Polysaccharide-based nanomedicines for cancer immunotherapy: a review

Cancer immunotherapy is an effective antitumor approach through activating immune systems to eradicate tumors by immunotherapeutics. However, direct administration of “naked” immunotherapeutic agents (such as nucleic acids, cytokines, adjuvants or antigens without delivery vehicles) often results in: (1) an unsatisfactory efficacy due to suboptimal pharmacokinetics; (2) strong toxic and side effects due to low targeting (or off-target) efficiency. To overcome these shortcomings, a series of polysaccharide-based nanoparticles have been developed to carry immunotherapeutics to enhance antitumor immune responses with reduced toxicity and side effects. Polysaccharides are a family of natural polymers that hold unique physicochemical and biological properties, as they could interact with immune system to stimulate an enhanced immune response. Their structures offer versatility in synthesizing multifunctional nanocomposites, which could be chemically modified to achieve high stability and bioavailability for delivering therapeutics into tumor tissues. This review aims to highlight recent advances in polysaccharide-based nanomedicines for cancer immunotherapy and propose new perspectives on the use of polysaccharide-based immunotherapeutics.info:eu-repo/semantics/publishedVersio

VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.Comment: Accepted to CVPR202

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging:Results from the Adolescent Brain Cognitive Development study^®

Background: Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health. Methods: We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers. Results: The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately. Conclusions: The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society

The Structural Characterization and Antigenicity of the S Protein of SARS-CoV

The corona-like spikes or peplomers on the surface of the virion under electronic microscope are the most striking features of coronaviruses. The S (spike) protein is the largest structural protein, with 1,255 amino acids, in the viral genome. Its structure can be divided into three regions: a long N-terminal region in the exterior, a characteristic transmembrane (TM) region, and a short C-terminus in the interior of a virion. We detected fifteen substitutions of nucleotides by comparisons with the seventeen published SARS-CoV genome sequences, eight (53.3%) of which are non-synonymous mutations leading to amino acid alternations with predicted physiochemical changes. The possible antigenic determinants of the S protein are predicted, and the result is confirmed by ELISA (enzyme-linked immunosorbent assay) with synthesized peptides. Another profound finding is that three disulfide bonds are defined at the C-terminus with the N-terminus of the E (envelope) protein, based on the typical sequence and positions, thus establishing the structural connection with these two important structural proteins, if confirmed. Phylogenetic analysis reveals several conserved regions that might be potent drug targets

Association between abnormal plasma metabolism and brain atrophy in alcohol-dependent patients

ObjectiveIn this study, we aimed to characterize the plasma metabolic profiles of brain atrophy and alcohol dependence (s) and to identify the underlying pathogenesis of brain atrophy related to alcohol dependence.MethodsWe acquired the plasma samples of alcohol-dependent patients and performed non-targeted metabolomic profiling analysis to identify alterations of key metabolites in the plasma of BA-ADPs. Machine learning algorithms and bioinformatic analysis were also used to identify predictive biomarkers and investigate their possible roles in brain atrophy related to alcohol dependence.ResultsA total of 26 plasma metabolites were significantly altered in the BA-ADPs group when compared with a group featuring alcohol-dependent patients without brain atrophy (NBA-ADPs). Nine of these differential metabolites were further identified as potential biomarkers for BA-ADPs. Receiver operating characteristic curves demonstrated that these potential biomarkers exhibited good sensitivity and specificity for distinguishing BA-ADPs from NBA-ADPs. Moreover, metabolic pathway analysis suggested that glycerophospholipid metabolism may be highly involved in the pathogenesis of alcohol-induced brain atrophy.ConclusionThis plasma metabolomic study provides a valuable resource for enhancing our understanding of alcohol-induced brain atrophy and offers potential targets for therapeutic intervention

Interfacial-hybridization-modified Ir Ferromagnetism and Electronic Structure in LaMnO3_3/SrIrO3_3 Superlattices

Artificially fabricated 3$d$/5$d$ superlattices (SLs) involve both strong electron correlation and spin-orbit coupling in one material by means of interfacial 3$d$-5$d$ coupling, whose mechanism remains mostly unexplored. In this work we investigated the mechanism of interfacial coupling in LaMnO$_3$/SrIrO$_3$ SLs by several spectroscopic approaches. Hard x-ray absorption, magnetic circular dichroism and photoemission spectra evidence the systematic change of the Ir ferromagnetism and the electronic structure with the change of the SL repetition period. First-principles calculations further reveal the mechanism of the SL-period dependence of the interfacial electronic structure and the local properties of the Ir moments, confirming that the formation of Ir-Mn molecular orbital is responsible for the interfacial coupling effects. The SL-period dependence of the ratio between spin and orbital components of the Ir magnetic moments can be attributed to the realignment of electron spin during the formation of the interfacial molecular orbital. Our results clarify the nature of interfacial coupling in this prototypical 3$d$/5$d$ SL system and the conclusion will shed light on the study of other strongly correlated and spin-orbit coupled oxide hetero-interfaces

Cross-talk between PRMT1-mediated methylation and ubiquitylation on RBM15 controls RNA splicing

RBM15, an RNA binding protein, determines cell-fate specification of many tissues including blood. We demonstrate that RBM15 is methylated by protein arginine methyltransferase 1 (PRMT1) at residue R578 leading to its degradation via ubiquitylation by an E3 ligase (CNOT4). Overexpression of PRMT1 in acute megakaryocytic leukemia cell lines blocks megakaryocyte terminal differentiation by downregulation of RBM15 protein level. Restoring RBM15 protein level rescues megakaryocyte terminal differentiation blocked by PRMT1 overexpression. At the molecular level, RBM15 binds to pre-mRNA intronic regions of genes important for megakaryopoiesis such as GATA1, RUNX1, TAL1 and c-MPL. Furthermore, preferential binding of RBM15 to specific intronic regions recruits the splicing factor SF3B1 to the same sites for alternative splicing. Therefore, PRMT1 regulates alternative RNA splicing via reducing RBM15 protein concentration. Targeting PRMT1 may be a curative therapy to restore megakaryocyte differentiation for acute megakaryocytic leukemia