Subcellular localization of Mitf in monocytic cells

Microphthalmia-associated transcription factor (Mitf) is a transcription factor that plays an important role in regulating the development of several cell lineages. The subcellular localization of Mitf is dynamic and is associated with its transcription activity. In this study, we examined factors that affect its subcellular localization in cells derived from the monocytic lineage since Mitf is present abundantly in these cells. We identified a domain encoded by Mitf exon 1B1b to be important for Mitf to commute between the cytoplasm and the nucleus. Deletion of this domain disrupts the shuttling of Mitf to the cytoplasm and results in its retention in the nucleus. M-CSF and RANKL both induce nuclear translocation of Mitf. We showed that Mitf nuclear transport is greatly influenced by ratio of M-CSF/Mitf protein expression. In addition, cell attachment to a solid surface also is needed for the nuclear transport of Mitf

Mitf Induction by RANKL Is Critical for Osteoclastogenesis

This study provides the first evidence that RANKL-induced Mitf is critical for osteoclastogenesis and Mitf is not completely redundant with Tfe3

Mitf regulates osteoclastogenesis by modulating NFATc1 activity

Transcription factors Mitf and NFATc1 share many downstream targets that are critical for osteoclastogenesis. Since RANKL signals induce/activate both NFATc1 and Mitf isoform-E (Mitf-E), a tissue-restricted Mitf isoform in osteoclasts, it is plausible that the two factors work together to promote osteoclastogenesis. Although Mitf was shown to function upstream of NFATc1 previously, this study showed that expression of Mitf had little effects on NFATc1 and NFATc1 was critical for the induction of Mitf-E. In Mitf(mi/mi) mice, the semi-dominant mutation in Mitf gene leads to arrest of osteoclastogenesis in the early stages. However, when stimulated by RANKL, the Mitf(mi/mi) preosteoclasts responded with a significant induction of NFATc1, despite that the cells cannot differentiate into functional osteoclasts. In the absence of RANKL stimulation, very high levels of NFATc1 are required to drive osteoclast development. Our data indicate that Mitf functions downstream of NFATc1 in the RANKL pathway, and it plays an important role in amplifying NFATc1-dependent osteoclastogenic signals, which contributes to the significant synergy between the two factors during osteoclastogenesis. We propose that Mitf-E functions as a tissue-specific modulator for events downstream of NFATc1 activation during osteoclastogenesis

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma

Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors

Despite a considerable expansion in the present therapeutic repertoire for other malignancy managements, mortality from head and neck cancer (HNC) has not significantly improved in recent decades. Moreover, the second primary cancer (SPC) diagnoses increased in patients with HNC, but studies providing evidence to support SPCs prediction in HNC are lacking. Several base classifiers are integrated forming an ensemble meta-classifier using a stacked ensemble method to predict SPCs and find out relevant risk features in patients with HNC. The balanced accuracy and area under the curve (AUC) are over 0.761 and 0.847, with an approximately 2% and 3% increase, respectively, compared to the best individual base classifier. Our study found the top six ensemble risk features, such as body mass index, primary site of HNC, clinical nodal (N) status, primary site surgical margins, sex, and pathologic nodal (N) status. This will help clinicians screen HNC survivors before SPCs occur