A Novel Biopsy Capsule Robot Based on High-Speed Cutting Tissue

ANRIL's Epigenetic Regulation and Its Implications for Cardiovascular Disorders

Cardiovascular disorders (CVDs) are a major global health concern, but their underlying molecular mechanisms are not fully understood. Recent research highlights the role of long noncoding RNAs (lncRNAs), particularly ANRIL, in cardiovascular development and disease. ANRIL, located in the human genome's 9p21 region, significantly regulates cardiovascular pathogenesis. It controls nearby tumor suppressor genes CDKN2A/B through epigenetic pathways, influencing cell growth and senescence. ANRIL interacts with epigenetic modifiers, leading to altered histone modifications and gene expression changes. It also acts as a transcriptional regulator, impacting key genes in CVD development. ANRIL's involvement in cardiovascular epigenetic regulation suggests potential therapeutic strategies. Manipulating ANRIL and its associated epigenetic modifiers could offer new approaches to managing CVDs and preventing their progression. Dysregulation of ANRIL has been linked to various cardiovascular conditions, including coronary artery disease, atherosclerosis, ischemic stroke, and myocardial infarction. This abstract provides insights from recent research, emphasizing ANRIL's significance in the epigenetic landscape of cardiovascular disorders. By shedding light on ANRIL's role in cellular processes and disease development, the abstract highlights its potential as a therapeutic target for addressing CVDs.

Capsule robots for the monitoring, diagnosis, and treatment of intestinal diseases

Current evidence suggests that the intestine as the new frontier for human health directly impacts both our physical and mental health. Therefore, it is highly desirable to develop the intelligent tool for the enhanced diagnosis and treatment of intestinal diseases. During the past 20 years, capsule robots have opened new avenues for research and clinical applications, potentially revolutionizing human health monitor, disease diagnosis and treatment. In this review, we summarize the research progress of edible multifunctional capsule robots in intestinal diseases. To begin, we introduce the correlation between the intestinal microbiome, intestinal gas and human diseases. After that, we focus on the technical structure of edible multifunctional robots. Subsequently, the biomedical applications in the monitoring, diagnosis and treatment of intestinal diseases are discussed in detail. Last but not least, the main challenges of multifunctional capsule robots during the development process are summarized, followed by a vision for future development opportunities.

Innovative PEEK in Dentistry of Enhanced Adhesion and Sustainability through AI-Driven Surface Treatments

This research investigates using Polyether ether ketone (PEEK) in dental prosthetics, focusing on enhancing the mechanical properties, adhesion capabilities, and environmental sustainability through AI-driven data analysis and advanced surface treatments. The objectives include improving PEEK's adhesion to dental types of cement, assessing its biocompatibility, and evaluating its environmental impact compared to traditional materials. The methodologies employed involve surface treatments such as plasma treatment and chemical etching, mechanical testing under ASTM standards, biocompatibility assessments, and lifecycle analysis. AI models predict and optimize mechanical properties based on extensive data. Significant findings indicate that surface-treated PEEK exhibits superior adhesion properties, maintaining robust mechanical integrity with no cytotoxic effects and supporting its use in direct contact with human tissues. Lifecycle analysis suggests PEEK offers a reduced environmental footprint due to lower energy-intensive production processes and recyclability. AI-driven analysis further enhances the material's performance prediction and optimization, ensuring better clinical outcomes. The study concludes that with improved surface treatments and AI optimization, PEEK is a promising alternative to conventional dental materials, combining enhanced performance with environmental sustainability, paving the way for broader acceptance in dental applications.

Advances of surgical robotics: image-guided classification and application

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future.

AI-Driven Data Analysis of Quantifying Environmental Impact and Efficiency of Shape Memory Polymers

This research investigates the environmental sustainability and biomedical applications of shape memory polymers (SMPs), focusing on their integration into 4D printing technologies. The objectives include comparing the carbon footprint, embodied energy, and water consumption of SMPs with traditional materials such as metals and conventional polymers and evaluating their potential in medical implants, drug delivery systems, and tissue engineering. The methodology involves a comprehensive literature review and AI-driven data analysis to provide robust, scalable insights into the environmental and functional performance of SMPs. Thermomechanical modeling, phase transformation kinetics, and heat transfer analyses are employed to understand the behavior of SMPs under various conditions. Significant findings reveal that SMPs exhibit considerably lower environmental impacts than traditional materials, reducing greenhouse gas emissions by approximately 40%, water consumption by 30%, and embodied energy by 25%. These polymers also demonstrate superior functionality and adaptability in biomedical applications due to their ability to change shape in response to external stimuli. The study concludes that SMPs are promising sustainable alternatives for biomedical applications, offering enhanced patient outcomes and reduced environmental footprints. Integrating SMPs into 4D printing technologies is poised to revolutionize healthcare manufacturing processes and product life cycles, promoting sustainable and efficient medical practices.

Review of robotic systems for thoracoabdominal puncture interventional surgery

Cancer, with high morbidity and high mortality, is one of the major burdens threatening human health globally. Intervention procedures via percutaneous puncture have been widely used by physicians due to its minimally invasive surgical approach. However, traditional manual puncture intervention depends on personal experience and faces challenges in terms of precisely puncture, learning-curve, safety and efficacy. The development of puncture interventional surgery robotic (PISR) systems could alleviate the aforementioned problems to a certain extent. This paper attempts to review the current status and prospective of PISR systems for thoracic and abdominal application. In this review, the key technologies related to the robotics, including spatial registration, positioning navigation, puncture guidance feedback, respiratory motion compensation, and motion control, are discussed in detail.

A Respiratory Motion Prediction Method Based on LSTM-AE with Attention Mechanism for Spine Surgery

Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery, resulting in inaccurate positional information of the target region and unexpected damage during the operation. In this paper, we propose a novel deep learning architecture for respiratory motion prediction, which can adapt to different patients. The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation. To ensure real-time performance, a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced. The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method. The experimental results demonstrate that the presented method (RMSE: 4.39%) outperforms other methods in terms of accuracy within a learning time of 2 min. The maximum predictive errors under the latency of 333 ms with respect to the x, y, and z axes of the optical camera system were 0.13, 0.07, and 0.10 mm, respectively, within a motion range of 2 mm.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

A Planar-Type Micro-Biopsy Tool for a Capsule-Type Endoscope Using a One-Step Nickel Electroplating Process

Millimeter-scale biopsy tools combined with an endoscope instrument have been widely used for minimal invasive surgery and medical diagnosis. Recently, a capsule-type endoscope was developed, which requires micromachining to fabricate micro-scale biopsy tools that have a sharp tip and other complex features, e.g., nanometer-scale end-tip sharpness and a complex scalpel design. However, conventional machining approaches are not cost-effective for mass production and cannot fabricate the micrometer-scale features needed for biopsy tools. Here, we demonstrate an electroplated nickel micro-biopsy tool which features a planar shape and is suitable to be equipped with a capsule-type endoscope. Planar-type micro-biopsy tools are designed, fabricated, and evaluated through in vitro tissue dissection experiments. Various micro-biopsy tools with a long shaft and sharp tip can be easily fabricated using a thick photoresist (SU8) mold via a simple one-step lithography and nickel electroplating process. The characteristics of various micro-biopsy tool design features, including a tip taper angle, different tool geometries, and a cutting scalpel, are evaluated for efficient tissue extraction from mice intestine. These fabricated biopsy tools have shown appropriate strength and sharpness with a sufficient amount of tissue extraction for clinical applications, e.g., cancer tissue biopsy. These micro-scale biopsy tools could be easily integrated with a capsule-type endoscope and conventional forceps.

Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot

Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges.

Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images

PURPOSE

The detection of where an organ starts and where it ends is achievable and, since this information can be delivered in real time, it could be quite important for several reasons. For one, by having the practical knowledge of the Wireless Endoscopic Capsule (WEC) transition through an organ's domain, we are able to align and control the endoscopic operation with any other possible protocol, i.e., delivering some form of treatment on the spot. Another is having greater anatomical topography information per session, therefore treating the individual in detail (not "in general"). Even the fact that by gathering more accurate information for a patient by merely implementing clever software procedures is a task worth exploiting, since the problems we have to overcome in real-time processing of the capsule findings (i.e., wireless transfer of images to another unit that will apply the necessary real time computations) are still challenging. This study proposes a computer-aided detection (CAD) tool, a CNN algorithm deployed to run on field programmable gate array (FPGA), able to automatically track the capsule transitions through the entrance (gate) of esophagus, stomach, small intestine and colon, in real time. The input data are the wireless transmitted image shots of the capsule's camera (while the endoscopy capsule is operating).

METHODS

We developed and evaluated three distinct multiclass classification CNNs, trained on the same dataset of total 5520 images extracted by 99 capsule videos (total 1380 frames from each organ of interest). The proposed CNNs differ in size and number of convolution filters. The confusion matrix is obtained by training each classifier and evaluating the trained model on an independent test dataset comprising 496 images extracted by 39 capsule videos, 124 from each GI organ. The test dataset was further evaluated by one endoscopist, and his findings were compared with CNN-based results. The statistically significant of predictions between the four classes of each model and the comparison between the three distinct models is evaluated by calculating the p-values and chi-square test for multi class. The comparison between the three models is carried out by calculating the macro average F1 score and Mattheus correlation coefficient (MCC). The quality of the best CNN model is estimated by calculations of sensitivity and specificity.

RESULTS

Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%.