Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database

Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database.

OBJECTIVE: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. METHOD: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. RESULTS: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. CONCLUSIONS: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

MicroRNA-124 negatively regulates STAT3 to alleviate hypoxic-ischemic brain damage by inhibiting oxidative stress.

MicroRNA-124 (miR-124) is implicated in various neurological diseases; however, its significance in hypoxic-ischaemic brain damage (HIBD) remains unclear. This study aimed to elucidate the underlying pathophysiological mechanisms of miR-124 in HIBD. In our study performed on oxygen-glucose deprivation followed by reperfusion (OGD)/R-induced primary cortical neurons, a substantial reduction in miR-124 was observed. Furthermore, the upregulation of miR-124 significantly mitigated oxidative stress, apoptosis, and mitochondrial impairment. We demonstrated that miR-124 interacts with the signal transducer and activator of transcription 3 (STAT3) to exert its biological function using the dual-luciferase reporter gene assay. As the duration of OGD increased, miR-124 exhibited a negative correlation with STAT3. STAT3 overexpression notably attenuated the protective effects of miR-124 mimics, while knockdown of STAT3 reversed the adverse effects of the miR-124 inhibitor. Subsequently, we conducted an HIBD model in rats. In vivo experiments, miR-124 overexpression attenuated cerebral infarction volume, cerebral edema, apoptosis, oxidative stress, and improved neurological function recovery in HIBD rats. In summary, the neuroprotective effects of the miR-124/STAT3 axis were confirmed in the HIBD model. MiR-124 may serve as a potential biomarker with significant therapeutic implications for HIBD.

MiR-155 promotes inflammation and apoptosis via targeting SIRT1 in hypoxic-ischemic brain damage.

Hypoxic-ischemic brain damage (HIBD) is one of the major causes of infant death and long-term neurological disturbances, which puts great pressure on families and society. Previous studies have reported that neuroinflammation regulates the pathogenesis of HIBD. MiR-155 has been reported to participate in many brain injuries; however, its direct implication and related mechanisms are not illuminated in HIBD. Herein, we identified that miR-155 plays a vital role in HIBD both in in vitro and in vivo models. We found that miR-155 promoted inflammation and apoptosis via targeting SIRT1 and negatively regulated its expression levels in oxygen-glucose deprivation/reoxygenation (OGD/R) in an in vitro model. Silencing of SIRT1 reversed the effects of miR-155 inhibitor on apoptosis and the NF-κB pathway in OGD/R-treated PC12 cells and microglia (BV2) cells. Moreover, in a neonatal rat HIBD model, miR-155 enhanced apoptosis and inflammation in the brains of rats with HIBD in vivo. Together, our results demonstrated that miR-155 exerted a negative effect in HIBD by targeting SIRT1, which could contribute to the treatment of neonatal patients with hypoxic-ischemic brain damage.

Preparation and performance of latanoprost-loaded hydrogels as a lacrimal suppository for the treatment of glaucoma.

Glaucoma is the leading cause of irreversible blindness, and its treatment is attracting widespread attention. Drug-loaded lacrimal suppositories can effectively treat xerophthalmia, but there is little research on the treatment of glaucoma with drug-loaded lacrimal suppositories. This article explored and expanded the non-pharmacological model of lacrimal suppository therapy for glaucoma by using a combination of lacrimal suppository and medication. The drug-loaded lacrimal suppository was rationally designed through the conjugation of gelatin with polyamide (PAM) via the formation of amide linkages, followed by Schiff base reaction grafting with latanoprost. In vitro drug release studies showed that latanoprost released from drug-loaded lacrimal embolus had sustained-release properties with a release time of 33 days and a drug release volume of 82.6%. The biological evaluation of drug-loaded lacrimal thrombus was carried out by IOP test, retinal potential test, and retinal H&E staining. The results showed that the IOP decreased to 27.125 ± 1.1254 mmHg, and the a and b waves of retinal potential increased to 4.39 ± 0.16 µV and 67.9 ± 2.17 µV, respectively. It indicated that latanoprost lacrimal suppository has a good therapeutic effect on glaucoma.

Identification of TCR repertoires in asymptomatic COVID-19 patients by single-cell T-cell receptor sequencing.

The T cell-mediated immune responses associated with asymptomatic infection (AS) of SARS-CoV-2 remain largely unknown. The diversity of T-cell receptor (TCR) repertoire is essential for generating effective immunity against viral infections in T cell response. Here, we performed the single-cell TCR sequencing of the PBMC samples from five AS subjects, 33 symptomatic COVID-19 patients and eleven healthy controls to investigate the size and the diversity of TCR repertoire. We subsequently analyzed the TCR repertoire diversity, the V and J gene segment deference, and the dominant combination of αß VJ gene pairing among these three study groups. Notably, we revealed significant TCR preference in the AS group, including the skewed usage of TRAV1-2-J33-TRBV6-4-J2-2 and TRAV1-2-J33-TRBV6-1-J2-3. Our findings may shed new light on understanding the immunopathogenesis of COVID-19 and help identify optimal TCRs for development of novel therapeutic strategies against SARS-CoV-2 infection.

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection.

Identification of Distinct Immune Cell Subsets Associated With Asymptomatic Infection, Disease Severity, and Viral Persistence in COVID-19 Patients.

The cell-mediated protective and pathogenic immune responses to SARS-CoV-2 infection remain largely elusive. Here we identified 76 distinct cell subsets in the PBMC samples that were associated with various clinical presentations of COVID-19 using scRNA-seq technology coupled with a deep and comprehensive analysis of unique cell surface markers and differentially expressed genes. We revealed that (TRAV1-2+CD8+)MAIT cells and (NCAM1hiCD160+)NK cells significantly enriched in the asymptomatic subjects whereas (LAG3+CD160+CD8+)NKT cells increased in the symptomatic patients. We also observed that (CD68-CSF1R-IL1BhiCD14+)classical monocytes were positively correlated with the disease severity. Moreover, (CD33-HLA-DMA-CD14+)classical monocytes and (CLEC10A-S100A9lo)pDC were associated with the viral persistence. The GO and KEGG analyses identified enriched pathways related to immune responses, inflammation, and apoptosis. These findings may enhance our understanding of the immunopathogenesis of COVID-19 and help develop novel strategies against SARS-CoV-2 infection.

Risk factors for COVID-19 progression and mortality in hospitalized patients without pre-existing comorbidities.

BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic continues to escalate intensively worldwide. Massive studies on general populations with SARS-CoV-2 infection have revealed that pre-existing comorbidities were a major risk factor for the poor prognosis of COVID-19. Notably, 49-75% of COVID-19 patients had no comorbidities, but this cohort would also progress to severe COVID-19 or even death. However, risk factors contributing to disease progression and death in patients without chronic comorbidities are largely unknown; thus, specific clinical interventions for those patients are challenging. METHODS: A multicenter, retrospective study based on 4806 COVID-19 patients without chronic comorbidities was performed to identify potential risk factors contributing to COVID-19 progression and death using LASSO and a stepwise logistic regression model. RESULTS: Among 4806 patients without pre-existing comorbidities, the proportions with severe progression and mortality were 34.29% and 2.10%, respectively. The median age was 47.00 years [interquartile range, 36.00-56.00], and 2162 (44.99%) were men. Among 51 clinical parameters on admission, age ≥ 47, oxygen saturation < 95%, increased lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen levels, dyspnea, increased blood glucose and prothrombin time levels were associated with COVID-19 mortality in the entire cohort. Of the 3647 patients diagnosed with non-severe COVID-19 on admission, 489(13.41%) progressed to severe disease. The risk factors associated with COVID-19 progression from non-severe to severe illness were increased procalcitonin levels, SpO2 < 95%, age ≥ 47, increased LDH, activated partial thromboplastin time levels, decreased high-density lipoprotein cholesterol levels, dyspnea and increased D-dimer levels. CONCLUSIONS: COVID-19 patients without pre-existing chronic comorbidities have specific traits and disease patterns. COVID-19 accompanied by severe bacterial infections, as indicated by increased procalcitonin levels, was highly associated with disease progression from non-severe to severe. Aging, impaired respiratory function, coagulation dysfunction, tissue injury, and lipid metabolism dysregulation were also associated with disease progression. Once factors for multi-organ damage were elevated and glucose increased at admission, these findings indicated a higher risk for mortality. This study provides information that helps to predict COVID-19 prognosis specifically in patients without chronic comorbidities.

Newly-Diagnosed Diabetes and Sustained Hyperglycemia are Associated with Poorer Outcomes in COVID-19 Inpatients Without Pre-Existing Diabetes.

PURPOSE: To analyze the impact of hyperglycemia on the clinical outcome of COVID-19 in patients with newly diagnosed diabetes (NDD). PATIENTS AND METHODS: We performed a retrospective study of 3114 cases of COVID-19 without pre-existing diabetes, 351 of which had NDD, in Hubei Province, China. The Cox regression model was used to calculate the risk of adverse clinical outcomes comparing the NDD vs non-NDD group before and after propensity score-matched (PSM) analysis. Patients with NDD were further divided into a sustained hyperglycemia group, a fluctuating group, and a remitted group based on their blood glucose levels during hospitalization as well as into hypoglycemic agent users and nonusers. RESULTS: Compared to the non-NDD individuals, individuals with NDD had a significantly increased risk of all-cause mortality (adjusted HR after PSM, 2.65; 95% CI, 1.49-4.72; P = 0.001) and secondary outcomes involving organ damage during the 28-day follow-up period. Subgroup analyses indicated that among individuals with NDD, the individuals with remitted hyperglycemia had the lowest 28-day mortality, whereas those with sustained hyperglycemia had the highest (IRR 24.27; 95% CI, 3.21-183.36; P < 0.001). Moreover, individuals treated with hypoglycemic agents had significantly lower all-cause mortality than those not treated with hypoglycemic agents (IRR 0.08; 95% CI, 0.01-0.56; P < 0.001). CONCLUSION: Our study reinforces the clinical message that NDD is strongly associated with poor outcomes in COVID-19 patients. Furthermore, resolved hyperglycemia in the later phase of the disease and the use of hypoglycemic agents were associated with improved prognosis in patients with NDD.

A risk score based on baseline risk factors for predicting mortality in COVID-19 patients.

BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. CONCLUSIONS: The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.

Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients.

BACKGROUND: To develop a sensitive risk score predicting the risk of mortality in patients with coronavirus disease 2019 (COVID-19) using complete blood count (CBC). METHODS: We performed a retrospective cohort study from a total of 13,138 inpatients with COVID-19 in Hubei, China, and Milan, Italy. Among them, 9,810 patients with ≥2 CBC records from Hubei were assigned to the training cohort. CBC parameters were analyzed as potential predictors for all-cause mortality and were selected by the generalized linear mixed model (GLMM). FINDINGS: Five risk factors were derived to construct a composite score (PAWNN score) using the Cox regression model, including platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio. The PAWNN score showed good accuracy for predicting mortality in 10-fold cross-validation (AUROCs 0.92-0.93) and subsets with different quartile intervals of follow-up and preexisting diseases. The performance of the score was further validated in 2,949 patients with only 1 CBC record from the Hubei cohort (AUROC 0.97) and 227 patients from the Italian cohort (AUROC 0.80). The latent Markov model (LMM) demonstrated that the PAWNN score has good prediction power for transition probabilities between different latent conditions. CONCLUSIONS: The PAWNN score is a simple and accurate risk assessment tool that can predict the mortality for COVID-19 patients during their entire hospitalization. This tool can assist clinicians in prioritizing medical treatment of COVID-19 patients. FUNDING: This work was supported by National Key R&D Program of China (2016YFF0101504, 2016YFF0101505, 2020YFC2004702, 2020YFC0845500), the Key R&D Program of Guangdong Province (2020B1111330003), and the medical flight plan of Wuhan University (TFJH2018006).

The Neutrophil-to-Lymphocyte Ratio Determines Clinical Efficacy of Corticosteroid Therapy in Patients with COVID-19.

Corticosteroid therapy is now recommended as a treatment in patients with severe COVID-19. But one key question is how to objectively identify severely ill patients who may benefit from such therapy. Here, we assigned 12,862 COVID-19 cases from 21 hospitals in Hubei Province equally to a training and a validation cohort. We found that a neutrophil-to-lymphocyte ratio (NLR) > 6.11 at admission discriminated a higher risk for mortality. Importantly, however, corticosteroid treatment in such individuals was associated with a lower risk of 60-day all-cause mortality. Conversely, in individuals with an NLR ≤ 6.11 or with type 2 diabetes, corticosteroid treatment was not associated with reduced mortality, but rather increased risks of hyperglycemia and infections. These results show that in the studied cohort corticosteroid treatment is associated with beneficial outcomes in a subset of COVID-19 patients who are non-diabetic and with severe symptoms as defined by NLR.

N-acetylserotonin Derivative Exerts a Neuroprotective Effect by Inhibiting the NLRP3 Inflammasome and Activating the PI3K/Akt/Nrf2 Pathway in the Model of Hypoxic-Ischemic Brain Damage.

Neonatal hypoxic-ischemic encephalopathy (HIE) is one of the main causes of neonatal disability and death. As a derivative of N-acetylserotonin, N-[2-(5-hydroxy-1H-indol-3-yl) ethyl]-2-oxopiperidine-3-carboxamide (HIOC) can easily cross the blood-brain barrier and have a long half-life in the brain. In this study, the hypothesis was verified that HIOC plays a neuroprotective role in the HIE model and its potential mechanism was evaluated. Firstly, an HIE rat model was established to deliver HIOC, revealing that it can reduce cerebral infarction volume, cerebral edema, and neuronal apoptosis. The results of immunofluorescence staining, Western blots and RT-PCR further showed that HIOC could inhibit the activation of the NLRP3 inflammasome and the expression of related proteins. Finally, the activation of the phosphatidylinositol-3-kinase (PI3K)/Akt/nuclear factor erythroid 2-related factor 2 (Nrf2) pathway by HIOC was verified in vitro and in vivo. It was discovered that HIOC could increase the nuclear translocation of Nrf2, and that this induction can be reversed by the PI3K/Akt pathway inhibitor LY294002. In general terms, the neuroprotective effect of HIOC was confirmed in the HIE model, which is related to the activation of the Pi3k/Akt/Nrf2 signal pathway and the inhibition of the NLRP3 inflammasome.

Kidney Function Indicators Predict Adverse Outcomes of COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a recently emerged respiratory infectious disease with kidney injury as a part of the clinical complications. However, the dynamic change of kidney function and its association with COVID-19 prognosis are largely unknown. METHODS: In this multicenter retrospective cohort study, we analyzed clinical characteristics, medical history, laboratory tests, and treatment data of 12,413 COVID-19 patients. The patient cohort was stratified according to the severity of the outcome into three groups: non-severe, severe, and death. FINDINGS: The prevalence of elevated blood urea nitrogen (BUN), elevated serum creatinine (Scr), and decreased blood uric acid (BUA) at admission was 6.29%, 5.22%, and 11.66%, respectively. The trajectories showed the elevation in BUN and Scr levels, as well as a reduction in BUA level for 28 days after admission in death cases. Increased all-cause mortality risk was associated with elevated baseline levels of BUN and Scr and decreased levels of BUA. CONCLUSIONS: The dynamic changes of the three kidney function markers were associated with different severity and poor prognosis of COVID-19 patients. BUN showed a close association with and high potential for predicting adverse outcomes in COVID-19 patients for severity stratification and triage. FUNDING: This study was supported by grants from the National Key R&D Program of China (2016YFF0101504), the National Science Foundation of China (81630011, 81970364, 81970070, 81970011, 81870171, and 81700356), the Major Research Plan of the National Natural Science Foundation of China (91639304), the Hubei Science and Technology Support Project (2019BFC582, 2018BEC473, and 2017BEC001), and the Medical Flight Plan of Wuhan University.

Metformin Is Associated with Higher Incidence of Acidosis, but Not Mortality, in Individuals with COVID-19 and Pre-existing Type 2 Diabetes.

The safety and efficacy of anti-diabetic drugs are critical for maximizing the beneficial impacts of well-controlled blood glucose on the prognosis of individuals with COVID-19 and pre-existing type 2 diabetes (T2D). Metformin is the most commonly prescribed first-line medication for T2D, but its impact on the outcomes of individuals with COVID-19 and T2D remains to be clarified. Our current retrospective study in a cohort of 1,213 hospitalized individuals with COVID-19 and pre-existing T2D indicated that metformin use was significantly associated with a higher incidence of acidosis, particularly in cases with severe COVID-19, but not with 28-day COVID-19-related mortality. Furthermore, metformin use was significantly associated with reduced heart failure and inflammation. Our findings provide clinical evidence in support of continuing metformin treatment in individuals with COVID-19 and pre-existing T2D, but acidosis and kidney function should be carefully monitored in individuals with severe COVID-19.

Redefining Cardiac Biomarkers in Predicting Mortality of Inpatients With COVID-19.

The prognostic power of circulating cardiac biomarkers, their utility, and pattern of release in coronavirus disease 2019 (COVID-19) patients have not been clearly defined. In this multicentered retrospective study, we enrolled 3219 patients with diagnosed COVID-19 admitted to 9 hospitals from December 31, 2019 to March 4, 2020, to estimate the associations and prognostic power of circulating cardiac injury markers with the poor outcomes of COVID-19. In the mixed-effects Cox model, after adjusting for age, sex, and comorbidities, the adjusted hazard ratio of 28-day mortality for hs-cTnI (high-sensitivity cardiac troponin I) was 7.12 ([95% CI, 4.60-11.03] P<0.001), (NT-pro)BNP (N-terminal pro-B-type natriuretic peptide or brain natriuretic peptide) was 5.11 ([95% CI, 3.50-7.47] P<0.001), CK (creatine phosphokinase)-MB was 4.86 ([95% CI, 3.33-7.09] P<0.001), MYO (myoglobin) was 4.50 ([95% CI, 3.18-6.36] P<0.001), and CK was 3.56 ([95% CI, 2.53-5.02] P<0.001). The cutoffs of those cardiac biomarkers for effective prognosis of 28-day mortality of COVID-19 were found to be much lower than for regular heart disease at about 19%-50% of the currently recommended thresholds. Patients with elevated cardiac injury markers above the newly established cutoffs were associated with significantly increased risk of COVID-19 death. In conclusion, cardiac biomarker elevations are significantly associated with 28-day death in patients with COVID-19. The prognostic cutoff values of these biomarkers might be much lower than the current reference standards. These findings can assist in better management of COVID-19 patients to improve outcomes. Importantly, the newly established cutoff levels of COVID-19-associated cardiac biomarkers may serve as useful criteria for the future prospective studies and clinical trials.

Silencing of long non-coding RNA CRNDE promotes autophagy and alleviates neonatal hypoxic-ischemic brain damage in rats.

Hypoxic-ischemic (HI) brain damage (HIBD) leads to high neonatal mortality and severe neurologic morbidity. Autophagy is involved in the pathogenesis of HIBD. This study aims to investigate the effect of long non-coding RNA colorectal neoplasia differentially expressed (CRNDE) on HIBD and to validate whether autophagy is involved in this process. A HIBD model in rat pups and a HI model in rat primary cerebrocortical neurons were established. Autophagy was evaluated by western blot. The HIBD in rats was evaluated by hematoxylin and eosin staining, TUNEL staining, triphenyl tetrazolium chloride staining, and morris water maze test. The HI injury in vitro was evaluated by determining cell viability and apoptosis. The results showed that CRNDE expression was time-dependently increased in the brain after HIBD. Administration with CRNDE shRNA-expressing lentiviruses alleviated pathological injury and apoptosis in rat hippocampus, decreased infarct volume, and improved behavior performance of rats subjected to HIBD. Furthermore, CRNDE silencing promoted cell viability and inhibited cell apoptosis in neurons exposed to HI. Moreover, CRNDE silencing promoted autophagy and the autophagy inhibitor 3-methyladenine counteracted the neuroprotective effect of CRNDE silencing on HI-induced neuronal injury both in vivo and in vitro. Collectively, CRNDE silencing alleviates HIBD, at least partially, through promoting autophagy.