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Clinical Gastroenterology and... Sep 2020
Topics: Alcoholism; Deep Learning; Disease Progression; Humans; Liver Diseases
PubMed: 32062043
DOI: 10.1016/j.cgh.2020.02.012 -
Alimentary Pharmacology & Therapeutics Jul 2017
Topics: Disease Progression; Humans; Proctitis
PubMed: 28621077
DOI: 10.1111/apt.14129 -
Frontiers in Immunology 2023Dendritic cells (DCs) are antigen presenting cells that link innate and adaptive immunity. DCs have been historically considered as the most effective and potent cell... (Review)
Review
Dendritic cells (DCs) are antigen presenting cells that link innate and adaptive immunity. DCs have been historically considered as the most effective and potent cell population to capture, process and present antigens to activate naïve T cells and originate favorable immune responses in many diseases, such as cancer. However, in the last decades, it has been observed that DCs not only promote beneficial responses, but also drive the initiation and progression of some pathologies, including inflammatory bowel disease (IBD). In line with those notions, different therapeutic approaches have been tested to enhance or impair the concentration and role of the different DC subsets. The blockade of inhibitory pathways to promote DCs or DC-based vaccines have been successfully assessed in cancer, whereas the targeting of DCs to inhibit their functionality has proved to be favorable in IBD. In this review, we (a) described the general role of DCs, (b) explained the DC subsets and their role in immunogenicity, (c) analyzed the role of DCs in cancer and therapeutic approaches to promote immunogenic DCs and (d) analyzed the role of DCs in IBD and therapeutic approaches to reduced DC-induced inflammation. Therefore, we aimed to highlight the "yin-yang" role of DCs to improve the understand of this type of cells in disease progression.
Topics: Humans; Dendritic Cells; Adaptive Immunity; Neoplasms; Inflammatory Bowel Diseases; Disease Progression
PubMed: 38239364
DOI: 10.3389/fimmu.2023.1321051 -
International Journal of Molecular... Feb 2024Prostate cancer (PCa) is the second most common cancer and the fifth highest cause of cancer-related death among men in the world [...].
Prostate cancer (PCa) is the second most common cancer and the fifth highest cause of cancer-related death among men in the world [...].
Topics: Male; Humans; Prostatic Neoplasms; Disease Progression
PubMed: 38473699
DOI: 10.3390/ijms25052451 -
Clinical and Translational Medicine Jul 2023
Topics: Humans; Prognosis; Disease Progression; Liver Diseases, Alcoholic
PubMed: 37381164
DOI: 10.1002/ctm2.1322 -
IEEE Transactions on Bio-medical... Aug 2021Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across...
Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.
Topics: Disease Progression; Forecasting; Humans; Neural Networks, Computer; Phenotype
PubMed: 33259292
DOI: 10.1109/TBME.2020.3041815 -
American Journal of Respiratory Cell... Aug 2022
Topics: Cilia; Disease Progression; Humans; Idiopathic Pulmonary Fibrosis
PubMed: 35612966
DOI: 10.1165/rcmb.2022-0201ED -
The Journal of Thoracic and... Jun 2018
Topics: Acute Kidney Injury; Cardiac Surgical Procedures; Disease Progression; Humans
PubMed: 29551539
DOI: 10.1016/j.jtcvs.2018.02.023 -
Heart (British Cardiac Society) Nov 2023
Topics: Humans; Heart Rate; Proteomics; Disease Progression
PubMed: 38011936
DOI: 10.1136/heartjnl-2023-323672 -
Nephrology, Dialysis, Transplantation :... Aug 2021
Topics: Disease Progression; Humans; Quality of Life; Renal Insufficiency, Chronic
PubMed: 33508092
DOI: 10.1093/ndt/gfab006