Objectives To investigate the effect of different dosages of low molecular weight heparin on acute pulmonary embolism and inhibition of pulmonary intimal hyperplasia in immature rats. Methods 90 male immature SD rats were randomly divided into ifve groups: sham group, pulmonary embolism group, low-low molecular heparin group (L-LMH), medium-low molecular heparin group (M-LMH) and high-low molecular heparin group (H-LMH). The model of acute pulmonary embolism was established through jugular vein injection with gel-foam solution. The rates in the L-LMH, M-LMH, H-LMH groups were treated with low molecular weight heparin by subcutaneous injection after surgery with a dosage of 0 . 005 ml/kg, 0 . 01 ml/kg, 0 . 02 ml/kg, twice a day. Animals in the control group were given saline injection. Arterial blood gas, pulmonary artery pressure (mPAP), right ventricular pressure (RVP), wall area/tube area, wall thickness/tube diameter, and the expression of PDGF-B and MCP-1 at gene and protein levels in lung tissue were detected on the 7 th ( 7 d), 14 th ( 14 d) and 28 th ( 28 d) after opration. Results There were signiifcant differences of PaO 2 among 5 groups on 7 d, 14 d and 28 d. PaO 2 in group M-LMH ( 105 . 1 ± 4 . 6 mm Hg) were signiifcantly higher than that of embolization group, L-LMH, but not H-LMH group at 28 d. mPAP of M-LMH group was lower than that in the other three intervention groups, but showed no signiifcant difference compared with sham group (P?>0 . 05 ). There were signiifcant differences of RVP on 7 d and 14 d. PDGF-B, MCP-1 of M-LMH group were signiifcantly lower compared with the other three intervention groups (P?0 . 05 ). Wall area/tube area, wall thickness/tube diameter scores of M-LMH group had no signiifcance differences compared with sham group on 28 d (P?>?0 . 05 ). Conclusion Medium dose of low molecular weight heparin could ameliorate the acute pulmonary embolism and inhibit the proliferation of pulmonary arteries in rats.

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.