Injectable Chitosan Hydrogels Doped with 2D Peptide Nanosheet-Drug Conjugates for Glutathione-Responsive Sustained Drug Delivery.

The development of novel and effective drug delivery systems aimed at enhancing therapeutic profile and efficacy of therapeutic agents is a critical challenge in modern medicine. This study presents an intelligent drug delivery system based on self-assembled two-dimensional peptide nanosheets (2D PNSs). Leveraging the tunable properties of amino acid structures and sequences, we design a peptide with the sequence of Fmoc-FKKGSHC, which self-assembles into 2D PNSs with uniform structure, high biocompatibility, and excellent degradability. Covalent attachment of thiol-modified doxorubicin (DOX) drugs to 2D PNSs via disulfide bond results in the peptide-drug conjugates (PDCs), which is denoted as PNS-SS-DOX. Subsequently, the PDCs are encapsulated within the injectable, thermosensitive chitosan (CS) hydrogels for drug delivery. The designed drug delivery system demonstrates outstanding pH-responsiveness and sustained drug release capabilities, which are facilitated by the characteristics of the CS hydrogels. Meanwhile, the covalently linked disulfide bond within the PNS-SS-DOX is responsive to intracellular glutathione (GSH) within tumor cells, enabling controlled drug release and significantly inhibiting the cancer cell growth. This responsive peptide-drug conjugate based on a 2D peptide nanoplatform paves the way for the development of smart drug delivery systems and has bright prospects in the future biomedicine field.

Assembling Ag2S quantum dots onto peptide nanosheet as a biomimetic two-dimensional nanoplatform for synergistic near infrared-II fluorescent imaging and photothermal therapy of tumor.

Fluorescent bioimaging and photothermal therapy (PTT) techniques have potential significance in cancer diagnosis and treatment and have been widely applied in biomedical and practical clinical trials. This study proposes the molecular design and biofabrication of a two-dimensional (2D) nanoplatform, exhibiting promising prospects for synergistic bioimaging and PTT of tumors. First, biocompatible 2D peptide nanosheets (PNSs) were designed and prepared through peptide self-assembly. These served as a support matrix for assembling polyethylene glycol-modified Ag2S quantum dots (PEG-Ag2SQDs) to form a 2D nanoplatform (PNS/PEG-Ag2SQDs) with unique fluorescent and photothermal properties. The designed 2D nanoplatform not only showed improved photothermal efficacy and an elevated photothermal conversion efficiency of 52.46 %, but also demonstrated significant lethality against tumors in both in vitro and in vivo cases. Additionally, it displays excellent imaging effects in the near-infrared II region, making it suitable for synergistic fluorescent imaging-guided PTT of tumors. This study not only provides a facile approach for devising and synthesizing 2D peptide assemblies but also presents new biomimetic strategies to create functional 2D organic/inorganic nanoplatforms for biomedical applications.

Recent advances in peptide-based bioactive hydrogels for nerve repair and regeneration: from material design to fabrication, functional tailoring and applications.

The injury of both central and peripheral nervous systems can result in neurological disorders and severe nervous diseases, which has been one of the challenges in the medical field. The use of peptide-based hydrogels for nerve repair and regeneration (NRR) provides a promising way for treating these problems, but the effects of the functions of peptide hydrogels on the NRR efficiency have been not understood clearly. In this review, we present recent advances in the material design, matrix fabrication, functional tailoring, and NRR applications of three types of peptide-based hydrogels, including pure peptide hydrogels, other component-functionalized peptide hydrogels, and peptide-modified polymer hydrogels. The case studies on the utilization of various peptide-based hydrogels for NRR are introduced and analyzed, in which the effects and mechanisms of the functions of hydrogels on NRR are illustrated specifically. In addition, the fabrication of medical NRR scaffolds and devices for pre-clinical application is demonstrated. Finally, we provide potential directions on the development of this promising topic. This comprehensive review could be valuable for readers to know the design and synthesis strategies of bioactive peptide hydrogels, as well as their functional tailoring, in order to promote their practical applications in tissue engineering, biomedical engineering, and materials science.

Nomogram for predicting venous thromboembolism after spinal surgery.

PURPOSE: This study aimed to establish a nomogram to predict the risk of venous thromboembolism (VTE), identifying potential risk factors, and providing theoretical basis for prevention of VTE after spinal surgery. METHODS: A retrospective analysis was conducted on 2754 patients who underwent spinal surgery. The general characteristics of the training group were initially screened using univariate logistic analysis, and the LASSO method was used for optimal prediction. Subsequently, multivariate logistic regression analysis was performed to identify independent risk factors for postoperative VTE in the training group, and a nomogram for predict risk of VTE was established. The discrimination, calibration, and clinical usefulness of the nomogram were separately evaluated using the C-index, receiver operating characteristic curve, calibration plot and clinical decision curve, and was validated using data from the validation group finally. RESULTS: Multivariate logistic regression analysis identified 10 independent risk factors for VTE after spinal surgery. A nomogram was established based on these independent risk factors. The C-index for the training and validation groups indicating high accuracy and stability of the model. The area under the receiver operating characteristic curve indicating excellent discrimination ability; the calibration curves showed outstanding calibration for both the training and validation groups. Decision curve analysis showed the clinical net benefit of using the nomogram could be maximized in the probability threshold range of 0.01-1. CONCLUSION: Patients undergoing spinal surgery with elevated D-dimer levels, prolonger surgical, and cervical surgery have higher risk of VTE. The nomogram can provide a theoretical basis for clinicians to prevent VTE.

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism.

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix.

Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

Identification of Turtle-Shell Growth Year Using Hyperspectral Imaging Combined with an Enhanced Spatial-Spectral Attention 3DCNN and a Transformer.

Turtle shell (Chinemys reecesii) is a prized traditional Chinese dietary therapy, and the growth year of turtle shell has a significant impact on its quality attributes. In this study, a hyperspectral imaging (HSI) technique combined with a proposed deep learning (DL) network algorithm was investigated for the objective determination of the growth year of turtle shells. The acquisition of hyperspectral images was carried out in the near-infrared range (948.72-2512.97 nm) from samples spanning five different growth years. To fully exploit the spatial and spectral information while reducing redundancy in hyperspectral data simultaneously, three modules were developed. First, the spectral-spatial attention (SSA) module was developed to better protect the spectral correlation among spectral bands and capture fine-grained spatial information of hyperspectral images. Second, the 3D convolutional neural network (CNN), more suitable for the extracted 3D feature map, was employed to facilitate the joint spatial-spectral feature representation. Thirdly, to overcome the constraints of convolution kernels as well as better capture long-range correlation between spectral bands, the transformer encoder (TE) module was further designed. These modules were harmoniously orchestrated, driven by the need to effectively leverage both spatial and spectral information within hyperspectral data. They collectively enhance the model's capacity to extract joint spatial and spectral features to discern growth years accurately. Experimental studies demonstrated that the proposed model (named SSA-3DTE) achieved superior classification accuracy, with 98.94% on average for five-category classification, outperforming traditional machine learning methods using only spectral information and representative deep learning methods. Also, ablation experiments confirmed the effectiveness of each module to improve performance. The encouraging results of this study revealed the potentiality of HSI combined with the DL algorithm as an efficient and non-destructive method for the quality control of turtle shells.

Recent advance in bioactive hydrogels for repairing spinal cord injury: material design, biofunctional regulation, and applications.

Functional hydrogels show potential application in repairing spinal cord injury (SCI) due to their unique chemical, physical, and biological properties and functions. In this comprehensive review, we present recent advance in the material design, functional regulation, and SCI repair applications of bioactive hydrogels. Different from previously released reviews on hydrogels and three-dimensional scaffolds for the SCI repair, this work focuses on the strategies for material design and biologically functional regulation of hydrogels, specifically aiming to show how these significant efforts can promoting the repairing performance of SCI. We demonstrate various methods and techniques for the fabrication of bioactive hydrogels with the biological components such as DNA, proteins, peptides, biomass polysaccharides, and biopolymers to obtain unique biological properties of hydrogels, including the cell biocompatibility, self-healing, anti-bacterial activity, injectability, bio-adhesion, bio-degradation, and other multi-functions for repairing SCI. The functional regulation of bioactive hydrogels with drugs/growth factors, polymers, nanoparticles, one-dimensional materials, and two-dimensional materials for highly effective treating SCI are introduced and discussed in detail. This work shows new viewpoints and ideas on the design and synthesis of bioactive hydrogels with the state-of-the-art knowledges of materials science and nanotechnology, and will bridge the connection of materials science and biomedicine, and further inspire clinical potential of bioactive hydrogels in biomedical fields.

Unsupervised Hyperspectral Band Selection via Multimodal Evolutionary Algorithm and Subspace Decomposition.

Unsupervised band selection is an essential task to search for representative bands in hyperspectral dimension reduction. Most of existing studies utilize the inherent attribute of hyperspectral image (HSI) and acquire single optimal band subset while ignoring the diversity of subsets. Moreover, the ordered property in HSI is expected to be focused in order to avoid choosing redundant bands. In this paper, we proposed an unsupervised band selection method based on the multimodal evolutionary algorithm and subspace decomposition to alleviate the problems. To explore the diversity of band subsets, the multimodal evolutionary algorithm is first employed in spectral subspace decomposition to seek out multiple global or local solutions. Meanwhile, in view of ordered property, we concentrate more on increasing the difference between neighbor band subspaces. Furthermore, to utilize the obtained multiple diverse band subsets, an integrated utilization strategy is adopted to improve the predicted performance. Experimental results on three popular hyperspectral remote sensing datasets and one collected composition prediction dataset show the effectiveness of the proposed method, and the superiority over state-of-the-art methods on predicted accuracy.

Preparation and Photodegradation Properties of Carbon-Nanofiber-Based Catalysts.

In this study, an iron oxide/carbon nanofibers (Fe2O3/CNFs) composite was prepared by a combination of electrospinning and hydrothermal methods. The characterization of Fe2O3/CNFs was achieved via scanning electron microscopy (SEM), infrared spectroscopy (IR), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). It is shown that when the hydrothermal reaction time was 180 °C and the reaction time was 1 h, the Fe2O3 nanoparticle size was about 90 nm with uniform distribution. The photodegradation performance applied to decolorize methyl orange (MO) was investigated by forming a heterogeneous Fenton catalytic system with hydrogen peroxide. The reaction conditions for the degradation of MO were optimized with the decolorization rate up to more than 99% within 1 h, which can decompose the dyes in water effectively. The degradation process of MO by Fenton oxidation was analyzed by a UV-visible NIR spectrophotometer, and the reaction mechanism was speculated as well.

Retropharyngeal Reduction Plate for Atlantoaxial Dislocation: A Cadaveric Test and Morphometric Trajectory Analysis.

OBJECTIVE: To evaluate the placement feasibility and safety of the newly designed retropharyngeal reduction plate by cadaveric test and to perform morphometric trajectory analysis. METHODS: The five cadaveric specimens with intact atlantoaxial joint were enrolled in this study. They were used for simulating the placement process and evaluating the placement feasibility of the retropharyngeal reduction plate. The atlantoaxial dislocation (AAD) of five cadaveric specimens were obtained by proper external force after dissecting ligaments. The retropharyngeal reduction plate was placed on atlantoaxial joint of cadaveric specimens. The X-ray and three-dimensional (3D) spiral CT were used for evaluating the placement safety of retropharyngeal reduction plate. The DICOM data was obtained after 3D spiral CT scanning for the morphometric trajectory analysis. RESULTS: The reduction plates were successfully placed on the atlantoaxial joint of five cadaveric specimens through the retropharyngeal approach, respectively. The X-ray and 3D spiral CT showed the accurate screw implantation and satisfying plate placement. The length of the left/right atlas screw trajectory (L/RAT) was, respectively, 1.73 ± 0.01 cm (LAT) and 1.71 ± 0.02 cm (RAT). The length of odontoid screw trajectory (OST) was 1.38 ± 0.02 cm. The length of the left/right axis screw trajectory (L/RAXT) was, respectively, 1.67 ± 0.02 cm (LAXT) and 1.67 ± 0.01 cm (RAXT). There was no statistical significance between left side and right side in terms of AT and AXT (P > 0.05). The angles of atlas screw trajectory angle (ASTA), axis screw trajectory angle (AXSTA), and odontoid screw trajectory angle (OSTA) were 38.04° ± 2.03°, 56.92° ± 2.66°, and 34.78° ± 2.87°, respectively. CONCLUSION: The cadaveric test showed that the retropharyngeal reduction plate is feasible to place on the atlantoaxial joint, which is also a safe treatment choice for atlantoaxial dislocation. The meticulous preoperative planning of screw trajectory based on individual differences was also vital to using this technique.

Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma.

PURPOSE: Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics. RESULTS: Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients. CONCLUSIONS: ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

Systematic profiling of diagnostic and prognostic value of autophagy-related genes for sarcoma patients.

BACKGROUND: Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking. METHODS: Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set. RESULTS: In total, 84 DEARGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between two clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.747 for the OS and DFS nomograms, respectively. CONCLUSION: This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

Surgical treatment of atlantoaxial subluxation by intraoperative skull traction and C1-C2 fixation.

BACKGROUND: Atlantoaxial subluxation (AAS) is a not rare abnormality between the atlas (C1) and axis (C2). For AAS patients with persistent neck pain and neurologic symptoms, surgical intervention is a good choice. Nevertheless, there were still few reports about the use of intraoperative skull traction and different fixation methods in treatment of AAS. METHODS: From January 2012 to December 2018, a total of 86 cases were admitted to our hospital and diagnosed as AAS. All the patients received atlantoaxial reduction with the help of intraoperative skull traction and C1-C2 fixation. Clinical and radiological parameters were collected through chart review. RESULTS: There were 86 cases included in this study. The mean operative time was 153.9 ± 73.9 min, and the mean amount of intraoperative blood loss was 219.1 ± 195.6 ml. 81 patients underwent posterior reduction, internal fixation and fusion. 5 patients underwent anterior release, followed by posterior internal fixation and fusion. 82 patients got satisfactory postoperative outcomes while complications occurred in 4 patients. Significant neurologic improvement was observed in these patients. Bone fusion was achieved on the midline sagittal reconstructed CT images at the latest follow-up in all these patients except 1 case. All the patients were followed up for 34.84 ± 15.86 months at average (range 12-60 months). The mean ADI value was 7.55 ± 1.67 mm at average preoperatively, and improved to 4.03 ± 1.21 mm postoperatively, and to 4.21 ± 0.99 mm at the latest follow-up. The mean A-A angle was 15.48 ± 9.82 degrees at average preoperatively, and improved to 21.61 ± 10.43 degrees postoperatively, and to 19.73 ± 8.13 degrees at the latest follow-up. The mean A-A height was 35.61 ± 7.66 mm at average preoperatively, and improved to 40.08 ± 8.5 mm postoperatively, and to 38.83 ± 6.97 mm at the latest follow-up. There were complications in 4 patients, including pedicle misplacement, pedicle screw fracture, infection and one death. CONCLUSION: Intraoperative skull traction can effectively facilitate the surgical procedures for ASS caused by different etiologies. Further research will be needed to investigate the safety and effectiveness of this method in the future.

Identification of Iron Metabolism-Related Gene Signatures for Predicting the Prognosis of Patients With Sarcomas.

Iron is one of the essential trace elements in the human body. An increasing amount of evidence indicates that the imbalance of iron metabolism is related to the occurrence and development of cancer. Here, we obtained the gene expression and clinical data of sarcoma patients from TCGA and the GEO database. The prognostic value of iron metabolism-related genes (IMRGs) in patients with sarcoma and the relationship between these genes and the immune microenvironment were studied by comprehensive bioinformatics analyses. Two signatures based on IMRGs were generated for the overall survival (OS) and disease-free survival (DFS) of sarcoma patients. At 3, 5, and 7 years, the areas under the curve (AUCs) of the OS signature were 0.708, 0.713, and 0.688, respectively. The AUCs of the DFS signature at 3, 5, and 7 years were 0.717, 0.689, and 0.702, respectively. Kaplan-Meier survival analysis indicated that the prognosis of high-risk patients was worse than that of low-risk patients. In addition, immunological analysis showed that there were different patterns of immune cell infiltration among patients in different clusters. Finally, we constructed two nomograms that can be used to predict the OS and DFS of sarcoma patients. The C-index was 0.766 (95% CI: 0.697-0.835) and 0.763 (95% CI: 0.706-0.820) for the OS and DFS nomograms, respectively. Both the ROC curves and the calibration plots showed that the two nomograms have good predictive performance. In summary, we constructed two IMRG-based prognostic models that can effectively predict the OS and DFS of sarcoma patients.