Titre : Metalloproteases

Metalloproteases : Questions médicales fréquentes

Termes MeSH sélectionnés :

Logistic Models
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"acceptedAnswer": { "@type": "Answer", "text": "Les niveaux de métalloprotéases dans le sérum ou les tissus peuvent servir de marqueurs diagnostiques." } }, { "@type": "Question", "name": "Les imageries sont-elles utiles pour le diagnostic ?", "position": 3, "acceptedAnswer": { "@type": "Answer", "text": "Oui, l'imagerie peut aider à visualiser les tissus affectés par des déséquilibres enzymatiques." } }, { "@type": "Question", "name": "Quels tests génétiques sont disponibles ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "Des tests génétiques peuvent identifier des mutations affectant la production de métalloprotéases." } }, { "@type": "Question", "name": "Comment évaluer l'activité enzymatique ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "Des tests enzymatiques spécifiques mesurent l'activité des métalloprotéases dans les échantillons." } }, { "@type": "Question", "name": "Quels symptômes indiquent une surproduction de métalloprotéases ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Une inflammation, des douleurs articulaires et des lésions tissulaires peuvent survenir." } }, { "@type": "Question", "name": "Les symptômes varient-ils selon le type de métalloprotéases ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Oui, différents types de métalloprotéases peuvent causer des symptômes variés selon les tissus affectés." } }, { "@type": "Question", "name": "Quels signes cliniques sont associés à une déficience ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Une cicatrisation lente et des anomalies dans la matrice extracellulaire peuvent être observées." } }, { "@type": "Question", "name": "Les symptômes sont-ils toujours visibles ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Non, certaines dysfonctions peuvent être asymptomatiques jusqu'à des stades avancés." } }, { "@type": "Question", "name": "Comment les symptômes évoluent-ils avec l'âge ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "L'activité des métalloprotéases peut augmenter avec l'âge, aggravant les symptômes dégénératifs." } }, { "@type": "Question", "name": "Comment prévenir les maladies liées aux métalloprotéases ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "Un mode de vie sain, incluant une alimentation équilibrée et de l'exercice, peut aider." } }, { "@type": "Question", "name": "Les suppléments nutritionnels sont-ils utiles ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Certains suppléments, comme les antioxydants, peuvent soutenir la fonction des métalloprotéases." } }, { "@type": "Question", "name": "L'évitement de certains aliments est-il recommandé ?", "position": 13, "acceptedAnswer": { "@type": "Answer", "text": "Oui, réduire les aliments pro-inflammatoires peut aider à réguler l'activité des métalloprotéases." } }, { "@type": "Question", "name": "Le contrôle du stress est-il important ?", "position": 14, "acceptedAnswer": { "@type": "Answer", "text": "Oui, le stress peut influencer l'activité des métalloprotéases, donc sa gestion est cruciale." } }, { "@type": "Question", "name": "Les examens réguliers sont-ils nécessaires ?", "position": 15, "acceptedAnswer": { "@type": "Answer", "text": "Des examens réguliers peuvent aider à détecter précocement des déséquilibres enzymatiques." } }, { "@type": "Question", "name": "Quels traitements ciblent les métalloprotéases ?", "position": 16, "acceptedAnswer": { "@type": "Answer", "text": "Des inhibiteurs spécifiques des métalloprotéases sont utilisés pour traiter certaines maladies." } }, { "@type": "Question", "name": "Les thérapies géniques sont-elles une option ?", "position": 17, "acceptedAnswer": { "@type": "Answer", "text": "Oui, la thérapie génique peut corriger des mutations affectant la production de métalloprotéases." } }, { "@type": "Question", "name": "Comment les anti-inflammatoires aident-ils ?", "position": 18, "acceptedAnswer": { "@type": "Answer", "text": "Ils peuvent réduire l'inflammation causée par une suractivité des métalloprotéases." } }, { "@type": "Question", "name": "Les traitements sont-ils personnalisés ?", "position": 19, "acceptedAnswer": { "@type": "Answer", "text": "Oui, les traitements peuvent être adaptés en fonction du type de métalloprotéases impliqué." } }, { "@type": "Question", "name": "Quels sont les effets secondaires des inhibiteurs ?", "position": 20, "acceptedAnswer": { "@type": "Answer", "text": "Les inhibiteurs peuvent causer des effets secondaires comme des troubles gastro-intestinaux." } }, { "@type": "Question", "name": "Quelles complications peuvent survenir avec une suractivité ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Des maladies dégénératives, comme l'arthrite, peuvent résulter d'une suractivité des métalloprotéases." } }, { "@type": "Question", "name": "Les complications sont-elles réversibles ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Certaines complications peuvent être réversibles avec un traitement approprié, d'autres non." } }, { "@type": "Question", "name": "Comment les complications affectent-elles la qualité de vie ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Elles peuvent entraîner des douleurs chroniques et une mobilité réduite, affectant la qualité de vie." } }, { "@type": "Question", "name": "Les complications sont-elles prévisibles ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Certaines complications peuvent être prévisibles en surveillant les niveaux d'activité enzymatique." } }, { "@type": "Question", "name": "Quelles sont les complications liées à une déficience ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Une cicatrisation lente et des infections peuvent survenir en raison d'une déficience en métalloprotéases." } }, { "@type": "Question", "name": "Quels facteurs augmentent le risque de dysfonction ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "L'âge, le mode de vie sédentaire et une alimentation déséquilibrée augmentent le risque." } }, { "@type": "Question", "name": "Les antécédents familiaux jouent-ils un rôle ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Oui, des antécédents familiaux de maladies liées aux métalloprotéases peuvent augmenter le risque." } }, { "@type": "Question", "name": "Le tabagisme est-il un facteur de risque ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "Oui, le tabagisme peut exacerber l'activité des métalloprotéases et augmenter les maladies associées." } }, { "@type": "Question", "name": "Le stress chronique influence-t-il le risque ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Oui, le stress chronique peut perturber l'équilibre des métalloprotéases, augmentant le risque de maladies." } }, { "@type": "Question", "name": "L'exposition à des toxines est-elle un facteur ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Oui, l'exposition à certaines toxines environnementales peut affecter l'activité des métalloprotéases." } } ] } ] }

Sous-catégories

20 au total
└─

Metalloendopeptidases

Metalloendopeptidases D008666 - D08.811.277.656.675.374
└─

Metalloexopeptidases

Metalloexopeptidases D045727 - D08.811.277.656.675.555
└─└─

Collagenases

Collagenases D017364 - D08.811.277.656.675.374.205
└─└─

Enzymes de conversion de l'endothéline

Endothelin-Converting Enzymes D000072396 - D08.811.277.656.675.374.229
└─└─

Gelatinases

Gelatinases D018093 - D08.811.277.656.675.374.252
└─└─

Insulinase

Insulysin D007339 - D08.811.277.656.675.374.300
└─└─

Lysostaphin

Lysostaphin D008248 - D08.811.277.656.675.374.452
└─└─

Matrix metalloproteinases

Matrix Metalloproteinases D020782 - D08.811.277.656.675.374.525
└─└─

Procollagen peptidase

Procollagen N-Endopeptidase D011348 - D08.811.277.656.675.374.664
└─└─

Bacillus thermoproteolyticus neutral proteinase

Thermolysin D013820 - D08.811.277.656.675.374.827
└─└─

Carboxypeptidases A

Carboxypeptidases A D043422 - D08.811.277.656.675.555.350
└─└─

Cystinyl aminopeptidase

Cystinyl Aminopeptidase D010122 - D08.811.277.656.675.555.400
└─└─

Glutamate carboxypeptidase II

Glutamate Carboxypeptidase II D043425 - D08.811.277.656.675.555.500
└─└─

Glutamyl aminopeptidase

Glutamyl Aminopeptidase D043384 - D08.811.277.656.675.555.600
└─└─

Leucyl Aminopeptidase

Leucyl Aminopeptidase D007931 - D08.811.277.656.675.555.700
└─└─

Lysine carboxypeptidase

Lysine Carboxypeptidase D001121 - D08.811.277.656.675.555.750
└─└─└─

Microbial collagenase

Microbial Collagenase D003012 - D08.811.277.656.675.374.205.500
└─└─└─└─

Matrix metalloproteinase 14

Matrix Metalloproteinase 14 D053511 - D08.811.277.656.675.374.525.300.500
└─└─└─└─

Matrix metalloproteinase 15

Matrix Metalloproteinase 15 D053517 - D08.811.277.656.675.374.525.300.625
└─└─└─└─

Matrix metalloproteinase 16

Matrix Metalloproteinase 16 D053513 - D08.811.277.656.675.374.525.300.750

Sources (10000 au total)

Comparison of static and rolling logistic regression models on predicting invasive mechanical ventilation or death from COVID-19-A retrospective, multicentre study.

COVID-19 virus has undergone mutations, and the introduction of vaccines and effective treatments have changed its clinical severity. We hypothesized that models that evolve may better predict invasiv... This retrospective study of adult patients with COVID-19 from six Michigan hospitals analysed 20 demographic, comorbid, vital sign and laboratory factors, one derived factor and nine factors represent... Of the 7041 patients, 534 (7.6%) required invasive mechanical ventilation or died within 14 days of arrival. Rolling models improved discrimination (0.865 ± 0.010, 0.856 ± 0.007 and 0.843 ± 0.005 for ... Rolling models with contemporaneous data maintained better metrics of performance than static models, which used older data....

Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration.

Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measu... We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all w... We developed a logistic regression model using seven feature variables derived from maternal socio-demographic and obstetric history from a preterm birth (n = 917) and a matched full-term (n = 100) co... The retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71-0.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66-0.76) and 0.78 (... Pending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous prete...

Factors associated with a better treatment efficacy among psoriasis patients: a study based on decision tree model and logistic regression in Shanghai, China.

Many effective therapies for psoriasis are being applied in clinical practice in recent years, however, some patients still can't achieve satisfied effect even with biologics. Therefore, it is crucial... We implemented an observational study and recruited 512 psoriasis patients in Shanghai Skin Diseases Hospital from 2021 to 2022. We used face-to-face questionnaire interview and physical examination t... The 512 patients were predominately males (72.1%), with a median age of 47.5 years. In this study, 245 patients achieved ≥ 75% improvement in psoriasis area and severity index (PASI) score in week 8 a... Psoriasis patients with higher education, without tobacco smoking, alcohol drinking and psoriasis family history had better treatment efficacy. Decision tree model had similar predicting effect with t...

Complete sources of cluster variation on the risk of under-five malaria in Uganda: a multilevel-weighted mixed effects logistic regression model approach.

Malaria, a major cause of mortality worldwide is linked to a web of determinants ranging from individual to contextual factors. This calls for examining the magnitude of the effect of clustering withi... This study applied a multilevel-weighted mixed effects logistic regression model to account for both individual and contextual factors.... Every additional year in a child's age was positively associated with malaria infection (AOR = 1.42; 95% CI 1.33-1.52). Children whose mothers had at least a secondary school education were less likel... Interventions towards reducing the burden of under-five malaria should be prioritized to improve individual-level characteristics compared to household-level features. Enumeration area (EA) specific i...

Prevalence of stunting and associated factors among under-five children in sub-Saharan Africa: Multilevel ordinal logistic regression analysis modeling.

Stunting is still a major public health problem all over the world, it affecting more than one-third of under-five children in the world that leads to growth retardation, life-threatening complication... This study was based on the most recent Demographic and Health Survey data of 36 sub-Saharan African countries. A total of 203,852(weighted sample) under-five children were included in the analysis. T... In this study, the prevalence of stunting among under-five children in Sub-Saharan Africa 34.04% (95% CI: 33.83%, 34.24%) with a large difference between specific countries which ranges from 16.14% in... Stunting among under-five children is still public health problem in Sub-Saharan Africa. Therefore designing interventions to address diarrhea and other infectious disease, improving the literacy leve...

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram.

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) his...

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods.

To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.... The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC ... LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM wer... The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs....

Risk prediction model for early postoperative death in patients with hepatocellular carcinoma: a retrospective study based on random forest algorithm and logistic regression.

At present, little is known about the risk factors of early postoperative death in patients with hepatocellular carcinoma (HCC).... We collected the data of patients who were diagnosed with primary liver cancer between 2010 and 2015 in the Surveillance, Epidemiology, and End Results database and further allocated them to the train... A total of 4154 patients were selected in this process, including 2647 patients with postoperative early death (outcome1) and 1507 patients with liver cancer-specific postoperative early death (outcom... This model provides an intuitive and practical tool for future studies based on large-scale cohorts by exploring the risk factors of early death in patients with HCCs undergoing surgery....