Modeling small objects under uncertainties : novel algorithms and applications.

Active Shape Models (ASM), Active Appearance Models (AAM) and Active Tensor Models (ATM) are common approaches to model elastic (deformable) objects. These models require an ensemble of shapes and textures, annotated by human experts, in order identify the model order and parameters. A candidate object may be represented by a weighted sum of basis generated by an optimization process. These methods have been very effective for modeling deformable objects in biomedical imaging, biometrics, computer vision and graphics. They have been tried mainly on objects with known features that are amenable to manual (expert) annotation. They have not been examined on objects with severe ambiguities to be uniquely characterized by experts. This dissertation presents a unified approach for modeling, detecting, segmenting and categorizing small objects under uncertainty, with focus on lung nodules that may appear in low dose CT (LDCT) scans of the human chest. The AAM, ASM and the ATM approaches are used for the first time on this application. A new formulation to object detection by template matching, as an energy optimization, is introduced. Nine similarity measures of matching have been quantitatively evaluated for detecting nodules less than 1 em in diameter. Statistical methods that combine intensity, shape and spatial interaction are examined for segmentation of small size objects. Extensions of the intensity model using the linear combination of Gaussians (LCG) approach are introduced, in order to estimate the number of modes in the LCG equation. The classical maximum a posteriori (MAP) segmentation approach has been adapted to handle segmentation of small size lung nodules that are randomly located in the lung tissue. A novel empirical approach has been devised to simultaneously detect and segment the lung nodules in LDCT scans. The level sets methods approach was also applied for lung nodule segmentation. A new formulation for the energy function controlling the level set propagation has been introduced taking into account the specific properties of the nodules. Finally, a novel approach for classification of the segmented nodules into categories has been introduced. Geometric object descriptors such as the SIFT, AS 1FT, SURF and LBP have been used for feature extraction and matching of small size lung nodules; the LBP has been found to be the most robust. Categorization implies classification of detected and segmented objects into classes or types. The object descriptors have been deployed in the detection step for false positive reduction, and in the categorization stage to assign a class and type for the nodules. The AAMI ASMI A TM models have been used for the categorization stage. The front-end processes of lung nodule modeling, detection, segmentation and classification/categorization are model-based and data-driven. This dissertation is the first attempt in the literature at creating an entirely model-based approach for lung nodule analysis

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

Funding Information: Funding Statement: This work was funded by the Researchers Supporting Project Number (RSP2023R 509) King Saud University, Riyadh, Saudi Arabia. This work was supported in part by the Higher Education Sprout Project from the Ministry of Education (MOE) and National Science and Technology Council, Taiwan, (109-2628-E-224-001-MY3), and in part by Isuzu Optics Corporation. Dr. Shih-Yu Chen is the corresponding author. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD

NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS

Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms. A critical step in defining gene structures and mRNA transcript variants is to accurately identify splice junctions. In the first work, we built the first deep learning-based splice junction classifier, DeepSplice. It outperforms the state-of-the-art classification tools in terms of both classification accuracy and computational efficiency. To uncover transcription factors governing metabolic reprogramming in non-small-cell lung cancer patients, we developed TFmeta, a machine learning approach to reconstruct relationships between transcription factors and their target genes in the second work. Our approach achieves the best performance on benchmark data sets. In the third work, we designed deep learning-based architectures to perform lesion detection in both 2D and 3D whole mammogram images

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Lung Pattern Analysis using Artificial Intelligence for the Diagnosis Support of Interstitial Lung Diseases

Interstitial lung diseases (ILDs) is a group of more than 200 chronic lung disorders characterized by inflammation and scarring of the lung tissue that leads to respiratory failure. Although ILD is a heterogeneous group of histologically distinct diseases, most of them exhibit similar clinical presentations and their diagnosis often presents a diagnostic dilemma. Early diagnosis is crucial for making treatment decisions, while misdiagnosis may lead to life-threatening complications. If a final diagnosis cannot be reached with the high resolution computed tomography scan, additional invasive procedures are required (e.g. bronchoalveolar lavage, surgical biopsy). The aim of this PhD thesis was to investigate the components of a computational system that will assist radiologists with the diagnosis of ILDs, while avoiding the dangerous, expensive and time-consuming invasive biopsies. The appropriate interpretation of the available radiological data combined with clinical/biochemical information can provide a reliable diagnosis, able to improve the diagnostic accuracy of the radiologists. In this thesis, we introduce two convolutional neural networks particularly designed for ILDs and a training scheme that employs knowledge transfer from the similar domain of general texture classification for performance enhancement. Moreover, we investigate the clinical relevance of breathing information for disease classification. The breathing information is quantified as a deformation field between inhale-exhale lung images using a novel 3D convolutional neural network for medical image registration. Finally, we design and evaluate the final end-to-end computational system for ILD classification using lung anatomy segmentation algorithms from the literature and the proposed ILD quantification neural networks. Deep learning approaches have been mostly investigated for all the aforementioned steps, while the results demonstrated their potential in analyzing lung images

Controlled Co-Delivery of Chemotherapeutic Drugs using Stimuli-Responsive Nano-formulations for Pancreatic Cancer

No abstract

Development of a Novel Humanized Single Chain Antibody-Streptococcal Superantigen-Derived Immunotherapy Targeting the 5T4 Oncofetal Antigen

Superantigens (SAgs) are microbial toxins that cross-link T cell receptors with major histocompatibility complex (MHC) class II (MHC II) molecules leading to the activation of large numbers of T cells. Herein, the development and preclinical testing of a novel tumour-targeted SAg (TTS) therapeutic built using the streptococcal pyrogenic exotoxin C (SpeC) SAg and targeting cancer cells expressing the 5T4 tumour-associated antigen (TAA) was described. To inhibit potentially harmful widespread immune cell activation, a SpeC mutation within the high-affinity MHC II binding interface was generated (SpeCD203A) that demonstrated a pronounced reduction in mitogenic activity, yet this mutant could still induce immune cell-mediated cancer cell death in vitro. To target 5T4+cancer cells, a humanized single-chain variable fragment (scFv) antibody to recognize 5T4 (scFv5T4) was engineered. Specific targeting of scFv5T4 was verified. SpeCD203A used to scFv5T4 maintained the ability to activate and induce immune cell-mediated cytotoxicity of colon cancer cells. Using a xenograft model of established human colon cancer, it was demonstrated that the SpeC-based TTS was able to control the growth and spread of large tumours in vivo. This required both TAA targeting by scFv5T4 and functional SAg activity. These studies lay the foundation for the development of streptococcal SAgs as \u27next-generation\u27 TTSs for cancer immunotherapy

Exploration Of Cancer Proliferative Signaling In Chemotherapy Drug Resistance And Mdig-Induced Tumorigenesis

ABSTRACT EXPLORATION OF CANCER PROLIFERATIVE SIGNALING IN CHEMOTHERAPY DRUG RESISTANCE AND MDIG-INDUCED TUMORIGENESIS by KAI WU August 2016 Advisor: Dr. Fei Chen Major: Pharmaceutical Sciences Degree: Doctor of Philosophy Aberrant intracellular signaling pathway is one of the major driving forces of malignancy through multiple stages of human cancers. Our study demonstrates that in cancer cells, the signaling pathways are profoundly and actively intertwined with each other so they can synergistically affect cell biology, including promoting development of malignancy and compensating the loss of proliferation or survival signals in responses to anti-tumor drug. Moreover, cancer cells can also adopt “non-canonical” mechanisms to modulate the activities of key protein regulators so the whole signaling pathway is strengthened. In the first project, we performed integrative studies to investigate the oncogenic role of a WTC (World Trade Center) dust-induced regulator, mdig, in multiple myeloma (MM). MM is a malignancy of plasma cells located within bone-marrow compartment and several post 9/11 health surveillance programs and epidemiological studies suggested an increased incidence rate of multiple myeloma (MM) among the individuals who intensively exposed to WTC dust. However, the potential connections between WTC dust and MM remain to be elucidated. Expressions of mdig were investigated in bronchial epithelial cells, B cells, MM cell lines and in the bone marrow specimens from the MM patients. We found that WTC dust is potent in inducing mdig protein and/or mRNA in bronchial epithelial cells, B cells and MM cell lines. An increased mdig expression in MM bone marrow was observed, which is associated with the disease progression and prognosis of the MM patients. Using integrative genomics and proteomics approaches, we further demonstrated that in MM cell lines, mdig directly interacts with c-myc and JAK1, which contributes to hyperactivation of the JAK-STAT3 signaling important for the pathogenesis of MM. Genetic silencing of mdig reduced activity of the major downstream effectors in the JAK-STAT3 pathway. Our results indicate that WTC dust induced-mdig overexpression bridges c-myc pathway and STAT3 pathway in MM, which is essential for the tumorigenesis of MM. In the second project, we focused on the underlying mechanisms of both primary and secondary resistance to EGFR TKI (Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor), including gefitinib, in NSCLC (Non-small cell lung cancer), which are two major obstacles compromising the clinical success of targeted therapy. In the part studying primary resistance, we observed that JAK2-STAT3 signaling axis in non-sensitive lung cancer cell lines is highly refractory to gefitinib treatment. Follow-up experiments further revealed a unique STAT3-dependent Akt restoration pattern in non-sensitive lung cancer cells, which impairs the efficacy of gefitinib. Mechanistically, gefitinib increased physical binding between EGFR and STAT3, which de-repressed STAT3 from SOCS3, an upstream suppressor of STAT3. Such a de-repression of STAT3 in turn fostered Akt activation. Genetic or pharmacological inhibition of STAT3 abrogated Akt activation and combined gefitinib with STAT3 inhibition synergistically reduced the growth of the tumor cells. In order to study the mechanisms of secondary resistance (acquired resistance), we established a gefitinib-resistant lung cancer (GR) cell line. Through profiling the gene expression pattern and investigating the alterations of intracellular signaling pathways, we discovered multiple resistance mechanisms in GR cells, including a unique hyperactivation pattern of STAT3. A rational co-inhibition of STAT3 and EGFR simultaneously suppressed several survival-related pathways in GR cells. As a result, such combinational targeting re-sensitized the GR cells to gefitinib treatment. Taken together, our study has unraveled novel mechanisms of resistance to EGFR TKI in lung cancer and has provided important information for rationale-based combinational targeting strategies to overcome drug resistance

Multi-Omics Investigation of Tumor Heterogeneity, Oncogenic Signaling, and Treatment Response in Human Cancers

Cancer is a highly complex disease with aberrations at the genetic, epigenetic, transcriptomic, and protein levels that drove its phenotypic diversity. Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, comprising roughly 80% of cases. To define the epigenetic and transcriptomic regulation of ccRCC at the single nucleus (sn) level, we performed snRNA-seq and snATAC-seq in 34 and 28 samples respectively, including primary tumors and normal adjacent tissues, and matched them with bulk proteogenomics data. We identified tumor-specific markers and tumor subpopulations using snRNA-seq, which demonstrated diverse pathway activity within and across patients. PBRM1 and BAP1 are two of the most frequently mutated genes in ccRCC, and both encode epigenetic regulators. However, the consequences of BAP1 and PBRM1 mutations on chromatin accessibility and downstream transcriptional networks remain largely unknown. Utilizing the combined analysis of snATAC-seq and snRNA-seq, we dissected chromatin accessibility and transcriptome changes associated with BAP1 and PBRM1 mutations, illuminating molecular alterations underlying differential phenotypes between BAP1- and PBRM1-mutant patients. For the treatment of RCC, patients with metastatic or inoperable tumors typically receive systemic treatment with targeted therapy and/or immunotherapy. Although these drugs have been proven effective to some extent, resistance eventually develops, and combinational therapy will be necessary to overcome such resistance. Patient-derived xenograft (PDX) models have proven valuable in studying treatment mechanisms and novel therapeutics for cancer, including renal cell carcinoma. Hence, we performed a series of drug tests on a set of RCC PDX models, in which cabozantinib and sapanisertib are the two most effective drugs, and found the combination of two drugs is effective for all six models. We collected PDX tumors at baseline and under treatments and performed bulk whole-exome sequencing, bulk RNA-seq, bulk proteomics and phosphoproteomics, and snRNA-seq. We revealed the pathways affected by the combination therapy and identified treatment-affected proteins that are associated with patient survival. We also identified baseline protein markers that may serve to predict treatment response, such as MET, with support from snRNA-seq data. This study proposed a potential new combination for RCC patients and revealed potential molecular alterations underlying tumor reduction induced by the combination treatment

Implantable antineoplastic-loaded antibody functionalized nanomicelles for human ovarian carcinoma cell targeting by molecular and in vivo investigations

A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, in fulfillment of the requirements for the degree of Doctor of Philosophy. Johannesburg, 2017.Epithelial ovarian cancer (EOC) is the most insidious, fatal gynaecological malignancy that accounts for millions of deaths in female population. Globally, the five-year survival period is between 15–20% for patience with clinical late stage ovarian malignancy in spite of surgery and platinum treatment. This study aimed to design and develop a novel drug delivery system employing antibody-ligand functionalized antineoplastic-loaded nanomicelles encapsulated with Chitosan-Poly(vinylpyrrolidone)-Poly (N-isopropylacrylamide) (C-P-N) hydrogel to form an in situ forming Implant (ISFI) which is responsive to temperature (body temperature 370C), pH (peritoneal fluid pH ~6.6) for cancer cell-targeting following intraperitoneal implantation to increase the residence time of the nanomicelles at tumor sites over a period exceeding one month, enhancing tumor uptake of drugs and prevent recurrence and chemo-resistance. An engineered-fabricated nanomicelle system (MTX)NM’s was formed by a novel thermal ring opening co-polymerization of hydrophobic L-Aspartic acid-N-carboxyanhydride onto the backbone of hydrophilic PNIPAAm-NH2 to form amphiphilic poly (N-isopropylacrylamide)-block-poly (aspartic acid) (PNIPAAm-b-PAsp) copolymer. PNIPAAm-b-PAsp copolymer exhibited competency in forming amphiphilic nanomicelles broadening areas of its nano-application in implantable drug delivery. Utilizing (PNIPAAm-b-PAsp) micelles, variables for an experimental design were obtained. A Face-Centred Central Composite experimental design approach generated thirteen formulations thoroughly screened in terms of variables (Amount of copolymer (mg) and homogenizer speed (rpm)) affecting responses (size (nm), drug entrapment efficiency (%) and mean dissolution time). Nanomicelles with sizes ranging from 51.67 to 76.45 nm, a yield/recovery of 46.8–89.8 mg and polydispersity index (PDI ≤ 0.5) were obtained. Drug encapsulation efficacy (DEE) was initially (65.3 ±0.5%) and was ultimately optimized to 80.6±0.3%. Optimal nanomicelle formulation was surface-functionalized with anti-MUC 16 (antibody) for the targeted delivery of methotrexate to human ovarian carcinoma (NIH:OVCAR-5) cells that expressed MUC 16 as a preferential form of intraperitoneal ovarian cancer chemotherapy. Furthermore, cross-linked interpenetrating C-P-N hydrogel was synthesized for the preparation of an in situ forming implant (ISFI) for ovarian carcinoma treatment. ISFI was fabricated by encapsulating a nanomicelle comprising of anti-MUC 16 (antibody) functionalized methotrexate (MTX)-loaded PNIPAAm-b-PAsp nanomicelles (AF(MTX)NM’s) within C-P-N hydrogel. Ex vivo endocytotic internalization via confocal fluorescent microscopy and intracellular imaging studies in (NIH:OVCAR-5) cells showed positive cellular uptake in both optimal (MTX)NM’s and (AF(MTX)NM’s) with exemplary results for (AF(MTX)NM’s) due to improved intracellular delivery. Chemotherapeutic efficacy of various treatment protocols including ISFI were invivo tested on the optimal Athymic nude mouse model that was intraperitoneally and subcutaneously induced with human ovarian carcinoma cells (NIH:OVCAR-5) and tumors with associated severe ascites grew within 10 days of inoculation. Results demonstrated tumor regression including reduction in mouse weight and tumor size, as well as a significant (p<0,05) reduction in mucin 16 levels in serum and ascitic fluid and improved survival of mice after treatment with the experimental anti-MUC16/CA125 antibody-bound nanotherapeutic implant drug delivery system (p<0,05). Low quantities of drug were found in the plasma but elevated levels were observed in the peritoneal cavity. In addition, the drug was present in the surrounding tissue in high concentration even after 10 days. Based on the results of this study, the antibody-bound nanotherapeutic implant drug delivery system should be considered a potentially important immuno-chemotherapeutic agent that can be employed in human clinical trials of advanced, and/or recurring, metastatic epithelial ovarian cancer (EOC). The development of this novel implantable drug delivery system may circumvent the treatment flaws experienced with conventional systemic therapies, effectively manage recurrent disease and ultimately prolong disease-free intervals in ovarian cancer patients.LG201