Efficacy of thymosin-α-1 in patients with COVID-19: A systematic review and meta-analysis
Notice bibliographique
Résumé
Objective To identify whether thymosin-α-1 (Tα1) is effective in patients with Coronavirus disease 2019 (COVID-19) and to determine a suitable population for Tα1 treatment. Methods We included studies with ≥10 cases and adults (aged ≥18 years) with laboratory-confirmed SARS-CoV-2 infection, data on mortality or length of hospitalization, disease severity, and study location, while excluded pregnant and breastfeeding women and minors. Publications were searched from November 1, 2019, to July 5, 2023, in six databases, including PubMed, Web of Science, Embase, Cochrane Library, China Knowledge Resource Integrated Database, and Wanfang Database. We separately utilized Newcastle-Ottawa Scale and Cochrane handbook methodology to evaluate risk of bias and used Review Manager (version 5.4, Cochrane Collaboration, Copenhagen, Denmark) to present and synthesize results. Relative risks (RR) and Standardized Mean Difference (SMD) with 95% confidence intervals (CI) were analyzed for dichotomous variables and continuous variables, respectively. Results Nine studies (participants = 5417) were included. No significant differences were found in mortality (nine studies; n = 5417; RR = 0.95; 95% CI: 0.56, −1.60; p = .84; I 2 = 90%) or length of hospitalization (four studies; n = 3688; SMD = 0.16; 95% CI: −0.38, −0.69; p = .57; I 2 = 96%) between patients with COVID-19 who did and did not receive Tα1. Participants were divided by the severity of the disease (serious and non-serious) and study location. Among the serious group, the incidence of death among patients who received Tα1 treatment was 0.67 times that of patients who did not receive Tα1 treatment (four studies; n = 1230; RR: 0.67; 95% CI: 0.58, −0.77; p < .00,001; I 2 = 0%). There was no significant difference in length of hospitalization between the groups (two studies; n = 410; SMD = 0.66; 95% CI: −0.06, −1.38; p = .07; I 2 = 87%). Among the non-serious group, compared to not having Tα1 treatment, receiving Tα1 treatment reduced hospitalization length (two studies; n = 3670; SMD = −0.28; 95% CI: −0.41, −0.14; p < .0001; I 2 = 51%), while no significant difference in mortality (three studies; n = 3775; RR = 1.06; 95% CI: 0.22, −5.03; p = .94; I 2 = 89%). Moreover, there was no significant difference between subgroups when divided by study locations (Studies within China: seven studies; n = 5263; RR = 1.14; 95% CI: 0.64, −2.04; p = .65; I 2 =92%; Studies outside of China: two studies; n = 154; RR = 0.41; 95% CI: 0.14, −1.24; p = .11; I 2 = 51%). Discussion For patients with serious types of COVID-19, Tα1 significantly decreased mortality, which supports the utilization of Tα1 in patients with severe and critical types of COVID-19. Moreover, regarding hospitalization length, patients with non-serious COVID-19 who used Tα1 reduced their hospitalization length compared to those that did not use Tα1. However, these results have high heterogeneity and limited generalizability.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,025 | 0,035 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».