The Potential Role of Nitric Oxide in Halting Cancer Progression Through Chemoprevention

Nitric oxide (NO) in general plays a beneficial physiological role as a vasorelaxant and the role of NO is decided by its concentration present in physiological environments. NO either facilitates cancer-promoting characters or act as an anti-cancer agent. The dilemma in this regard still remains unanswered. This review summarizes the recent information on NO and its role in carcinogenesis and tumor progression, as well as dietary chemopreventive agents which have NO-modulating properties with safe cytotoxic profile. Understanding the molecular mechanisms and cross-talk modulating NO effect by these chemopreventive agents can allow us to develop better therapeutic strategies for cancer treatment

An Efficient Supervised Machine Learning Technique for Forecasting Stock Market Trends

Background/introduction: In recent years, stock market forecasting has received a lot of attention from researchers. This attention and the growing stock market investments have highlighted this as an important and emerging application of machine learning.Methods: In this research work, we present a stock trend forecasting system with a focus on reducing the amount of sparseness in the data collected using machine learning. We conduct an outlier detection of the data available for reducing dimensionality and implement a K-nearest neighbor algorithm to classify stock trends.Results and conclusions: The experimental results show the performance and effectiveness of the proposed trend forecasting system compared to the existing systems. The proposed system’s model (i.e., KNN classifier) gives better results of low error (MSE = 0.00005, MAE = 0.005 and Logcosh = 0.004) on KSE dataset as compared to previous works

(-)-Epigallocatechin-3-gallate reverses the expression of various tumor-suppressor genes by inhibiting DNA methyltransferases and histone deacetylases in human cervical cancer cells

There has been increasing evidence that numerous bioactive dietary agents can hamper the process of carcinogenesis by targeting epigenetic alterations including DNA methylation. This therapeutic approach is considered as a significant goal for cancer therapy due to the reversible nature of epigenetic-mediated gene silencing and warrants further attention. One such dietary agent, green tea catechin, (-)-epigallocatechin-3-gallate (EGCG) has been shown to modulate many cancer-related pathways. Thus, the present study was designed to investigate the role of EGCG as an epigenetic modifier in HeLa cells. DNA methyltransferase (DNMT) and histone deacetylase (HDAC) inhibition assays were conducted, and the transcription levels of DNMT3β and HDAC1 were assessed by enzymatic activity assay and RT-PCR, respectively. Furthermore, we studied the binding interaction of EGCG with DNMT3β and HDAC1 by molecular modeling as well as promoter DNA methylation and expression of retinoic acid receptor-â (RARâ), cadherin 1 (CDH1) and death-associated protein kinase-1 (DAPK1) in EGCG-treated HeLa cells by RT-PCR and MS-PCR. In the present study, time-dependent EGCG-treated HeLa cells were found to have a significant reduction in the enzymatic activity of DNMT and HDAC. However, the expression of DNMT3β was significantly decreased in a time-dependent manner whereas there was no significant change in HDAC1 expression. Molecular modeling data also supported the EGCG-mediated DNMT3β and HDAC1 activity inhibition. Furthermore, time-dependent exposure to EGCG resulted in reactivation of known tumor-suppressor genes (TSGs) in HeLa cells due to marked changes in the methylation of the promoter regions of these genes. Overall, the present study suggests that EGCG may have a significant impact on the development of novel epigenetic-based therapy

Analysis of Human Gait Cycle with Body Equilibrium based on leg Orientation

Gait analysis identifies the posture during movement in order to provide the correct actions for a normal gait. A person\u27s gait may differ from others and can be recognized by specific patterns. Healthy individuals exhibit normal gait patterns, while lower limb amputees exhibit abnormal gait patterns. To better understand the pitfalls of gait, it is imperative to develop systems capable of capturing the gait patterns of healthy individuals. The main objective of this research was to introduce a new concept in gait analysis by computing the static and dynamic equilibrium in a real-world environment. A relationship was also presented among the parameters stated as static \& dynamic equilibrium, speed, and body states. A sensing unit was installed on the designed metal-based leg mounting assembly on the lateral side of the leg. An algorithm was proposed based on two variables: the position of the leg in space and the angle of the knee joint measured by an IMU sensor and a rotary encoder. It was acceptable to satisfy the static conditions when the body was in a fixed position and orientation, whether lying down or standing. While walking and running, the orientation is determined by the position and knee angle variables, which fulfill the dynamic condition. High speed reveals a rapid change in orientation, while slow speed reveals a slow change in orientation. The proposed encoder-based feedback system successfully determined the flexion at 47$^\circ$, extension at 153$^\circ$, and all seven gait cycle phases were recognized within this range of motion. Body equilibrium facilitates individuals when they are at risk of falling or slipping

Nanoemulgel: For Promising Topical and Systemic Delivery

Nanoemulgel delivery system is a fusion of two different delivery systems, wherein the physical state of drug containing nanoemulsion is changed by adding it to the gel matrix, thus enabling more lipophilic drugs to be used in treatment therapies. It solves the major issues such as limiting use of lipophilic drugs, poor oral bioavailability, and unpredictable pharmacokinetic and absorption variations. Simultaneously, its nongreasy nature and easily spreading ability support the patient compliance. Nanoemulgel can be widely used in the treatment of acne, pimple, psoriasis, fungal infection, and inflammation cause by osteoarthritis and rheumatoid arthritis. The delivery of drug via ocular, vaginal, dental, and nose to brain routes for the treatment of diverse local and systemic ailments for instance alopecia, periodontitis, and Parkinson’s are possible. In the cosmetic industries, UV absorber nanoemulgel protected skin from sunburn

Sulforaphane Reverses the Expression of Various Tumor Suppressor Genes by Targeting DNMT3B and HDAC1 in Human Cervical Cancer Cells

© 2015 Munawwar Ali Khan et al. Sulforaphane (SFN) may hinder carcinogenesis by altering epigenetic events in the cells; however, its molecular mechanisms are unclear. The present study investigates the role of SFN in modifying epigenetic events in human cervical cancer cells, HeLa. HeLa cells were treated with SFN (2.5 μM) for a period of 0, 24, 48, and 72 hours for all experiments. After treatment, expressions of DNMT3B, HDAC1, RARβ, CDH1, DAPK1, and GSTP1 were studied using RT-PCR while promoter DNA methylation of tumor suppressor genes (TSGs) was studied using MS-PCR. Inhibition assays of DNA methyl transferases (DNMTs) and histone deacetylases (HDACs) were performed at varying time points. Molecular modeling and docking studies were performed to explore the possible interaction of SFN with HDAC1 and DNMT3B. Time-dependent exposure to SFN decreases the expression of DNMT3B and HDAC1 and significantly reduces the enzymatic activity of DNMTs and HDACs. Molecular modeling data suggests that SFN may interact directly with DNMT3B and HDAC1 which may explain the inhibitory action of SFN. Interestingly, time-dependent reactivation of the studied TSGs via reversal of methylation in SFN treated cells correlates well with its impact on the epigenetic alterations accumulated during cancer development. Thus, SFN may have significant implications for epigenetic based therapy

Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications in various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances

Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances

Performance and emissions of diesel engine with circulation nonsurfactant emulsion fuel system

Diesel engine is known for its durable operation and capability of utilizing various type of fuels, however, dangerous exhaust emissions are emitted from diesel engines. Nonsurfactant emulsion fuel is a potential fuel for diesel engine to reduce for Nitrogen oxides (NOx) and Particulate matter (PM) emission compare to conventional diesel fuel in a diesel engine. In this study, emulsion fuel was prepared using a mixer known as Circulation Non-Surfactant Emulsion Fuel System. The study carried out with different water percentages in the emulsion fuel given as follows: 3%, 6%, and 9% and at a different engine load condition from 1-4 kW with a constant speed of 3200 rpm. Results show that, 6% emulsion fuel shows average 4.38% reduction in NOx emission and 1.10% reduction in fuel consumption. 9% emulsion fuel show higher amount of CO emission compare to Diesel while it reduces CO2 emission. Overall, 6% when prepared are recommended for the formation of non-surfactant emulsion fuel