Abraham Lin unpaid staff

Research Abraham Lin

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Expertise

I am a Postdoctoral Researcher at the University of Antwerp in the field of non-thermal plasma medicine. My research aims to understand plasma-cell interactions and develop non-thermal plasma systems for biomedical applications. Topics of Investigation: 1) Electrical and chemical characterization of plasma treatment regimes for biological applications (e.g. cancer immunotherapy, surface decontamination, neuroregeneration) 2) Development of plasma devices and systems for medical applications including robotics and neural network integration 3) Modulation of tumor-expressing immunosuppressive signals via wet lab experiments and computational modelling 4) Plasma-induced intracellular pathways for sensitivity and resistance development via cancer bioinformatics tools 5) Plasma effects on immunogenic cell death (ICD), tumor microenvironment (TME), epithelial–mesenchymal transition (EMT), and metastasis 6) Combination cancer therapies with plasma 7) Plasma-induced cell death mechanisms

Development of a plasma device for rapid disinfection of contaminated hospital materials: Hospital‐Use Plasma Unit (HUP‐Unit). 01/09/2022 - 31/08/2023

Abstract

The SARS‐CoV‐2 pandemic has exposed how unprepared our society was in preventing the propagation of highly infectious diseases, protecting the healthcare providers and patients, and efficiently organizing the logistics, while managing large numbers of patients. For the past two years, hospitals have battled to mitigate the spread of the virus in their facilities, a challenge that included the need to daily dispose of thousands of unused, individually‐packaged medical products that could not be disinfected with the traditional disinfection methods. On average, the Antwerp University Hospital (UZA) produced around 250,000 kg of medical waste per year. In 2021, the amounts of medical waste increased by more than 10% compared to the pre‐COVID period. Globally, the pandemic not only increased the cost for hospitals, but it also increased the generation of waste around the world by 400‐500%. Moreover, at the height of the pandemic, there was even a critical shortage of medical supplies. Therefore, this was not only an environmental and financial issue, but also a serious healthcare burden. In order to be better prepared for future pandemics, we have prepared a mission‐oriented innovation project, which responds to a specific request from the Intensive Care Unit (ICU) at UZA. In our IOF‐POC CREATE project here, we aim to develop a non‐thermal plasma (NTP)‐based disinfection device to rapidly eliminate viruses from unused, individually‐packaged medical products: the hospital‐use plasma unit (HUP‐unit). Our HUP‐device will utilize a completely innovative cylindrical geometry design feature with materials to be disinfected, to enhance NTP generation and contact with a large volume of material, and ensure complete, uniform treatment. Indeed, we have to design a completely novel NTP device concept, which we will categorize as a 'moving‐bed' dielectric barrier discharge (DBD). By using the individually‐packaged hospital products as part of the NTP generation mechanism, our 'moving‐bed' DBD HUP‐unit offers a scalable solution to provide rapid disinfection in the hospital. Based on our understanding of plasma dynamics and computational plasma simulations, we have developed this theoretical design, but the feasibility of creating a working prototype remains to be seen. Therefore, in this IOF‐POC CREATE project, we will produce and validate our prototype HUP‐unit in the lab. If successful, our HUP‐unit will allow us to: i) mitigate shortages in individually‐packaged medical products; ii) reduce the waste produced by healthcare facilities and associated waste management cost; iii) reduce the incidence of hospital‐acquired infections.

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Award 'Robert Oppenheimer' - 2021. 01/12/2021 - 31/12/2022

Abstract

Non-thermal plasma (NTP) technology has been investigated for its anti-cancer and immunogenic effects for cancer therapy. NTP systems for biomedical applications have been thoroughly characterized for in vitro systems, which include component analysis (e.g. pulsed-electric fields, UV radiation), gas-phase measurements of excited and reactive species, and liquid chemistry studies. Already it has been shown to induce immunogenic cell death (ICD), a highly favorable type of cell death for cancer immunotherapy that is characterized by the release of 'danger signals', known to stimulate key immune cells for initiating an adaptive immune response. Despite promising advances in plasma therapy for cancers (a field now coined as 'plasma oncology'), several gaps in knowledge still remain which hinder translation of this technology including: 1) determining the proper 'NTP treatment dose' and 2) defining effective treatment schedules for combination therapies. In this study, we aim to correlate NTP treatment dose to anti-cancer effect and determine the efficacy of NTP treatment in strategic combination other therapies. To investigate this, an in vivo cancer model will be used in order take into account the complex interactions between NTP, the tumor, the tumor microenvironment, and the immune system. We will use the B16-F10 melanoma cell line with syngeneic C57BL/6J mice. Results of these experiments will provide insight into standardizing NTP treatment and help streamline adoption of NTP technologies into the clinic.

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OrBITS Platform: A Cloud-Based Image Analysis and Drug Screening Service. 01/09/2021 - 31/08/2022

Abstract

Advances in artificial intelligence (AI) have facilitated the development of solutions for numerous industrial, academic, and research challenges. We have developed a software, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), for image-based analysis of 2D and 3D cancer cell cultures using computer vision technology combined with a convolutional network, machine learning approach (priority patents filed). As such, our OrBITS software can provide 2 major services: 1) software as a service for image-based research analysis and 2) high-throughput screening of therapeutic compounds. The technology and services are already of high interest to both industrial and academic partners, and we have begun performing image analysis and drug screening services for both internal and external groups. However, in order to facilitate and expand our service capacity, some technological and operational gaps must be met. Notably, we require dedicated personnel to perform drug screening of compounds provided by the clients, conduct routine maintenance of biological cultures, and integrate our current workflow with recently acquired state-of-the-art equipment (e.g. Tecan Spark Cyto live imager, Tecan D300e drug printer, Opentron OT-2 pipetting robot). The documentation and standardization of this workflow will streamline future expansion and increase service capacity. Furthermore, we aim to migrate our software to a cloud-based system to centralize data storage and training of the software AI network. Setting up this cloud system will resolve many issues associated with image-based analysis (e.g. inadequate data storage and traffic, inefficient and incremental software updates, absent data sets), which are described by our industrial and academic collaborators. Setting up this cloud-based system will further allow our AI software training unit to stay up-to-date and relevant with the fast past of scientific and translational research, thus keeping our image-based analysis and drug screening services at the cutting edge. Lastly, we aim to work with a dedicated business developer to perform market analysis, optimize pricing and advertisement strategies, and develop a business plan for our service platform, as currently we are working on a collaborative (case-by-case) modality. The technology and business development aims proposed here will enable the establishment of the OrBITS Platform to become a self-sustaining service provider with the ability to scale as the clients and needs increase.

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Investigation of non-thermal plasma therapy with first-line treatments of recurrent and metastatic head and neck squamous cell carcinoma: a novel combination with platinum-based chemotherapy and immunotherapy. 01/11/2020 - 31/10/2024

Abstract

Head and neck squamous cell carcinoma (HNSCC) is the 6th most common cancer worldwide, and advanced HNSCC patients often experience relapse or metastasis (R/M HNSCC) resulting in dismal prognoses. These patients receive immunotherapy (ICI) alone or in combination with platinum-based chemotherapeutics (PLAT) as first-line treatment. While these combination treatments have some clinical benefit, they are limited by low response rates and severe side effects in already weakened patients. To address this, I will investigate a novel combination strategy with non-thermal plasma (NTP). NTP, an ionised gas, is a localised therapy that induces immunogenic cancer cell death (ICD), which can activate the patient's anti-cancer immunity. To date, no adverse side effects have been reported with the clinical use of NTP. Therefore, we hypothesize that combining NTP with PLAT/ICI will be well-tolerated and improve clinical efficacy in R/M HNSCC. In this project, I will perform 3D in vitro experiments on cell lines and primary patient material, and two mouse models will be used to validate the safety and clinical efficacy of this combination strategy. The successful completion of my project will help integrate NTP into current first-line therapies of R/M HNSCC as a new combination strategy to improve treatment efficacy and quality of life for those patients. This study will also be a stepping stone towards a broader implementation of NTP technology in other cancer types.

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From physical plasma to cellular pathway: a multi-disciplinary approach to unravel the response pathways induced by nonthermal plasma for cancer therapy. 01/10/2020 - 30/09/2023

Abstract

Cancer therapy has been rapidly transforming in part due to progress in seemingly unrelated fields. This has led to the development of profound tools for studying cancer pathways and innovative therapies. Non-thermal plasma (NTP) is a novel treatment that has been emerging for cancer immunotherapy. Bioinformatics is another field experiencing rapid growth, as the ability to collect and process large amounts of 'omics' data has become increasingly accessible. In the context of oncology, this has led to success in elucidating therapy-induced pathways and therapy target discovery. Therefore, in my project, I will use a combination of experimental and bioinformatics approaches to study fundamental effects of NTP on cancerous cells: 1) mechanisms driving cell sensitivity and 2) immunological changes to be exploited for combination therapy. In vitro experiments will be performed to categorize cells into sensitivity groups based on NTP-induced cell death; cellular redox and death modalities will also be studied. Transcriptome analysis and bioinformatics techniques will be used to uncover the activated pathways. Signature gene sets from transcriptome data will be studied to obtain a more comprehensive picture of the immunologic changes in NTP-treated cells. All in silico results will be validated experimentally. Success of this project will benefit multiple science fields and open new lines of research while providing insight into underlying mechanisms of NTP-induced cancer response.

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Development of a precision clinical plasma treatment system using environmental sensing and robotic controls. 01/07/2020 - 31/12/2021

Abstract

In the context of clinical treatment of cancers, a major challenge involves the precise delivery of therapeutic agents to the tumor while limiting off-target effects. This is true for multiple treatment modalities including radiotherapy and non-thermal plasma (NTP) therapy. Hence, the main focus of this research project is to introduce the design of a supervisory control structure into a patient-in-the-loop therapeutic application. This system will be developed by integrating 3 components: 1) environmental sensors, 2) a robotic control unit, and 3) a therapeutic device (NTP generator). Since NTP treatment is highly dependent on parameters such as treatment time, application distance, etc. a feedback approach is necessary to compensate for tumor motion induced by the patient during treatment (e.g. respiration). To this end, artificial intelligence tools, including neural networks, will be employed to model the dynamic disturbances of the tumor. The developed self-learning artificial intelligence models will be embedded within model-based controllers to predict and minimize the effect of disturbances. Performance of the control structure will be validated with real-time experiments of plasma delivery in biological systems. The proposed methodology has the potential to improve the precision and accuracy of clinical NTP treatment and consequently minimize damage to healthy tissue.

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Investigating fundamental plasma effects on tumor microenvironment through development of a controlled plasma treatment system for clinical cancer therapy. 01/01/2020 - 31/12/2023

Abstract

Non-thermal plasma technology is gaining attention as a novel cancer therapeutic. In the clinic, plasma has been applied to patients with head and neck squamous cell carcinoma, the 6th most common cancer worldwide with long-term survival below 50%. While initial studies are promising (e.g. partial remission, decreased levels of pain, no reported side-effects), a critical issue became apparent when translating plasma technology from the laboratory to the clinic: low reproducibility of treatment. Current plasma devices are handheld and require the operator (clinician) to make a judgement as to how long to treat the patient. This leads to large variability, which becomes even more pronounced when the clinician must move the plasma applicator over a large area of treatment. We aim to develop a robotic plasma treatment system that will enable us to investigate fundamental plasma effects on the tumor for clinical cancer therapy. We will use multiple sensors to detect the patient environment, artificial intelligence to 'learn and predict' patient disturbance patterns (e.g. breathing), and a robotic arm to deliver plasma. We will test our developed system in 3D and mouse cancer models and study the consequence of plasma treatment in the tumor, and to the survival of the animal. Altogether, our project will progress plasma technology for clinical translation by elucidating previously unknown biological responses to plasma and addressing issues in the clinic.

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Elucidating mechanisms of plasma-induced immunogenic cancer cell death and determining efficacy to elicit anti-tumor immunity: An experimental and computational study 01/10/2017 - 30/09/2020

Abstract

Cancer is still a major healthcare issue and many conventional therapies overlook the role of the immune system in the resolution of this disease. Non-equilibrium plasma is emerging as a novel cancer treatment. Promising results showed that plasma can kill cancerous cells and stimulate immune cells, but experiments have largely been in vitro. To address this, we will perform in vivo mouse experiments to validate therapeutic efficacy of plasma and assess immune responses required to eliminate cancer. While treatment may be efficacious, the underlying mechanisms of plasma cancer therapy are not fully understood. When plasma is generated, a complex environment of electric fields, ultraviolet light, charged particles and neutral species is produced. To date, it is unclear which plasma components and reactive species play the major role in cancer therapy. Therefore, we will also delineate the components of plasma that elicit anti-cancer responses. In addition, we will develop a computational model that will predict the behavior of plasma-generated species in liquid, as cells and tissue are treated in the presence of liquid. This will be compared to chemical analysis of plasma-treated liquid for validation and establishment of crucial species for cancer therapy. Altogether, this project will support development of plasma technology for cancer immunotherapy and provide insight into the underlying mechanisms.

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