Neuroprognostication value of serum neurofilament light chain for out-of-hospital cardiac arrest: A systematic review and meta-analysis.

BACKGROUND: Neurofilament light chain (NfL) is a novel biomarker for the assessment of neurological function after cardiac arrest (CA). Although meta-analysis has confirmed its predictive value, it has not conducted a more detailed analysis of its research. We conducted a meta-analysis to evaluate the relationship between serum NfL level and neurological prognosis in patients with spontaneous circulation recovery after CA, and subgroup analysis was conducted according to sample collection time, time to assess neurological function, study design, whether TTM was received, the method of specimen determination, and the presence of neurological disease in patients. To analyze the influence of these factors on the predictive value of serum NfL. METHODS: Published Cochrane reviews and an updated, extended search of MEDLINE, Cochrane Library, Embase, Scopus, ClinicalKey, CINAHL, and Web of Science for relevant studies until March 2022 were assessed through inclusion and exclusion criteria. The standard mean difference and 95% confidence interval were calculated using the random-effects model or fixed-effects model to assess the association between one variable factor NfL level and the outcome of CA patients. Subgroup analysis according to sample collection time was performed. The prognosis analysis and publication bias were also assessed using Egger's and Begg's tests. RESULTS: Among 1209 related articles for screening, 6 studies (1360 patients) met the inclusion criteria and were selected for meta-analysis. The level of serum NfL in the good prognosis group (CPC1-2, CPC: cerebral performance category score) was significantly lower than that in the poor prognosis group (CPC3-5)SMD(standardized mean difference) = 0.553, 95%CI(confidence interval) = 0.418-0.687, I2 = 65.5% P<0.05). And this relationship also exists at each sampling time point (NfL specimens were collected on admission: SMD:0.48,95%CI:0.24-0.73; Samples were collected 24 hours after CA: SMD:0.60,95%CI:0.32-0.88;Specimens were obtained 48 hours after CA: SMD:0.51, 95%CI:0.18-0.85;Specimens were obtained 72 hours after CA: SMD:0.59, 95%CI:0.38-0.81). CONCLUSION: NfL may play a potential neuroprognostication role in postcardiac arrest patients with spontaneous circulation, regardless of when the sample was collected after CA.

Risk factors and prognosis of early neurological deterioration in patients with posterior circulation cerebral infarction.

BACKGROUND: The incidence, risk factors, and pathogenesis of early neurological deterioration (END) in posterior circulation stroke are still unclear. In this study, we aimed to determine the risk factors and prognosis of END in patients with acute posterior circulation cerebral infarction. METHODS: Acute posterior circulation ischemic stroke patients who had completed neuroimaging within 72 h of onset were selected from a prospective registry study Demographic characteristics, physiological data, medical history, laboratory data, in-hospital evaluation, neurological severity and TOAST classification, treatment, and the modified Rankin Scale (mRS) score of patients were assessed. Early neurological deterioration was defined as an increase of 2 points in the National Institutes of Health Stroke Scale score between the baseline and 72 h evaluation. Favorable and poor outcomes were defined as mRSs of 02 and≥ 3, respectively, at 3 months. The incidence and risk factors were evaluated by univariate and multivariate regression analysis (step-back method). RESULTS: The analysis included 455 subjects with an acute posterior circulation non-cardiac ischemic stroke, 330 (72.53 %) of them male, with an average age of 63.12 ( ± 10.14) years and with 47 (10.33 %) having END. The results of univariate and multivariate logistic regression analysis showed that BATMAN scores ≥ 5 (OR: 0.1, 95 % CI: 0.02-0.53, P < 0.01), large artery atherosclerosis (OR: 11.55, 95 % CI: 4.18-31.93, P < 0.01), vascular stenosis > 50 % (OR: 2.44, 95 % CI: 1.1-5.42, P = 0.029), reperfusion therapy (OR: 4.21, 95 % CI: 1.66-10.64, P < 0.01), and the distribution of pontine lesions (OR: 5.66, 95 % CI: 2.39-13.44, P < 0.01) were significantly associated with END. Patients with END had a lower rate of favorable outcomes at discharge and long-term follow-up (P < 0.001), regardless of whether they received reperfusion therapy. CONCLUSION: The lesion distribution of the pons, the progression of temporo-occipital lobe lesions, and large arterial atherosclerosis are independent risk factors of END that might predict a poor short- and long-term prognosis.

A network pharmacology approach to reveal the pharmacological targets and biological mechanism of compound kushen injection for treating pancreatic cancer based on WGCNA and in vitro experiment validation.

BACKGROUND: Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated. METHODS: Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis. RESULTS: The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis. CONCLUSIONS: Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI.