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Scientific Data Jul 2024
PubMed: 38956109
DOI: 10.1038/s41597-024-03548-x -
Scientific Reports Jul 2024Distinguishing between microscopic variances in temperature in both space and time with high precision can open up new opportunities in optical sensing. In this paper,...
Distinguishing between microscopic variances in temperature in both space and time with high precision can open up new opportunities in optical sensing. In this paper, we present a novel approach to optically measure temperature from the fluorescence of erbium:ytterbium doped tellurite glass, with fast temporal resolution at micron-scale localisation over an area with sub millimetre spatial dimensions. This confocal-based approach provides a micron-scale image of temperature variations over a 200 m 200 m field of view at sub-1 second time intervals. We test our sensing platform by monitoring the real-time evaporation of a water droplet over a wide field of view and track it's evaporative cooling effect on the glass where we report a net temperature change of 6.97 K ± 0.03 K. This result showcases a confocal approach to thermometry to provide high temporal and spatial resolution over a microscopic field of view with the goal of providing real-time measures of temperature on the micro-scale.
PubMed: 38956091
DOI: 10.1038/s41598-024-65529-9 -
Scientific Reports Jul 2024Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and...
Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.
Topics: Celiac Disease; Humans; Spectrum Analysis, Raman; Deep Learning; Female; Male; Adult; Neural Networks, Computer; Case-Control Studies; Middle Aged
PubMed: 38956075
DOI: 10.1038/s41598-024-64621-4 -
Scientific Data Jul 2024Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image...
Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease. Here, we release the HVSMR-2.0 dataset, comprising 60 CMR scans alongside manual segmentation masks of the 4 cardiac chambers and 4 great vessels. The images showcase a wide range of heart defects and prior surgical interventions. The dataset also includes masks of required and optional extents of the great vessels, enabling fairer comparisons across algorithms. Detailed diagnoses for each subject are also provided. By releasing HVSMR-2.0, we aim to encourage development of robust segmentation algorithms and clinically relevant tools for congenital heart disease.
Topics: Humans; Heart Defects, Congenital; Magnetic Resonance Imaging; Heart; Imaging, Three-Dimensional; Algorithms
PubMed: 38956063
DOI: 10.1038/s41597-024-03469-9 -
Nature Communications Jul 2024The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we...
The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.
Topics: Cryoelectron Microscopy; Deep Learning; Proteins; Protein Conformation; Molecular Dynamics Simulation; Neural Networks, Computer; Databases, Protein; Computational Biology
PubMed: 38956032
DOI: 10.1038/s41467-024-49858-x -
Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review.Journal of Racial and Ethnic Health... Jul 2024Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to... (Review)
Review
BACKGROUND
Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to perpetuate disparities among historically marginalized populations.
OBJECTIVE
Following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we conducted a narrative review of current literature on AI and health disparities in the United States. We aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities?
METHODS
We searched the Ovid MEDLINE electronic database to identify and retrieve publications discussing AI and its impact on racial/ethnic health disparities. Articles were included if they discussed AI as a tool to mitigate racial health disparities with or without bias in developing and using AI.
RESULTS
This review included 65 articles. We identified six themes of limitations in AI that impact health equity: (1) biases in AI can perpetuate and exacerbate racial and ethnic inequities; (2) equity in algorithms should be a priority; (3) lack of diversity in the field of AI is concerning; (4) the need for regulation and testing algorithms for accuracy; (5) ethical standards for AI in health care are needed; and (6) the importance of promoting transparency and accountability.
CONCLUSIONS
While AI promises to enhance healthcare outcomes and address equity concerns, risks and challenges are associated with its implementation. To maximize the use of AI, it must be approached with an equity lens during all phases of development.
PubMed: 38955956
DOI: 10.1007/s40615-024-02057-2 -
International Urology and Nephrology Jul 2024This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of...
PURPOSE
This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of anterior urethral stricture.
METHODS
The study enrolled 63 male patients with diagnosed anterior urethral strictures alongside 10 healthy volunteers to serve as controls. The imaging protocol utilized a high-frequency 3D ultrasound system combined with a linear stepper motor, which enabled precise and rapid image acquisition. For image analysis, an advanced AI-based segmentation process using a modified U-net algorithm was implemented to perform real-time, high-resolution segmentation and three-dimensional reconstruction of the urethra. A comparative analysis was performed against the surgically measured stricture lengths. Spearman's correlation analysis was executed to assess the findings.
RESULTS
The AI model completed the entire processing sequence, encompassing recognition, segmentation, and reconstruction, within approximately 5 min. The mean intraoperative length of urethral stricture was determined to be 14.4 ± 8.4 mm. Notably, the mean lengths of the urethral strictures reconstructed by manual and AI models were 13.1 ± 7.5 mm and 13.4 ± 7.2 mm, respectively. Interestingly, no statistically significant disparity in urethral stricture length between manually reconstructed and AI-reconstructed images was observed. Spearman's correlation analysis underscored a more robust association of AI-reconstructed images with intraoperative urethral stricture length than manually reconstructed 3D images (0.870 vs. 0.820). Furthermore, AI-reconstructed images provided detailed views of the corpus spongiosum fibrosis from multiple perspectives.
CONCLUSIONS
The research heralds the inception of an innovative, efficient AI-driven sonographic approach for three-dimensional visualization of urethral strictures, substantiating its viability and superiority in clinical application.
PubMed: 38955940
DOI: 10.1007/s11255-024-04137-y -
The AAPS Journal Jul 2024The paper highlights the necessity for a robust regulatory framework for assessing nanomedicines and their off-patent counterparts, termed as nanosimilar, which could be... (Review)
Review
The paper highlights the necessity for a robust regulatory framework for assessing nanomedicines and their off-patent counterparts, termed as nanosimilar, which could be considered as 'similar' to the prototype nanomedicine,based on essential criteria describing the 'similarity'. The term 'similarity' should be focused on criteria that describe nanocarriers, encompassing their physicochemical, thermodynamic, morphological, and biological properties, including surface interactions and pharmacokinetics. Nanocarriers can be regarded as advanced self-assembled excipients (ASAEs) due to their complexity and chaotic behavior and should be evaluated by using essential criteria in order for off-patent nanomedicines be termed as nanosimilars, from a regulatory perspective. Collaboration between the pharmaceutical industry, regulatory bodies, and artificial intelligence (AI) startups is pivotal for the precise characterization and approval processes for nanomedicines and nanosimilars and embracing innovative tools and terminology facilitates the development of a sustainable regulatory framework, ensuring safety and efficacy. This crucial shift toward precision R&D practices addresses the complexity inherent in nanocarriers, paving the way for therapeutic advancements with economic benefits.
Topics: Nanomedicine; Humans; Biosimilar Pharmaceuticals; Artificial Intelligence; Nanoparticles; Drug Industry; Drug Approval; Drug Carriers
PubMed: 38955936
DOI: 10.1208/s12248-024-00942-6 -
Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary Sciences,... Jul 2024Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In... (Review)
Review
Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.
PubMed: 38955920
DOI: 10.1007/s12539-024-00626-x -
International Journal of Computer... Jul 2024This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature)...
PURPOSE
This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.
METHODS
For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.
RESULTS
The results showed that MLR combined with the scaled preprocessing achieved the highest R coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R = 0.8253).
CONCLUSIONS
The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.
PubMed: 38955902
DOI: 10.1007/s11548-024-03218-8