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Background Ovarian injury is one of the side effects of chemotherapy in female patients, which seriously endangers female reproductive health. Indole-3-carbinol (I3C), a natural substance abundant in cruciferous vegetables, has been reported to attenuate tissue damage. This study aimed to investigate whether I3C could prevent from ovarian damage induced by chemotherapy. Methods A mouse model of ovarian damage was established by intraperitoneal injection of cisplatin or co-treatment with I3C. Then the ovarian index and estrous cycle was assessed. Meanwhile, follicles counting was conducted to evaluate the effect of I3C in follicular development. Also, we performed the TUNEL and IHC staining to analyze the level of apoptosis and fibrosis, respectively. Western blot and qRT-PCR was used as quantitative methods to evaluate the expression of relative markers and TGF-β1/Smad pathway. Hela cells and Caski cells was used to investigate the anti-tumor activity of I3C by cell counting kit-8, the wound healing assay and colony formation assay in vitro. Results Our results showed that administration of I3C restored the ovary index and improved estrous cycle disorders. Follicle counting results showed that I3C is able to inhibit primordial follicles over-activation caused by cisplatin treatment, and maintained primordial follicle pool. We also found that I3C can down-regulate the levels of Bax and γH2ax, and inhibit the apoptosis of ovarian granulosa cells. In addition, I3C also reduced ovarian fibrosis and inhibited α-SMA and Collagen I expression levels. Further research revealed that I3C treatment significantly down-regulated the activity of the TGF-β1/smad signaling pathway. Finally, we demonstrated that I3C could inhibit the proliferation, migration and colony formation of cervical cancer cells in vitro. Conclusions In summary, I3C alleviates primordial follicular over-activation, granulosa cell apoptosis and ovarian fibrosis induced by cisplatin, and exhibits antitumor activity. Our study provides an innovative therapeutic strategy for preventing ovarian function from chemotherapy in female cancer patients.

N6-methyladenosine (m6A) is the most abundant modification in eukaryotic messenger RNAs (mRNAs), and plays important roles in many bioprocesses. However, its functions in bladder cancer (BCa) remain elusive. Here, we discovered that methyltransferase-like 3 (METTL3), a major RNA N6-adenosine methyltransferase, was significantly up-regulated in human BCa. Knockdown of METTL3 drastically reduced BCa cell proliferation, invasion, and survival in vitro and tumorigenicity in vivo. On the other hand, overexpression of METTL3 significantly promoted BCa cell growth and invasion. Through transcriptome sequencing, m6A sequencing and m6A methylated RNA immuno-precipitation quantitative reverse-transcription polymerase chain reaction, we revealed the profile of METTL3-mediated m6A modification in BCa cells for the first time. AF4/FMR2 family member 4 (AFF4), two key regulators of NF-κB pathway (IKBKB and RELA) and MYC were further identified as direct targets of METTL3-mediated m6A modification. In addition, we showed that besides NF-κB, AFF4 binds to the promoter of MYC and promotes its expression, implying a novel multilevel regulatory network downstream of METTL3. Our results uncovered an AFF4/NF-κB/MYC signaling network operated by METTL3-mediated m6A modification and provided insight into the mechanisms of BCa progression.

Bifurcation detection in coronary arteries is significant since it influences the treatment strategy selection and optimization. Bifurcations are also reliable landmarks for image registration. Intravascular optical coherence tomography (IVOCT) is a high-resolution imaging modality that is very useful in percutaneous coronary intervention stenting optimization. We present a bifurcation identification method utilizing pullback characteristics for IVOCT, which can effectively identify the bifurcations with a small size. The longitudinal view of the pullback will appear as an outward discontinuity in the bifurcation area. By detecting this discontinuity, bifurcation can be identified with high accuracy. We also use the normal vectors method to extract the ostium of bifurcation. We compare the proposed method with the widely-used distance transformation method by clinical 5302 IVOCT images from 22 pullbacks. The average metrics of true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) for the proposed method are 86.97%, 98.50%, 85.56%, and 98.67%, respectively. TPR, PPV, and NPV by the proposed method are improved by 40.24%, 9.31%, 3.90%, and TNR is on par compared with the distance transformation method. Especially in the small bifurcation identification, TPR of the proposed method is 64.71% higher than the distance transformation method with a bifurcation area ratio less than 0.2.

In catheter based polarization sensitive optical coherence tomography (PS-OCT), a optical fiber with a rapid rotation in the catheter can cause low signal-to-noise ratio (SNR), polarization state instability, phase change of PS-OCT signals and then heavy noise-induced depolarization, which has a strong impact on the phase retardation measurement of the sample. In this paper, we analyze the noise-induced depolarization and find that the effect of depolarization can be reduced by polar decomposition after incoherent averaging in the Mueller matrix averaging (MMA) method. Namely, MMA can reduce impact of noise on phase retardation mapping. We present a Monte Carlo method based on PS-OCT to numerically describe noise-induced depolarization effect and contrast phase retardation imaging results by MMA and Jones matrix averaging (JMA) methods. The peak signal to noise ratio (PSNR) of simulated images processed by MMA is higher than about 8.9 dB than that processed by JMA. We also implement experiments of multiple biological tissues using the catheter based PS-OCT system. From the simulation and experimental results, we find the polarization contrasts processed by the MMA are better than those by JMA, especially at areas with high depolarization, because the MMA can reduce effect of noise-induced depolarization on the phase retardation measurement.

Summary It has been reported that functionally distinct cancer stem cells (CSCs) exist in human bladder cancer (BCa). Here, we found that Sox2, a transcription factor that is well characterized as a marker for stem cells, is upregulated in both mouse and human BCa. Sox2 expression is absent in normal urothelial cells, but it begins to be expressed in pre-neoplastic bladder tumors and continues to be expressed in invasive mouse BCa. Using s as a reporter of Sox2 transcriptional expression, we demonstrated that Sox2-expressing cells mark a subpopulation of tumor cells that fuel the growth of established BCa. SOX2-positive cells also expressed other previously reported BCa CSC markers, including Keratin14 (KRT14) and CD44v6. Ablation of Sox2-expressing cells within primary invasive BCa led to enhanced tumor regression, supporting the essential role of SOX2-positive cells in regulating BCa maintenance and progression. Our data show that Sox2 is a marker of bladder CSCs and indicate it as a potential clinical target for BCa therapy., Highlights • Sox2 expression marks bladder cancer stem cells in vivo • SOX2-positive cells are a subpopulation of KRT14-expressing cancer stem cells • SOX2-positive cells are a subpopulation of CD44v6-expressing cancer stem cells • Elimination of Sox2-expressing cells led to reduction of bladder cancer progression, In this article, Yang and colleagues assess the renewal and differentiation potential of Sox2-expressing cells in bladder cancer, by in vitro clonogenic assays, in vivo transplantation, and lineage-tracing experiments. Their data indicate that Sox2 expression marks the bladder cancer stem cells, which are necessary for tumor growth and maintenance.

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Low dose treatment with the DNA methylation inhibitor decitabine has been shown to be applicable for the management of certain types of cancer. However, its antitumor effect and mechanisms are context dependent and its activity has never been systematically studied in bladder cancer treatment. We used mouse models, cultured cell lines and patient-derived xenografts to demonstrate that low dose decitabine treatment remarkably enhanced the effects of cisplatin and gemcitabine on basal-like bladder cancer both in vivo and in vitro. Genetic lineage tracing revealed that the stemness of a bladder cancer stem cell population was inhibited by decitabine treatment in mice. These effects were accompanied by decreases in genome-wide DNA methylation, gene re-expression, and changes in key cellular regulatory pathways such as STAT3 signaling. These results indicate that this DNA-demethylating reagent is a promising therapeutic approach for basal-like bladder cancer treatment.

The role of transforming growth factor-β (TGF-β) signaling in cancer progression is still under debate. To determine the function of TGF-β signaling in bladder cancer progression, we conditionally knocked out the Tgfbr2 in mouse model after a N-butyl-N-4-hydroxybutyl Nitrosamine induced bladder carcinogenesis. We found the ablation of TGF-β signaling could inhibit the cancer cell proliferation, cancer stem cell population and EMT, hence suppressed the invasive cancer progression, which is similar with the result of TGF-β receptor I inhibitor treatment. These findings recognize the roles and mechanisms of TGF-β signaling in bladder cancer progression in vivo for the first time.

Current practice for diagnosing nasopharyngeal carcinoma (NPC) is based on invasive tissue biopsy. This study aims to explore the feasibility of using Raman spectroscopy to differentiate cancerous and non-cancerous nasopharyngeal tissue smears, expecting to realize minimal invasive diagnosis using smears from in vivo mucosa tissue by Raman spectroscopy. Biopsy tissue smears were acquired from 74 patients with pathologically diagnosed nasopharyngeal diseases and measured using confocal Raman spectroscopy. Both fingerprint region and high wavenumber Raman spectra were acquired with distinguish features. Multivariate statistical analysis was used to differentiate cancerous and non-cancerous groups, achieving a diagnostic sensitivity of 87.2 and specificity of 85.7 % for differentiating NPC from nasopharyngeal non-cancerous smears. This work indicates that the method has a unique advantage in microanalysis for tissue smears which may provide a promising minimal invasive (or noninvasive) diagnosing tool for cancer diagnosis.

Aiming at the desired status self-validation of traditional multifunctional sensor, a novel multifunctional self-validating sensor functional model is employed to improve the measurement reliability. Detailed self-validating functions which consist of faults detection, isolation and recovery, validated uncertainty estimation and health levels evaluation of sensors are presented, especially the proposed multivariable relevance vector machine (MVRVM)-based signal reconstruction emphasized in this paper. Being different from traditional single measured physical signal, MVRVM has expanded into simultaneous reconstruction of multiple physical variables with one sparser model. Compared with previous one output with single model, the computational burden of this paper is much lower, which benefits the on-line status validation of sensors. The working principle of MVRVM is emphasized for multiple measured signals reconstruction, which is very suitable for the final validated measurement values of multiple measured components. A real experimental system of multifunctional self-validating sensor was designed to produce the actual samples, and further verify the proposed methodology. Experimental results demonstrate that the proposed strategy could provide a good solution to the signal reconstruction of multifunctional self-validating sensors under both normal and off-normal situations.

Multiple different quantities are often measured by the multifunctional sensor in industrial applications. The sensor may not be truly healthy or fault-free, which could reduce the reliability of measurement results. In this paper, a new prototype of multifunctional self-validating sensor was put forward, and detailed intelligent functions were illustrated and generalized. Specifically, we 1) employ the multivariable relevant vector machine (MVRVM) coupled with faults detection, isolation and recovery (FDIR) technology for the final validated measurement value (VMV), in which the polynomial predictive filters with low computation complexity is used for the data validation and then the incorrect measurements are validated or corrected on line. This MVRVM is very suitable for multiple measured components; 2) propose a novel random fuzzy variable (VRFV) based uncertainty evaluation strategy for the on-line validated uncertainty (VU) estimation, in which negative effects of different faults are fully considered. As a more general theory, VRFV has taken both the nonrandom and random effects into account; 3) define some measurement value status (MVS) to imply how the VMV are obtained, which enhance the security in application. The methodology presented is illustrated on an experimental system for hydrogen concentration, temperature and humidity, and results demonstrate that the proposed scheme provides a good solution to the status self-validation of the multifunctional self-validating sensor under both normal and abnormal faults situations.

Health Monitoring Systems is an implementation of monitoring strategies for complex systems whereby avoiding catastrophic failure, extending life and leading to improved asset management. A Health Monitoring Systems generally encompasses intelligence at many levels and sub-systems including sensors, actuators, devices, etc. In this paper, a smart sensor is studied, which is use to detect transient transitions of liquid-propellant rocket engines test bed. In consideration of dramatic changes of variable condition, wavelet decomposition is used to work real time in areas. Contrast to traditional Fourier transform method, the major advantage of adding wavelet analysis is the ability to detect transient transitions as well as obtaining the frequency content using a much smaller data set. Historically, transient transitions were only detected by offline analysis of the data. The methods proposed in this paper provide an opportunity to detect transient transitions automatically as well as many additional data anomalies, and provide improved data-correction and sensor health diagnostic abilities. The developed algorithms have been tested on actual rocket test data.

Recent progression in cancer immunotherapy made checkpoint blockade and T-cell engineering excitingly successful in clinical trials. The melanoma antigen gene (MAGE) protein family has been noticed for their restricted expression in reproductive tissues and a wide variety of cancer types for decades. Tumor-specific expression pattern and antigenic properties made MAGE family members a promising cancer immunotherapy target. This review introduces the background of cancer immunotherapy and MAGE family members while highlighting the recent developments in the implication of MAGE family in different contexts of cancer immunotherapy and enlisting the outstanding questions in the field.

A novel multifunctional self-validating sensor functional model is proposed for evaluation, improvement, and forecast of measurement value reliability. This paper presents a detailed introduction of main content—self-validating function, especially the quantitative health evaluation and remaining using life forecasting. As a fundamental self-validating function, a novel strategy based on relevance vector machine (RVM) coupled with wavelet analysis is proposed for failure detection, isolation, and recovery (FDIR) of multifunctional self-validating sensor. It centers on the working principle and on-line updating algorithm of WRVM predictor. The proposed WRVM predictor can also distinguish the fault free signal with sudden change from the failure signal and it is updated online for pursuit of signal change. A multifunctional self-validating sensor experimental system is designed to evaluate the performance of novel strategy. Results demonstrate that the proposed methodology provides a good solution to the FDIR of multifunctional self-validating sensor.