Menopause

Predicting Neo-Adjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images

Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, resulting in limited targeted treatment options. Neoadjuvant chemotherapy (NACT) is the standard treatment for early-stage TNBC, with pathologic complete response (pCR) serving as a key prognostic ...

Molecular Insights into the Binding of Organophosphate Flame Retardant Key Metabolites with Mineralocorticoid and Estrogen Receptors.

Flame retardants (FR) encompass a wide range of chemicals designed to inhibit and reduce the spread of fire by forming protective layers on materials. While originally considered relatively safe due to their rapid metabolism, growing evidence indicates that organophosphate flame retardants (OPFRs) can be extensively released into the environment, leading to toxic effects in humans, particularly en...

A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors.

Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automat...

Improving the discovery of near-Earth objects with machine-learning methods

We present a comprehensive analysis of the digest2 parameters for candidates of the Near-Earth Object Confirmation Page (NEOCP) that were reported b...

Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients.

Identifying patients suitable for conversion therapy through early non-invasive screening is crucial for tailoring treatment in advanced gastric cance...

Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset

In this study, we propose a novel remote sensing change detection task, non-registration change detection, to address the increasing number of emerg...

Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma.

The transcription factor SOX9 plays a critical role in various diseases, including hepatocellular carcinoma (HCC), and has been implicated in resista...

A Mixed-Methods Evaluation of LLM-Based Chatbots for Menopause.

The integration of Large Language Models (LLMs) into healthcare settings has gained significant attention, particularly for question-answering tasks. ...

Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

Introduction: Timely identification of intracranial hemorrhage (ICH) subtypes on non-contrast computed tomography is critical for prognosis predicti...

Using machine learning methods to predict the diabatic bond dissociation energy of non-heme iron complexes.

Bond dissociation energy (BDE) is an important property in chemical research. In the process of non-heme iron complex catalytic reactions, diabatic BD...

Total radius BMD correlates with the hip and lumbar spine BMD among post-menopausal patients with fragility wrist fracture in a machine learning model.

UNLABELLED: Osteoporosis screening should be systematic in the group of over 50-year-old females with a radius fracture. We tested a phantom combined ...

Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective.

: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary...

Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.

BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportuniti...

A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.

Combination therapies are one potential approach to improve the outcomes of patients with refractory or relapsed disease. However, comprehensive testi...

AI-Enabled Exoskeletal Robotics for Enhancing Mobility, Bone Regeneration, and Functional Rehabilitation in Osteoporosis: A Literature Review.

The care of osteoporosis is being revolutionized by developments in AI-enabled exoskeletal robotics, which can improve mobility and facilitate the rec...

Lipid nanoparticle (LNP) mediated mRNA delivery in neurodegenerative diseases.

Neurodegenerative diseases (NDD) are characterized by the progressive loss of neurons and the impairment of cellular functions. Messenger RNA (mRNA) h...

Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography-SleepAI.

Home sleep apnea tests (HSATs) have emerged as alternatives to in-laboratory polysomnography (PSG), but Type IV HSATs often show limited diagnostic pe...

Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.

BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) ...

[Use of Machine Learning Methods to Identify Soil Parent Materials in a High-cadmium Geological Background Area].

Recently, the characteristics of high Cd content and low Cd mobility in karstic soil of a high geological background area in south China have received...

Molecular Association Assay Systems for Imaging Protein-Protein Interactions in Mammalian Cells.

Molecular imaging probes play a pivotal role in assaying molecular events in various physiological systems. In this study, we demonstrate a new genre ...