Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

Cancer remains a major global health challenge, with conventional treatments like chemotherapy and radiotherapy often hindered by significant side effects, lack of specificity, and limited efficacy in advanced cases. Among emerging therapeutic strategies, mRNA vaccines have shown remarkable potential due to their adaptability, rapid production, and capability for personalized cancer treatment. This review provides an in-depth analysis of messenger RNA (mRNA) vaccines as a therapeutic approach for cancer immunotherapy, focusing on their molecular biology, classification, mechanisms, and clinical studies. Derived from reported literature and data on clinicaltrials.gov, it examines studies on mRNA vaccines encoding tumor-specific antigens (TSAs), tumor-associated antigens (TAAs), immunomodulators, and chimeric antigen receptors (CARs) across various cancer types. The review highlights the ability of mRNA vaccines to encode TSAs and TAAs, enabling personalized cancer treatments, and classifies these vaccines into non-replicating and self-amplifying types. It further explores their mechanisms of action, including antigen presentation and immune activation, while emphasizing findings from clinical studies that demonstrate the potential of mRNA vaccines in cancer therapy. Despite their promise, challenges remain in enhancing delivery systems, improving immunogenicity, and addressing tumor heterogeneity. Overcoming these obstacles will require further investigation to fully harness the potential of mRNA vaccines in personalized cancer treatment.
Humans ; Cancer Vaccines/immunology* ; Neoplasms/immunology* ; mRNA Vaccines/therapeutic use* ; Immunotherapy/methods* ; Antigens, Neoplasm/genetics* ; RNA, Messenger/therapeutic use*