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1. Graphene/gold nanoparticle composites for ultrasensitive and versatile biomarker assay using single-particle inductively-coupled plasma/mass spectrometry

   https://doi.org/10.1039/d0an01019g  

   Xing, Yuqian ; Han, Juan ; Wu, Xu ; Pierce, David T. ; Zhao, Julia Xiaojun ( January 2020 , The Analyst)

   An ultrasensitive and versatile assay for biomarkers has been developed using graphene/gold nanoparticles (AuNPs) composites and single-particle inductively-coupled plasma/mass spectrometry (spICP-MS). Thrombin was chosen as a model biomarker for this study. AuNPs modified with thrombin aptamers were first non-selectively adsorbed onto the surface of graphene oxide (GO) to form GO/AuNPs composites. In the presence of thrombin, the AuNPs desorbed from the GO/AuNPs composites due to a conformation change of the thrombin aptamer after binding with thrombin. The desorbed AuNPs were proportional to the concentration of thrombin and could be quantified by spICP-MS. By counting the individual AuNPs in the spICP-MSmore »measurement, the concentration of thrombin could be determined. This assay achieved an ultralow detection limit of 4.5 fM with a broad linear range from 10 fM to 100 pM. The method also showed excellent selectivity and reproducibility when a complex protein matrix was evaluated. Furthermore, the diversity and ready availability of ssDNA ligands make this method a versatile new technique for ultrasensitive detection of a wide variety of biomarkers in clinical diagnostics.« less
2. Graphene/gold nanoparticle composites for ultrasensitive and versatile biomarker assay using single-particle inductively-coupled plasma/mass spectrometry

   Xing, Yuqian ; Han, Juan ; Wu, Xu ; Pierce, David T. ; Zhao, Julia Xiaojun ( October 2020 , Analyste)

   An ultrasensitive and versatile assay for biomarkers has been developed using graphene/gold nanoparticles (AuNPs) composites and single-particle inductively-coupled plasma/mass spectrometry (spICP-MS). Thrombin was chosen as a model biomarker for this study. AuNPs modified with thrombin aptamers were first non-selectively adsorbed onto the surface of graphene oxide (GO) to form GO/AuNPs composites. In the presence of thrombin, the AuNPs desorbed from the GO/AuNPs composites due to a conformation change of the thrombin aptamer after binding with thrombin. The desorbed AuNPs were proportional to the concentration of thrombin and could be quantified by spICP-MS. By counting the individual AuNPs in the spICP-MSmore »measurement, the concentration of thrombin could be determined. This assay achieved an ultralow detection limit of 4.5 fM with a broad linear range from 10 fM to 100 pM. The method also showed excellent selectivity and reproducibility when a complex protein matrix was evaluated. Furthermore, the diversity and ready availability of ssDNA ligands make this method a versatile new technique for ultrasensitive detection of a wide variety of biomarkers in clinical diagnostics.« less
3. Rapid single-molecule digital detection of protein biomarkers for continuous monitoring of systemic immune disorders

   https://doi.org/10.1182/blood.2019004399  

   Song, Yujing ; Sandford, Erin ; Tian, Yuzi ; Yin, Qingtian ; Kozminski, Andrew G. ; Su, Shiuan-Haur ; Cai, Tao ; Ye, Yuxuan ; Chung, Meng Ting ; Lindstrom, Ryan ; et al ( March 2021 , Blood)

   Abstract Digital protein assays have great potential to advance immunodiagnostics because of their single-molecule sensitivity, high precision, and robust measurements. However, translating digital protein assays to acute clinical care has been challenging because it requires deployment of these assays with a rapid turnaround. Herein, we present a technology platform for ultrafast digital protein biomarker detection by using single-molecule counting of immune-complex formation events at an early, pre-equilibrium state. This method, which we term “pre-equilibrium digital enzyme-linked immunosorbent assay” (PEdELISA), can quantify a multiplexed panel of protein biomarkers in 10 µL of serum within an unprecedented assay incubation time of 15more »to 300 seconds over a 104 dynamic range. PEdELISA allowed us to perform rapid monitoring of protein biomarkers in patients manifesting post-chimeric antigen receptor T-cell therapy cytokine release syndrome, with ∼30-minute sample-to-answer time and a sub–picograms per mL limit of detection. The rapid, sensitive, and low-input volume biomarker quantification enabled by PEdELISA is broadly applicable to timely monitoring of acute disease, potentially enabling more personalized treatment.« less
4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmetmore »is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
5. Label-Free Optical Detection of Multiple Biomarkers in Sweat, Plasma, Urine, and Saliva

   https://doi.org/https://doi.org/10.1021/acssensors.9b00301  

   Ray, P. ; Steckl, A. J. ( March 2019 , ACS sensors)

   We report a novel label-free quantitative detection of human performance “stress” biomarkers in different body fluids based on optical absorbance of the biomarkers in the ultraviolet (UV) region. Stress biomarker (hormones and neurotransmitters) concentrations in bodily fluids (blood, sweat, urine, saliva) predict the physical and mental state of the individual. The stress biomarkers primarily focused on in this manuscript are cortisol, serotonin, dopamine, norepinephrine, and neuropeptide Y. UV spectroscopy of stress biomarkers performed in the 190–400 nm range has revealed primary and secondary absorption peaks at near-UV wavelengths depending on their molecular structure. UV characterization of individual and multiple biomarkersmore »is reported in various biofluids. A microfluidic/optoelectronic platform for biomarker detection is reported, with a prime focus toward cortisol evaluation. The current limit of detection of cortisol in sweat is ∼200 ng/mL (∼0.5 μM), which is in the normal (healthy) range. Plasma samples containing both serotonin and cortisol resulted in readily detectable absorption peaks at 203 (serotonin) and 247 (cortisol) nm, confirming feasibility of simultaneous detection of multiple biomarkers in biofluid samples. UV spectroscopy performed on various stress biomarkers shows a similar increasing absorption trend with concentration. The detection mechanism is label free, applicable to a variety of biomarker types, and able to detect multiple biomarkers simultaneously in various biofluids. A microfluidic flow cell has been fabricated on a polymer substrate to enable point-of-use/care UV measurement of target biomarkers. The overall sensor combines sample dispensing and fluid transport to the detection location with optical absorption measurements with a UV light emitting diode (LED) and photodiode. The biomarker concentration is indicated as a function of photocurrent generated at the target wavelength.« less