may involve comparing the performance of the device against existing diagnostic methods or standards.
  4. Informed Consent: Participants in the clinical trial must provide informed consent before joining the study. This involves explaining the purpose, risks, and benefits of the trial to participants in a way that they can understand. Informed consent is an ethical requirement to ensure that participants make voluntary and informed decisions about their participation.
  5. Recruitment of Participants: Participants meeting the inclusion criteria are recruited for the study. The recruitment process follows strict guidelines to ensure that participants are representative of the target population.
  6. Data Collection: Data is collected according to the study protocol. For blood-based diagnostics, this may involve collecting blood samples from participants and assessing the device’s performance in detecting specific biomarkers or conditions.
  7. Data Analysis: The collected data is analyzed using statistical methods to assess the diagnostic device's sensitivity, specificity, accuracy, and other relevant parameters.
  8. Regulatory Submission: After completing the clinical trial, the study results, along with detailed reports, are submitted to the regulatory authorities for evaluation. This is a critical step in obtaining approval for the diagnostic kit's commercialization and use.
  9. Publication and Reporting: The results of the clinical trial are typically published in scientific journals, contributing to the broader scientific community's knowledge. Additionally, regulatory reporting requirements must be fulfilled.

Throughout this process, adherence to ethical standards, rigorous study design, and transparent reporting are essential to ensure the reliability and validity of the clinical trial results.


For commercialization our first step is to conduct thorough market research identifying the target audience, market size, and potential demand for our kit. This will help us understand the current landscape and any regulatory considerations that might be required. Then we shall look for potential partnerships with healthcare providers, clinics, hospitals, and research institutions to get a credible user base. This will then be followed by collaborating with key opinion leaders in the field of psychiatry and mental health to establish credibility and gain endorsement for the tool. To find the best distribution channels for reaching the target audience effectively, we will use marketing methods such as online advertising, social media marketing, and targeted outreach to healthcare professionals. Our next step would be educating potential users about the benefits of early diagnosis and the ease of using the blood biomarker-based diagnostic tool as an aid for MDD detection. At last it will be essential to continuously monitor the performance and effectiveness of the product along with gathering feedback from users, healthcare professionals, and patients to improve the device and address any of its limitations or concerns.
For more details about our market and business plan, kindly checkout the entrepreneurship page.


Developing a microfluidic biomarker-based diagnostic tool for mental health doesn’t come without a comprehensive set of challenges, spanning technical, administrative, and social domains.

Please click on the different buttons to read the challenges faced in technical, administrative, and social domains.

Technical Challenges

Biomarkers and assays
Selecting and validating biomarkers is pivotal. The validation process must extend beyond Cacasian populations to ensure broad applicability. Issues like miRNA stability and assay standardization must be rigorously addressed to guarantee accurate results.
Microfluidic chip
Optimizing mixing and sample dispersion on the microfluidic chip is crucial, emphasizing cost-effectiveness in mass production. Sustainable material use and waste disposal methods are also key considerations for environmental impact.
enhancing sensitivity and specificity of the fluorimeter, along with seamless integration between the chip and sensors, demands meticulous attention. Calibration procedures and quality control measures are essential for reliable results.
Data Collection:
Integrating machine learning (ML) into data collection processes is imperative for scalability and efficiency, allowing for large-scale analysis of diverse datasets.

Administrative Challenges

Navigating regulatory requirements, including the establishment and continuous oversight of an ethics committee, certification acquisition, and quality control measures, is a persistent challenge.
User Training
Ensuring comprehensible instructions in multiple languages and thorough user training is vital. Ongoing user support from the development team is necessary for effective implementation.
Conducting thorough market research to understand potential applications is essential. Tailoring marketing and pricing strategies, conducting risk assessments, and anticipating market demands are integral aspects of successful produtc management.
Scaling Business
Handling increased demand requires adept resource management and allocation, addressing supply chain logistics, organizing customer support, and establishing robust revenue streams.
IP and Patents
Developing a strategic approach for IP and patent filings, including trademark protection, due diligence, and licensing agreements, is critical for protecting intellectual property.

Social Challenges

Data Management and Privacy
Safeguarding sensitive data involves stringent security measures, adherence to legal and ethical requirements, defining data retention periods, obtaining patient consent, and prioritizing patient privacy.
Addressing the stigma associated with mental health requires a concerted effort in education and sensitivity training, both within the user community and the broader public.

In summary, overcoming the challenges in this ambitious project demands a synergistic approach, combining technical innovation, meticulous administrative planning, and a keen understanding of social dynamics. As the project progresses, continuous adaptation, collaboration, and a commitment to ethical and inclusive practices will be essential to realize the full potential of OASYS.


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