Search Results (335)

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Large language models (LLMs) are increasingly being applied to clinical tasks, but it remains unclear whether medical-specific models consistently outperform smaller, generalpurpose ones. This study investigates that assumption in the context of cardiovascular disease (CVD) risk assessment. We fine-tuned eight LLMs—both general-purpose and medical-specific—using textualized data from the Behavioral Risk Factor Surveillance System (BRFSS) to classify individuals as “High Risk” or “Low Risk”. To provide actionable insights, we integrated a Retrieval-Augmented Generation (RAG) framework for personalized recommendation generation and deployed the system within an interactive chatbot interface. Notably, Gemma2, a compact 2B-parameter general-purpose model, achieved a high recall (0.907) and F1-score (0.770), performing on par with larger or medical-specialized models such as Med42 and BioBERT. These findings challenge the common assumption that larger or specialized models always yield superior results, and highlight the potential of lightweight, efficiently fine-tuned LLMs for clinical decision support—especially in resource-constrained settings. Overall, our results demonstrate that general-purpose models, when fine-tuned appropriately, can offer interpretable, high-performing, and accessible solutions for CVD risk assessment and personalized healthcare delivery. Full article

Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized—particularly within early-onset CRC (EOCRC) and across diverse treatment and demographic contexts. We present AI-HOPE-JAK-STAT, a novel conversational artificial intelligence platform built to enable the real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC. The platform integrates clinical, genomic, and treatment data to support dynamic, hypothesis-generating analyses for precision oncology. Methods: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal. Users define analytical queries in plain English, which are translated into executable code for cohort selection, survival analysis, odds ratio testing, and mutation profiling. To validate the platform, we replicated known associations involving JAK1, JAK3, and STAT3 mutations. Additional exploratory analyses examined age, treatment exposure, tumor stage, and anatomical site. Results: The platform recapitulated established trends, including improved survival among EOCRC patients with JAK/STAT pathway alterations. In FOLFOX-treated CRC cohorts, JAK/STAT-altered tumors were associated with significantly enhanced overall survival (p < 0.0001). Stratification by age revealed survival advantages in younger (age < 50) patients with JAK/STAT mutations (p = 0.0379). STAT5B mutations were enriched in colon adenocarcinoma and correlated with significantly more favorable trends (p = 0.0000). Conversely, JAK1 mutations in microsatellite-stable tumors did not affect survival, emphasizing the value of molecular context. Finally, JAK3-mutated tumors diagnosed at Stage I–III showed superior survival compared to Stage IV cases (p = 0.00001), reinforcing stage as a dominant clinical determinant. Conclusions: AI-HOPE-JAK-STAT establishes a new standard for pathway-level interrogation in CRC by empowering users to generate and test clinically meaningful hypotheses without coding expertise. This system enhances access to precision oncology analyses and supports the scalable, real-time discovery of survival trends, mutational associations, and treatment-response patterns across stratified patient cohorts. Full article

Microbial fuel cells (MFCs) are bio-electrochemical systems that harness microorganisms to convert organic pollutants in wastewater directly into electricity, offering a dual solution for sustainable wastewater treatment and renewable energy generation. This paper presents a holistic techno-economic and environmental feasibility assessment of large-scale MFC deployment in Dhaka’s industrial zone, Bangladesh, as a relevant case study. Here, treating 100,000 cubic meters of wastewater daily would require a capital investment of approximately USD 500 million, with a total project cost ranging between USD 307.38 million and 1.711 billion, depending on system configurations. This setup has an estimated theoretical energy recovery of 478.4 MWh/day and a realistic output of 382 MWh/day, translating to a per-unit energy cost of USD 0.2–1/kWh. MFCs show great potential for treating wastewater and addressing energy challenges. However, this paper explores remaining challenges, including high capital costs, electrode and membrane inefficiencies, and scalability issues. Full article

Biodegradable mulch films (BDMs) are considered a promising solution for mitigating plastic residue pollution in agroecosystems. However, the degradation behavior and ecological impacts of their residues on soil–plant systems remain unclear. Here, a pot experiment was conducted using an acidic purple soil (AS), a neutral purple soil (NS), and a calcareous purple soil (CS) to investigate the degradation of 1% (w/w) microplastics derived from polyethylene mulch film (PE-MPs) and polybutylene adipate terephthalate/polylactic acid (PBAT/PLA) mulch film (Bio-MPs), as well as their effects on soil properties, bacterial communities, and radish growth. PE-MPs degraded slightly, while the degradation of Bio-MPs followed the order of NS > CS > AS. PE-MPs and Bio-MPs enhanced the nitrification and radish growth in AS but had no significant effects on soil properties and radish growth in CS. Bio-MPs notably increased the relative abundance of PBAT/PLA degradation-related bacteria, such as Ramlibacter, Bradyrhizobium, and Microbacterium, across the three soils. In NS, Bio-MPs raised soil pH and enriched nitrogen-fixing and denitrifying bacteria, leading to a decrease in NO₃⁻-N content and radish biomass. Overall, the effects of Bio-MPs on soil–plant systems varied with soil properties, which are closely related to their degradation rates. These findings highlight the need to assess the ecological risks of BDM residues before their large-scale use in agriculture. Full article

This study presents the development of a sustainable composite material by incorporating by-products from the cork industry into acrylonitrile butadiene styrene (ABS), with the aim of reducing the environmental impact of plastic composites while maintaining their performance. ABS, a petroleum-based polymer, was used as the matrix, and maleic anhydride (MAH) with dicumyl peroxide (DCP) served as a compatibilizing system to improve interfacial adhesion with cork microparticles. Composites were prepared with 10% w/w cork in various particle sizes and characterized via FTIR, X-ray computed tomography, SEM, mechanical testing, and thermal analysis. The best performing formulation (CPC-125) showed a reduction of only ~16% in tensile modulus and ~7% in tensile strength compared with ABS-g-MAH, with a more pronounced decrease in strain at break (3.23% vs. 17.47%) due to the cork’s inherent rigidity. Thermogravimetric and calorimetric analysis confirmed that thermal stability and processing temperatures remained largely unaffected. These results demonstrate the feasibility of incorporating cork microparticles as a bio-based reinforcing filler in ABS composites, offering a promising strategy to reduce the use of virgin plastics in applications compatible with conventional injection molding. Full article

Introduction: Early-onset colorectal cancer (EOCRC) is rising rapidly, particularly among the Hispanic/Latino (H/L) populations, who face disproportionately poor outcomes. The transforming growth factor-beta (TGF-β) signaling pathway plays a critical role in colorectal cancer (CRC) progression by mediating epithelial-to-mesenchymal transition (EMT), immune evasion, and metastasis. However, integrative analyses linking TGF-β alterations to clinical features remain limited—particularly for diverse populations—hindering translational research and the development of precision therapies. To address this gap, we developed AI-HOPE-TGFbeta (Artificial Intelligence agent for High-Optimization and Precision Medicine focused on TGF-β), the first conversational artificial intelligence (AI) agent designed to explore TGF-β dysregulation in CRC by integrating harmonized clinical and genomic data via natural language queries. Methods: AI-HOPE-TGFbeta utilizes a large language model (LLM), Large Language Model Meta AI 3 (LLaMA 3), a natural language-to-code interpreter, and a bioinformatics backend to automate statistical workflows. Tailored for TGF-β pathway analysis, the platform enables real-time cohort stratification and hypothesis testing using harmonized datasets from the cBio Cancer Genomics Portal (cBioPortal). It supports mutation frequency comparisons, odds ratio testing, Kaplan–Meier survival analysis, and subgroup evaluations across race/ethnicity, microsatellite instability (MSI) status, tumor stage, treatment exposure, and age. The platform was validated by replicating findings on the SMAD4, TGFBR2, and BMPR1A mutations in EOCRC. Exploratory queries were conducted to examine novel associations with clinical outcomes in H/L populations. Results: AI-HOPE-TGFbeta successfully recapitulated established associations, including worse survival in SMAD4-mutant EOCRC patients treated with FOLFOX (fluorouracil, leucovorin and oxaliplatin) (p = 0.0001) and better outcomes in early-stage TGFBR2-mutated CRC patients (p = 0.00001). It revealed potential population-specific enrichment of BMPR1A mutations in H/L patients (OR = 2.63; p = 0.052) and uncovered MSI-specific survival benefits among SMAD4-mutated patients (p = 0.00001). Exploratory analysis showed better outcomes in SMAD2-mutant primary tumors vs. metastatic cases (p = 0.0010) and confirmed the feasibility of disaggregated ethnicity-based queries for TGFBR1 mutations, despite small sample sizes. These findings underscore the platform’s capacity to detect both known and emerging clinical–genomic patterns in CRC. Conclusions: AI-HOPE-TGFbeta introduces a new paradigm in cancer bioinformatics by enabling natural language-driven, real-time integration of genomic and clinical data specific to TGF-β pathway alterations in CRC. The platform democratizes complex analyses, supports disparity-focused investigation, and reveals clinically actionable insights in underserved populations, such as H/L EOCRC patients. As a first-of-its-kind system studying TGF-β, AI-HOPE-TGFbeta holds strong promise for advancing equitable precision oncology and accelerating translational discovery in the CRC TGF-β pathway. Full article

Webbed-foot gliding water entry is a characteristic water-landing strategy employed by swans and other large waterfowls, demonstrating exceptional low-impact loading and remarkable motion stability. These distinctive biomechanical features offer significant potential for informing the design of cross-medium vehicles’ (CMVs’) water-entry systems. To analyze the hydrodynamic mechanisms and flow characteristics during swan webbed-foot gliding entry, the three-dimensional bionic webbed-foot water-entry process was investigated through a computational fluid dynamics (CFD) method coupled with global motion mesh (GMM) technology, with a particular emphasis on elucidating the regulatory effects of entry parameters on dynamic performance. The results demonstrated that the gliding water-entry process can be divided into two distinct phases: stable skipping and surface gliding. During the stable skipping phase, the motion trajectory exhibits quasi-sinusoidal periodic fluctuations, accompanied by multiple water-impact events and significant load variations. In the surface-gliding phase, the kinetic energy of the bionic webbed foot progressively decreases while maintaining relatively stable load characteristics. Increasing the water-entry velocity will enhance impact loads while simultaneously increasing the skipping frequency and distance. Increasing the water-entry angle will primarily intensify the impact load magnitude while slightly reducing the skipping frequency and distance. An optimal pitch angle of 20° provides maximum glide-skip stability for the bio-inspired webbed foot, with angles exceeding 25° or below 15° leading to motion instability. This study on webbed-foot gliding entry behavior provided insights for developing novel bio-inspired entry strategies for cross-medium vehicles, while simultaneously advancing the optimization of impact-mitigation designs in gliding water-entry systems. Full article

Polyimine-based composites have emerged as a promising class of dynamic covalent thermosets, combining high mechanical strength, thermal stability, self-healing, recyclability, and reprocessability. This review systematically summarizes recent advances in polyimine synthesis, highlighting dynamic covalent chemistry (DCC) strategies such as imine exchange and reversible Schiff base reactions. Structural customization can be achieved by incorporating reinforcing phases such as carbon nanotubes, graphene, and bio-based fibers. Advanced fabrication methods—including solution casting, hot pressing, and interfacial polymerization—enable precise integration of these components while preserving structural integrity and adaptability. Mechanical performance analysis emphasizes the interplay between dynamic bonds, interfacial engineering, and multiscale design strategies. Polyimine composites exhibit outstanding performance characteristics, including a self-healing efficiency exceeding 90%, a tensile strength reaching 96.2 MPa, and remarkable chemical recyclability. Emerging engineering applications encompass sustainable green materials, flexible electronics, energy storage devices, and flame-retardant systems. Key challenges include balancing multifunctionality, enhancing large-scale processability, and developing low-energy recycling strategies. Future efforts should focus on interfacial optimization and network adaptivity to accelerate the industrial translation of polyimine composites, advancing next-generation sustainable materials. Full article

Bio-inspired design, a paradigm-shifting methodology that translates evolutionary mechanisms into engineering solutions, has established itself as a cornerstone for pioneering innovation in multifaceted technological systems. Despite its promise, the inherent complexity of biological systems and interdisciplinary knowledge gaps hinder the effective translation of biological principles into practical engineering solutions. This study introduces a structured framework integrating large language models (LLMs) with a function–behavior–characteristic–environment (F-B-C-E) paradigm to systematize biomimetic design processes. We propose a standardized F-B-C-E knowledge model to formalize biological strategy representations, coupled with a BERT-based pipeline for automated inversion of biological strategies into engineering applications. To optimize strategy selection, a hybrid decision-making methodology combining VIKOR multi-criteria analysis and rank correlation is developed. The framework’s functional robustness is validated via aquatic robotic system implementations, wherein three biomimetic propulsion modalities—oscillatory caudal propulsion, pulsed hydrodynamic thrust generation, and autonomous peristaltic locomotion—demonstrate quantifiable enhancements in locomotion efficiency and environmental adaptability metrics. These results underscore the robustness of the proposed inversion methodology in resolving intricate engineering problems through systematic biomimetic translation. Full article

Unfired bricks offer a sustainable alternative to traditional fired bricks by enabling the large-scale reuse of industrial, construction, and municipal wastes while significantly reducing energy consumption and greenhouse gas emissions. This review contributes to eliminating knowledge fragmentation by systematically organising stabiliser technologies, performance metrics, and sustainability indicators across a wide variety of unfired brick systems. It thus provides a coherent reference framework to support further development and industrial translation. Emphasis is placed on the role of stabilisers—including cement, lime, geopolymers, and microbial or bio-based stabilisers—in improving mechanical strength, moisture resistance, and durability. Performance data are analysed in relation to compressive strength, water absorption, drying shrinkage, thermal conductivity, and resistance to freeze–thaw and wet–dry cycles. The findings indicate that properly stabilised unfired bricks can achieve compressive strengths above 20 MPa and water absorption rates below 10%, with notable improvements in insulation and acoustic properties. Additionally, life-cycle comparisons reveal up to 90% reductions in CO₂ emissions and energy use relative to fired clay bricks. Despite technical and environmental advantages, broader adoption remains limited due to standardisation gaps and market unfamiliarity. The paper concludes by highlighting the importance of hybrid stabiliser systems, targeted certification frameworks, and waste valorisation policies to support the transition toward low-carbon, resource-efficient construction practices. Full article

The sustainable valorization of lignocellulosic biomass into high-value platform chemicals presents a crucial pathway for reducing reliance on fossil resources. Gamma (γ)-valerolactone (GVL) has gained recognition as a versatile bio-derived compound with broad applications in renewable energy systems and green chemical synthesis. While conventional GVL production strategies from carbohydrate biomass typically depend on noble metal catalysts paired with high-pressure hydrogen gas, these approaches face substantial technical barriers including catalyst costs, hydrogen storage requirements, and operational safety concerns in large-scale applications. This work develops an innovative catalytic system utilizing earth-abundant iron for in situ hydrogen generation through water splitting, integrated with Raney Ni as the hydrogenation catalyst. The designed two-stage process enables direct conversion of cellulose—first through acid hydrolysis to levulinic acid (LA) followed by catalytic hydrogenation to GVL without intermediate purification. Through systematic parameter optimization, a remarkable 61.9% overall GVL yield from cellulose feedstock was achieved. Furthermore, the methodology’s versatility was demonstrated through wheat straw conversion experiments, yielding 24.6% GVL. This integrated methodology explores a technically feasible pathway for direct cellulose-to-GVL conversion utilizing abundant water as the hydrogen source, effectively overcoming the critical limitations associated with conventional hydrogenation technologies regarding hydrogen infrastructure and process safety. Full article

The main goal of this study is to investigate the possibility of using residual materials (biomass derived from used cooking oils and lignocellulosic biomass from plant waste) on a large scale for producing renewable fuels and, in particular, the best way to collect them. The methodology of Geographic Information Systems (GIS) as well as spatial analysis (SA) techniques were used to investigate the Greek case for this. The data recorded in the geographic database were quantities of waste cooking and household oils as well as quantities of lignocellulosic biomass. The most common global and local indices of spatial autocorrelation were used. Concerning the biomass derived from used cooking oils, it was found that their quantities were important (163.17 million L/year), and these can be used to produce green diesel in the context of the circular economy. Although the dispersion of the used cooking oils was wide, there is no doubt that their concentration in large cities and tourist areas is higher. This finding suggests a collection process that could be carried out mainly in these areas through the development of small autonomous collection units in each neighborhood and central processing plants in small regional units. The investigation of the geographical–spatial distribution of residual lignocellulosic biomass showed the geographical fragmentation and heterogeneity of the distributions. The quantities recorded were significant (4.5 million tons/year) but widely dispersed, such that the cost of collecting and transporting the biomass to central processing plants could be prohibitive. The “geography” of the problem itself suggests solutions of small mobile collection units in every part of the country. The lignocellulosic biomass would be collected and converted in situ into bio-oil by rapid pyrolysis carried out in a tanker vehicle. This would transport the produced bio-oil to the nearest oil refineries for the conversion of bio-oil into biofuels through deoxygenation processes. Full article

This paper presents a cloud-based system that builds upon the FIT4NER framework to support medical experts in training machine learning models for named entity recognition (NER) using Microsoft Azure. The system is designed to simplify complex cloud configurations while providing an intuitive interface for managing and converting large-scale training and evaluation datasets across formats such as PDF, DOCX, TXT, BioC, spaCyJSON, and CoNLL-2003. It also enables the configuration of transformer-based spaCy pipelines and orchestrates Azure cloud services for scalable and efficient NER model training. Following the structured Nunamaker research methodology, the paper introduces the research context, surveys the state of the art, and highlights key challenges faced by medical professionals in cloud-based NER. It then details the modeling, implementation, and integration of the system. Evaluation results—both qualitative and quantitative—demonstrate enhanced usability, scalability, and accessibility for non-technical users in medical domains. The paper concludes with insights gained and outlines directions for future work. Full article