Bioinspired cellular membrane-derived vesicles for mRNA delivery.

The rapid advancement of mRNA as vaccines and therapeutic agents in the biomedical field has sparked hope in the fight against untreatable diseases. Successful clinical application of mRNA therapeutics largely depends on the carriers. Recently, a new and exciting focus has emerged on natural cell-derived vesicles. These nanovesicles offer many functions, including enhanced drug delivery capabilities and immune evasion, thereby presenting a unique and promising platform for the effective and safe delivery of mRNA therapeutics. In this study, we summarize the characteristics and properties of biomimetic delivery systems for mRNA therapeutics. In particular, we discuss the unique features of cellular membrane-derived vesicles (CDVs) and the combination of synthetic nanovesicles with CDVs.

Probing structural superlubricity of two-dimensional water transport with atomic resolution.

Low-dimensional water transport can be drastically enhanced under atomic-scale confinement. However, its microscopic origin is still under debate. In this work, we directly imaged the atomic structure and transport of two-dimensional water islands on graphene and hexagonal boron nitride surfaces using qPlus-based atomic force microscopy. The lattice of the water island was incommensurate with the graphene surface but commensurate with the boron nitride surface owing to different surface electrostatics. The area-normalized static friction on the graphene diminished as the island area was increased by a power of ~-0.58, suggesting superlubricity behavior. By contrast, the friction on the boron nitride appeared insensitive to the area. Molecular dynamic simulations further showed that the friction coefficient of the water islands on the graphene could reduce to <0.01.

Enhanced Sampling Molecular Dynamics Simulations Reveal Transport Mechanism of Glycoconjugate Drugs through GLUT1.

Glucose transporters GLUT1 belong to the major facilitator superfamily and are essential to human glucose uptake. The overexpression of GLUT1 in tumor cells designates it as a pivotal target for glycoconjugate anticancer drugs. However, the interaction mechanism of glycoconjugate drugs with GLUT1 remains largely unknown. Here, we employed all-atom molecular dynamics simulations, coupled to steered and umbrella sampling techniques, to examine the thermodynamics governing the transport of glucose and two glycoconjugate drugs (i.e., 6-D-glucose-conjugated methane sulfonate and 6-D-glucose chlorambucil) by GLUT1. We characterized the specific interactions between GLUT1 and substrates at different transport stages, including substrate recognition, transport, and releasing, and identified the key residues involved in these procedures. Importantly, our results described, for the first time, the free energy profiles of GLUT1-transporting glycoconjugate drugs, and demonstrated that H160 and W388 served as important gates to regulate their transport via GLUT1. These findings provide novel atomic-scale insights for understanding the transport mechanism of GLUT1, facilitating the discovery and rational design of GLUT1-targeted anticancer drugs.

Imaging surface structure and premelting of ice Ih with atomic resolution.

Ice surfaces are closely relevant to many physical and chemical properties, such as melting, freezing, friction, gas uptake and atmospheric reaction1-8. Despite extensive experimental and theoretical investigations9-17, the exact atomic structures of ice interfaces remain elusive owing to the vulnerable hydrogen-bonding network and the complicated premelting process. Here we realize atomic-resolution imaging of the basal (0001) surface structure of hexagonal water ice (ice Ih) by using qPlus-based cryogenic atomic force microscopy with a carbon monoxide-functionalized tip. We find that the crystalline ice-Ih surface consists of mixed Ih- and cubic (Ic)-stacking nanodomains, forming 19 × 19 periodic superstructures. Density functional theory reveals that this reconstructed surface is stabilized over the ideal ice surface mainly by minimizing the electrostatic repulsion between dangling OH bonds. Moreover, we observe that the ice surface gradually becomes disordered with increasing temperature (above 120 Kelvin), indicating the onset of the premelting process. The surface premelting occurs from the defective boundaries between the Ih and Ic domains and can be promoted by the formation of a planar local structure. These results put an end to the longstanding debate on ice surface structures and shed light on the molecular origin of ice premelting, which may lead to a paradigm shift in the understanding of ice physics and chemistry.

The role of coagulopathy and subdural hematoma thickness at admission in predicting the prognoses of patients with severe traumatic brain injury: A multicenter retrospective cohort study from China.

BACKGROUND: Traumatic brain injury (TBI) is one of the diseases with high disability and mortality worldwide. Recent studies have shown that TBI-related factors may change the complex balance between bleeding and thrombosis, leading to coagulation disorders. The aim of this retrospective study was to investigate the prediction of coagulopathy and subdural hematoma thickness at admission using the Glasgow Outcome Scale (GOS) in patients with severe TBI at 6 months after discharge. METHODS: In this retrospective cohort study, a total of 1,006 patients with severe TBI in large medical centers in three different provinces of China from June 2015 to June 2021 were enrolled after the exclusion criteria, and 800 patients who met the enrollment criteria were included. A receiver operating characteristic (ROC) curve was used to determine the best cut-off values of platelet (PLT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and subdural hematoma (SDH) thickness. The ROC curve, nomogram, calibration curve, and the decision curve were used to evaluate the predictive effect of the coagulopathy and Coagulopathy-SDH(X1) models on the prognoses of patients with severe TBI, and the importance of predictive indicators was ranked by machine learning. RESULTS: Among the patients with severe TBI on admission, 576/800 (72%) had coagulopathy, 494/800 (61%) had SDH thickness ≥14.05 mm, and 385/800 (48%) had coagulopathy combined with SDH thickness ≥14.05 mm. Multivariate logistic regression analyses showed that age, pupil, brain herniation, WBC, CRP, SDH, coagulopathy, and X1 were independent prognostic factors for GOS after severe TBI. Compared with other single indicators, X1 as a predictor of the prognosis of severe TBI was more accurate. The GOS of patients with coagulopathy and thick SDH (X1, 1 point) at 6 months after discharge was significantly worse than that of patients with coagulopathy and thin SDH (X1, 2 points), patients without coagulopathy and thick SDH (X1, 3 point), and patients without coagulopathy and thin SDH (X1, 4 points). In the training group, the C-index based on the coagulopathy nomogram was 0.900. The C-index of the X1-based nomogram was 0.912. In the validation group, the C-index based on the coagulopathy nomogram was 0.858. The C-index of the X1-based nomogram was 0.877. Decision curve analysis also confirmed that the X1-based model had a higher clinical net benefit of GOS at 6 months after discharge than the coagulopathy-based model in most cases, both in the training and validation groups. In addition, compared with the calibration curve based on the coagulopathy model, the prediction of the X1 model-based calibration curve for the probability of GOS at 6 months after discharge showed better agreement with actual observations. Machine learning compared the importance of each independent influencing factor in the evaluation of GOS prediction after TBI, with results showing that the importance of X1 was better than that of coagulopathy alone. CONCLUSION: Coagulopathy combined with SDH thickness could be used as a new, accurate, and objective clinical predictor, and X1, based on combining coagulopathy with SDH thickness could be used to improve the accuracy of GOS prediction in patients with TBI, 6 months after discharge.

Non-Hermitian Strongly Interacting Dirac Fermions.

Exotic quantum phases and phase transition in the strongly interacting Dirac systems have attracted tremendous interests. On the other hand, non-Hermitian physics, usually associated with dissipation arising from the coupling to environment, emerges as a frontier of modern physics in recent years. In this Letter, we investigate the interplay between non-Hermitian physics and strong correlation in Dirac-fermion systems. We generalize the projector quantum Monte-Carlo (PQMC) algorithm to the non-Hermitian interacting fermionic systems. Employing PQMC simulation, we decipher the ground-state phase diagram of the honeycomb Hubbard model with spin resolved non-Hermitian asymmetric hopping processes. The antiferromagnetic (AFM) ordering induced by Hubbard interaction is enhanced by the non-Hermitian asymmetric hopping. Combining PQMC simulation and renormalization group analysis, we reveal that the quantum phase transition between Dirac semi-metal and AFM phases belongs to Hermitian chiral XY universality class, implying that a Hermitian Gross-Neveu transition is emergent at the quantum critical point although the model is non-Hermitian.

Prolonged near-infrared fluorescence imaging of microRNAs and proteases in vivo by aggregation-enhanced emission from DNA-AuNC nanomachines.

Developing a comprehensive strategy for imaging various biomarkers (i.e., microRNAs and proteases) in vivo is an exceptionally formidable task. Herein, we have designed a deoxyribonucleic acid-gold nanocluster (DNA-AuNC) nanomachine for detecting tumor-related TK1 mRNA and cathepsin B in living cells and in vivo. The DNA-AuNC nanomachine is constructed using AuNCs and DNA modules that incorporate a three component DNA hybrid (TD) and a single-stranded fuel DNA (FD). Upon being internalized into tumor cells, the TK1 mRNA initiates the DNA-AuNC nanomachine through DNA strand displacement cascades, leading to the amplified self-assembly and the aggregation-enhanced emission of AuNCs for in situ imaging. Furthermore, with the aid of a protease nanomediator consisting of a mediator DNA/peptide complex and AuNCs (DpAuNCs), the DNA-AuNC nanomachine can be triggered by the protease-activated disassembly of the DNA/peptide complex on the nanomediator, resulting in the aggregation of AuNCs for in vivo protease amplified detection. It is worth noting that our study demonstrates the impressive tumor permeability and accumulation capabilities of the DNA-AuNC nanomachines via in situ amplified self-assembly, thereby facilitating prolonged imaging of TK1 mRNA and cathepsin B both in vitro and in vivo. This strategy presents a versatile and biomarker-specific paradigm for disease diagnosis.

Emergence of Competing Orders and Possible Quantum Spin Liquid in SU(N) Fermions.

In the past few decades, tremendous efforts have been made toward understanding the exotic physics emerging from competition between various ordering tendencies in strongly correlated systems. Employing state-of-the-art quantum Monte Carlo simulation, we investigate an interacting SU(N) fermionic model with varying interaction strength and value of N, and we unveil the ground-state phase diagram of the model exhibiting a plethora of exotic phases. For small values of N-namely, N=2, 3-the ground state is an antiferromagnetic (AFM) phase, whereas in the large-N limit, a staggered valence bond solid (VBS) order is dominant. For intermediate values of N such as N=4, 5, remarkably, our study reveals that distinct VBS orders appear in the weak and strong coupling regimes. More fantastically, the competition between staggered and columnar VBS ordering tendencies gives rise to a Mott insulating phase without spontaneous symmetry breaking (SSB), existing in a large interacting parameter regime, which is consistent with a gapped quantum spin liquid. Our study not only provides a platform to investigate the fundamental physics of quantum many-body systems-it also offers a novel route toward searching for exotic states of matter such as quantum spin liquid in realistic quantum materials.

Bone Targeting Nanoparticles for the Treatment of Osteoporosis.

Osteoporosis (OP) affects millions of people worldwide, especially postmenopausal women and the elderly. Although current available anti-OP agents can show promise in slowing down bone resorption, most are not specifically delivered to the hard tissue, causing significant toxicity. A bone-targeted nanodrug delivery system can reduce side effects and precisely deliver drug candidates to the bone. This review focuses on the progress of bone-targeted nanoparticles in OP therapy. We enumerate the existing OP medications, types of bone-targeted nanoparticles and categorize pairs of the most common bone-targeting functional groups. Finally, we summarize the potential use of bone-targeted nanoparticles in OP treatment. Ongoing research into the development of targeted ligands and nanocarriers will continue to expand the possibilities of OP-targeted therapies into clinical application.

Phage Display Screening of Anchor Peptides for Red Blood Cell-Derived Extracellular Vesicles.

Extracellular vesicles (EVs) are increasingly used for disease diagnosis and treatment. Among them, red blood cell-derived EVs (RBC-EVs) have attracted great attention due to their abundant sources and low risks of gene transfer (RBC-EVs lack nuclear and mitochondrial DNA). Here, we first revealed the high expression level of membrane protein solute carrier family 4 member 1 (SLC4A1) in RBC-EVs through proteomic analysis. We then identified several binding peptides with high affinity for the SLC4A1 extracellular domain (SLC4A1-EC) from phage display library screening. A high affinity of SLC4A1-EC and the three peptides (XRB2, XRE4, and XRH7) were assessed in vitro using surface plasmon resonance analysis and SDS-polyacrylamide gel electrophoresis (SDS-PAGE). The binding sites of SLC4A1-EC and polypeptides were further predicted by LigPlot + analysis, and the results showed that these three polypeptides could bind to part of the hydrophobic residues of SLC4A1-EC. The binding efficiency of the anchor peptides to the RBC-EVs was further verified by flow cytometry and fluorescence imaging. In conclusion, we successfully screened three specific RBC-EV-targeting peptides which could potentially be utilized for isolating RBC-derived EVs from serum samples. More importantly, this peptide could be coupled with targeting peptides to modify RBC-EVs for drug delivery. Our work will provide a viable method for optimizing the function of RBC-EVs.

Targeted immunotherapy: harnessing the immune system to battle multiple myeloma.

Multiple myeloma (MM) remains an incurable hematological malignancy disease characterized by the progressive dysfunction of the patient's immune system. In this context, immunotherapy for MM has emerged as a prominent area of research in recent years. Various targeted immunotherapy strategies, such as monoclonal antibodies, antibody-drug conjugates, bispecific antibodies, chimeric antigen receptor T cells/natural killer (NK) cells, and checkpoint inhibitors have been developed for MM. This review aims to discuss promising experimental and clinical evidence as well as the mechanisms of action underlying these immunotherapies. Specifically, we will explore the design of exosome-based bispecific monoclonal antibodies that offer cell-free immunotherapy options. The treatment landscape for myeloma continues to evolve with the development of numerous emerging immunotherapies. Given their significant advantages in modulating the MM immune environment through immune-targeted therapy, these approaches provide novel perspectives in selecting cutting-edge treatments for MM.

Nanoscale one-dimensional close packing of interfacial alkali ions driven by water-mediated attraction.

The permeability and selectivity of biological and artificial ion channels correlate with the specific hydration structure of single ions. However, fundamental understanding of the effect of ion-ion interaction remains elusive. Here, via non-contact atomic force microscopy measurements, we demonstrate that hydrated alkali metal cations (Na+ and K+) at charged surfaces could come into close contact with each other through partial dehydration and water rearrangement processes, forming one-dimensional chain structures. We prove that the interplay at the nanoscale between the water-ion and water-water interaction can lead to an effective ion-ion attraction overcoming the ionic Coulomb repulsion. The tendency for different ions to become closely packed follows the sequence K+ > Na+ > Li+, which is attributed to their different dehydration energies and charge densities. This work highlights the key role of water molecules in prompting close packing and concerted movement of ions at charged surfaces, which may provide new insights into the mechanism of ion transport under atomic confinement.

Brain-derived extracellular vesicles: Potential diagnostic biomarkers for central nervous system diseases.

Extracellular vesicles (EVs) are membrane-enclosed nanovesicles secreted by cells into the extracellular space and contain functional biomolecules, e.g. signaling receptors, bioactive lipids, nucleic acids, and proteins, which can serve as biomarkers. Neurons and glial cells secrete EVs, contributing to various physiological and pathological aspects of brain diseases. EVs confer their role in the bidirectional crosstalk between the central nervous system (CNS) and the periphery owing to their distinctive ability to cross the unique blood-brain barrier (BBB). Thus, EVs in the blood, cerebrospinal fluid (CSF), and urine can be intriguing biomarkers, enabling the minimally invasive diagnosis of CNS diseases. Although there has been an enormous interest in evaluating EVs as promising biomarkers, the lack of ultra-sensitive approaches for isolating and detecting brain-derived EVs (BDEVs) has hindered the development of efficient biomarkers. This review presents the recent salient findings of exosomal biomarkers, focusing on brain disorders. We summarize highly sensitive sensors for EV detection and state-of-the-art methods for single EV detection. Finally, the prospect of developing advanced EV analysis approaches for the non-invasive diagnosis of brain diseases is presented.

The effect of surface hydrophobicity and hydrophilicity on ion-ion interactions at water-solid interfaces.

Condensation and arrangement of ions at water-solid interfaces are of great importance in the formation of electrical double layers (EDL) and the transport of ions under a confined geometry. So far, the microscopic understanding of interfacial ion configurations is still far from complete, especially when the local ion concentration is high and ion-ion interactions become prominent. In this study, we directly visualized alkali metal cations within the hydrogen-bonding network of water on graphite and Cu(111)-supported graphene surfaces, using qPlus-based noncontact atomic force microscopy (NC-AFM). We found that the codeposition of the alkali cations and water molecules on the hydrophobic graphite surface leads to the formation of an ion-doped bilayer hexagonal ice (BHI) structure, where the ions are repelled from each other and scattered in a disordered distribution. In contrast, the hydrated alkali cations aggregate in one dimension on the more hydrophilic graphene/Cu(111) surface, forming a nematic state with a long-range order. Such a nematic state arises from the delicate interplay between water-ion and water-water interactions under surface confinement. These results reveal the high sensitivity of ion-ion interactions and ionic ordering to the surface hydrophobicity and hydrophilicity.

Harvesting Energy from Changes in Relative Humidity Using Nanoscale Water Capillary Bridges.

We show that nanoscale water capillary bridges (WCB) formed between patchy surfaces can extract energy from the environment when subjected to changes in relative humidity (RH). Our results are based on molecular dynamics simulations combined with a modified version of the Laplace-Kelvin equation, which is validated using the nanoscale WCB. The calculated energy density harvested by the nanoscale WCB is relevant, ≈1700 kJ/m3, and is comparable to the energy densities harvested using available water-responsive materials that expand and contract due to changes in RH.

Programming assembly of biomimetic exosomes: An emerging theranostic nanomedicine platform.

Exosomes have emerged as a promising cell-free therapeutic approach. However, challenges in large-scale production, quality control, and heterogeneity must be overcome before they can be used clinically. Biomimetic exosomes containing key components of natural exosomes have been assembled through extrusion, artificial synthesis, and liposome fusion to address these limitations. These exosome-mimetics (EMs) possess similar morphology and function but provide higher yields, faster large-scale production, and similar size compared to conventional exosomes. This article provides an overview of the chemical and biological properties of various synthetic exosome systems, including nanovesicles (NVs), EMs, and hybrid exosomes. We highlight recent advances in the production and applications of nanobiotechnology and discuss the advantages, limitations, and potential clinical applications of programming assembly of exosome mimetics.

Harnessing knee joint resident mesenchymal stem cells in cartilage tissue engineering.

Osteoarthritis (OA) is a widespread clinical disease characterized by cartilage degeneration in middle-aged and elderly people. Currently, there is no effective treatment for OA apart from total joint replacement in advanced stages. Mesenchymal stem cells (MSCs) are a type of adult stem cell with diverse differentiation capabilities and immunomodulatory potentials. MSCs are known to effectively regulate the cartilage microenvironment, promote cartilage regeneration, and alleviate OA symptoms. As a result, they are promising sources of cells for OA therapy. Recent studies have revealed the presence of resident MSCs in synovial fluid, synovial membrane, and articular cartilage, which can be collected as knee joint-derived MSCs (KJD-MSC). Several preclinical and clinical studies have demonstrated that KJD-MSCs have great potential for OA treatment, whether applied alone, in combination with biomaterials, or as exocrine MSCs. In this article, we will review the characteristics of MSCs in the joints, including their cytological characteristics, such as proliferation, cartilage differentiation, and immunomodulatory abilities, as well as the biological function of MSC exosomes. We will also discuss the use of tissue engineering in OA treatment and introduce the concept of a new generation of stem cell-based tissue engineering therapy, including the use of engineering, gene therapy, and gene editing techniques to create KJD-MSCs or KJD-MSC derivative exosomes with improved functionality and targeted delivery. These advances aim to maximize the efficiency of cartilage tissue engineering and provide new strategies to overcome the bottleneck of OA therapy. STATEMENT OF SIGNIFICANCE: This research will provide new insights into the medicinal benefit of Joint resident Mesenchymal Stem Cells (MSCs), specifically on its cartilage tissue engineering ability. Through this review, the community will further realize promoting joint resident mesenchymal stem cells, especially cartilage progenitor/MSC-like progenitor cells (CPSC), as a preventive measure against osteoarthritis and cartilage injury. People and medical institutions may also consider cartilage derived MSC as an alternative approach against cartilage degeneration. Moreover, the discussion presented in this study will convey valuable information for future research that will explore the medicinal benefits of cartilage derived MSC.

Quantum criticality of a Z_{3}-symmetric spin chain with long-range interactions.

Based on large-scale density matrix renormalization group techniques, we investigate the critical behaviors of quantum three-state Potts chains with long-range interactions. Using fidelity susceptibility as an indicator, we obtain a complete phase diagram of the system. The results show that as the long-range interaction power α increases, the critical points f_{c}^{*} shift towards lower values. In addition, the critical threshold α_{c}(≈1.43) of the long-range interaction power is obtained for the first time by a nonperturbative numerical method. This indicates that the critical behavior of the system can be naturally divided into two distinct universality classes, namely the long-range (α<α_{c}) and short-range (α>α_{c}) universality classes, qualitatively consistent with the classical ϕ^{3} effective field theory. This work provides a useful reference for further research on phase transitions in quantum spin chains with long-range interaction.

A Comparison of Clinical Characteristics in Overweight/Obese and Normal Weight Patients with Psoriasis Vulgaris: A Bicentric Retrospective Observational Study.

Background: Psoriasis is a chronic, inflammatory skin disease that is often accompanied by multiple comorbidities. Obesity is considered an independent risk factor for the development of psoriasis. However, most of the related data are derived from epidemiological studies conducted in the United States of America and Europe. This study aimed to compare the clinical characteristics of patients with psoriasis who are overweight/obese and patients with psoriasis with normal weight in China. Methods: We reviewed the medical records of 208 patients with psoriasis. Based on their body mass index (BMI), the patients were divided into two groups: patients with psoriasis who were overweight/obese and patients with psoriasis with normal weight. Results: The most patients enrolled in this study were men (77.40%). Patients with psoriasis who were overweight/obese had a higher mean age, longer disease duration, and significantly higher Psoriasis Area and Severity Index (PASI) values (P=0.032). Additionally, the incidence of fatty liver, hyperlipidemia, hyperuricemia, and abnormal liver function was higher among patients with psoriasis who were overweight/obese (P<0.05). Linear regression analysis revealed a linear relationship between PASI values and BMI (P=0.016). Moreover, patients with psoriasis who were overweight/obese had significantly higher levels of serum alanine transaminase (ALT), aspartate transaminase (AST), uric acid (UC), total cholesterol (TC), low-density lipoprotein (LDL), and fasting plasma glucose (FPG) (P<0.05) and lower serum high-density lipoprotein (HDL) levels and absolute lymphocyte count (ALC) (P<0.05). Conclusion: Patients with psoriasis who are overweight/obese have more severe psoriatic lesions and metabolic comorbidities. Detailed assessment of the BMI of patients with psoriasis revealed that weight loss may be necessary for patients who are overweight/obese to reduce the risk of metabolic disorders.

Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images.

Relevant to broad applied fields and natural processes, interfacial ionic hydrates have been widely studied by using ultrahigh-resolution atomic force microscopy (AFM). However, the complex relationship between the AFM signal and the investigated system makes it difficult to determine the atomic structure of such a complex system from AFM images alone. Using machine learning, we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images, including the position of each atom and the orientations of water molecules. Furthermore, it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network trained with easily available interfacial water data. Thus, this work provides an efficient and economical methodology that not only opens up avenues to determine atomic structures of more complex systems from AFM images, but may also help to interpret other scientific studies involving sophisticated experimental results.