Vascular transplantation is an effective treatment for severe vascular lesions. The design of the bioactive and mechanical properties of small-caliber vascular grafts is critical for their application in tissue engineering. In this study, we sought to develope a small-caliber vascular graft by electrospinning a mixture of a human acellular amniotic membrane (HAAM) and polycaprolactone (PCL).

Heart disease patients necessitate precise monitoring to ensure the safety and efficacy of their physical activities when managing conditions such as hypertension or heart failure. This study, therefore, aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of pulse rate (PR), interbeat-intervals (IB-I) and oxygen saturation (SpO2) during high-intensity interval training (HIIT).

Sarcopenia is an age-related, insidious, crippling but curable degenerative disease if diagnosed and treated early. However, no accessible and accurate early screening method is available for community settings that does not require specialized personnel. One of the hallmarks of sarcopenia is the pathological changes of muscle fiber type composition and motor unit firing patterns. Surface electromyography (sEMG) may serve as an effective tool for detecting differences between healthy and sarcopenic individuals due to its superior wearability and accessibility compared to other screening methods such as medical imaging and bioimpedance measurements, making it ideal for community-based sarcopenic screening. Our study aims to explore sEMG biomarkers that can be used for screening or diagnosis of sarcopenia.

Extracellular vesicles (EVs) have emerged as an exciting tool for targeted delivery of therapeutics for a wide range of diseases. As nano-scale membrane-bound particles derived from living cells, EVs possess inherent capabilities as carriers of biomolecules. However, the translation of EVs into viable therapeutic delivery vehicles is challenged by lengthy and inefficient processes for cargo loading and pre- and post-loading purification of EVs, resulting in limited quantity and consistency of engineered EVs.

Oral health is closely linked to systemic conditions, particularly non-communicable diseases (NCDs), which can exacerbate oral issues. Essential oils (EOs) have emerged as potential alternatives for oral health due to their antibacterial, anti-inflammatory, and antioxidant properties. Among these, rosemary essential oil (REO) shows promise due to its various biological activities. This study investigates the potential of REO in dental applications using microfluidic devices and electrochemical impedance spectroscopy (EIS) to analyze the electrical properties of REO in artificial saliva (AS) mixtures.

The purpose of this study was to evaluate the repeatability and agreement of multispectral refraction topography (MRT) in measuring retinal refraction before and after cycloplegia in children. The results of this study will provide valuable insights into the accuracy and reliability of MRT as a tool for assessing retinal refraction in pediatric patients.

Blister aneurysms of the internal carotid artery (ICA) are rare and are primarily documented in the literature through small series and case reports. The intraoperative observation of a hemorrhage in the artery wall proximal to the aneurysmal bulge led to the hypothesis that some of these aneurysms might develop in a retrograde manner.

This study aimed to develop and validate an ultrasound radiomics model for distinguishing invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS) by combining intratumoral and peritumoral features.

The use of human dental pulp stem cells (hDPSCs) as autologous stem cells for tissue repair and regenerative techniques is a significant area of global research. The objective of this study was to investigate the effects of long-term in vitro culture on the multidifferentiation potential of hDPSCs and the potential molecular mechanisms involved.

Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition.

In mechanically ventilated neonates, the arterial partial pressure of ( ) is an important indicator for the adequacy of ventilation settings. Determining the is commonly done using invasive blood gas analyses, which constitute risks for neonates and are typically only available infrequently. An accurate, reliable, and continuous estimation of is of high interest for medical staff, giving the possibility of a closer monitoring and faster reactions to changes. We aim to present a non-invasive estimation method for in neonates on the basis of end-tidal ( ) with inclusion of different physiological and ventilation parameters. The estimation method should be more accurate than an estimation by unaltered measurements with regard to the mean absolute error and the standard deviation.

Manually bent, standard-of-care, Ti-6Al-4V, mandibular graft fixation devices are associated with a significant post-operative failure rate. These failures require the patient to endure stressful and expensive re-operation. The approach recommended in this report demonstrates the optimization of graft fixation device mechanical properties via "stiffness-matching" by varying the fixation device's location, shape, and material composition through simulation of the device's post-operative performance. This provides information during pre-operative planning that may avoid future device failure. Optimized performance may combine translation of all loading into compression of the bone graft with the adjacent bone segments and elimination or minimization of post-healing interruption of normal stress-strain (loading) trajectories.

The probability density function (PDF) of the surface electromyogram (sEMG) depends on contraction force. This dependence, however, has so far been investigated by having the subject generate force at a few fixed percentages of MVC. Here, we examined how the shape of the sEMG PDF changes with contraction force when this force was gradually increased from zero.

Manual annotation of medical image datasets is labor-intensive and prone to biases. Moreover, the rate at which image data accumulates significantly outpaces the speed of manual annotation, posing a challenge to the advancement of machine learning, particularly in the realm of supervised learning. Self-supervised learning is an emerging field that capitalizes on unlabeled data for training, thereby circumventing the need for extensive manual labeling. This learning paradigm generates synthetic pseudo-labels through pretext tasks, compelling the network to acquire image representations in a pseudo-supervised manner and subsequently fine-tuning with a limited set of annotated data to achieve enhanced performance. This review begins with an overview of prevalent types and advancements in self-supervised learning, followed by an exhaustive and systematic examination of methodologies within the medical imaging domain from 2018 to September 2024. The review encompasses a range of medical image modalities, including CT, MRI, X-ray, Histology, and Ultrasound. It addresses specific tasks, such as Classification, Localization, Segmentation, Reduction of False Positives, Improvement of Model Performance, and Enhancement of Image Quality. The analysis reveals a descending order in the volume of related studies, with CT and MRI leading the list, followed by X-ray, Histology, and Ultrasound. Except for CT and MRI, there is a greater prevalence of studies focusing on contrastive learning methods over generative learning approaches. The performance of MRI/Ultrasound classification and all image types segmentation still has room for further exploration. Generally, this review can provide conceptual guidance for medical professionals to combine self-supervised learning with their research.

Diseases of the esophagus affect its function and often lead to replacement of long sections of the organ. Current healing methods involve the use of bioscaffolds processed from other animal models. Although the properties of these animal models are not exactly the same as those of the human esophagus, they nevertheless present a reasonable means of assessing the biomechanical properties of the esophageal tissue. Besides, sheep bear many similarities physiologically to humans and they also suffer from same diseases as humans. The morphology of their esophagus is also comparable to that of humans. Thus, in the study, an ovine esophagus was studied. Studies on the planar biaxial tests of the gross esophageal anatomy are limited. The composite nature of the gross anatomy of the esophagus makes the application of structure-based models such as Holzapfel-type models very difficult. In current studies the tissue is therefore often separated into specific layers with substantial collagen content. The effects of adipose tissue and other non-collagenous tissue often make the mechanical behavior of the esophagus widely diverse and unpredictable using deterministic structure-based models. Thus, it may be very difficult to predict its mechanical behavior. In the study, an NARX neural network was used to predict the stress-strain response of the gross anatomy of the ovine esophagus. The results show that the NARX model was able to achieve a correlation above 99.9% within a fitting error margin of 16%. Therefore, the use of artificial neural networks may provide a more accurate way of predicting the biaxial stress-strain response of the esophageal tissue, and lead to further improvements in the design and development of synthetic replacement materials for esophageal tissue.

Peri-implantitis (PI), a pathological condition associated with plaque, affects the tissues around dental implants. In addition, peri-implant mucositis (PIM) is a precursor to the destructive inflammatory PI and is an inflammation of the soft tissues surrounding the dental implant. It is challenging to eradicate and regulate the PI treatment due to its limited effectiveness. Currently, there is a significant interest in the development and research of additional biocompatible materials to prevent the failure of dental implants. Nanotechnology has the potential to address or develop solutions to the significant challenge of implant failure caused by cytotoxicity and biocompatibility in dentistry. Nanoparticles (NPs) may be used as carriers for the release of medicines, as well as to make implant coatings and supply appropriate materials for implant construction. Furthermore, the bioactivity and therapeutic efficacy of metal NPs in peri-implant diseases (PID) are substantiated by a plethora of in vitro and in vivo studies. Furthermore, the use of silver (Ag), gold (Au), zinc oxide, titanium oxide (TiO), copper (Cu), and iron oxide NPs as a cure for dental implant infections brought on by bacteria that have become resistant to several medications is the subject of recent dentistry research. Because of their unique shape-dependent features, which enhance bio-physio-chemical functionalization, antibacterial activity, and biocompatibility, metal NPs are employed in dental implants. This study attempted to provide an overview of the application of metal and metal oxide NPs to control and increase the success rate of implants while focusing on the antimicrobial properties of these NPs in the treatment of PID, including PIM and PI. Additionally, the study reviewed the potential benefits and drawbacks of using metal NPs in clinical settings for managing PID, with the goal of advancing future treatment strategies for these conditions.

Alzheimer's disease (AD) is a chronic disease among people aged 65 and older. As the aging population continues to grow at a rapid pace, AD has emerged as a pressing public health issue globally. Early detection of the disease is important, because increasing evidence has illustrated that early diagnosis holds the key to effective treatment of AD. In this work, we developed and refined a multi-layer cyclic Residual convolutional neural network model, specifically tailored to identify AD-related submicroscopic characteristics in the facial images of mice. Our experiments involved classifying the mice into two distinct groups: a normal control group and an AD group. Compared with the other deep learning models, the proposed model achieved a better detection performance in the dataset of the mouse experiment. The accuracy, sensitivity, specificity and precision for AD identification with our proposed model were as high as 99.78%, 100%, 99.65% and 99.44%, respectively. Moreover, the heat maps of AD correlation in the facial images of the mice were acquired with the class activation mapping algorithm. It was proven that the facial images contained AD-related submicroscopic features. Consequently, through our mouse experiments, we validated the feasibility and accuracy of utilizing a facial image-based deep learning model for AD identification. Therefore, the present study suggests the potential of using facial images for AD detection in humans through deep learning-based methods.

Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center-edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.

This article provides an overview of recent progress in the achievement of non-contact intraoperative image control through the use of vision and sensor technologies in operating room (OR) environments. A discussion of approaches to improving and optimizing associated technologies is also provided, together with a survey of important challenges and directions for future development aimed at improving the use of non-contact intraoperative image access systems.