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Article: Curcumin: The Golden Nutraceutical on the Road to Cancer Prevention and Therapeutics. A Clinical Perspective.

Kumar, Aviral / Hegde, Mangala / Parama, Dey / Kunnumakkara, Ajaikumar B

2023 Volume 27, Issue 3, Page(s) 33–63

Abstract: Cancer is considered as the major public health scourge of the 21st century. Although remarkable strides were made for developing targeted therapeutics, these therapies suffer from lack of efficacy, high cost, and debilitating side effects. Therefore, ... ...

Abstract	Cancer is considered as the major public health scourge of the 21st century. Although remarkable strides were made for developing targeted therapeutics, these therapies suffer from lack of efficacy, high cost, and debilitating side effects. Therefore, the search for safe, highly efficacious, and affordable therapies is paramount for establishing a treatment regimen for this deadly disease. Curcumin, a known natural, bioactive, polyphenol compound from the spice turmeric (Curcuma longa), has been well documented for its wide range of pharmacological and biological activities. A plethora of literature indicates its potency as an anti-inflammatory and anti-cancer agent. Curcumin exhibits anti-neoplastic attributes via regulating a wide array of biological cascades involved in mutagenesis, proliferation, apoptosis, oncogene expression, tumorigenesis, and metastasis. Curcumin has shown a wide range of pleiotropic anti-proliferative effect in multiple cancers and is a known inhibitor of varied oncogenic elements, including nuclear factor kappa B (NF-κB), c-myc, cyclin D1, Bcl-2, VEGF, COX-2, NOS, tumor necrosis factor alpha (TNF-α), interleukins, and MMP-9. Further, curcumin targets different growth factor receptors and cell adhesion molecules involved in tumor growth and progression, making it a most promising nutraceutical for cancer therapy. To date, curcumin-based therapeutics have completed more than 50 clinical trials for cancer. Although creative experimentation is still elucidating the immense potential of curcumin, systematic validation by proper randomized clinical trials warrant its transition from lab to bedside. Therefore, this review summarizes the outcome of diverse clinical trials of curcumin in various cancer types.
MeSH term(s)	Humans ; Curcumin/therapeutic use ; Dietary Supplements ; NF-kappa B/metabolism ; Apoptosis ; Anti-Inflammatory Agents/pharmacology ; Neoplasms/drug therapy ; Neoplasms/prevention & control
Chemical Substances	Curcumin (IT942ZTH98) ; NF-kappa B ; Anti-Inflammatory Agents
Language	English
Publishing date	2023-06-12
Publishing country	United States
Document type	Review ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1036388-9
ISSN	0893-9675
ISSN	0893-9675
DOI	10.1615/CritRevOncog.2023045587
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Book ; Online: Efficient Deep Reinforcement Learning Requires Regulating Overfitting

Li, Qiyang / Kumar, Aviral / Kostrikov, Ilya / Levine, Sergey

2023

Abstract: Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are ... ...

Abstract	Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial for enabling data-efficient RL, a general understanding of the bottlenecks in data-efficient RL has remained unclear. Consequently, it has been difficult to devise a universal technique that works well across all domains. In this paper, we attempt to understand the primary bottleneck in sample-efficient deep RL by examining several potential hypotheses such as non-stationarity, excessive action distribution shift, and overfitting. We perform thorough empirical analysis on state-based DeepMind control suite (DMC) tasks in a controlled and systematic way to show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms, and prior methods that lead to good performance do in fact, control the validation TD error to be low. This observation gives us a robust principle for making deep RL efficient: we can hill-climb on the validation TD error by utilizing any form of regularization techniques from supervised learning. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks. Comment: 26 pages, 18 figures, 3 tables, The International Conference on Learning Representations (ICLR) 2023
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
Subject code	006
Publishing date	2023-04-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MicroRNA signatures differentiate types, grades, and stages of breast invasive ductal carcinoma (IDC): miRNA-target interacting signaling pathways.

Verma, Vinod Kumar / Beevi, Syed Sultan / Nair, Rekha A / Kumar, Aviral / Kiran, Ravi / Alexander, Liza Esther / Dinesh Kumar, Lekha

Cell communication and signaling : CCS

2024 Volume 22, Issue 1, Page(s) 100

Abstract: Background: Invasive ductal carcinoma (IDC) is the most common form of breast cancer which accounts for 85% of all breast cancer diagnoses. Non-invasive and early stages have a better prognosis than late-stage invasive cancer that has spread to lymph ... ...

Abstract	Background: Invasive ductal carcinoma (IDC) is the most common form of breast cancer which accounts for 85% of all breast cancer diagnoses. Non-invasive and early stages have a better prognosis than late-stage invasive cancer that has spread to lymph nodes. The involvement of microRNAs (miRNAs) in the initiation and progression of breast cancer holds great promise for the development of molecular tools for early diagnosis and prognosis. Therefore, developing a cost effective, quick and robust early detection protocol using miRNAs for breast cancer diagnosis is an imminent need that could strengthen the health care system to tackle this disease around the world. Methods: We have analyzed putative miRNAs signatures in 100 breast cancer samples using two independent high fidelity array systems. Unique and common miRNA signatures from both array systems were validated using stringent double-blind individual TaqMan assays and their expression pattern was confirmed with tissue microarrays and northern analysis. In silico analysis were carried out to find miRNA targets and were validated with q-PCR and immunoblotting. In addition, functional validation using antibody arrays was also carried out to confirm the oncotargets and their networking in different pathways. Similar profiling was carried out in Brca2/p53 double knock out mice models using rodent miRNA microarrays that revealed common signatures with human arrays which could be used for future in vivo functional validation. Results: Expression profile revealed 85% downregulated and 15% upregulated microRNAs in the patient samples of IDC. Among them, 439 miRNAs were associated with breast cancer, out of which 107 miRNAs qualified to be potential biomarkers for the stratification of different types, grades and stages of IDC after stringent validation. Functional validation of their putative targets revealed extensive miRNA network in different oncogenic pathways thus contributing to epithelial-mesenchymal transition (EMT) and cellular plasticity. Conclusion: This study revealed potential biomarkers for the robust classification as well as rapid, cost effective and early detection of IDC of breast cancer. It not only confirmed the role of these miRNAs in cancer development but also revealed the oncogenic pathways involved in different progressive grades and stages thus suggesting a role in EMT and cellular plasticity during breast tumorigenesis per se and IDC in particular. Thus, our findings have provided newer insights into the miRNA signatures for the classification and early detection of IDC.
MeSH term(s)	Animals ; Female ; Mice ; Biomarkers ; Biomarkers, Tumor/genetics ; Breast Neoplasms/pathology ; Carcinoma, Ductal/genetics ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Signal Transduction
Chemical Substances	Biomarkers ; Biomarkers, Tumor ; MicroRNAs
Language	English
Publishing date	2024-02-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2126315-2
ISSN	1478-811X ; 1478-811X
ISSN (online)	1478-811X
ISSN	1478-811X
DOI	10.1186/s12964-023-01452-2
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Book ; Online: Confidence-Conditioned Value Functions for Offline Reinforcement Learning

Hong, Joey / Kumar, Aviral / Levine, Sergey

2022

Abstract: Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offline RL methods must handle distributional shift between the dataset and the ... ...

Abstract	Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offline RL methods must handle distributional shift between the dataset and the learned policy. The most common approach is to learn conservative, or lower-bound, value functions, which underestimate the return of out-of-distribution (OOD) actions. However, such methods exhibit one notable drawback: policies optimized on such value functions can only behave according to a fixed, possibly suboptimal, degree of conservatism. However, this can be alleviated if we instead are able to learn policies for varying degrees of conservatism at training time and devise a method to dynamically choose one of them during evaluation. To do so, in this work, we propose learning value functions that additionally condition on the degree of conservatism, which we dub confidence-conditioned value functions. We derive a new form of a Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability. By conditioning on confidence, our value functions enable adaptive strategies during online evaluation by controlling for confidence level using the history of observations thus far. This approach can be implemented in practice by conditioning the Q-function from existing conservative algorithms on the confidence.We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence. Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains. Comment: published as a paper in ICLR 2023; 16 pages
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	005
Publishing date	2022-12-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Data-Driven Offline Decision-Making via Invariant Representation Learning

Qi, Han / Su, Yi / Kumar, Aviral / Levine, Sergey

2022

Abstract: The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active interaction. These problems appear in many forms: offline reinforcement ... ...

Abstract	The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active interaction. These problems appear in many forms: offline reinforcement learning (RL), where we must produce actions that optimize the long-term reward, bandits from logged data, where the goal is to determine the correct arm, and offline model-based optimization (MBO) problems, where we must find the optimal design provided access to only a static dataset. A key challenge in all these settings is distributional shift: when we optimize with respect to the input into a model trained from offline data, it is easy to produce an out-of-distribution (OOD) input that appears erroneously good. In contrast to prior approaches that utilize pessimism or conservatism to tackle this problem, in this paper, we formulate offline data-driven decision-making as domain adaptation, where the goal is to make accurate predictions for the value of optimized decisions ("target domain"), when training only on the dataset ("source domain"). This perspective leads to invariant objective models (IOM), our approach for addressing distributional shift by enforcing invariance between the learned representations of the training dataset and optimized decisions. In IOM, if the optimized decisions are too different from the training dataset, the representation will be forced to lose much of the information that distinguishes good designs from bad ones, making all choices seem mediocre. Critically, when the optimizer is aware of this representational tradeoff, it should choose not to stray too far from the training distribution, leading to a natural trade-off between distributional shift and learning performance. Comment: This is an extended version of the NeurIPS 2022 conference paper titled: "Data-Driven Offline Model-Based Optimization via Invariant Representation Learning"
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-11-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Dual Generator Offline Reinforcement Learning

Vuong, Quan / Kumar, Aviral / Levine, Sergey / Chebotar, Yevgen

2022

Abstract: In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously overestimated values. In principle, generative adversarial networks (GAN) can ...

Abstract	In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously overestimated values. In principle, generative adversarial networks (GAN) can provide an elegant solution to do so, with the discriminator directly providing a probability that quantifies distributional shift. However, in practice, GAN-based offline RL methods have not performed as well as alternative approaches, perhaps because the generator is trained to both fool the discriminator and maximize return -- two objectives that can be at odds with each other. In this paper, we show that the issue of conflicting objectives can be resolved by training two generators: one that maximizes return, with the other capturing the ``remainder'' of the data distribution in the offline dataset, such that the mixture of the two is close to the behavior policy. We show that not only does having two generators enable an effective GAN-based offline RL method, but also approximates a support constraint, where the policy does not need to match the entire data distribution, but only the slice of the data that leads to high long term performance. We name our method DASCO, for Dual-Generator Adversarial Support Constrained Offline RL. On benchmark tasks that require learning from sub-optimal data, DASCO significantly outperforms prior methods that enforce distribution constraint. Comment: NeurIPS 2022
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-11-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

Kumar, Aviral / Hong, Joey / Singh, Anikait / Levine, Sergey

2022

Abstract: Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly ... ...

Abstract	Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly suboptimal data, a scenario where imitation learning finds suboptimal solutions that do not improve over the demonstrator that generated the dataset. However, another common use case for practitioners is to learn from data that resembles demonstrations. In this case, one can choose to apply offline RL, but can also use behavioral cloning (BC) algorithms, which mimic a subset of the dataset via supervised learning. Therefore, it seems natural to ask: when can an offline RL method outperform BC with an equal amount of expert data, even when BC is a natural choice? To answer this question, we characterize the properties of environments that allow offline RL methods to perform better than BC methods, even when only provided with expert data. Additionally, we show that policies trained on sufficiently noisy suboptimal data can attain better performance than even BC algorithms with expert data, especially on long-horizon problems. We validate our theoretical results via extensive experiments on both diagnostic and high-dimensional domains including robotic manipulation, maze navigation, and Atari games, with a variety of data distributions. We observe that, under specific but common conditions such as sparse rewards or noisy data sources, modern offline RL methods can significantly outperform BC. Comment: ICLR 2022. First two authors contributed equally
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-04-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Pulsed electric field (PEF): Avant-garde extraction escalation technology in food industry

Naliyadhara, Nikunj / Kumar, Aviral / Girisa, Sosmitha / Daimary, Uzini Devi / Hegde, Mangala / Kunnumakkara, Ajaikumar B.

Trends in Food Science & Technology. 2022 Apr., v. 122 p.238-255

2022

Abstract: Pulsed Electric Field (PEF) technology is a non-thermal approach mainly used to preserve food with higher electrical conductivity, such as liquid or semi-liquid foods. PEF uses an electric field to create irreversible poration in the cell membrane, ... ...

Abstract	Pulsed Electric Field (PEF) technology is a non-thermal approach mainly used to preserve food with higher electrical conductivity, such as liquid or semi-liquid foods. PEF uses an electric field to create irreversible poration in the cell membrane, increasing membrane permeability. The application of PEF to increase the extraction rate in the food and nutraceutical industry is still in its infancy. This study describes the mechanism of PEF treatment and its effect on food's quantitative and qualitative attributes. This review emphasizes the significance of PEF treatment on the extraction of various compounds from the food matrix like juices, edible oils, bioactive compounds, and carbohydrates. A plethora of literature evinced that the application of PEF treatment to extract fruit juices, edible oils, various bioactive compounds, and carbohydrate compounds on a laboratory scale and pilot-level scale have significantly affected the extraction yield. Moreover, PEF treatment allows preserving the end product's quality in terms of sensory and nutritional aspects. Various studies have shown PEF as a productive tool to utilize the waste of agro-processing industries for recovering multiple bioactive compounds that could be economically beneficial. PEF becomes an optimal choice for pretreatment before extraction due to its desirable traits such as minimal operational period and efficient power consumption. Though extensive research has been carried out at a laboratory scale, there is a need to explore the PEF on the industrial scale further and optimize various PEF parameters to obtain high productivity.
Keywords	carbohydrates ; cell membranes ; dietary supplements ; electric field ; electrical conductivity ; energy use and consumption ; food industry ; food matrix ; fruits ; liquids ; membrane permeability ; pulsed electric fields ; wastes ; PV ; VOO ; EVOO ; VCO
Language	English
Dates of publication	2022-04
Size	p. 238-255.
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	1049246-x
ISSN	1879-3053 ; 0924-2244
ISSN (online)	1879-3053
ISSN	0924-2244
DOI	10.1016/j.tifs.2022.02.019
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: EMT in breast cancer metastasis: an interplay of microRNAs, signaling pathways and circulating tumor cells.

Kumar, Aviral / Golani, Aparna / Kumar, Lekha Dinesh

Frontiers in bioscience (Landmark edition)

2020 Volume 25, Issue 5, Page(s) 979–1010

Abstract: Epithelial to Mesenchymal Transition (EMT) is a biological process characterized by the transition from immotile epithelial cells to motile mesenchymal cells. Though shown to be implicated in many biological processes, it has also been identified to ... ...

Abstract	Epithelial to Mesenchymal Transition (EMT) is a biological process characterized by the transition from immotile epithelial cells to motile mesenchymal cells. Though shown to be implicated in many biological processes, it has also been identified to enhance migration and invasion of cancer cells leading to metastasis. A class of microRNAs called "oncomiRs" plays a significant role in the regulation of malignant transformation and metastasis. In this review, the ability of different signaling pathways in controlling EMT through well-defined regulatory networks, and the role exerted by oncomiRs in regulating the specific signaling pathways like TGF-β, Wnt, Notch and Hedgehog in modulating breast cancer metastasis have been discussed with updated information. Further, this review focuses on the significance of up and down regulated microRNAs in the pathogenesis and progression of breast cancer and how such microRNAs could be treated as potential therapeutic targets to circumvent cancer. As a prospective strategy, we highlight the importance of circulating tumor cells (CTCs) and their derived microRNAs as prognostic indicators and cancer therapy monitoring tools.
MeSH term(s)	Breast Neoplasms/genetics ; Breast Neoplasms/pathology ; Epithelial-Mesenchymal Transition/genetics ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; MicroRNAs/genetics ; Neoplasm Metastasis ; Neoplastic Cells, Circulating/metabolism ; Neoplastic Cells, Circulating/pathology ; Precision Medicine/methods ; Signal Transduction/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2020-03-01
Publishing country	Singapore
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2704569-9
ISSN	2768-6698 ; 1093-9946
ISSN (online)	2768-6698
ISSN	1093-9946
DOI	10.2741/4844
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.

Dutta, Pratik / Saha, Sriparna / Pai, Sanket / Kumar, Aviral

Scientific reports

2020 Volume 10, Issue 1, Page(s) 665

Abstract: In the field of computational bioinformatics, identifying a set of genes which are responsible for a particular cellular mechanism, is very much essential for tasks such as medical diagnosis or disease gene identification. Accurately grouping (clustering) ...

Abstract	In the field of computational bioinformatics, identifying a set of genes which are responsible for a particular cellular mechanism, is very much essential for tasks such as medical diagnosis or disease gene identification. Accurately grouping (clustering) the genes is one of the important tasks in understanding the functionalities of the disease genes. In this regard, ensemble clustering becomes a promising approach to combine different clustering solutions to generate almost accurate gene partitioning. Recently, researchers have used generative model as a smart ensemble method to produce the right consensus solution. In the current paper, we develop a protein-protein interaction-based generative model that can efficiently perform a gene clustering. Utilizing protein interaction information as the generative model's latent variable enables enhance the generative model's efficiency in inferring final probabilistic labels. The proposed generative model utilizes different weak supervision sources rather utilizing any ground truth information. For weak supervision sources, we use a multi-objective optimization based clustering technique together with the world's largest gene ontology based knowledge-base named Gene Ontology Consortium(GOC). These weakly supervised labels are supplied to a generative model that eventually assigns all genes to probabilistic labels. The comparative study with respect to silhouette score, Biological Homogeneity Index (BHI) and Biological Stability Index (BSI) proves that the proposed generative model outperforms than other state-of-the-art techniques.
MeSH term(s)	Cluster Analysis ; Computational Biology ; Datasets as Topic ; Gene Ontology ; Genomics ; Humans ; Models, Genetic ; Models, Statistical ; Multigene Family ; Protein Interaction Domains and Motifs/genetics ; Transcriptome
Language	English
Publishing date	2020-01-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-57437-5
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