RibMR - A Mixed Reality Visualization System for Rib Fracture Localization in Surgical Stabilization of Rib Fractures: Phantom, Preclinical, and Clinical Studies.

In surgical stabilization of rib fractures (SSRF), the current standard relies on preoperative CT imaging and often incorporates ultrasound (US) imaging. As an alternative, mixed reality (MR) technology holds promise for improving rib fracture localization. This study presents an MR-based visualization system designed for SSRF in a clinical setting.

Denatonium inhibits RANKL-induced osteoclast differentiation and rescues the osteoporotic phenotype by blocking p65 signaling pathway.

Background: Bone remodeling is a critical process that maintains skeletal integrity, orchestrated by the balanced activities of osteoclasts, which resorb bone, and osteoblasts, which form bone. Osteoclastogenesis, the formation of osteoclasts, is primarily driven by NFATc1, a process activated through c-Fos and NF-κB signaling pathways in response to receptor activator of nuclear factor κB ligand (RANKL). Dysregulation of RANKL signaling is a key contributor to pathological bone loss, as seen in conditions such as osteoporosis.

The quality and safety of using generative AI to produce patient-centred discharge instructions.

Patient-centred instructions on discharge can improve adherence and outcomes. Using GPT-3.5 to generate patient-centred discharge instructions, we evaluated responses for safety, accuracy and language simplification.

Head-Mounted Displays in Context-Aware Systems for Open Surgery: A State-of-the-Art Review.

Surgical context-aware systems (SCAS), which leverage real-time data and analysis from the operating room to inform surgical activities, can be enhanced through the integration of head-mounted displays (HMDs). Rather than user-agnostic data derived from conventional, and often static, external sensors, HMD-based SCAS relies on dynamic user-centric sensing of the surgical context. The analyzed context-aware information is then augmented directly into a user's field of view via augmented reality (AR) to directly improve their task and decision-making capability.

Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images.

Early survival prediction is vital for the clinical management of cancer patients, as tumors can be better controlled with personalized treatment planning. Traditional survival prediction methods are based on radiomics feature engineering and/or clinical indicators (e.g.

The autophagy-targeting compound V46 enhances antimicrobial responses to Mycobacteroides abscessus by activating transcription factor EB.

Mycobacteroides abscessus (Mabc) is a rapidly growing nontuberculous mycobacterium that poses a considerable challenge as a multidrug-resistant pathogen causing chronic human infection. Effective therapeutics that enhance protective immune responses to Mabc are urgently needed. This study introduces trans-3,5,4'-trimethoxystilbene (V46), a novel resveratrol analogue with autophagy-activating properties and antimicrobial activity against Mabc infection, including multidrug-resistant strains.

Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging.

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes.

A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children.

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images.

Engineering properties as a supplement cementitious material of ground copper reduction slag extracted valuable metal from Cu slag.

We have examined whether the copper reduction slag (CRS) generated after recovering valuable metals from copper slag (CS) by reduction process can be used as supplementary cementitious materials (SCMs). According to the test results, the Cu secondary slag with low Fe, Cu, and heavy metal contents had a suitable oxide composition for using as a SCM. CRS showed better grinding efficiency than that of ground blast furnace slag (GGBS).

Optimal Limestone Content on Hydration Properties of Ordinary Portland Cement with 5% Ground Granulated Blast-Furnace Slag.

In this study, the effect of limestone content on the mechanical performance and the heat of hydration of ordinary Portland cement (OPC) was investigated. Changes in the phase assemblage were analyzed through XRD and thermodynamic modeling. The purpose of the study was to identify the optimal limestone content in OPC.

Extracellular C1qbp inhibits myogenesis by suppressing NFATc1.

Aging and lack of exercise are the most important etiological factors for muscle loss. We hypothesized that new factors that contribute to muscle loss could be identified from ones commonly altered in expression in aged and exercise-limited skeletal muscles. Mouse gastrocnemius muscles were subjected to mass spectrometry-based proteomic analysis.

Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet.

Objectives: Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.

Methods: In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss.

Bactericidal Effects and Quality Impact of Peroxyacetic Acid and Sodium Hypochlorite on Chicken Carcasses.

There is an urgent need to develop efficient and environmentally friendly decontaminants for poultry products. In this study, we aimed to evaluate the practical application of peroxyacetic acid (PAA) as a replacement for sodium hypochlorite (SH) to sterilize fresh chicken carcasses, using microbial, color, and electronic-nose analyses. We evaluated the decontamination effects of different concentrations of PAA and SH on chicken carcasses.

Enzymatic extraction of anthocyanins from the pomace of aronia (Aronia melanocarpa).

Efficient extraction of natural pigments is a key focus in enhancing the utilization of by-products for applications in the food industry. In this study, an enzymatic extraction method using Pectinex Ultra SP-L, Pectinex XXL, Novoshape, and Celluclast was used to investigate natural pigment production from the pomace of aronia, a commercially important plant. The method's performance was monitored using high-performance liquid chromatography with diode-array detection by measuring total and individual anthocyanin levels.

Age-related cholesterol and colorectal cancer progression: Validating squalene epoxidase for high-risk cases.

As people age, the risk and progression of colorectal cancer (CRC), along with cholesterol levels, tend to increase. Nevertheless, epidemiological studies on serum lipids and CRC have produced conflicting results. We previously demonstrated that the reduction of squalene epoxidase (SQLE) due to accumulated cholesterol within cells accelerates CRC progression through the activation of the β-catenin pathway.

CM1, a Chrysin Derivative, Protects from Endotoxin-Induced Lethal Shock by Regulating the Excessive Activation of Inflammatory Responses.

Sepsis, a leading cause of death worldwide, is a harmful inflammatory condition that is primarily caused by an endotoxin released by Gram-negative bacteria. Effective targeted therapeutic strategies for sepsis are lacking. In this study, using an in vitro and in vivo mouse model, we demonstrated that CM1, a derivative of the natural polyphenol chrysin, exerts an anti-inflammatory effect by inducing the expression of the ubiquitin-editing protein TNFAIP3 and the NAD-dependent deacetylase sirtuin 1 (SIRT1).

Panoramic imaging errors in machine learning model development: a systematic review.

Objectives: To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models.

Methods: This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature.

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology.

CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images.

Positron emission tomography (PET) is the most sensitive molecular imaging modality routinely applied in our modern healthcare. High radioactivity caused by the injected tracer dose is a major concern in PET imaging and limits its clinical applications. However, reducing the dose leads to inadequate image quality for diagnostic practice.

SSPT-bpMRI: A Self-supervised Pre-training Scheme for Improving Prostate Cancer Detection and Diagnosis in Bi-parametric MRI.

Prostate cancer (PCa) is one of the most prevalent cancers in men. Early diagnosis plays a pivotal role in reducing the mortality rate from clinically significant PCa (csPCa). In recent years, bi-parametric magnetic resonance imaging (bpMRI) has attracted great attention for the detection and diagnosis of csPCa.

Exploring the Performance of Geometry-Based Markerless Registration in a Simulated Surgical Environment: A Comparative Study of Registration Algorithms in Medical Augmented Reality.

Augmented Reality (AR) has been utilized in multiple applications in the medical field, such as augmenting Computed Tomography (CT) images onto the patient's body during surgery. However, one of the challenges in its utilization is to register the pre-operative CT images to the patient's body accurately. The current registration process requires prior attachment of tracking markers, and their localization within the body and CT images.

A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm.

The analysis of maternal factors that impact the normal development of the fetal thalamus is an emerging field of research and requires the retrospective measurement of fetal thalamus diameter (FTD). Unfortunately, FTD is not measured in routine 2D ultrasound (2D-US) screenings of fetuses. Manual measurement of FTD is a laborious, difficult, and error-prone process because the thalamus lacks well-defined boundaries in 2D-US images of the fetal brain as it has a similar echogenicity to the surrounding brain tissue.

Multi-Level Swin Transformer Enabled Automatic Segmentation and Classification of Breast Metastases.

Detection of metastatic breast cancer lesions is a challenging task in breast cancer treatment. The recent advancements in deep learning gained attention owing to its robustness, particularly in addressing automated segmentation and classification issues in medical images. In this paper, we proposed a modified Swin Transformer model (mST) integrated with a novel Multi-Level Adaptive Feature Fusion (MLAFF) Module.

Diagnosis of Crohn's disease and ulcerative colitis using the microbiome.

Background: Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with the severity of the disease.