Biomedical Engineering Application: Disease Diagnosis and Treatment

• Author / Creator

  Zhang, Wei

• Nowadays, with the rapid development of science and technology, human healthcare has become a hot topic and attracts more and more attention. A lot of researchers work on different technologies to contribute to our healthcare no matter disease diagnosis and prognosis or disease treatment. In this thesis, we first introduce two different automated diseases diagnosis approaches and then design a novel gene delivery system that can help treat genetic diseases.
  Artificial intelligence (AI) is a popular research topic now and a lot of researchers are working on it. AI has various successful applications in computer vision, automatic speech recognition, natural language processing, audio recognition, bioinformatics and has been proven to be used in disease diagnosis. Two automated diseases diagnosis approaches are designed for depression and tuberculosis using different machine learning (ML) algorithms, respectively. Depression is one of the most common mental disorders, and rates of depression in individuals continuously increase each year. Traditional diagnosis methods are mostly based on the professional judgment of mental health, which is prone to individual bias. Therefore, it is crucial to design an effective and robust model for automated depression detection. I proposed a multimodal fusion model comprised of text, audio, and video for both depression detection and assessment tasks. For the text modality, a pre-trained sentence embedding algorithm was utilized to extract semantic representation along with bidirectional Long Short-Term Memory (BiLSTM) to predict depression. We also used principal component analysis (PCA) to reduce the dimensionality of the input feature space and fed it into a support vector machine (SVM) to predict depression based on audio modality. For the video modality, XGBoost was employed to conduct both feature selection and depression detection. The final predictions were given by outputs of different modalities with an ensemble voting algorithm. Experiments on the Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WOZ) dataset showed our proposed model outperforms the baseline in both depression detection and assessment tasks and has comparable performance with other existing state-of-the-art depression detection methods.
  Tuberculosis (TB) is a major public health burden affecting about a quarter of the world’s population annually according to the World Health Organization(WHO). Among many control steps, early diagnosis and treatment of TB are critical. Commonly used diagnostic techniques, such as X-ray, TB culture test, TB skin test, and Sputum acid-fast bacillus, have their major limitations. Therefore, new cost-effective diagnostic methods are urgently needed. In this thesis, we used high-resolution liquid chromatography-mass spectrometry (LC-MS) to screen 191 blood samples and discovered kynurenine (Kyn), tryptophan (Trp) and their ratio, Indoleamine 2, 3-dioxygenase (IDO) are excellent TB biomarkers. We employed the logistic regression algorithm to detect pulmonary TB and got excellent performance for classifying health control (HC) vs active tuberculosis (ATB) and latent tuberculosis infection (LTBI) vs ATB. When we used IDO and t-spot to distinguish between nontuberculous lung disease (NTB) and ATB, the results are always satisfying both on the validation set and external independent cohort.
  For the gene delivery system, we synergistically combine non-viral chemical materials, magnetic nanoparticles (MNPs), and physical technique, low-intensity pulsed ultrasound (LIPUS), to achieve efficiently and targeted gene delivery. The MNPs are iron oxide super-paramagnetic nanoparticles, coated with polyethyleneimine (PEI) giving a highly positively charged surface, which is favorable for the binding of genetic materials. Driven by the paramagnetic properties of the MNPs, the application of an external magnetic field increases transfection efficiency while LIPUS stimulation enhances cell viability and permeability. By combining the effect of the external magnetic field and LIPUS, the genetic material (GFP or Cherry Red plasmid) can enter the cells. The flow cytometry results showed that by using just a magnetic field to direct the genetic material, the transfection efficiency on HEK 293 cells that were treated by our MNPs coupled with LIPUS stimulation, increased a lot and was much higher than the positive control (Lipofectamine 2000) and showed less toxicity. Cell viability after transfection was greatly promoted compared to the standard transfection technique.

• Subjects / Keywords

  • Machine Learning
  • Disease Diagnosis
  • Depression
  • Pulmonary Tuberculosis
  • Gene Delivery

• Graduation date

  Spring 2022

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-1waq-px35

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.