The Role of the Abdominal Perforator Exchange (APEX) Technique in the Perforator Selection Algorithm for Delayed Deep Inferior Epigastric Perforator (DIEP) Flap Breast Reconstruction.

: The deep inferior epigastric artery perforator (DIEP) flap is currently the most widely used method for autologous breast reconstruction. Its primary advantage over the transverse rectus abdominis muscle (TRAM) flap is the reduction in donor-site morbidity, as it preserves the integrity of the abdominal muscles and motor nerves. Importantly, each patient's unique vascular anatomy requires an individualized approach to perforator selection and the surgical technique.

Combining chromosome conformation capture and exome sequencing for simultaneous detection of structural and single-nucleotide variants.

Background: Effective molecular diagnosis of congenital diseases hinges on comprehensive genomic analysis, traditionally reliant on various methodologies specific to each variant type-whole exome or genome sequencing for single nucleotide variants (SNVs), array CGH for copy-number variants (CNVs), and microscopy for structural variants (SVs).

Methods: We introduce a novel, integrative approach combining exome sequencing with chromosome conformation capture, termed Exo-C. This method enables the concurrent identification of SNVs in clinically relevant genes and SVs across the genome and allows analysis of heterozygous and mosaic carriers.

Multimodal GPT model for assisting thyroid nodule diagnosis and management.

Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI models lack transparency and interpretability. We developed a multimodal generative pre-trained transformer for thyroid nodules (ThyGPT), aiming to provide a transparent and interpretable AI copilot model for thyroid nodule risk assessment and management. Ultrasound data from 59,406 patients across nine hospitals were retrospectively collected to train and test the model.

Medical image translation with deep learning: Advances, datasets and perspectives.

Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to another, preserving both anatomical structures and cross-modal features, thus enabling efficient and accurate modality transfer and offering unique advantages for model development and clinical practice. This paper reviews the latest advancements in deep learning(DL)-based medical image translation.

Multi-User Opportunistic Spectrum Access for Cognitive Radio Networks Based on Multi-Head Self-Attention and Multi-Agent Deep Reinforcement Learning.

Aiming to address the issue of multi-user dynamic spectrum access in an opportunistic mode in cognitive radio networks leading to low sum throughput, we propose a multi-user opportunistic spectrum access method based on multi-head self-attention and multi-agent deep reinforcement learning. First, an optimization model for joint channel selection and power control in multi-user systems is constructed based on centralized training with a decentralized execution framework. In the training phase, the decision-making policy is optimized using global information, while in the execution phase, each agent makes decisions according to its observations.

Multicenter Study Protocol: Research on Evaluation and Detection of Surgical Wound Complications with AI-Based Recognition (REDSCAR-Trial).

The increasing use of telemedicine in surgical care has shown promise in improving patient outcomes and optimizing healthcare resources. Surgical site infections (SSIs) are a major cause of healthcare-associated infections (HAIs), leading to significant economic and health burdens. A pilot study already demonstrated that RedScar© achieved 100% sensitivity and 83.

A dataset for two-echelon electric vehicle routing problems.

This paper introduces a dataset generated for research on Two-Echelon Electric Vehicle Routing Problems (2E-EVRPs) with additional constraints, including time windows, simultaneous pickup and delivery (SPD), and partial deliveries. The dataset is derived from established benchmark instances from the VRP and EVRP literature and further extended using methodologies from the literature. It features diverse scenarios designed to challenge and validate solution approaches proposed for two-echelon routing algorithms under various constraints.

Research on the optimization of optimal blasting parameters and fragmentation control based on coal seam geological conditions.

Open-pit coal mining often employs loosening blasting, with perforation blasting accounting for a significant portion of the coal seam mining costs. For coal of the same quality, the price of lump coal is much higher than that of crushed coal. Therefore, reducing the percentage of crushed coal in the blasting process is an important means to improve quality and efficiency in open-pit coal mining.

Epidermal electronic-tattoo for plant immune response monitoring.

Real-time monitoring of plant immune responses is crucial for understanding plant immunity and mitigating economic losses from pathogen and pest attacks. However, current methods relying on molecular-level assessment are destructive and time-consuming. Here, we report an ultrathin, substrate-free, and highly conductive electronic tattoo (e-tattoo) designed for plants, enabling immune response monitoring through non-invasive electrical impedance spectroscopy (EIS).

A flexible transoral swab sampling robot system with visual-tactile fusion approach.

A significant number of individuals have been affected by pandemic diseases, such as COVID-19 and seasonal influenza. Nucleic acid testing is a common method for identifying infected patients. However, manual sampling methods require the involvement of numerous healthcare professionals.

Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study.

Background: Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable datasets. Given the vastness of Chinese medical literature and the unique value of traditional Chinese medicine, precise classification of these abstracts is vital for advancing global medical research.

Anatomy education potential of the first digital twin of a Korean cadaver.

The objective of this study is to explore innovative integration within the field of anatomy education by leveraging HoloLens 2 Augmented Reality Head-Mounted Display (AR HMD) technology and real-time cloud rendering. Initial 3D datasets, comprising extensive anatomical information for each bone, were obtained through the 3D scanning of a full-body cadaver of Korean male origin. Subsequently, these datasets underwent refinement processes aimed at enhancing visual fidelity and optimizing polygon counts, utilizing Blender software.

The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review.

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities.

An online intelligent detection method for slurry density in concept drift data streams based on collaborative computing.

In industrial environments, slurry density detection models often suffer from performance degradation due to concept drift. To address this, this article proposes an intelligent detection method tailored for slurry density in concept drift data streams. The method begins by building a model using Gaussian process regression (GPR) combined with regularized stochastic configuration.

Impact of Western diet on milk miRNAs and target genes in offspring adipose tissue: modulation by betaine during suckling.

Objective: We investigated how a maternal Western diet (WD) affects milk microRNA (miRNA) profile and associates with metabolic programming in adipose tissues in pups. We also explored the impact of betaine supplementation during suckling, as betaine levels are reported to be reduced in WD-fed dams' milk.

Methods: A microarray analysis was performed to profile miRNA expression in dams' milk.

Visualizing Reactive Oxygen Species-Induced DNA Damage Process in Higher-Ordered Origami Nanostructures.

The genetic information on organisms is stored in the cell nucleus in the form of higher-ordered DNA structures. Here, we use DNA framework nanostructures (DFNs) to simulate the compaction and stacking density of nucleosome DNA for precise conformational and structure determination, particularly the dynamic structural changes, preferential reaction regions, and sites of DFNs during the reactive oxygen species (ROS) reaction process. By developing an atomic force microscopy-based single-particle analysis (SPA) data reconstruction method to collect and reanalyze imaging information, we demonstrate that the geometric morphology of DFNs constrains their reaction kinetics with ROS, where local mechanical stress and regional base distribution are two key factors affecting their kinetics.

[Cross-modal hash retrieval of medical images based on Transformer semantic alignment].

Medical cross-modal retrieval aims to achieve semantic similarity search between different modalities of medical cases, such as quickly locating relevant ultrasound images through ultrasound reports, or using ultrasound images to retrieve matching reports. However, existing medical cross-modal hash retrieval methods face significant challenges, including semantic and visual differences between modalities and the scalability issues of hash algorithms in handling large-scale data. To address these challenges, this paper proposes a Medical image Semantic Alignment Cross-modal Hashing based on Transformer (MSACH).

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023.

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart's structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data.

MSCMamba: Prediction of Antimicrobial Peptide Activity Values by Fusing Multiscale Convolution with Mamba Module.

Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been devoted to the identification of AMPs and the qualitative prediction of their functional activities, few methods address the quantitative prediction of their activity values. In this paper, we propose a regression model called MSCMamba, which fuses multiscale convolutional neural network with Mamba module to accurately predict the activity values of AMPs.

A Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing.

Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing.

An efficient heuristic for geometric analysis of cell deformations.

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis.

Sources, levels, and determinants of indoor air pollutants in Europe: A systematic review.

Clean air is a requirement for life, and the quality of indoor air is a health determinant since people spend most of their daily time indoors. The aim of this study was to systematically review the available evidence regarding the sources, determinants and concentrations of indoor air pollutants in a set of scenarios under study in K-HEALTHinAIR project. To this end, a systematic review was performed to review the available studies published between the years 2013-2023, for several settings (schools, homes, hospitals, lecture halls, retirement homes, public transports and canteens), conducted in Europe, where sources and determinants of the indoor pollutants concentrations was assessed.

Identifying multilayer network hub by graph representation learning.

The recent advances in neuroimaging technology allow us to understand how the human brain is wired in vivo and how functional activity is synchronized across multiple regions. Growing evidence shows that the complexity of the functional connectivity is far beyond the widely used mono-layer network. Indeed, the hierarchical processing information among distinct brain regions and across multiple channels requires using a more advanced multilayer model to understand the synchronization across the brain that underlies functional brain networks.

Enhancing Surgical Wound Monitoring: A Paired Cohort Study Evaluating a New AI-Based Application for Automatic Detection of Potential Infections.

This study assessed the feasibility and security of remote surgical wound monitoring using the RedScar© smartphone app, which employs automated diagnosis for early visual detection of infections without direct healthcare personnel involvement. Additionally, patient satisfaction with telematic care was evaluated as a secondary aim. Surgical site infection (SSI) is the second leading cause of healthcare-associated infections (HAIs), leading to prolonged hospital stays, heightened patient distress, and increased healthcare costs.

Progressive Evolution of Flow and Heat Transfer Channels in Hot Dry Rock Stimulated by Liquid Nitrogen Cold Shock.

Hot dry rock (HDR) is a novel green, low-carbon energy. Its development requires the creation of fracture channels in deep thermal reservoirs. Traditional methods such as hydraulic fracturing have limited effectiveness in reservoir stimulation, so a method of liquid nitrogen cold shock was proposed.