Does hCG-trigger in the mild stimulation protocol for endometrial preparation have any effect on pregnancy outcome in frozen-thawed embryo transfer?

Background: Recent literature has explored the role of human chorionic gonadotropin (hCG) triggering in frozen embryo transfer (FET) cycles with natural endometrial preparation. Despite this, the impact of hCG triggering on pregnancy outcomes following endometrial preparation with mild stimulation (mST) using Letrozole and Gonadotropins remains inadequately characterized. This study aimed to elucidate the effects of hCG-trigger on pregnancy outcomes in mST-FET cycles.

Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images.

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening.

The Effect of Change Position on Cardiac Function Patterns on Patients with COVID-19 Diagnosis: A Clinical Trial Study.

Background: Various methods are used to treat patients with coronavirus, including drug therapy and alternative and non-invasive therapies Research has been done on the effects of body position on cardiac function in patients with COVID-19 diagnosis.

Methods: This study was performed on patients admitted with COVID-19 diagnosis. Patients with inclusion criteria were selected based on purpose and entered into the study.

Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.

Objectives: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis of AD patients. Convolutional neural networks (CNN) approaches have demonstrated exceptional performance in the automated stratification of AD, mild cognitive impairment (MCI) and cognitively normal (CN) participants using MRI, owing to their high predictive accuracy and reliability.