Injectable Regenerated Silk Fibroin Micro/Nanosphere with Enhanced Permeability and Stability for Osteoarthritis Therapy.

In the therapy of early-stage osteoarthritis, to accomplish full infiltration of subchondral bone and cartilage, and to target osteoclast and chondrocyte simultaneously remain challenges in biomaterials design. Herein, a novel hierarchical drug delivery system is introduced, with micrometer-scale outer layer spheres composed of regenerated silk fibroin, characterized by connected porous structure through the n-butanol and regenerated silk fibroin combined emulsion route and freezing method. The design effectively resists clearance from the joint cavity, ensuring stable delivery and prolonged residence time within the joint space. Additionally, the system incorporates phenylboronic acid-enriched silk fibroin nanoparticles, stabilized through chemical cross-linking, which encapsulate isoliquiritin derived from Glycyrrhiza uralensis. These nanoparticles facilitate complete penetration of the cartilage extracellular matrix, exhibit pH-responsive behavior, neutralize reactive oxygen species, and enable controlled drug release, thereby enhancing therapeutic efficacy. The in vitro and in vivo experiments both demonstrate that the composite micro/nanospheres not only inhibit osteoclastogenesis with bone loss in subchondral bone and osteophyte formation, but also mitigate chondrocytes apoptosis, reduce oxidative stress associated with cartilage degeneration, and ameliorate neuropathic hyperalgesia, with the underlying mechanisms being elucidated. The study indicates that such an injectable strategy combining organic biomaterials with Chinese medicine holds substantial promise for the treatment of early osteoarthritis.

Correction: Wang et al. Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation. Sensors 2023, 23, 9488.

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Long-Range π-π Stacking Brings High Electron Delocalization for Enhanced Photocatalytic Activity in Hydrogen-Bonded Organic Framework.

Unlike many studies that regulate transport and separation behaviour of photogenerated charge carriers through controlling the chemical composite, our work demonstrates this goal can be achieved through simply tuning the molecular π-π packing from short-range to long-range within hydrogen-bonded organic frameworks (HOFs) without altering the building blocks or network topology. Further investigations reveal that the long-range π-π stacking significantly promotes electron delocalization and enhances electron density, thereby effectively suppressing electron-hole recombination and augmenting the charge transfer rate. Simultaneously, acting as a porous substrate, it boosts electron density of Pd nanoparticle loaded on its surfaces, resulting in remarkable CO2 photoreduction catalytic activity (CO generation rate: 48.1 µmol/g/h) without the need for hole scavengers. Our study provide insight into regulating the charge carrier behaviours in molecular assemblies based on hydrogen bonds, offering a new clue for efficient photocatalyst design.

The effect of neutrophil extracellular traps in venous thrombosis.

Neutrophil extracellular traps (NETs) as special release products of neutrophils have received extensive attention. They are composed of decondensed chromatin and coated with nucleoproteins, including histones and some granulosa proteins. NETs can form a network structure to effectively capture and eliminate pathogens and prevent their spread. Not only that, recent studies have shown that NETs also play an important role in venous thrombosis. This review provides the most important updated evidence regarding the mechanism of NETs formation and the role of NETs in the process of venous thrombosis. The potential prophylactic and therapeutic value of NETs in venous thrombotic disease will also be discussed.

Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation.

Medical image segmentation primarily utilizes a hybrid model consisting of a Convolutional Neural Network and sequential Transformers. The latter leverage multi-head self-attention mechanisms to achieve comprehensive global context modelling. However, despite their success in semantic segmentation, the feature extraction process is inefficient and demands more computational resources, which hinders the network's robustness. To address this issue, this study presents two innovative methods: PTransUNet (PT model) and C-PTransUNet (C-PT model). The C-PT module refines the Vision Transformer by substituting a sequential design with a parallel one. This boosts the feature extraction capabilities of Multi-Head Self-Attention via self-correlated feature attention and channel feature interaction, while also streamlining the Feed-Forward Network to lower computational demands. On the Synapse public dataset, the PT and C-PT models demonstrate improvements in DSC accuracy by 0.87% and 3.25%, respectively, in comparison with the baseline model. As for the parameter count and FLOPs, the PT model aligns with the baseline model. In contrast, the C-PT model shows a decrease in parameter count by 29% and FLOPs by 21.4% relative to the baseline model. The proposed segmentation models in this study exhibit benefits in both accuracy and efficiency.

Design of an achromatic optical polarization-insensitive zoom metalens.

A diffractive lens based on metasurfaces has many advantages such as flatness, small aberrations, and compactness. The focal length can be adjusted by changing the lateral displacement between a pair of conjugate metasurfaces while fixing their axial distance, thereby forming a very compact zoom lens. However, chromatic aberration of diffractive optical elements restricts this system to working at one wavelength. This Letter proposes a metalens design method based on novel three-layer polarization-insensitive nanoposts, which can improve transmission amplitude and satisfy the achromatic zoom function with wavelengths ranging from 1310 nm to 1550 nm. The focal length can be adjusted from 31.2 µm to 19.8 µm, corresponding to extremal wavelength-dependent focal length relative deviation of 8.98%. This achromatic zoom metalens design method could have applications in various fields including augmented reality and integrated optical systems.

Fe-ZIF8 Coating Cu Foil Derived Carbon as A pH-Universal Electrocatalyst for Efficient Oxygen Reduction Reaction.

It is a great challenge to fabricate highly efficient pH-universal electrocatalysts for oxygen reduction reaction (ORR). Herein, a facile strategy, which includes coating the Fe modified ZIF8 on Cu foil and in situ pyrolysis to evaporate and dope Cu into the MOF derived carbon, is developed to fabricate Fe/Cu-N co-doped carbon material (Cu/Fe-NC). Profiting from the modulated electron distribution and textual properties, well-designed Cu/Fe-NC exhibits superior half-wave potential (E1/2 ) of 0.923 V in alkaline, 0.757 V in neutral and comparable 0.801 V in acid electrolytes, respectively. Furthermore, the ultralow peroxides yield of ORR demonstrates the high selectivity of Cu/Fe-NC in full pH scale electrolytes. As expected, the self-made alkaline and neutral zinc-air batteries equipped with Cu/Fe-NC cathode display excellent discharge voltages, outstanding peak power densities and remarkable stability. This work opens a new way to fabricate highly efficient and pH-universal electrocatalysts for ORR through strategy of Fe/Cu-N co-doping, Cu foil evaporation and carbon defects capture.

Enhanced Isolation of Fetal Nucleated Red Blood Cells by Enythrocyte-Leukocyte Hybrid Membrane-Coated Magnetic Nanoparticles for Noninvasive Pregnant Diagnostics.

Fetal nucleated red blood cells (fNRBCs) in maternal peripheral blood containing the whole genetic information of the fetus may serve for noninvasive pregnant diagnostics (NIPD). However, the fetal cell-based NIPD is seriously limited by the poor purity of the isolated fNRBCs. Recently, the biomimetic cell membrane-camouflaged nanoparticles containing outstanding features have been widely used to detect and isolate rare cells from the peripheral blood samples. In this work, enythrocyte (RBC) and leukocyte (WBC) membranes are fused and coated onto magnet nanoparticles and then modified with anti-CD147 to isolate fNRBCs from the maternal peripheral blood with significant efficiency (∼90%) and purity (∼87%) in simulated spiked blood samples. Further, fNRBCs were isolated and identified from a series of maternal peripheral blood samples coming from pregnant women of 11-13 gestational weeks, and different chromosomal aneuploidies were diagnosed using fNRBCs isolated from maternal blood in early pregnancy. Our strategy may offer additional opportunity to overcome the limitations of current cell-based NIPD platforms.

Cyclin F and KIF20A, FOXM1 target genes, increase proliferation and invasion of ovarian cancer cells.

Increased expression of FOXM1 is observed in a variety of human malignancies. The downstream target genes of FOXM1 involved in tumorigenesis and development are not fully elucidated in ovarian cancer. Here, we identified Cyclin F, a substrate recognition subunit of SCF (Skp1-Cul1-F-box protein) complex, and Kinesin Family Member 20A (KIF20A) were transcriptionally regulated by FOXM1 in ovarian cancer. Accordingly, Cyclin F and KIF20A were commonly overexpressed in ovarian cancer. Functionally, forced expression of Cyclin F or KIF20A significantly enhanced while knockdown of them decreased proliferation and invasion of ovarian cancer cells. Importantly, high levels of Cyclin F and KIF20A correlated with poor prognosis in patients with ovarian cancer. Our findings indicate that Cyclin F and KIF20A are functional targets of FOXM1 which might be potential drug targets in ovarian cancer.

Silica microbeads capture fetal nucleated red blood cells for noninvasive prenatal testing of fetal ABO genotype.

ABO hemolytic disease of the newborn (ABO-HDN), which may cause neonatal jaundice and polycythemia, or even stillbirth or neonatal death, is widespread in China. Prenatal testing for the fetal ABO blood group can reduce unnecessary concerns or ensure prompt treatment. Herein, we presented a method to employ high-density silica microbeads (SiO2 MBs) for capturing fetal nucleated red blood cells (fnRBCs) in maternal peripheral blood, and we detected the ABO genotype of the fetus using these captured cells. We evaluated 52 patients using the SiO2 MBs. Among 26 pregnant women with type O blood, 8 (30.8%) of the fetuses had type A blood, 5 (19.2%) had type B blood, and 13 (50%) had type O blood. SRY genes were detected in all 27 male fetuses. This study represents a simple and effective method for noninvasive prenatal detection of the fetal ABO genotype. We believe that this method has great potential for noninvasive prenatal testing of the fetal Rh blood group and other fetal diseases as well.

High-throughput isolation of fetal nucleated red blood cells by multifunctional microsphere-assisted inertial microfluidics.

Being easy, safe and reliable, non-invasive prenatal diagnosis (NIPD) has been greatly pursued in recent years. Holding the complete genetic information of the fetus, fetal nucleated red blood cells (fNRBCs) are viewed as a suitable target for NIPD application. However, effective separating fNRBCs from maternal peripheral blood for clinic use still faces great challenges, given that fNRBCs are extremely rare in maternal blood circulation. Here, by combining the high-throughput inertial microfluidic chip with multifunctional microspheres as size amplification, we develop a novel method to isolate fNRBCs with high performance. To enlarge the size difference between fNRBCs and normal blood cells, we use the gelatin coated microspheres to capture fNRBCs with anti-CD147 as specific recognizer at first. The size difference between fNRBCs captured by the microspheres and normal blood cells makes it easy to purify the captured fNRBCs through the spiral microfluidic chip. Finally, the purified fNRBCs are mildly released from the microspheres by enzymatically degrading the gelatin coating. Cell capture efficiency about 81%, high purity of 83%, as well as cell release viability over 80% were achieved using spiked samples by this approach. Additionally, fNRBCs were successfully detected from peripheral blood of pregnant women with an average of 24 fNRBCs per mL, suggesting the great potential of this method for clinical non-invasive prenatal diagnosis.

Electrospun degradable Zn-Mn oxide hierarchical nanofibers for specific capture and efficient release of circulating tumor cells.

Constructing biological affinity devices is considered as an effective strategy for isolating circulating tumor cells (CTCs), and electrospun nanofibers (ESNFs) have recently received attention. However, the current research focuses on polymer fibers, and fabricating stimuli-responsive inorganic nanofibers for cancer diagnosis and analysis is still challenging. In this work, Zn-Mn oxide nanofibers (ZnMnNFs) are used to capture and purify cancer cells after modification with specific antibodies. Then, the hierarchical nanofibers are degraded by reductive weak acid to release the captured cells efficiently without residues. Fusion of Zn and Mn, two transition metals, enhances the surface activity of oxides so that ZnMnNFs are easier to be degraded and modified. By using MCF-7 cancer cells, the cell capture efficiency of ZnMnNFs is up to 88.2%. Furthermore, by using citric acid, it is discovered that, by comparison with Mn oxide nanofibers, the cell release efficiency of ZnMnNFs is improved to 95.1% from 15.4%. In addition, the viability of released cells exceeds 90%. Lastly, the robustness of ZnMnNFs substrates is tested in peripheral blood from breast cancer patients (BCP) and colorectal cancer patients (CCP). Combined with fluorescence labeling, CTCs are confirmed to be isolated from all the clinical samples. This is the first trial of using ternary inorganic ESNFs for cancer cell capture. It is anticipated that the degradable ESNFs will provide biocompatible theranostic platforms and overcome the current limitations of cell release for high-precision gene analysis.

Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

Motivation: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Results: Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. Availability and implementation: The PrabHot webserver is freely available at http://denglab.org/PrabHot/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification.

Motivation: Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Results: Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. Availability and implementation: The source code and data are available at http://denglab.org/NeuraNetL2GO/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

A valve-based microfluidic device for on-chip single cell treatments.

Assays toward single-cell analysis have attracted the attention in biological and biomedical researches to reveal cellular mechanisms as well as heterogeneity. Yet nowadays microfluidic devices for single-cell analysis have several drawbacks: some would cause cell damage due to the hydraulic forces directly acting on cells, while others could not implement biological assays since they could not immobilize cells while manipulating the reagents at the same time. In this work, we presented a two-layer pneumatic valve-based platform to implement cell immobilization and treatment on-chip simultaneously, and cells after treatment could be collected non-destructively for further analysis. Target cells could be encapsulated in sodium alginate droplets which solidified into hydrogel when reacted with Ca2+ . The size of hydrogel beads could be precisely controlled by modulating flow rates of continuous/disperse phases. While regulating fluid resistance between the main channel and passages by the integrated pneumatic valves, on-chip capture and release of hydrogel beads was implemented. As a proof of concept for on-chip single-cell treatments, we showed cellular live/dead staining based on our devices. This method would have potential in single cell manipulation for biochemical cellular assays.

TiO2 nanopillar arrays coated with gelatin film for efficient capture and undamaged release of circulating tumor cells.

Circulating tumor cells (CTCs) are important for the detection and treatment of cancer. Nevertheless, a low density of circulating tumor cells makes the capture and release of CTCs an obstacle. In this work, TiO2 nanopillar arrays coated with gelatin film were synthesized for efficient capture and undamaged release of circulating tumor cells. The scanning electron microscope and atomic force microscope images demonstrate that the substrate has a certain roughness. The interaction between the cell membrane and the nanostructure substrate contributes to the efficient capture of CTC (capture efficiency up to 94.98%). The gelatin layer has excellent biocompatibility and can be rapidly digested by matrix metalloproteinase (MMP9), which realizes the non-destructive release of CTCs (0.1 mg ml-1, 5 min, nearly 100% release efficiency, activity 100%). Therefore, by our strategy, the CTCs can be efficiently captured and released undamaged, which is important for subsequent analysis.

Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network.

BACKGROUND: Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs. RESULTS: In this work, we propose a novel approach, PLIPCOM, using two groups of network features to detect protein-lncRNA interactions. In particular, diffusion features and HeteSim features are extracted from protein-lncRNA heterogenous network, and then combined to build the prediction model using the Gradient Tree Boosting (GTB) algorithm. Our study highlights that the topological features of the heterogeneous network are crucial for predicting protein-lncRNA interactions. The cross-validation experiments on the benchmark dataset show that PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting protein-lncRNA interactions. We also prove the robustness of the proposed method on three unbalanced data sets. Moreover, our case studies demonstrate that our method is effective and reliable in predicting the interactions between lncRNAs and proteins. AVAILABILITY: The source code and supporting files are publicly available at: http://denglab.org/PLIPCOM/ .

Highly sensitive and rapid isolation of fetal nucleated red blood cells with microbead-based selective sedimentation for non-invasive prenatal diagnostics.

Non-invasive prenatal diagnostics (NIPD) has been an emerging field for prenatal diagnosis research. Carrying the whole genome coding of the fetus, fetal nucleated red blood cells (FNRBCs) have been pursued as a surrogate biomarker traveling around in maternal blood. Here, by combining a unique microbead-based centrifugal separation and enzymatic release, we demonstrated a novel method for FNRBC isolation from the blood samples. First, the gelatin-coated silica microbeads were modified with FNRBC-specific antibody (anti-CD147) to capture the target cells in the blood samples. Then, the density difference between microbead-bound FNRBCs and normal blood cells enables the purification of FNRBCs via an improved high-density percoll-based separation. The non-invasive release of FNRBCs can then be achieved by enzymatically degrading the gelatin film on the surface of the microbeads, allowing a gentle release of the captured target cells with as high as 84% efficiency and ∼80% purity. We further applied it to isolate fetal cells from maternal peripheral blood. The released cells were analyzed by real-time polymerase chain reaction to verify their fetal origin and fluorescent in situ hybridization to detect fetal chromosome disorders. This straightforward and reliable alternative platform for FNRBC detection may have the potential for realizing facile NIPD.

A boosting approach for prediction of protein-RNA binding residues.

BACKGROUND: RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex. RESULTS: We propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties. In cross-validation experiments on the RBP170 data set show that PredRBR achieves an overall accuracy of 0.84, a sensitivity of 0.85, MCC of 0.55 and AUC of 0.92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost. We further calculate the feature importance of different feature categories and find that structural neighborhood characteristics are critical in the recognization of RNA binding residues. Also, PredRBR yields significantly better prediction accuracy on an independent test set (RBP101) in comparison with other state-of-the-art methods. CONCLUSIONS: The superior performance over existing RNA-binding residue prediction methods indicates the importance of the gradient tree boosting algorithm combined with the optimal selected features.

Cancer-Associated Fibroblasts Autophagy Enhances Progression of Triple-Negative Breast Cancer Cells.

BACKGROUND Cancer-associated fibroblasts (CAFs) are key factors in malignant tumor initiation, progression, and metastasis. However, the effect of CAFs autophagy on triple-negative breast cancer (TNBC) cells is not clear. In this study, the growth effect of TNBC cells regulated by CAFs autophagy was evaluated. MATERIAL AND METHODS CAFs were obtained from invasive TNBC tumors and identified by Western blot and immunofluorescence staining assay. CAFs were co-cultured with TNBC cells, and migration and invasion were evaluated by Matrigel-coated Transwell and Transwell inserts. TNBC cells growth was detected by MTT assay, and epithelial-mesenchymal transition (EMT) regulated by CAFs was evaluated by Western blot assay. RESULTS CAFs were identified by the high expression of α-smooth muscle actin (α-SMA) protein. Autophagy-relevant Beclin 1 and LC3-II/I protein conversion levels in CAFs were higher than those in NFs (P<0.05). TNBC cells migration, invasion, and proliferation levels were significantly improved in the CAFs-conditioned medium (CAFs-CM) group, compared with the other 3 groups (P<0.05). TNBC cells vimentin and N-cadherin protein levels were upregulated and E-cadherin protein level was downregulated in the CAFs-CM group compared with the control group (P<0.05). Further study indicated b-catenin and P-GSK-3ß protein levels, which are the key proteins in the Wnt/ß-catenin pathway, were upregulated in the CAFs-CM group compared with the control group (P<0.05). CONCLUSIONS Our data demonstrated CAFs autophagy can enhance TNBC cell migration, invasion, and proliferation, and CAFs autophagy can induce TNBC cells to engage in the EMT process through the Wnt/ß-catenin pathway.