Z-number network data envelopment analysis approach: A case study on the Iranian insurance industry.

The main aim of this research is to present an innovative method known as fuzzy network data envelopment analysis (FNDEA) in order to assess the performance of network decision-making units (DMUs) that possess a two-stage structure while taking into account the uncertainty of data. To attain this goal, we utilize various methodologies including the non-cooperative game (leader-follower) NDEA method, the concept of Z-number, credibility theory, and chance-constrained programming (CCP) to develop a model for the fuzzy NDEA approach. The FNDEA approach offers several advantages, such as the linearity of the presented FNDEA models, the ability to rank two-stage DMUs in situations of ambiguity, the provision of a unique efficiency decomposition method in an uncertain environment, and the capability to handle Z-information. To demonstrate the applicability and effectiveness of the proposed approach, we implement the Z-number network data envelopment analysis (ZNDEA) approach in assessing the performance of Iranian private insurance companies. The results of this implementation reveal that the proposed ZNDEA method is suitable and effective for measuring and ranking insurance companies in situations where data ambiguity is present.

Analysis of trace elements in processed products of grapes and potential health risk assessment.

Raisins and grape pekmez are consumed commonly by human all over the globe. Consumption of contaminated foods may be the likely pathway of heavy metal exposure. Therefore, the objectives of the present research were to quantify trace elements concentration in raisins and grape pekmez produced from locally grown grapes in Gonabad and to assess non-carcinogenic (HQ and HI) and carcinogenic (total cancer risk, CRt) health risks caused by trace elements exposure via oral intake of these products for children, teenagers, and adults. For this purpose, a totally 30 (15 raisins and 15 grape pekmez) samples were purchased from the vineyard gardeners and examined for ten trace elements including As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn. The HI values signaled that the studied population consuming these products is at risk. The HQ, HI, and CRt values of the elements were in order of children > teenagers > adults. The highest cancer risk contribution was attributed to As and Ni for all the studied age groups in both raisins and grape pekmez samples. However, it is recommended that the concentration of trace elements in the soil and crops of the study area and its related health risks be regularly monitored to avoid significant health risks in the future.

sRNA-Effector: A tool to expedite discovery of small RNA regulators.

microRNAs (miRNAs) are small regulatory RNAs that repress target mRNA transcripts through base pairing. Although the mechanisms of miRNA production and function are clearly established, new insights into miRNA regulation or miRNA-mediated gene silencing are still emerging. In order to facilitate the discovery of miRNA regulators or effectors, we have developed sRNA-Effector, a machine learning algorithm trained on enhanced crosslinking and immunoprecipitation sequencing and RNA sequencing data following knockdown of specific genes. sRNA-Effector can accurately identify known miRNA biogenesis and effector proteins and identifies 9 putative regulators of miRNA function, including serine/threonine kinase STK33, splicing factor SFPQ, and proto-oncogene BMI1. We validated the role of STK33, SFPQ, and BMI1 in miRNA regulation, showing that sRNA-Effector is useful for identifying new players in small RNA biology. sRNA-Effector will be a web tool available for all researchers to identify potential miRNA regulators in any cell line of interest.

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest.

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 µM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.

Boosting the adsorptive and photocatalytic performance of MIL-101(Fe) against methylene blue dye through a thermal post-synthesis modification.

Photocatalytic degradation under ultra-low powered light is a viable advanced oxidation process technique against extensive emerging contaminants. As a new and remarkable class of nanoporous materials, metal-organic frameworks (MOFs), attract interest for the supreme adsorptive and photocatalytic functionalities. An outstanding MOF, MIL-101(Fe) chosen as a photocatalyst template for the synthesis of α-Fe2O3 by a simple thermal modification to improve the structural properties toward methylene blue (MB) eradication. Octahedron-like α-Fe2O3 photocatalyst (Modified MIL-101(Fe), M-MIL-101(Fe)) was superior in dispersion and separation properties in aqueous medium. Moreover, the adsorptive and catalytic performance was increased for modified form by ~ 7.3% and ~ 17.1% compared to pristine MIL-101(Fe), respectively. Synergistic improvement of MB removal achieved by simultaneous adsorption/degradation under 5-W LED irradiation. Parametric study indicated an 18.1% and 44.5% improvement in MB removal was observed by increasing pH from 4 to 10, and M-MIL-101(Fe) dose from 0.2 to 1 g L-1, respectively. MB removal followed the pseudo-second-order kinetics model and the process efficiency dropped by 38% as MB concentration increased from 5 to 20 mg L-1. Radical trapping tests revealed the significant role of [Formula: see text] and electron radicals as the major participants in dye degradation. A significant loss in the efficiency of M-MIL-101(Fe) was observed in the reusability tests that is good to study further. In conclusion, a simple thermal post-synthesis modification on MIL-101(Fe) improved its structural, catalytic, and adsorptive properties against MB.

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks.

Anomaly detection is a significant task in sensors' signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors' applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies. We developed autoencoder-based prediction models to automatically detect anomalous data recorded by three electrochemical aptasensors, with variations in the signals' lengths for particular concentrations, analytes, and bioreceptors. Prediction models employed autoencoder networks and the kernel density estimation (KDE) method for finding the threshold to detect anomalies. Moreover, the autoencoder networks were vanilla, unidirectional long short-term memory (ULSTM), and bidirectional LSTM (BLSTM) autoencoders for the training stage of the prediction models. However, the decision-making was based on the result of these three networks and the integration of vanilla and LSTM networks' results. The accuracy as a performance metric of anomaly prediction models showed that the performance of vanilla and integrated models were comparable, while the LSTM-based autoencoder models showed the least accuracy. Considering the integrated model of ULSTM and vanilla autoencoder, the accuracy for the dataset with the lengthier signals was approximately 80%, while it was 65% and 40% for the other datasets. The lowest accuracy belonged to the dataset with the least normal data in its dataset. These results demonstrate that the proposed vanilla and integrated models can automatically detect abnormal data when there is sufficient normal data for training the models.

The Effect of Palm Pollen Extract on Sexual Disorders in Postmenopausal Women: A Randomized Clinical Trial.

Background: Considering the numerous nutritional and estrogenic compounds of palm pollen and their effect on sexual function, this study was performed to investigate the effect of palm pollen extract on sexual disorders in postmenopausal women. Methods: In this three-blind clinical trial, 110 postmenopausal women from December 2019 to December 2020 from Rafsanjan comprehensive health service centers were randomly assigned to two groups, using a lottery method. The intervention group received 300 mg capsule of palm pollen extract, and the control group received placebo for 4 weeks. Sexual disorders were assessed with a 6-item female sexual function index before, at the end of the intervention, and 4 weeks after the end of the intervention. Independent t-test, Chi-square and repeated measures ANOVA were used to analyze the data through SPSS software version 21. The statistically significant level was considered P value less than 0.05. Results: The mean scores of sexual disorders before the intervention in the intervention and control groups were 15.36±5.01 and 14.13±4.67 (P=0.68); at the end of the intervention, they were 15.18±4.50 and 14.22±3.91 (P=0.43) and 4 weeks after the end of the intervention we obtained 15.7±4.77 and 14.44±3.78, respectively (P=0.90). Conclusions: According to the results, daily consumption of 300 mg of date pollen extract had no effect on improving sexual disorders in postmenopausal women. Further studies in this field are suggested.Trial Registration Number: IRCT20160308026971N1.

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks.

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor's performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals' lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks' performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.

Dual-specificity phosphatases: therapeutic targets in cancer therapy resistance.

PURPOSE: Therapy resistance is the principal obstacle to achieving cures in cancer patients and its successful tackling requires a deep understanding of the resistance mediators. Increasing evidence indicates that tumor phosphatases are novel and druggable targets in translational oncology and their modulation may hinder tumor growth and motility and potentiate therapeutic sensitivity in various neoplasms via regulation of various signal transduction pathways. Dual-specificity phosphatases (DUSPs) are key players of cell growth, survival and death and have essential roles in tumor initiation, malignant progression and therapy resistance through regulation of the MAPK signaling pathway. In this review, different aspects of DUSPs are discussed. METHODS: A comprehensive literature review was performed using various websites including PubMed. RESULTS: We provide mechanistic insights into the roles of well-known DUSPs in resistance to a wide range of cancer therapeutic approaches including chemotherapy, radiation and molecular targeted therapy in human malignancies. Moreover, we discuss the development of DUSP modulators, with a focus on DUSP1 and 6 inhibitors. Ultimately, the preclinical investigations of small molecule inhibitors of DUSP1 and 6 are outlined. CONCLUSION: Emerging evidence indicates that the DUSP family is aberrantly expressed in human malignancies and plays critical roles in determining sensitivity to a wide range of cancer therapeutic strategies through regulation of the MAPK signaling pathways. Consequently, targeting DUSPs and their downstream molecules can pave the way for more effective cancer therapies.

Cediranib, a pan-inhibitor of vascular endothelial growth factor receptors, inhibits proliferation and enhances therapeutic sensitivity in glioblastoma cells.

AIMS: Glioblastoma (GB) is the most aggressive type of brain tumor. Rapid progression, active angiogenesis, and therapy resistance are major reasons for its high mortality. Elevated expression of members of the vascular endothelial growth factor (VEGF) family suggests that anti-VEGF therapies may be potent anti-glioma therapeutic approaches. Here, we evaluated the anti-tumor activity of cediranib, a pan inhibitor of the VEGF receptors, on GB cells. MATERIALS AND METHODS: Anti-proliferative effects of cediranib were determined using MTT, crystal-violet staining, clonogenic and anoikis resistance assays. Apoptosis induction was assessed by Annexin V/PI staining and Western blot analysis and aggressive abilities of GB cells were investigated using cell migration/invasion assays and zymography. Small-interfering RNA (siRNA)-mediated Knockdown was used to study resistance mechanisms. The anti-proliferative and apoptotic effects of cediranib in combination with radiotherapy, temozolomide, bevacizumab were also evaluated using MTT, Annexin V/PI staining and Western blot analysis for cleaved PARP-1. KEY FINDINGS: Cediranib reduced GB cell proliferation, induced apoptotic cell death and inhibited the aggressive abilities of GB cells. Cediranib synergistically increased the anti-proliferative and apoptotic effects of radiotherapy and bevacizumab and augmented the sensitivity of GB cells to temozolomide chemotherapy. In addition, knockdown of MET and AKT potentiated cediranib sensitivity in cediranib-resistant GB cells. SIGNIFICANCE: These findings suggest that cediranib, alone or in combination with other therapeutics, is a promising strategy for the treatment of GB and provide a rationale for further investigation of the therapeutic potential of cediranib for the treatment of this fatal malignancy.

Algal blooms historical outbreaks in the northern coastal waters of the Persian Gulf and Oman Sea (1980-2015).

Harmful algal blooms (HABs) have been serious environmental problems in the Persian Gulf and Oman Sea in recent years. There has been an increase in occurrence of HABs in coastal waters of Hormuzgan Province (to the north of the Persian Gulf and Oman Sea) in recent decade, due to eutrophication as a result of human activities. In this review, HAB phenomena in coastal waters of Hormuzgan Province are summarized, including, causative species, timing and the location of blooms during a 35-year time span from 1980 to 2015. This review illustrates that 436 algal blooms have been recorded in the north of the Persian Gulf, formed by 17 species of phytoplankton; 270 of harmful dinoflagellate (Margalefidinium polykrikoides) blooms have led to huge catastrophic impacts on the economy, environment, and society. In addition, most algal blooms (49%) have occurred in the coasts of Bandar Abbas. The data in this review suggest supporting the establishment of an algal bloom monitoring and control program in the coastal waters of the northern part of the Persian Gulf and Oman Sea (Hormuzgan Province).

The Effects of Taurine Supplementation on Metabolic Profiles, Pentosidine, Soluble Receptor of Advanced Glycation End Products and Methylglyoxal in Adults With Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial.

OBJECTIVES: Advanced glycation end products, along with methylglyoxal (MGO) as their precursor, play a major role in increased complications of type 2 diabetes mellitus (T2DM). Taurine (2-aminoethanesulphonic acid), a conditionally essential amino acid, is found in most mammalian tissues. Taurine is known as an antiglycation compound. This study was designed to investigate the effects of taurine supplementation on metabolic profiles, pentosidine, MGO and soluble receptors for advanced glycation end products in patients with T2DM. METHODS: In this double-blind randomized controlled trial, 46 patients with T2DM were randomly allocated into taurine and placebo groups. Participants received either 3,000 mg/day taurine or placebo for 8 weeks. Metabolic profiles, pentosidine, MGO and soluble receptors for advanced glycation end products levels were assessed after 12 h of fasting at baseline and completion of the clinical trial. Independent t test, paired t test, Pearson correlation and analysis of covariance were used for analysis. RESULTS: The mean serum levels of fasting blood sugar (p=0.01), glycated hemoglobin (p=0.04), insulin (p=0.03), homeostasis model assessment-insulin resistance (p=0.004), total cholesterol (p=0.01) and low-density lipoprotein cholesterol (p=0.03) significantly were reduced in the taurine group at completion compared with the placebo group. In addition, after completion of the study, pentosidine (p=0.004) and MGO (p=0.006) were significantly reduced in the taurine group compared with the placebo group. CONCLUSIONS: The results of this trial show that taurine supplementation may decrease diabetes complications through improving glycemic control and advanced glycation end products.

Cediranib, an inhibitor of vascular endothelial growth factor receptor kinases, inhibits proliferation and invasion of prostate adenocarcinoma cells.

Prostate Cancer is the second cause of cancer-related death in men and development of metastatic castration-resistant prostate cancer (mCRPC) is the major reason for its high mortality rate. Despite various treatments, all patients succumb to resistant disease, suggesting that there is a pressing need for novel and more efficacious treatments. Members of the vascular endothelial growth factor (VEGF) family play key roles in the tumorigenesis of mCRPC, indicating that VEGF-targeted therapies may have potential anti-tumor efficacy in this malignancy. However, due to compensatory activation of other family members, clinical trials with single-targeted VEGF inhibitors were discouraging. Here, we determined the anti-neoplastic activity of Cediranib, a pan-VEGF receptor inhibitor, in the mCRPC cell lines. Anti-growth effects of Cediranib were studied by MTT and BrdU cell proliferation assays and crystal violet staining. Annexin V/PI, radiation therapy and cell motility assays were carried out to examine the effects of Cediranib on apoptosis, radio-sensitivity and cell motility. Quantitative reverse transcription-PCR (qRT-PCR) and Western blot analyses were conducted to determine the molecular mechanisms underlying the anti-tumor activity of Cediranib. Cediranib decreased cell viability and induced apoptosis via inhibition of the anti-apoptotic proteins. Combination with Cediranib synergistically increased Docetaxel sensitivity and potentiated the effects of radiation therapy. Furthermore, Cediranib impaired cell motility via decrease in the expression of the epithelial-to-mesenchymal transition markers. These findings suggest that Cediranib may have anti-tumor activity in mCRPC cells and warrant further investigation on the therapeutic activity of this pan-VEGF receptor inhibitor in mCRPC.

The effects of taurine supplementation on glycemic control and serum lipid profile in patients with type 2 diabetes: a randomized, double-blind, placebo-controlled trial.

Previous studies have suggested that taurine has hypoglycemic and hypolipidemic effects on experimental diabetic models. Therefore, this clinical trial was designed to explore the impacts of taurine supplementation on glycemic control and lipid profile in the patients with T2DM. This study was conducted on 45 patients with T2DM in Tabriz Sheikhor-raees Polyclinic and Imam-Reza Hospital Endocrine Center. Subjects were randomly divided into taurine and placebo groups. Accordingly, the taurine group (n = 23) received taurine 3000 mg/daily and the placebo group (n = 22) took crystalline microcellulose/daily for the duration of 8 weeks. At baseline and after the trial completion, fasting blood samples were obtained from the patients to assess the glycemic indicators and lipid profile. Independent t test, paired t test, Pearson's correlation, and analysis of covariance was used for analysis. At the end of the study, levels of FBS (p = 0.01), insulin (p = 0.01), HOMA-IR (p = 0.003), TC (p = 0.013), and LDL-C (p = 0.041) significantly decreased in the taurine group compared to the placebo group. In addition, there was no significant changes in HbA1c, triglyceride, HDL-C, anthropometric indicators or dietary intakes by passing 8 weeks from the intervention. In conclusion, the findings of the current study indicated that taurine supplementation (3000 mg/day) for 8 weeks could improve the glycemic indexes and lipid profiles including TC and LDL-C in the patients with T2DM.

Anti-tumor activity of cediranib, a pan-vascular endothelial growth factor receptor inhibitor, in pancreatic ductal adenocarcinoma cells.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is the most common and lethal subtype of pancreatic cancer, with a 5-year survival rate of < 3%. Early tumor dissemination, late diagnosis and insensitivity to conventional treatment are the major reasons for its high mortality rate. Members of the vascular endothelial growth factor (VEGF) family are overexpressed in PDAC and play important roles in its malignant progression, suggesting that VEGF-targeted therapies may interrupt the proliferation and motility of PDAC cells. Here, we evaluated the anti-tumor activity of cediranib, a pan-VEGF receptor inhibitor, on PDAC cells. METHODS: Anti-proliferative effects of cediranib were determined using cell proliferation and crystal violet staining assays. Annexin V/PI staining, radiation therapy, and cell migration and invasion assays were carried out to examine the effects of cediranib on apoptosis, radio-sensitivity and cell motility, respectively. Quantitative reverse transcription-PCR (qRT-PCR) and Western blot analyses were applied to elucidate the molecular mechanisms underlying the anti-tumor activity of cediranib. RESULTS: We found that cediranib decreased PDAC cell proliferation and clonogenic survival and induced apoptotic cell death through inhibition of the anti-apoptotic proteins cIAP1, XIAP, MCL-1 and survivin. Combination with cediranib synergistically increased the sensitivity of PDAC cells to chemotherapeutic agents such as gemcitabine and paclitaxel, and potentiated the effects of radiation therapy on PDAC cell growth inhibition and apoptosis induction. Furthermore, we found that treatment with cediranib impaired PDAC cell migration and invasion via expression reduction of the epithelial-to-mesenchymal transition (EMT) markers ZEB1, N-cadherin and Snail. CONCLUSIONS: Our data indicate that cediranib may exhibit anti-tumor activity in PDAC cells and provide a rationale for further investigation of the potential of VEGF receptor-targeted therapies for the treatment of PDAC.

An overview of management and monitoring of harmful algal blooms in the northern part of the Persian Gulf and Oman Sea (Hormuzgan Province).

Algal bloom as a common phenomenon in the Persian Gulf and the Sea of Oman had catastrophic effects on environmental, social, economic, and human health aspects from 2008 to 2009. Since 2008, the Persian Gulf and the Sea of Oman Ecological Research Institute (PGSOERI) has monitored and managed algal blooms in the Hormuzgan coast along the northern Persian Gulf and the Sea of Oman. Management strategies have included regular monitoring of chlorophyll, water quality, and remote sensing. In this regard, relevant departments and the Regional Organization for Protection of Marine Environment (ROPME) collaborated with each other to prevent and forecast algal blooms. We reviewed historical and current monitoring, mitigation, and management systems of algal blooms in the Hormuzgan coast. In addition, complications and challenges of algal bloom monitoring and management were also discussed. Documenting algal bloom monitoring and research, improving forecasting and modeling of blooms, educating the public and fishermen, developing a cooperative monitoring framework, and controlling pollution input entering the ROPME region are the main challenges of algal bloom management in the Hormuzgan coast.

The ERBB receptor inhibitor dacomitinib suppresses proliferation and invasion of pancreatic ductal adenocarcinoma cells.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC), the most common malignancy of the pancreas, is the fourth most common cause of cancer-related death in the USA. Local progression, early tumor dissemination and low efficacy of current treatments are the major reasons for its high mortality rate. The ERBB family is over-expressed in PDAC and plays essential roles in its tumorigenesis; however, single-targeted ERBB inhibitors have shown limited activity in this disease. Here, we examined the anti-tumor activity of dacomitinib, a pan-ERBB receptor inhibitor, on PDAC cells. METHODS: Anti-proliferative effects of dacomitinib were determined using a cell proliferation assay and crystal violet staining. Annexin V/PI staining, radiation therapy and cell migration and invasion assays were carried out to examine the effects of dacomitinib on apoptosis, radio-sensitivity and cell motility, respectively. Quantitative reverse transcription-PCR (qRT-PCR) and Western blot analyses were applied to elucidate the molecular mechanisms underlying the anti-tumor activity of dacomitinib. RESULTS: We found that dacomitinib diminished PDAC cell proliferation via inhibition of FOXM1 and its targets Aurora kinase B and cyclin B1. Moreover, we found that dacomitinib induced apoptosis and potentiated radio-sensitivity via inhibition of the anti-apoptotic proteins survivin and MCL1. Treatment with dacomitinib attenuated cell migration and invasion through inhibition of the epithelial-to-mesenchymal transition (EMT) markers ZEB1, Snail and N-cadherin. In contrast, we found that the anti-tumor activity of single-targeted ERBB agents including cetuximab (anti-EGFR mAb), trastuzumab (anti-HER2 mAb), H3.105.5 (anti-HER3 mAb) and erlotinib (EGFR small molecule inhibitor) were marginal. CONCLUSIONS: Our findings indicate that dacomitinib-mediated blockade of the ERBB receptors yields advantages over single-targeted ERBB inhibition and provide a rationale for further investigation of the therapeutic potential of dacomitinib in the treatment of ERBB-driven PDAC.

IL-6/IL-6R pathway is a therapeutic target in chemoresistant ovarian cancer.

INTRODUCTION:: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide and despite an initial response to therapeutic agents, the majority of patients have chemoresistant disease. There is no treatment strategy with proven efficacy against chemoresistant EOC and in this setting, overcoming therapy resistance is the key to successful treatment. METHODS:: This study aimed to investigate expression of interleukin-6 (IL-6) (IL-6) and IL-6 receptor (IL-6R) in a panel of the EOC cell lines. To achieve this, the expression of IL-6 and its receptor were compared in the EOC cells using quantitative reverse transcription polymerase chain reaction. MTT assay was performed to obtain chemosensitivity of the EOC cells. RESULTS:: In this report, we show that expressions of IL6 and IL6R are higher in therapy-resistant EOC cells compared to sensitive ones. Higher expression of IL6 and its receptor correlated with resistance to certain chemotherapeutic agents. Moreover, our findings showed that combination of tocilizumab (Actemra; Roche), an anti-IL-6R monoclonal antibody, with carboplatin synergistically inhibited growth and proliferation of the EOC cells and the most direct axis for IL-6 gene expression was NF-κB pathway. CONCLUSION:: Collectively, our findings suggest that blockade of the IL-6 signaling pathway with anti-IL-6 receptor antibody tocilizumab might resensitize the chemoresistant cells to the current chemotherapeutics.

Inhibition of bromodomain and extraterminal domain reduces growth and invasive characteristics of chemoresistant ovarian carcinoma cells.

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy worldwide. Development of chemoresistance and peritoneal dissemination are the major reasons for low survival rate in the patients. The bromodomain and extraterminal domain (BET) proteins are known as epigenetic 'readers,' and their inhibitors are novel epigenetic strategies for cancer treatment. Accumulating body of evidence indicates that epigenetic modifications have critical roles in development of EOC, and overexpression of the BET family is a key step in the induction of important oncogenes. Here, we examined the mechanistic activity of I-BET151, a pan-inhibitor of the BET family, in therapy-resistant EOC cells. Our findings showed that I-BET151 diminished cell growth, clonogenic potential, and induced apoptosis. I-BET151 inhibited cell proliferation through down-modulation of FOXM1 and its targets aurora kinase B and cyclin B1. I-BET151 attenuated migration and invasion of the EOC cells by down-regulation of epithelial-mesenchymal transition markers fibronectin, ZEB2, and N-cadherin. I-BET151 synergistically enhanced cisplatin chemosensitivity by down-regulation of survivin and Bcl-2. Our data provide insights into the mechanistic activity of I-BET151 and suggest that BET inhibition has potential as a therapeutic strategy in therapy-resistant EOC. Further in vivo investigations on the therapeutic potential of I-BET151 in EOC are warranted.

Blockade of nuclear factor-κB (NF-κB) pathway inhibits growth and induces apoptosis in chemoresistant ovarian carcinoma cells.

Epithelial ovarian cancer (EOC) has exhibited marginal improvement in survival rate, despite advances in surgical debulking and chemotherapy regimens. Although the majority of EOC patients achieve a clinical remission after induction therapy, over 80% relapse and succumb to chemoresistant disease. In this regard, it is of paramount importance to elucidate molecular mechanisms and signaling pathways which promote therapy resistance in EOC in order to devise novel and more effective treatment strategies. In this study, we showed that activation of nuclear factor-κB (NF-κB) is significantly higher in therapy-resistant EOC cells compared to chemosensitive counterparts, which was positively associated with resistance to cisplatin, carboplatin, paclitaxel and erlotinib. Bay 11-7082, a highly selective NF-κB inhibitor, reduced cell proliferation, clonogenicity and anoikis resistance in the therapy-resistant EOC cells and induced apoptotic cell death. Moreover, Bay 11-7082 decreased the expression of pro-survival, inflammatory and metastatic genes and synergistically increased anti-proliferative efficacy of cisplatin, carboplatin, paclitaxel and erlotinib. Altogether, these findings suggest that NF-κB is an attractive therapeutic target in EOC to be exploited in translational oncology and Bay 11-7082 is a potential anti-cancer drug to overcome chemoresistance and inhibit proliferation of the EOC cells.