Menopause

From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.

Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effects, as well as to G protein-coupled estrogen receptor (GPER) located on the membrane, thereby inducing downstream nongenomic effects rapidly. However, due to the relatively scarce ligand data, receptor-based or ligand-based screening models for GPER a...

Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice.

It is well established that the early inflammatory response following fracture is essential for initiating subsequent bone regeneration. An imbalance in inflammation, whether within the innate or adaptive immune response, can result in impaired fracture healing. In our previous studies, we demonstrated that, for example, mice with ovariectomy-induced osteoporosis exhibit altered immune cell popula...

Comparative examination of the chemistry and biology of AI-driven gold NPs in Theranostics: New insights into biosensing, bioimaging, genomics, diagnostics, and therapy.

Integrating artificial intelligence (AI) with nanomedicine is transforming Theranostics, driving advances in biosensing, bioimaging, genomics, diagnos...

Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review.

Neurodegenerative disorders present a significant challenge to healthcare systems, mainly due to the limited availability of effective treatment optio...

Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.

Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important bio...

Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.

Suicide causes over 700,000 deaths annually worldwide. Mental disorders are closely linked to suicidal ideation, but predicting suicide remains comple...

Data-driven survival modeling for breast cancer prognostics: A comparative study with machine learning and traditional survival modeling methods.

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast c...

Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.

RATIONALE AND OBJECTIVES: To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep lea...

Comparative analysis of ChatGPT-4o mini, ChatGPT-4o and Gemini Advanced in the treatment of postmenopausal osteoporosis.

BACKGROUND: Osteoporosis is a sex-specific disease. Postmenopausal osteoporosis (PMOP) has been the focus of public health research worldwide. The pur...

A prediction model of pediatric bone density from plain spine radiographs using deep learning.

Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decreased mechanical strength and an increased fractur...

The Role of ChatGPT in osteoporosis management: a comparative analysis with clinical expertise.

UNLABELLED: This study evaluates the role of ChatGPT in osteoporosis management, demonstrating 91% diagnostic accuracy and significantly faster respon...

Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.

Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD...

Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.

The aim of this study was to establish the optimal prediction model by comparing the prediction effect of 6 kinds of prediction models containing bioc...

A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults.

BACKGROUND: Osteoporosis has become a significant public health concern that necessitates the application of appropriate techniques to calculate disea...

Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.

BACKGROUND: The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale pop...

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histol...

Traumatic brain injury: Bridging pathophysiological insights and precision treatment strategies.

Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression ...

An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test.

BACKGROUND: Individuals with anxious-depressed (AD) symptoms have more severe mood disorders and cognitive impairment than those with non-anxious depr...

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper.

Effective drug development for bone-related diseases, such as osteoporosis and metastasis, is hindered by the lack of physiologically relevant in vitr...

Predictive modelling of knee osteoporosis.

OBJECTIVE: The objective of this research was to develop a machine learning-based predictive model for osteoporosis screening using demographic and cl...