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.

Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm.

OBJECTIVE: The significance of noncontrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorrhage (ICH) within different time intervals in the initial 24 hours after onset may be uncertain. Hence, our objective was to examine the predictive value of clinical factors and CT image markers for HE within the initial 24 hours using machine learning algorithms. METHODS: Four machine learning algorithms, including extreme gradient boosting (XGBoost), support vector machine, random forest, and logistic regression, were employed to assess the predictive efficacy of HE within every 6-hour interval during the first 24 hours post-ICH. The area under the receiver operating characteristic curves was utilized to appraise predictive performance across various time periods within the initial 24 hours. RESULTS: A total of 604 patients were included, with 326 being male, and 112 experiencing hematoma expansion (HE). The findings from machine learning algorithms revealed that computed tomography (CT) image markers, baseline hematoma volume, and other factors could accurately predict HE. Among these algorithms, XGBoost demonstrated the most robust predictive model results. XGBoost's accuracy at different time intervals was 0.89, 0.82, 0.87, and 0.94, accompanied by F1-scores of 0.89, 0.80, 0.87, and 0.93, respectively. The corresponding area under the curve was 0.96, affirming the precision of the predictive capability. CONCLUSIONS: Computed tomography (CT) imaging markers and clinical factors could effectively predict HE within the initial 24 hours across various time periods by machine learning algorithms. In the expansive landscape of big data and multimodal cerebral hemorrhage, machine learning held significant potential within the realm of neuroscience.

Comparison of the Endoscopic Endonasal Approach with the Endoscopic Supraorbital Keyhole Approach to the Tuberculum Sellae Region: A Quantitatively Cadaveric Study.

BACKGROUND: The endoscopic endonasal approach (EEA) and the endoscopic supraorbital keyhole approach (eSKA) provide minimally invasive access to tuberculum sellae (TS) tumors. Evaluation of the operating maneuverability is helpful for approach selection. Herein, we compared the two approaches and aimed to provide quantitative anatomic data for surgical decision-making in the management of TS lesions. METHODS: Fifteen dissections were performed on five silicone-injected cadaveric heads. The EEA and eSKA (both right and left) were performed on each head. Surgical freedom and working angles in the axial and sagittal planes were calculated using the stereotactic navigation system in the selected six targets: the midpoint of the leading edge of the sphenoid sinus (leSS), the midpoint of the edge of the dorsum sellae (eDS), the ipsilateral medial opticocarotid recess (imOCR), the contralateral medial opticocarotid recess (cmOCR), the ipsilateral lateral opticocarotid recess (ilOCR), and the contralateral lateral opticocarotid recess (clOCR). RESULTS: The surgical freedom at the ilOCR and the axial working angles at the leSS, ilOCR, and imOCR (imOCR with excessive manipulation of the optic apparatus) were greater in the eSKA. The EEA provided greater surgical freedom and/or working angles at most targets than eSKA (the surgical freedom at the imOCR, cmOCR, clOCR, and eDS; the axial working angles at the cmOCR and clOCR; and the sagittal working angles at the leSS, imOCR, cmOCR, clOCR, and eDS). CONCLUSION: The EEA provides greater surgical freedom and working angles for paramedian lesions, whereas the eSKA provides better surgical maneuverability for lesions with lateral extension.

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.

Comprehensive analysis of m6A subtype classification for immune microenvironment of pituitary adenomas.

BACKGROUND: N6-methyladenosine (m6A) RNA methylation and tumor immune microenvironment (IME) have an essential role in tumor development. However, their relationships in pituitary adenomas (PAs) remains unclear. METHODS: PA datasets from the Gene Expression Omnibus (GEO) and European Bioinformatics Institute (EMBL-EBI) were used. We utilized hierarchical clustering algorithms based on the m6A regulator gene set to identify m6A subtypes. ESTIMATE and CIBERSORT algorithms were applied to explore the compositions of stromal and immune cells. A nomogram model was constructed for the prediction of m6A subtypes in PAs. Immunohistochemistry and multiplex immunofluorescence staining were used to analyze the expression level of m6A regulator YTHDF2 in relation to M2 macrophages and immune checkpoints in PAs. RESULTS: We concluded the IME landscape of m6A subtype classification and characterized two emerging m6A subtypes. Different IME between these two m6A subtypes were identified. Simultaneously, a polygenic nomogram model was constructed for predicting m6A subtype classification, with excellent predictive performance (training set, AUC = 0.984; validation set, AUC = 0.986). YTHDF2 was highly expressed in PAs and accompanied by upregulated M2 macrophages and expression of PD-L1. CONCLUSIONS: We proposed two novel m6A subtypes in PAs for the first time and constructed a reliable and clinically accessible nomogram model for them. Meanwhile, YTHDF2 was first identified as a promising biomarker for immunotherapy and potential molecular target in PAs.

Predictors and dynamic online nomogram for postoperative delayed hyponatremia after endoscopic transsphenoidal surgery for pituitary adenomas: a single-center, retrospective, observational cohort study with external validation.

BACKGROUND: Postoperative delayed hyponatremia (PDH) is a major cause of readmission after endoscopic transsphenoidal surgery (eTSS) for pituitary adenomas (PAs). However, the risk factors associated with PDH have not been well established, and the development of a dynamic online nomogram for predicting PDH is yet to be realized. We aimed to investigate the predictive factors for PDH and construct a dynamic online nomogram to aid in its prediction. METHODS: We analyzed the data of 226 consecutive patients who underwent eTSS for PAs at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020. An additional 97 external patients were included for external validation. PDH was defined as a serum sodium level below 137 mmol/L, occurring on the third postoperative day (POD) or later. RESULTS: Hyponatremia on POD 1-2 (OR = 2.64, P = 0.033), prothrombin time (PT) (OR = 1.78, P = 0.008), and percentage of monocytes (OR = 1.22, P = 0.047) were identified as predictive factors for PDH via multivariable logistic regression analysis. Based on these predictors, a nomogram was constructed with great discrimination in internal validation (adjusted AUC: 0.613-0.688) and external validation (AUC: 0.594-0.617). Furthermore, the nomogram demonstrated good performance in calibration plot, Brier Score, and decision curve analysis. Subgroup analysis revealed robust predictive performance in patients with various clinical subtypes and mild to moderate PDH. CONCLUSIONS: Preoperative PT and the percentage of monocytes were, for the first time, identified as predictive factors for PDH. The dynamic nomogram proved to be a valuable tool for predicting PDH after eTSS for PAs and demonstrated good generalizability. Patients could benefit from early identification of PDH and optimized treatment decisions.

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.

N6-methyladenosine-related microRNAs risk model trumps the isocitrate dehydrogenase mutation status as a predictive biomarker for the prognosis and immunotherapy in lower grade gliomas.

Aim: Lower grade gliomas [LGGs; World Health Organization (WHO) grades 2 and 3], owing to the heterogeneity of their clinical behavior, present a therapeutic challenge to neurosurgeons. The aim of this study was to explore the N6-methyladenosine (m6A) modification landscape in the LGGs and to develop an m6A-related microRNA (miRNA) risk model to provide new perspectives for the treatment and prognostic assessment of LGGs. Methods: Messenger RNA (mRNA) and miRNA expression data of LGGs were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. An m6A-related miRNA risk model was constructed via least absolute shrinkage and selection operator (LASSO), univariate, and multivariate Cox regression analysis. Next, Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment analysis, immune infiltrate analysis, dynamic nomogram, and drug sensitivity prediction were used to evaluate this risk model. Results: Firstly, six m6A-related miRNAs with independent prognostic value were selected based on clinical information and used to construct a risk model. Subsequently, compared with low-risk group, LGGs in the high-risk group had a higher m6A writer and reader scores, but a lower eraser score. Moreover, LGGs in the high-risk group had a significantly worse clinical prognosis than those in the low-risk group. Simultaneously, this risk model outperformed other clinicopathological variables in the prognosis prediction of LGGs. Immune infiltrate analysis revealed that the proportion of M2 macrophages, regulatory T (Treg) cells, and the expression levels of exhausted immune response markers were significantly higher in the high-risk group than in the low-risk group. Finally, this study constructed an easy-to-use and free dynamic nomogram to help clinicians use this risk model to aid in diagnosis and prognosis assessment. Conclusions: This study developed a m6A-related risk model and uncovered two different m6A modification landscapes in LGGs. Moreover, this risk model may provide guidance and help in clinical prognosis assessment and immunotherapy response prediction for LGGs.

Roles of the m6A Modification of RNA in the Glioblastoma Microenvironment as Revealed by Single-Cell Analyses.

Purpose: Glioblastoma multiforme (GBM) is a common and aggressive form of brain tumor. The N6-methyladenosine (m6A) mRNA modification plays multiple roles in many biological processes and disease states. However, the relationship between m6A modifications and the tumor microenvironment in GBM remains unclear, especially at the single-cell level. Experimental Design: Single-cell and bulk RNA-sequencing data were acquired from the GEO and TCGA databases, respectively. We used bioinformatics and statistical tools to analyze associations between m6A regulators and multiple factors. Results: HNRNPA2B1 and HNRNPC were extensively expressed in the GBM microenvironment. m6A regulators promoted the stemness state in GBM cancer cells. Immune-related BP terms were enriched in modules of m6A-related genes. Cell communication analysis identified genes in the GALECTIN signaling network in GBM samples, and expression of these genes (LGALS9, CD44, CD45, and HAVCR2) correlated with that of m6A regulators. Validation experiments revealed that MDK in MK signaling network promoted migration and immunosuppressive polarization of macrophage. Expression of m6A regulators correlated with ICPs in GBM cancer cells, M2 macrophages and T/NK cells. Bulk RNA-seq analysis identified two expression patterns (low m6A/high ICP and high m6A/low ICP) with different predicted immune infiltration and responses to ICP inhibitors. A predictive nomogram model to distinguish these 2 clusters was constructed and validated with excellent performance. Conclusion: At the single-cell level, m6A modification facilitates the stemness state in GBM cancer cells and promotes an immunosuppressive microenvironment through ICPs and the GALECTIN signaling pathway network. And we also identified two m6A-ICP expression patterns. These findings could lead to novel treatment strategies for GBM patients.

A prognostic scoring system for operated acute epidural hematoma based on gray-white matter ratio.

ABSTRACT: To determine the prognostic risk factors of patients with acute epidural hematoma (AEDH), a scoring system was established based on gray-white matter ratio (GWR) and internal verification was performed.All patients with AEDH who underwent surgical treatment in Qinghai Provincial People's Hospital from January 2013 to June 2019 were continuously collected. The clinical and imaging data of the patients were collected. According to Glasgow Outcome Scale at 3 months after operation, the patients were divided into poor and good prognosis groups, respectively. The GWR value of the nonhematoma side was measured at the inner capsule area. Univariate and multivariate analyses were used. Independent predictors significantly related to the prognosis of AEDH were screened out and a nomogram was established based on these factors.A total of 170 cases were included in this study, the Glasgow Coma Score (severe and moderate), cerebral hernia, midline shift, preoperative GWR, postoperative GWR, hematoma thickness/midline shift, time from coma to surgery, and decompression of bone flap were the independent risk factors for predicting the poor prognosis of AEDH. Moreover, the prediction ability of nomogram was higher than any other independent predictive factors.The nomogram model established represents the most effective factor to predict the prognosis of operated AEDH. The scoring system is characterized by high accuracy, simplicity and feasibility, with a wide range of clinical application prospects.

Ambient fine particulate matter of diameter ≤ 2.5 µm and risk of hemorrhagic stroke: a systemic review and meta-analysis of cohort studies.

Ambient fine particulate matter of 2.5 µm or less in diameter (PM2.5) of environment contamination is deemed as a risk factor of cerebrovascular diseases. Yet there is still no explicit evidence strongly supporting that PM2.5 with per unit increment can increase the risk of hemorrhagic stroke (HS). Literatures were searched from PubMed, Cochrane, and Embase. After the systemic review of relevant studies, random effects model was used to perform meta-analysis and to evaluate the association between PM2.5 and risk of HS. Seven cohort studies were finally included, involving more than 6 million people and 37,667 endpoint events (incidence or mortality of HS). Total scores of quality assessment were 50. Pooled hazard ratio (HR) for crude HRs was 1.13 (95%CI: 1.09-1.17) (CI for confidence interval). Pooled HR of subgroup analysis for current smoking with exposure to growing PM2.5 was 1.14 (95%CI: 0.92-2.15) and for never and former smoking was 1.04 (95%CI: 0.74-1.46). Ambient PM2.5 level is significantly associated with the risk of HS, which might be a potential risk factor of HS. Smoking does not further increase the risk of HS under exposure of PM2.5.

Prediction of Poor Outcome in Intracerebral Hemorrhage Based on Computed Tomography Markers.

INTRODUCTION: Intracerebral hemorrhage (ICH) is the most fatal type of stroke worldwide. Herein, we aim to develop a predictive model based on computed tomography (CT) markers in an ICH cohort and validate it in another cohort. METHODS: This retrospective observational cohort study was conducted in 3 medical centers in China. The values of CT markers, including hypodensities, hematoma density, blend sign, black hole sign, island sign, midline shift, baseline hematoma volume, and satellite sign, in predicting poor outcome were analyzed by logistic regression analysis. A nomogram was developed based on the results of multivariate logistic regression analysis in development cohort. Area under curve (AUC) and calibration plot were used to assess the accuracy of nomogram in this development cohort and validate in another cohort. RESULTS: A total of 1,498 patients were included in this study. Multivariate logistic regression analysis indicated that hypodensities, black hole sign, island sign, midline shift, and baseline hematoma volume were independently associated with poor outcome in development cohort. The AUC was 0.75 (95% confidence interval [CI]: 0.73-0.76) in the internal validation with development cohort and 0.74 (95% CI: 0.72-0.75) in the external validation with validation cohort. The calibration plot in development and validation cohort indicated that the nomogram was well calibrated. CONCLUSIONS: CT markers of hypodensities, black hole sign, and island sign might predict poor outcome of ICH patients within 90 days.

The effect of tranexamic acid in patients with TBI: a systematic review and meta-analysis of randomized controlled trials.

To conduct a systematic review and meta-analysis and evaluate the effect of tranexamic acid in patients with traumatic brain injury. PubMed, EMBASE, and CENTRAL (Cochrane Central Register of Controlled Trials) were searched to identify randomized controlled trials and evaluate the effect of tranexamic acid in traumatic brain injury patients. The primary outcome was mortality. Two reviewers extracted the data independently. The random effect meta-analysis was used to estimate the aggregate effect size of 95% confidence intervals. Six randomized controlled trials investigating tranexamic acid versus placebo and 30073 patients were included. Compared with placebo, tranexamic acid decreased the mortality (RR = 0.92; 95% CI, 0.87-0.96; p < 0.001) and growth of hemorrhagic mass (RR = 0.78; 95% CI, 0.61-0.99; p = 0.04). However, tranexamic acid could not decrease disability or independent, neurosurgery, vascular embolism, and stroke. Current evidence suggested that compared with placebo, tranexamic acid could reduce mortality and growth of hemorrhagic mass. This finding indicated that patients with traumatic brain injury should be treated with tranexamic acid.

A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients.

OBJECTIVE: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH). METHODS: The clinical and CT image data of the patients with ICH in Qinghai Provincial People's Hospital and Xuzhou Central Hospital were collected. The risk factors related to the poor outcome of the patients were determined by univariate and multivariate logistic regression analysis. To determine the effect of factors related to poor outcome, the nomogram model was made by software of R 3.5.2 and the support vector machine operation was completed by software of SPSS Modelor. RESULTS: A total of 8265 patients were collected and 1186 patients met the criteria of the study. Age, hospitalization days, blend sign, intraventricular extension, subarachnoid hemorrhage, midline shift, diabetes and baseline hematoma volume were independent predictors of poor outcome. Among these factors, baseline hematoma volume๥20ml (odds ratio:13.706, 95% confidence interval:9.070-20.709, p < 0.001) was the most significant factor for poor outcome, followed by the volume among 10ml-20ml (odds ratio:11.834, 95% confidence interval:7.909-17.707, p < 0.001). It was concluded that the highest percentage of weight in outcome was baseline hematoma volume (25.0%), followed by intraventricular hemorrhage (23.0%). CONCLUSION: This predictive model might accurately predict the outcome of patients with ICH. It might have a wide range of application prospects in clinical.

Nomogram Model for Predicting Hematoma Expansion in Spontaneous Intracerebral Hemorrhage: Multicenter Retrospective Study.

OBJECTIVE: To establish a new nomogram model and provide a new theoretical basis for the diagnosis and treatment of spontaneous intracerebral hemorrhage. METHODS: The clinical data and noncontrast computed tomography images of patients with spontaneous intracerebral hemorrhage in 3 tertiary medical centers were collected continuously. Univariate and binary logistic regression analysis were performed to screen out the independent predictors that were significantly associated with hematoma expansion. The nomogram model was drawn by R programming language. According to the related risk factors of nomogram, decision curve analysis and clinical impact curve were established. RESULTS: The numbers of the 3 cooperative units were 554, 582, and 202, respectively. Island sign, blend sign, swirl sign, intraventricular hemorrhage, history of diabetes, time to baseline computed tomography scan, and baseline hematoma volume were independent predictors of hematoma expansion. Baseline hematoma volume >20 mL (odds ratio, 4.088; 95% confidence interval, 2.802-5.964; P < 0.0001) was the most dangerous factor for predicting hematoma expansion, followed by the time to baseline computed tomography scan ≤1 hour (odds ratio, 4.188; 95% confidence interval, 2.598-6.750; P < 0.0001). Decision curve analysis showed that the net benefit of patients was the highest when nomogram score existed. When the threshold probability was >40%, the prediction probability of hematoma expansion was close to the actual probability. CONCLUSIONS: This nomogram model could accurately predict hematoma expansion of spontaneous intracerebral hemorrhage, which provided a theoretical basis for clinicians to intervene in the early stage.

Establishment and Verification of the Hematoma Expansion Prediction Score of Intracerebral Hemorrhage in the Qinghai-Tibetan Plateau.

OBJECTIVE: The latest World Health Organization data showed that stroke was the highest mortality in China, accounting for 23.7% of the total mortality from 2000 to 2012. Intracerebral hemorrhage (ICH) was the most deadly and incurable type of stroke. In the Qinghai-Tibetan Plateau, the incidence of stroke was relatively higher. Several studies showed that the shape and heterogeneity of hematoma and image markers on brain computed tomography scan had predictive effects on hematoma expansion (HE). The study aimed to find relative factors and established a nomogram model to predict the HE of ICH. METHODS: All patients with ICH in Qinghai Provincial People's Hospital from January 1, 2012, to May 22, 2018, were continuously collected. A total of 402 patients were included in the study. This was a single-center retrospective study. Univariate and binary logistic regression analysis were performed to screen out the independent predictors that were significantly associated with HE. RESULTS: The total incidence of HE in ICH was 30.9%, whereas the incidence of HE in the basal ganglia and nonbasal ganglia was 36.4% and 17.2%, respectively. Diabetes, basal ganglia hemorrhage, time of onset to baseline computed tomography, island sign, blend sign, black hole sign, and swirl sign were independent predictors of HE. Based on these predictors, a nomogram model was established and the accuracy was 81.6%, the sensitivity was 91.1%, and the specificity was 70.5%. CONCLUSIONS: This model had a high accuracy of predicting HE in the Qinghai-Tibetan Plateau. Because this model is noninvasive, rapid, and low cost, it is easy to promote and has wide application prospects in clinical practice.