With the increasing interests of using graphene and its derivatives in the area of biomedicine, the systematic evaluation of their potential risks and impacts to biological systems is becoming critically important. In this work, we carefully study how surface coatings affect the cytotoxicity and extracellular biodegradation behaviors of graphene oxide (GO) and its derivatives. Although naked GO could induce significant toxicity to macrophages, coating those two-dimensional nanomaterials with biocompatible macromolecules such as polyethylene glycol (PEG) or bovine serum albumin (BSA) could greatly attenuate their toxicity, as independently evidenced by several different assay approaches. On the other hand, although GO can be gradually degraded through enzyme induced oxidization by horseradish peroxidase (HRP), both PEG and BSA coated GO or reduced GO (RGO) are rather resistant to HRP-induced biodegradation. In order to obtain biocompatible functionalized GO that can still undergo enzymatic degradation, we conjugate PEG to GO via a cleavable disulfide bond, obtaining GO-SS-PEG with negligible toxicity and considerable degradability, promising for further biomedical applications.

Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraoperative histology of laryngeal tissue is crucial for providing optimal surgical outcomes. We hypothesized that deep-learning based stimulated Raman scattering (SRS) microscopy could provide automated and accurate diagnosis of laryngeal squamous cell carcinoma on fresh, unprocessed surgical specimens without fixation, sectioning or staining. Methods : We first compared 80 pairs of adjacent frozen sections imaged with SRS and standard hematoxylin and eosin histology to evaluate their concordance. We then applied SRS imaging on fresh surgical tissues from 45 patients to reveal key diagnostic features, based on which we have constructed a deep learning based model to generate automated histologic results. 18,750 SRS fields of views were used to train and cross-validate our 34-layered residual convolutional neural network, which was used to classify 33 untrained fresh larynx surgical samples into normal and neoplasia. Furthermore, we simulated intraoperative evaluation of resection margins on totally removed larynxes. Results : We demonstrated near-perfect diagnostic concordance (Cohen's kappa, κ > 0.90) between SRS and standard histology as evaluated by three pathologists. And deep-learning based SRS correctly classified 33 independent surgical specimens with 100% accuracy. We also demonstrated that our method could identify tissue neoplasia at the simulated resection margins that appear grossly normal with naked eyes. Conclusion : Our results indicated that SRS histology integrated with deep learning algorithm provides potential for delivering rapid intraoperative diagnosis that could aid the surgical management of laryngeal cancer.

SummaryObjectives We aimed to determine the predictive factors for central compartment lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC). Design and patients We undertook a retrospective study of 291 patients treated for PTMC. The following criteria were assessed to predict the presence of central compartment LNM: sex, age, tumour multifocality, tumour size, tumour bilaterality, extracapsular spread (ECS), lateral neck LNM, coexistence of chronic lymphocytic thyroiditis, BRAF V600E mutation and ultrasonography (US) features. Univariate and multivariate analyses were performed to identify clinicopathological characteristics and US findings in predicting central compartment LNM from PTMC.Results The central compartment LNM affected 133 (45Á7%) of 291 patients. With use of univariate and multivariate analyses, male gender (OR 2Á020; P = 0Á039), tumour size (>5 mm) (OR 3Á687; P = 0Á015), ESC (OR 2Á330; P = 0Á044), lateral LNM (OR 15Á075; P = 0Á000) and BRAF V600E mutation (OR 2Á464; P = 0Á000) were independently correlated with central compartment LNM. Age, tumour multifocality, tumour bilaterality, coexistence of chronic lymphocytic thyroiditis and US characteristics were not significantly related to the presence of central compartment LNM. We have also developed a nomogram to predict the probability of central compartment LNM for an individual patient. The sensitivity was 71Á9% and specificity was 70Á3%, with an under the receiver operating characteristic (ROC) curve of 0Á772.Conclusions A prophylactic neck dissection of the central compartment should be considered particularly in PTMC patients with male gender, a >5 mm tumour size, ECS of the tumours, lateral LNM and positive BRAF V600E mutation.

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