Enhancing Multichannel Fiber Optic Sensing Systems with IFFT-DNN for Remote Water Level Monitoring.

This paper proposes a novel approach to enhance the multichannel fiber optic sensing systems by integrating an Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) to accurately predict sensor responses despite signals overlapping and crosstalk between sensors. The IFFT-DNN leverages both frequency and time domain information, enabling a comprehensive feature extraction which enhances the prediction accuracy and reliability performance. To investigate the IFFT-DNN's performance, we propose a multichannel water level sensing system based on Free Space Optics (FSO) to measure the water level at multiple points in remote areas. The experimental results demonstrate the system's high precision, with a Mean Absolute Error (MAE) of 0.07 cm, even in complex conditions. Hence, this system provides a cost-effective and reliable remote water level sensing solution, highlighting its practical applicability in various industrial settings.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Loss of MuRF1 in Duroc pigs promotes skeletal muscle hypertrophy.

Muscle mass development depends on increased protein synthesis and reduced muscle protein degradation. Muscle ring-finger protein-1 (MuRF1) plays a key role in controlling muscle atrophy. Its E3 ubiquitin ligase activity recognizes and degrades skeletal muscle proteins through the ubiquitin-proteasome system. The loss of Murf1, which encodes MuRF1, in mice leads to the accumulation of skeletal muscle proteins and alleviation of muscle atrophy. However, the function of Murf1 in agricultural animals remains unclear. Herein, we bred F1 generation Murf1+/- and F2 generation Murf1-/- Duroc pigs from F0 Murf1-/- pigs to investigate the effect of Murf1 knockout on skeletal muscle development. We found that the Murf1+/- pigs retained normal levels of muscle growth and reproduction, and their percentage of lean meat increased by 6% compared to that of the wild type (WT) pigs. Furthermore, the meat color, pH, water-holding capacity, and tenderness of the Murf1+/- pigs were similar to those of the WT pigs. The drip loss rate and intramuscular fat decreased slightly in the Murf1+/- pigs. However, the cross-sectional area of the myofibers in the longissimus dorsi increased in the adult Murf1+/- pigs. The skeletal muscle proteins MYBPC3 and actin, which are targeted by MuRF1, accumulated in the Murf1+/- and Murf1-/- pigs. Our findings show that inhibiting muscle protein degradation in MuRF1-deficient Duroc pigs increases the size of their myofibers and their percentage of lean meat without influencing their growth or pork quality. Our study demonstrates that Murf1 is a target gene for promoting skeletal muscle hypertrophy in pig breeding.

Clinical and imaging characteristics of patients with bronchogenic cysts: a single-center retrospective analysis.

BACKGROUND: Bronchogenic cysts (BCs) are rare and usually asymptomatic malformations detected during imaging examinations. We aimed to investigate the clinical and imaging characteristics of patients with BCs. METHODS: We retrospectively evaluated patients who received surgery to remove their BCs from January 2015 to January 2019. Their baseline characteristics, clinical information, and imaging results were reviewed. RESULTS: Our study included 129 patients, with 57 males and 72 females and a mean age of 42.7 years old. The most common location for BCs was the mediastinum (67 patients, 51.9%). Fewer than half of the patients (53 patients, 41.1%) reported clinical symptoms, with chest pain being the most common (16 patients, 30.2%). Neck BCs were more frequently observed in young patients (P = 0.002) and were more often associated with thyroid cancer (P = 0.007). A computed tomography scan was the most commonly used method to diagnose BCs in the lung and mediastinum, whereas ultrasound was the most commonly used diagnostic method for neck BCs. The characteristic images were well-defined, thin-wall cystic lesions in varying densities. A few lesions showed small, calcified spots along the rim or cavities. CONCLUSIONS: Although most BCs were found in the mediastinum, their locations could vary in different sex and age groups. Particular attention should be paid to young patients with BCs in the neck to rule out thyroid cancer.

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch.

Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people's activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.

High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network.

In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance.

Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition.

Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver-silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10-20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.

Ferroelectric PVDF nanofiber membrane for high-efficiency PM0.3 air filtration with low air flow resistance.

The significant public health concerns related to particulate matter (PM) air pollutants and the airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have led to considerable interest in high-performance air filtration membranes. Highly ferroelectric polyvinylidene fluoride (PVDF) nanofiber (NF) filter membranes are successfully fabricated via electrospinning for high-performance low-cost air filtration. Spectroscopic and ferro-/piezoelectric analyses of PVDF NF show that a thinner PVDF NF typically forms a ferroelectric ß phase with a confinement effect. A 70-nm PVDF NF membrane exhibits the highest fraction of ß phase (87%) and the largest polarization behavior from piezoresponse force microscopy. An ultrathin 70-nm PVDF NF membrane exhibits a high PM0.3 filtration efficiency of 97.40% with a low pressure drop of 51 Pa at an air flow of 5.3 cm/s owing to the synergetic combination of the slip effect and ferroelectric dipole interaction. Additionally, the 70-nm PVDF NF membrane shows excellent thermal and chemical stabilities with negligible filtration performance degradation (air filtration efficiency of 95.99% and 87.90% and pressure drop of 55 and 65 Pa, respectively) after 24 h of heating at 120 °C and 1 h immersion in isopropanol.

Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology.

In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development.

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection.

Wearing a safety helmet is important in construction and manufacturing industrial activities to avoid unpleasant situations. This safety compliance can be ensured by developing an automatic helmet detection system using various computer vision and deep learning approaches. Developing a deep-learning-based helmet detection model usually requires an enormous amount of training data. However, there are very few public safety helmet datasets available in the literature, in which most of them are not entirely labeled, and the labeled one contains fewer classes. This paper presents the Safety HELmet dataset with 5K images (SHEL5K) dataset, an enhanced version of the SHD dataset. The proposed dataset consists of six completely labeled classes (helmet, head, head with helmet, person with helmet, person without helmet, and face). The proposed dataset was tested on multiple state-of-the-art object detection models, i.e., YOLOv3 (YOLOv3, YOLOv3-tiny, and YOLOv3-SPP), YOLOv4 (YOLOv4 and YOLOv4pacsp-x-mish), YOLOv5-P5 (YOLOv5s, YOLOv5m, and YOLOv5x), the Faster Region-based Convolutional Neural Network (Faster-RCNN) with the Inception V2 architecture, and YOLOR. The experimental results from the various models on the proposed dataset were compared and showed improvement in the mean Average Precision (mAP). The SHEL5K dataset had an advantage over other safety helmet datasets as it contains fewer images with better labels and more classes, making helmet detection more accurate.

Association of LncRNA-GAS5 gene polymorphisms and PBMC LncRNA-GAS5 level with risk of systemic lupus erythematosus in Chinese population.

Growth arrest-specific 5 (GAS5) is a kind of long non-coding RNAs (lncRNAs). Previous studies showed that down-regulation of LncRNA-GAS5 was involved in the development of systemic lupus erythematosus (SLE). However, the regulatory mechanism of down-expressed LncRNA-GAS5 in SLE remains obscure. In this study, we aimed to investigate the association of LncRNA-GAS5 polymorphism with SLE risk. And further explore how LncRNA-GAS5 is involved in the occurrence of SLE. Here, we evaluated the relationship between the risk for the development of SLE and the 5-base pair (AGGCA/-) insertion/deletion (I/D) polymorphism (rs145204276) in the LncRNA-GAS5 promoter region. A custom 36-Plex SNPscan kit was used for genotyping the LncRNA-GAS5 polymorphisms. The LncRNA-GAS5 and miR-21 target prediction was performed using bioinformatics software. Enzyme-linked immunosorbent assay (ELISA) and quantitative real-time PCR (qRT-PCR) were performed to assess GAS5 and miR-21 mRNA expression and PTEN protein expression. The results revealed that rs145204276 resulted in a decreased risk of SLE (DD genotypes vs II genotypes: adjusted OR = 0.538, 95% CI, 0.30-0.97, P = .039; ID genotypes vs II genotypes: adjusted OR = 0.641, 95% CI, 0.46-0.89, P = .007; ID/DD genotypes vs II genotypes: adjusted OR = 0.621, 95% CI, 0.46-0.84, P = .002; D alleles vs I alleles: adjusted OR = 0.680, 95% CI, 0.53-0.87, P = .002). A reduced incidence of renal disorders in SLE was found to be related to ID/DD genotypes and D alleles (ID/DD genotypes vs II genotypes: OR = 0.57, 95% CI, 0.36-0.92, P = .020; D alleles vs I alleles: OR = 0.63, 95% CI, 0.43-0.93, P = .019). However, no significant association of rs2235095, rs6790, rs2067079 and rs1951625 polymorphisms with SLE risk was observed (P > .05). Additionally, haplotype analysis showed that a decreased SLE risk resulted from the A-A-C-G-D haplotype (OR = 0.67, 95% CI, 0.49-0.91, P = .010). Also, patients in the SLE group showed a down-regulated expression of LncRNA-GAS5 and PTEN than the healthy volunteers; however, patients with rs145204276 ID/DD genotypes showed up-regulated expression of LncRNA-GAS5 and PTEN compared with patients carrying the II genotype. Furthermore, the miR-21 levels were considerably up-regulated in the SLE group than the healthy volunteers, and patients with rs145204276 ID/DD genotype had lower miR-21 levels than the ones with the II genotype. Thus, we found that the LncRNA-GAS5/miR-21/PTEN signalling pathway was involved in the development of SLE, where LncRNA-GAS5 acted as an miR-21 target, and miR-21 regulated the expression of PTEN. These findings indicated that the rs145204276 ID/DD genotypes in the LncRNA-GAS5 gene promoter region may be protected against SLE by up-regulating the expression of LncRNA-GAS5, which consecutively regulated miR-21 and PTEN levels.

A highly efficient nanofibrous air filter membrane fabricated using electrospun amphiphilic PVDF-g-POEM double comb copolymer.

Current global emergencies, such as the COVID-19 pandemic and particulate matter (PM) pollution, require urgent protective measures. Nanofibrous air filter membranes that can capture PM0.3 and simultaneously help in preventing the spread of COVID-19 are essential. Therefore, a highly efficient nanofibrous air filter membrane based on amphiphilic poly(vinylidene fluoride)-graft-poly(oxyethylene methacrylate) (PVDF-g-POEM) double comb copolymer was fabricated using atomic transfer radical polymerization (ATRP) and electrospinning. Fourier transform infrared spectroscopy, X-ray diffraction, proton nuclear magnetic resonance, transmission electron microscopy, differential scanning calorimetry, and thermogravimetric analysis were employed to successfully characterize the molecular structure of the fabricated amphiphilic PVDF-g-POEM double comb copolymer. The nanofibrous air filter membrane based on amphiphilic PVDF-g-POEM double comb copolymer achieved a low air resistance of 4.69 mm H2O and a high filtration efficiency of 93.56 % due to enhanced chemical and physical adsorption properties.

Towards Privacy-Preserved Aging in Place: A Systematic Review.

Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.

Sound Source Localization Using a Convolutional Neural Network and Regression Model.

In this research, a novel sound source localization model is introduced that integrates a convolutional neural network with a regression model (CNN-R) to estimate the sound source angle and distance based on the acoustic characteristics of the interaural phase difference (IPD). The IPD features of the sound signal are firstly extracted from time-frequency domain by short-time Fourier transform (STFT). Then, the IPD features map is fed to the CNN-R model as an image for sound source localization. The Pyroomacoustics platform and the multichannel impulse response database (MIRD) are used to generate both simulated and real room impulse response (RIR) datasets. The experimental results show that an average accuracy of 98.96% and 98.31% are achieved by the proposed CNN-R for angle and distance estimations in the simulation scenario at SNR = 30 dB and RT60 = 0.16 s, respectively. Moreover, in the real environment, the average accuracies of the angle and distance estimations are 99.85% and 99.38% at SNR = 30 dB and RT60 = 0.16 s, respectively. The performance obtained in both scenarios is superior to that of existing models, indicating the potential of the proposed CNN-R model for real-life applications.

Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network.

Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos. To reduce the amount of the training data, the posture information is represented by skeleton data extracted from the 300 frames of one film. The two-stream method was applied to increase the accuracy of recognition by using the spatial and motion features of skeleton sequences. The relations of adjacent skeletal joints were employed to build the direct acyclic graph (DAG) matrices, source matrix, and target matrix. Two features were transferred by DAG matrices and expanded as color texture images. The linear-map CNN had a two-dimensional linear map at the beginning of each layer to adjust the number of channels. A two-dimensional CNN was used to recognize the actions. We applied the RGB videos from the action recognition datasets of the NTU RGB+D database, which was established by the Rapid-Rich Object Search Lab, to execute model training and performance evaluation. The experimental results show that the obtained precision, recall, specificity, F1-score, and accuracy were 86.9%, 86.1%, 99.9%, 86.3%, and 99.5%, respectively, in the cross-subject source, and 94.8%, 94.7%, 99.9%, 94.7%, and 99.9%, respectively, in the cross-view source. An important contribution of this work is that by using the skeleton sequences to produce the spatial and motion features and the DAG matrix to enhance the relation of adjacent skeletal joints, the computation speed was faster than the traditional schemes that utilize single frame image convolution. Therefore, this work exhibits the practical potential of real-life action recognition.

Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.

The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident's house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.

A Slug-dependent mechanism is responsible for tumor suppression of p53-stabilizing compound CP-31398 in p53-mutated endometrial carcinoma.

Mutation in the tumor suppressor gene p53 is the most frequent molecular defect in endometrial carcinoma (EC). Recently, CP-31398, a p53-stabilizing compound, has been indicated to possess the ability to alter the expression of non-p53 target genes in addition to p53 downstream genes in tumor cells. Herein, we explore the alternative mechanisms underlying the restoration of EC tumor suppressor function in mutant p53 by CP-31398. A p53-mutated EC cell was constructed in AN3CA cells with restored or partial loss of Slug using lentiviral vectors, followed by treatment with 25 µM CP-31398. A p53-independent mechanism of CP-31398 was confirmed by the interaction between mouse double minute 2 homolog (MDM2) and Slug AN3CA cells treated with IWR-1 (inhibitor of Wnt response 1). Furthermore, the AN3CA cells were treated with short hairpin RNA against Slug, Wnt-specific activators (LiCl) or inhibitors (XAV-939) followed by CP-31398 treatment. Moreover, AN3CA cell proliferation and apoptosis were examined. A tumorigenicity assay was conducted in nude mice. CP-31398 could promote the apoptosis of p53-mutated EC cells, while Slug reversed this effect. Slug ubiquitination was found to occur via binding of Slug to MDM2 in AN3CA cells. We found that CP-31398 increased the GSK-3ß, p-Slug, Puma, Wtp53, and Bax expressions whereas Wnt, Mtp-53, Slug, Bcl-2, and Ki-67 expressions were decreased. However, these findings were reversed following the activation of the Wnt pathway and overexpression of Slug. Finally, the in vivo experimental evidence confirmed that CP-31398 with depleted Slug suppressed tumor growth by downregulating the Slug. Collectively, CP-31398-regulated Slug downregulation represses the p53-mutated EC via the p53/Wnt/Puma pathway.

CP-31398 inhibits the progression of cervical cancer through reversing the epithelial mesenchymal transition via the downregulation of PAX2s.

CP-31398, a styrylquinazoline, emerges from a screen for therapeutic agents that restore the wild-type DNA-binding conformation of mutant p53 to suppress tumors in vivo, but its effects on cervical cancer (CC) remain unknown. Hence, this study aimed to explore the effects CP-31398 has on the CC cells and to investigate whether it is associated with paired box 2 (PAX2) expression. CC cells were treated with different concentrations of CP-31398 (1, 2, 4, 6, 8, and 10 µg/ml) to determine the optimum concentration using fluorometric microculture cytotoxicity assay. After constructing the sh-PAX2 vector, CC cells were transfected with sh-PAX2 or treated with CP-31398. The effects of CP-31398 or PAX2 silencing on CC cell proliferation, apoptosis, invasion, and migration were evaluated. Epithelial mesenchymal transition (EMT)-related genes such as E-cadherin, vimentin, N-cadherin, snail, and twist in CC cells were detected. Tumor formation experiment in nude mice was performed to observe tumor growth. The optimum concentration of CP-31398 was 2 µg/ml. PAX2 was overexpressed in CC cells. CC cells treated with CP-31398 or treated with sh-PAX2 inhibited proliferation, invasion, and migration but promoted apoptosis with decreased PAX2 expression. The EMT process in CC cells was also reversed after treatment with CP-31398 or sh-PAX2. Moreover, the tumor formation experiment in nude mice revealed the inhibitory activity of CP-31398 in CC tumor in nude mice by suppressing PAX2. Our results provide evidence that CP-31398 could inhibit EMT and promote apoptosis of CC cells to curb CC tumor growth by downregulating PAX2.

Deletion of INMAP postpones mitotic exit and induces apoptosis by disabling the formation of mitotic spindle.

INMAP was first identified as a spindle protein that plays important roles in cell-cycle progression, and previous studies have revealed that its abnormal expression leads to mitotic disorder and the growth inhibition of human tumor xenografts, but the underlying mechanism is still unclear. In this study, we knocked out INMAP in HEK293T cells, a strain of human embryonic renal cells, through CRISPR-Cas9 gene editing technology, resulting in obvious cell growth inhibition. In this system, the deletion of INMAP caused obviously apoptosis. And we also found that knockout of INMAP caused micronuclei formation, chromosome aberration, and γH2AX expression upregulation, suggesting DNA damage induction and genomic stability impairment. As a principal component of spindle, the expression of ß-tubulin, detected through Western blot, is obviously upregulated in HEK293T-INMAP-/-. Meanwhile, the level of Cyclin B is also upregulated, whereas, that of Cyclin E, downregulated, with the postponement of mitotic exit and the assembly anomaly of spindle. These results suggest that the deletion of INMAP block the formation of spindle, leading to arrest of cell cycle and DNA damage, finally blocking cell proliferation and inducing apoptosis. Therefore, INMAP is an indispensable factor for genomic integrity and normal mitotic exit.

A novel long non-coding RNA-KAT7 is low expressed in colorectal cancer and acts as a tumor suppressor.

BACKGROUND: The abnormal expression of many long non-coding RNAs (lncRNAs) has been reported in the progression of various tumors. However, the potential biological roles and regulatory mechanisms of long non-coding RNAs in the development of colorectal cancer (CRC) have not yet been fully elucidated. Therefore, it is crucial to identify that lncRNAs can be used for the clinical prevention and treatment of CRC. METHODS: In our previous work, we identificated a novel lncRNA, lncRNA-KAT7, and found that the expression of lncRNA-KAT7 in CRC tissues was significantly lower than that in matched normal intestinal tissues, and the expression in CRC cell lines was lower than that of normal intestinal epithelial cells (P < 0.05). Besides, the expression of lncRNA-KAT7 is negative associated with age, tumor size, tumor differentiation, lymph node metastasis of CRC patients. The potential biological effects and molecular mechanisms of lncRNA-KAT7 in CRC were evaluated using a series of CCK-8 assay, clone formation assay, EdU proliferation assay, scratch determination, transwell determination, western blot analysis, and nude subcutaneous tumorigenesis model construction cell and animal experiments. RESULTS: The expression of lncRNA-KAT7 in CRC tissues was lower than that in matched normal tissues and normal intestinal epithelial cells (P < 0.05). Decreased expression of lncRNA-KAT7 is associated with clinicopathological features of poor CRC patients. In vitro experiments showed that up-regulation of lncRNA-KAT7 expression in CRC cells inhibited cell proliferation and migration. In vivo animal experiments showed that the lncRNA-KAT7 also inhibited tumor growth. Western blot analysis showed that the expression of lncRNA-KAT7 was up-regulated in HCT116 cells, the expression of E-cadherin increased, and the expression of Vimentin, MMP-2 and ß-catenin protein was down-regulated so did the phosphorylation NF-κB P65. The results confirm that the expression of lncRAN-KAT7 can inhibit the malignant phenotype of CRC cells. CONCLUSIONS: Up to now, as a novel lncRNA, lncRNA-KAT7 has not any relevant research and reports. The results confirm that the expression of lncRNA-KAT7 can inhibit the malignant phenotype of CRC cells. And it can be used as a new diagnostic biomarker and therapeutic target for the development of CRC.