Search Results (225)

Precise monitoring of forearm muscle groups is crucial for decoding motor intentions in human–machine interfaces (HMIs) and rehabilitation. However, traditional surface electromyography (sEMG) electrodes face significant challenges in densely packed muscle regions with large skin deformations, leading to severe signal crosstalk and unstable contact. Here, we report a flexible, low-cost 16-channel copper electrode array system designed for the high-density monitoring of multiple forearm muscle activities. Through a facile fabrication process, rigid copper is transformed into a conformable sensing interface. The optimized serpentine interconnects endow the array with excellent stretchability and effectively isolate motion-induced stress, ensuring high-quality signal acquisition under complex deformations. The high-density 2 × 8 array enables the spatiotemporal mapping of distributed flexor and extensor muscle groups. Integrated with a customized wireless data acquisition system, the array successfully demonstrates real-time, multi-channel sEMG monitoring of various hand movements (e.g., fist clenching, wrist flexion/extension), clearly revealing specific muscle activation patterns. This low-cost, high-performance flexible sensor array provides a highly promising tool for complex gesture decoding, electromyographic imaging, and next-generation wearable HMIs. Full article

Breast cancer remains the most frequently diagnosed malignancy in women worldwide, accounting for approximately 2.3 million new cases and 670,000 deaths annually. The diagnostic landscape has undergone a paradigm shift over the past two decades, evolving from morphology-based classification toward molecularly informed, precision-guided strategies. Early and accurate diagnosis is fundamental to improving outcomes; advances in imaging technology, including digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and abbreviated magnetic resonance imaging (MRI), have improved sensitivity and specificity in diverse patient populations. Simultaneously, the integration of artificial intelligence (AI) and radiomics into screening workflows offers unprecedented potential for risk stratification and a reduction in false-positives. At the pathological level, multi-gene expression profiling assays such as Oncotype DX, MammaPrint, Prosigna, and EndoPredict have refined prognostic classification and guide adjuvant chemotherapy decisions in early-stage hormone receptor-positive disease. The emergence of liquid biopsy, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomal biomarkers provides minimally invasive tools for real-time monitoring of response, residual disease, and the evolution of resistance mechanisms. Precision diagnostics now encompass next-generation sequencing (NGS)-based comprehensive genomic profiling, enabling identification of actionable alterations such as PIK3CA mutations, HER2 amplification, BRCA1/2 pathogenic variants, and NTRK fusions, each linked to approved therapeutic agents. The purpose of this review is to provide a comprehensive synthesis of current and emerging diagnostic modalities in breast cancer—from population-level screening to individualized molecular profiling—and to examine how integrative, multimodal diagnostic platforms are reshaping clinical decision-making in the era of precision medicine. Full article

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

A comprehensive understanding of plant diversity is essential for ecological research, conservation planning, and sustainable resource management. Advances in genetic technologies have transformed the assessment of plant biodiversity, enabling more precise and efficient characterization of genetic variation. Early molecular markers, widely used in the late 2000s, have largely been replaced by polymerase chain reaction (PCR)-based tools that require less DNA, are easier to use, and are supported by accessible commercial kits. The 2020s have seen the emergence of new, more accessible tools driven by cost reduction and efficiency improvements. High-throughput sequencing (HTS) technologies have further revolutionized the field by providing genome-wide insights into allelic diversity, structural polymorphisms, and epigenetic modifications. These innovations enhance the detection of adaptive variation, improve understanding of spatial genetic structure, and support the evaluation of environmental impacts on plant populations. Marker-assisted selection, now common in modern breeding, leverages genomic data to develop cultivars with enhanced resistance and desirable agronomic traits. Emerging tools such as environmental DNA (eDNA) analysis, high-throughput phenotyping, and advanced bioinformatics workflows expand the capacity to monitor species, assess population viability, and identify key traits linked to adaptation. The present review aims to highlight these technological advancements and the more recent and useful tools available from Next-Generation Sequencing to genotyping-by-sequencing, discussing their role for conserving plant genetic resources, improving breeding programs, and deepening knowledge of plant biodiversity within changing ecosystems. Full article

This review evaluates the emerging role of circulating tumour cells (CTCs) as clinically meaningful, minimally invasive biomarkers for oral squamous cell carcinoma (OSCC). Despite advances in management, OSCC continues to demonstrate high morbidity and mortality, largely due to late diagnosis and the absence of validated biomarkers for early detection or real-time monitoring. Conventional diagnostic tools, tissue biopsy, and imaging provide only static snapshots and fail to capture tumour heterogeneity or evolving biological behaviour. CTCs offer a novel and significant opportunity to address these limitations. Key findings from recent studies highlight that CTC enumeration correlates with tumour burden, nodal metastasis, recurrence, and overall prognosis. Molecular and phenotypic characterisation further reveals dynamic traits such as epithelial–mesenchymal transition, stemness, and therapy resistance, providing insights into metastatic potential and treatment failure. Technological advances, including immunocytochemistry, microfluidic capture platforms, PCR-based assays, and next-generation sequencing, have enhanced the sensitivity and specificity of CTC detection and enabled detailed multi-omic profiling. Collectively, evidence suggests that integrating CTC analysis into OSCC clinical workflows could improve early detection, refine risk stratification, personalise therapeutic strategies, and support longitudinal monitoring of disease dynamics. As research progresses, CTC-based diagnostics represent a promising frontier in shifting OSCC management toward more precise, adaptive, and biologically informed care. Full article

This review examines the current state of development and application of artificial intelligence (AI) tools for monitoring nutrition and physical activity in individuals with obesity, with a focus on the physiological complexity of energy balance and the role of chrono-nutrition. Energy intake and expenditure are dynamically coupled and circadian-regulated: meal timing and movement patterns influence insulin sensitivity, thermogenesis, and Non-Exercise Activity Thermogenesis within the same day. Traditional monitoring methods suffer from recall bias and low granularity, while isolated sensors operate in data silos, limiting accuracy. Effective solutions require multimodal, continuous, and temporally aligned data streams. Current AI models exhibit critical limitations in obesity-specific contexts: inaccurate gait and energy expenditure estimates due to biomechanical differences, dietary models underestimating glycemic variability, poor performance on mixed dishes, sauces, and culturally diverse foods, and a lack of validation against gold standards such as doubly labelled water (DLW) and weighed food records. This review proposes a paradigm shift toward obesity-specific AI design, including enriched datasets and multimodal integration. Physical activity monitoring faces similar challenges: systematic measurement bias in wearables, sensor placement issues, and algorithms trained on normal-weight cohorts. In the GLP-1/GIP era, if transparency, ethical safeguards, and equitable access are ensured, AI will act as a catalyst for personalized care, remote monitoring, trial optimization, and next-generation drug discovery. In conclusion, the integration of AI with rigorous validation procedures and inclusive sampling strategies is essential to achieve reliable, fair, and clinically relevant monitoring approaches for obesity management. Full article

Human papillomavirus (HPV)-driven oropharyngeal squamous cell carcinoma (OPSCC) has emerged as a biologically distinct entity, typically affecting younger, non-smoking patients and showing improved survival compared to HPV-negative tumors. Accurate HPV status determination is essential for correct staging, prognostic assessment, and treatment de-escalation. Despite advances, substantial variability persists among diagnostic methods and clinical workflows. A narrative review of PubMed, Scopus, and Web of Science databases was conducted up to July 2025. Studies addressing HPV detection techniques in OPSCC—including p16^INK4a^ immunohistochemistry (IHC), HPV DNA and RNA assays, liquid biopsy approaches, and computational surrogates—were critically analyzed regarding diagnostic accuracy, clinical applicability, and emerging innovations. Tissue-based assays remain the diagnostic reference standard. p16 IHC provides high sensitivity but limited specificity and should be confirmed with nucleic acid-based methods such as DNA PCR, in situ hybridization (ISH), or E6/E7 mRNA detection. Combined or “orthogonal” testing minimizes discordance and refines risk stratification. Liquid biopsy detection of circulating HPV DNA using droplet digital PCR or next-generation sequencing has shown high sensitivity and specificity in cohorts of patients with HPV-associated OPSCC, supporting its potential role as a complementary biomarker for treatment monitoring and surveillance. However, circulating HPV DNA alone does not unequivocally identify the anatomic source of HPV DNA and should be interpreted together with clinical, radiologic, and tissue-based findings. Oral rinse and saliva assays show moderate diagnostic performance, while artificial intelligence-based radiomic and histopathologic models are emerging as complementary tools. Reliable HPV attribution in OPSCC requires a multimodal diagnostic strategy integrating p16 IHC, molecular confirmation, and ctHPV-DNA monitoring. Methodological standardization and prospective validation are essential to implement precision-guided, cost-effective workflows in routine clinical practice. Full article

Metagenomic and targeted next-generation sequencing (NGS) technologies are rapidly transforming diagnosis and management for infectious diseases. This review comprehensively examines the current applications of metagenomic NGS (mNGS) and targeted NGS (tNGS) in clinical microbiology, highlighting their roles in pathogen detection, antimicrobial resistance profiling, virulence characterization, and outbreak investigation—particularly in complex cases such as pneumonia, critical illness with pulmonary infections, and pediatric acute respiratory illnesses. We discuss the diagnostic performance, advantages, and limitations of these approaches, including challenges related to sensitivity, specificity, standardization, bioinformatic complexity, and cost-effectiveness. Furthermore, we explore emerging opportunities for integrating NGS-based surveillance with public health strategies, such as wastewater epidemiology, to monitor healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) at the population level. Finally, we outline key steps needed to translate these powerful genomic tools from research settings into routine clinical and public health practice. Full article

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

Oral squamous cell carcinoma (OSCC) is a prevalent form of head and neck cancer that typically develops on the lip or within the oral cavity. Although there have been advances in early detection and treatment, the prognosis for patients, particularly those with advanced-stage disease, remains poor. Liquid biopsy, particularly through the analysis of cell-free DNA (cfDNA) in plasma and urine, has emerged as a promising tool for non-invasive cancer detection and monitoring. This study assessed cfDNA concentration dynamics in plasma and urine samples from 32 OSCC patients, with 5 undergoing genomic characterization by targeted next-generation sequencing (NGS). CfDNA levels were higher in patients compared to healthy controls and showed transient increases following treatment initiation, likely reflecting tumor cell death, followed by a gradual return to baseline. However, cfDNA concentrations were not significantly associated with tumor stage, recurrence, or progression-free survival. Targeted NGS analysis revealed a heterogeneous mutational landscape, identifying 76 variants across tumor tissue and initial cfDNA, with 30.3% shared between both sources. Recurrent hotspot mutations were detected in several important genes, including TP53, PIK3CA, KRAS, APC, and FBXW7. Urine cfDNA also captured several mutations absent from plasma or tissue, supporting its complementary value. These findings demonstrate that cfDNA analysis can dynamically reflect treatment response and capture tumor heterogeneity in OSCC. While informative, cfDNA quantification alone offers limited prognostic reliability, reinforcing the need for a multidimensional approach that includes genomic and clinical evaluation. Overall, this study supports the potential of liquid biopsy as a real-time, non-invasive tool for molecular monitoring and personalized management of OSCC patients. Full article

In recent decades, Artificial Neural Networks (ANNs) have become a robust tool for addressing complex engineering challenges. This paper implements an ANN-based methodology to determine the natural frequencies of rotating sandwich composite beams with core defects. The study focuses on the influence of rotation speed and defect characteristics (size and location) on a beam made of carbon fiber face-sheets and a honeycomb core, selected for its high strength-to-weight ratio in next-generation designs. The primary novelty lies in providing a simplified model that, through an ANN-based surrogate, establishes an automated and high-speed process for frequency prediction. This approach bypasses the prohibitive computational costs of 3D-FEM simulations, enabling near-instantaneous results essential for real-time Structural Health Monitoring (SHM) applications. Full article

Neoantigen vaccines have revitalized cancer vaccination by targeting tumor-specific mutant epitopes largely absent from central tolerance. Yet, clinical benefits remain inconsistent, in part because conventional vaccine platforms often do not reliably deliver antigens within an inflammatory tumor context, struggle to overcome immunosuppressive tumor microenvironments, and may not rapidly adapt to tumor heterogeneity and evolution. Oncolytic viruses (OVs) provide a mechanistically distinct route to “vaccinate in situ” by coupling tumor-selective infection and immunogenic cancer cell death with local innate immune activation, antigen release, and remodeling of the tumor microenvironment. In parallel, advances in sequencing, neoantigen prediction (e.g., updated NetMHCpan and MHCflurry tools as of 2025), and antigen presentation validation have enabled rational selection of patient-specific targets. At the same time, modern OV engineering supports insertion of neoantigen payloads and immune-modulatory transgenes. Here, we summarized principles that underpin neoantigen-encoded OVs as personalized cancer vaccines, emphasizing how OV adjuvanticity and antigenicity interact to drive priming, epitope spreading, and durable systemic immunity. We discussed major OV platforms with respect to payload capacity, expression control, manufacturability, and clinical track records, including lessons learned from approved or late-stage OVs such as talimogene laherparepvec (T-VEC) and teserpaturev/G47Δ. We also discussed design choices for encoding neoantigens (polyepitope strings, minigenes, long peptides; class I/II balancing), prioritizing translational biomarkers and immune-monitoring strategies, and outlining regulatory and GMP considerations for “platform-plus-variable insert” products. Finally, we propose a pragmatic clinical workflow for rapid personalization to maximize therapeutic index. Tightly integrating neoantigen science with immunovirotherapy, including recent 2025 preclinical advances like oncolytic adenovirus neoantigen delivery sensitizing low-TMB tumors to PD-1 blockade, could enable next-generation personalized cancer vaccines capable of converting “cold” tumors into responsive, systemically controlled disease. Full article

Background/Objectives: Colorectal cancer (CRC) liver metastases present a significant clinical challenge due to high recurrence risks post-resection. Traditional diagnostics often fail to detect early-stage minimal residual disease (MRD). This preliminary pilot study evaluated ctDNA dynamics in 10 patients with liver metastases using a personalized multistep approach. Methods: Following primary tumor Next-Generation Sequencing (NGS) to identify somatic mutations in KRAS, NRAS, TP53, RET, APC, and WRN, custom TaqMan assays were designed for longitudinal plasma analysis. Four methodologies were compared: HRM-PCR, PNA-enhanced qPCR, and two digital platforms (dPCR and ddPCR). Results: While HRM-PCR sensitivity was limited in plasma, digital platforms demonstrated 100% qualitative concordance. MRD-negative status (VAF 0.00%) was identified in 70% of cases (P01, P03, P06, P07, P08, P09, P10), while detectable ctDNA in patients P02, P04, and P05 strongly correlated with aggressive progression. Digital PCR enabled the ultra-low detection of Variant Allele Frequencies (VAFs), identifying high molecular burdens (e.g., P05, VAF 49%) correlating with rapid decline, and capturing early molecular residue in P04 (VAF 0.62%). Conclusions: Our preliminary findings confirm that personalized longitudinal VAF tracking via digital PCR provides superior prognostic value, serving as a robust tool for recurrence monitoring in personalized CRC therapy. Full article

Background: Liquid biopsy, particularly through the analysis of circulating tumor DNA (ctDNA), represents a significant advancement in oncology. Unlike traditional tissue biopsies, ctDNA offers a minimally invasive, real-time approach to cancer management. It has demonstrated considerable potential in early cancer detection, monitoring of therapeutic responses, and assessing minimal residual disease (MRD) to predict recurrence. By enabling comprehensive molecular profiling through a simple blood test, ctDNA supports the core principles of precision oncology, facilitating more personalized and adaptive treatment strategies. Methods: In the following article we describe the recent developments focused on refining ctDNA detection assays to improve sensitivity and specificity. Advanced technologies, including next-generation sequencing (NGS) and digital PCR, are commonly employed. The integration of artificial intelligence (AI) and multi-omics approaches—such as combining genomic, epigenomic, and transcriptomic data—has further enhanced the analytical power of ctDNA assays. Results: Emerging evidence shows that ctDNA-based liquid biopsy enables dynamic, real-time tracking of tumor evolution and therapeutic resistance. Clinical studies have demonstrated its efficacy in detecting early-stage cancers, guiding treatment selection, and predicting relapse with higher accuracy than some conventional methods. Moreover, AI-enhanced algorithms have improved signal detection, allowing for more precise and earlier identification of actionable mutations and MRD. Conclusions: ctDNA analysis via liquid biopsy is poised to revolutionize cancer care by offering a non-invasive, precise, and adaptive tool for tumor characterization and monitoring. Although obstacles remain—particularly regarding assay sensitivity, standardization, and economic feasibility—ongoing technological innovations and multi-omics integration are rapidly advancing its clinical viability. With continued progress, ctDNA-based liquid biopsy is likely to become a cornerstone of routine oncology practice. Full article

Electrochemical biosensors have emerged as indispensable detection tools with rapid advancements in recent years, offering high sensitivity, specificity, and cost-effectiveness for quantifying diverse analytes, including amino acids, proteins, pathogens, cells, antigens, and organic/inorganic compounds, thereby advancing analytical detection technologies across multiple fields. Aptamers, synthetic in vitro-evolved ligands with exceptional binding affinity and stability, serve as superior biorecognition elements for electrochemical sensing interfaces. Compared with other bioreceptors such as antibodies, they are generally easier and faster to produce, more uniform between batches, and easier to modify chemically; they also maintain greater stability than protein antibodies or enzymes across varying pH, temperature, and ionic conditions, enabling targeted recognition and measurable signal transduction. This review systematically summarizes recent advances in electrochemical aptasensors across three core domains: biomedical diagnostics (covering tumor markers, infectious disease pathogens, cardiovascular and metabolic biomarkers), food safety monitoring (targeting antibiotics, mycotoxins, foodborne pathogens, and pesticide residues), and environmental hazard detection (including heavy metals, toxic compounds, and biotoxins). Key technological innovations such as nanomaterial modification, signal amplification strategies, and novel sensor architectures are highlighted. Additionally, it critically discusses prominent challenges, including complex matrix interference, limited aptamer repertoires, poor reproducibility, and lack of standardization, along with future prospects. This work aims to provide a comprehensive reference for the rational design, optimization, and clinical/field application of next-generation electrochemical aptasensing technologies. Full article