Comment diagnostiquer un besoin de biofeedback ?

Un professionnel évalue les symptômes et l'historique médical pour déterminer l'indication.

Quels tests sont utilisés pour le biofeedback ?

Des tests de stress, d'anxiété et des mesures physiologiques comme la fréquence cardiaque.

Le biofeedback est-il adapté à tous ?

Non, il peut ne pas convenir aux personnes avec certaines conditions médicales graves.

Quels professionnels pratiquent le biofeedback ?

Psychologues, thérapeutes et médecins spécialisés en médecine comportementale.

Comment évaluer l'efficacité du biofeedback ?

Par l'observation des changements dans les symptômes et les mesures physiologiques.

Quels symptômes peuvent être traités par biofeedback ?

Anxiété, stress, migraines, douleurs chroniques et troubles du sommeil.

Le biofeedback aide-t-il à la gestion du stress ?

Oui, il enseigne des techniques de relaxation et de contrôle physiologique.

Quels signes indiquent un besoin de biofeedback ?

Tensions musculaires, palpitations, insomnie ou difficultés de concentration.

Le biofeedback peut-il réduire les migraines ?

Oui, il aide à identifier et à gérer les déclencheurs de la migraine.

Quels effets secondaires peut-on attendre du biofeedback ?

Généralement minimes, mais certains peuvent ressentir de la fatigue ou de l'irritabilité.

Le biofeedback peut-il prévenir des maladies ?

Oui, il aide à gérer le stress et à prévenir des troubles liés à l'anxiété.

Comment le biofeedback aide-t-il à la prévention du stress ?

Il enseigne des techniques de relaxation et de gestion des émotions.

Le biofeedback est-il utile pour les sportifs ?

Oui, il améliore la concentration et la gestion du stress avant les compétitions.

Peut-on utiliser le biofeedback à domicile ?

Oui, des dispositifs portables permettent de pratiquer le biofeedback à domicile.

Quels conseils de prévention peut-on donner ?

Pratiquer la méditation, l'exercice régulier et utiliser le biofeedback pour gérer le stress.

Comment se déroule une séance de biofeedback ?

Le patient est connecté à des capteurs qui mesurent des fonctions corporelles et reçoit des retours.

Combien de séances de biofeedback sont nécessaires ?

Cela varie, mais généralement entre 5 et 20 séances pour des résultats optimaux.

Le biofeedback est-il efficace pour l'hypertension ?

Oui, il peut aider à réduire la pression artérielle en apprenant des techniques de relaxation.

Peut-on combiner biofeedback et médicaments ?

Oui, le biofeedback peut être utilisé en complément des traitements médicamenteux.

Quels appareils sont utilisés en biofeedback ?

Des appareils mesurant la fréquence cardiaque, la température corporelle et l'activité musculaire.

Quelles complications peuvent survenir avec le biofeedback ?

Peu de complications, mais une mauvaise utilisation peut entraîner frustration ou stress.

Le biofeedback peut-il aggraver certains symptômes ?

Rarement, mais une mauvaise interprétation des signaux peut causer de l'anxiété.

Y a-t-il des contre-indications au biofeedback ?

Oui, certaines conditions psychologiques graves peuvent rendre le biofeedback inapproprié.

Comment gérer les complications du biofeedback ?

Consulter un professionnel pour ajuster les techniques et les attentes.

Le biofeedback peut-il créer une dépendance ?

Non, il s'agit d'un outil d'apprentissage, pas d'un traitement dépendant.

Quels facteurs augmentent le besoin de biofeedback ?

Stress élevé, troubles anxieux, douleurs chroniques et mode de vie sédentaire.

Les antécédents familiaux influencent-ils le biofeedback ?

Oui, des antécédents de troubles anxieux ou de migraines peuvent augmenter le besoin.

Le mode de vie affecte-t-il l'efficacité du biofeedback ?

Oui, un mode de vie sain améliore les résultats du biofeedback.

Les jeunes adultes ont-ils plus besoin de biofeedback ?

Ils peuvent être plus exposés au stress et à l'anxiété, augmentant le besoin.

Les femmes sont-elles plus susceptibles de bénéficier du biofeedback ?

Certaines études montrent que les femmes peuvent répondre différemment au biofeedback.

Rétroaction biologique (psychologie) - Informations médicales vérifiées

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

27 Nov 2023

DOI: 10.1186/s12859-023-05563-y PMID: 38012590

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...

Humans DNA Copy Number Variations Single-Cell Gene Expression Analysis Neoplasms Transcriptome +2 autres

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

03 Jul 2023

DOI: 10.1186/s12864-023-09487-y PMID: 37394518

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 ...

Humans Gene Expression Profiling Single-Cell Gene Expression Analysis Induced Pluripotent Stem Cells RNA-Seq +2 autres

Protocols for single-cell RNA-seq and spatial gene expression integration and interactive visualization.

17 03 2023

DOI: 10.1016/j.xpro.2023.102047 PMID: 36853708

There is a wealth of software that utilizes single-cell RNA-seq (scRNA-seq) data to deconvolve spatial transcriptomic spots, which currently are not yet at single-cell resolution. Here we provide prot...

Humans Sequence Analysis, RNA Single-Cell Gene Expression Analysis Single-Cell Analysis Gene Expression Profiling +1 autres

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

30 Jul 2024

DOI: 10.1186/s12859-024-05872-w PMID: 39080559

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...

Single-Cell Analysis Gene Expression Profiling Sequence Analysis, RNA Humans Software +3 autres

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

18 Sep 2024

DOI: 10.1186/s12864-024-10728-x PMID: 39294558

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....

Single-Cell Analysis Humans RNA-Seq Algorithms Gene Expression Profiling +4 autres

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

25 May 2023

DOI: 10.1186/s12864-023-09374-6 PMID: 37231345

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...

Gene Expression Profiling Single-Cell Gene Expression Analysis Single-Cell Analysis Cluster Analysis Sequence Analysis, RNA +1 autres

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

01 11 2023

DOI: 10.1097/HC9.0000000000000304 PMID: 37889536

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....

Humans Pyruvate Kinase Single-Cell Gene Expression Analysis Liver Neoplasms Macrophages +1 autres

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

24 Apr 2023

DOI: 10.1186/s12885-023-10830-5 PMID: 37095455

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...

Humans Leukocytes, Mononuclear Single-Cell Gene Expression Analysis Precursor B-Cell Lymphoblastic Leukemia-Lymphoma Killer Cells, Natural +4 autres

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

2023

DOI: 10.3389/fimmu.2023.1247131 PMID: 38239341

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....

Humans Single-Cell Gene Expression Analysis Sepsis Algorithms B-Lymphocytes +1 autres

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

08 07 2023

DOI: 10.18632/aging.204856 PMID: 37421595

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...

Humans Single-Cell Gene Expression Analysis Atherosclerosis Plaque, Atherosclerotic Macrophages +2 autres

Rétroaction biologique (psychologie) : Questions médicales fréquentes

Termes MeSH sélectionnés :

Questions fréquentes et termes MeSH associés

Diagnostic 5

Symptômes 5

Prévention 5

Traitements 5

Complications 5

Facteurs de risque 5

Contenu validé par Dr Olivier Menir

Sous-catégories

Rétroaction neurologique

Auteurs principaux

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