Tropism-shifted AAV-PHP.eB-mediated bFGF gene therapy promotes varied neurorestoration after ischemic stroke in mice.

ABSTRACT: AAV-PHP.eB is an artificial adeno-associated virus (AAV) that crosses the blood-brain barrier and targets neurons more efficiently than other AAVs when administered systematically. While AAV-PHP.eB has been used in various disease models, its cellular tropism in cerebrovascular diseases remains unclear. In the present study, we aimed to elucidate the tropism of AAV-PHP.eB for different cell types in the brain in a mouse model of ischemic stroke and evaluate its effectiveness in mediating basic fibroblast growth factor (bFGF) gene therapy. Mice were injected intravenously with AAV-PHP.eB either 14 days prior to (pre-stroke) or 1 day following (post-stroke) transient middle cerebral artery occlusion. Notably, we observed a shift in tropism from neurons to endothelial cells with post-stroke administration of AAV-PHP.eB-mNeonGreen (mNG). This endothelial cell tropism correlated strongly with expression of the endothelial membrane receptor lymphocyte antigen 6 family member A (Ly6A). Furthermore, AAV-PHP.eB-mediated overexpression of bFGF markedly improved neurobehavioral outcomes and promoted long-term neurogenesis and angiogenesis post-ischemic stroke. Our findings underscore the significance of considering potential tropism shifts when utilizing AAV-PHP.eB-mediated gene therapy in neurological diseases and suggest a promising new strategy for bFGF gene therapy in stroke treatment.

Dissecting the shared genetic architecture between anti-Müllerian hormone and age at menopause based on genome-wide association study.

BACKGROUND: While the phenotypic association between anti-Müllerian hormone (AMH) and age at menopause has been widely studied, the role of AMH in predicting the age at menopause is currently controversial, and the genetic architecture or causal relationships underlying these two traits is not well understood. AIM: We aimed to explore the shared genetic architecture between AMH and age at menopause, to identify shared pleiotropic loci and genes, and to investigate causal association and potential causal mediators. STUDY DESIGN: Using summary statistics from publicly available genome-wide association studies on AMH (N=7,049) and age at menopause (N=201,323) in Europeans, we investigated the global genetic architecture between AMH and age at menopause through linkage disequilibrium score regression. We employed pleiotropic analysis under composite null hypothesis (PLACO), Functional Mapping and Annotation of Genetic Associations (FUMA), Multimarker analysis of GenoMic annotation (MAGMA), and colocalization analysis to identify loci and genes with pleiotropic effects. Tissue enrichment analysis based on GTEx data was conducted using the Linkage Disequilibrium Score for the specific expression of genes analysis (LDSC-SEG). Functional genes that were shared were additionally identified through summary data-based Mendelian randomization (SMR). The relationship between AMH and age at menopause was examined through two-sample Mendelian randomization (MR), and potential mediators were further explored using colocalization and metabolite-mediated analysis. RESULTS: A positive genetic association (correlation coefficient = 0.88, P = 1.33 × 10-5) was observed between AMH and age at menopause. By using PLACO and FUMA, 42 significant pleiotropic loci were identified that were associated with AMH and age at menopause, and ten of these (rs10734411, rs61913600, rs2277339, rs75770066, rs28416520, rs9796, rs11668344, rs403727, rs6011452, and rs62237617) had colocalized loci. Additionally, 245 significant pleiotropic genes were identified by MAGMA. Genetic associations between AMH and age at menopause were markedly concentrated in various tissues including whole blood, brain, heart, liver, muscle, pancreas, and kidneys. Further, SMR analysis revealed nine genes that may have a causative effect on both AMH and age at menopause. A potential causal effect of age at menopause on AMH was suggested by two-sample MR analysis, with very-low-density lipoprotein identified as a potential mediator. CONCLUSIONS: Our study revealed a shared genetic architecture between AMH and age at menopause, providing a basis for experimental investigations and individual therapies to enhance reproductive outcomes. Furthermore, our findings emphasized that relying solely on AMH is not sufficient for accurately predicting the age at menopause, and a combination of other factors needs to be considered. Exploring new therapeutics aimed at delaying at the onset of menopause holds promise, particularly when targeting shared genes based on their shared genetic architecture.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

A transient transcriptional activation governs unpolarized-to-polarized morphogenesis during embryo implantation.

During implantation, embryos undergo an unpolarized-to-polarized transition to initiate postimplantation morphogenesis. However, the underlying molecular mechanism is unknown. Here, we identify a transient transcriptional activation governing embryonic morphogenesis and pluripotency transition during implantation. In naive pluripotent embryonic stem cells (ESCs), which represent preimplantation embryos, we find that the microprocessor component DGCR8 can recognize stem-loop structures within nascent mRNAs to sequester transcriptional coactivator FLII to suppress transcription directly. When mESCs exit from naive pluripotency, the ERK/RSK/P70S6K pathway rapidly activates, leading to FLII phosphorylation and disruption of DGCR8/FLII interaction. Phosphorylated FLII can bind to transcription factor JUN, activating cell migration-related genes to establish poised pluripotency akin to implanting embryos. Resequestration of FLII by DGCR8 drives poised ESCs into formative pluripotency. In summary, we identify a DGCR8/FLII/JUN-mediated transient transcriptional activation mechanism. Disruption of this mechanism inhibits naive-poised-formative pluripotency transition and the corresponding unpolarized-to-polarized transition during embryo implantation, which are conserved in mice and humans.

Dynamic evolution of MADS-box genes in extant ferns via large-scale phylogenomic analysis.

Introduction: Several studies of MADS-box transcription factors in flowering plants have been conducted, and these studies have indicated that they have conserved functions in floral organ development; MIKC-type MADS-box genes has been proved to be expanded in ferns, however, few systematic studies of these transcription factors have been conducted in non-seed plants. Although ferns and seed plants are sister groups, they exhibit substantial morphological differences. Methods: Here, we clarified the evolution of MADS-box genes across 71 extant fern species using available transcriptome, genome, and gene expression data. Results: We obtained a total of 2,512 MADS-box sequences, ranging from 9 to 89 per species. The most recent common ancestor (MRCA) of ferns contained approximately three type I genes and at least 5-6 type II MADS-box genes. The domains, motifs, expression of type I and type II proteins, and the structure of the both type genes were conserved in ferns as to other land plants. Within type II genes, MIKC*-type proteins are involved in gametophyte development in ferns; MIKCC-type proteins have broader expression patterns in ferns than in seed plants, and these protein sequences are likely conserved in extant seed plants and ferns because of their diverse roles in diploid sporophyte development. More than 90% of MADS-box genes are type II genes, and MIKCC genes, especially CRM1 and CRM6-like genes, have undergone a large expansion in leptosporangiate ferns; the diverse expression patterns of these genes might be related to the fuctional diversification and increased complexity of the plant body plan. Tandem duplication of CRM1 and CRM6-like genes has contributed to the expansion of MIKCC genes. Conclusion or Discussion: This study provides new insights into the diversity, evolution, and functions of MADS-box genes in extant ferns.

Heat stress induces calcium dyshomeostasis to subsequent cognitive impairment through ERS-mediated apoptosis via SERCA/PERK/eIF2α pathway.

Heat exposure is an environmental stressor that has been associated with cognitive impairment. However, the neural mechanisms that underlie this phenomenon have yet to be extensively investigated. The Morris water maze test was utilized to assess cognitive performance. RNA sequencing was employed to discover the primary regulators and pathological pathways involved in cognitive impairment caused by heat. Before heat exposure in vivo and in vitro, activation of the sarco/endoplasmic reticulum (SR/ER) calcium (Ca2+)-ATPase (SERCA) was achieved by CDN1163. Hematoxylin-Eosin, Nissl staining, calcium imaging, transmission electron microscopy, western blot, and immunofluorescence were utilized to visualize histological changes, intracellular calcium levels, endoplasmic reticulum stress (ERS) markers, apoptosis, and synaptic proteins alterations. Heat stress (HS) significantly induced cognitive decline and neuronal damage in mice. By the transcriptome sequencing between control (n = 5) and heat stress (n = 5) mice in hippocampal tissues, we identified a reduction in the expression of the atp2a gene encoding SERCA, accompanied by a corresponding decrease in its protein level. Consequently, this dysregulation resulted in an excessive accumulation of intracellular calcium ions. Furthermore, HS exposure also activated ERS and apoptosis, as evidenced by the upregulation of p-PERK, p-eIF2α, CHOP, and caspase-3. Consistently, a reduction in postsynaptic density protein 95 (PSD95) and synaptophysin (SYN) expressions indicated modifications in synaptic function. Notably, the impacts on neurons caused by HS were found to be mitigated by CDN1163 treatment both in vivo and in vitro. Additionally, SERCA-mediated ERS-induced apoptosis was attenuated by GSK2606414 treatment via inhibiting PERK-eIF2α-CHOP axis that not only curtailed the level of caspase-3 but also elevated the levels of PSD95 and SYN. These findings highlight the significant impact of heat stress on cognitive impairment, and further elucidate the underlying mechanism involving SERCA/PERK/eIF2α pathway.

Full Selectivity Control over the Catalytic Hydrogenation of Nitroaromatics into Six Products.

A full selectivity control over the catalytic hydrogenation of nitroaromatics leads to the production of six possible products, i.e., nitroso, hydroxylamine, azoxy, azo, hydrazo or aniline compounds, which has however not been achieved in the field of heterogeneous catalysis. Currently, there is no sufficient evidence to support that the catalytic hydrogenation of nitroaromatics with the use of heterogeneous metal catalysts would follow the Haber's mechanistic scheme based on electrochemical reduction. We now demonstrate in this work that it is possible to fully control the catalytic hydrogenation of nitroaromatics into their all six products using a single catalytic system under various conditions. Employing SnO2-supported Pt nanoparticles facilitated by the surface coordination of ethylenediamine and vanadium species enabled this unprecedented selectivity control. Through systematic investigation into the controlled production of all products and their chemical reactivities, we have constructed a detailed reaction network for the catalytic hydrogenation of nitroaromatics. Crucially, the application of oxygen-isolated characterization techniques proved indispensable in identifying unstable compounds such as nitroso, hydroxylamine, hydrazo compounds. The insights gained from this research offer invaluable guidance for selectively transforming nitroaromatics into a wide array of functional N-containing compounds, both advancing fundamental understanding and fostering practical applications in various fields.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution.

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-ß (Aß), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aß clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aß plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.

Introduction: Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods: By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results: By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion: In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

Targeting a mTOR/autophagy axis: a double-edged sword of rapamycin in spontaneous miscarriage.

Immune dysfunction is a primary culprit behind spontaneous miscarriage (SM). To address this, immunosuppressive agents have emerged as a novel class of tocolytic drugs, modulating the maternal immune system's tolerance towards the embryo. Rapamycin (PubChem CID:5284616), a dual-purpose compound, functions as an immunosuppressive agent and triggers autophagy by targeting the mTOR pathway. Its efficacy in treating SM has garnered significant research interest in recent times. Autophagy, the cellular process of self-degradation and recycling, plays a pivotal role in numerous health conditions. Research indicates that autophagy is integral to endometrial decidualization, trophoblast invasion, and the proper functioning of decidual immune cells during a healthy pregnancy. Yet, in cases of SM, there is a dysregulation of the mTOR/autophagy axis in decidual stromal cells or immune cells at the maternal-fetal interface. Both in vitro and in vivo studies have highlighted the potential benefits of low-dose rapamycin in managing SM. However, given mTOR's critical role in energy metabolism, inhibiting it could potentially harm the pregnancy. Moreover, while low-dose rapamycin has been deemed safe for treating recurrent implant failure, its potential teratogenic effects remain uncertain due to insufficient data. In summary, rapamycin represents a double-edged sword in the treatment of SM, balancing its impact on autophagy and immune regulation. Further investigation is warranted to fully understand its implications.

Stromal mediated DNA damage promotes high grade serous ovarian cancer initiation.

The fundamental steps in high-grade serous ovarian cancer (HGSOC) initiation are unclear, thus providing critical barriers to the development of prevention or early detection strategies for this deadly disease. Increasing evidence demonstrates most HGSOC starts in the fallopian tube epithelium (FTE). Current models propose HGSOC initiates when FTE cells acquire increasing numbers of mutations allowing cells to evolve into serous tubal intraepithelial carcinoma (STIC) precursors and then to full blown cancer. Here we report that epigenetically altered mesenchymal stem cells (termed high risk MSC-hrMSCs) can be detected prior to the formation of ovarian cancer precursor lesions. These hrMSCs drive DNA damage in the form of DNA double strand breaks in FTE cells while also promoting the survival of FTE cells in the face of DNA damage. Indicating the hrMSC may actually drive cancer initiation, we find hrMSCs induce full malignant transformation of otherwise healthy, primary FTE resulting in metastatic cancer in vivo . Further supporting a role for hrMSCs in cancer initiation in humans, we demonstrate that hrMSCs are highly enriched in BRCA1/2 mutation carriers and increase with age. Combined these findings indicate that hrMSCs may incite ovarian cancer initiation. These findings have important implications for ovarian cancer detection and prevention.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Discovery of a Promising CBP/p300 Degrader XYD129 for the Treatment of Acute Myeloid Leukemia.

The epigenetic target CREB (cyclic-AMP responsive element binding protein) binding protein (CBP) and its homologue p300 were promising therapeutic targets for the treatment of acute myeloid leukemia (AML). Herein, we report the design, synthesis, and evaluation of a class of CBP/p300 PROTAC degraders based on our previously reported highly potent and selective CBP/p300 inhibitor 5. Among the compounds synthesized, 11c (XYD129) demonstrated high potency and formed a ternary complex between CBP/p300 and CRBN (AlphaScreen). The compound effectively degraded CBP/p300 proteins and exhibited greater inhibition of growth in acute leukemia cell lines compared to its parent compound 5. Furthermore, 11c demonstrated significant inhibition of tumor growth in a MOLM-16 xenograft model (TGI = 60%) at tolerated dose schedules. Our findings suggest that 11c is a promising lead compound for the treatment of AML.

Capturing totipotency in human cells through spliceosomal repression.

The cleavage of zygotes generates totipotent blastomeres. In human 8-cell blastomeres, zygotic genome activation (ZGA) occurs to initiate the ontogenesis program. However, capturing and maintaining totipotency in human cells pose significant challenges. Here, we realize culturing human totipotent blastomere-like cells (hTBLCs). We find that splicing inhibition can transiently reprogram human pluripotent stem cells into ZGA-like cells (ZLCs), which subsequently transition into stable hTBLCs after long-term passaging. Distinct from reported 8-cell-like cells (8CLCs), both ZLCs and hTBLCs widely silence pluripotent genes. Interestingly, ZLCs activate a particular group of ZGA-specific genes, and hTBLCs are enriched with pre-ZGA-specific genes. During spontaneous differentiation, hTBLCs re-enter the intermediate ZLC stage and further generate epiblast (EPI)-, primitive endoderm (PrE)-, and trophectoderm (TE)-like lineages, effectively recapitulating human pre-implantation development. Possessing both embryonic and extraembryonic developmental potency, hTBLCs can autonomously generate blastocyst-like structures in vitro without external cell signaling. In summary, our study provides key criteria and insights into human cell totipotency.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Molecular dynamics (MD) study to predict performances of a novel hexanitrohexaazaisowurtzitane/1,4-dinitroimidazole (CL-20/1,4-DNI) energetic cocrystal under different temperatures.

CONTEXT: To estimate the influence of temperature on properties of 2,4,6,8,10,12-hexanitro- 2,4,6,8,10,12-hexaazaisowurtzitane/1,4-dinitroimidazole (CL-20/1,4-DNI) cocrystal explosive, the supercell crystal of CL-20/1,4-DNI cocrystal model was established. The mechanical properties, sensitivity, and stability of cocrystal model under different temperatures (T = 225 K, 250 K, 275 K, 300 K, 325 K, 350 K) were predicted. Results show that mechanical parameters, including bulk modulus, tensile modulus and shear modulus are the lowest when temperature is 300 K, while Cauchy pressure is the highest, indicating that CL-20/1,4-DNI cocrystal model has better mechanical properties at 300 K. Cohesive energy density (CED) and its components energies decrease monotonically with the increase of temperature, illustrating that the CL-20 and 1,4-DNI molecules are activated and the safety of cocrystal explosive is worsened with the increase of temperature. Cocrystal model has relatively higher binding energy when the temperature is 300 K, implying that the CL-20/1,4-DNI cocrystal explosive is more stable under this condition. METHODS: The CL-20/1,4-DNI cocrystal model was optimized and the properties were predicted through molecular dynamics (MD) method. The MD simulation was performed with COMPASS force field and the ensemble was set as NPT, external pressure was set as 0.0001 GPa.

Neuropharmacology involvement of dopamine D3 receptor in impulsive choice decision-making in male rats.

Impulsive decision-making has been linked to impulse control disorders and substance use disorders. However, the neural mechanisms underlying impulsive choice are not fully understood. While previous PET imaging and autoradiography studies have shown involvement of dopamine and D2/3 receptors in impulsive behavior, the roles of distinct D1, D2, and D3 receptors in impulsive decision-making remain unclear. In this study, we used a food reward delay-discounting task (DDT) to identify low- and high-impulsive rats, in which low-impulsive rats exhibited preference for large delayed reward over small immediate rewards, while high-impulsive rats showed the opposite preference. We then examined D1, D2, and D3 receptor gene expression using RNAscope in situ hybridization assays. We found that high-impulsive male rats exhibited lower levels of D2 and D3, and particularly D3, receptor expression in the nucleus accumbens (NAc), with no significant changes in the insular, prelimbic, and infralimbic cortices. Based on these findings, we further explored the role of the D3 receptor in impulsive decision-making. Systemic administration of a selective D3 receptor agonist (FOB02-04) significantly reduced impulsive choices in high-impulsive rats but had no effects in low-impulsive rats. Conversely, a selective D3 receptor antagonist (VK4-116) produced increased both impulsive and omission choices in both groups of rats. These findings suggest that impulsive decision-making is associated with a reduction in D3 receptor expression in the NAc. Selective D3 receptor agonists, but not antagonists, may hold therapeutic potentials for mitigating impulsivity in high-impulsive subjects.

Practical Nomograms and Risk Stratification System for Predicting the Overall and Cancer-specific Survival in Patients with Anaplastic Astrocytoma.

OBJECTIVE: Anaplastic astrocytoma (AA) is an uncommon primary brain tumor with highly variable clinical outcomes. Our study aimed to develop practical tools for clinical decision-making in a population-based cohort study. METHODS: Data from 2997 patients diagnosed with AA between 2004 and 2015 were retrospectively extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The LASSO and multivariate Cox regression analyses were applied to select factors and establish prognostic nomograms. The discriminatory ability of these nomogram models was evaluated using the concordance index (C-index) and receiver operating characteristic curve (ROC). Risk stratifications were established based on the nomograms. RESULTS: Selected 2997 AA patients were distributed into the training cohort (70%, 2097) and the validation cohort (30%, 900). Age, household income, tumor site, extension, surgery, radiotherapy, and chemotherapy were identified as independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). In the training cohort, our nomograms for OS and CSS exhibited good predictive accuracy with C-index values of 0.752 (95% CI: 0.741-0.764) and 0.753 (95% CI: 0.741-0.765), respectively. Calibration and DCA curves showed that the nomograms demonstrated considerable consistency and satisfactory clinical utilities. With the establishment of nomograms, we stratified AA patients into high- and low-risk groups, and constructed risk stratification systems for OS and CSS. CONCLUSIONS: We constructed two predictive nomograms and risk classification systems to effectively predict the OS and CSS rates in AA patients. These models were internally validated with considerable accuracy and reliability and might be helpful in future clinical practices.