Comment évaluer le niveau de protéines sanguines ?

Un dosage sanguin peut mesurer les niveaux de protéines totales et spécifiques.

Quels tests diagnostiques sont utilisés ?

Les tests incluent l'électrophorèse des protéines et les dosages d'albumine.

Quels signes indiquent une anomalie des protéines ?

Des symptômes comme l'œdème, la fatigue ou des saignements peuvent indiquer des anomalies.

Quand faire un bilan des protéines sanguines ?

Un bilan est recommandé en cas de maladies hépatiques, rénales ou d'infections chroniques.

Quel rôle joue l'albumine dans le diagnostic ?

L'albumine aide à évaluer la fonction hépatique et l'état nutritionnel du patient.

Quels symptômes sont liés à une carence en protéines ?

Les symptômes incluent fatigue, œdème, et troubles de la coagulation.

Comment reconnaître un excès de protéines ?

Un excès peut provoquer des problèmes rénaux, des nausées et des troubles digestifs.

Les douleurs articulaires sont-elles liées aux protéines ?

Oui, certaines protéines inflammatoires peuvent contribuer à des douleurs articulaires.

Quels signes indiquent une maladie des protéines ?

Des signes comme des saignements, des infections fréquentes ou des œdèmes peuvent apparaître.

Les troubles de la coagulation sont-ils liés aux protéines ?

Oui, des anomalies dans les protéines de coagulation peuvent entraîner des troubles hémorragiques.

Comment prévenir les carences en protéines ?

Une alimentation équilibrée et variée est essentielle pour prévenir les carences.

Quels aliments sont riches en protéines ?

Les viandes, poissons, œufs, produits laitiers, légumineuses et noix sont riches en protéines.

La supplémentation est-elle nécessaire ?

La supplémentation peut être nécessaire pour les personnes ayant des besoins accrus ou des restrictions alimentaires.

Comment éviter les excès de protéines ?

Équilibrer l'apport alimentaire et consulter un nutritionniste peut aider à éviter les excès.

Les examens réguliers sont-ils importants ?

Oui, des examens réguliers aident à surveiller les niveaux de protéines et la santé globale.

Comment traiter une carence en protéines ?

Le traitement inclut une alimentation riche en protéines et des suppléments si nécessaire.

Quels médicaments affectent les protéines sanguines ?

Certains médicaments comme les anticoagulants peuvent influencer les niveaux de protéines.

Comment gérer un excès de protéines ?

La gestion inclut une réduction de l'apport protéique et un suivi médical régulier.

Les transfusions de protéines sont-elles courantes ?

Oui, les transfusions de protéines, comme l'albumine, sont utilisées en cas de besoin.

Quel rôle des immunoglobulines dans le traitement ?

Les immunoglobulines sont utilisées pour traiter certaines infections et maladies auto-immunes.

Quelles complications peuvent survenir avec une carence ?

Des complications incluent des infections fréquentes, des troubles de la croissance et des œdèmes.

Les excès de protéines peuvent-ils causer des maladies ?

Oui, un excès peut entraîner des maladies rénales et des troubles métaboliques.

Comment les maladies hépatiques affectent-elles les protéines ?

Les maladies hépatiques peuvent altérer la production de protéines, entraînant des déséquilibres.

Les troubles de la coagulation peuvent-ils être graves ?

Oui, ils peuvent entraîner des saignements excessifs ou des thromboses, nécessitant une attention médicale.

Quelles sont les conséquences d'une inflammation chronique ?

L'inflammation chronique peut perturber les niveaux de protéines et affecter la santé globale.

Quels facteurs augmentent le risque de carence ?

Les facteurs incluent une malnutrition, des maladies chroniques et des régimes restrictifs.

Les personnes âgées sont-elles à risque ?

Oui, les personnes âgées peuvent être à risque de carences en protéines en raison de l'alimentation.

Les maladies rénales augmentent-elles le risque d'excès ?

Oui, les maladies rénales peuvent entraîner une accumulation de protéines dans le sang.

Le stress peut-il affecter les niveaux de protéines ?

Oui, le stress chronique peut perturber la synthèse et le métabolisme des protéines.

Les habitudes alimentaires influencent-elles les niveaux ?

Oui, une alimentation déséquilibrée peut entraîner des carences ou des excès de protéines.

Protéines du sang - Informations médicales vérifiées

Developing a nomogram model for 3-month prognosis in patients who had an acute ischaemic stroke after intravenous thrombolysis: a multifactor logistic regression model approach.

24 Jul 2024

DOI: 10.1136/bmjopen-2023-079428 PMID: 39053953

This study is to establish a nomination graph model for individualised early prediction of the 3-month prognosis of patients who had an acute ischaemic stroke (AIS) receiving intravenous thrombolysis ... For the period from January 2016 through August 2022, 991 patients who had an acute stroke eligible for intravenous thrombolysis were included in the retrospective analysis study. The study was based ... Patients who received treatment from January 2016 to February 2021 were included in the training cohort, and those who received treatment from March 2021 to August 2022 were included in the testing co... Each patient received intravenous thrombolysis within 4.5 hours of onset, with treatment doses divided into standard doses (0.9 mg/kg).... The primary outcome measure was a 3-month adverse outcome (modified Rankin Scale 3-6).... The National Institutes of Health Stroke Scale Score after thrombolysis (OR=1.18; 95% CI: 1.04 to 1.36; p = 0.015), door-to-needle time (OR=1.01; 95% CI: 1.00 to 1.02; p = 0.003), baseline blood gluco... A reliable nomogram model (DGHM2N model) was developed and validated in this study. This nomogram could individually predict the adverse outcome of patients who had an AIS receiving intravenous thromb...

Humans Male Female Ischemic Stroke Thrombolytic Therapy +9 autres

Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling.

23 11 2022

DOI: 10.1186/s12874-022-01774-8 PMID: 36418976

This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with c... Cohort study using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus. Exposure variables include whether patients received pr... There were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus in our cohort. The rate of preventative treatments in the first six months follo... The use of random forest models to analyze data and provide predictions for patients holds great potential in identifying modifiable patient-level and health-system factors and cohorts for increased s...

United States Humans Aged Aged, 80 and over Logistic Models +6 autres

A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on motor vehicle crashes: Logistic regression approach.

2023

DOI: 10.1080/15389588.2023.2212392 PMID: 37436170

Chest injuries that occur in motor vehicle crashes (MVCs) include rib fractures, pneumothorax, hemothorax, and hemothorax depending on the injury mechanism. Many risk factors are associated with serio... Among 3,697 patients who visited the emergency room in regional emergency medical centers after MVCs between 2011 and 2018, we analyzed data from 1,226 patients with chest injuries. Vehicle damage was... Among the 1,226 patients with chest injuries, 484 (39.5%) had serious chest injuries. Patients in the serious group were older than those in the non-serious group (p=.001). In analyses based on vehicl... Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it...

Humans Accidents, Traffic Logistic Models Hemothorax Thoracic Injuries +3 autres

Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms.

Apr 2023

DOI: 10.1111/1753-0407.13375 PMID: 36890429

Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pre... Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational ... A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706-0.815), and 0.748 (95% CI 0.659-0.837) for t... We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and c...

Infant Pregnancy Infant, Newborn Female Humans +6 autres

Using logistic regression models to investigate the effects of high-sensitivity cardiac troponin T confounders on ruling in acute myocardial infarction.

27 06 2023

DOI: 10.1515/cclm-2022-1004 PMID: 36698327

Confounding factors, including sex, age, and renal dysfunction, affect high-sensitivity cardiac troponin T (hs-cTnT) concentrations and the acute myocardial infarction (AMI) diagnosis. This study asse... This retrospective study included a primary derivation cohort of 18,022 emergency department (ED) patients at a US medical center and a validation cohort of 890 ED patients at a Canadian medical cente... The area under the curve of the best-fitted model was 0.95. The model achieved a 90.0% diagnostic accuracy in the validation cohort. The optimal model cutoff yielded comparable performance (90.5% accu... The integrated prediction model incorporating confounding factors does not outperform hs-cTnT delta thresholds. Sex-specific hs-cTnT delta thresholds remain to provide the highest diagnostic accuracy....

Male Female Humans Troponin T Logistic Models +4 autres

Factors related to self-rated health of older adults in rural China: A study based on decision tree and logistic regression model.

2022

DOI: 10.3389/fpubh.2022.952714 PMID: 36530664

This study aimed to explore the related factors of self-rated health (SRH) by using decision tree and logistic regression models among older adults in rural China.... Convenience sampling was employed with 1,223 enrolled respondents who met the inclusion criteria from 10 randomly selected villages in M County in China. The content of the questionnaire covered demog... Notably, 817 (68.7%) subjects had good SRH. The logistic regression model showed that living standard, alcohol consumption, sleep quality, labor, hospitalization, discomfort, the number of chronic dis... Decision tree and logistic regression models complement each other and can describe the factors related to the SRH of the elderly in rural China from different aspects. Our findings indicated that men...

Humans Aged Logistic Models China Rural Population +2 autres

Application of O-RADS Ultrasound Lexicon-Based Logistic Regression Analysis Model in the Diagnosis of Solid Component-Containing Ovarian Malignancies.

2022

DOI: 10.1155/2022/7187334 PMID: 36330455

To use the logistic regression model to evaluate the value of ultrasound characteristics in the Ovarian-Adnexal Reporting and Data System ultrasound lexicon in determining ovarian solid component-cont... We retrospectively analyzed the data of 172 patients with adnexal masses discovered by ultrasound, and diagnosis was confirmed by postoperative pathological tests from January 2019 to December 2021. T... Of the 172 adnexal tumors, 104 were benign, and 68 were malignant. There were differences in cancer antigen 125, maximum mass diameter, maximum solid component diameter, multilocular cyst with solid c... The logistic regression model containing the maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence/absence of ascites can hel...

Female Humans Logistic Models Retrospective Studies Ascites +4 autres

Factors influencing the turnover intention for disease control and prevention workers in Northeast China: an empirical analysis based on logistic-ISM model.

22 Oct 2024

DOI: 10.1186/s12913-024-11738-x PMID: 39434108

This study aimed to determine the current turnover intention among workers at Centers for Disease Control and Prevention (CDCs) in Northeast China and to investigate the factors contributing to this p... The cross-sectional study was conducted in May 2023 across four northeastern provinces of China. The study included a total of 11,912 valid participants who were CDC workers selected using a stratifie... The study found that 28.8% of the respondents reported high turnover intention. The binary logistic regression suggested that the risk factors of turnover intention among employees included gender, ag... Nearly one-third of the respondents expressed a strong desire to resign from their employment. Turnover intention among CDC workers was subject to diverse influences. Early identification, detection, ...

Humans China Personnel Turnover Male Female +8 autres

Prevalence of childhood hypertension and associated factors in Zhejiang Province: a cross-sectional analysis based on random forest model and logistic regression.

03 Aug 2024

DOI: 10.1186/s12889-024-19630-3 PMID: 39097727

With childhood hypertension emerging as a global public health concern, understanding its associated factors is crucial. This study investigated the prevalence and associated factors of hypertension a...

Humans Cross-Sectional Studies Male Female Hypertension +7 autres

An analysis of factors influencing cognitive dysfunction among older adults in Northwest China based on logistic regression and decision tree modelling.

07 May 2024

DOI: 10.1186/s12877-024-05024-y PMID: 38714934

Cognitive dysfunction is one of the leading causes of disability and dependence in older adults and is a major economic burden on the public health system. The aim of this study was to investigate the... A cross-sectional study was conducted using a multistage sampling method. The questionnaires were distributed through the Elderly Disability Monitoring Platform to older adults aged 60 years and above... A total of 12,494 valid questionnaires were collected, including 2617 from participants in the cognitive dysfunction group and 9877 from participants in the normal cognitive function group. Univariate... Traditional risk factors (including BMI, literacy, and alcohol consumption) and potentially modifiable risk factors (including balance function, ability to care for oneself in daily life, and widowhoo...

Humans Male Female China Aged +10 autres

Protéines du sang : Questions médicales fréquentes

Termes MeSH sélectionnés :

Sous-catégories

Protéine de la phase aigüe

Fibrine

Immunoprotéines

Sérumalbumine

Protéine amyloïde A sérique

Activateurs du plasminogène

Mousse de fibrine

Colle de fibrine

Protéine C-réactive

Protéines du système du complément

Protéines des granules de l'éosinophile

Opsonines

Préalbumine

Sérumalbumine bovine

Sérum-albumine humaine

Activité de type insulinique non suppressible

Streptokinase

Activateur du plasminogène de type urokinase

Anaphylatoxines

Enzymes activatrices du complément

Complément C1

Complément C2

Complément C3

Complément C4

Complément C5

Complément C6

Complément C7

Complément C8

Complément C9

Protéines inhibitrices du complément

Complexe d'attaque membranaire du complément

Protéine cationique de l'éosinophile

Neurotoxine dérivée des éosinophiles

Protéine basique majeure de l'éosinophile

Eosinophil Peroxidase

Sérumalbumine radioiodée

Anistreplase

Streptodornase et streptokinase

Ténectéplase

Complement C3-C5 Convertases

Facteur D du complément

Complément C1q

Complément C1r

Complément C1s

Complément C2a

Complément C2b

Complément C3a

Complément C3b

Complément C4a

Complément C4b

Complément C5a

Complément C5b

Protéines inhibitrices de la fraction C3b du complément

Protéine de liaison à C4b

Alternative pathway C3-C5 Convertases

Classical pathway C3-C5 Convertases

Mannose-Binding Protein-Associated Serine Proteases

Complément C3c

Complément C3d

Complément C5a des-Arg

Facteur I du complément

Alternative pathway complement C3 convertase

Alternative pathway C5 convertase

Classical pathway complement C3 convertase

Classical pathway C5 convertase

Sources (10000 au total)