Recent Advances Research Articles

Schema Extraction in NoSQL Databases: A Systematic Literature Review

Introduction: Nowadays, NoSQL databases have taken on an increasingly important role in the storage of massive data within companies. Due to a common property called schema-less, NoSQL databases offer great flexibility, particularly for the storage of data in different formats. However, despite their success in data storage, schema-less databases are a major obstacle in areas requiring precise knowledge of this schema, especially in the field of data integration. Method: This study presents a Systematic Literature Review (SLR) to explore, evaluate, and discuss relevant existing research and endeavors using novel schema extraction approaches. Furthermore, we conducted this study using a well-defined methodology to examine and study the problem of schema extraction from NoSQL databases. Results: Our research results highlight and emphasize the scheme extraction approaches and provide knowledge to researchers and practitioners by proposing schema extraction approaches and their limitations, which contributes to inventing new, more efficient approaches. Conclusion: In our future work, inspired by the recent advances in quantum computing and the emergence of post-quantum cryptography (PQC), we aim to propose a schema extraction approach that blends cutting-edge technologies with a strong focus on database security.

Recent Advancements in the Treatment of Alzheimer's Disease: A Multitarget-directed Ligand Approach.

Alzheimer's disease (AD) is a neurodegenerative disease and one of the leading causes of progressive dementia, affecting 50 million people worldwide. Many pathogenic processes, including amyloid β aggregation, tau hyperphosphorylation, oxidative stress, neuronal death, and deterioration of the function of cholinergic neurons, are associated with its progression. The one-compound-one-target treatment paradigm was unsuccessful in treating AD due to the multifaceted nature of Alzheimer's disease. The recent development of multitarget-directed ligand research has been explored to target the complementary pathways associated with the disease. We aimed to find the key role and progress of MTDLs in treating AD; thus, we searched for the past ten years of literature on "Pub- Med", "ScienceDirect", "ACS" and "Bentham Science" using the keywords neurodegenerative diseases, Alzheimer's disease, and multitarget-directed ligands. The literature was further filtered based on the quality of work and relevance to AD. Thus, this review highlights the current advancement and advantages of multitarget-directed ligands over traditional single-targeted drugs and recent progress in their development to treat AD.

Recent Advances in Therapeutic Approaches Against Ebola Virus Infection.

Ebola virus (EBOV) is a genus of negative-strand RNA viruses belonging to the family Filoviradae that was first described in 1976 in the present-day Democratic Republic of the Congo. It has intermittently affected substantial human populations in West Africa and presents itself as a global health menace due to the high mortality rate of patients, high transmission rate, difficult patient management, and the emergence of complicated autoimmune disease-like conditions post-infection. EBOV or other EBOV-like species as a biochemical weapon pose a significant risk; hence, the need to develop both prophylactic and therapeutic medications to combat the virus is unquestionable. In this review work, we have compiled the literature pertaining to transmission, pathogenesis, immune response, and diagnosis of EBOV infection. We included detailed structural details of EBOV along with all the available therapeutics against EBOV disease. We have also highlighted current developments and recent advances in therapeutic approaches against Ebola virus disease (EVD). The development of preventive vaccines against the virus is proving to be a successful effort as of now; however, problems concerning logistics, product stability, multi- dosing, and patient tracking are prominent in West Africa. Monoclonal antibodies that target EBOV proteins have also been developed and approved in the clinic; however, no small drug molecules that target these viral proteins have cleared clinical trials. An understanding of clinically approved vaccines and their shortcomings also serves an important purpose for researchers in vaccine design in choosing the right vector, antigen, and particular physicochemical properties that are critical for the vaccine's success against the virus across the world. Our work brings together a comprehensive review of all available prophylactic and therapeutic medications developed and under development against the EBOV, which will serve as a guide for researchers in pursuing the most promising drug discovery strategies against the EBOV and also explore novel mechanisms of fighting against EBOV infection.

Polymersomes as Next Generation Nanocarriers for Drug Delivery: Recent Advances, Patents, Synthesis and Characterization

Background: Polymersomes (PS), self-assembled nanostructures formed by amphiphilic block copolymers, have garnered significant attention in recent years due to their unique properties and versatile applications in the fields of drug delivery and biomedicine. They are being prepared for a wide range of complex medicinal compounds, including nucleic acids, proteins, and enzymes. Polymersomes have lately been used as vehicles for delivering varied therapeutic substances and regulating ROS (reactive oxygen species). Due to their immunogenic features, polymersomes could play a critical role in enhancing subunit vaccine and drug delivery against COVID-19 infection. Objective: The prime purpose of this manuscript is to furnish an extensive overview of polymersomes, highlighting their recent advances, fabrication methods, characterization techniques, and pharmaceutical applications. Methods: The article has been amassed using several online and offline manuscripts from reputed journals, books, and other resources. Besides this, various user-friendly interfaces, like Pubmed, Google Scholar, etc, have been utilized to gather the latest data about polymersomes. This domain encompasses recent advancements in the realm of innovations about the delivery of drugs through polymeric vesicles. This field involves innovations or developments in nanocarrier systems as they are efficaciously employed to deliver the desired moiety to the targeted site. Results: PS have been discovered to exhibit remarkable promise in addressing various challenges associated with inadequate bioavailability, targeted drug delivery, dosing frequency, and diminished toxic effects. Over the past decade, such nanovesicles have been effectively employed as a complementary approach to address the issues arising from poorly soluble medications. However, this domain still requires further focus on novel breakthroughs. Conclusion: Polymersomes demonstrate unparalleled potential as innovative carriers, exhibiting remarkable versatility and exceptional biocompatibility. This concise review underscores their extraordinary prospects in diverse fields, accentuating their distinctive attributes and opening new avenues for groundbreaking applications.

Organoids Research for Colorectal Cancer: Promising Approach for Precision Medicine, their Applications and Future Perspectives

Background: Organoids are three-dimensional (3D) constructs designed to emulate the complexity and functionality of organs in the body. Organoids have recently been used as powerful instruments for modeling and investigating several diseases, including colorectal cancer. Colorectal cancer is caused by altering colonic epithelial cells, which produce adenomas and carcinomas. Objective: The objective of present study was to investigate impact of organoids on colorectal cancer and their therapeutic outcome in cancer research. Organoids can be grown from stem cells in vitro, which closely resemble the structure and function of the organ they are derived from. They have been used in a variety of research applications, including disease modeling, drug screening, and personalized medicine. Organoids have allowed researchers to understand better the mechanisms underlying colorectal cancer initiation, progression, and resistance to therapy. Methods: The literature review was surveyed, and keywords related to cancer management, organoids, modelling, personized medicine, 3D structures were screened for colorectal cancer management were screened in SCI-hub, SCOPUS, WOS, and ABC Journals. Results: The findings of studies suggested that organoids derived from patient tumors can recapitulate the histopathology and genetic alterations of the original tumor, making them a valuable tool for personalized medicine. Conclusion: Organoids have been used to develop high-throughput drug screening assays and investigate the tumor microenvironment's contribution to colorectal cancer progression. In this review, we summarize recent advances in the use of organoids to study colorectal cancer and discuss their potential applications in the clinic.

Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges.

Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with computers. Previous studies have achieved significant breakthroughs and extensive applications in the past decade, such as public opinion analysis and intelligent voice service. With the rapid development of deep learning, SA based on various modalities has become a research hotspot. However, only individual modality has been analyzed separately, lacking a systematic carding of comprehensive SA methods. Meanwhile, few surveys covering the topic of multimodal SA (MSA) have been explored yet. In this article, we first take the modality as the thread to design a novel framework of SA tasks to provide researchers with a comprehensive understanding of relevant advances in SA. Then, we introduce the general workflows and recent advances of single-modal in detail, discuss the similarities and differences of single-modal SA in data processing and modeling to guide MSA, and summarize the commonly used datasets to provide guidance on data and methods for researchers according to different task types. Next, a new taxonomy is proposed to fill the research gaps in MSA, which is divided into multimodal representation learning and multimodal data fusion. The similarities and differences between these two methods and the latest advances are described in detail, such as dynamic interaction between multimodalities, and the multimodal fusion technologies are further expanded. Moreover, we explore the advanced studies on multimodal alignment, chatbots, and Chat Generative Pre-trained Transformer (ChatGPT) in SA. Finally, we discuss the open research challenges of MSA and provide four potential aspects to improve future works, such as cross-modal contrastive learning and multimodal pretraining models.

A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis

Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.

Nanotechnology in Drug Delivery: Overcoming Poor Solubility Challenges through Nanoformulations

Abstract: The pharmaceutical sector continues to face difficulties with poorly soluble drug solubility. Insufficiently soluble drugs have low bioavailability, and their effectiveness is frequently affected. Numerous approaches have been developed in response to this challenge, including using various dosage forms, solid dispersions, nano-suspensions, self-emulsifying drug delivery systems, and cyclodextrin complexes. By improving drug dissolving, decreasing drug particle size, and increasing drug dispersion, these dosage forms seek to increase drug solubility. Nanotechnology is one of the latest advances that has the potential to revolutionize the delivery of drugs and significantly improve the solubility of drugs that are now poorly soluble. Since they have a larger surface area and can pass through biological barriers, nanoparticles are particularly well suited for the delivery of drugs. These technologies can potentially enable the development of more effective and efficient drug formulations for the treatment of various diseases. In addition, the review highlights recent advances in the field, including emerging technologies such as nanotechnology, which can revolutionize drug delivery and significantly improve the solubility of poorly soluble drugs with their potential applications.

Using AI to expand the “Toolbox” for EAP writing instruction

Abstract The recent advent of AI-based digital assistants such as ChatGPT offers the potential for a new tool in the instructional “toolbox” for EAP courses. We report on a retrospective survey of students’ experiences using ChatGPT in first- and second-year EAP core writing courses at a continental European university. Students reported that they saw ChatGPT as a social agent, had moderately fluent experiences interacting with it, and had moderately positive perceptions of ChatGPT as an instructional tool. Both self-reported fluency of the interaction and perceptions of ChatGPT’s social agentic qualities were positively associated with perceptions of ChatGPT’s value as an instructional tool. Students reported that ChatGPT was less helpful than their professors, and neither more nor less helpful than their peers. Implications for EAP instruction are discussed.

Advancing diabetes management: Exploring pancreatic beta-cell restoration’s potential and challenges

Diabetes mellitus, characterized by chronic hyperglycemia due to insulin deficiency or resistance, poses a significant global health burden. Central to its pathogenesis is the dysfunction or loss of pancreatic beta cells, which are res-ponsible for insulin production. Recent advances in beta-cell regeneration research offer promising strategies for diabetes treatment, aiming to restore endogenous insulin production and achieve glycemic control. This review explores the physiological basis of beta-cell function, recent scientific advan-cements, and the challenges in translating these findings into clinical applications. It highlights key developments in stem cell therapy, gene editing technologies, and the identification of novel regenerative molecules. Despite the potential, the field faces hurdles such as ensuring the safety and long-term efficacy of regen-erative therapies, ethical concerns around stem cell use, and the complexity of beta-cell differentiation and integration. The review highlights the importance of interdisciplinary collaboration, increased funding, the need for patient-centered approaches and the integration of new treatments into comprehensive care strategies to overcome these challenges. Through continued research and collaboration, beta-cell regeneration holds the potential to revolutionize diabetes care, turning a chronic condition into a manageable or even curable disease.

Left atrial appendage volume estimated by artificial intelligence predicts atrial fibrillation recurrence after cryobaloon ablation

Abstract Background/Introduction The volume of the left atrial appendage (LAA), as measured in contrast-enhanced cardiac computed tomography (CCTA), has been previously reported as a predictor of long-term recurrence of atrial fibrillation (AF) following cryoballoon ablation procedures. Unfortunately manual assessment of LAA volume, is tedious and time-consuming, making it impractical for clinical practice. The recent advent of artificial intelligence (AI)-based methods offers a potential solution to this limitation. Purpose This study aims to evaluate the utility of LAA volume, as automatically measured by a state-of-the-art AI model for the fully automatic segmentation of LAA in CCTA, in predicting AF recurrence following cryoballoon ablation procedures. Methods We retrospectively analyzed a cohort of patients who underwent cryoballoon ablation for pulmonary vein isolation at our facility between 2010 and 2016. AF recurrence was determined through in-person visits and Holter monitoring at 3, 6, and 12 months post-ablation, followed by annual visits. The first three months post-ablation were considered a blanking period. An open-source deep neural network AI model [1] was utilized to perform automatic segmentation and calculation of LAA and left atrium (LA) volumes in. Receiver-operating characteristic curves were used to obtain optimal cut-off values for increased LAA and LA volumes. We then assessed the association of increased LAA and LA volume, and the LAA to LA volume ratio with recurrence-free survival using the Cox regression model. Results The cohort consisted of 212 patients (median age 62, inter-quartile range [IQR]: 55–65), with 25% having persistent AF before the procedure. Subjects were observed for a median period of 12 months (IQR: 12–36), with 70 of them (33%) experiencing a documented recurrent AF episode. The optimal thresholds for predicting AF recurrence were established as 9.8ml for LAA volume, 124ml for LA volume, and 0.086 for the LAA to LA volume ratio, respectively. An increased LAA volume demonstrated the strongest association with AF recurrence (hazard ratio [HR]: 2.6, 95% confidence interval [CI]: 1.55–4.2), followed by an increased LA volume (HR: 2.1, 95% CI: 1.3–3.4), persistence of AF (HR: 2, 95% CI: 1.2–3.3), and increased LAA to LA volume ratio (HR: 1.9, 95% CI: 1.1–3). Kaplan-Meier plots illustrating AF-free survival, are presented in Figure 1. After adjusting for age, persistent AF, left ventricular ejection fraction, and body surface area, all AI measurements remained significantly associated with a higher AF recurrence (Table 1). Conclusions Increased LAA volume, as assessed automatically by AI in CCTA, is a useful predictor of AF recurrence after catheter ablation for atrial fibrillation. The use of freely available, AI-assisted analysis of CCTA could be instrumental in assessing LAA volume, among other predictors, for long-term ablation success.Figure 1.

How can ethology inform the neuroscience of fear, aggression and dominance?

The study of behaviour is dominated by two approaches. On the one hand, ethologists aim to understand how behaviour promotes adaptation to natural contexts. On the other, neuroscientists aim to understand the molecular, cellular, circuit and psychological origins of behaviour. These two complementary approaches must be combined to arrive at a full understanding of behaviour in its natural setting. However, methodological limitations have restricted most neuroscientific research to the study of how discrete sensory stimuli elicit simple behavioural responses under controlled laboratory conditions that are only distantly related to those encountered in real life. Fortunately, the recent advent of neural monitoring and manipulation tools adapted for use in freely behaving animals has enabled neuroscientists to incorporate naturalistic behaviours into their studies and to begin to consider ethological questions. Here, we examine the promises and pitfalls of this trend by describing how investigations of rodent fear, aggression and dominance behaviours are changing to take advantage of an ethological appreciation of behaviour. We lay out current impediments to this approach and propose a framework for the evolution of the field that will allow us to take maximal advantage of an ethological approach to neuroscience and to increase its relevance for understanding human behaviour.

Coding latent concepts: a human and LLM-coordinated content analysis procedure

ABSTRACT Measuring complex and latent concepts at a large-scale poses significant challenges for communication researchers. While computational and crowdsourced methods offer solutions, they often require high professional thresholds or incur significant costs. The recent advent of large language models has revolutionized content analysis. This paper employs a human and LLM-coordinated analysis procedure to measure complex and latent concepts in 1,000 public comments, exemplified by the multi-dimension concept of “deliberativeness.” We showcase the collaboration between humans and LLMs in completing complex coding tasks by designing and refining a codebook for human use and corresponding prompts for LLMs. Surprisingly, we find that fine-tuned GPT-3.5-turbo-1106 with smaller datasets can surpass GPT-4o-2024-05-13’s performance and match manual content analyses. This paper provides communication researchers with an efficient and cost-effective reference for measuring latent concepts.

Organelle Targeted Drug Delivery: Key Challenges, Recent Advancements and Therapeutic Implications.

Organelle-specific targeted drug delivery has emerged as a promising approach in the field of drug delivery and therapeutics. This innovative strategy involves the precise delivery of therapeutic agents to specific organelles within cells, such as the nucleus, mitochondria, endoplasmic reticulum, or lysosomes, with the aim of enhancing drug efficacy while minimizing offtarget effects. Despite its tremendous potential, organelle-specific drug delivery faces several key challenges. One major challenge is the development of delivery systems that can accurately navigate the complex intracellular environment and deliver drugs exclusively to the desired organelles. Achieving this level of precision demands advanced nanotechnology and biomaterials engineering. Furthermore, ensuring the safety and biocompatibility of these delivery systems is paramount. Recent advancements in this field include the development of nanocarriers, such as liposomes, nanoparticles, and dendrimers, designed to target specific organelles through ligandreceptor interactions or pH-responsive mechanisms. Additionally, advancements in molecular biology and genetic engineering have enabled the design of genetically encoded organellespecific drug delivery systems. The therapeutic implications of organelle-specific drug delivery are vast. This approach has the potential to revolutionize the treatment of diseases with organelle- specific pathologies, such as neurodegenerative disorders, cancer, and mitochondrial diseases. By precisely targeting the organelles involved in disease progression, the efficacy of therapies can be significantly improved while minimizing collateral damage to healthy tissues.

Recent Advances in the Preparation of Protein/peptide Microspheres by Solvent Evaporation Method.

Protein/peptide drugs are extensively used to treat various chronic and serious diseases. The short half-life in vivo of protein and peptide as therapeutics drug limit the realization of complete effects. Encapsulating drugs in microspheres can slow the speed of drug release and prolong the efficacy of drugs. The solvent evaporation method is widely used to prepare protein/ peptide microspheres because of its facile operation and minimal equipment requirements. This method has several challenges in the lower encapsulation efficiency, fluctuant release profiles and the stabilization of protein/peptides, which researchers believe may be solved by adjusting the preparation parameter or formulation of microspheres. The article discusses the formulation parameters that govern the preparation of protein/peptide-loaded microspheres by the solvent evaporation method, which provides an overview of the current promising strategies for solvent evaporation for protein/peptide microspheres. The article takes parameter evaluation as the framework, facilitating subsequent researchers to quickly find possible solutions when encountering problems.

Beyond Limitations of 5G with RIS: Field Trial in a Commercial Network, Recent Advances, and Future Directions

Beyond Limitations of 5G with RIS: Field Trial in a Commercial Network, Recent Advances, and Future Directions

Few-Shot Relation Extraction With Dual Graph Neural Network Interaction.

Recent advances in relation extraction with deep neural architectures have achieved excellent performance. However, current models still suffer from two main drawbacks: 1) they require enormous volumes of training data to avoid model overfitting and 2) there is a sharp decrease in performance when the data distribution during training and testing shift from one domain to the other. It is thus vital to reduce the data requirement in training and explicitly model the distribution difference when transferring knowledge from one domain to another. In this work, we concentrate on few-shot relation extraction under domain adaptation settings. Specifically, we propose DUAL GRAPH, a novel graph neural network (GNN) based approach for few-shot relation extraction. DUAL GRAPH leverages an edge-labeling dual graph (i.e., an instance graph and a distribution graph) to explicitly model the intraclass similarity and interclass dissimilarity in each individual graph, as well as the instance-level and distribution-level relations across graphs. A dual graph interaction mechanism is proposed to adequately fuse the information between the two graphs in a cyclic flow manner. We extensively evaluate DUAL GRAPH on FewRel1.0 and FewRel2.0 benchmarks under four few-shot configurations. The experimental results demonstrate that DUAL GRAPH can match or outperform previously published approaches. We also perform experiments to further investigate the parameter settings and architectural choices, and we offer a qualitative analysis.

The Mechanisms of miRNAs on Target Regulation and their Recent Advances in Atherosclerosis.

miRNAs are crucial regulators in a variety of physiological and pathological processes, while their regulation mechanisms were usually described as negatively regulating gene expression by targeting the 3'-untranslated region(3'-UTR) of target gene miRNAs through seed sequence in tremendous studies. However, recent evidence indicated the existence of non-canonical mechanisms mediated by binding other molecules besides mRNAs. Additionally, accumulating evidence showed that functions of intracellular and intercellular miRNAs exhibited spatiotemporal patterns. Considering that detailed knowledge of the miRNA regulating mechanism is essential for understanding the roles and further clinical applications associated with their dysfunction and dysregulation, which is complicated and not fully clarified. Based on that, we summarized the recently reported regulation mechanisms of miRNAs, including recognitions, patterns of actions, and chemical modifications. And we also highlight the novel findings of miRNAs in atherosclerosis progression researches to provide new insights for non-coding RNA-based therapy in intractable diseases.

Topical Anti-ulcerogenic Effect of the Beta-adrenergic Blockers on Diabetic Foot Ulcers: Recent Advances and Future Prospectives.

Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes. The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs. Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review. The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures. There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

Targeting Cervical Cancer Stem Cells by Phytochemicals.

Cervical cancer (CaCx) poses a significant global health challenge, ranking as the fourth most common cancer among women worldwide. Despite the emergence of advanced treatment strategies, recurrence remains a bottleneck in favorable treatment outcomes and contributes to poor prognosis. The chemo- or radio-therapy resistance coupled with frequent relapse of more aggressive tumors are some key components that contribute to CaCx-related mortality. The onset of therapy resistance and relapse are attributed to a small subset of, slow-proliferating Cancer Stem Cells (CSC). These CSCs possess the properties of tumorigenesis, self-renewal, and multi-lineage differentiation potential. Because of slow cycling, these cells maintain themselves in a semi-quiescent stage and protect themselves from different anti-proliferative anti-cancer drugs. Keeping in view recent advances in their phenotypic and functional characterization, the feasibility of targeting CSC and associated stem cell signaling bears a strong translational value. The presence of CSC has been reported in CaCx (CCSC) which remains a forefront area of research. However, we have yet to identify clinically useful leads that can target CCSC. There is compelling evidence that phytochemicals, because of their advantages over synthetic anticancer drugs, could emerge as potential therapeutic leads to target these CCSCs. The present article examined the potential of phytochemicals with reported anti-CSC properties and evaluated their future in preclinical and clinical applications against CaCx.