Bioengineering最新文献

Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.

Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, is limited.

Objectives: The aim of this work is to develop a binary-outcome mixed-effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aim to assess its performance and compare it to fixed-effects Xgboost and traditional logistic regression models.

Methods: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 January 2012 and 31 March 2019, were included. The dataset was split into two cohorts: training/validation (n = 157,196; 2012-2016) and holdout (n = 69,891; 2017-2019). The outcome variable was 30-day mortality with hospitals considered as the clustering variable. The logistic regression, mixed-effects logistic regression, Xgboost and binary-outcome mixed-effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach.

Results: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of the mixed-effects models. Unstandardized Xgboost BME demonstrated marked improvements in prediction power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glms) and generalized linear mixed-effects models (glmers) followed similar results, with the Xgboost models also excelling at greater sample sizes.

Conclusions: These findings suggest that integrating mixed effects into machine learning models can enhance their performance on datasets where the sample size is small.

In the present study, a customized device (Epi-ExPer) was designed and fabricated to facilitate an epithelial organ culture, allowing for controlled exposure to exogenous chemical stimuli and accommodating the evaluation of permeation of the tissue after treatment. The Epi-ExPer system was fabricated using a stereolithography (SLA)-based additive manufacturing (AM) method. Human and porcine oral epithelial mucosa tissues were inserted into the device and exposed to resinous monomers commonly released by dental restorative materials. The effect of these xenobiotics on the morphology, viability, permeability, and expression of relevant markers of the oral epithelium was evaluated. Tissue culture could be performed with the desired orientation of air-liquid interface (ALI) conditions, and exposure to xenobiotics was undertaken in a spatially guarded and reproducible manner. Among the selected monomers, HEMA and TEGDMA reduced tissue viability at high concentrations, while tissue permeability was increased by the latter. Xenobiotics affected the histological image by introducing the vacuolar degeneration of epithelial cells and increasing the expression of panCytokeratin (pCK). Epi-ExPer device offers a simple, precise, and reproducible study system to evaluate interactions of oral mucosa with external stimuli, providing a biocompatibility and permeability assessment tool aiming to an enhanced in vitro/ex vivo-to-in vivo extrapolation (IVIVE) that complies with European Union (EU) and Food and Durg Administration (FDI) policies.

Diagnosing atypical pigmented facial lesions (aPFLs) is a challenging topic for dermatologists. Accurate diagnosis of these lesions is crucial for effective patient management, especially in dermatology, where visual assessment plays a central role. Incorrect diagnoses can result in mismanagement, delays in appropriate interventions, and potential harm. AI, however, holds the potential to enhance diagnostic accuracy and provide reliable support to clinicians. This work aimed to evaluate and compare the effectiveness of machine learning (logistic regression of lesion features and patient metadata) and deep learning (CNN analysis of images) models in dermoscopy diagnosis and the management of aPFLs. This study involved the analysis of 1197 dermoscopic images of facial lesions excised due to suspicious and histologically confirmed malignancy, classified into seven classes (lentigo maligna-LM; lentigo maligna melanoma-LMM; atypical nevi-AN; pigmented actinic keratosis-PAK; solar lentigo-SL; seborrheic keratosis-SK; and seborrheic lichenoid keratosis-SLK). Image samples were collected through the Integrated Dermoscopy Score (iDScore) project. The statistical analysis of the dataset shows that the patients mean age was 65.5 ± 14.2, and the gender was equally distributed (580 males-48.5%; 617 females-51.5%). A total of 41.7% of the sample constituted malignant lesions (LM and LMM). Meanwhile, the benign lesions were mainly PAK (19.3%), followed by SL (22.2%), AN (10.4%), SK (4.0%), and SLK (2.3%). The lesions were mainly localised in the cheek and nose areas. A stratified analysis of the assessment provided by the enrolled dermatologists was also performed, resulting in 2445 evaluations of the 1197 images (2.1 evaluations per image on average). The physicians demonstrated higher accuracy in differentiating between malignant and benign lesions (71.2%) than in distinguishing between the seven specific diagnoses across all the images (42.9%). The logistic regression model obtained a precision of 39.1%, a sensitivity of 100%, a specificity of 33.9%, and an accuracy of 53.6% on the test set, while the CNN model showed lower sensitivity (58.2%) and higher precision (47.0%), specificity (90.8%), and accuracy (59.5%) for melanoma diagnosis. This research demonstrates how AI can enhance the diagnostic accuracy in complex dermatological cases like aPFLs by integrating AI models with clinical data and evaluating different diagnostic approaches, paving the way for more precise and scalable AI applications in dermatology, showing their critical role in improving patient management and the outcomes in dermatology.

Walking, a basic physical movement of the human body, is a resource for observers in forming interpersonal impressions. We have previously investigated the expression and perception of the attractiveness of female gaits. In this paper, drawing on our previous research, additional analysis, and reviewing previous studies, we seek to deepen our understanding of the function of gait attractiveness. First, we review previous research on gait as nonverbal information. Then, we show that fashion models' gaits reflect sociocultural genderlessness, while nonmodels express reproductive-related biological attractiveness. Next, we discuss the functions of gait attractiveness based on statistical models that link gait parameters and attractiveness scores. Finally, we focus on observers' perception of attractiveness, constructing a model of the visual information processing with respect to gait attractiveness. Overall, our results suggest that there are not only biological but also sociocultural criteria for gait attractiveness, and men and women place greater importance on the former and latter criteria, respectively, when assessing female gait attractiveness. This paper forms a major step forward in neuroaesthetics to understand the beauty of the human body and the generation of biological motions.

Stem cell fate decisions, including proliferation, differentiation, morphological changes, and viability, are impacted by microenvironmental cues such as physical and biochemical signals. However, the specific impact of matrix elasticity on kidney cell development and function remains less understood due to the lack of models that can closely recapitulate human kidney biology. An established protocol to differentiate podocytes from human-induced pluripotent stem (iPS) cells provides a promising avenue to elucidate the role of matrix elasticity in kidney tissue development and lineage determination. In this study, we synthesized polyacrylamide hydrogels with different stiffnesses and investigated their ability to promote podocyte differentiation and biomolecular characteristics. We found that 3 kPa and 10 kPa hydrogels significantly support the adhesion, differentiation, and viability of podocytes. Differentiating podocytes on a more compliant (0.7 kPa) hydrogel resulted in significant cell loss and detachment. Further investigation of the mechanosensitive proteins yes-associated protein (YAP) and synaptopodin revealed nuanced molecular distinctions in cellular responses to matrix elasticity that may otherwise be overlooked if morphology and cell spreading alone were used as the primary metric for selecting matrices for podocyte differentiation. Specifically, hydrogels with kidney-like rigidities outperformed traditional tissue culture plates at modulating the molecular-level expression of active mechanosensitive proteins critical for podocyte health and function. These findings could guide the development of physiologically relevant platforms for kidney tissue engineering, disease modeling, and mechanistic studies of organ physiology and pathophysiology. Such advances are critical for realizing the full potential of in vitro platforms in accurately predicting human biological responses.

Atherosclerosis, as the main root cause, makes cardiovascular diseases (CVDs) a substantial worldwide health concern. Inflammation and disrupted cholesterol metabolism are the primary clinical risk elements contributing to the onset of atherosclerosis. Few works exist on the improvement effect of gut microbiota on atherosclerosis. One specific probiotic strain, Enterococcus faecium NCIMB11508, has shown promise in mitigating inflammation. Consequently, it is critical to investigate its potential in reducing the progression of atherosclerosis. In our study, we administered E. faecium NCIMB11508 orally to ApoE^-/- mice, resulting in a decrease in the formation of atherosclerotic lesions. Additionally, it demonstrated the ability to lower the inflammatory factor levels both in the aorta and blood serum while maintaining the integrity of the small intestine against lipopolysaccharides. Moreover, E. faecium NCIMB11508 had a beneficial impact on the gut microbiota composition by increasing the levels of short-chain fatty acids (SCFAs), which in turn helped to reduce inflammation and protect the intestine. The probiotic E. faecium NCIMB11508, according to our research, has a definitive capacity to prevent atherosclerosis progression by beneficially altering the SCFA composition in the gut microbiota of ApoE^-/- mice.

This paper presents a deep-learning architecture for segmenting retinal fluids in patients with Diabetic Macular Oedema (DME) and Age-related Macular Degeneration (AMD). Accurate segmentation of multiple fluid types is critical for diagnosis and treatment planning, but existing techniques often struggle with precision. We propose an encoder-decoder network inspired by U-Net, processing enhanced OCT images and their edge maps. The encoder incorporates Residual and Inception modules with an autoencoder-based multiscale attention mechanism to extract detailed features. Our method shows superior performance across several datasets. On the RETOUCH dataset, the network achieved F1 Scores of 0.82 for intraretinal fluid (IRF), 0.93 for subretinal fluid (SRF), and 0.94 for pigment epithelial detachment (PED). The model also performed well on the OPTIMA and DUKE datasets, demonstrating high precision, recall, and F1 Scores. This architecture significantly enhances segmentation accuracy and edge precision, offering a valuable tool for diagnosing and managing retinal diseases. Its integration of dual-input processing, multiscale attention, and advanced encoder modules highlights its potential to improve clinical outcomes and advance retinal disease treatment.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Two of the greatest challenges in breast reconstruction are mastectomy skin flap necrosis (MSFN) and autologous flap failure. This review summarizes current evidence regarding the usage of indocyanine green angiography (ICGA) in breast reconstruction, identifies knowledge gaps, and provides directions for future studies. An umbrella review was conducted to identify related syntheses in Embase, Ovid Medline, Scopus, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, and the Clinical Trials databases. Data were extracted from systematic reviews (SRs) and meta-analyses (MAs) that discussed the use of ICGA in breast reconstruction. Sixteen syntheses were included (10 SRs and 6 MAs). Syntheses showed much evidence that ICGA usage typically reduces MSFN rates. However, it tends to overpredict necrosis and is best utilized in high-risk patients or those with an unclear clinical picture. ICGA is also useful in autologous breast reconstruction by reducing rates of breast fat necrosis (BFN), total flap loss, and reoperation. ICGA usage may also aid in perforator mapping and selection intraoperatively, with minimal complication risk. Most syntheses had moderate quality scores; however, they were small with significant heterogeneity in protocols and complication definitions. The use of ICGA in breast reconstruction is safe and useful in decreasing rates of MSFN, BFN, and reoperation after free flap reconstruction.