Investigation of Tumor Suppressing Function of CACNA2D3 in Esophageal Squamous Cell Carcinoma.

Background: Deletion of 3p is one of the most frequent genetic alterations in esophageal squamous cell carcinoma (ESCC), suggesting the existence of one or more tumor suppressor genes (TSGs) within these regions. In this study, one TSG, CACNA2D3 at 3p21.1, was characterized. Methods: Expression of CACNA2D3 in ESCCs was tested by quantitative real-time PCR and tissue microarray. The mechanism of CACNA2D3 downregulation was investigated by methylation-specific polymerase chain reaction (MS-PCR). The tumor suppressive function of CACNA2D3 was characterized by both in vitro and in vivo tumorigenic assays, cell migration and invasion assays. Results: CACNA2D3 was frequently downregulated in ESCCs (24/48, 50%), which was significantly associated with promoter methylation and allele loss (P<0.05). Tissue microarray result showed that downregulation of CACNA2D3 was detected in (127/224, 56.7%) ESCCs, which was significantly associated with lymph node metastasis (P = 0.01), TNM staging (P = 0.003) and poor outcome of ESCC patients (P<0.05). Functional studies demonstrated that CACNA2D3 could inhibit tumorigenicity, cell motility and induce apoptosis. Mechanism study found that CACNA2D3 could arrest cell cycle at G1/S checkpoint by increasing expressions of p21 and p53 and decreasing expression of CDK2. In addition, CACNA2D3 could upregulate intracellular free cytosolic Ca2+ and subsequently induce apoptosis. Conclusion: CACNA2D3 is a novel TSG responsible to the 3p21 deletion event and plays a critical suppressing role in the development and progression of ESCC. © 2013 Li et al.link_to_OA_fulltex

Association of polymicrobial interactions with dental caries development and prevention

Dental caries is a common oral disease. In many cases, disruption of the ecological balance of the oral cavity can result in the occurrence of dental caries. There are many cariogenic microbiota and factors, and their identification allows us to take corresponding prevention and control measures. With the development of microbiology, the caries-causing bacteria have evolved from the traditional single Streptococcus mutans to the discovery of oral symbiotic bacteria. Thus it is necessary to systematically organized the association of polymicrobial interactions with dental caries development. In terms of ecology, caries occurs due to an ecological imbalance of the microbiota, caused by the growth and reproduction of cariogenic microbiota due to external factors or the disruption of homeostasis by one’s own factors. To reduce the occurrence of dental caries effectively, and considering the latest scientific viewpoints, caries may be viewed from the perspective of ecology, and preventive measures can be taken; hence, this article systematically summarizes the prevention and treatment of dental caries from the aspects of ecological perspectives, in particular the ecological biofilm formation, bacterial quorum sensing, the main cariogenic microbiota, and preventive measures

Adaptive Fault Detection with Two Time-Varying Control Limits for Nonlinear and Multimodal Processes

A novel fault detection method is proposed for detection process with nonlinearity and multimodal batches. Calculating the Mahalanobis distance of samples, the data with the similar characteristics are replaced by the mean of them; thus, the number of training data is reduced easily. Moreover, the super ball regions of mean and variance of training data are presented, which not only retains the statistical properties of original training data but also avoids the reduction of data unlimitedly. To accurately identify faults, two control limits are determined during investigating the distributions of distances and angles between training samples to their nearest neighboring samples in the reduced database; thus, the traditional k-nearest neighbors (only considering distances) fault detection (FD-kNN) method is developed. Another feature of the proposed detection method is that the control limits vary with updating database such that an adaptive fault detection technique is obtained. Finally, numerical examples and case study are given to illustrate the effectiveness and advantages of the proposed method

Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set

A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case

Optimization of Fermentation Conditions for the Production of Angiotensin-Converting Enzyme (ACE) Inhibitory Peptides from Cow Milk by Lactobacillus bulgaricus LB6

The purpose of this research was to screen out the optimal -producing peptide conditions for cow milk fermented by Lactobacillus bulgaricus LB6. The effects of temperature, inoculation size, time and skim milk concentration on the ACE inhibition rate of fermented milk were investigated by single factor experiment, and the optimal fermentation conditions were determined by orthogonal experiment. The conditions of the single factor experiment were: Temperatures were 37° C, 39° C, 42° C, 44° C and 46° C. The inoculation amount was 1%, 3%, 5%, 7% and 9%, the time was 8h and 10h. At 12h, 14h and 16h, the concentration of skim milk was 8%, 10%, 12%, 14% and 16%, respectively. The results showed that the optimal fermentation conditions for ACE inhibitory peptide produced by Lactobacillus bulgaricus LB6 were 4% inoculation, 13h in time, 42°C in temperature and 13% in skim milk. Under this condition, the ACE inhibition rate reached 76.50% and the OD value was 0.330. The titration acidity was 116.4°T, the pH was 4.62, and the sensory evaluation was 75 scores

Self-supervised Blind2Unblind deep learning scheme for OCT speckle reductions

As a low-coherence interferometry-based imaging modality, optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiply scattered photons. Speckles hide tissue microstructures and degrade the accuracy of disease diagnoses, which thus hinder OCT clinical applications. Various methods have been proposed to address such an issue, yet they suffer either from the heavy computational load, or the lack of high-quality clean images prior, or both. In this paper, a novel self-supervised deep learning scheme, namely, Blind2Unblind network with refinement strategy (B2Unet), is proposed for OCT speckle reduction with a single noisy image only. Specifically, the overall B2Unet network architecture is presented first, and then, a global-aware mask mapper together with a loss function are devised to improve image perception and optimize sampled mask mapper blind spots, respectively. To make the blind spots visible to B2Unet, a new re-visible loss is also designed, and its convergence is discussed with the speckle properties being considered. Extensive experiments with different OCT image datasets are finally conducted to compare B2Unet with those state-of-the-art existing methods. Both qualitative and quantitative results convincingly demonstrate that B2Unet outperforms the state-of-the-art model-based and fully supervised deep-learning methods, and it is robust and capable of effectively suppressing speckles while preserving the important tissue micro-structures in OCT images in different cases.Published versionNational Natural Science Foundation of China (62220106006); Basic and Applied Basic Research Foundation of Guangdong Province (2021B1515120013); Key Research and Development Projects of Shaanxi Province (2021SF-342); Key Research Project of Shaanxi Higher Education Teaching Reform (21BG005)