Improving the color stability of red Monascus pigments by direct protein microgelation.

The application of red Monascus pigments (RMPs) in food storage as well as food processing usually endures harsh environmental conditions. Here, we presented a new encapsulation strategy to improve the stability of RMPs. At first, we managed to realize the azaphilic substitution reaction between orange Monascus pigments (OMPs) and soy protein to produce water-soluble RMPs, i.

Nitration of Tyr37 alters the aggregation pathway of hIAPP and enhances its cytotoxicity.

The amyloid aggregation of hIAPP and the increased level of oxidative stress are closely related to the occurrence and development of type 2 diabetes (T2D). Protein tyrosine nitration is a common post-translational modification under oxidative stress conditions. We previously found that tyrosine nitrated hIAPP (3-NT-hIAPP) has higher cytotoxicity than wild type hIAPP.

Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development.

The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood.

Interpretable Surrogate Learning for Electronic Material Generation.

Despite many accessible AI models that have been developed, it is an open challenge to fully exploit interpretable insights to enable effective materials design and develop materials with desired properties for target applications. Here, we introduce an interpretable surrogate learning framework that can actively design and generate electronic materials (EMGen), akin to producing updated materials with requirements by screening all possible elements and fractions. Taking the materials system with required band gaps as a case study, EMGen exhibits a benchmarking predictive error and a running time of 1.

Enhanced CRISPR/Cas12a Fluorimetry via a DNAzyme-Embedded Framework Nucleic Acid Substrate.

CRISPR/Cas12a fluorimetry has been extensively developed in the biosensing arena, on account of its high selectivity, simplicity, and rapidness. However, typical CRISPR/Cas12a fluorimetry suffers from low sensitivity due to the limited trans-cleavage efficiency of Cas12a, necessitating the integration of other preamplification techniques. Herein, we develop an enhanced CRISPR/Cas12a fluorimetry via a DNAzyme-embedded framework nucleic acid (FNAzyme) substrate, which was designed by embedding four CLICK-17 DNAzymes into a rigid tetrahedral scaffold.

The U -shape relationship between free fatty acid level and adverse outcomes in coronary artery disease patients with hypertension: evidence from a large prospective cohort study.

Background: Evidence is scarce on the effect of free fatty acid (FFA) level in the prognosis of coronary artery disease (CAD) patients with hypertension. This study.

Methods: A large prospective cohort study with a follow-up period of average 2 years was conducted at Xinjiang Medical University Affiliated First Hospital from December 2016 to October 2021.

Detection of foodborne methicillin-resistant Staphylococcus aureus via fluorescence-encoded microsphere and Argonaute-mediated decoding.

Molecular diagnosis of foodborne methicillin-resistant Staphylococcus aureus (MRSA) is crucial for controlling its dissemination and ensuring food safety. However, existing genetic methods are limited by susceptibility to aerosol contamination and restricted to single-gene detection. Herein, a fluorescent biosensor employing fluorescence-encoded microspheres and Argonaute-mediated decoding is developed, enabling ultrasensitive, accurate, and duplex detection of MRSA genes.

Unlocking Potential of Pyrochlore in Energy Systems via Soft Voting Ensemble Learning.

In traditional machine learning (ML)-based material design, the defects of low prediction accuracy, overfitting and low generalization ability are mainly caused by the training of a single ML model. Here, a Soft Voting Ensemble Learning (SVEL) approach is proposed to solve the above issues by integrating multiple ML models in the same scene, thus pursuing more stable and reliable prediction. As a case study, SVEL is applied to develop the broad chemical space of novel pyrochlore electrocatalysts with the molecular formula of ABO, to explore promising pyrochlore oxides and accelerate predictions of unknown pyrochlore in the periodic table.

Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy.

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer.

Ethanolic extract from Sophora moorcroftiana inhibit cell proliferation and alter the mechanical properties of human cervical cancer.

Background: Cervical cancer is one of the most common gynecological malignancies. Previous studies have shown that the ethanol extract of Sophora moorcroftiana seeds (EESMS) possesses an antiproliferative effect on several tumors in vitro. Therefore, in this study, we assessed the impact of EESMS on human cervical carcinoma (HeLa) cell proliferation.

High-Entropy Alloy Electrocatalysts Bidirectionally Promote Lithium Polysulfide Conversions for Long-Cycle-Life Lithium-Sulfur Batteries.

High-entropy alloys (HEAs) have attracted considerable attention, owing to their exceptional characteristics and high configurational entropy. Recent findings demonstrated that incorporating HEAs into sulfur cathodes can alleviate the shuttling effect of lithium polysulfides (LiPSs) and accelerate their redox reactions. Herein, we synthesized nano PtCuFeCoNi HEAs on hollow carbons (HCs; denoted as HEA/HC) by a facile pyrolysis strategy.

4D-Printed MXene-Based Artificial Nerve Guidance Conduit for Enhanced Regeneration of Peripheral Nerve Injuries.

Repairing larger defects (>5 mm) in peripheral nerve injuries (PNIs) remains a significant challenge when using traditional artificial nerve guidance conduits (NGCs). A novel approach that combines 4D printing technology with poly(L-lactide-co-trimethylene carbonate) (PLATMC) and TiCT MXene nanosheets is proposed, thereby imparting shape memory properties to the NGCs. Upon body temperature activation, the printed sheet-like structure can quickly self-roll into a conduit-like structure, enabling optimal wrapping around nerve stumps.

Sea Cucumber-Inspired Microneedle Nerve Guidance Conduit for Synergistically Inhibiting Muscle Atrophy and Promoting Nerve Regeneration.

Muscle atrophy resulting from peripheral nerve injury (PNI) poses a threat to a patient's mobility and sensitivity. However, an effective method to inhibit muscle atrophy following PNI remains elusive. Drawing inspiration from the sea cucumber, we have integrated microneedles (MNs) and microchannel technology into nerve guidance conduits (NGCs) to develop bionic microneedle NGCs (MNGCs) that emulate the structure and piezoelectric function of sea cucumbers.

Anode-Free Aqueous Aluminum Ion Batteries.

Aqueous aluminum ion batteries (AAIBs) possess the advantages of high safety, cost-effectiveness, eco-friendliness and high theoretical capacity. However, the AlO film on the Al anode surface, a natural physical barrier to the plating of hydrated aluminum ions, is a key factor in the decomposition of the aqueous electrolyte and the severe hydrogen precipitation reaction. To circumvent the obnoxious Al anode, a proof-of-concept of an anode-free AAIB is first proposed, in which AlTiO, as a cathode pre-aluminum additive (Al source), can replenish Al loss by over cycling.

Safety and clinical efficacy of microwave ablation combined with percutaneous vertebroplasty in the treatment of multisegmental spinal metastases.

Objective: To evaluate the safety and efficacy of microwave ablation (MWA) combined with percutaneous vertebroplasty (PVP) in the treatment of multisegmental (2-3 segments) osteolytic spinal metastases.

Materials And Methods: This study comprised a retrospective analysis of data from 20 patients with multisegmental (2-3 segments) osteolytic spinal metastases who received MWA combined with PVP. The visual analog scale (VAS) score, Oswestry Disability Index (ODI) score, Quality of Life Questionnaire-Bone Metastases 22 (QLQ-BM22), and local recurrence before and after the operation were measured.

3D printing nacre powder/sodium alginate scaffold loaded with PRF promotes bone tissue repair and regeneration.

Bone defects are a common complication of bone diseases, which often affect the quality of life and mental health of patients. The use of biomimetic bone scaffolds loaded with bioactive substances has become a focal point in the research on bone defect repair. In this study, composite scaffolds resembling bone tissue were created using nacre powder (NP) and sodium alginate (SA) through 3D printing.

Clinical efficacy and safety of microwave ablation combined with percutaneous osteoplasty for palliative treatment in pelvic osteolytic metastases.

Objectives: To evaluate the impact of microwave ablation (MWA) on pain relief, quality of life, mobility, and local tumour progression in adult patients with pelvic osteolytic bone metastasis and to test the safety of MWA.

Methods: This study retrospectively analysed the data from 20 patients with pelvic osteolytic metastases who received MWA combined with percutaneous osteoplasty (POP). The visual analogue scale (VAS), musculoskeletal tumour society system (MSTS), and Quality of Life Questionnaire-Bone Metastases 22 (QLQ-BM22) were used to evaluate the pain, limb function, and quality of life.

Mitochondrial Localized In Situ Self-Assembly Reprogramming Tumor Immune and Metabolic Microenvironment for Enhanced Cancer Therapy.

The inherent immune and metabolic tumor microenvironment (TME) of most solid tumors adversely affect the antitumor efficacy of various treatments, which is an urgent issue to be solved in clinical cancer therapy. In this study, a mitochondrial localized in situ self-assembly system is constructed to remodel the TME by improving immunogenicity and disrupting the metabolic plasticity of cancer cells. The peptide-based drug delivery system can be pre-assembled into nanomicelles in vitro and form functional nanofibers on mitochondria through a cascade-responsive process involving reductive release, targeted enrichment, and in situ self-assembly.

Surfactants Influencing the Biocatalytic Performance of Natural Alkane-Degrading Bacteria via Interfacial Biocatalysis in Pickering Emulsions.

Setting superhydrophobic sp. as an example, the hydrophobic bacteria acting as demulsifying agents of surfactant-stabilized conventional emulsions, vice versa, the synergistic/antagonistic influence of nonionic surfactants (Tween 80 or Span 80) on the stability of the bacteria-stabilized Pickering emulsions was investigated. At the same time, the activated/suppression effect of nonionic surfactants on microbial degradation of tetradecane, which exhibited a dose-response relationship, was also found.

The Tautomerase Activity of Tumor Exosomal MIF Promotes Pancreatic Cancer Progression by Modulating MDSC Differentiation.

Pancreatic cancer is a deadly disease that is largely resistant to immunotherapy, in part because of the accumulation of immunosuppressive cells in the tumor microenvironment (TME). Much evidence suggests that tumor-derived exosomes (TDE) contribute to the immunosuppressive activity mediated by myeloid-derived suppressor cells (MDSC) within the pancreatic cancer TME. However, the underlying mechanisms remain elusive.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future.

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system.

RotNet: A Rotationally Invariant Graph Neural Network for Quantum Mechanical Calculations.

Deep learning has proven promising in biological and chemical applications, aiding in accurate predictions of properties such as atomic forces, energies, and material band gaps. Traditional methods with rotational invariance, one of the most crucial physical laws for predictions made by machine learning, have relied on Fourier transforms or specialized convolution filters, leading to complex model design and reduced accuracy and efficiency. However, models without rotational invariance exhibit poor generalization ability across datasets.