Pregnancy

Computational prediction and SLN formulation of Narcissin for reverse transcriptase inhibition and controlled drug delivery applications.

The aim of this paper was to construct a stable drug delivery mechanism of Narcissin, which is phytoconstituent of Aerva lanata that has Reverse Transcriptase potential of anti-breast cancer. The Rand Forest Classifier was the most successful machine learning algorithm with an accuracy of 86.43 and independent test set validation of 80.85. High binding affinity to Narcissin (-13.3 kcal/mol) with f...

Channel-wise joint disentanglement representation learning for B-mode and super-resolution ultrasound based CAD of breast cancer.

B-mode ultrasound (BUS) is widely used in breast cancer diagnosis, while the emerging super-resolution ultrasound (SRUS) provides microvascular information with high spatial resolution, which has shown great potential in improving breast cancer diagnosis. However, as a new ultrasound modality, its diagnosis remains highly dependent on the clinical experience of sonologists, highlighting the need f...

Deep learning in fetal, infant, and toddler neuroimaging research.

Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analys...

The application of artificial intelligence in blind ultrasound sweep diagnostics for prenatal medicine: A systematic literature review.

INTRODUCTION: Obstetric ultrasound is fundamental in prenatal care for gestational age (GA) estimation, fetal monitoring, and complication screening. ...

Artificial intelligence and machine learning guided optimization in drug delivery.

The optimisation of drug delivery systems is a complex, multidimensional challenge involving the interplay of formulation composition, process paramet...

Machine Learning Prediction of Incomplete Hysteroscopic Myomectomy Using Preoperative Clinical and Imaging Variables.

STUDY OBJECTIVE: To develop and validate a machine-learning (ML) model using preoperative clinical and imaging variables including ultrasound and diag...

Machine learning-based prediction of central lymph node metastasis in unifocal papillary thyroid microcarcinoma.

OBJECTIVE: This study aims to develop a machine learning (ML) model to predict the risk of central lymph node metastasis (CLNM) in patients with papil...

Deep Learning-Based Prediction of Cardiopulmonary Disease in Retinal Images of Premature Infants.

IMPORTANCE: Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. OBJEC...

Lipid Nanoparticle Development in Practice: Challenges and Collective Insights.

Lipid nanoparticles (LNPs) have emerged as a versatile delivery platform for improving pharmacokinetic performance, protecting nucleic acid cargo, and...

Nanotechnology and machine learning synergies for improving the bioavailability and functional efficacy of dietary polyphenols.

Dietary polyphenols exhibit diverse bioactivities, but their clinical application is limited by poor bioavailability due to low solubility, rapid meta...

Artificial intelligence and machine learning driven nanorobotics for targeted brain delivery: Redefining blood-brain barrier navigation.

The blood-brain barrier (BBB) plays a central role in preserving central nervous system (CNS) homeostasis, and its dysfunction is implicated in variou...

The role of the kynurenine pathway in the pathophysiology of autism-like phenotype induced by maternal inflammation in male mice.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with core symptoms that may include deficits in communication, social challenges, and ...

Development of a Deep Learning Algorithm for Posterior Fossa Abnormality Recognition on First-Trimester US Screening Scans: AIRFRAME Study Part 1.

Purpose To develop a deep learning algorithm to automatically assess the posterior fossa on first-trimester US screening scans and identify open spina...

Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning.

Neonatal prematurity leads to considerable morbidity and mortality, partly because of acquired conditions such as bronchopulmonary dysplasia (BPD), in...

[Predictive value of a multimodal radiomics model for central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma based on machine learning].

OBJECTIVES: To develop and validate a multimodal radiomics model based on machine learning for predicting central lymph node metastasis (CLNM) in pati...

Associations between prenatal exposure to indoor air pollution and ambient particulate matter with infant growth.

Despite growing concern, the developmental effects of prenatal exposure to indoor and ambient particulate pollution remain poorly characterized. This ...

The role of artificial intelligence in obstetrics and gynecology: Innovations, challenges, and opportunities explored through a bibliometric analysis.

OBJECTIVE: Artificial intelligence (AI) applications have garnered increasing interest in obstetrics and gynecology. This study aims to analyze the ev...

Therapeutic nanofibrous scaffolds in precision drug delivery: formulation design, challenges and regulatory frameworks.

INTRODUCTION: Drug-loaded nanofibrous systems represent a breakthrough in drug delivery, overcoming limitations of conventional formulations. They dem...

Biofabrication of 3D bioprinted and organ-on-chip blood-brain barrier models using hCMEC/D3 for intranasal delivery of central nervous system therapeutics.

The BBB remains a major obstacle to effective treatment of CNS disorders by limiting the entry of most therapeutics into the brain. The hCMEC/D3 is wi...

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation effica...