TAF2, within the TFIID complex, regulates the expression of a subset of protein-coding genes.

TFIID, one of the general transcription factor (GTF), regulates transcriptional initiation of protein-coding genes through direct binding to promoter elements and subsequent recruitment of other GTFs and RNA polymerase II. Although generally required for most protein-coding genes, accumulated studies have also demonstrated promoter-specific functions for several TFIID subunits in gene activation. Here, we report that TBP-associated factor 2 (TAF2) specifically regulates TFIID binding to a small subset of protein-coding genes and is essential for cell growth of multiple cancer lines. Co-immunoprecipitation assays revealed that TAF2 may be sub-stoichiometrically associated with the TFIID complex, thus indicating a minor fraction of TAF2-containing TFIID in cells. Consistently, integrated genome-wide profiles show that TAF2 binds to and regulates only a small subset of protein-coding genes. Furthermore, through the use of an inducible TAF2 degradation system, our results reveal a reduction of TBP/TFIID binding to several ribosomal genes upon selective ablation of TAF2. In addition, depletion of TAF2, as well as the TAF2-regulated ribosomal protein genes RPL30 and RPL39, decreases ribosome assembly and global protein translation. Collectively, this study suggests that TAF2 within the TFIID complex is of functional importance for TBP/TFIID binding to and expression of a small subset of protein-coding genes, thus establishing a previously unappreciated promoter-selective function for TAF2.

Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation.

We aimed to develop an accurate and efficient skin cancer classification system using deep-learning technology with a relatively small dataset of clinical images. We proposed a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet's deep convolutional neural networks were trained using a dataset of 1215 clinical images of skin tumors diagnosed at Taichung and Taipei Veterans General Hospital between 2015 and 2020. The dataset comprised five categories: benign nevus, seborrheic keratosis, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. The SkinFLNet's performance was evaluated using 463 clinical images between January and December 2021. SkinFLNet achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%, outperforming other deep convolutional neural network models. We also compared SkinFLNet's performance with that of three board-certified dermatologists, and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. Our study presents an efficient skin cancer classification system utilizing model fusion and lifelong learning technologies that can be trained on a relatively small dataset. This system can potentially improve skin cancer screening accuracy in clinical practice.

Worsening Quality of Life in Young Adult, Highly Educated, and Married Female Patients with Vitiligo: A Hospital-Based Case Control Study in Taiwan.

Vitiligo is an acquired chronic depigmentation disorder that can have a negative impact on the quality of life (QoL). This is especially true for patients with non-white skin. Only few studies have investigated the QoL of Asian patients with vitiligo. We aimed to investigate the QoL in Taiwanese vitiligo patients and identify the factors that influence their QoL. The cross-sectional study recruited 100 vitiligo patients and 100 controls with general skin diseases in the Department of Dermatology of Changhua Christian Hospital. Data were obtained using a structured questionnaire for demographic information and modified Skindex-21 instruments. The QoL was not significantly different between vitiligo patients and controls. Among the vitiligo patients, adults exhibited deteriorated emotional levels and total QoL as compared with non-adults. Married females reported greater levels of emotional disturbance than the unmarried ones. A higher educational level and shorter history of disease were associated with greater emotional impacts. The patients with a generalized type of vitiligo suffered more in total QoL. After multivariate adjustment, the young adult patients aged 20-39 were associated with poorer total QoL. It is suggested that vitiligo patients who are aged between 20 and 39, are married females, are highly educated, have a shorter disease history, and suffer from the generalized type of this disease demonstrate more deterioration in their life quality compared with other vitiligo patients. Care providers should tailor the psychological counseling and treatment accordingly.