Molecular oxygen (O2) is essential for most biological reactions in mammalian cells. When the intracellular oxygen content decreases, it is called hypoxia. The process of hypoxia is linked to several biological processes, including pathogenic microbe infection, metabolic adaptation, cancer, acute and chronic diseases, and other stress responses. The mechanism underlying cells respond to oxygen changes to mediate subsequent signal response is the central question during hypoxia. Hypoxia-inducible factors (HIFs) sense hypoxia to regulate the expressions of a series of downstream genes expression, which participate in multiple processes including cell metabolism, cell growth/death, cell proliferation, glycolysis, immune response, microbe infection, tumorigenesis, and metastasis. Importantly, hypoxia signaling also interacts with other cellular pathways, such as phosphoinositide 3-kinase (PI3K)-mammalian target of rapamycin (mTOR) signaling, nuclear factor kappa-B (NF-κB) pathway, extracellular signal-regulated kinases (ERK) signaling, and endoplasmic reticulum (ER) stress. This paper systematically reviews the mechanisms of hypoxia signaling activation, the control of HIF signaling, and the function of HIF signaling in human health and diseases. In addition, the therapeutic targets involved in HIF signaling to balance health and diseases are summarized and highlighted, which would provide novel strategies for the design and development of therapeutic drugs.

Background: Ultrasound (US) examination is helpful in the differential diagnosis of thyroid nodules (malignant vs. benign), but its accuracy relies heavily on examiner experience. Therefore, the aim of this study was to develop a less subjective diagnostic model aided by machine learning. Methods: A total of 2064 thyroid nodules (2032 patients, 695 male; M age = 45.25-13.49 years) met all of the following inclusion criteria: (i) hemi-or total thyroidectomy, (ii) maximum nodule diameter 2.5 cm, (iii) examination by conventional US and real-time elastography within one month before surgery, and (iv) no previous thyroid surgery or percutaneous thermotherapy. Models were developed using 60% of randomly selected samples based on nine commonly used algorithms, and validated using the remaining 40% of cases. All models function with a validation data set that has a pretest probability of malignancy of 10%. The models were refined with machine learning that consisted of 1000 repetitions of derivatization and validation, and compared to diagnosis by an experienced radiologist. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Results: A random forest algorithm led to the best diagnostic model, which performed better than radiologist diagnosis based on conventional US only (AUC = 0.924 [confidence interval (CI) 0.895-0.953] vs. 0.834 [CI 0.815-0.853]) and based on both conventional US and real-time elastography (AUC = 0.938 [CI 0.914-0.961] vs. 0.843 [CI 0.829-0.857]). Conclusions: Machine-learning algorithms based on US examinations, particularly the random forest classifier, may diagnose malignant thyroid nodules better than radiologists.

BackgroundIdentification of cancer stem cells (CSCs) and their behaviors will provide insightful information for the future control of human cancers. This study investigated CD44 and CD24 cell surface markers as breast cancer CSC markers in vitro and in vivo.MethodsFlow cytometry with CD44 and CD24 markers was used to sort breast cancer MCF7 cells for scanning electron microscopy (SEM), tumor cell invasion assay, and nude mouse xenograft assay.ResultsFlow cytometry assay using CD44 and CD24 markers sorted MCF7 cells into four subsets, i.e., CD44+/CD24-/low, CD44-/CD24+, CD44+/CD24+, and CD44-/CD24-. The SEM data showed that there were many protrusions on the surface of CD44+/CD24-/low cells. CD44+/CD24-/low cells had many microvilli and pseudopodia. The CD44+/CD24-/low cells had a higher migration and invasion abilities than that of the other three subsets of the cells. The in vivo tumor formation assay revealed that CD44+/CD24- cells had the highest tumorigenic capacity compared to the other three subsets.ConclusionCD44 and CD24 could be useful markers for identification of breast CSCs because CD44+/CD24-/low cells had unique surface ultrastructures and the highest tumorigenicity and invasive abilities.

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