Search Results (245)

The rapid evolution of healthcare technology is being driven by advancements in Micro-Electro-Mechanical Systems (MEMS), BioMEMS (Biological MEMS), and the expanding concept of the Internet of Bodies (IoB). This review explores the convergence of these three domains and their transformative impact on personalized medicine (PM), with a focus on smart, connected biomedical devices. Starting from the historical development of MEMS for medical sensing and diagnostics, the review traces the emergence of BioMEMS as biocompatible, minimally invasive solutions for continuous monitoring and real-time intervention. The integration of such devices within the IoB ecosystem enables data-driven, remote, and predictive healthcare, offering tailored diagnostics and treatment for chronic and acute conditions alike. The paper classifies IoB-associated technologies into non-invasive, invasive, and incorporated devices, reviewing wearable systems such as smart bracelets, e-tattoos, and smart footwear, as well as internal devices including implantable and ingestible. Alongside these opportunities, significant challenges persist, particularly in device biocompatibility, data interoperability, cybersecurity, and ethical regulation. By synthesizing recent advances and critical perspectives, this review aims to provide a comprehensive understanding of the current landscape, clinical potential, and future directions of MEMS, BioMEMS, and IoB-enabled personalized healthcare. Full article

Background: The efficacy and safety of the anti-TNF inhibitor golimumab in ulcerative colitis (UC) have been demonstrated in pivotal randomized controlled trials (RCTs). However, real-world data are needed to assess its effectiveness and safety in routine clinical practice, where patient populations and treatment settings are more heterogeneous. Methods: This pooled, retrospective–prospective cohort analysis draws on primary data from four IBD registries that contribute UC data to the UMBRELLA-IBD data warehouse: BioColitis, RUN-UC, TARGET-IBD, and VEDO-IBD. Data for each registry were collected across multiple centers under routine clinical care conditions in Germany. Eligible patients had a confirmed diagnosis of UC according to DGVS/ECCO criteria, were ≥18 years of age, and had newly initiated treatment with golimumab between 2017 and 2023. In total, 222 patients met these criteria and were included in the analysis. Statistical analyses included descriptive summaries, group comparisons, and Kaplan–Meier analysis of treatment persistence. Adverse events (AEs) and serious adverse events (SAEs) were also assessed and compared to other anti-TNF therapies from UMBRELLA-IBD. Results: Of the 222 patients who newly initiated golimumab, 134 had a documented month 12 visit with a documented pMayo score and were included in the modified intention-to-treat (mITT) analysis. A high proportion of the patients in the study had previously received treatment with at least two biologics (81%). In this mITT population, clinical remission was achieved in 38.1% and steroid-free clinical remission (SFCR) in 36.6% at 12 months. In the full cohort, treatment persistence at 12 months was 67.2%. Safety data on adverse events (AEs) and serious adverse events (SAEs) were reported in 14.8% and 5.8% of cases, respectively, with no significant differences compared with other anti-TNF therapies. Conclusions: In addition to the positive findings from the pivotal RCTs, these real-world data further support the clinical effectiveness and safety of golimumab in routine care for UC. Full article

Background: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local Small- and Medium-Sized Enterprises (SMEs) has been proposed as a strategic approach to enhance supply chain localization, yet barriers such as limited visibility, qualification hurdles, and integration difficulties persist. Methods: This study proposes a comprehensive knowledge graph driven framework for representing and discovering SMEs, implemented as a proof-of-concept in the U.S. BioPharma sector. The framework constructs a curated knowledge graph in Neo4j, converts it to Resource Description Framework (RDF) format, and aligns it with the Schema.org vocabulary to enable semantic interoperability and enhance the discoverability of SMEs. Results: The developed knowledge graph, consisting of 488 nodes and 11,520 edges, enabled accurate multi-hop SME discovery with query response times under 10 milliseconds. RDF serialization produced 16,086 triples, validated across platforms to confirm interoperability and semantic consistency. Conclusions: The proposed framework provides a scalable, adaptable, and generalizable solution for SME discovery and supply chain localization, offering a practical pathway to strengthen resilience in diverse manufacturing industries. Full article

Electronic health records (EHRs) are typically stored in relational databases, making them difficult to query for nontechnical users, especially under privacy constraints. We evaluate two practical clinical NLP workflows, natural language to SQL (NL2SQL) for EHR querying and retrieval-augmented generation for clinical question answering (RAG-QA), with a focus on privacy-preserving deployment. We benchmark nine large language models, spanning open-weight options (DeepSeek V3/V3.1, Llama-3.3-70B, Qwen2.5-32B, Mixtral-8 × 22B, BioMistral-7B, and GPT-OSS-20B) and proprietary APIs (GPT-4o and GPT-5). The models were chosen to represent a diverse cross-section spanning sparse MoE, dense general-purpose, domain-adapted, and proprietary LLMs. On MIMICSQL (27,000 generations; nine models × three runs), the best NL2SQL execution accuracy (EX) is 66.1% (GPT-4o), followed by 64.6% (GPT-5). Among open-weight models, DeepSeek V3.1 reaches 59.8% EX, while DeepSeek V3 reaches 58.8%, with Llama-3.3-70B at 54.5% and BioMistral-7B achieving only 11.8%, underscoring a persistent gap relative to general-domain benchmarks. We introduce SQL-EC, a deterministic SQL error-classification framework with adjudication, revealing string mismatches as the dominant failure (86.3%), followed by query-join misinterpretations (49.7%), while incorrect aggregation-function usage accounts for only 6.7%. This highlights lexical/ontology grounding as the key bottleneck for NL2SQL in the biomedical domain. For RAG-QA, evaluated on 100 synthetic patient records across 20 questions (54,000 reference–generation pairs; three runs), BLEU and ROUGE-L fluctuate more strongly across models, whereas BERTScore remains high on most, with DeepSeek V3.1 and GPT-4o among the top performers; pairwise t-tests confirm that significant differences were observed among the LLMs. Cost–performance analysis based on measured token usage shows per-query costs ranging from USD 0.000285 (GPT-OSS-20B) to USD 0.005918 (GPT-4o); DeepSeek V3.1 offers the best open-weight cost–accuracy trade-off, and GPT-5 provides a balanced API alternative. Overall, the privacy-conscious RAG-QA attains strong semantic fidelity, whereas the clinical NL2SQL remains brittle under lexical variation. SQL-EC pinpoints actionable failure modes, motivating ontology-aware normalization and schema-linked prompting for robust clinical querying. Full article

The persistence of the synthetic plastic waste problem makes it one of the most pressing environmental challenges. Sustainable material is an alternative approach to reduce petroleum plastics. In this research, our work aims to convert two biomaterials, water hyacinth (WH) and cassava chip (CC), into value-added biopolymer composite sheets (BCS). The raw materials of both WH and CC were prepared and characterized using physical and chemical treatments. Alkali treatments and chemical modifications were applied to remove lignin, protein, lipid, and other inhibiting components. After that, the two main raw materials of the WH and CC components were varied (100:0, 90:10, 80:20, 70:30, and 60:40, respectively) to investigate the optimal conditions for mixing, blending, and forming processes. Finally, mechanical properties (tensile strength), physical properties (surface morphology using a scanning electron microscope (SEM), crystalline structure by X-ray diffraction (XRD), and water solubility were also evaluated. The results obtained obviously revealed that the BCS reached an optimal ratio of 80:20 and exhibited outstanding properties. We were successful in exploring the potential use of a combination of two kinds of biopolymers under optimal conditions to produce an effective and environmentally friendly BCS in a manner that promotes a sustainable bio-circular economy and zero-waste concepts. Full article

Background/Objectives: Spanish-speaking patients face persistent barriers in accessing equitable audiological care, particularly when standardized language-appropriate tools are lacking. Two Spanish-language sentence recognition tests, the Spanish AzBio Sentence (SAzB) and the Latin American Hearing in Noise Test (LAH), are commonly used to evaluate speech perception in adults with hearing loss. However, performance differences between these measures may influence referral decisions for hearing intervention, such as cochlear implantation. This study compared test performance under varying noise and spatial conditions to guide appropriate test selection and reduce the risk of misclassification that may contribute to healthcare disparities. Methods: Twenty-one bilingual Spanish/English speaking adults with normal bilateral hearing completed speech perception testing using both the SAzB and LAH. Testing was conducted under two spatial configurations: (1) speech and noise presented from the front (0° azimuth) and (2) speech to the simulated poorer ear and noise to the better ear (90°/270° azimuth). Conditions included quiet and three signal-to-noise ratios (+10, +5, and 0 dB). Analyses included paired t-tests and one-way ANOVAs. Results: Participants scored significantly higher on the LAH than on the SAzB across all SNR conditions and configurations, with ceiling effects observed for the LAH. SAzB scores varied by language dominance, while LAH scores did not. No other differences were observed based on any further demographic information. Conclusions: The SAzB provides a more challenging and informative assessment of speech perception in noise. Relying on easier tests like the LAH may obscure real-world difficulties and delay appropriate referrals for hearing loss intervention, including cochlear implant evaluation. Selecting the most appropriate test is critical to avoiding under-referral and ensuring Spanish-speaking patients receive equitable and accurate care. Full article

Cardiorenal syndrome (CRS) reflects the intricate and bidirectional interplay between cardiac and renal dysfunction, commonly resulting in diagnostic uncertainty, therapeutic dilemmas and poor outcomes. While traditional biomarkers like serum creatinine (Cr) and natriuretic peptides remain widely used, their limitations in specificity, timing and contextual interpretation often hinder optimal management. This narrative review synthesizes the current evidence on established and emerging biomarkers in CRS, with emphasis on their clinical relevance, integration into real-world practice, and potential to inform precision therapy. Markers of glomerular filtration rate beyond creatinine—such as cystatin C—offer more accurate assessment in frail or sarcopenic patients, while tubular injury markers such as NGAL, KIM-1, and urinary L-FABP (uL-FABP) provide early signals of structural renal damage. The FDA-approved NephroCheck^® test—based on TIMP-2 and IGFBP7— enables risk stratification for imminent AKI up to 24 h before functional decline. Congestion-related markers such as CA125 and bio-adrenomedullin outperform natriuretic peptides in certain CRS phenotypes, particularly in right-sided heart failure or renally impaired patients. Fibrosis and inflammation markers (galectin-3, sST2, GDF-15) add prognostic insights, especially when combined with NT-proBNP or troponin. Rather than presenting biomarkers in isolation, this review proposes a framework that links them to specific clinical contexts—such as suspected decongestion-related renal worsening or persistent congestion despite therapy—to support actionable interpretation. A tailored, scenario-based, multi-marker strategy may enhance diagnostic precision and treatment safety in CRS. Future research should prioritize prospective biomarker-guided trials and standardized pathways for clinical integration. Full article

Nitrile-containing compounds are integral to pharmaceuticals, agrochemicals and polymer industries, yet their environmental persistence and toxicity pose major challenges. Biocatalytic approaches using nitrile-converting enzymes—particularly nitrilases and nitrile hydratases—offer sustainable alternatives to conventional hydrolysis, enabling the selective transformation of nitriles into amides and acids under mild conditions. This review presents an industrial perspective on nitrile-converting enzymes, summarising their catalytic potential, current limitations, and emerging strategies for stability, activity and performance enhancement. Advances in protein engineering, metagenomic discovery and biocatalytic optimisation have already expanded their wider applicability, while synthetic biology and protein design tools are accelerating the development of tailored biocatalysts. The integration of these enzymes into cascades and chemoenzymatic processes supports scalable and innovative solutions to green manufacturing. Collectively, these emerging strategies position nitrile-converting enzymes as versatile tools for sustainable catalysis, with growing relevance in fine chemical synthesis, waste remediation, and bio-based synthetic platforms. Full article

Climate change poses a serious threat to biodiversity, particularly for woody species with limited dispersal capacity such as Prunus avium L. (wild cherry). In this study, we assessed potential shifts in its suitable distribution range (SDR) across Türkiye by applying an ensemble modeling framework that combined Generalized Additive Models (GAM), Maximum Entropy (MaxEnt), and Random Forest (RF). We used updated occurrence data (including GBIF and EUFORGEN records) and 11 ecologically relevant bioclimatic variables under SSP2-4.5 and SSP5-8.5 scenarios. Model performance was validated using AUC (Area Under the ROC Curve) and TSS (True Skill Statistic) metrics. Results suggest that while 60–70% of current SDRs remain stable by 2100, approximately 10% may be lost, with 20–23% new expansions. Temperature seasonality (Bio4) and seasonal precipitation (Bio15) were consistently identified as dominant predictors across models. Notably, newly suitable habitats are expected to be spatially isolated, limiting natural colonization. Our findings highlight the necessity of proactive conservation planning, including assisted migration and drought-resistant genotype selection, to ensure long-term persistence of wild cherry under changing climates. These results offer actionable insights for adaptive forest management and biodiversity conservation in Mediterranean-type ecosystems. Full article

Gentiana pneumonanthe L., a wetland specialist and exclusive host of the Alcon Blue (Phengaris alcon), is highly vulnerable to climate change. This study assessed the future climate suitability of the Iberian Peninsula (IP) for G. pneumonanthe. From 14 bioclimatic variables (ISIMIP3b, processed by CHELSA method at 1 km²) and two topographic variables, four bio-ecological indicators were selected using Pearson correlation and Variance Inflation Factors: Thermicity Index, Ombrothermic Index, Accumulated summer precipitation from June to August, and Maximum of the daily maximum temperature of August. A species distribution model platform (Biomod2) was applied for historical (1995–2014) and future periods (2041–2060, 2081–2100) under two anthropogenic radiative forcing scenarios (SSP3-7.0, SSP5-8.5). The ensemble model created shows a strong predictive performance (BOYCE: 0.98). Historically, 13.4% of the IP was climatically suitable, mainly in mountain areas. Under SSP3-7.0, suitable areas are projected to decline by 74.2% (2041–2060) and 99.3% (2081–2100); under SSP5-8.5, by 75.5% and 99.9%, respectively. While small gains may occur in the Pyrenees, most conservation protected areas (Natura 2000, RAMSAR) may lose suitability for species persistence. Such losses could disrupt ecological ecosystems and directly threaten the survival of P. alcon. These findings highlight the urgent need for climate-informed land-use planning and effective habitat conservation. Full article

Feeding a growing population under the pressures of climate change requires solutions that safeguard yields while strengthening agricultural resilience. Integrated Crop Management (ICM)—which combines precise fertilization, efficient water use, and targeted pest control—offers a promising framework. Hydrogels, with their water retention and controlled release properties, can enhance ICM by improving fertilizer efficiency, reducing water loss, and supporting soil health. Despite extensive research, their optimal use in agriculture remains unclear, and limitations continue to restrict large-scale adoption. To address this gap, this study applies the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology alongside bibliometric analysis to examine hydrogel applications in ICM from 2000 to 2024. Ninety Scopus-indexed publications were analyzed across four domains: pesticides, nutritional growth inputs, soil conditioners, and bioactive substances. The results reveal a marked increase in hydrogel structural complexity, greater diversity in characterization methods, ongoing reliance on high-impact pesticides despite advances in bio-based hydrogels, and persistent gaps in assessing environmental impacts and regulatory compliance. These findings underscore the need for stronger collaboration between academia and industry to translate hydrogel research into effective, sustainable agricultural practices under changing climatic conditions. Full article

Spent coffee grounds (SCGs), a globally abundant by-product of the coffee industry, represent a significant source of lignocellulosic biomass with considerable valorization potential. Rich in organic compounds, lipids, and antioxidants, SCGs are increasingly recognized as a sustainable feedstock for energy, materials, and environmental applications within a circular bioeconomy framework. This review critically examines recent advances in SCG valorization via thermochemical, biochemical, and material-based pathways. The review focuses on the conversion of SCGs into biofuels (biodiesel, bioethanol, biogas, and bio-oil), activated carbon for water and air purification, biodegradable polymers, and soil-enhancing amendments. Comparative analyses of process conditions, product yields, and techno-economic feasibility are provided through summarized tables. Although laboratory-scale studies demonstrate promising outcomes, challenges persist in terms of process scalability, environmental impacts, feedstock variability, and lack of regulatory standardization. Furthermore, comprehensive life cycle assessments and policy integration remain underdeveloped. By merging all findings, this review identifies key knowledge gaps and outlines strategic directions for future research, including the development of integrated valorization platforms, hybrid conversion systems, and industrial-scale implementation. The findings support the role of SCG valorization in advancing sustainable resource management and contribute directly to the achievement of multiple Sustainable Development Goals. Full article

Biological swarms communicate through decentralized, adaptive behaviors shaped by local interactions, selective attention, and symbolic signaling. These principles of animal communication enable robust coordination without centralized control or persistent connectivity. This work presents a proof of concept that identifies, evaluates, and translates biological communication strategies into a generative visual language for unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-denied environments. Drawing from natural exemplars such as bee waggle dancing, white-tailed deer flagging, and peacock feather displays, we construct a configuration space that encodes visual messages through trajectories and LED patterns. A large language model (LLM), preconditioned using retrieval-augmented generation (RAG), serves as a generative translation layer that interprets perception data and produces symbolic UAV responses. Five test cases evaluate the system’s ability to preserve and adapt signal meaning through within-modality fidelity (maintaining symbolic structure in the same modality) and cross-modal translation (transferring meaning across motion and light). Covariance and eigenvalue-decomposition analysis demonstrate that this bio-agentic approach supports clear, expressive, and decentralized communication, with motion-based signaling achieving near-perfect clarity and expressiveness (0.992, 1.000), while LED-only and multi-signal cases showed partial success, maintaining high expressiveness (~1.000) but with much lower clarity (≤0.298). Full article

Phosphorus pollution represents a persistent and significant threat to aquatic ecosystems, particularly within the Mediterranean region, where ongoing eutrophication continues to compromise both water quality and biodiversity. Concurrently, the accumulation of Posidonia oceanica residues along coastal areas presents a biomass management challenge. This study explores the sustainable use of thermally treated Posidonia ash as a low-cost, bio-based adsorbent for phosphate removal from water. Batch experiments under varying phosphate concentrations, pH, hardness, and alkalinity revealed high removal capacities (33.5–58.7 mg/g). A novel surface complexation model (SCM) was developed and validated using spectroscopic techniques to elucidate the mechanisms of phosphate retention. The SCM outperformed conventional isotherm models by providing mechanistic insights into adsorption behavior. Phosphate adsorption was found to be pH-dependent, occurring via surface complexation to neutral and basic surface sites. The release of Ca²⁺ and Mg²⁺ ions facilitated ternary complex formation and precipitation. Under alkaline conditions, competitive adsorption between phosphate and carbonate ions was observed. This study demonstrates the dual benefit of Posidonia oceanica ash: efficient phosphate removal and its reuse as a phosphorus reservoir, offering a circular strategy for tackling nutrient pollution and promoting coastal biomass valorization. Full article

The rapid shift toward electric vehicles (EVs) has underscored the critical importance of battery pack crashworthiness, creating a demand for lightweight, energy-absorbing protective systems. This review systematically explores bio-inspired cellular structures as promising solutions for improving the impact resistance of EV battery packs. Inspired by natural geometries, these designs exhibit superior energy absorption, controlled deformation behavior, and high structural efficiency compared to conventional configurations. A comprehensive analysis of experimental, numerical, and theoretical studies published up to mid-2025 was conducted, with emphasis on design strategies, optimization techniques, and performance under diverse loading conditions. Findings show that auxetic, honeycomb, and hierarchical multi-cell architectures can markedly enhance specific energy absorption and deformation control, with improvements often exceeding 100% over traditional structures. Finite element analyses highlight their ability to achieve controlled deformation and efficient energy dissipation, while optimization strategies, including machine learning, genetic algorithms, and multi-objective approaches, enable effective trade-offs between energy absorption, weight reduction, and manufacturability. Persistent challenges remain in structural optimization, overreliance on numerical simulations with limited experimental validation, and narrow focus on a few bio-inspired geometries and thermo-electro-mechanical coupling, for which engineering solutions are proposed. The review concludes with future research directions focused on geometric optimization, multi-physics modeling, and industrial integration strategies. Collectively, this work provides a comprehensive framework for advancing next-generation crashworthy battery pack designs that integrate safety, performance, and sustainability in electric mobility. Full article