Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage.

BACKGROUND: Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH. METHODS: We employed single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of OS across various cellular tiers following ICH, aiming to acquire biological insights into ICH. We utilized AUCell, Ucell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, to identify hub genes influencing high OS post-ICH. Furthermore, we employed four machine learning algorithms, eXtreme Gradient Boosting, Boruta, Random Forest, and Least Absolute Shrinkage and Selection Operator, to identify the optimal feature genes. To validate the accuracy of our analysis, we conducted validation in ICH animal experiments. RESULTS: After analyzing the scRNA-seq dataset using various algorithms, we found that OS activity exhibited heterogeneity across different cellular layers following ICH, with particularly heightened activity observed in monocytes. Further integration of bulk data and machine learning algorithms revealed that ANXA2 and COTL1 were closely associated with high OS after ICH. Our animal experiments demonstrated an increase in OS expression post-ICH. Additionally, the protein expression of ANXA2 and COTL1 was significantly elevated and co-localized with microglia. Pearson correlation coefficient analysis revealed a significant correlation between ANXA2 and OS, indicating strong consistency (r = 0.84, p < 0.05). Similar results were observed for COTL1 and OS (r = 0.69, p < 0.05). CONCLUSIONS: Following ICH, ANXA2 and COTL1 might penetrate the brain via monocytes, localize within microglia, and enhance OS activity. This might help us better understand OS after ICH.

The relationship between sleeptime and depression among middle-aged and elderly Chinese participant during COVID-19 epidemic and non-epidemic phases.

Background: The global impact of the COVID-19 pandemic had significantly altered the daily routines of people worldwide. This study aimed to compare how sleeptime and depression among Chinese residents had differed between periods during and outside the epidemic. Furthermore, it delved into the interactive effect of age in this relationship. Method: Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) study in 2015 and the recently released data from 2020, which covered the pandemic period. Depression was assessed using Center for Epidemiologic Studies Depression Scale (CESD-10), considering a score of 10 or higher as indicative of depression. Participants were categorized based on age, specifically those aged 60 years and older. multivariate logistic regression and interaction analyses were employed to assess the interplay of age, supported by subgroup and sensitivity analyses to reinforce our findings. Results: The 2020 database comprised 19,331 participants, while the 2015 database had 10,507 participants. Our findings demonstrated a significant correlation between sleeptime and depression in both unadjusted models and models adjusted for all variables in both datasets (p<0.001). Upon stratifying by age and adjusting for relevant factors, we identified an interaction effect among age, sleeptime, and depression (p=0.004 for the interaction in the 2020 database, compared to 0.004 in 2015). The restricted cubic spline analysis in both datasets showcased a nonlinear relationship between sleeptime and depression. Conclusions: During both epidemic and non-epidemic periods in China, there existed a correlation between sleep duration and depression, which interacts with age.

A Preoperative Nomogram for Prediction of Postoperative Hypocortisolism in Patients with Pituitary Adenomas: A Single-Center Retrospective Cohort Study.

BACKGROUND: Patients with pituitary adenomas (PAs) are at an increased risk preoperatively and postoperatively for hypopituitarism. Postoperative hypocortisolism is associated with increased mortality and morbidity as well as poor quality of life. However, research about the risk factors for postoperative hypocortisolism is limited, and a predictive nomogram for postoperative hypocortisolism has not yet been developed. We aimed to investigate the predictive factors for postoperative hypocortisolism and construct a dynamic online nomogram. METHODS: Our database included 438 consecutive PA patients who were hospitalized and treated with transsphenoidal surgery by experienced neurosurgeons from the different medical teams in the Neurosurgery Department, Jinling Hospital, between January 2018 and October 2020. The final study group included 238 eligible patients. Data on possible predictors, including age, sex, treatment history of PAs, preoperative signs and symptoms, primary recurrence subtype, and clinical subtypes, were collected. Univariable and multivariable logistic regression analyses were applied to identify independent predictors, which were included in constructing the nomogram model. The calibration curve and receiver operating characteristic curve were computed to evaluate the predictive performance of the nomogram model. RESULTS: The incidence of postoperative hypocortisolism was 12.08%. Three preoperative predictors were identified to construct the nomogram: surgical type (microscopic or endoscopic, with endoscopic surgery proven to be the protective factor) (odds ratio, 0.24; 95% confidence interval [CI], 0.093-0.610; P = 0.003), prothrombin time (odds ratio, 2.40; 95% CI, 1.332-4.326; P = 0.004), and basophil cell count (odds ratio, 5.25; 95% CI, 1.270-21.816; P = 0.022,). The area under the curve of receiver operating characteristic curve for the constructed nomogram was 0.749 (95% CI, 0.640-0.763); a well-fixed calibration curve was generated for the nomogram model. An interactive web-based dynamic nomogram application was also constructed. CONCLUSIONS: In this study, surgical type, prothrombin time, and basophil cell count were the most relevant predictive factors for postoperative hypocortisolism. A predictive nomogram that can preoperatively assess the risk of hypocortisolism after surgical treatment of PAs was developed. This nomogram could be helpful in identifying high-risk patients who require close monitoring of serum cortisol levels and initiating clinical procedures for patients requiring cortisol administration therapy as a lifesaving strategy.

Advances in Optogenetics Applications for Central Nervous System Injuries.

Injuries to the central nervous system (CNS) often lead to severe neurological dysfunction and even death. However, there are still no effective measures to improve functional recovery following CNS injuries. Optogenetics, an ideal method to modulate neural activity, has shown various advantages in controlling neural circuits, promoting neural remapping, and improving cell survival. In particular, the emerging technique of optogenetics has exhibited promising therapeutic methods for CNS injuries. In this review, we introduce the light-sensitive proteins and light stimulation system that are important components of optogenetic technology in detail and summarize the development trends. In addition, we construct a comprehensive picture of the current application of optogenetics in CNS injuries and highlight recent advances for the treatment and functional recovery of neurological deficits. Finally, we discuss the therapeutic challenges and prospective uses of optogenetics therapy by photostimulation/photoinhibition modalities that would be suitable for clinical applications.