Advanced Interdisciplinary Science and Technology
The Advanced Interdisciplinary Science and Technology journal is a high-tier, international publication dedicated to pioneering research that seamlessly integrates multiple scientific and engineering fields. Its core mission is to move beyond multidisciplinary work and foster true interdisciplinary synthesis, where the combination of fields creates novel solutions to complex global challenges and opens new technological frontiers. The journal covers a wide spectrum of converging disciplines, from nano-biotechnology and AI-driven discovery to sustainable energy systems. AIST ensures quality through a rigorous peer-review process managed by an interdisciplinary editorial board, guaranteeing that articles are judged on both their technical excellence and their successful integration of concepts. Operating a hybrid open-access model, the journal is designed to achieve high visibility and impact, serving as a premier platform for researchers aiming to drive scientific revolution and address pressing global issues.
Edge Intelligence Empowering Primary Healthcare: A Review of Health Monitoring Systems for Resource-Limited Communities
Approximately billions of people worldwide live in regions with scarce medical resources, where unstable electricity supplies and inadequate high-speed internet coverage render traditional telemedicine solutions unworkable. This paper systematically reviews recent advances in edge artificial intelligence (Edge AI) for health monitoring in resource-constrained environments, with a focus on the intelligent health monitoring system as a core case study. The system employs a modified low-cost smartphone paired with a portable multi-parameter sensor, with all AI models running locally on the device—completely eliminating dependence on internet connectivity. Following simple training, community health workers can collect patient vital signs, and the system provides real-time triage recommendations. In field deployments across rural Oman, the system achieved a preliminary triage accuracy of 91% for common infectious diseases and chronic conditions, with a cost of less than $0.50 per use. This paper further explores the theoretical implications and practical challenges of edge intelligence for health equity in Global South countries, proposing that a “plug-and-play” edge intelligence architecture represents a viable technological pathway to bridge the global health divide.
Copyright (c) 2026 Advanced Interdisciplinary Science and Technology https://creativecommons.org/licenses/by/4.0
From Treatment to Prevention: A Paradigm Shift in Acute Exacerbation Management of Chronic Obstructive Pulmonary Disease Using a Multimodal Wearable Intelligent Sensing System
Chronic Obstructive Pulmonary Disease (COPD) ranks among the leading causes of death globally, with acute exacerbations serving as the primary driver of hospitalizations and fatalities. The traditional healthcare model, centered on “treatment,” lacks early warning capabilities for exacerbations. This paper systematically reviews the multimodal wearable intelligent sensing system for COPD exacerbation management, using research from Dr. Sofia Ramirez’s team at the University of Barcelona as a starting point. The team integrated four sensors—measuring heart rate, blood oxygen levels, acceleration, and skin conductance—into a lightweight wristband. By deploying lightweight deep learning models locally for real-time data processing on the edge, the system achieved early warnings for acute exacerbations an average of 48 hours in advance (F1 score: 0.89). This paper further provides an in-depth analysis from three dimensions: multimodal physiological sensing, edge intelligence computing architecture, and clinical validation and effectiveness assessment. It explores the dual value of the edge computing architecture in safeguarding patient privacy while reducing data transmission load. The research demonstrates that wearable intelligent sensing technology is driving a fundamental shift in the healthcare paradigm from “treatment” to “prevention,” representing a value leap in the life sciences from mechanistic deconstruction to technological intervention.
Multidimensional Analysis of Obesity in Middle School Students: Causes, Effects and Systematic Intervention Strategies
This study conducts a systematic analysis of the multidimensional causes, health impacts, and intervention strategies related to obesity among Chinese middle school students. Through a systematic review of 98 Chinese and English literature articles published between 2018 and 2023, adhering to the PRISMA protocol, three core mechanisms contributing to obesity were identified: dietary imbalance (17.3%–53.1% of students had irregular breakfast routines, OR = 1.325–2.833), reduced physical activity (only 18.4% met daily exercise standards), and psychological compensation (33.2% engaged in emotional eating, OR = 3.11 for bullying-related depression). Obesity was found to increase systolic blood pressure by 4.36–10.79 mmHg (P < 0.01) and reduce academic performance by 2.743 standard deviations. Systemic risks are also associated with community environments, with low-income areas having only one-third the sports facility density of high-income regions. The study validated the distinct benefits of resistance training (reducing acylated ghrelin, SMD = -1.05) and high-intensity interval training (reducing ghrelin, SMD = -0.88). A four-tier intervention framework based on the socio-ecological model—encompassing individual micro-behavioral adjustments, family contracts, school policies, and social legislation—was proposed. This framework provides a viable approach for achieving the "Healthy China 2030" goal of zero growth in adolescent obesity adolescent obesity.
Copyright (c) 2025 Advanced Interdisciplinary Science and Technology https://creativecommons.org/licenses/by/4.0
Prompts for Writing Scientific Research Papers Using Large Language Models
The rapid advance of artificial intelligence has endowed Large Language Models (LLMs) with formidable capabilities in natural-language generation, textual comprehension and knowledge integration. Consequently, these systems are increasingly deployed to support scholarly paper writing. Prompts—the principal interface between user and model—exert a decisive influence on the quality and usability of the generated text. This study systematically examines design principles, strategies and optimization techniques for prompts employed in LLM-assisted academic writing, and evaluates their effectiveness across discrete research phases through illustrative cases. Findings indicate that prompts characterized by clear structure, sufficient context and unambiguous task specification significantly enhance the logical coherence and disciplinary professionalism of model output. Nevertheless, LLMs remain prone to knowledge hallucination and citation distortion; human verification and ethical safeguards are therefore indispensable. The paper proposes an operational framework for prompt engineering that can guide researchers and foster the responsible integration of AI into academic writing.
Ethical Challenges and Solutions for Explainable AI in Clinical Decision Support Systems: From Black-Box Prediction to Trustworthy
The rapid application of artificial intelligence (AI) in the healthcare sector has brought about unprecedented improvements in diagnostic accuracy; however, its inherent "black-box" nature creates an irreconcilable conflict with the fundamental requirement for transparency in clinical decision-making. This paper systematically reviews the ethical challenges and technical solutions associated with Explainable AI (XAI) within clinical decision support systems. It focuses specifically on analyzing how knowledge-enhanced hybrid models—by integrating biomedical knowledge graphs with lightweight neural networks—can generate natural-language explanations while simultaneously maintaining high predictive accuracy. Research indicates that knowledge graph-based multi-relational graph neural networks can effectively integrate heterogeneous, multi-source data—including genes, diseases, drugs, and phenotypes—demonstrating significant performance improvements over traditional methods in tasks involving isolated node prediction and rare disease inference (with AUC scores rising from 0.31 to 0.95). Furthermore, this paper proposes a four-dimensional ethical evaluation framework encompassing fairness, traceability, privacy protection, and the governance of algorithmic bias. A pilot study involving 1,200 patients across three hospitals in Italy demonstrated that this framework can effectively detect and mitigate algorithmic bias, successfully limiting the disparity in predictive accuracy among patients of different genders, ages, and socioeconomic backgrounds to within 2%. This paper argues that the deep integration of life sciences and comprehensive healthcare must be grounded in the cornerstones of explainability and fairness; this is not merely a technical issue, but a matter concerning the fundamental principles of medical ethics.
From Single-Modality Sequencing to Multi-Omics Intelligence: A Profound Paradigm Shift in Rare Disease Diagnosis
Although high-throughput sequencing technologies have significantly enhanced the molecular diagnostic capabilities for rare diseases, more than half of rare disease patients worldwide still lack a definitive diagnosis. The core bottleneck of this dilemma has shifted from data acquisition to data integration—specifically, the siloed storage and analysis of multi-omics information (including genomics, transcriptomics, and epigenomics), which results in the burial of numerous potential diagnostic clues. This paper systematically reviews the latest advancements in multi-omics data fusion methods within the field of rare disease diagnosis. It focuses on analyzing how deep learning frameworks—exemplified by multi-modal variational autoencoders and attention mechanisms—enable the unified representation and collaborative inference of heterogeneous omics data.
ISLSIH 2025 Paris Conference: A New International Chapter in the Convergence of Life Sciences and Holistic Health
From December 15 to 17, 2025, the ISLSIH 2025 International Symposium was held in Paris, France, focusing on the theme of "Life Sciences and Intelligent Health." Over one hundred experts and scholars from countries including France, Italy, Spain, Oman, and Japan engaged in in-depth discussions across five key areas: genomics, clinical ethics, wearable sensing, rural health monitoring, and robotic surgery training. The event featured the presentation of five original research papers and culminated in the compilation of a comprehensive conference proceedings report. The Pasteur Institute in France introduced a multi-omics deep learning framework that boosted the diagnostic yield for rare diseases from 55% to 82%. The University of Milan in Italy showcased an interpretable and unbiased AI-driven clinical decision-support system. The University of Barcelona in Spain unveiled a wearable device capable of providing a 48-hour early warning for acute exacerbations of Chronic Obstructive Pulmonary Disease (COPD). Sultan Qaboos University in Oman launched a low-cost, edge-AI-based health monitoring system tailored for rural settings, achieving a triage accuracy rate of 91%. Meanwhile, Kyoto University in Japan presented an AI-assisted robotic surgery training system that accelerated the rate of skill acquisition among physicians by 35%. Roundtable discussions focused on critical issues such as data privacy, barriers to clinical implementation, and the global health divide. The symposium issued a call for increased support for health technologies in resource-constrained environments, aiming to ensure that the benefits of advancements in life sciences reach every individual. The next ISLSIH symposium is scheduled to take place in Melbourne, Australia, in 2026.
Artificial Intelligence and Future Medicine
Wearable Sensing and Chronic Disease Management
Edge Computing and Real-time Early Warning Systems
Bridging the Global Health Technology Divide
Foundations of Interdisciplinary Science and Technology: From Disciplinary Depth to Convergent Synthesis
This review article, written for the inaugural issue of Advanced Interdisciplinary Science and Technology (AIST), provides a systematic framework for understanding, evaluating, and practicing genuine interdisciplinarity. It distinguishes multidisciplinary adjacency, cross-disciplinary collaboration, and true interdisciplinary synthesis, then formalizes the core principles of knowledge integration across disparate fields. The review synthesizes insights from philosophy of science, complex systems theory, empirical studies of high-performing convergent teams, and frontier domains such as AI-driven discovery, nano-biotechnology, and climate-systems modeling. Key contributions include: (1) the concept of translational literacy as a core competency; (2) methodological protocols for cross-domain data fusion under divergent uncertainty conventions; (3) design principles for bio-inspired nanomaterials that unify surface chemistry, protein engineering, and systems biology; and (4) institutional conditions that enable genuine synthesis. The review concludes with a baseline agenda for evaluating progress in interdisciplinary science and a set of irreducible research questions that demand simultaneous engagement across nanoscale fabrication, physiological modeling, and ethical risk assessment.
A Self-Regulating Bio-Climate Adaptive Envelope Integrating Microbial Mineralization, Hygroscopic Hydrogels, and AI Optimization
The building sector accounts for over one-third of global energy consumption and carbon emissions; consequently, traditional mechanical approaches to environmental control face increasingly severe sustainability challenges. This paper proposes the concept of a "bio-climate adaptive envelope"—a paradigm extending beyond conventional passive design—which integrates three key technological pillars: (1) Self-healing and carbon sequestration mechanisms based on microbial mineralization, leveraging the carbonate mineralization capabilities of ureolytic bacteria (e.g., *Bacillus* species) to achieve autonomous crack repair and biological CO₂ fixation; (2) Hygroscopic hydrogels serving as passive actuation elements, enabling energy-free ventilation regulation through humidity-responsive deformation at the molecular-rotor level; and (3) An AI optimization framework based on Graph Neural Networks and Reinforcement Learning, facilitating multi-scale prediction of indoor and outdoor environments alongside the coordinated management of biological processes. This paper systematically reviews the latest advancements in these technological domains, elucidates the coupling mechanisms between microbial metabolism and the water absorption-desorption cycles of hydrogels, and discusses critical challenges such as maintaining long-term microbial viability, ensuring cross-scale structural-biological stability, and conducting full life-cycle assessments. The research indicates that this integrated system holds the potential to reduce HVAC energy consumption by 30–50%, while simultaneously achieving a biological CO₂ fixation rate of 0.5–1.5 kg per square meter of facade per year. Finally, this paper proposes a new paradigm of symbiosis among architectural, environmental, and biological systems, providing a theoretical framework for the next generation of intelligent building technologies.
Microbially Induced Carbonate Precipitation (MICP)
From Data to Materials: An Active Learning-Based Cross-Scale "Synthesis-Characterization-Performance" Closed-Loop System for Extreme Environment Materials Development
The launch of Advanced Interdisciplinary Science and Technology (AIST) responds to a critical paradigm shift in modern research, where the most profound scientific and technological challenges are inherently complex and transcend traditional disciplinary boundaries. This article articulates the foundational philosophy and urgent necessity for a dedicated platform fostering interdisciplinary synthesis. It argues that progress in frontier domains—from sustainable energy and bio-digital twins to AI-driven discovery—is increasingly contingent on the integration of diverse fields such as materials science, data analytics, engineering, ethics, and the life sciences. The article outlines AIST's role not merely as an archival publication but as a dynamic ecosystem designed to catalyze collaboration. It details the journal's commitment to a rigorous, multi-tiered peer-review process specifically tailored to evaluate the integrity and innovation of convergent research. Furthermore, it positions AIST as a vital conduit for translating integrated research into tangible societal impact, addressing global challenges through a holistic lens. The journal is presented as an essential agent in shaping the future of scientific inquiry, providing a structured and incentivized venue for the pioneering work that lies at the interfaces between disciplines.
A KRAS-Targeted mRNA Cancer Vaccines: Synergistic Mechanisms and Future Perspectives in Combination Immunotherapy
KRAS is one of the most frequently mutated oncogenes in human cancers and was long considered "undruggable." Although the successful development of small molecule inhibitors targeting the KRAS G12C mutation, such as Sotorasib and Adagrasib, represents a historic breakthrough in targeted therapy, their clinical application is hampered by significant challenges, including limited target coverage, modest objective response rates, and the inevitable development of acquired resistance. Concurrently, the success of mRNA vaccine technology in infectious disease prevention has validated its potential as a powerful platform, offering a new paradigm for cancer therapy. By presenting mutated KRAS as a neoantigen, mRNA cancer vaccines can elicit a specific T-cell mediated immune response to precisely eliminate tumor cells. This approach holds the unique advantages of covering a broader range of KRAS mutations (e.g., G12D, G12V) and inducing long-lasting immunological memory. This article systematically elucidates an innovative "targeted therapy plus immunotherapy" combination strategy, integrating KRAS small molecule inhibitors with mutation-specific KRAS mRNA vaccines. The central thesis is that these two modalities can exert synergistic anti-tumor effects through complementary mechanisms. On one hand, KRAS inhibitors not only directly suppress tumor growth but may also foster a "hot tumor" microenvironment more permissive to T-cell infiltration by inducing immunogenic cell death and reversing immunosuppression. On the other hand, the mRNA vaccine can precisely eradicate tumor cells that have developed resistance to inhibitors via mechanisms such as bypass pathway activation, as well as clear minimal residual disease, thereby overcoming drug resistance and preventing tumor recurrence. By reviewing the mechanisms of action, clinical progress, and limitations of each therapy, this paper provides an in-depth analysis of the theoretical underpinnings for their synergistic combination, offering a strong rationale and a forward-looking perspective for the development of a more effective and durable treatment paradigm for KRAS-mutant cancers.
A Management of Type 2 Diabetes: Mechanistic Insights, Dietary Models and Machine Learning Applications
Type 2 diabetes is a rapidly increasing metabolic disease worldwide, driven by unhealthy dietary patterns, chronic inflammation, nutrient imbalances, sedentary behavior and disturbed circadian rhythm. This review summarizes mechanistic evidence linking high intakes of saturated fat, refined carbohydrates, and UPFs to insulin resistance through activation of inflammatory pathways, hepatic triglyceride accumulation, glucolipotoxicity, gut barrier impairment, and disturbances in gut microbiota. The roles of fiber, antioxidant nutrients, and micronutrients in preserving insulin sensitivity and β-cell function are highlighted. Major evidence-based dietary patterns, including the MD, DASH Diet, low-carbohydrate diets are also introduced. Other factors including intermittent fasting, lifestyle habits and psychological states are also mentioned in relation to blood sugar and lipid levels as well as metabolic risks. The review further discusses dietary strategies tailored to Chinese populations, established a dietary structure with cultural characteristics and emphasized traditional functional foods such as tea. Finally, we outline emerging applications of ML in diabetes care, including risk prediction, data identification and personalized dietary response. These insights support an integrated framework to improve long-term outcomes in T2D management.
The Forging the Future at the Frontiers: The Interdisciplinary Imperative of Advanced Interdisciplinary Science and Technology (AIST)
Copyright (c) 2025 Advanced Interdisciplinary Science and Technology https://jist.islsih.org/index.php/aist_journal/article/view/3
A Theranostic Nanorobotic Platform for Cancer: Integrating Smart Nanomaterials and Biological Computers into Tumor Microenvironment-Responsive Systems
Conventional nanomedicine approaches for cancer therapy face a fundamental limitation: they lack the capacity for autonomous sensing and decision-making, rendering them incapable of adapting to the dynamic and heterogeneous nature of the tumor microenvironment (TME). This review presents a comprehensive framework for a next-generation theranostic nanorobot that integrates three transformative technologies: DNA origami for structural precision, nucleic acid-based logic gates for onboard computing, and externally actuated propulsion systems for targeted navigation. We propose a closed-loop architecture wherein the nanorobot senses multiple TME biomarkers—including pH, enzymatic activity, and hypoxia—processes these inputs through Boolean logic operations (AND/OR gates), and executes therapeutic actions exclusively upon satisfaction of predefined criteria. This paradigm shifts from passive accumulation to active computation, enabling unprecedented specificity in tumor targeting. We critically examine recent advances in DNA nanotechnology for logic-gated molecular systems, magnetic and acoustic actuation platforms for in vivo navigation, and optoacoustic imaging modalities for real-time tracking. Key challenges addressed include biocompatibility, bioenergetics for autonomous function, and integration of synthetic biology tools for metabolic support. Finally, we envision a future "sense-compute-respond" platform that embodies the convergence of nanobiotechnology, systems biology, and precision medicine.