Exploring the Cocktail Factor Approach to Generate Salivary Gland Progenitors through Co-Culture Techniques.

BACKGROUND: The derivation of salivary gland (SG) progenitors from pluripotent stem cells (PSCs) presents significant potential for developmental biology and regenerative medicine. However, the existing protocols for inducing SG include limited factors, making it challenging to mimic the in vivo microenvironment of embryonic SGs. METHODS: We reported a cocktail factor approach to promote the differentiation of mouse embryonic stem cell (mESC)-derived oral epithelium (OE) into SG progenitors through a three-dimensional co-culture method. Upon confirming that the embryonic SG can promote the differentiation of mESC-derived OE, we performed RNA sequence analysis to identify factors involved in the differentiation of SG progenitors. RESULTS: Our findings highlight several efficient pathways related to SG development, with frequent appearances of four factors: IFN-γ, TGF-ß2, EGF, and IGF-1. The combined treatment using these cocktail factors increased the expression of key SG progenitor markers, including Sox9, Sox10, Krt5, and Krt14. However, absence of any one of these cocktail factors did not facilitate differentiation. Notably, aggregates treated with the cocktail factor formed SG epithelial-like structures and pre-bud-like structures on the surface. CONCLUSION: In conclusion, this study offers a novel approach to developing a differentiation protocol that closely mimics the in vivo microenvironment of embryonic SGs. This provides a foundation for generating PSC-derived organoids with near-physiological cell behaviors and structures.

Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.

Differentiation and Characterization of Cystic Fibrosis Transmembrane Conductance Regulator Knockout Human Pluripotent Stem Cells into Salivary Gland Epithelial Progenitors.

The differentiation of pluripotent stem cells has been used to study disease mechanisms and development. We previously described a method for differentiating human pluripotent stem cells (hPSCs) into salivary gland epithelial progenitors (SGEPs). Here, cystic fibrosis transmembrane conductance regulator (CFTR) knockout hPSCs were differentiated into SGEPs derived from CFTR knockout hESCs (CF-SGEPs) using the same protocol to investigate whether the hPSC-derived SGEPs can model the characteristics of CF. CF-a disease that affects salivary gland (SG) function-is caused by mutations of the CFTR gene. Firstly, we successfully generated CFTR knockout hPSCs with reduced CFTR protein expression using the CRISPR-Cas9 system. After 16 days of differentiation, the protein expression of CFTR decreased in SGEPs derived from CFTR knockout hESCs (CF-SGEPs). RNA-Seq revealed that multiple genes modulating SG development and function were down-regulated, and positive regulators of inflammation were up-regulated in CF-SGEPs, correlating with the salivary phenotype of CF patients. These results demonstrated that CFTR suppression disrupted the differentiation of hPSC-derived SGEPs, which modeled the SG development of CF patients. In summary, this study not only proved that the hPSC-derived SGEPs could serve as manipulable and readily accessible cell models for the study of SG developmental diseases but also opened up new avenues for the study of the CF mechanism.

Novel Preparation of Graphene Foam with Adjustable Dielectric Properties for Efficient Microwave Absorption.

Graphene foams (GFs) prepared via a hydrothermal method can be vacuum-dried directly without the need for freezing by adding naphthalene to the graphene hydrogels. By optimizing the GF preparation process, it is also possible to adjust the GF's dielectric properties by varying the amount of naphthalene added. Based on the comparison results, it was observed that controlling the addition of naphthalene could also modify the internal structure of GF and effectively regulate its dielectric properties. GF-80, synthesized with 80 g of naphthalene, exhibited exceptional microwave absorption (MA) performance. At a mass content of only 2% and a matching thickness of 3.38 mm, a minimum reflection loss (RLmin) of -55.89 dB was achieved. Moreover, GF-80, with a thickness of 2.31 mm, was shown to achieve a bandwidth of RL less than -10 dB across 6.88 GHz.

Functionalized hydrogel-microsphere composites stimulating neurite outgrowth for vascularized bone regeneration.

Neurovascularized bone regeneration remains an enormous challenge in the clinic. Biomaterials mimicking the developmental microenvironment might be promising tools to enhance tissue regeneration. In this study, functionalized hydrogel-microsphere composites are developed to enhance bone regeneration via a recapitulating neurovascularized microenvironment. RGD peptide and the porous structure generated by the degradation of gelatin microspheres (GMs) are beneficial for the proliferation and migration of human mesenchymal stem cells (hMSCs); mesoporous silica nanoparticles (MSNs) promote osteogenic differentiation of hMSCs through the delivery of BFP-1 peptide; the QK peptide from the GMs is sustained-released to recruit endogenous endothelial cells (ECs), and IK19 peptide grafted on the hydrogel guides the neurite outgrowth. The in vivo results show that the hydrogel-microsphere composites not only promote new bone formation, but also facilitate nerve infiltration and angiogenesis. Furthermore, the neurovascularized niche created by this composite stimulated neurite growth through MAPK, PI3K, IL17 and TNF signaling pathways, enabling vascularized bone regeneration. The findings suggest a novel bioengineering approach to guide the construction of neurovascularized bone repair materials, which is beneficial for achieving functional bone regeneration and repair.

Application of functionalized hyaluronic acid hydrogel with the activity of regulating the behaviors of stem cells in repairing rabbit knee articular cartilage.

Using injectable hydrogels loaded with mesenchymal stem cells (MSCs) to repair chondral defects is a new trend of cartilage tissue engineering in recent years. In this study, hyaluronic acid (HA) hydrogels containing the system of sustained-release Kartogenin (KGN) and modified by RGD and HAV peptides were used to facilitate repair of cartilage defect in the knee joint of rabbits. Different groups of implants were injected into osteochondral defects, and samples were taken 4 weeks after operation. Through the qualitative and quantitative analysis of Micro-CT, it can be seen that both FH (unloaded cell group) and R + FH groups (allogeneic cell group) can repair osteochondral defects well, and the amount of bone formation is high, which is close to the intact cartilage groups. Macroscopic observation and histological staining analysis showed that except for the intact cartilage group, FH group obtained the highest score. The morphology of the cartilage tissue in the FH groups was more regular and continuous than that in R + FH and H + FH (xenogeneic cell group) groups, approaching that of native cartilage. Immunohistochemical staining of Collagen II (Col II) showed that the expression and morphology of Col II in FH groups were similar to those in intact cartilage tissue. Interestingly, through in vivo experiments, this functionalized hyaluronic acid hydrogel can effectively promote the rapid repair of rabbit knee cartilage defects within one month.

MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI.

Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.

Orchestrating Chemical and Physical Cross-Linking in Protein Hydrogels to Regulate Embryonic Stem Cell Growth.

Protein hydrogels are ideal candidates for next-generation biomaterials due to their genetically programmable properties. Herein, we report an entirely protein-based hydrogel as an artificial extracellular matrix (ECM) for regulating the embryonic stem cell growth. A synergy between chemical and physical cross-linking was achieved in one step by SpyTag/SpyCatcher reaction and P zipper association at 37 °C. The hydrogels' stress relaxation behaviors can be tuned across a broad spectrum by single-point mutation on a P zipper. It has been found that faster relaxation can promote the growth of HeLa tumor spheroids and embryonic stem cells, and mechanical regulation of embryonic stem cells occurs via retention of the cells at the G1 phase. The results highlight the promise of genetically encoded protein materials as a platform of artificial ECM for understanding and controlling the complex cell-matrix interactions in a 3D cell culture.

Controlling the electrochemical activity of dahlia-like ß-NiS@rGO by interface polarization.

Transition metal sulfides have become more and more important in the field of energy storage due to their superior chemical and physical properties. Herein, dahlia ß-NiS with a rough surface and ß-NiS@reduced graphene oxide (rGO) have been green synthesized by a one-step hydrothermal method. The interface characteristics of ß-NiS@ rGO composites have been systematically studied by XPS, Raman, and first-principles calculations. It is found that the residual O atoms in the interface and the polarization charge generated by them play an important role in performance enhancement. The NiS@rGO composite material has the best electrochemical performance when the C/O ratio is 6.48. Furthermore, we designed a NiS@rGO//rGO asymmetric supercapacitor with a potential window of 1.7 V. Its excellent energy density and power density demonstrate the advantages of the optimized NiS@rGO electrode. When the power density is 850 W kg-1, the energy density can reach 40.4 W h kg-1. Even at a power density of up to 6800 W kg-1, the energy density can be maintained at 17.6 W h kg-1. These encouraging results provide a possible pathway for designing asymmetric supercapacitors with ultra-high performance and a feasible strategy for the precise control of electrochemical performance.

Multi-model adaptive fusion-based graph network for Alzheimer's disease prediction.

Alzheimer's disease (AD) is a common cognitive disorder. Recently, many computer-aided diagnostic techniques have been used for AD prediction utilizing deep learning technology, among which graph neural networks have received increasing attention owing to their ability to model sample relationships on large population graphs. Most of the existing graph-based methods predict diseases according to a single model, which makes it difficult to select an appropriate node embedding algorithm for a certain classification task. Moreover, integrating data from different patterns into a unified model to improve the quality of disease diagnosis remains a challenge. Hence, in this study, we aimed to develop a multi-model fusion framework for AD prediction. A spectral graph attention model was used to aggregate intra- and inter-cluster node embeddings of normal and diseased populations, whereafter, a bilinear aggregation model was applied as an auxiliary model to enhance the abnormality degree in different categories of populations, and finally, an adaptive fusion module was designed to dynamically fuse the results of both models and enhance AD prediction. Compared to other comparison methods, the model proposed in this study provides the best results.

A non-heat-source process for preparing graphene oxide with low energy consumption.

As the most widely used method for preparing graphene oxide (GO), Hummers' method always involves a key step, that is adding water to concentrated sulfuric acid. We found that if this process is cancelled, the oxidation degree of GO will be significantly reduced. This means that the heat released during concentrated sulfuric acid dilution will promote further oxidation of GO. In this paper, we fully utilize the heat released during concentrated sulfuric acid dilution to develop a new non-heat-source process without any low-/high-temperature auxiliar, exponentially reducing the energy consumption and largely avoiding the frequent temperature control. The result shows that GO prepared by Hummers' method and that prepared by the proposed process show a similar structure, composition, morphology, and defect degree. Meanwhile, the corresponding reduced GO (rGO) obtained after reduction shows similar capacitive behavior. Their specific capacitances are 243.6 F g-1 and 240.3 F g-1 at 1 A g-1, respectively, and they both have a long-term cycling performance (with a 100% capacitance retention after 10 000 cycles at 30 A g-1). This study provides a new strategy for the preparation of GO with low energy consumption.

Starch-Based Ion-Conductive Organo-Hydrogels with Self-Healing, Anti-Freezing, and High Mechanical Properties toward Strain Sensors.

Fully bio-based ion-conductive organo-hydrogels with multi-functionalities such as high mechanical properties, self-healing, anti-freezing, and non-drying capabilities are still extremely rare so far, and achieving it remains a great challenge. In this work, a starch/natural rubber composite hydrogel is first obtained by a simple one-pot method, and then an ion-conductive organo-hydrogel composed of starch, natural rubber, lithium chloride, and glycerol with adjustable mechanical properties (ultimate tensile stress of 0.15-2.33 MPa with a failure strain of 675-1367%, elastic modulus of 0.087-15.2 MPa) is fabricated by a solvent replacement strategy. The organo-hydrogels exhibit excellent fatigue resistance, elasticity, and good self-healing, anti-freezing, non-drying properties (with no obvious change after 10 days at ambient environment). The obtained hydrogels are successfully applied to monitor human movement with high durability (over 1000 cycles) and low hysteresis. In addition, the sensors exhibit high stability in a wide temperature range from -20 °C to 100 °C that endows it with a wide range of potential applications in flexible sensing and wearable devices.

Derivation of Human Salivary Epithelial Progenitors from Pluripotent Stem Cells via Activation of RA and Wnt Signaling.

Derivation of salivary gland epithelial progenitors (SGEPs) from human pluripotent stem cells (hPSCs) has great potential in developmental biology and regenerative medicine. At present, no efficient method is available to generate salivary gland cells from hPSCs. Here, we described for the first time a robust protocol for direct differentiation of hPSCs into SGEPs by mimicking retinoic acid and Wnt signaling. These hPSC-derived SGEPs expressed SOX9, KRT5, and KRT19, important progenitor markers of developing salivary glands. CD24 and α-SMA positive cells, capable of restoring the functions of injured salivary glands, were also present in SGEP cultures. Importantly, RNA-sequencing revealed that the SGEPs resembled the transcript profiles of human fetal submandibular glands. Therefore, we provided an efficient protocol to induce hPSCs differentiation into SGEPs. Our study provides a foundation for generating functional hPSCs derived salivary gland acinar cells and three-dimensional organoids, potentially serving as new models for basic study and future translational research.

A DUSP6 inhibitor suppresses inflammatory cardiac remodeling and improves heart function after myocardial infarction.

Acute myocardial infarction (MI) results in loss of cardiomyocytes and abnormal cardiac remodeling with severe inflammation and fibrosis. However, how cardiac repair can be achieved by timely resolution of inflammation and cardiac fibrosis remains incompletely understood. Our previous findings have shown that dual-specificity phosphatase 6 (DUSP6) is a regeneration repressor from zebrafish to rats. In this study, we found that intravenous administration of the DUSP6 inhibitor (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) improved heart function and reduced cardiac fibrosis in MI rats. Mechanistic analysis revealed that BCI attenuated macrophage inflammation through NF-κB and p38 signaling, independent of DUSP6 inhibition, leading to the downregulation of various cytokines and chemokines. In addition, BCI suppressed differentiation-related signaling pathways and decreased bone-marrow cell differentiation into macrophages through inhibiting DUSP6. Furthermore, intramyocardial injection of poly (D, L-lactic-co-glycolic acid)-loaded BCI after MI had a notable effect on cardiac repair. In summary, BCI improves heart function and reduces abnormal cardiac remodeling by inhibiting macrophage formation and inflammation post-MI, thus providing a promising pro-drug candidate for the treatment of MI and related heart diseases. This article has an associated First Person interview with the first author of the paper.

Estimation of mechanical properties of Mg-5Zn-0.5Al-xSn alloy based on virtual crystal approximation.

First-principles calculations of multi-component alloys have been studied in detail. Herein, the first-principles calculations of Mg-5Zn-0.5Al-xSn alloys were performed by using the virtual crystal approximation (VCA) method. By calculating the lattice constants and elastic constants of the Mg-5Zn-0.5Al-xSn doping models, it was found that the mechanical properties and micro-hardness were related with the content of Sn. With the increase of Sn content, and the best ductility and the smallest micro-hardness were achieved at Sn = 2 wt.%. To verify the calculation results, the Mg-5Zn-0.5Al-xSn alloys were prepared and micro-hardness and tensile tests were conducted. The experiments demonstrate that the trends in mechanical properties obtained from the experiments are in agreement with the VCA computational results. These findings indicate that the VCA method has guiding significance in industries for rapid screening of high-performance Mg alloys.

Functionalized Microscaffold-Hydrogel Composites Accelerating Osteochondral Repair through Endochondral Ossification.

Osteochondral regeneration remains a key challenge because of the limited self-healing ability of the bone and its complex structure and composition. Biomaterials based on endochondral ossification (ECO) are considered an attractive candidate to promote bone repair because they can effectively address the difficulties in establishing vascularization and poor bone regeneration via intramembranous ossification (IMO). However, its clinical application is limited by the complex cellular behavior of ECO and the long time required for induction of the cell cycle. Herein, functionalized microscaffold-hydrogel composites are developed to accelerate the developmental bone growth process via recapitulating ECO. The design comprises arginine-glycine-aspartic acid (RGD)-peptide-modified microscaffolds loaded with kartogenin (KGN) and wrapped with a layer of RGD- and QK-peptide-comodified alginate hydrogel. These microscaffolds enhance the proliferation and aggregation behavior of the human bone marrow mesenchymal stem cells (hBMSCs); the controlled release of kartogenin induces the differentiation of hBMSCs into chondrocytes; and the hydrogel grafted with RGD and QK peptide facilitates chondrocyte hypertrophy, which creates a vascularized niche for osteogenesis and finally accelerates osteochondral repair in vivo. The findings provide an efficient bioengineering approach by sequentially modulating cellular ECO behavior for osteochondral defect repair.

Alzheimer's Disease Prediction Algorithm Based on Group Convolution and a Joint Loss Function.

Alzheimer's disease (AD) can effectively predict by 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) of the brain, but current PET images still suffer from indistinct lesion features, low signal-to-noise ratios, and severe artefacts, resulting in poor prediction accuracy for patients with mild cognitive impairment (MCI) and unclear lesion features. In this paper, an AD prediction algorithm based on group convolution and a joint loss function is proposed. First, a group convolutional backbone network based on ResNet18 is designed to extract lesion features from multiple channels, which makes the expression ability of the network improved to a great extent. Then, a hybrid attention mechanism is presented, which enables the network to focus on target regions and learn feature weights, so as to enhance the network's learning ability of the lesion regions that are relevant to disease diagnosis. Finally, a joint loss function, that avoids the overfitting phenomenon, increases the generalization of the model, and improves prediction accuracy by adding a regularization loss function to the conventional cross-entropy function, is proposed. Experiments conducted on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the algorithm we proposed gives a prediction accuracy improvement of 2.4% over that of the current AD prediction algorithm, thus proving the effectiveness and availability of the new algorithm.

Advances and Challenges of Self-Healing Elastomers: A Mini Review.

In the last few decades, self-healing polymeric materials have been widely investigated because they can heal the damages spontaneously and thereby prolong their service lifetime. Many ingenious synthetic procedures have been developed for fabricating self-healing polymers with high performance. This mini review provides an impressive summary of the self-healing polymers with fast self-healing speed, which exhibits an irreplaceable role in many intriguing applications, such as flexible electronics. After a brief introduction to the development of self-healing polymers, we divide the development of self-healing polymers into five stages through the perspective of their research priorities at different periods. Subsequently, we elaborated the underlying healing mechanism of polymers, including the self-healing origins, the influencing factors, and direct evidence of healing at nanoscopic level. Following this, recent advance in realizing the fast self-healing speed of polymers through physical and chemical approaches is extensively overviewed. In particular, the methodology for balancing the mechanical strength and healing ability in fast self-healing elastomers is summarized. We hope that it could afford useful information for research people in promoting the further technical development of new strategies and technologies to prepare the high performance self-healing elastomers for advanced applications.

Retinoic acid and FGF10 promote the differentiation of pluripotent stem cells into salivary gland placodes.

BACKGROUND: Salivary glands produce saliva that play essential roles in digestion and oral health. Derivation of salivary gland organoids from pluripotent stem cells (PSCs) provides a powerful platform to model the organogenesis processes during development. A few studies attempted to differentiate PSCs into salivary gland organoids. However, none of them could recapitulate the morphogenesis of the embryonic salivary glands, and most of the protocols involved complicated manufacturing processes. METHODS: To generate PSC-derived salivary gland placodes, the mouse embryonic stem cells were first differentiated into oral ectoderm by treatment with BMP4 on day 3. Retinoic acid and bFGF were then applied to the cultures from day 4 to day 6, followed by a 4-day treatment of FGF10. The PSC-derived salivary gland placodes on day 10 were transplanted to kidney capsules to determine the regenerative potential. Quantitative reverse transcriptase-polymerase chain reaction, immunofluorescence, and RNA-sequencing were performed to identify the PSC-derived SG placodes. RESULTS: We showed that step-wise treatment of retinoic acid and FGF10 promoted the differentiation of PSCs into salivary gland placodes, which can recapitulate the early morphogenetic events of their fetal counterparts, including the thickening, invagination, and then formed initial buds. The PSC-derived salivary gland placodes also differentiated into developing duct structures and could develop to striated and excretory ducts when transplanted in vivo. CONCLUSIONS: The present study provided an easy and safe method to generate salivary gland placodes from PSCs, which offered possibilities for studying salivary gland development in vitro and developing new cell therapies.

Doping-driven electronic structure and conductivity modification of nickel sulfide.

The lack of electrical conductivity limits the electrochemical kinetic rate of the electrode material, resulting in the inability to reach its theoretical capacity. A facile method is adopted to improve the intrinsic conductivity of binary NiS2/Ni3S4 hybrid nickel sulfide, with the doping of transition metal atoms Co, Mn and Ag. Through the introduction of heteroatoms, the electronic structure of the electrode material is modified and the electrical conductivity is significantly improved, thus enhancing its electrochemical performance. The improvement of conductivity is attributed to the formation of intermediate bands of transition metals and the redistribution of electrons, and the result is demonstrated by experimental and density functional theory (DFT) calculations. As a result, the NiS2/Ni3S4 hybrid nickel sulfide after 0.5% amount of Co-doping reaches the highest specific capacitance of 2874 F g-1 at 1 A g-1, increasing specific capacitance of 653 F g-1 as 29.4% of the specific capacitance of non-doped nickel sulfide. The Co doped nickel sulfide also exhibits remarkable cycling stability compared with non-doped nickel sulfide. The assembled 2% Co-doped nickel sulfide//rGO, 0.5% Mn-doped nickel sulfide//rGO and 0.5% Ag-doped nickel sulfide//rGO asymmetric supercapacitors show a specific energy density of 36.6, 36.1 and 36.0 W h kg-1 at a power density of 800 W kg-1. This study provides a useful insight into the fabrication of high performance pseudocapacitive materials.