The Concerted Action of Type 2 and Type 3 Deiodinases Regulates the Cell Cycle and Survival of Basal Cell Carcinoma Cells

BACKGROUND

Thyroid hormones (THs) mediate pleiotropic cellular processes involved in metabolism, cellular proliferation, and differentiation. The intracellular hormonal environment can be tailored by the type 1 and 2 deiodinase enzymes D2 and D3, which catalyze TH activation and inactivation respectively. In many cellular systems, THs exert well-documented stimulatory or inhibitory effects on cell proliferation; however, the molecular mechanisms by which they control rates of cell cycle progression have not yet been entirely clarified. We previously showed that D3 depletion or TH treatment influences the proliferation and survival of basal cell carcinoma (BCC) cells. Surprisingly, we also found that BCC cells express not only sustained levels of D3 but also robust levels of D2. The aim of the present study was to dissect the contribution of D2 to TH metabolism in the BCC context, and to identify the molecular changes associated with cell proliferation and survival induced by TH and mediated by D2 and D3.

METHODS

We used the CRISPR/Cas9 technology to genetically deplete D2 and D3 in BCC cells and studied the consequences of depletion on cell cycle progression and on cell death. Cell cycle progression was analyzed by fluorescence activated cell sorting analysis of synchronized cells, and the apoptosis rate by annexin V incorporation.

RESULTS

Mechanistic investigations revealed that D2 inactivation accelerates cell cycle progression thereby enhancing the proportion of S-phase cells and cyclin D1 expression. Conversely, D3 mutagenesis drastically suppressed cell proliferation and enhanced apoptosis of BCC cells. Furthermore, the basal apoptotic rate was oppositely regulated in D2- and D3-depleted cells.

CONCLUSION

Our results indicate that BCC cells constitute an example in which the TH signal is finely tuned by the concerted expression of opposite-acting deiodinases. The dual regulation of D2 and D3 expression plays a critical role in cell cycle progression and cell death by influencing cyclin D1-mediated entry into the G1-S phase. These findings reinforce the concept that TH is a potential therapeutic target in human BCC.

A sorting system for hierarchical grading of diabetic fundus images: a preliminary study

Diabetic retinopathy is a leading cause of blindness in developed countries. Diabetic patients can prevent severe visual loss by attending regular eye examinations and receiving timely treatments. In the United States, standard protocols have been developed and refined for years to provide better screening and evaluation procedures of the fundus images. Due to the emerging number of diabetic retinopathy cases, accurate and efficient evaluations of the fundus images have become a serious burden for the ophthalmologists or care providers. While diabetic retinopathy remains too complicated to call for an automatic diagnosis system, an efficient tool to facilitate the grading process with a limited number of personnel is in great demand. The current study is to develop a sorting system with a user-friendly interface, based upon the standardized early treatment diabetic retinopathy study (ETDRS) protocol, to assist the professional graders. The raw fundus images will be screened and assigned to different graders according to their skill levels and experiences. The developed hierarchical sorting process will greatly support the graders and enhance their efficiency and throughput. The proposed hybrid intelligent system with multilevel knowledge representation is used to construct this sorting system. A preliminary case study is conducted using only the features of the spot lesion group coupled with the ETDRS standard to demonstrate its feasibility and performance. The results obtained from the case study show a promising future.

Extracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification

A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REM-Sleep, Non-REM Sleep Stage 1, Stage 2, and Stage 3-4. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143 +/- 39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83.9 +/- 0.4% of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleep-wake classification system.

Fault classification on vibration data with wavelet based feature selection scheme

Fault classification based upon vibration measurements is an essential building block of a conditional based health usage monitoring system. Multiple sensors are incorporated to assure the redundancy and to achieve the desired reliability and accuracy. The shortcoming of using multiple sensors is the need to deal with a high dimensional feature set, a computationally expensive task in classification. It is vital to reduce the feature dimension via an effective feature extraction and feature selection algorithm. A simple wavelet based feature selection scheme is proposed herein, uniquely built by local discriminant bases and genetic optimization. This scheme overcomes the disadvantages faced by the existing feature selection methods by producing a generic feature set, reducing the dimensionality of features, and requiring no prior information of the problem domain. The proposed feature selection scheme is based upon the strategy of "divide and conquer" that significantly reduce the computation time without compromising the classification performance. The simulation results show the proposed feature selection scheme provides at least 65% reduction of the total number of features at no cost of the classification accuracy.

Feature generation using genetic programming with application to fault classification

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

ACCURACY, COMPREHENSIBILITY AND COMPLETENESS EVALUATION OF A FUZZY EXPERT SYSTEM

Using optimization tools such as genetic algorithms to construct a fuzzy expert system (FES) focusing only on its accuracy without considering the comprehensibility may not result in a system that produces understandable expressions. To exploit the transparency characteristics of FES for reasoning in a higher-level knowledge representation, a FES should provide high comprehensibility while preserving its accuracy. The completeness of fuzzy sets and rule structures should also be considered to guarantee that every data point has a response output. This paper proposes some quantitative measures for a FES to determine the degree of the accuracy, the comprehensibility of the fuzzy sets, and the completeness of fuzzy rule structure. These quantitative measures are then used as a fitness function for a genetic algorithm in optimally refining a FES.