Search Results (152)

Accurate and automated detection and tracking of cells in microscopy images is a persistent challenge in biotechnology and biomedical research. Effective detection and tracking are crucial for understanding biological processes and extracting meaningful data for subsequent simulations. In this study, we present an integrated pipeline that leverages a fine-tuned YOLOv8x model for detecting cells and cell divisions across microscopy image series. While YOLOv8x exhibits strong detection capabilities, it occasionally misses certain cells, leading to gaps in data. To mitigate this, we incorporate the DeepSORT tracking algorithm, which enhances data association and reduces the cells’ identity (ID) switches by utilizing a pre-trained convolutional network for robust multi-object tracking. This combination ensures continuous detection and compensates for missed detections, thereby improving overall recall. Our approach achieves a recall of 93.21% with the enhanced DeepSORT algorithm, compared to the 53.47% recall obtained by the original YOLOv8x model. The proposed pipeline effectively extracts detailed information from structured image datasets, providing a reliable approximation of cellular processes in culture environments. Full article

Health and well-being, both mental and physical, depend largely on adequate sleep. Many conditions arise from a disrupted sleep cycle, significantly deteriorating the quality of life of those affected. The analysis of the sleep cycle provide valuable information about sleep stages, which are employed in sleep medicine for the diagnosis of numerous diseases. The clinical standard for sleep data recording is polysomnography (PSG), which records electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and other signals during sleep activity. Recently, machine learning approaches have exhibited high accuracy in applications such as the classification and prediction of biomedical signals. This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. The objective is to design a framework for automated feature extraction. To address class imbalance, an epoch-level random undersampling (E-LRUS) method is proposed, discarding full epochs from majority classes while preserving the temporal structure, unlike traditional methods that remove individual samples. This method has been tested on EEG recordings acquired from the public Sleep EDF Expanded database, achieving an overall accuracy rate of 78.67% along with an F1-score of 72.10%. The findings show that this method proves to be effective for sleep stage classification in patients. Full article

In the past decade, nanozymes have been attracting increasing interest in academia due to their stable performance, low cost, and easy modification. With the catalytic signal amplification feature, nanozymes not only find wide use in traditional “lock-and-key” single-target detection but hold great potential in high-throughput multiobjective analysis via fabricating sensor arrays. In particular, the rise of machine learning in recent years has greatly advanced the design, construction, signal processing, and utilization of sensor arrays. The constructive collaboration of nanozymes, sensor arrays, and machine learning is accelerating the development of biochemical sensors. To highlight the emerging field, in this minireview, we created a concise summary of machine-learning-assisted nanozyme-based sensor arrays. First, the construction of nanozyme-involved sensor arrays is introduced from several aspects, including nanozyme materials and activities, sensing variables, and signal outputs. Then, the roles of machine learning in signal treatment, information extraction, and outcome feedback are emphasized. Afterwards, typical applications of machine-learning-assisted nanozyme-involved sensor arrays in environmental detection, food analysis, and biomedical sensing are discussed. Finally, the promise of machine-learning-assisted nanozyme-based sensor arrays in biochemical sensing is highlighted, and some future trends are also pointed out to attract more interest and effort to promote the emerging field for better practical use. Full article

Clinician notes are a rich source of patient information, but often contain inconsistencies due to varied writing styles, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder their direct use in patient care and degrade the performance of downstream computational applications that rely on these notes as input, such as quality improvement, population health analytics, precision medicine, clinical decision support, and research. We present a large-language-model (LLM) approach to the preprocessing of 1618 neurology notes. The LLM corrected spelling and grammatical errors, expanded acronyms, and standardized terminology and formatting, without altering clinical content. Expert review of randomly sampled notes confirmed that no significant information was lost. To evaluate downstream impact, we applied an ontology-based NLP pipeline (Doc2Hpo) to extract biomedical concepts from the notes before and after editing. F1 scores for Human Phenotype Ontology extraction improved from 0.40 to 0.61, confirming our hypothesis that better inputs yielded better outputs. We conclude that LLM-based preprocessing is an effective error correction strategy that improves data quality at the level of free text in clinical notes. This approach may enhance the performance of a broad class of downstream applications that derive their input from unstructured clinical documentation. Full article

Phase wrapping is a common phenomenon in optical full-field imaging or measurement systems. It arises from large phase retardations and results in wrapped-phase maps that contain essential information about surface roughness and topology. However, these maps are often degraded by noise, such as speckle and Gaussian, which reduces the measurement accuracy and complicates phase reconstruction. Denoising such data is a fundamental problem in computer vision and plays a critical role in biomedical imaging modalities like Full-Field Optical Interferometry. In this paper, we propose WPD-Net (Wrapped-Phase Denoising Network), a lightweight deep learning-based neural network specifically designed to restore phase images corrupted by high noise levels. The network architecture integrates a shallow feature extraction module, a series of Residual Dense Attention Blocks (RDABs), and a dense feature fusion module. The RDABs incorporate attention mechanisms that help the network focus on critical features and suppress irrelevant noise, especially in high-frequency or complex regions. Additionally, WPD-Net employs a growth-rate-based feature expansion strategy to enhance multi-scale feature representation and improve phase continuity. We evaluate the model’s performance on both synthetic and experimentally acquired datasets and compare it with other state-of-the-art deep learning-based denoising methods. The results demonstrate that WPD-Net achieves superior noise suppression while preserving fine structural details even with mixed speckle and Gaussian noises. The proposed method is expected to enable fast image processing, allowing unwrapped biomedical images to be retrieved in real time. Full article

This work introduces TrialSieve, a novel framework for biomedical information extraction that enhances clinical meta-analysis and drug repurposing. By extending traditional PICO (Patient, Intervention, Comparison, Outcome) methodologies, TrialSieve incorporates hierarchical, treatment group-based graphs, enabling more comprehensive and quantitative comparisons of clinical outcomes. TrialSieve was used to annotate 1609 PubMed abstracts, 170,557 annotations, and 52,638 final spans, incorporating 20 unique annotation categories that capture a diverse range of biomedical entities relevant to systematic reviews and meta-analyses. The performance (accuracy, precision, recall, F1-score) of four natural-language processing (NLP) models (BioLinkBERT, BioBERT, KRISSBERT, PubMedBERT) and the large language model (LLM), GPT-4o, was evaluated using the human-annotated TrialSieve dataset. BioLinkBERT had the best accuracy (0.875) and recall (0.679) for biomedical entity labeling, whereas PubMedBERT had the best precision (0.614) and F1-score (0.639). Error analysis showed that NLP models trained on noisy, human-annotated data can match or, in most cases, surpass human performance. This finding highlights the feasibility of fully automating biomedical information extraction, even when relying on imperfectly annotated datasets. An annotator user study (n = 39) revealed significant (p < 0.05) gains in efficiency and human annotation accuracy with the unique TrialSieve tree-based annotation approach. In summary, TrialSieve provides a foundation to improve automated biomedical information extraction for frontend clinical research. Full article

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments. Full article

Optical differential operations can directly extract edge information from images and have significant application potential in fields such as image processing and object recognition. In this work, we propose an optical spatial differentiator based on the birefringence effect of uniaxial crystals. The system comprises two orthogonal polarizers and a uniaxial crystal, offering advantages of structural simplicity, operational stability, low cost, and seamless compatibility with conventional optical systems. Experimental results demonstrate that the proposed differentiator achieves clear edge imaging for both amplitude and phase objects, while also enabling dark-field differential imaging of transparent biological cells, thereby substantially enhancing imaging quality and contrast. This efficient and robust design provides a promising solution for advancing optical differentiation techniques in applications ranging from data processing to biomedical imaging. Full article

The extraction of Adverse Drug Reactions from biomedical text is a critical task in the field of healthcare and pharmacovigilance. It serves as a cornerstone for improving patient safety by enabling the early identification and mitigation of potential risks associated with pharmaceutical treatments. This process not only helps in detecting harmful side effects that may not have been evident during clinical trials but also contributes to the broader understanding of drug safety in real-world settings, ultimately guiding regulatory actions and informing clinical practices. In this study, we conducted a comprehensive evaluation of eleven transformer-based models for ADR extraction, focusing on two widely used datasets: CADEC and SMM4H. The task was approached as a sequence labeling problem, where each token in the text is classified as part of an ADR or not. Various transformer architectures, including BioBERT, PubMedBERT, and SpanBERT, were fine-tuned and evaluated on these datasets. BioBERT demonstrated superior performance on the CADEC dataset, achieving an impressive F1 score of 86.13%, indicating its strong capability in recognizing ADRs within patient narratives. On the other hand, SpanBERT emerged as the top performer on the SMM4H dataset, with an F1 score of 84.29%, showcasing its effectiveness in processing the more diverse and challenging social media data. These results highlight the importance of selecting appropriate models based on the specific characteristics such as text formality, domain-specific language, and task complexity to achieve optimal ADR extraction performance. Full article

Silk sericin (SS), a by-product of the textile industry, has gained significant attention for its biomedical potential due to its biocompatibility and regenerative potential. However, the literature lacks information on SS processing methods and the resulting physicochemical properties. This study represents the first step in protocol optimization and standardization. In the present work, different processing techniques were studied and compared on SS extracted from boiling water: evaporation, rotary evaporation, lyophilization, and dialysis, which presented a recovery yield of approximately 27–32%. The goal was to find the most promising process to concentrate extracted SS solutions, and to ensure that the SS structure was highly preserved. As a result, a new cryo-lyophilization methodology was proposed. The proposed method allows for the preservation of the amorphous structure, which offers significant advantages including complete dissolution in water and PBS, an increase in storage stability, and the possibility of scaling-up, making it highly suitable for industrial and biomedical applications. The second part of the work focused on addressing another challenge in SS processing: efficient and non-destructive sterilization. Supercritical CO₂ (scCO₂) has been gaining momentum in the last years for sterilizing sensitive biopolymers and biological materials due to its non-toxicity and mild processing conditions. Thus, scCO₂ technology was validated as a mild technique for the terminal sterilization of SS. In this way, it was possible to engineer a sequential cryo-lyophilization/scCO₂ sterilization process which was able to preserve the original properties of this natural silk protein. Overall, we have valorized SS into a sterile, off-the-shelf, bioactive, and water-soluble material, with the potential to be used in the biomedical, pharmaceutical, or cosmetic industries. Full article

Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data. Full article

Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human–computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage. Full article

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems. Full article

Fucoidan is a sulfated polysaccharide found in brown seaweed. Due to its reported biological activities, including antiviral, antibacterial and anti-inflammatory activities, it has garnered significant attention for potential biomedical applications. However, the direct relationship between fucoidan extracts’ chemical structures and bioactivities is unclear, making it extremely challenging to predict whether an extract will possess a given bioactivity. This relationship is further complicated by a lack of uniformity in the recent literature in terms of the assessment and reporting of extract properties, yield and chemical composition (e.g., sulfate, fucose, uronic acid and monosaccharide contents). These inconsistencies pose significant challenges when directly comparing extraction techniques across studies. This review collected data on extract contents and properties from a selection of available studies. Where information was unavailable directly, efforts were made to extrapolate data. This approach enabled a comprehensive examination of the correlation between extraction techniques and the characteristics of the resulting extracts. A holistic framework is presented for the selection of fucoidan extraction methods, outlining key heuristics to consider when capturing the broader context of a seaweed bioprocess. Future work should focus on developing knowledge within these heuristic categories, such as the creation of technoeconomic models of each extraction process. This framework should allow for a robust extraction selection process that integrates process scale, cost and constraints into decision making. Key quality attributes for biologically active fucoidan are proposed, and areas for future research are identified, such as studies for specific bioactivities aimed at elucidating fucoidan’s mechanism of action. This review also sets out future work required to standardize the reporting of fucoidan extract data. Standardization could positively enhance the quality and depth of data on fucoidan extracts, enabling the relationships between physical, chemical and bioactive properties to be identified. Recommendations on best practices for the production of high-quality fucoidan with desirable yield, characteristics and bioactivity are highlighted. Full article

In the expanding landscape of biomedical literature, numerous latent associations outlined in scholarly papers await discovery and integration into biomedical databases. Biomedical Natural Language Processing (NLP) research focuses on automating knowledge extraction and mining from this literature, particularly emphasizing the essential task of Relation Extraction (RE). However, existing models have limitations, mainly in their applicability to partial datasets for RE tasks. Moreover, conventional models often treat RE as a binary classification challenge, which proves suboptimal given the diverse relationships, including intricate ones like similarity and hierarchy, present in the RE dataset. These limitations are exacerbated by the models’ inability to capture word-level positional nuances and sentence-level language features. In response to these challenges, we present a novel RE model called BicapBert. This model integrates neural networks and capsule networks, enhancing them with hybrid knowledge graph embeddings to extract relevant features. BicapBert utilizes PubMedBERT and capsule networks to extract word-level positional and sentence-level language features. It further captures knowledge features from a biomedical knowledge graph, integrating them with the aforementioned linguistic features. The amalgamated information is then input into a multi-layer perceptron, culminating in results derived through a softmax classifier. Experimental evaluations on three extensive RE task datasets showcase the state-of-the-art performance of our proposed model. Additionally, we validate the model’s efficacy on three randomly selected biomedical datasets for various tasks, further affirming its superiority. Full article