Translational research : the journal of laboratory and clinical medicine
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Inflammatory and immune signaling has been documented as a root cause of many cardiovascular pathologies. In this review, we explore the emerging role of tumor necrosis factor receptor-associated factor 6 (TRAF6)-nuclear factor kappa B (NF-κB) signaling axis in atherosclerosis, ischemic heart disease, pathologic cardiac hypertrophy or heart failure, myocarditis, and sepsis-induced cardiomyopathy. We discuss the current understanding of cardiac inflammation in heart disease, present the TRAF6 signaling axis in the heart, then summarize what is known about TRAF6 in pathophysiology of heart disease including proof-of-concept studies that identify the utility of blocking TRAF6 to attenuate cardiac dysfunction, which suggests that TRAF6 is a novel, druggable target in treating cardiovascular disease incurred by inflammatory processes.
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It is assumed that platelets in diseased conditions share similar properties to platelets in healthy conditions, although this has never been examined in detail for myocardial infarction (MI). We examined platelets from patients with ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) compared with platelets from healthy volunteers to evaluate for differences in platelet phenotype and function. Platelet activation was examined and postreceptor signal transduction pathways were assessed. ⋯ Matrix metalloproteinase 9 (MMP9) protein content and enzymatic activity were several-fold greater in platelets with MI than in control. Mean plasma MMP9 concentration in patients with MI distinguished between STEMI and NSTEMI (area under curve [AUC] 75% [confidence interval (CI) 60-91], P = 0.006) which was superior to troponin T (AUC 66% [CI 48-85, P = 0.08), predicting STEMI with 80% sensitivity (95% CI 56-94), 90% specificity (CI 68-99), 70% AUC (CI 54-86, P < 0.0001), and NSTEMI with 50% sensitivity (CI 27-70), 90% specificity (CI 68-99), 70% AUC (CI 54-86, P = 0.03). Platelets from patients with STEMI and NSTEMI show differences in platelet surface receptor activation and postreceptor signal transduction, suggesting the healthy platelet phenotype in which antiplatelet agents are often evaluated in preclinical studies is different from platelets in patients with MI.
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Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. ⋯ We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.
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Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. ⋯ We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data-a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions.
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Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. ⋯ Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.