Anamnesis: A 18-year-old woman suffered from severe multi-trauma in combination with acute brain injury (Glasgow Coma Scale Score = 4) after road accident. After prolonged rescue measures and emergency stabilisation the patient was transferred by helicopter to the emergency department of our clinic.
Investigations: Cranial computer tomography showed a severe general cerebral edema and a marked reduction in cerebral perfusion. Additionally, blunt abdominal injury, severe chest injury and multiple fractures were seen. Due to the severe and diffuse brain injury, a neurosurgical intervention was not possible. The patient was transferred to the intensive care unit.
Therapy And Course: Intensive supportive therapy was started (artificial ventilation, massive transfusion, volume replacement, insertion of a chest tube, renal replacement therapy). Control cerebral computer tomography indicated a complete destruction of the cerebral parenchyma and infarction. Sedation was stopped. After 48-hours of intensive care therapy brain death was stated and the approval for organ donation was given by the next of kin. Heart and kidneys were explanted and transplanted successfully.
Conclusion: Even under conditions of limited organ functions early identification and maximal supportive therapy may help to supply organ donation. Under certain condition, multiorgan failure may be reversible in possible organ donors.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1055/s-2006-955031 | DOI Listing |
J Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
Proc Natl Acad Sci U S A
February 2025
Department of Construction Sciences, Lund University, Lund SE-22100, Sweden.
Preemptive identification of potential failure under loading of engineering structures is a critical challenge. Our study presents an innovative approach to design built-in prefailure indicators within multiscale structural designs with optimized load carrying capabilities utilizing the design freedom of topology optimization. The indicators are engineered to visibly signal load conditions approaching the global critical buckling load at predefined locations.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Biology, Swarthmore College, Swarthmore, Pennsylvania, United States of America.
Mental illnesses put a tremendous burden on afflicted individuals and society. Identification of novel drugs to treat such conditions is intrinsically challenging due to the complexity of neuropsychiatric diseases and the need for a systems-level understanding that goes beyond single molecule-target interactions. Thus far, drug discovery approaches focused on target-based in silico or in vitro high-throughput screening (HTS) have had limited success because they cannot capture pathway interactions or predict how a compound will affect the whole organism.
View Article and Find Full Text PDFDetecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!