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Scientific Reports Jul 2024Postural sway has not been investigated before or after a neck exercise intervention in individuals with chronic whiplash-associated disorders (WAD). The aim of the...
Postural sway has not been investigated before or after a neck exercise intervention in individuals with chronic whiplash-associated disorders (WAD). The aim of the study was to investigate postural sway in individuals with chronic WAD grades 2 and 3: (a) compared with healthy matched controls at baseline; (b) after three months of neck-specific exercise and (c) to investigate the correlation between postural sway with self-reported dizziness during motion and balance problems/unsteadiness. This is a longitudinal prospective experimental case-control intervention study. Individuals with WAD (n = 30) and age- and gender-matched healthy volunteers (n = 30) participated. Postural sway was assessed using an iPhone application. Measurements were carried out at baseline, and for those with WAD a second measurement was performed at the three-month follow-up when neck-specific exercise intervention ended. The WAD group performed significantly worse than the healthy group in both pathway and ellipse area double stance eyes closed at baseline (main outcome), but not at the three-month follow-up. The WAD group significantly improved after rehabilitation in both pathway double stance eyes closed and pathway single stance eyes open. The correlation between postural sway and self-rated dizziness during motion and balance problems was low to moderate. One may conclude that postural sway was improved after a neck-specific exercise programme. The study results strengthen earlier findings that individuals with WAD have worse balance outcome when they have to rely on neck proprioception (eyes closed). The study results may be important for the development of improved rehabilitation methods for WAD.
Topics: Humans; Male; Female; Whiplash Injuries; Postural Balance; Adult; Case-Control Studies; Longitudinal Studies; Exercise Therapy; Middle Aged; Prospective Studies; Dizziness; Neck; Chronic Disease
PubMed: 38956135
DOI: 10.1038/s41598-024-66176-w -
Scientific Reports Jul 2024Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing...
Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net's robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications.
Topics: Retinal Vessels; Humans; Neural Networks, Computer; Deep Learning; Image Processing, Computer-Assisted; Algorithms
PubMed: 38956117
DOI: 10.1038/s41598-024-63496-9 -
Scientific Reports Jul 2024Trainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct... (Randomized Controlled Trial)
Randomized Controlled Trial
Trainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct instrument handling. This expert-guided training faces significant limitations in objectively assessing skills in real-time and tracking learning. It is unknown whether AI systems can effectively replicate nuanced real-time feedback, risk identification, and guidance in mastering surgical technical skills that expert instructors offer. This randomized controlled trial compared real-time AI feedback to in-person expert instruction. Ninety-seven medical trainees completed a 90-min simulation training with five practice tumor resections followed by a realistic brain tumor resection. They were randomly assigned into 1-real-time AI feedback, 2-in-person expert instruction, and 3-no real-time feedback. Performance was assessed using a composite-score and Objective Structured Assessment of Technical Skills rating, rated by blinded experts. Training with real-time AI feedback (n = 33) resulted in significantly better performance outcomes compared to no real-time feedback (n = 32) and in-person instruction (n = 32), .266, [95% CI .107 .425], p < .001; .332, [95% CI .173 .491], p = .005, respectively. Learning from AI resulted in similar OSATS ratings (4.30 vs 4.11, p = 1) compared to in-person training with expert instruction. Intelligent systems may refine the way operating skills are taught, providing tailored, quantifiable feedback and actionable instructions in real-time.
Topics: Humans; Clinical Competence; Artificial Intelligence; Female; Male; Adult; Simulation Training
PubMed: 38956112
DOI: 10.1038/s41598-024-65716-8 -
Scientific Reports Jul 2024Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive...
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.
Topics: Bayes Theorem; Uncertainty; Models, Biological; Computer Simulation; Humans; Signal Transduction
PubMed: 38956095
DOI: 10.1038/s41598-024-65196-w -
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 -
Cardiovascular Engineering and... Jul 2024Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and... (Review)
Review
BACKGROUND AND OBJECTIVE
Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.
METHODS
The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.
RESULTS
ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.
CONCLUSION
ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.
PubMed: 38956008
DOI: 10.1007/s13239-024-00737-y -
Therapeutic Innovation & Regulatory... Jul 2024Clinical trials have become larger and more complex. Thus, eSource should be used to enhance efficiency. This study aimed to evaluate the impact of the multisite...
BACKGROUND
Clinical trials have become larger and more complex. Thus, eSource should be used to enhance efficiency. This study aimed to evaluate the impact of the multisite implementation of eSource direct data capture (DDC), which we define as eCRFs for direct data entry in this study, on efficiency by analyzing data from a single investigator-initiated clinical trial in oncology.
METHODS
Operational data associated with the targeted study conducted in Japan was used to analyze time from data occurrence to data entry and data finalization, and number of visits to the site and time spent at the site by clinical research associates (CRAs). Additionally, simulations were performed on the change in hours at the clinical sites during the implementation of eSource DDC.
RESULTS
No difference in time from data occurrence to data entry was observed between the DDC and the transcribed data fields. However, the DDC fields could be finalized 4 days earlier than the non-DDC fields. Additionally, although no difference was observed in the number of visits for source data verification (SDV) by CRAs, a comparison among sites that introduced eSource DDC and those that did not showed that the time spent at the site for SDV was reduced. Furthermore, the simulation results indicated that even a small amount of data to be collected or a small percentage of DDC-capable items may lead to greater efficiency when the number of subjects per site is significant.
CONCLUSIONS
The implementation of eSource DDC may enhance efficiency depending on the study framework and type and number of items to be collected.
PubMed: 38956005
DOI: 10.1007/s43441-024-00671-0 -
Attention, Perception & Psychophysics Jul 2024A key aspect of efficient visual processing is to use current and previous information to make predictions about what we will see next. In natural viewing, and when...
A key aspect of efficient visual processing is to use current and previous information to make predictions about what we will see next. In natural viewing, and when looking at words, there is typically an indication of forthcoming visual information from extrafoveal areas of the visual field before we make an eye movement to an object or word of interest. This "preview effect" has been studied for many years in the word reading literature and, more recently, in object perception. Here, we integrated methods from word recognition and object perception to investigate the timing of the preview on neural measures of word recognition. Through a combined use of EEG and eye-tracking, a group of multilingual participants took part in a gaze-contingent, single-shot saccade experiment in which words appeared in their parafoveal visual field. In valid preview trials, the same word was presented during the preview and after the saccade, while in the invalid condition, the saccade target was a number string that turned into a word during the saccade. As hypothesized, the valid preview greatly reduced the fixation-related evoked response. Interestingly, multivariate decoding analyses revealed much earlier preview effects than previously reported for words, and individual decoding performance correlated with participant reading scores. These results demonstrate that a parafoveal preview can influence relatively early aspects of post-saccadic word processing and help to resolve some discrepancies between the word and object literatures.
PubMed: 38956003
DOI: 10.3758/s13414-024-02916-4 -
Pharmaceutical Research Jul 2024To develop a toolkit of test methods for characterizing potentially critical quality attributes (CQAs) of topical semisolid products and to evaluate how CQAs influence...
Topical Semisolid Drug Product Critical Quality Attributes with Relevance to Cutaneous Bioavailability and Pharmacokinetics: Part I-Bioequivalence of Acyclovir Topical Creams.
PURPOSE
To develop a toolkit of test methods for characterizing potentially critical quality attributes (CQAs) of topical semisolid products and to evaluate how CQAs influence the rate and extent of active ingredient bioavailability (BA) by monitoring cutaneous pharmacokinetics (PK) using an In Vitro Permeation Test (IVPT).
METHODS
Product attributes representing the physicochemical and structural (Q3) arrangement of matter, such as attributes of particles and globules, were assessed for a set of test acyclovir creams (Aciclostad® and Acyclovir 1A Pharma) and compared to a set of reference acyclovir creams (Zovirax® US, Zovirax® UK and Zovirax® Australia). IVPT studies were performed with all these creams using heat-separated human epidermis, evaluated with both, static Franz-type diffusion cells and a flow through diffusion cell system.
RESULTS
A toolkit developed to characterize quality and performance attributes of these acyclovir topical cream products identified certain differences in the Q3 attributes and the cutaneous PK of acyclovir between the test and reference sets of products. The cutaneous BA of acyclovir from the set of reference creams was substantially higher than from the set of test creams.
CONCLUSIONS
This research elucidates how differences in the composition or manufacturing of product formulations can alter Q3 attributes that modulate myriad aspects of topical product performance. The results demonstrate the importance of understanding the Q3 attributes of topical semisolid drug products, and of developing appropriate product characterization tests. The toolkit developed here can be utilized to guide topical product development, and to mitigate the risk of differences in product performance, thereby supporting a demonstration of bioequivalence (BE) for prospective topical generic products and reducing the reliance on comparative clinical endpoint BE studies.
PubMed: 38955999
DOI: 10.1007/s11095-024-03736-9 -
Annals of Surgical Oncology Jul 2024Immediate lymphatic reconstruction (ILR) has been proposed to decrease lymphedema rates. The primary aim of our study was to determine whether ILR decreased the...
BACKGROUND
Immediate lymphatic reconstruction (ILR) has been proposed to decrease lymphedema rates. The primary aim of our study was to determine whether ILR decreased the incidence of lymphedema in patients undergoing axillary lymph node dissection (ALND).
METHODS
We conducted a two-site pragmatic study of ALND with or without ILR, employing surgeon-level cohort assignment, based on breast surgeons' preferred standard practice. Lymphedema was assessed by limb volume measurements, patient self-reporting, provider documentation, and International Classification of Diseases, Tenth Revision (ICD-10) codes.
RESULTS
Overall, 230 patients with breast cancer were enrolled; on an intention-to-treat basis, 99 underwent ALND and 131 underwent ALND with ILR. Of the 131 patients preoperatively planned for ILR, 115 (87.8%) underwent ILR; 72 (62.6%) were performed by one breast surgical oncologist and 43 (37.4%) by fellowship-trained microvascular plastic surgeons. ILR was associated with an increased risk of lymphedema when defined as ≥10% limb volume change on univariable analysis, but not on multivariable analysis, after propensity score adjustment. We did not find a statistically significant difference in limb volume measurements between the two cohorts when including subclinical lymphedema (≥5% inter-limb volume change), nor did we see a difference in grade between the two cohorts on an intent-to-treat or treatment received basis. For all patients, considering ascertainment strategies of patient self-reporting, provider documentation, and ICD-10 codes, as a single binary outcome measure, there was no significant difference in lymphedema rates between those undergoing ILR or not.
CONCLUSION
We found no significant difference in lymphedema rates between patients undergoing ALND with or without ILR.
PubMed: 38955992
DOI: 10.1245/s10434-024-15715-w