BTG2 acts as an inducer of muscle stem cell senescence.

BACKGROUND: Muscle aging is associated with muscle stem cell (MuSC) senescence, a process of whose DNA damage accumulation is considered as one of the leading causes. BTG2 had been identified as a mediator of genotoxic and cellular stress signaling pathways, however, its role in senescence of stem cells, including MuSC, remains unknown. METHOD: We first compared MuSCs isolated from young and old mice to evaluate our in vitro model of natural senescence. CCK8 and EdU assays were utilized to assess the proliferation capacity of the MuSCs. Cellular senescence was further assessed at biochemical levels by SA-ß-Gal and γHA2.X staining, and at molecular levels by quantifying the expression of senescence-associated genes. Next, by performing genetic analysis, we identified Btg2 as a potential regulator of MuSC senescence, which was experimentally validated by Btg2 overexpression and knockdown in primary MuSCs. Lastly, we extended our research to humans by analyzing the potential links between BTG2 and muscle function decline in aging. RESULTS: BTG2 is highly expressed in MuSCs from elder mice showing senescent phenotypes. Overexpression and knockdown of Btg2 stimulates and prevents MuSCs senescence, respectively. In humans, high level of BTG2 is associated with low muscle mass in aging, and is a risk factor of aging-related diseases, such as diabetic retinopathy and HDL cholesterol. CONCLUSION: Our work demonstrates BTG2 as a regulator of MuSC senescence and may serve as an intervention target for muscle aging.

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.

Endocrine Outcomes and Associated Predictive Factors for Somatotrophin Pituitary Adenoma after Endoscopic Endonasal Transsphenoidal Surgery: 10 Years of Experience in a Single Institute.

Objective Biochemical remission rates of endoscopic endonasal transsphenoidal surgery (EETS) and its associated predictive factors were evaluated in patients with somatotrophin pituitary adenomas. Methods The patients who underwent EETS in Jinling Hospital were identified between 2011 and 2020. The surgeons' experience, preoperative insulin-like growth factor 1 (IGF-1), basal growth hormone (GH) levels, nadir GH levels, and the tumor characteristics were analyzed for their relationships with endocrine outcomes. Total 98 patients were included for single factor analysis and regression analysis. They were divided into three groups according to the admission chronologic order. Results The overall remission rate of the patients was 57% (56/98) for all the patients over 10 years. In the single factor analysis, we found that the tumor size, cavernous invasion, and sellar invasion were valuable to predict the endocrine outcome after surgery. As for the suprasellar invasion, no significant difference was found between the noninvasive group and the invasive group. The preoperative IGF-1 level ( p = 0.166), basal GH level ( p = 0.001), and nadir GH level ( p = 0.004) were also different between the remission group and the nonremission group in the single factor analysis. The logistic regression analysis indicated that the preoperative nadir GH (odds ratio = 0.930, 95% confidence interval = 0.891-0.972, p = 0.001) was a significant predictor for the endocrine outcomes after surgery. Conclusion The surgeons' experience is an important factor that can affect the patients' endocrine outcomes after surgery. The macroadenomas with lateral invasion are more difficult to cure. Patients with higher preoperative nadir GH levels are less likely to achieve remission.

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.

Human skeletal muscle aging atlas.

Skeletal muscle aging is a key contributor to age-related frailty and sarcopenia with substantial implications for global health. Here we profiled 90,902 single cells and 92,259 single nuclei from 17 donors to map the aging process in the adult human intercostal muscle, identifying cellular changes in each muscle compartment. We found that distinct subsets of muscle stem cells exhibit decreased ribosome biogenesis genes and increased CCL2 expression, causing different aging phenotypes. Our atlas also highlights an expansion of nuclei associated with the neuromuscular junction, which may reflect re-innervation, and outlines how the loss of fast-twitch myofibers is mitigated through regeneration and upregulation of fast-type markers in slow-twitch myofibers with age. Furthermore, we document the function of aging muscle microenvironment in immune cell attraction. Overall, we present a comprehensive human skeletal muscle aging resource ( https://www.muscleageingcellatlas.org/ ) together with an in-house mouse muscle atlas to study common features of muscle aging across species.

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.

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.

Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3-5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery.