Purpose Of Review: The exstrophy-epispadias complex (EEC) represents a group of congenitally acquired malformations involving the musculoskeletal, gastrointestinal, and genitourinary systems. Classic bladder exstrophy (CBE) is the most common and best studied entity within the EEC. In this review, imaging features of CBE anatomy will be presented with surgical correlation.
Recent Findings: Magnetic resonance imaging (MRI) has emerged as a useful modality for pre- and postnatal assessment of the abdominal wall, pelvic floor, and gastrointestinal and genitourinary systems of children with CBE. The authors' experience supports use of preoperative MRI, in conjunction with navigational software, as a method for identifying complex CBE anatomy. Imaging facilitates surgical approach and improves visualization of complex anatomy, potentially helping to avoid complications. Continued investigation of imaging guidance in CBE repair is needed as surgical techniques improve.
Life is a dynamic and systematic whole, with organs interacting with each other. Traditional Chinese medicine(TCM) discusses the interaction between human tissue and organs through the Zangfu relations, while modern biological medicine clarifies physiological and pathological relationships through "organ crosstalk". From the perspective of "organ crosstalk", this article discussed the five classical Zangfu relations(lungs and large intestine, heart and small intestine, spleen and stomach, liver and gallbladder, and kidney and bladder) in TCM and their extension.
Vesicovaginal reflux (VVR) is a functional disorder seen in girls and women. It may be asymptomatic or cause incontinence and dribbling symptoms. It is often associated with high body mass index (BMI) and infrequent voiding or abnormal voiding position.
Bladder exstrophy-epispadias-cloacal exstrophy complex (BEEC) is a spectrum of congenital urologic anomalies that involve the bladder, urethra, genitalia, and pelvic musculoskeletal system, and can affect urinary continence, sexual health, and fertility. BEEC includes a wide spectrum of anatomical abnormalities with different levels of severity: epispadias represents the mildest phenotype, classic bladder exstrophy (CBE) is the most common defect, and cloacal exstrophy (CE) - often referred to as omphalocele, exstrophy, imperforate anus, and spinal defects (OEIS) complex - is the most severe form. BEEC disorders cause significant health problems and affect the health-related quality of life (QoL) of affected individuals.
This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. We applied various machine learning techniques to biomarker data from the previous IP4IC and ICRS studies to predict the presence of IC/BPS, a disorder impacting the urinary bladder. Data were sourced from two nationwide, crowd-sourced collections of urine samples involving 2009 participants.
