搜索结果：Diagnostics (Basel, Switzerland)

The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2-90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0-41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics.

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Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung-thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ1 = 1.75 × 10-9 and λ2 = 8.91 × 10-10). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k01 = 240 → 239.5; k02 = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease.

In the original publication [...].

Antimicrobial resistance (AMR) is an ever-growing threat to global healthcare. It is largely driven by delayed or inadequate pathogen identification and antimicrobial susceptibility testing in routine clinical workflows. By the time the clinician receives results to guide treatment from traditional culture-based diagnostics, several days may have elapsed, leading to the use and potential over-prescription of broad-spectrum antibiotics and the development of resistant pathogens. A rapid and clinically actionable diagnostic approach at the clinical point of care (POC) may help address this gap. This review examines current and emerging POC diagnostic technologies for AMR and outlines the fundamental principles and mechanistic classifications of POC diagnostic technologies. These include phenotypic, genotypic, immunological, and biosensor-based approaches. A critical overview of key technological platforms, including rapid phenotypic antimicrobial susceptibility testing (AST), microfluidics and isothermal nucleic acid amplification (e.g., LAMP and RPA), CRISPR-based diagnostics, nanomaterial-enhanced biosensors, and mobile-integrated systems is provided. The impact of POC diagnostics on antimicrobial stewardship, time to appropriate therapy, and patient outcomes in primary care settings, hospitals, intensive care units, and resource-limited settings is presented and discussed. In addition to clinical implementation challenges, this review considers the issues of analytical performance, workflow, regulatory pathways, cost, and implementation readiness. In addition, it outlines key trends regarding digital integration, surveillance, workforce training, and policy frameworks. Overall, the review outlines the role of POC diagnostics in enhancing antimicrobial response surveillance and the global fight against AMR. Among emerging platforms, rapid phenotypic AST, microfluidic and isothermal-based assays, CRISPR-based diagnostics, and integrated biosensor systems show the greatest potential for near-term clinical impact; however, widespread implementation remains constrained by challenges related to clinical validation, cost, workflow integration, and alignment with antimicrobial stewardship frameworks.

Background/Objectives: Melioidosis, caused by Burkholderia pseudomallei, is a severe tropical infectious disease associated with high mortality in endemic regions. Early diagnosis remains challenging because conventional diagnostic methods, including culture, serological assays, and molecular techniques, have limitations in sensitivity, specificity, processing time, and accessibility in resource-limited settings. This review evaluates current diagnostic approaches and highlights the potential of short peptide biomarkers for improving melioidosis detection. Methods: A narrative literature review was conducted using four electronic databases (PubMed, Scopus, Web of Science, and Google Scholar) covering publications from 2000 to 2024. Relevant studies were identified using predefined keywords related to melioidosis diagnostics, biomarkers, and peptide-based approaches, and were screened based on relevance to diagnostic methods and peptide biomarker development in Burkholderia pseudomallei. Results: Several biomarkers have been investigated for melioidosis diagnostics, including capsular polysaccharide (CPS), type III secretion system 1 (TTS1), and other virulence-associated proteins such as Hcp1 and BPSS1187. Among these, CPS and TTS1 are highly conserved and specific targets widely used in molecular and antigen-based detection methods. Short peptide epitopes derived from these antigens demonstrate promising advantages over whole proteins, including improved stability, high specificity, easier synthesis, and reduced production costs. Advances in epitope prediction technologies and peptide-based biosensors have further expanded the potential applications of short peptides in rapid diagnostic platforms, including ELISA, lateral flow immunoassays, and biosensor-based detection systems. Conclusions: Short peptide-based biomarkers represent a promising strategy for developing rapid, sensitive, and cost-effective diagnostic tools for melioidosis, particularly in endemic and resource-limited settings.

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next τ=0.2 s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor (w=d=0.1 s, k=2) is warnable on only 18.3% of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget α with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at τ=0.2 s, the scorer achieves AUPRC ≈0.251 against a base rate of 0.638%, AUROC ≈0.986, and ECE ≈0.034. After calibration at α=5%, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FARrun, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported μ-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions.

Background/Objectives: The manual analysis of sperm morphology, crucial for male infertility diagnosis, is subjective and time-consuming. Automated methods using deep learning, offer a promising alternative; however, standard deep models are prone to overfitting when applied to small, heavily unbalanced clinical datasets, limiting their generalization capability. This study proposes a knowledge distillation approach that functions as a strong regularizer, improving the robustness of automated sperm morphology analysis. Methods: We utilize soft distillation to transfer knowledge from a set of high-capacity teacher models to a smaller student model (SwinV2-base). The teacher architectures include SwinV2-large, EfficientNetV2-m, and ConvNeXtV2-large. To maximize performance, we investigated two distillation strategies: a single-teacher approach, where the student learns from one specific architecture, and a multi-teacher approach, where the student learns from an averaged response of multiple teachers. The models were trained on the imbalanced Hi-LabSpermMorpho dataset, which comprises 18 different sperm morphology categories derived from three differently stained (BesLab, Histoplus, GBL) sample sets. We adopted a cross-dataset training approach in which the teacher models were fine-tuned using the combination of two stained datasets, and the student model was trained on the third, distinct stained dataset. The global loss function combined cross-entropy loss with Kullback-Leibler divergence, employing the teacher's soft probabilities to prevent the student from over-confidence. Results: The experimental results demonstrate that the student model trained in a multi-teacher setup with augmentation and soft distillation attains higher accuracies (70.94% on BesLab, 73.61% on Histoplus, 71.63% on GBL) than the baseline models. Conclusions: This approach mitigates challenges associated with data scarcity and heavily unbalanced sperm morphology datasets, providing consistent improvements and offering a highly generalizable solution for clinical diagnostics.

Non-coding RNAs (ncRNAs) have emerged as important regulators of gene expression and cellular homeostasis, and their dysregulation is now recognized as a hallmark of cancer. Over the past decades, extensive research has demonstrated that diverse ncRNA classes, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and other small ncRNA species, participate in complex regulatory networks that influence tumor initiation, progression, metastasis, and therapy response. Through mechanisms such as transcriptional regulation, post-transcriptional gene silencing, epigenetic modulation, and competitive endogenous RNA interactions, ncRNAs shape the molecular circuitry underlying cancer development. In addition to their functional roles in tumor biology, many ncRNAs are released into biological fluids and can be detected as circulating molecules in blood, urine, saliva, and other biofluids. Their remarkable stability in extracellular environments has generated considerable interest in their use as minimally invasive biomarkers in liquid biopsy applications. Emerging evidence has shown that circulating ncRNAs (c-ncRNAs) can support cancer detection, disease stratification, and treatment monitoring. This narrative review provides an integrated view that links ncRNA-mediated regulatory networks with their application as liquid biopsy biomarkers, positioning ncRNAs as comprehensive indicators of tumor conditions. Particular emphasis is placed on c-ncRNA biomarkers, the integration of multiple ncRNA classes, and multi-analyte biomarker strategies that combine ncRNAs with complementary circulating molecules such as cell-free DNA and protein markers. Finally, we discuss the technical and clinical challenges that currently limit the translation of ncRNA-based diagnostics into clinical practice and highlight future directions for advancing ncRNA-guided liquid biopsy approaches in precision oncology.

The dynamic field of radiopharmaceuticals is currently experiencing an explosion of growth due in part to excitement over the emerging field of theranostics (therapy and diagnostics). Radiopharmaceuticals use physiological targeting methods to deliver radionuclides with medically relevant decay properties to disease biomarkers for diagnosis and treatment, offering opportunities for early disease imaging and radiation therapy treatment in disease pathologies that are inoperable or refractory to other forms of radiotherapy. Sustaining this rapidly growing field depends heavily on the continued design and production of novel, effective radiopharmaceuticals. Effective therapeutic radiopharmaceuticals cause complex and varied cellular responses, and to choose radionuclides that maximize therapeutic response, researchers must understand radiation biology. Cellular radiation response depends heavily on factors including linear energy transfer (LET), dose, dose rate, targeted location, direct or indirect energy deposition mechanisms, the broader cellular matrix, cellular stress signaling pathways, and endogenous radiation protection mechanisms. Because of the extensive application of low-LET external beam radiation on clinical cancer treatments, biological responses to low-LET form the basis of radiation biology and are generally considered transferable to high-LET radiopharmaceuticals. However, increased focus on high-LET, radiopharmaceutical therapy-specific radiation biology is motivated by differences between low- and high-LET radiation, external beam versus radiopharmaceutical therapy-induced biological response, and the observed varied clinical responses to radiopharmaceutical therapies. This review article summarizes historical understanding of low- and high-LET radiation responses within cells, with emphasis on radiopharmaceutical-specific responses when available, and discusses current gaps in understanding in the radiation biology of radiotheranostic pharmaceuticals.

This study presents a novel fabrication approach for the precise patterning of conductive polymer coatings (graphene/PEDOT:PSS) on flexible substrates. Traditional lithographic methods often result in chemical or thermal degradation of polymer chains, compromising electrical conductivity. The proposed method utilizes an inversely structured gold nanocoating (400-450 nm) as a sacrificial template. By employing a selective lift-off process in a potassium iodide solution, high-resolution polymer topologies are achieved without damaging the active material. The resulting structures exhibit a sheet resistance of 90-100 Ω/sq and maintain linear sensitivity to temperature and humidity, making them suitable for next-generation wearable medical diagnostics.

Early bearing faults are often difficult to identify because their characteristic components are weak and easily masked by strong interference. To improve weak-fault feature extraction, this paper proposes a particle-swarm-optimization-based FitzHugh-Nagumo stochastic resonance (FHN-SR) method for bearing vibration signals. The raw signal is first preprocessed by de-meaning, Hilbert envelope demodulation, and standardization to construct a stable stochastic resonance (SR) input. Then, the key model parameters are adaptively optimized by maximizing the output signal-to-noise ratio around the target fault characteristic frequency. To evaluate the proposed method comprehensively, comparisons are carried out with classical SR, underdamped bistable stochastic resonance (UBSR), and a Fast-Kurtogram-based envelope-analysis scheme. Experimental validation is performed on three fault cases, including the rolling element fault case from the Case Western Reserve University (CWRU) dataset and the inner-race and outer-race fault cases from the Machinery Comprehensive Diagnostics Simulator (MCDS) platform. The results show that FHN-SR produces a clearer concentration of fault-related energy and achieves a higher output signal-to-noise ratio (SNR) than the compared methods in most cases. In particular, under degraded noise conditions, FHN-SR maintains more stable enhancement performance, indicating stronger robustness to interference. These results demonstrate that the proposed method provides an effective approach for extracting weak bearing fault features under complex noise backgrounds.

Extracellular vesicles (EVs) facilitate intercellular communication in glioblastoma (GBM) by transferring microRNAs (miRNAs). GBM is the most aggressive primary brain tumor in adults, and despite multimodal therapy, the median survival remains approximately 15 months. Current diagnostic approaches, including contrast-enhanced MRI, are insufficient to reliably distinguish true tumor progression from pseudoprogression. Moreover, therapeutic efficacy is limited by intratumoral heterogeneity, acquired resistance, and the restrictive nature of the blood-brain barrier (BBB). In this context, EV-associated miRNAs (EV-miRNAs) contribute to GBM progression by regulating proliferation, angiogenesis, invasion, therapeutic resistance, and immune evasion. Notably, several EV-miRNAs are dysregulated in both GBM and neurodegenerative diseases (NDDs), suggesting shared molecular pathways across central nervous system (CNS) disorders. Circulating tumor-derived EV-miRNAs represent promising liquid biopsy biomarkers for diagnosis, prognosis, and longitudinal treatment monitoring. Beyond their biomarker potential, EVs can be engineered as nanocarriers capable of crossing the BBB to deliver therapeutic cargo, including inhibitors of oncogenic miRNAs (e.g., miR-21) or tumor-suppressive miRNAs (e.g., miR-124). This review summarizes the molecular functions, biomarker applications, and therapeutic strategies of EV-miRNAs in GBM. We further discuss current challenges related to methodological standardization, scalable production, and clinical translation. Collectively, advancing the understanding and clinical implementation of EV-miRNAs may provide new opportunities for precision diagnostics and therapeutic innovation in GBM.

Base running performance in baseball depends on the ability to efficiently transition between linear and curvilinear sprinting; however, current assessment approaches provide limited insight into how speed is developed, maintained, or lost across these phases. This perspective presents a methodological framework for using GPS technology to enhance the analysis and interpretation of base running performance through segment-specific velocity and time diagnostics. GPS data were collected during 54.7 m linear sprints and home-to-second-base curvilinear sprints in three high-school baseball players with differing performance profiles. Sprint paths were divided into standardized linear (L1-L4) and curvilinear (C1-C4) segments, allowing examination of speed changes between successive phases to identify acceleration, maintenance, and deceleration patterns. Comparative case analyses illustrate how athletes differ in their ability to negotiate the curve around first base, reaccelerate toward second base, and maintain speed under increasing curvilinear demands. In addition, a base running efficiency ratio (BREr) is introduced to quantify how effectively linear sprint capacity is preserved during curvilinear base running, both globally and across early and late phases of the sprint. The three players' data illustrated that GPS-derived velocity-time profiles may provide useful insights into individual running strategies, path selection, and segment-specific performance limitations that are not captured by traditional timing methods. Rather than establishing normative benchmarks, this paper emphasizes the applied value of GPS technology as a diagnostic tool to potentially inform individualized assessment and monitoring in applied settings related to linear and curvilinear sprint performance in baseball.

Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and coupled thermal-fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies.

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light-matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field.

Traditional cancer therapies such as surgery, chemotherapy, and antibody-based treatments often face significant barriers, including systemic toxicity, a lack of selectivity, and the emergence of drug resistance. These issues demand innovative and targeted solutions. Peptide-based therapeutics have gained prominence for their ability to disrupt cancer pathways and facilitate targeted drug delivery, offering structural flexibility, precise targeting, and low immunogenicity with minimal effects on healthy tissues. Concurrently, aptamers, which are structured nucleic acid molecules capable of high-affinity molecular recognition, are being developed as both direct therapeutic agents and as targeting ligands for the improved delivery of anticancer drugs. Combining peptide and aptamer technologies with engineered exosomes provides a modular drug delivery system that enhances targeting specificity, stability, and the ability to cross complex biological barriers such as the blood-brain barrier. The emergence of peptide-decorated, aptamer-decorated exosomes represents a new frontier in precision oncology, promising highly selective, biocompatible, and tunable cancer therapies. Further advances are required to overcome challenges in pharmacokinetics, scalable production, and regulatory compliance, but ongoing bioengineering and nanotechnology research continues to accelerate the translation of these innovative strategies toward improved cancer diagnostics and treatment outcomes. This review discusses the synergistic integration of peptides and aptamers with exosome-based delivery systems, highlighting their current applications and future possibilities.

Inertial measurement units (IMUs) present promising avenues for performance diagnostics in skateboarding, yet systematic validation of their accuracy and applicability remains limited. This study validates the commercially available Spinnax Freak IMU system in the context of skateboarding, with a focus on selected trick detection and classification, distance measurement, maximal horizontal speed, maximal vertical height of the skateboard and airtime during a jump trick. A total of 23 skateboarders (4 females, 19 males; 27.4 ± 10.9 years) participated in this study. Validation methods included comparisons with established reference systems such as laser ranging for maximal horizontal speed (LAVEG), 2D video analysis for maximal vertical height of the skateboard (Kinovea), light barrier measurements for airtime detection (OptoJump Next), and a fixed metric reference (10 m) for rolling distance measurements. The evaluation was supported by statistical analyses including mean absolute error (MAE), root mean-square error (RMSE), mean absolute percentage error (MAPE), t-tests, Bland-Altman plots, linear regression, and ICC(3,1). The Spinnax Freak system demonstrated high validity in detecting trick events and in providing distance measurements that were statistically equivalent to the reference. Trick classification, maximal horizontal speed, maximal vertical height of the skateboard and airtime showed substantial errors, indicating that these outputs are not reliable for biomechanical interpretation at this point. These findings highlight both the potential and the current constraints of single-sensor setups for field-based motion capture in skateboarding. Future developments should prioritize algorithmic refinement, improved temporal resolution, and optimized event classification to enhance measurement accuracy and expand applicability in biomechanical analysis and automated training documentation in skateboarding.

Background: Mucopolysaccharidoses (MPS) are rare lysosomal storage disorders caused by deficiencies in glycosaminoglycan (GAG)-degrading enzymes, leading to progressive multisystem involvement. Methods: We evaluated two unrelated consanguineous Pakistani families, each with three individuals showing features consistent with MPS. Affected individuals in Family 1 presented with developmental regression, severe cognitive impairment, behavioral abnormalities and facial dysmorphisms. The affected individuals in Family 2 showed classical skeletal dysplasia consistent with Morquio syndrome. Whole-exome sequencing (WES), segregation analysis, and in silico protein modeling were performed to identify and characterize pathogenic gene variants. Results: Analysis of WES data revealed a homozygous missense variant in the SGSH gene [c.548G>A (p.Cys183Tyr)] in the three cases of Family 1 and a homozygous splice-site variant in the GALNS gene (c.423-1G>A) in the cases of Family 2. The SGSH variant, located within the sulfatase catalytic domain and classified as likely pathogenic (ACMG), is consistent with the Sanfilippo A phenotype and represents the first clinical characterization of this allele. Regarding Family 2, we identified the GALNS mutation as a recurrent pathogenic founder allele previously reported in individuals of South Asian descent. Structural modeling of SGSH p.Cys183Tyr predicted disruption of a conserved cysteine residue and altered protein stability, likely supporting its deleterious effect. Conclusions: This study expands the spectrum of MPS-associated variants in Pakistan. The findings underscore the importance of genomic diagnostics for enabling early detection, accurate classification, and genetic counseling in populations with high consanguinity.