ABSTRACT
Background: Post-stroke depression (PSD) is a well-established psychiatric complication following stroke. Nevertheless, the relationship between early-onset PSD and homocysteine (Hcy) or fibrinogen remains uncertain. Methods: Acute ischemic stroke (AIS) patients who met the established criteria were enrolled in this study. Early-onset PSD was diagnosed two weeks after the stroke. The severity of depressive symptoms was assessed by the Hamilton Depression Scale-17 items (HAMD-17), with patients scored ≥7 assigned to the early-onset PSD group. Spearman rank correlation analysis was employed to evaluate the associations between Hcy, fibrinogen, and HAMD scores across all patients. Logistic regression analysis was conducted to investigate the relationship between Hcy, fibrinogen, and early-onset PSD. Receiver operating characteristic curve (ROC) analysis was ASSDalso performed to detect the predictive ability of Hcy and fibrinogen for early-onset PSD. Results: Among the 380 recruited patients, a total of 106 (27.89%) patients were diagnosed with early-onset PSD. The univariate analysis suggested that patients in the PSD group had a higher admission National Institutes of Health Stroke Scale (NIHSS) score, modified Rankin Scale score (mRS), Hcy, and fibrinogen levels than patients in the non-PSD group (P<0.05). The logistic regression model indicated that Hcy (odds ratio [OR], 1.344; 95% confidence interval [CI] 1.209-1.494, P<0.001) and fibrinogen (OR, 1.57 6; 95% CI 1.302-1.985, P<0.001) were independently related to early-onset PSD. Area under curve (AUC) of Hcy, fibrinogen, and Hcy combined fibrinogen to predict early-onset PSD was 0.754, 0.698, and 0.803, respectively. Conclusion: This study indicates that Hcy and fibrinogen may be independent risk factors for early-onset PSD and can be used as predictive indicators for early-onset PSD.
ABSTRACT
Background: Inflammation and platelet activation play pivotal roles in acute ischemic stroke (AIS) pathogenesis. Early response to thrombolysis is a vital indicator for the long-term prognosis of AIS. However, the correlation between fibrinogen or the neutrophil-to-lymphocyte ratio (NLR) and the early response to intravenous thrombolysis in patients with AIS remains unclear. Methods: AIS patients undergoing intravenous thrombolysis were enrolled between January 2018 and May 2023. Blood cell counts were sampled before thrombolysis. A good response was defined as a National Institutes of Health Stroke Scale (NIHSS) score decreased ≥4 or complete recovery 24 h after thrombolysis treatment. A poor response was defined as any increase in the NIHSS score or a decrease in the NIHSS score <4 at the 24 h after thrombolysis treatment compared with that at admission. Logistic regression analysis was performed to explore the relationship of the fibrinogen level and NLR with a poor thrombolysis response. Receiver operating characteristic (ROC) analysis was used to assess the ability of the fibrinogen level and NLR to discriminate poor responders. Results: Among 700 recruited patients, 268 (38.29%) were diagnosed with a good response, and 432 (61.71%) were diagnosed with a poor response to intravenous thrombolysis. A binary logistic regression model indicated that an elevated fibrinogen level (odds ratio [OR], 1.693; 95% confidence interval [CI] 1.325-2.122, P < 0.001) and NLR (OR, 1.253; 95% CI, 1.210-2.005, P = 0.001) were independent factors for a poor response. The area under the curve (AUC) values for the fibrinogen level, NLR and fibrinogen level combined with the NLR for a poor response were 0.708, 0.605, and 0.728, respectively. Conclusions: Our research indicates that the levels of fibrinogen and NLR at admission can be used as a prognostic factor to predict early poor response to intravenous thrombolysis.
ABSTRACT
Cancer stem cells (CSCs) play an important role in cancer growth, self-renewal, metastasis, recurrence and radio/chemotherapy. However, the underlying mechanisms remain elusive. In this review, we explore the roles of CSCs in cancer's relapse and progression and discuss the biomarkers of CSCs to predict clinical outcome and their diagnostic potential. The different approaches of CSC therapies are also reviewed, including cytotoxic, radiation, differentiation and targeting signaling pathways. We also discuss the challenge of targeting CSCs in cancer therapy. In addition, non-coding RNAs in CSC therapies are also discussed.
