Introduction: Neurogenic lower urinary tract (NLUTD), bowel (NBD), and sexual dysfunction (SD) are commonly observed in patients with (pw) multiple sclerosis (MS) and diminish the patients' quality of life (QoL). This systematic review aim to evaluate and discuss the current algorithms for the management of these issues.
Methods: A systematic review was conducted on the PubMed in June 2024. The primary search criterion was the presence of the term 'algorithm/s' or 'management/ing' in the title and/or abstract, followed by the MeSH term 'multiple sclerosis' and a combination of free-text keywords referring to NLUTD, NBD or SD.
Results: Fifteen articles regarding NLUTD were considered eligible, only one regarding SD while none addressed NBD.
Discussion: Numerous studies emphasize the profound impact of urinary and bowel symptoms on the QoL and morbidity in pwMS. Few algorithms addressing NLUTD are designed for first-line physicians and addresses the key priorities in MS care. Specific approaches to NBD management in pwMS are lacking. Screening for SD requires a structured assessment to deliver appropriate solutions.
Conclusion: NLUTD, NBD, and SD are underdiagnosed and undertreated. The implementation of straightforward algorithms for first-line physicians could enhance the management of these common issues, improve the QoL, reduce costs, and ensure appropriate referral to specialists.
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http://dx.doi.org/10.1016/j.msard.2024.105884 | DOI Listing |
Urologie
January 2025
Klinik für Urologie, Campus Lübeck, Universitätsklinikum Schleswig-Holstein, Lübeck, Deutschland.
This article provides a comprehensive overview of the current treatment options for patients with metastatic castration-resistant prostate cancer (mCRPC) following the failure of first-line therapy. Although significant progress has been made in the primary treatment of hormone-sensitive prostate cancer, the management of mCRPC remains a clinical challenge. The article outlines the diagnostic criteria for mCRPC, which can be confirmed through biochemical progression and imaging techniques.
View Article and Find Full Text PDFDrug Des Devel Ther
January 2025
Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.
Purpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.
Methods: A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China.
Perfusion
January 2025
Fraser Health, Surrey, BC, Canada.
Severe accidental hypothermia can lead to cardiac arrest. The most efficient method of resuscitating and warming is by ECMO (Extracorporeal Membrane Oxygenation). While the convention is to use VA ECMO (Veno Arterial ECMO), using VV ECMO (Veno Venous ECMO) in which the blood is returned directly into the right ventricle could be an alternative and lead to conversion to life sustaining cardiac rhythm.
View Article and Find Full Text PDFBackground: For patients with suspected traumatic vertebral artery injury (TVAI), CT angiography (CTA) is the first-line screening modality. Digital subtraction angiography (DSA) serves as the confirmatory diagnostic imaging, and is the gold standard for cerebrovascular injury assessment, due to its higher sensitivity and specificity. Among patients with TVAI based on CTA who have undergone follow-up DSA, this study aims to investigate how diagnostic information with additional imaging affects clinical management.
View Article and Find Full Text PDFNat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
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