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Abstract The CARLO (cold ablation robot-guided laser osteotome) is a compact device with integrated multiple sensory, steering and safety checking elements. A multi-center study was performed to evaluate the CARLO device for the linear part of midface osteotomy in 28 patients. Feasibility, success rate, safety, performance and experience of the surgeons were assessed and evaluated. All 28 procedures were completed with CARLO without falling back to the conventional methods, giving a technical success rate of 100%. For 27 (96%) cases, procedural success was achieved with cutting lines deviation less than 2 mm. For 25 procedures, the CARLO-cutting was conducted smoothly. In the other 3 cases, some minor difficulties related to the reference markers were reported. For 18 procedures, no change for the cutting path was necessary. For the other 10 cases, cut path was adapted. Intraoperative re-planning was possible, easy and quick without significant delay of the procedures. No CARLO-related adverse events were recorded. Especially, there was no unexpected and unusual bleeding during the CARLO-conducted osteotomy. The time needed for the registration ranged from 1 to 12 min (median = 4). The CARLO-cutting lasted for 5 to 21 min (median = 7). The present study demonstrated feasibility, simplicity, safety, reliability and accuracy of CARLO for the linear part of midface osteotomy.

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent malignancy worldwide, with high incidence and poor survival rates. RBP1 is highly expressed in several kinds of cancer and plays a potential prognostic factor. However, the relationship between RBP1 and HNSCC were analyzed based on The Cancer Genome Atlas (TCGA) database. Materials and methods RBP1 expression and clinical information were obtained from the Cancer Genome Atlas (TCGA) database. Tumor tissue and adjacent normal tissue of 6 HNSCC patients were collected to analyze the RBP1 mRNA expression level by quantitative PCR. Cox regression analysis was used to evaluate the prognostic values of RBP1 and clinical data in HNSCC. A nomogram was also established to predict the impact of RBP1 on prognosis based on Cox multivariate results. The methylation level of RBP1 in HNSC and its prognosis were analyzed in UALACN and MethSurv. Finally, the potential biological functions of RBP1 were investigated using gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA). Results The mRNA expression levels of RBP1 were highly expressed in HNSCC tissue. The Cox analyses demonstrate that highly-expressed RBP1 is an independent prognosis marker(P

IntroductionHead and neck cancer (HNC) is a complex disease, and multiple risk factors can lead to its progression. Observational studies indicated that herpes simplex virus (HSV) may be correlated with the risk of HNC. However, the causal effects and direction between them were still unclear.MethodsThis study utilized a Mendelian randomization (MR) approach for causality assessment between HSV infection and Head and neck cancer based on the latest public health data and Genome-Wide Association Study (GWAS) data. The causal effects were estimated using IVW, weighted median, and MR-Egger. A reverse MR analysis was subsequently performed. Cochrans Q test, MR‐Egger intercept test, leave one out analysis, and the funnel plot were all used in sensitivity analyses.ResultsGenetically predicted higher level of HSV-1 IgG was causally related to HNC (OR=1.0019, 95%CI=1.0003–1.0036, p=0.0186, IVW) and oral and oropharyngeal cancer (OR=1.0018, 95%CI=1.0004–1.0033, p=0.0105, IVW). The reverse MR analysis did not demonstrate a reverse causal relationship between HSV and HNC. However, HSV-2 infection was not causally related to HNC data and oropharyngeal cancer data. Sensitivity analysis was performed and revealed no heterogeneity and horizontal pleiotropy.ConclusionCollectively, a significant association was noted between HSV infection and increased risk of HNC, providing valuable insights into the etiology of this malignancy. Further in-depth study is needed to validate these findings and elucidate the underpinning mechanisms.

Abstract Bioactive material concepts for targeted therapy have been an important research focus in regenerative medicine for years. The aim of this study was to investigate a proof-of-concept composite structure in the form of a membrane made of natural silk fibroin (SF) and extracellular vesicles (EVs) from gingival fibroblasts. EVs have multiple abilities to act on their target cell and can thus play crucial roles in both physiology and regeneration. This study used pH neutral, degradable SF-based membranes, which have excellent cell- and tissue-specific properties, as the carrier material. The characterization of the vesicles showed a size range between 120 and 180 nm and a high expression of the usual EV markers (e.g. CD9, CD63 and CD81), measured by nanoparticle tracking analysis (NTA) and single-EV flow analysis (IFCM). An initial integration of the EVs into the membrane was analyzed using scanning and transmission electron microscopy (SEM and TEM) and vesicles were successfully detected, even if they were not homogeneously distributed in the membrane. Using direct and indirect tests, the cytocompatibility of the membranes with and without EVs could be proven and showed significant differences compared to the toxic control (p 0.05).

In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.