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Conservation of Endangered Cordyceps sinensis Through Artificial Cultivation Strategies of C. militaris, an Alternate
Cordyceps, an entomopathogenic fungus belonging to the Ascomycota phylum, is a familiar remedial mushroom that is extensively used in the traditional medicinal system, especially in South Asian nations. The significance of this genus members in a range of therapeutic and biotechnological applications has long been acknowledged. The exceedingly valuable fungus Ophiocordyceps sinensis (Cordyceps sinensis) is found in the alpine meadows of Bhutan, Nepal, Tibet, and India, where it is severely harvested. Driven by market demand and ecological concerns, the study highlights challenges in natural C. sinensis collection and emphasizes the shift towards sustainable artificial cultivation methods. This in-depth review navigates Cordyceps cultivation strategies, focusing on C. sinensis and the viable alternative, C. militaris. The escalating demand for Cordyceps fruiting bodies and bioactive compounds prompts a shift toward sustainable artificial cultivation. While solid-state fermentation on brown rice remains a traditional method, liquid culture, especially submerged and surface/static techniques, emerges as a key industrial approach, offering shorter cultivation periods and enhanced cordycepin production. The review accentuates the adaptability and scalability of liquid culture, providing valuable insights for large-scale Cordyceps production. The future prospects of Cordyceps cultivation require a holistic approach, combining scientific understanding, technological innovation, and sustainable practices to meet the demand for bioactive metabolites while ensuring the conservation of natural Cordyceps populations. Graphical Abstract: (Figure presented.). The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
CHARACTER STRENGTHS INTERVENTIONS IN HIGHER EDUCATION STUDENTS: A LITERATURE REVIEW
This review provides a comprehensive overview of interventions on character strengths in college and university students. Both qualitative and quantitative studies were reviewed. The review showed that focusing on character strengths leads to improved well-being, stronger interpersonal relationships, and reduced levels of stress, depression, anxiety, and academic pressure among students. The review also suggests that such interventions can be integrated into elective courses, first-year programs, and short-term training sessions tailored to address the specific needs of students. The interventions can offer a cost-effective alternative to traditional mental health strategies and could be implemented within college counseling centers. The limitations and practical implications of character strengths intervention modules designed specifically for college students are pointed out. By highlighting positive attributes and nurturing personal growth, character strengths interventions emerge as a valuable tool in bolstering the overall well-being of college students. The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 ). -
Exploring the Use of the Therapists Self in Therapy: A Systematic Review
Purpose: This systematic qualitative review explored how psychotherapists use their self in therapy within the psychotherapy literature. It sought to examine the key documented ways through which the therapists self is intentionally used in therapy and the process of using the therapists self. Methods: Following PRISMA guidelines, databases including PubMed, ProQuest, APA PsycArticles, and APA PsycINFO were searched. The review question How do therapists use their self in therapy? guided the search using derivative keywords. Of the 149 screened articles, 20 underwent full-text review, and only four studies met inclusion criteria. Findings: All studies that met the inclusion criteria were from the West. Therapeutic self-disclosure (TSD) emerged as the primary way through which therapists used their self in therapynotably, the only way documented in the studies reviewed. Studies discussed the nature, rationale, influencing factors, and effectiveness of TSD. This article elaborates upon the themes from the reviewed studies. It critically examines existing literature, lists avenues for future research, and discusses implications for psychotherapy practice. Conclusions: The review underscores a significant gap in empirical qualitative research regarding therapists use of their self beyond TSD in therapy. There is an urgent need for further exploration in this domain. 2024 The Author(s). -
Shaping sustainable paths for perishable food supply chains - contemporary insights and future prospects
The pursuit for a sustainable system for perishable Food Supply Chains (FSCs) centres on delivering sustenance to potential stakeholders while minimizing environmental repercussions and conserving precious natural resources. Despite extensive literature review on process and quality in FSCs, the existing gap between research and industry practice still remains a challenge. A lack of comprehensive framework encompassing all stages of perishable FSCs and a dearth of understanding regarding consumer preferences and recent market trends emphasize the need for further research. The present study efforts to address the existing gap by employing an advanced bibliographical technique that categorizes emerging research trends and transcends conventional literature approaches. Through an extensive analysis, the present study objectives to scrutinize the current state of sustainability within perishable FSCs while also mapping out a blueprint for forthcoming research. This comprehensive exploration identifies five pivotal themes: Strategic Governance and Innovation, Consumer Attitudes and Short FSCs, Sustainable Supply Chains Network Design, Sustainable Supply Chains and Circular Economy, and Integration of Emerging Technologies. By probing into prescriptive content analysis, the study delves into research gaps and trends, providing an all-encompassing panorama of prior sustainable FSC research. Furthermore, the study concentrates on two generic research questions that pave the way for embracing sustainable approaches in the realm of perishable FSC research, thereby propelling the field towards a more impactful and sustainable future. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Exploring ocean pH dynamics via a mathematical modeling with the Caputo fractional derivative
Global warming is a complex problem with far-reaching global implications. One of its notable repercussions is the escalation of CO2 levels in the atmosphere, resulting in the phenomenon known as Ocean Acidification (OA). In this research, we have established a correlation between four key factors: marine species, human population, CO2 levels, and ocean pH. By formulating a Caputo fractional differential equation, we investigated the dynamics of these variables to evaluate the significance of this climatic phenomenon. The model analysis reveals that the rise in anthropogenic CO2 emissions causes a reduction in the oceans pH level and increases OA.This process, in turn, decreases the oceans ability to absorb CO2, making it less effective in mitigating climate change. In this study, it was demonstrated that elevated levels of CO2 result in a reduction in pH levels, which in turn causes a decrease in the population of marine species that play a critical role in numerous economic sectors such as tourism, aquaculture, and fisheries. Moreover, we conducted a comprehensive analysis of the influence exerted by the intrinsic growth rate of the human population. We examined various theoretical aspects, including the assessment of existence and uniqueness. Numerical simulations are carried out to illustrate the effect of key parameters on the dynamics of the system using the generalized AdamsBashforthMoulton method. The Author(s) 2024. -
Review of Geopolymer Composites Synthesized Using Different Industrial By-products
Managing the substantial volume of industrial waste is challenging due to diminishing landfill capacity and associated risks to people and the environment. The optimal approach is to repurpose or find alternative applications for these waste products. Previous studies have investigated using industrial waste and chemicals to enhance soil stability. Common binders like cement, while offering significant stabilization potential, raise concerns about economic feasibility and environmental impact. Recently, theres a growing interest in low carbon emission cementing agents. This trend leads to using waste by-products for geopolymer binder production, potentially strengthening soft soil in an eco-friendly way. Unconfined compressive strength, vital in construction foundation design, has been a focus of extensive research to enhance soil strength over the years. This paper provides a brief overview of several studies that highlight the utilization of various industrial waste products in the synthesis of geopolymers. Also, this comprehensive review centers on investigations related to the application of geopolymers derived from industrial solid waste as a soil stabilizer. The review delves into the impact of various parameters, including different percentage mixes (%), molarity (M), temperature (T), curing time (days), on the unconfined compressive strength of the soil. It has been observed that, a variety of industrial by-products like Bagasse ash (BA), Blast furnace slag (BFS), Egg shell powder (ESP), Fly ash (FA), Ground Granulated Blast Furnace Slag (GGBS), Iron Ore Tailings (IOT), Metakaolin (MK), Palm Oil Fuel Ash (POFA), Recycled Asphalt Pavement (RAP), Rice husk ash (RHA), Red Mud (RM), etc. can serve as valuable source materials for geopolymerization. In most of the studies, the commonly utilized alkaline activator consists of a blend of sodium hydroxide and sodium silicate solution. The unconfined compressive strength of geopolymerized industrial waste products relies on specific parameters, including optimal alkaline concentration, activator liquid to raw material mass ratio, and sodium silicate to sodium hydroxide solution ratio. Diverse curing conditions are also necessary, varying with raw materials and activators. The Author(s), under exclusive licence to Chinese Society of Pavement Engineering 2024. -
Evidence-Based Interventions for Improved Psychosocial Outcomes in Harmful Alcohol Use: A Scoping Review
Background. Harmful alcohol use is defined as a drinking pattern that lasts at least one month or has occurred often during the preceding 12 months and that negatively impacts multiple facets of life. It has a high recurrence rate and a poor prognosis, despite the availability of cognitive-behavioral and psychosocial therapy. Emerging neuromodulation techniques for treating harmful alcohol use are gaining traction in the field of psychotherapy, but knowing their efficacy in terms of psychosocial outcomes necessitates an adjuvant approach. This scoping review aims to investigate the existing evidence on the effectiveness of various psychosocial interventions that improve quality of life (QoL) dimensions in conjunction with neurotherapies for individuals with harmful alcohol use. Methods. The review utilized a five-stage technique to search for research papers from 2000 to 2022. After screening and reviewing 41 full-text papers, 29 were found to meet the inclusion criteria. Conclusion. The articles highlighted the advantages of integrated therapeutic interventions such as motivation enhancement therapy, cognitive behavior therapy, neurotherapy, multimodal therapy, supportive therapy, and 12-step facilitation programs. However, limited studies have explored the effectiveness of combining neurotherapy with psychosocial interventions. Implications. Future research should focus on the efficacy of combining neurofeedback with psychosocial therapies to improve QoL for individuals with harmful alcohol use. 2024. Thakuria and Bennett. -
Food Additives and Evolved Methods of Detection: A Review
Food additives are essential constituents of food products in the modern world. The necessity of food processing went up rapidly as to meet requirements including, imparting desirable properties like preservation, enhancement and regulation of color and taste. The methods of identification and analysis of such substances are crucial. With the advancement of technology, a variety of techniques are emerging for this purpose which have many advantages over the existing conventional ways. This review is on different kinds of additives used in the food industry and few prominent methods for their determination ranging from conventional chromatographic techniques to the recently evolved nano-sensor techniques. 2024 Taylor & Francis Group, LLC. -
A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis
Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental 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, sensitiv-ity, 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, at-lases, 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 methodolo-gy. 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 im-pressive 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 da-tasets, 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. 2024 Bentham Science Publishers. -
Can financial markets help attain carbon goals? Evidence from systematic literature review, bibliometric analysis and topic modelling
Purpose: This paper presents a conceptual framework based on an extensive literature review. The aim of this study is to deepen understanding of the relationship between carbon performance and the financial market by applying qualitative research approaches. Design/methodology/approach: The investigation has identified 372 articles sourced from Scopus databases, subjecting the bibliographic data to a comprehensive qualitativequantitative analysis. The research uses established protocols for a structured literature review, adhering to PRISMA guidelines, machine learning-based structural topic modelling using Python and bibliometric citation analysis. Findings: The results identified the leading academic authors, institutions and countries concerning carbon performance and financial markets literature. Quantitative studies dominate this research theme. The study has identified six knowledge clusters using topic modelling related to environmental reporting; price drivers of carbon markets; environmental policy and capital markets; financial development and carbon emissions; carbon risk and financial markets; and environmental performance and firm value. The results of the study also present the opportunities associated with carbon performance and the financial market and propose future research agendas on research through theory, characteristics, context and methodology. Practical implications: The results of the study offer insights to practitioners, researchers and academicians regarding scientific development, intricate relationships and the complexities involved in the intersection of carbon performance and financial markets. For policymakers, a better understanding of carbon performance and financial markets will contribute to designing policies to set up priorities for countering carbon emissions. Social implications: The study highlights the critical areas that require attention to limit greenhouse gas emissions and promote decarbonisation effectively. Policymakers can leverage these insights to develop targeted and evidence-based policies that facilitate the transition to a more sustainable and low-carbon economy. Originality/value: The study initially attempts to discuss the research stream on carbon performance and financial markets literature from a systematic literature review. 2024, Emerald Publishing Limited. -
Parental Perspectives on Stress and Challenges in Raising Autistic Children: A Meta-Synthesis
Raising autistic children can be challenging, and the current meta-synthesis explores the stress and challenges the parents encounter across life domains. Database searches (JSTOR, ProQuest, EBSCO, PsycINFO, and Google Scholar) were done using the SPIDER method, and 463 articles published between 2011 and 2021 were reviewed. The meta-synthesis adhered to the PRISMA guidelines and included 28 eligible studies centered on stress in parents of children up to the age of 12 years diagnosed with autism. This comprehensive analysis encompassed a collective participant pool of n-505 individuals. Eight stressors were derived using the line of argument synthesis method, which include parental stress due to emotional impact, diagnosis process, social stigma, financial aspects, work-life balance, lack of resources and social support, marital life, and academic setting. Multiple stressors exert a combined effect of individual and systemic factors across domains of life, leading to parental stress. Interventions must be designed considering the complex nature of the parental stress and its interaction with the environment. Psycho-education for awareness and empowerment contribute to parental well-being. The Author(s), under exclusive licence to Springer Nature India Private Limited 2024. -
Advancements in Sustainable Techniques for Dried Meat Production: an Updated Review
Dried meat is one of the ethnic and aesthetic food products popular among global civilizations and communities. The background of the production is associated with several methods practiced conventionally in the olden days. This review focused on investigating the advantages, challenges, research gaps, and technological intervention in dried meat production in the modern era. Moreover, it presented a gestalt of cutting-edge thermal and non-thermal food processing technologies and their effectiveness in extending shelf life. It delved into the specific characteristics of dried meat, including biochemical, sensory, and microbiological properties and processing techniques, and addressed the contamination sources. The pros and cons of various drying methods like hot-air drying, vacuum pulsed electric field, microwave-assisted techniques, and non-thermal drying processes are comprehended. The impact on meat's structural properties, nutritional value, shelf-life, quality control, and food safety are thoroughly presented. Moreover, the review explored the biochemical dynamics of the drying process and underscored the health risks associated with mycotoxin contamination in dried meat products. Furthermore, the study also presented the avenues of AI-based platforms and non-destructive technology for validating the quality of dried meat products. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Deciphering the global research trends and significance of moral intelligence via bibliometric analysis
Introduction: Moral Intelligence (MI) as a concept has gained importance in recent years due to its wide applicability in individual, organizational, and clinical settings or even policy making. The present study employed Bibliometric analysis to understand the emerging topics associated with MI and its global research trend. This papers primary aim was (i) to explore the temporal and geographic growth trends of the research publication on MI. (ii) to identify the most prolific countries, institutions, and authors, working on MI, (iii) to identify the most frequent terminologies, (iv) to explore research topics and to provide insight into potential collaborations and future directions, and (v) to explore the significance of the concept of moral intelligence. Method: Bibliometric analysis was used to understand the emerging topics associated with MI and its global research trend using the SCOPUS database. VOS viewer and R were employed to analyze the result. Through the analysis conducted, the development of the construct over time was analyzed. Results: Results have shown that Iran and the United States and these two combined account for 53.16% of the total country-wise publications. Switzerland has the highest number of Multi-county publications. Authors from Iran and Switzerland have the most number of publications. Emerging topics like decision-making, machine ethics, moral agents, artificial ethics, co-evolution of human and artificial moral agents, green purchase intention etc were identified. Discussion: The application of MI in organisational decision-making, education policy, artificial intelligence and measurement of moral intelligence are important areas of application as per the results. Research interest in MI is projected to increase according to the results delineated in this article. Copyright 2024 Bagchi, Srivastava and Tushir. -
Women and Fairness: Navigating an Unfair World
[No abstract available] -
A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms
Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiogramscommonly used in clinics and hospitals to detect abnormal heart rhythmsare not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024. -
A review on the efficacy of artificial intelligence for managing anxiety disorders
Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy. Copyright 2024 Das and Gavade. -
Impact of nanoparticles on immune cells and their potential applications in cancer immunotherapy
Nanoparticles represent a heterogeneous collection of materials, whether natural or synthetic, with dimensions aligning in the nanoscale. Because of their intense manifestation with the immune system, they can be harvested for numerous bio-medical and biotechnological advancements mainly in cancer treatment. This review article aims to scrutinize various types of nanoparticles that interact differently with immune cells like macrophages, dendritic cells, T lymphocytes, and natural killer (NK) cells. It also underscores the importance of knowing how nanoparticles influence immune cell functions, such as the production of cytokines and the presentation of antigens which are crucial for effective cancer immunotherapy. Hence overviews of bio-molecular mechanisms are provided. Nanoparticles can improve antigen presentation, boost T-cell responses, and overcome the immunosuppressive tumor environment. The regulatory mechanisms, signaling pathways, and nanoparticle characteristics are also presented for a comprehensive understanding. We review the nanotechnology platform options and challenges in nanoparticles-based immunotherapy, from an immunotherapy perspective including precise targeting, immune modulation, and potential toxicity, as well as personalized approaches based on individual patient and tumor characteristics. The development of emerging multifunctional nanoparticles and theranostic nanoparticles will provide new solutions for the precision and efficiency of cancer therapies in next-generation practice. Copyright 2024 The Authors. -
Calcium Sulfide Based NanophosphorsA Review on Synthesis Techniques, Characterization and Applications
Calcium sulfide (CaS) is a widely investigated alkaline earth sulfide nanophosphor with promising applications in optoelectronics and biomedical fields due to its excellent photoluminescence properties. The selection of the synthesis method is a crucial factor in determining the efficacy of nanophosphors for various applications. This review provides a comprehensive overview of the various synthesis techniques employed to develop CaS nanophosphors, including solvothermal, alkoxide, sol-gel, microwave, wet chemical co-precipitation, solid-state diffusion, and single-source precursor methods. The structural and optical properties of CaS nanophosphors are discussed in detail, highlighting the influence of different dopants on the emission color, which can be tuned from blue to red. The review also explores the potential applications of CaS nanophosphors in optoelectronics and biomedicine. This review serves as a valuable resource for researchers interested in developing CaS nanophosphors for various optoelectronic and biomedical applications, providing insights into the latest advancements and future prospects in this field. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Job Satisfaction Among Health-Care Practitioners: A Bibliometric Analysis
Purpose: The study aims to throw light on job satisfaction among health-care practitioners based on the metadata of published literature from Scopus database with the help of bibliometric analysis. Design/methodology/approach: Metadata of 6,998 publications from the Scopus database were extracted. Bibliometric analysis was done with country-based co-authorship analysis, all keywords-based co-occurrence analysis, sources-based citation analysis, cited authors-based co-citation analysis, and term co-occurrence based on text data. Findings: It was found that United States had the highest number of publications at 2,037. The Journal Of Nursing Management had the highest number of publications (332). Term co-occurrence based on text data reveals that job satisfaction, turnover intention, work engagement, compassion fatigue, job stress, organizational commitment, job demand, job performance, workplace violence, job burnout, career satisfaction, safety climate, organizational support, transformational leadership, leadership style, discrimination, workplace bullying, and job strain are the most prominent terms. The paper also highlights the factors affecting job satisfaction of employees in the health-care sector. Conclusion: The paper tries to highlight the publication trends on the job satisfaction among health-care practitioners. Since health care is a primary sector, prosperity of other sectors in the society depends on the job satisfaction level of employees in this sector. 2024 Association for Radiologic & Imaging Nursing -
Advances in detecting non-steroidal anti-inflammatory drugs (NSAIDs) using molecular receptors and nanostructured assemblies
The detection and quantification of non-steroidal anti-inflammatory drugs (NSAIDs) are crucial due to their widespread use and potential impact on human health and the environment. This review provides a comprehensive survey of the recent advancements in sensing technologies for NSAIDs, focusing on molecular receptors and nanostructured assemblies. Molecular receptors based on different fluorescent molecules such as anthracene, naphthalimide, squaraine, quinoline, BINOL, etc. offer high selectivity and sensitivity for NSAID detection. In parallel, nanostructured assemblies including CdSe/ZnS, Cd/S quantum dots (QDs), carbon dot-containing imprinted polymers, Ag and Au nanoparticles (NPs), hydrogel-embedded chemosensors, etc. were utilized for NSAID detection. This review highlights the different binding pathways with the change of various photophysical properties combining molecular recognition elements with nanomaterials to develop innovative sensors that achieve rapid, sensitive, and selective detection of NSAIDs. The review also discusses current challenges and future prospects in the field and based on reported designed receptors and nanostructured assemblies. To the best of our knowledge, no reviews have been reported on this topic so far. Thus, this review will fruitfully guide researchers to design various new molecular receptors and nanostructured materials to detect NSAIDs. 2024 RSC.