Computers in biology and medicine最新文献

The etiology of spaceflight-associated neuro-ocular syndrome (SANS) remains unclear. Recent murine studies indicate there may be a link between the space environment and retinal endothelial dysfunction.

Post-fixed control (N = 4) and 14-day tail-suspended (TS) (N = 4) mice eye samples were stained and imaged for the vessel plexus and co-located regions of endothelial cell death. A custom workflow combined whole-mounted and tear reconstructed three-dimensional (3D) spherical retinal plexus models with computational fluid dynamics (CFD) simulation that accounted for the Fåhræus-Lindqvist effect and boundary conditions that accommodated TS fluid pressure measurements and deeper capillary layer blood flow distribution.

TS samples exhibited reduced surface area (4.6 ± 0.5 mm² vs. 3.5 ± 0.3 mm², P = 0.010) and shorter lengths between branches in small vessels (<10 μm, 69.5 ± 0.6 μm vs. 60.4 ± 1.1 μm, P < 0.001). Wall shear stress (WSS) and pressure were higher in TS mice compared to controls, particularly in smaller vessels (<10 μm, WSS: 6.57 ± 1.08 Pa vs. 4.72 ± 0.67 Pa, P = 0.034, Pressure: 72.04 ± 3.14 mmHg vs. 50.64 ± 6.74 mmHg, P = 0.004). Rates of retinal endothelial cell death were variable in TS mice compared to controls. WSS and pressure were generally higher in cell death regions, both within and between cohorts, but significance was variable and limited to small to medium-sized vessels (<20 μm).

These findings suggest a link may exist between emulated microgravity and retinal endothelial dysfunction that may have implications for SANS development. Future work with increased sample sizes of larger species or spaceflight cohorts should be considered.

Several general-purpose language model (LM) architectures have been proposed with demonstrated improvement in text summarization and classification. Adapting these architectures to the medical domain requires additional considerations. For instance, the medical history of the patient is documented in the Electronic Health Record (EHR) which includes many medical notes drafted by healthcare providers. Direct processing of these notes may not be possible because the computational complexity of LMs imposes a limit on the length of input text. Therefore, previous applications resorted to content selection using truncation or summarization of the text. Unfortunately, these text processing techniques may lead to information loss, redundancy or irrelevance. In the present paper, a decision-focused content selection technique is proposed. The objective of this technique is to select a subset of sentences from the medical notes of a patient that are relevant to the target outcome over a predefined observation period. This decision-focused content selection methodology is then used to develop a dementia risk prediction model based on the Longformer LM architecture. The results show that the proposed framework delivers an AUC of 78.43 when the summary is restricted to 1024 tokens, outperforming previously proposed content selection techniques. This performance is notable given that the model estimates dementia risk with a one year prediction horizon, relies on an observation period of only one year and solely uses medical notes without other EHR data modalities. Moreover, the proposed techniques overcome the limitation of machine learning models that use a tabular representation of the text by preserving contextual content, enable feature engineering from raw text and circumvent the computational complexity of language models.

Sections stained in periodic acid-Schiff (PAS), periodic acid-methenamine silver (PAM), hematoxylin and eosin (H&E), and Masson's trichrome (MT) stain with minimal morphological discordance are helpful for pathological diagnosis in renal biopsy. Here, we propose an artificial intelligence-based re-stainer called PPHM-GAN (PAS, PAM, H&E, and MT-generative adversarial networks) with multi-stain to multi-stain transformation capability. We trained three GAN models on 512 × 512-pixel patches from 26 training cases. The model with the best transformation quality was selected for each pair of stain transformations by human evaluation. Frechet inception distances, peak signal-to-noise ratio, structural similarity index measure, contrast structural similarity, and newly introduced domain shift inception score were calculated as auxiliary quality metrics. We validated the diagnostic utility using 5120 × 5120 patches of ten validation cases for major glomerular and interstitial abnormalities. Transformed stains were sometimes superior to original stains for the recognition of crescent formation, mesangial hypercellularity, glomerular sclerosis, interstitial lesions, or arteriosclerosis. 23 of 24 glomeruli (95.83 %) from 9 additional validation cases transformed to PAM, PAS, or MT facilitated recognition of crescent formation. Stain transformations to PAM (p = 4.0E-11) and transformations from H&E (p = 4.8E-9) most improved crescent formation recognition. PPHM-GAN maximizes information from a given section by providing several stains in a virtual single-section view, and may change the staining and diagnostic strategy.

The advancement of medical informatization necessitates extracting entities and their relationships from electronic medical records. Presently, research on electronic medical records predominantly concentrates on single-entity relationship extraction. However, clinical electronic medical records frequently exhibit overlapping complex entity relationships, thereby heightening the challenge of information extraction. To rectify the absence of a clinical medical relationship extraction dataset, this study utilizes electronic medical records from 584 patients in a hospital to create a compact clinical medical relationship extraction dataset. To address the pipelined relationship extraction model’s limitation in overlooking the one-to-many correlation problem between entities and relationships, this paper introduces a cascading relationship extraction model. This model integrates the MacBERT pre-training model, gated recurrent network, and multi-head self-attention mechanism to enhance the extraction of text features. Simultaneously, adversarial learning is incorporated to bolster the model’s robustness. In scenarios involving one-to-many relationships between entities, a two-phase task is employed. Initially, the main entity is predicted, followed by predicting the associated object and their correspondences. Employing this cascade-structured approach enables the model to flexibly manage intricate entity relationships, thereby enhancing extraction accuracy. Experimental results demonstrate the model’s efficiency, yielding F1-scores of 82.8%, 76.8%, and 88.2% for fulfilling relational extraction requirements and tasks on DuIE, CHIP-CDEE, and private datasets, respectively. These scores represent improvements over the benchmark model. The findings indicate the model’s applicability in practical domains, particularly in tasks such as biomedical information extraction.

Background

In electrocardiographic imaging (ECGI), selecting an optimal regularization parameter (λ) is crucial for obtaining accurate inverse electrograms. The effects of signal and geometry uncertainties on the inverse problem regularization have not been thoroughly quantified, and there is no established methodology to identify when λ is sub-optimal due to these uncertainties. This study introduces a novel approach to λ selection using Tikhonov regularization and L-curve optimization, specifically addressing the impact of electrical noise in body surface potential map (BSPM) signals and geometrical inaccuracies in the cardiac mesh.

Methods

Nineteen atrial simulations (5 of regular rhythms and 14 of atrial fibrillation) ensuring variability in substrate complexity and activation patterns were used for computing the ECGI with added white Gaussian noise from 40 dB to -3dB. Cardiac mesh displacements (1–3 cm) were applied to simulate the uncertainty of atrial positioning and study its impact on the L-curve shape. The regularization parameter, the maximum curvature, and the most horizontal angle of the L-curve (β) were quantified. In addition, BSPM signals from real patients were used to validate our findings.

Results

The maximum curvature of the L-curve was found to be inversely related to signal-to-noise ratio and atrial positioning errors. In contrast, the β angle is directly related to electrical noise and remains unaffected by geometrical errors. Our proposed adjustment of λ, based on the β angle, provides a more reliable ECGI solution than traditional corner-based methods. Our findings have been validated with simulations and real patient data, demonstrating practical applicability.

Conclusion

Adjusting λ based on the amount of noise in the data (or on the β angle) allows finding optimal ECGI solutions than a λ purely found at the corner of the L-curve. It was observed that the relevant information in ECGI activation maps is preserved even under the presence of uncertainties when the regularization parameter is correctly selected. The proposed criteria for regularization parameter selection have the potential to enhance the accuracy and reliability of ECGI solutions.

Recent advancements in retinal vessel segmentation, which employ transformer-based and domain-adaptive approaches, show promise in addressing the complexity of ocular diseases such as diabetic retinopathy. However, current algorithms face challenges in effectively accommodating domain-specific variations and limitations of training datasets, which fail to represent real-world conditions comprehensively. Manual inspection by specialists remains time-consuming despite technological progress in medical imaging, underscoring the pressing need for automated and robust segmentation techniques. Additionally, these methods have deficiencies in handling unlabeled target sets, requiring extra preprocessing steps and manual intervention, which hinders their scalability and practical application in clinical settings. This research introduces a novel framework that employs semi-supervised domain adaptation and contrastive pre-training to address these limitations. The proposed model effectively learns from target data by implementing a novel pseudo-labeling approach and feature-based knowledge distillation within a temporal convolutional network (TCN) and extracts robust, domain-independent features. This approach enhances cross-domain adaptation, significantly enhancing the model's versatility and performance in clinical settings. The semi-supervised domain adaptation component overcomes the challenges posed by domain shifts, while pseudo-labeling utilizes the data's inherent structure for enhanced learning, which is particularly beneficial when labeled data is scarce. Evaluated on the DRIVE and CHASE_DB1 datasets, which contain clinical fundus images, the proposed model achieves outstanding performance, with accuracy, sensitivity, specificity, and AUC values of 0.9792, 0.8640, 0.9901, and 0.9868 on DRIVE, and 0.9830, 0.9058, 0.9888, and 0.9950 on CHASE_DB1, respectively, outperforming current state-of-the-art vessel segmentation methods. The partitioning of datasets into training and testing sets ensures thorough validation, while extensive ablation studies with thorough sensitivity analysis of the model's parameters and different percentages of labeled data further validate its robustness.

Colorectal cancer (CRC) is a significant health issue globally, affecting approximately 10 % of the world's population. The prevalence of CRC highlights the need for effective treatments and prevention strategies. The current therapeutic option, such as chemotherapy, has significant side effects. Thus, this study investigated the anticancer properties of Sanguinarine derivatives, an alkaloid found in traditional herbs via chemoinformatic approaches. Six Sanguinarine derivatives were discovered through virtual screening and molecular docking to determine their binding affinities against the mixed lineage kinase (MLK4) protein which is responsible for CRC. All the compounds were found to be more effective than standard drug used for colorectal cancer treatment, with Sanguinarine derivative 11 showing the highest affinity. The stability of the drug was confirmed through molecular dynamics simulations at 500 ns. This suggests that compound 11 has a higher chance of replacing 5-Fluorouracil, which is currently a widely used chemotherapy drug. Before molecular dynamics simulations, the pharmacokinetic and chemical properties of Sanguinarine derivatives were determined using pkCSM server and DFT method, respectively. The results support that compound 11 is a good drug candidate, as evidenced by Lipinski's Rule of Five. Therefore, compound 11 is recommended for further analysis via in vivo and in vitro studies to confirm its efficacy and safety.

Introduction

High-frequency chest wall oscillation (HFCWO) devices are used to improve airway clearance in various respiratory conditions. This study comprehensively assesses the evidence on efficacy and safety and identifies trends in scientific publications and patents across geographic regions.

Methods

This study utilized an integrated approach, combining bibliographic and bibliometric research with artificial intelligence (AI) tools. Four databases – PubMed, Europe Pubmed Central, Cochrane Database of Systematic Reviews, and CINAHL – were searched for systematic reviews on the effectiveness of treatment options for HFCWO. The AMSTAR-2 tool was used to evaluate the risk of bias in systematic reviews. Bibliographic research synthesized the evidence following PRISMA and Cochrane guidelines. The Dimensions platform was used for bibliometric analysis to provide insights into the global landscape. AI tools with prompt engineering tools Chain-of-Thoughts (CoT) and Tree of Thoughts (ToT) were used to enhance data extraction capabilities.

Results

The umbrella review identified 12 systematic reviews supporting the effectiveness of HFCWO in improving pulmonary function parameters, sputum characteristics, dyspnea, health scores, and quality of life in conditions including cystic fibrosis, bronchiectasis, chronic obstructive pulmonary disease (COPD), or neuromuscular diseases, with varying evidence of certainty. Eight of the twelve reviews had a moderate to high AMSTAR-2 confidence level, while several studies lacked sufficient descriptions of methods, treatment regimens, outcome measures, and adverse effects. Despite the absence of adverse events, the overall evidence quality between studies is evaluated as low to very low. Bibliometric analysis found a significant increase in global interest over the past two decades, with 230 research publications, 137 patents, and 56 clinical trials.

Conclusions

The study highlights the potential of HFCWO devices in respiratory care but demands more robust evidence. The increasing interest in airway clearance devices highlights the necessity for HFCWO mechanism and safety research, underlining its therapeutic relevance in respiratory medicine. The interdisciplinary integration of AI tools and prompt engineering contributes to a nuanced understanding of the available evidence.

Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED.

Computer simulations of emergency departments (EDs) are tools that can support managing and optimising ED operations. A core component of ED simulation models is patient trajectories, defined as the series of activities patients undergo in the ED from arrival to discharge. The combined duration of these activities, and waiting times between them, determines a patient’s length of stay (LOS). Patient trajectories are often calibrated and validated solely based on the estimated acuity of patients assigned upon arrival. However, acuity is a prospective patient indicator that inconsistently reflects patients’ actual urgency and resource usage as seen retrospectively upon discharge. Here, we propose a data-driven ED simulation model in which patient trajectories are modelled based on both acuity and retrospective patient indicators. We show that including retrospective patient indicators recovers the observed LOS distributions more accurately than when using acuity alone. We also demonstrate how the use of retrospective patient indicators leads to more plausible estimates of the impact of increased stress in the ED on patients’ LOS. Our work exemplifies how we can better utilise ED data to make the development and evaluation of ED simulation models more accurate and robust, enabling them to provide more reliable and useful operational insights.