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Abstract Schizophrenia is a serious neuropsychiatric disorder with abnormal age-related neurodevelopmental (or neurodegenerative) trajectories. Although an accelerated aging hypothesis of schizophrenia has been proposed, the quantitative study of the disruption of the physiological trajectory caused by schizophrenia is inconclusive. In this study, we employed 3 “epigenetic clock” methods to quantify the epigenetic age of a large sample size of whole blood (1069 samples from patients with schizophrenia vs 1264 samples from unaffected controls) and brain tissues (500 samples from patients with schizophrenia vs 711 samples from unaffected controls). We observed significant positive correlations between epigenetic age and chronological age in both blood and brain tissues from unaffected controls and patients with schizophrenia, as estimated by 3 methods. Furthermore, we observed that epigenetic age acceleration was significantly delayed in schizophrenia from the whole blood samples (aged 20–90 years) and brain frontal cortex tissues (aged 20–39 years). Intriguingly, the genes regulated by the epigenetic clock also contained schizophrenia-associated genes, displaying differential expression and methylation in patients with schizophrenia and involving in the regulation of cell activation and development. These findings were further supported by the dysregulated leukocyte composition in patients with schizophrenia. Our study presents quantitative evidence for a neurodevelopmental model of schizophrenia from the perspective of a skewed “epigenetic clock.” Moreover, landmark changes in an easily accessible biological sample, blood, reveal the value of these epigenetic clock genes as peripheral biomarkers for schizophrenia.

Although antibody-drug conjugates (ADCs) have shown significant efficacy in cancer treatment, hematotoxicity remains a serious issue. This study aims to investigate the relationship between ADCs and hematological toxicity. Our study was conducted using data extracted from the U.S. Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2011 to the second quarter of 2024. We used four disproportionality analysis methods to measure risk signals. In addition, we analyzed the time-to-onset of hematotoxicity adverse events (AEs). A total of 4,803 cases of hematotoxicity AEs associated with ADCs were identified, the median age of patients was 60 years (IQR: 47-72). Different ADCs have different hematotoxicity profiles, among which brentuximab vedotin (BV) and sacituzumab govitecan (SG) were more likely to lead to serious outcomes. The median time-to-onset of hematotoxicity AEs was the shortest for SG at 12 days and the longest for trastuzumab deruxtecan (TG) at 22 days. The hospitalization and mortality rates with hematotoxicity AEs were 30.38% and 18.30%, respectively. ADCs are significantly associated with increased reporting of hematotoxicity. A novel hematotoxicity signal that was not disclosed in the drug specifications was observed. The most prominent hematotoxicity AE signals were cytopenia related to inotuzumab ozogamicin (IO), polatuzumab vedotin (PV), loncastuximab tesirine (LT), and tisotumab vedotin (TV); febrile bone marrow aplasia related togemtuzumab ozogamicin (GO), BV, and SG; and myelosuppression related to BV, trastuzumab emtansine (TE), andenfortumab vedotin (EV). Our findings need to be validated by large-scale prospective studies.

Although antibody-drug conjugates (ADCs) have shown significant efficacy in cancer treatment, hematotoxicity remains a serious issue. This study aims to investigate the relationship between ADCs and hematological toxicity. Our study was conducted using data extracted from the U.S. Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2011 to the second quarter of 2024. We used four disproportionality analysis methods to measure risk signals. In addition, we analyzed the time-to-onset of hematotoxicity adverse events (AEs). A total of 4,803 cases of hematotoxicity AEs associated with ADCs were identified, the median age of patients was 60 years (IQR: 47-72). Different ADCs have different hematotoxicity profiles, among which brentuximab vedotin (BV) and sacituzumab govitecan (SG) were more likely to lead to serious outcomes. The median time-to-onset of hematotoxicity AEs was the shortest for SG at 12 days and the longest for trastuzumab deruxtecan (TG) at 22 days. The hospitalization and mortality rates with hematotoxicity AEs were 30.38% and 18.30%, respectively. ADCs are significantly associated with increased reporting of hematotoxicity. A novel hematotoxicity signal that was not disclosed in the drug specifications was observed. The most prominent hematotoxicity AE signals were cytopenia related to inotuzumab ozogamicin (IO), polatuzumab vedotin (PV), loncastuximab tesirine (LT), and tisotumab vedotin (TV); febrile bone marrow aplasia related togemtuzumab ozogamicin (GO), BV, and SG; and myelosuppression related to BV, trastuzumab emtansine (TE), andenfortumab vedotin (EV). Our findings need to be validated by large-scale prospective studies.

Background Growth retardation is common in glycogen storage disease (GSD), though the relative contributions of hormonal and metabolic factors remain unclear. We compared clinical and biochemical features between GSD I and non-GSD I patients and identified independent predictors of height standard deviation score (SDS). Methods Thirty-eight children with GSD (24 with GSD I; 14 with GSD III/VI/IX; mean age: 7.5 years) underwent evaluation of height SDS, BMI SDS, IGF1 SDS, and metabolic parameters. After excluding three patients with inflammatory bowel disease (final n  = 35), multiple regression was used to identify factors associated with height SDS. In GSD I ( n  = 24), Lasso regression selected variables, and 1,000 bootstrap resamples assessed coefficient stability. Results GSD I patients had lower height SDS (–2.30 vs. − 1.17; p  = 0.021) and higher lactate (3.94 vs. 1.48 mmol/L; p  < 0.001), uric acid (431.04 vs. 283.79µmol/L; p  < 0.001) and triglyceride levels (2.38 vs. 1.29 mmol/L, p  = 0.002) compared to non-GSD I. In combined-cohort regression, lactate was the only independent negative predictor of height SDS ( p  = 0.011); glucose levels and IGF1 SDS did not reach statistical significance. In GSD I, Lasso retained lactate (β = − 0.682), glucose (β = − 0.625), and IGF1 SDS (β = 0.524), and bootstrap validation showed only IGF1 SDS remained consistently significant. Conclusions Hyperlactatemia is significant predictor of growth impairment in GSD, while IGF1 is a stable predictor in GSD I. These findings highlight metabolic and hormonal targets for future hypothesis-driven research in this population.

Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. Digital phenotyping, which involves using data from smartphones or other digital devices to assess mental health, has emerged as a promising tool for distinguishing between these two disorders. This systematic review aimed to achieve two goals: (1) to summarize the existing literature on the use of digital phenotyping to directly distinguish between UD and BD and (2) to review studies that use digital phenotyping to classify UD, BD, and healthy control (HC) individuals. Furthermore, the review sought to identify gaps in the current research and propose directions for future studies. We systematically searched the Scopus, IEEE Xplore, PubMed, Embase, Web of Science, and PsycINFO databases up to March 20, 2025. Studies were included if they used portable or wearable digital tools to directly distinguish between UD and BD, or to classify UD, BD, and HC. Original studies published in English, including both journal and conference papers, were included, while reviews, narrative reviews, systematic reviews, and meta-analyses were excluded. Articles were excluded if the diagnosis was not made through a professional medical evaluation or if they relied on electronic health records or clinical data. For each included study, the following information was extracted: demographic characteristics, diagnostic criteria or psychiatric assessments, details of the technological tools and data types, duration of data collection, data preprocessing methods, selected variables or features, machine learning algorithms or statistical tests, validation, and main findings. We included 21 studies, of which 11 (52%) focused on directly distinguishing between UD and BD, while 10 (48%) classified UD, BD, and HC. The studies were categorized into 4 groups based on the type of digital tool used: 6 (29%) used smartphone apps, 3 (14%) used wearable devices, 11 (52%) analyzed audiovisual recordings, and 1 (5%) used multimodal technologies. Features such as activity levels from smartphone apps or wearable devices emerged as potential markers for directly distinguishing UD and BD. Patients with BD generally exhibited lower activity levels than those with UD. They also tended to show higher activity in the morning and lower in the evening, while patients with UD showed the opposite pattern. Moreover, speech modalities or the integration of multiple modalities achieved better classification performance across UD, BD, and HC groups, although the specific contributing features remained unclear. Digital phenotyping shows potential in distinguishing BD from UD, but challenges like data privacy, security concerns, and equitable access must be addressed. Further research should focus on overcoming these challenges and refining digital phenotyping methodologies to ensure broader applicability in clinical settings. PROSPERO CRD42024624202; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024624202.

Organic montmorillonite/brominated butyl rubber nanocomposites are prepared by improved solution blending, i.e., preparation of organic clay suspension and suspension blending with brominated butyl rubber solution by adding polytetramethylene ether glycol as interface agent. Scanning electron microscopy and atomic force microscopy show that polytetramethylene ether glycol has good dispersing effect on organic clay. Transmission electron microscopy and X‐ray diffraction analysis demonstrates that both intercalated and exfoliated structures are obtained in organic montmorillonite/brominated butyl rubber nanocomposites. The mechanical performances and the gas barrier properties of the organic montmorillonite/brominated butyl rubber nanocomposites are improved greatly, which proves that the high‐performance clay/rubber nanocomposites are prepared in nonpolar solvent. The organic montmorillonite is nano‐dispersed in the brominated butyl rubber by the combination effect of solution compounding method and unique interface agent. The nanocomposites have excellent mechanical properties and gas barrier properties. In particular, the gas barrier properties are improved by more than 50%, which has great potential in the preparation of green tire inner layer.

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 11.3% to 48.8% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor https://bioconductor.org/packages/release/bioc/html/ADImpute.html ) that automatically determines the best imputation method for each gene in a dataset. Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.

Blood–brain barrier (BBB) leakage is an important cause of the exacerbation of pathological features of cerebral ischemia reperfusion injury (CIRI). However, the specific mechanism of BBB leakage is not clear. It was found that the CIRI resulted in RIPK1 activation and subsequent RIPK1-dependent apoptosis (RDA). Inhibition of RIPK1 significantly reduced BBB breakdown and brain damage. The aim of this study is to investigate the mechanism of RIPK1 in the BBB leakage during CIRI. It was discovered by proteomics that autophagy activation resulting from ischemia and reperfusion significantly downregulated the level of A20 protein. A20 is an important protein that regulates RIPK1 and RDA. It was hypothesized that activation of autophagy caused by ischemic reperfusion led to a decrease in A20 protein, which, in turn, caused the activation of RIPK1 and the occurrence of RDA, leading to leakage of the BBB. The findings in this study revealed the role of RIPK1 in the cell death and BBB leakage upon cerebral ischemia reperfusion injury, and these findings provide a novel perspective for the treatment of ischemic reperfusion.

The conserved long noncoding RNA MeXis has anti-atherosclerotic effects in mice by acting with the nuclear hormone receptor LXR in macrophages to promote cholesterol efflux. Nuclear receptors regulate gene expression in response to environmental cues, but the molecular events governing the cell type specificity of nuclear receptors remain poorly understood. Here we outline a role for a long noncoding RNA (lncRNA) in modulating the cell type–specific actions of liver X receptors (LXRs), sterol-activated nuclear receptors that regulate the expression of genes involved in cholesterol homeostasis and that have been causally linked to the pathogenesis of atherosclerosis. We identify the lncRNA MeXis as an amplifier of LXR-dependent transcription of the gene Abca1 , which is critical for regulation of cholesterol efflux. Mice lacking the MeXis gene show reduced Abca1 expression in a tissue-selective manner. Furthermore, loss of MeXis in mouse bone marrow cells alters chromosome architecture at the Abca1 locus, impairs cellular responses to cholesterol overload, and accelerates the development of atherosclerosis. Mechanistic studies reveal that MeXis interacts with and guides promoter binding of the transcriptional coactivator DDX17. The identification of MeXis as a lncRNA modulator of LXR-dependent gene expression expands understanding of the mechanisms underlying cell type–selective actions of nuclear receptors in physiology and disease.

Catalyst design relies heavily on electronic metal‐support interactions, but the metal‐support interface with an uncontrollable electronic or coordination environment makes it challenging. Herein, we outline a promising approach for the rational design of catalysts involving heteroatoms as anchors for Pd nanoparticles for ethanol oxidation reaction (EOR) catalysis. The doped B and N atoms from dimethylamine borane (DB) occupy the position of the Ti3C2 lattice to anchor the supported Pd nanoparticles. The electrons transfer from the support to B atoms, and then to the metal Pd to form a stable electronic center. A strong electronic interaction can be produced and the d‐band center can be shifted down, driving Pd into the dominant metallic state and making Pd nanoparticles deposit uniformly on the support. As‐obtained Pd/DB–Ti3C2 exhibits superior durability to its counterpart (∼14.6% retention) with 91.1% retention after 2000 cycles, placing it among the top single metal anodic catalysts. Further, in situ Raman and density functional theory computations confirm that Pd/DB–Ti3C2 is capable of dehydrogenating ethanol at low reaction energies. A catalyst comprising both a stable electronic center and strong bonds to anchor Pd nanoparticles via dual‐atom codoping is developed to apply for efficient ethanol oxidation reaction (EOR) catalysis. The strong electronic Pd/MXene interaction and uniform distribution of Pd nanoparticles make the catalyst have high electrocatalytic activity and superior long‐term durability during the EOR process.