Titre : Vinblastine

Vinblastine : Questions médicales fréquentes

Termes MeSH sélectionnés :

Single-Cell Gene Expression Analysis

Questions fréquentes et termes MeSH associés

Diagnostic 2

#1

Comment la vinblastine est-elle utilisée dans le diagnostic du cancer ?

La vinblastine n'est pas un outil de diagnostic, mais un traitement pour les cancers diagnostiqués.
Vinblastine Cancer Diagnostic médical
#2

Quels tests précèdent l'administration de vinblastine ?

Des tests sanguins et d'imagerie sont effectués pour évaluer la maladie avant le traitement.
Tests de laboratoire Imagerie médicale Vinblastine

Symptômes 2

#1

Quels sont les effets secondaires courants de la vinblastine ?

Les effets secondaires incluent nausées, vomissements, fatigue et perte de cheveux.
Effets secondaires Vinblastine Nausées
#2

La vinblastine provoque-t-elle des douleurs ?

Elle peut causer des douleurs musculaires ou articulaires, mais ce n'est pas systématique.
Douleur Vinblastine Effets indésirables

Prévention 2

#1

La vinblastine a-t-elle un rôle préventif dans le cancer ?

Non, la vinblastine est un traitement et ne prévient pas le développement du cancer.
Prévention du cancer Vinblastine Traitement
#2

Peut-on prévenir les effets secondaires de la vinblastine ?

Des médicaments antiémétiques peuvent aider à réduire les nausées causées par la vinblastine.
Effets secondaires Vinblastine Médicaments antiémétiques

Traitements 2

#1

Dans quels types de cancer la vinblastine est-elle utilisée ?

Elle est utilisée pour traiter les lymphomes, le cancer du poumon et le cancer du testicule.
Lymphome Cancer du poumon Vinblastine
#2

Comment la vinblastine est-elle administrée ?

La vinblastine est généralement administrée par injection intraveineuse sous supervision médicale.
Administration intraveineuse Vinblastine Traitement du cancer

Complications 2

#1

Quelles complications peuvent survenir avec la vinblastine ?

Des complications incluent des infections, des saignements et des problèmes neurologiques.
Complications Vinblastine Infections
#2

La vinblastine peut-elle affecter la moelle osseuse ?

Oui, la vinblastine peut entraîner une dépression de la moelle osseuse, réduisant les globules blancs.
Moelle osseuse Vinblastine Dépression médullaire

Facteurs de risque 2

#1

Qui est à risque d'effets indésirables avec la vinblastine ?

Les patients avec des antécédents de maladies cardiaques ou hépatiques sont à risque accru.
Facteurs de risque Vinblastine Maladies cardiaques
#2

Les enfants peuvent-ils prendre de la vinblastine ?

Oui, la vinblastine peut être administrée aux enfants, mais avec précaution et surveillance.
Vinblastine Pédiatrie Traitement du cancer
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Dr Olivier Menir

Contenu validé par Dr Olivier Menir

Expert en Médecine, Optimisation des Parcours de Soins et Révision Médicale


Validation scientifique effectuée le 05/05/2025

Contenu vérifié selon les dernières recommandations médicales

Auteurs principaux

Eric Bouffet

2 publications dans cette catégorie

Affiliations :
  • Programme in Developmental and Stem Cell Biology, Division of Haematology/Oncology, Hospital for Sick Children, Toronto, Ontario, Canada.

Yaser Hassan Dewir

2 publications dans cette catégorie

Affiliations :
  • Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sarah E O'Connor

2 publications dans cette catégorie

Affiliations :
  • Department of Natural Product Biosynthesis, Max Planck Institute for Chemical Ecology, Jena, Germany.

G A Amos Burke

2 publications dans cette catégorie

Affiliations :
  • Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK.

Riccardo Finotello

2 publications dans cette catégorie

Affiliations :
  • Department of Small Animal Clinical Sciences, Department of Veterinary Anatomy Physiology and Pathology, Institute of Infection Veterinary and Ecological Sciences, University of Liverpool, Chester High Rd, Neston CH64 7TE, UK.

Yoshihiro Nishida

2 publications dans cette catégorie

Affiliations :
  • Department of Rehabilitation Medicine, Nagoya University Hospital, Nagoya, Japan.
  • Department of Orthopaedic Surgery, Nagoya University Hospital, Nagoya, Japan.

Shunsuke Hamada

2 publications dans cette catégorie

Affiliations :
  • Department of Orthopaedic Surgery, Nagoya University Hospital, Nagoya, Japan.

Tomohisa Sakai

2 publications dans cette catégorie

Affiliations :
  • Department of Orthopaedic Surgery, Nagoya University Hospital, Nagoya, Japan.

Hiroshi Koike

2 publications dans cette catégorie

Affiliations :
  • Department of Orthopaedic Surgery, Nagoya University Hospital, Nagoya, Japan.

Magdalena Markowicz-Piasecka

2 publications dans cette catégorie

Affiliations :
  • Laboratory of Bioanalysis, Department of Pharmaceutical Chemistry, Drug Analysis and Radiopharmacy, Medical University of Lodz, ul. Muszyńskiego 1, 90-151, Lodz, Poland; Department of Applied Pharmacy, Medical University of Lodz, ul. Muszyńskiego 1, 90-151, Lodz, Poland.

Sources (10000 au total)

scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.

Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatment... scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories,... The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumo...

Robustness of single-cell RNA-seq for identifying differentially expressed genes.

A common feature of single-cell RNA-seq (scRNA-seq) data is that the number of cells in a cell cluster may vary widely, ranging from a few dozen to several thousand. It is not clear whether scRNA-seq ... We addressed this question by performing scRNA-seq and poly(A)-dependent bulk RNA-seq in comparable aliquots of human induced pluripotent stem cells-derived, purified vascular endothelial and smooth m... Findings of the current study provide a quantitative reference for designing studies that aim for identifying DEGs for specific cell clusters using scRNA-seq data and for interpreting results of such ...

Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis.

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technica...

Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.

The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and... We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sam... DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples....

scMEB: a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data.

Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data. Obtaining a perfect clustering result is of central importance f... Here, we propose single-cell minimum enclosing ball (scMEB), a novel and fast method for detecting single-cell DEGs without prior cell clustering results. The proposed method utilizes a small part of ... We compared scMEB to two different approaches that could be used to identify DEGs without cell clustering. The investigation of 11 real datasets revealed that scMEB outperformed rival methods in terms...

Single-cell transcriptome analysis reveals the key genes associated with macrophage polarization in liver cancer.

The aim of this study was to reveal the key genes associated with macrophage polarization in liver cancer.... Data were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas databases (TCGA). R package Seurat 4.0 was used to preprocess the downloaded single-cell sequencing data, princi... Two thousand highly variable genes were obtained after the normalization of single-cell profiles. In all, 16 principal components and 15 cell clusters were obtained. Monocytes and macrophages were the... The key genes associated with macrophage polarization, namely CD53, TGFBI, S100A4, pyruvate kinase M, LSP1, and SPP1, may be potential therapeutic targets for liver cancer....

Single-cell transcriptome analysis profiles the expression features of TMEM173 in BM cells of high-risk B-cell acute lymphoblastic leukemia.

As an essential regulator of type I interferon (IFN) response, TMEM173 participates in immune regulation and cell death induction. In recent studies, activation of TMEM173 has been regarded as a promi... Quantitative real-time PCR (qRT-PCR) and western blotting (WB) were applied to determine the mRNA and protein levels of TMEM173 in peripheral blood mononuclear cells (PBMCs). TMEM173 mutation status w... The mRNA and protein levels of TMEM173 were increased in PBMCs from B-ALL patients. Besides, frameshift mutation was presented in TMEM173 sequences of 2 B-ALL patients. ScRNA-seq analysis identified t... Our findings provide insights into the transcriptomic features of TMEM173 in the BM of high-risk B-ALL patients. Targeted activation of TMEM173 in specific cells might provide new therapeutic strategi...

Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis.

The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and developm... We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of d... Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, wit... We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis....

Integrating the characteristic genes of macrophage pseudotime analysis in single-cell RNA-seq to construct a prediction model of atherosclerosis.

Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of ... Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSOR... Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages ... IL1RN...