Steroids

Use of neural networks to predict a potential osteoporotic metabolic condition based on the panoramic mandibular index.

OBJECTIVE: This study investigates the use of neural networks to predict potential osteoporotic metabolic conditions using the Panoramic Mandibular Index (PMI) derived from panoramic radiographs. STUDY DESIGN: A total of n = 750 radiographs were collected and 1,500 regions of interest (ROIs) were analysed. Convolutional neural network (CNN), specifically ResNet-50, were used with transfer learning...

Evaluating bone density with ultrasonic backscatter: Leveraging time-frequency analyses and convolutional neural networks.

Osteoporosis is a disease characterized by decreased bone density and increased fracture risk. This study proposes a convolutional neural network (CNN) method for detecting changes in bone density using spectrograms or scalograms generated from ultrasonic backscatter signals of 55 human cancellous bone specimens from the proximal femur. After applying a simple grid search to optimize three CNN hyp...

AI Screening Tool Based on X-Rays Improves Early Detection of Decreased Bone Density in a Clinical Setting.

PURPOSE: Osteoporosis is an under-screened musculoskeletal disorder that results in diminished quality of life and significant burden to the healthcar...

An AI-based pipeline for osteoporosis/osteopenia prediction using hip radiographs.

OBJECTIVE: To develop and validate an artificial intelligence-based tool for the diagnosis of osteoporosis/osteopenia using hip radiographs. The tool ...

A Retrospective Cohort Study on 28-Day Mortality in Immunosuppressed Sepsis: An Interpretability-Based Predictive Model Using MIMIC-IV V2.2.

BACKGROUND: Sepsis in immunosuppressed patients is associated with significantly higher mortality rates, yet predictive models tailored to this high-r...

Toward a unified pipeline for natural product discovery: tools and strategies for NRPS and PKS pathway exploration and engineering.

Covering: up to 2025.Non-ribosomal peptide synthetases and polyketide synthases are modular biosynthetic systems that produce structurally diverse and...

Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a sign...

A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes.

BACKGROUND: Orthopantomograms (OPGs) are essential diagnostic tools in dental and maxillofacial care, providing a panoramic view of the jaws, teeth, a...

Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.

Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We...

Development of Artificial Intelligence-Assisted Lumbar and Femoral BMD Estimation System Using Anteroposterior Lumbar X-Ray Images.

The early detection and treatment of osteoporosis and prevention of fragility fractures are urgent societal issues. We developed an artificial intelli...

Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning.

Spinal cord injury (SCI) is a severe neurological disorder, with glucocorticoids like methylprednisolone commonly used for treatment. However, their e...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher...

Quantitative CT Imaging in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations....

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk ...

Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with a poor prognosis due to its aggressive nature and limited tr...

Development of a prediction model by combining tumor diameter and clinical parameters of adrenal incidentaloma.

When adrenal incidentalomas are detected, diagnostic procedures are complicated by the need for endocrine-stimulating tests and imaging using various ...

Folate metabolism-associated CYP26A1 is a clinico-immune target in colorectal cancer.

Folic acid plays a crucial role in cellular regulation and metabolism, commonly included in dietary supplements. Despite this, its involvement in colo...

Discovery of novel cathepsin K inhibitors for osteoporosis treatment using a deep learning-based strategy.

BACKGROUND: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeut...

Differential Phase Contrast Imaging to Predict MSC Immune Function.

Mesenchymal stromal cells (MSCs) exhibit significant immunomodulatory potential, making them promising candidates for cell-based therapies in autoimmu...

Regulation and induction of fungal secondary metabolites: a comprehensive review.

Fungal secondary metabolites (SMs) represent a vast reservoir of bioactive compounds with immense therapeutic, agricultural, and industrial potential....