Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Stereogenic-at-Cobalt(III) Complex Catalyzed Halocyclization of Alkynes: Enantioselective Access to Axially Chiral ortho-Halo-C2-indoles.

Here, we report an anionic stereogenic-at-cobalt(III) complex catalysis strategy for the enantioselective halocyclization of ortho-alkynylanilines using N-halosuccinimide (NXS) as the halogen source. This protocol provides a distinct atroposelective approach to access the axially chiral ortho-halo-C2-indole skeletons in excellent yields with good to high enantioselectivities (up to 99% yield, 99:1 er).

Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach.

Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying and segmenting different structures. In this study, we employ the latest developments in 3D semi-supervised learning to create cutting-edge models for the 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductors scans. We illustrate our approach to locating the region of interest of the structures, their individual components, and their void defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefit of contrastive learning in the data pre-selection step for our detection model and multi-scale Mean Teacher training paradigm in 3D semantic segmentation to achieve better performance compared with the state of the art. Our extensive experiments have shown that our method achieves competitive performance and is able to outperform by up to 16% on object detection and 7.8% on semantic segmentation. Additionally, our automated metrology package shows a mean error of less than 2 µm for key features such as Bond Line Thickness and pad misalignment.

Risk factors for inferior vena cava filter thrombosis in traumatic fracture patients with deep venous thrombosis of lower extremity: A single-center experience.

OBJECTIVE: To explore the risk factors for inferior vena cava filter (IVCF) thrombus in orthopedic trauma patients who underwent filter placement with ongoing anticoagulation in clinical settings. METHODS: We retrospectively analyzed clinical data from fracture patients with lower extremity acute deep vein thrombosis (DVT) implanted with an IVCF admitted to Tianjin Hospital from January 2017 to December 2019. Potential risk factors, such as gender, age, diabetes, hypertension, fracture sites, thrombus location, free-floating thrombus, filter type, Injury Severity Score (ISS), and postoperative D-dimer values, were analyzed by the Chi-square test, t-test, logistic regression, and receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 662 patients were included in our study, and filter-related thrombosis was present in 67 (10.1%) patients. No significant differences were observed in age, gender, hypertension, diabetes, fracture site, free-floating thrombus, filter type, indwelling time, and postoperative D-dimer level. Thrombus location and ISS were significantly different (p < 0.05). Popliteal DVT (P-DVT) (odds ratio [OR]: 2.130, p = 0.018) and ISS (OR: 1.135, p = 0.000) were associated with filter thrombus. Patients with P-DVT were prone to a small filter thrombus (OR: 3.231, p = 0.037). From the ROC curve analysis, the diagnostic value of ISS was 24.5 and 26.5 for patients with filter and massive filter thrombus, respectively. CONCLUSION: Thrombus location and ISS were independent risk factors for filter thrombus in patients with traumatic fractures. P-DVT had a higher potential to result in a small filter thrombus and an ISS value >26.5, which was considered a significant massive filter thrombus predictor.

The effectiveness of percutaneous mechanical thrombectomy in the treatment of acute thromboembolic occlusions of lower extremity.

OBJECTIVES: The objective of this study was to evaluate the effectiveness of percutaneous mechanical thrombectomy as the initial thrombus removal method in the treatment of acute lower extremity ischemia. METHODS: The patients with acute lower limb ischemia who underwent percutaneous mechanical thrombectomy between August 2016 and February 2018 were retrospectively reviewed. The patients were diagnosed by clinical examination and computed tomography angiography. The percutaneous mechanical thrombectomy was performed as the initial thrombus removal method, followed by anticoagulation therapy. The patients were followed up by clinical examination, imaging, and ankle brachial index (ABI) examination. RESULTS: Thirty-two patients (21 males, 11 females; average age of 68.53 ± 8.05; three cases of grade III, 29 cases of grade IIB) were reviewed. Recanalization of the thromboembolic occlusions were achieved in all patients. ABI significantly (p < 0.01) increased postoperatively (preoperative ABI: 0.51 ± 0.13; postoperative ABI: 0.85 ± 0.65, ABI at three months postoperatively: 0.84 ± 0.66). Eleven patients underwent balloon dilation and three patients had stent placement. Complete thrombus removal was achieved in all patients. The primary patency at 3 months, 6 months, and 12 months postoperatively was 90%, 85%, and 56%, respectively. The secondary patency at 3 months, 6 months, and 12 months postoperatively was 93%, 87%, and 65%, respectively. CONCLUSIONS: The immediate result appeared to be effective to use percutaneous mechanical thrombectomy as the first thrombus removal method in the treatment of acute thromboembolic occlusions in the lower extremity, while the midterm result needs to be further improved.

IrF8 Molecular Crystal under High Pressure.

An important goal in chemistry is to prepare F-rich transition metal fluorides due to the high oxidation states and potential applications such as oxidating and fluorinating agents. Thus far, the highest F stoichiometry in the neutral transition metal fluorides is 7. Here, we identify a hitherto unknown IrF8 compound through first-principles swarm-intelligence structure search calculations under high pressure. The three identified IrF8 phases exhibit typical molecular crystal characters, showing +8 oxidation state in Ir. The spatial symmetry of the basic building block in the three IrF8 phases gradually increases with pressure (e.g., dodecahedron [Formula: see text] square antiprism [Formula: see text] quasicube). The pressure-induced faster increase of Ir 5d orbital energy level with respect to F 2p provides a strong charge transfer driving force from Ir 5d to F 2p, facilitating the formation of F-rich compounds. More interestingly, the predicted electron affinities of the three predicted IrF8 phases are comparable/larger than that of PtF6, the strongest oxidation agent in the third row transition metal hexafluorides. The built high-pressure phase diagram of Ir-F binary compounds provides useful information for experimental synthesis.

Two-Dimensional PC6 with Direct Band Gap and Anisotropic Carrier Mobility.

Graphene and phosphorene are two major types of atomically thin two-dimensional materials under extensive investigation. However, the zero band gap of graphene and the instability of phosphorene greatly restrict their applications. Here, we make first-principle unbiased structure search calculations to identify a new buckled graphene-like PC6 monolayer with a number of desirable functional properties. The PC6 monolayer is a direct-gap semiconductor with a band gap of 0.84 eV, and it has an extremely high intrinsic conductivity with anisotropic character (i.e., its electron mobility is 2.94 × 105 cm2 V-1 s-1 along the armchair direction, whereas the hole mobility reaches 1.64 × 105 cm2 V-1 s-1 along the zigzag direction), which is comparable to that of graphene. On the other hand, PC6 shows a high absorption coefficient (105 cm-1) in a broad band, from 300 to 2000 nm. Additionally, its direct band gap character can remain within a biaxial strain of 5%. All these appealing properties make the predicted PC6 monolayer a promising candidate for applications in electronic and photovoltaic devices.

Predicted Pressure-Induced Superconducting Transition in Electride Li_{6}P.

Electrides are unique compounds where most of the electrons reside at interstitial regions of the crystal behaving as anions, which strongly determines its physical properties. Interestingly, the magnitude and distribution of interstitial electrons can be effectively modified either by modulating its chemical composition or external conditions (e.g., pressure). Most of the electrides under high pressure are nonmetallic, and superconducting electrides are very rare. Here we report that a pressure-induced stable Li_{6}P electride, identified by first-principles swarm structure calculations, becomes a superconductor with a predicted superconducting transition temperature T_{c} of 39.3 K, which is the highest among the already known electrides. The interstitial electrons in Li_{6}P, with dumbbell-like connected electride states, play a dominant role in the superconducting transition. Other Li-rich phosphides, Li_{5}P, Li_{11}P_{2}, Li_{15}P_{2}, and Li_{8}P, are also predicted to be superconducting electrides, but with a lower T_{c}. Superconductivity in all these compounds can be attributed to a combination of a weak electronegativity of phosphorus (P) with a strong electropositivity of lithium (Li), and opens up the interest to explore high-temperature superconductivity in similar binary compounds.

TiC3 Monolayer with High Specific Capacity for Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) have attracted considerable attention due to the intrinsic safety and high abundance of sodium. However, the lack of high-performance anode materials becomes a main obstacle for the development of SIBs. Here, we identify an ideal anode material, a metallic TiC3 monolayer with not only remarkably high storage capacity of 1278 mA h g-1 but also low barrier energy and open-circuit voltage, through first-principles swarm-intelligence structure calculations. TiC3 still keeps metallic after adsorbing two-layer Na atoms, ensuring good electrical conductivity during the battery cycle. Besides, high melting point and superior dynamical stability are in favor of practical application. Its excellent performance can be mainly attributed to the presence of an unusual n-biphenyl unit in the TiC3 monolayer. High cohesive energy, originating from multibonding coexistence (e.g., covalent, ionic, and metal bonds) in the TiC3 monolayer, provides strong feasibility for experimental synthesis. In comparison with TiC3, functionalized TiC3 with oxygen shows a higher storage capacity; meanwhile, it keeps nearly the same barrier energy. This is in sharp contrast with metal-rich MXenes. These intriguing properties make the TiC3 monolayer a promising anode material for SIBs.

Anisotropic carrier mobility in buckled two-dimensional GaN.

Developing nanoelectronic engineering requires two-dimensional (2d) materials with both usable carrier mobility and proper large band-gap. In this study, we present a detailed theoretical investigation of the intrinsic carrier mobilities of buckled 2d GaN. This buckled 2d GaN is accessed by hydrofluorination (FGaNH) and hydrogenation (HGaNH). We predict that the anisotropic carrier mobilities of buckled 2d GaN can exceed those of 2d MoS2 and can be altered by an alterable surface chemical bond (convert from a Ga-F-Ga bond of FGaNH to a Ga-H bond of HGaNH). Moreover, converting FGaNH to HGaNH can significantly suppress hole mobility (even close to zero) and result in a transition from a p-type-like semiconductor (FGaNH) to an n-type-like semiconductor (HGaNH). These features make buckled 2d GaN a promising candidate for application in future conductivity-adjustable electronics.

Unfolding/Refolding Study on Collagen from Sea Cucumber Based on 2D Fourier Transform Infrared Spectroscopy.

We aimed to explore the differences of thermal behaviors between insoluble collagen fibrils (ICFs) and pepsin-solubilized collagens (PSCs) from sea cucumber Stichopus japonicus. The unfolding/refolding sequences of secondary structures of ICFs and PSCs during the heating and cooling cycle (5 → 70 → 5 °C) were identified by Fourier transform infrared spectrometry combined with curve-fitting and 2D correlation techniques. ICFs showed a higher proportion of α-helical structures and higher thermostability than PSCs, and thus had more-stable triple helical structures. The sequences of changes affecting the secondary structures during heating were essentially the same between ICFs and PSCs. In all cases, α-helix structure was the most important conformation and it disappeared to form a ß-sheet structure. In the cooling cycle, ICFs showed a partially refolding ability, and the proportion of ß-sheet structure rose before the increasing proportion of α-helix structure. PSCs did not obviously refold during the cooling stage.

Dysregulated miR1254 and miR579 for cardiotoxicity in patients treated with bevacizumab in colorectal cancer.

Methods for detecting circulating microRNAs (miRNAs), small RNAs that control gene expression, at high sensitivity and specificity in the blood have been reported in recent studies. The goal of this study was to determine if detectable levels of specific miRNAs are released into the circulation for bevacizumab-induced cardiotoxicity. A miRNA array analysis was performed using RNA isolated from 10 control patients in bevacizumab treatment, and n=10 patients have been confirmed to have bevacizumab-induced cardiotoxicity. From the array, we selected 19 candidate miRNA for a second validation study in 90 controls and 88 patients with bevacizumab-induced cardiotoxicity. Consistent with the data obtained from the microRNA array, circulating levels of five miRNAs were significantly increased in patients with bevacizumab-induced cardiotoxicity compared with controls. To confirm these data, we compared selected miRNAs in the plasma of patients with bevacizumab-induced cardiotoxicity with those of 66 patients with acute myocardial infarction (AMI). Moreover, we went on to analyze what factors may influence the levels of potential biomarker miRNAs. Consistent with the data obtained from the microRNA array, circulating levels of five miRNAs were significantly increased in patients with bevacizumab-induced cardiotoxicity compared with those of healthy bevacizumab treatment controls. However, only miRNA1254 and miRNA579 showed high specificity in the validation experiments. Moreover, we went on to analyze what factors may influence the levels of potential biomarker miRNAs. We identify two miRNAs that are specifically elevated in patients with bevacizumab-induced cardiotoxicity, miR1254 and miRNA579, and miRNA1254 shows the strongest correlation to the clinical diagnosis of bevacizumab-induced cardiotoxicity.

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space [Formula: see text]. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter.

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

Efficient Perturbation Inference and Expandable Network for continual learning.

Although humans are capable of learning new tasks without forgetting previous ones, most neural networks fail to do so because learning new tasks could override the knowledge acquired from previous data. In this work, we alleviate this issue by proposing a novel Efficient Perturbation Inference and Expandable Network (EPIE-Net), which dynamically expands lightweight task-specific decoders for new classes and utilizes a mixed-label uncertainty strategy to improve the robustness. Moreover, we calculate the average probability of perturbed samples at inference, which can generally improve the performance of the model. Experimental results show that our method consistently outperforms other methods with fewer parameters in class incremental learning benchmarks. For example, on the CIFAR-100 10 steps setup, our method achieves an average accuracy of 76.33% and the last accuracy of 65.93% within only 3.46M average parameters.

A multiplex method for detection of SARS-CoV-2 variants based on MALDI-TOF mass spectrometry.

The recent outbreak of the coronavirus disease 2019 (COVID-19) pandemic and the continuous evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have highlighted the significance of new detection methods for global monitoring and prevention. Although quantitative reverse transcription PCR (RT-qPCR), the current gold standard for diagnosis, performs excellently in genetic testing, its multiplexing capability is limited because of the signal crosstalk of various fluorophores. Herein, we present a highly efficient platform which combines 17-plex assays with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), enabling the targeting of 14 different mutation sites of the spike gene. Diagnosis using a set of 324 nasopharyngeal swabs or sputum clinical samples with SARS-CoV-2 MS method was identical to that with the RT-qPCR. The detection consistency of mutation sites was 97.9% (47/48) compared to Sanger sequencing without cross-reaction with other respiratory-related pathogens. Therefore, the MS method is highly potent to track and assess SARS-CoV-2 changes in a timely manner, thereby aiding the continuous response to viral variation and prevention of further transmission.

LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical Image Segmentation.

While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven and time-consuming nature of pixel-level annotations in clinical practices, and (ii) failure to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. Recent unsupervised domain adaptation (UDA) techniques leverage abundant labeled source data together with unlabeled target data to reduce the domain gap, but these methods degrade significantly with limited source annotations. In this study, we address this underexplored UDA problem, investigating a challenging but valuable realistic scenario, where the source domain not only exhibits domain shift w.r.t. the target domain but also suffers from label scarcity. In this regard, we propose a novel and generic framework called "Label-Efficient Unsupervised Domain Adaptation" (LE-UDA). In LE-UDA, we construct self-ensembling consistency for knowledge transfer between both domains, as well as a self-ensembling adversarial learning module to achieve better feature alignment for UDA. To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images. Experimental results demonstrate that the proposed LE-UDA can efficiently leverage limited source labels to improve cross-domain segmentation performance, outperforming state-of-the-art UDA approaches in the literature.

Rapid Detection of Predominant SARS-CoV-2 Variants Using Multiplex High-Resolution Melting Analysis.

Coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a considerable threat to global public health. This study developed and evaluated a rapid, low-cost, expandable, and sequencing-free high-resolution melting (HRM) assay for the direct detection of SARS-CoV-2 variants. A panel of 64 common bacterial and viral pathogens that can cause respiratory tract infections was employed to evaluate our method's specificity. Serial dilutions of viral isolates determined the sensitivity of the method. Finally, the assay's clinical performance was assessed using 324 clinical samples with potential SARS-CoV-2 infection. Multiplex HRM analysis accurately identified SARS-CoV-2 (as confirmed with parallel reverse transcription-quantitative PCR [qRT-PCR] tests), differentiating between mutations at each marker site within approximately 2 h. For each target, the limit of detection (LOD) was lower than 10 copies/reaction (the LOD of N, G142D, R158G, Y505H, V213G, G446S, S413R, F486V, and S704L was 7.38, 9.72, 9.96, 9.96, 9.50, 7.80, 9.33, 8.25, and 8.25 copies/reaction, respectively). No cross-reactivity occurred with organisms of the specificity testing panel. In terms of variant detection, our results had a 97.9% (47/48) rate of agreement with standard Sanger sequencing. The multiplex HRM assay therefore offers a rapid and simple procedure for detecting SARS-CoV-2 variants. IMPORTANCE In the face of the current severe situation of increasing SARS-CoV-2 variants, we developed an upgraded multiplex HRM method for the predominant SARS-CoV-2 variants based on our original research. This method not only could identify the variants but also could be utilized in subsequent detection of novel variants since the assay has great performance in terms of flexibility. In summary, the upgraded multiplex HRM assay is a rapid, reliable, and economical detection method, which could better screen prevalent virus strains, monitor the epidemic situation, and help to develop measures for the prevention and control of SARS-CoV-2.

Freezing of water and melting of ice: theoretical modeling at the nanoscale.

Freezing of water and melting of ice at the nanoscale play critical roles in science and technology fields, including aviation systems, infrastructures, and other broad spectrum of technologies. To cope with the icing challenge, nanoscale anti-icing surface technology has been developed. The freezing and melting temperatures can be tailored by manipulating the size (the radius of water or ice); however, it lacks systemic research. In this work, the size effect on the melting temperature of ice nanocrystals was first established, which considered the variation of bond energy and equivalent heat energy from the perspective of the force-heat equivalence energy density principle. Based on the heterogeneous nucleation mode and by further considering the size and temperature effects on the interface energy involved solid-liquid energy and liquid-vapor energy as well as the above developed melting temperature model, another model is established to accurately predict the freezing temperature of water nanodroplets. The parameters required by the two models established in this paper have a clear physical meaning and establish the quantitative relationships among freezing temperature, melting temperature, surface stress, interface energy, and other thermodynamic parameters. The agreement between model prediction and experimental simulation data confirms the validity and universality of the established models. The higher prediction accuracy of this work compared to the other theoretical models, due to the more detailed consideration and the reference point, captures the errors introduced by the experiment or simulation. This study contributes to a deeper understanding of the underlying mechanism of freezing of water and melting of ice nanocrystals and provides theoretical guidance for the design of cryopreservation systems and anti-icing systems for aviation.

Electrodeposition of chitosan/graphene oxide conduit to enhance peripheral nerve regeneration.

Currently available commercial nerve guidance conduits have been applied in the repair of peripheral nerve defects. However, a conduit exhibiting good biocompatibility remains to be developed. In this work, a series of chitosan/graphene oxide (GO) films with concentrations of GO varying from 0-1 wt% (collectively referred to as CHGF-n) were prepared by an electrodeposition technique. The effects of CHGF-n on proliferation and adhesion abilities of Schwann cells were evaluated. The results showed that Schwann cells exhibited elongated spindle shapes and upregulated expression of nerve regeneration-related factors such as Krox20 (a key myelination factor), Zeb2 (essential for Schwann cell differentiation, myelination, and nerve repair), and transforming growth factor ß (a cytokine with regenerative functions). In addition, a nerve guidance conduit with a GO content of 0.25% (CHGFC-0.25) was implanted to repair a 10-mm sciatic nerve defect in rats. The results indicated improvements in sciatic functional index, electrophysiology, and sciatic nerve and gastrocnemius muscle histology compared with the CHGFC-0 group, and similar outcomes to the autograft group. In conclusion, we provide a candidate method for the repair of peripheral nerve defects using free-standing chitosan/GO nerve conduits produced by electrodeposition.