scholarly journals Is Curcumin the Answer to Future Chemotherapy Cocktail?

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4329
Author(s):  
Wei Yang Kong ◽  
Siew Ching Ngai ◽  
Bey Hing Goh ◽  
Learn-Han Lee ◽  
Thet Thet Htar ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Clinical Evidence ◽  
Research Direction ◽  
Future Research ◽  
Cancer Therapies ◽  
Combination Treatments ◽  
Anticancer Effects ◽  
Critical Insight ◽  
New Cancer Therapies ◽  
Continuous Use

The rise in cancer cases in recent years is an alarming situation worldwide. Despite the tremendous research and invention of new cancer therapies, the clinical outcomes are not always reassuring. Cancer cells could develop several evasive mechanisms for their survivability and render therapeutic failure. The continuous use of conventional cancer therapies leads to chemoresistance, and a higher dose of treatment results in even greater toxicities among cancer patients. Therefore, the search for an alternative treatment modality is crucial to break this viscous cycle. This paper explores the suitability of curcumin combination treatment with other cancer therapies to curb cancer growth. We provide a critical insight to the mechanisms of action of curcumin, its role in combination therapy in various cancers, along with the molecular targets involved. Curcumin combination treatments were found to enhance anticancer effects, mediated by the multitargeting of several signalling pathways by curcumin and the co-administered cancer therapies. The preclinical and clinical evidence in curcumin combination therapy is critically analysed, and the future research direction of curcumin combination therapy is discussed.

ECONOMIC GROWTH ANALYSIS: A STUDY ON MALAYSIAN MARKET

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Suhaily Maizan Abdul Manaf ◽  
Shuhada Mohamed Hamidi ◽  
Nur Shafini Mohd Said ◽  
Siti Rapidah Omar Ali ◽  
Nur Dalila Adenan
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Direct Investment ◽  
Inflation Rate ◽  
Government Expenditure ◽  
Correlation Method ◽  
Research Direction ◽  
Least Square ◽  
Future Research ◽  
Internal And External Factors

Economic performance of a country is mostly determined by the growth and any other internal and external factors. In this study, researchers purposely focused on Malaysian market by examining the relationship between export, inflation rate, government expenditure and foreign direct investment towards economic growth in Malaysia by applying the yearly data of 47 years from 1970 to 2016 using descriptive statistics, regression model and correlation method analysis. By applying Ordinary Least Square (OLS) method, the result suggests that export, government expenditure and foreign direct investment are positively and significantly correlated with the economic growth. However, inflation rate has negative and insignificant relationship with the economic growth. The outcome of the study is suggested to be useful in providing the future research direction towards the economic growth in Malaysia. Keywords: economic growth; export; inflation rate; government expenditure

Co-Enzyme Q10 to Treat Neurological Disorders: Basic Mechanisms, Clinical Outcomes, and Future Research Direction

2013 ◽  
Vol 12 (5) ◽  
pp. 641-664 ◽  
Author(s):  
Mohamed Salama ◽  
Ti-Fei Yuan ◽  
Sergio Machado ◽  
Eric Murillo-Rodriguez ◽  
Jose Vega ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Neurological Disorders ◽  
Research Direction ◽  
Future Research ◽  
Future Research Direction

scholarly journals Recent Advancements on Immunomodulatory Mechanisms of Resveratrol in Tumor Microenvironment

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1343
Author(s):  
Gagan Chhabra ◽  
Chandra K. Singh ◽  
Deeba Amiri ◽  
Neha Akula ◽  
Nihal Ahmad
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Cancer Therapies ◽  
Regulatory Cells ◽  
Immunomodulatory Effects ◽  
Cancer Management ◽  
Natural Agents ◽  
Anticancer Effects ◽  
Cancer Types ◽  
Immune Related Genes

Immunomodulation of the tumor microenvironment is emerging as an important area of research for the treatment of cancer patients. Several synthetic and natural agents are being investigated for their ability to enhance the immunogenic responses of immune cells present in the tumor microenvironment to impede tumor cell growth and dissemination. Among them, resveratrol, a stilbenoid found in red grapes and many other natural sources, has been studied extensively. Importantly, resveratrol has been shown to possess activity against various human diseases, including cancer. Mechanistically, resveratrol has been shown to regulate an array of signaling pathways and processes involving oxidative stress, inflammation, apoptosis, and several anticancer effects. Furthermore, recent research suggests that resveratrol can regulate various cellular signaling events including immune cell regulation, cytokines/chemokines secretion, and the expression of several other immune-related genes. In this review, we have summarized recent findings on resveratrol’s effects on immune regulatory cells and associated signaling in various cancer types. Numerous immunomodulatory effects of resveratrol suggest it may be useful in combination with other cancer therapies including immunotherapy for effective cancer management.

scholarly journals Structural and Functional Remodeling of Mitochondria in Cardiac Diseases

2021 ◽  
Vol 22 (8) ◽  
pp. 4167
Author(s):  
Xiaonan Sun ◽  
Jalen Alford ◽  
Hongyu Qiu
Keyword(s):  
Regulatory Network ◽  
Molecular Basis ◽  
Therapeutic Potential ◽  
Research Direction ◽  
Future Research ◽  
Cardiac Diseases ◽  
Functional Remodeling ◽  
Underlying Mechanisms ◽  
Mitochondrial Remodeling ◽  
Normal Cellular Function

Mitochondria undergo structural and functional remodeling to meet the cell demand in response to the intracellular and extracellular stimulations, playing an essential role in maintaining normal cellular function. Merging evidence demonstrated that dysregulation of mitochondrial remodeling is a fundamental driving force of complex human diseases, highlighting its crucial pathophysiological roles and therapeutic potential. In this review, we outlined the progress of the molecular basis of mitochondrial structural and functional remodeling and their regulatory network. In particular, we summarized the latest evidence of the fundamental association of impaired mitochondrial remodeling in developing diverse cardiac diseases and the underlying mechanisms. We also explored the therapeutic potential related to mitochondrial remodeling and future research direction. This updated information would improve our knowledge of mitochondrial biology and cardiac diseases’ pathogenesis, which would inspire new potential strategies for treating these diseases by targeting mitochondria remodeling.

scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

scholarly journals Recent Progress of Fluxgate Magnetic Sensors: Basic Research and Application

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1500
Author(s):  
Songrui Wei ◽  
Xiaoqi Liao ◽  
Han Zhang ◽  
Jianhua Pang ◽  
Yan Zhou
Keyword(s):  
Recent Progress ◽  
Electronic Devices ◽  
Basic Research ◽  
Research Direction ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Future Research ◽  
Calibration Methods ◽  
Wearable Electronic ◽  
Wearable Electronic Devices

Fluxgate magnetic sensors are especially important in detecting weak magnetic fields. The mechanism of a fluxgate magnetic sensor is based on Faraday’s law of electromagnetic induction. The structure of a fluxgate magnetic sensor mainly consists of excitation windings, core and sensing windings, similar to the structure of a transformer. To date, they have been applied to many fields such as geophysics and astro-observations, wearable electronic devices and non-destructive testing. In this review, we report the recent progress in both the basic research and applications of fluxgate magnetic sensors, especially in the past two years. Regarding the basic research, we focus on the progress in lowering the noise, better calibration methods and increasing the sensitivity. Concerning applications, we introduce recent work about fluxgate magnetometers on spacecraft, unmanned aerial vehicles, wearable electronic devices and defect detection in coiled tubing. Based on the above work, we hope that we can have a clearer prospect about the future research direction of fluxgate magnetic sensor.

scholarly journals Flow and Particle Modelling of Dry Powder Inhalers: Methodologies, Recent Development and Emerging Applications

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Zhanying Zheng ◽  
Sharon Shui Yee Leung ◽  
Raghvendra Gupta
Keyword(s):  
Dry Powder Inhaler ◽  
Research Direction ◽  
Particle Methods ◽  
Future Research ◽  
High Dose ◽  
Dry Powder ◽  
Multi Scale ◽  
Wide Range ◽  
Computational Fluid Dynamics Cfd ◽  
Discrete Element Methods

Dry powder inhaler (DPI) is a device used to deliver a drug in dry powder form to the lungs. A wide range of DPI products is currently available, with the choice of DPI device largely depending on the dose, dosing frequency and powder properties of formulations. Computational fluid dynamics (CFD), together with various particle motion modelling tools, such as discrete particle methods (DPM) and discrete element methods (DEM), have been increasingly used to optimise DPI design by revealing the details of flow patterns, particle trajectories, de-agglomerations and depositions within the device and the delivery paths. This review article focuses on the development of the modelling methodologies of flow and particle behaviours in DPI devices and their applications to device design in several emerging fields. Various modelling methods, including the most recent multi-scale approaches, are covered and the latest simulation studies of different devices are summarised and critically assessed. The potential and effectiveness of the modelling tools in optimising designs of emerging DPI devices are specifically discussed, such as those with the features of high-dose, pediatric patient compatibility and independency of patients’ inhalation manoeuvres. Lastly, we summarise the challenges that remain to be addressed in DPI-related fluid and particle modelling and provide our thoughts on future research direction in this field.

scholarly journals A New Model Averaging Approach in Predicting Credit Risk Default

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 114
Author(s):  
Paritosh Navinchandra Jha ◽  
Marco Cucculelli
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Model Averaging ◽  
Research Direction ◽  
Future Research ◽  
Classification Problems ◽  
Average Technique ◽  
Model Average ◽  
Novel Approach ◽  
Unbalanced Dataset

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

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