Prediction of obstetric outcomes in sickle cell patients based on tricuspid regurgitant velocity.

Background: Transthoracic echocardiography, a validated tool for risk assessment in non-pregnant population with sickle cell disease (SCD), uses tricuspid regurgitant velocity (TRV) over 2.5 m/s is an independent mortality risk factor. Its applicability in obstetrics lacks sufficient evidence.

Early Outcomes of Preoperative Short Course Radiotherapy With Simultaneous Integrated Boost and Response-adapted Chemotherapy for Advanced Rectal Cancer.

Background And Purpose: Limited evidence exists for dose escalation in neoadjuvant short course radiotherapy (SCRT) for rectal cancer. With enhanced imaging and radiotherapy techniques over the past decades along with the valuable endpoint of pathological complete response (pCR), we believe SCRT with simultaneous integrated boost could potentially provide deeper pathological responses and improve local control.

Methods And Materials: Between January 2020 and December 2022, locoregional-advanced rectal cancer patients that were treated with neoadjuvant SCRT with simultaneous integrated boost up to 5.

Guideline No. 452: Diagnosis and Management of Intrahepatic Cholestasis of Pregnancy.

Objective: To summarize the current evidence and to make recommendations for the diagnosis and management of intrahepatic cholestasis of pregnancy.

Target Population: Pregnant people with intrahepatic cholestasis of pregnancy.

Options: Diagnosing the condition using fasting or non-fasting bile acids, classifying disease severity, determining what treatment to offer, establishing how to monitor for antenatal fetal wellbeing, identifying when to perform elective birth.

Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.

Background: Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach.