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Quantifying the spin-orbit torque (SOT) efficiency with changing the layer thickness is crucial for understanding the physical background of SOT. This study investigates the Nb-thickness-dependent SOT efficiency of two types of layered heterostructures: Ta/Nb/CoFeB and Pt/Nb/CoFeB. We find that the Nb thickness dependence of the SOT efficiency in the two samples is quite different. In the Pt/Nb series, the SOT sign changes according to the thickness variation because Pt and Nb have different spin-orbit coupling signs. We observe the resulting reversal in switching polarity through current-induced SOT switching experiments. However, due to the same spin-orbit coupling signs of Ta and Nb, no such polarity reversal was observed in Ta/Nb series. Further, we extract the spin diffusion length of Nb in each heterostructure. These results provide a systematic understanding of the material- and thickness-dependent SOT characteristics., (© 2021. The Author(s).)

The aim of the present study was to examine the conditions, characteristics, and risk factors of level IIb lymph node metastases in oral squamous cell carcinoma and to formulate surgical criteria for level IIb lymph node dissection. We analyzed clinical and pathological records for 541 oral squamous carcinoma patients in relation to level IIb metastasis. Univariate and multivariate analyses were performed to detect risk factors for level IIb lymph node metastasis; a predictive model was built based on multivariate analysis and tested in a validation group. Univariate and multivariate analyses using the training group indicated that level IIa metastasis and Lymphovascular permeation (LVP) were two independent risk factors for level IIb lymph node metastasis. This model was built and tested in a validation group, the area under the curve being 0.697 (P < .0.001). The model's sensitivity was 66.7% and specificity was 77.4%. Nomogram incorporating validated variables was developed for level IIb metastasis prediction. Expected survival probabilites were analysed to specify significance of model's variable on patients' overall survival and recurrence. Level IIb dissection should be performed in patients with level IIa metastasis and LVP. However, thorough consideration of the oncologic safety of omitting level IIb dissection is compulsory., (© 2021. The Author(s).)

The purpose of this study was to investigate the effect of administering intermittent parathyroid hormone (iPTH) before tooth extraction versus after tooth extraction on the risk of developing MRONJ in experimental animal model. Twenty-five ovariectomized rats received 6 weeks of bisphosphonate therapy. They were classified into 3 groups, based on the timing of the medication, as Control, Pre-PTH and Post-PTH groups. For Control group, normal saline was administered before and after tooth extraction. iPTH was administered during 4 weeks before tooth extraction for Pre-PTH group and after tooth extraction for Post-PTH group. The animals were euthanized 8 weeks after tooth extraction. Macroscopic, histological, micro-computed tomography (micro-CT), and histomorphometric examinations were conducted. The incidences of impaired healing were 11.11% both in Pre-PTH and Post-PTH groups, which was lower than the Control group (42.86%). Bone healing in the extraction socket, based on micro-CT and histomorphometry evaluations, was best in Post-PTH and worst in Control group. The Pre-PTH group showed moderate healing pattern. Despite of limitations in this study, the authors identified Pre-PTH group seems to have positive effect on extraction socket healing. With regard to timing, administering iPTH after tooth extraction was superior to applying it before tooth extraction.

It has been recognized that systemic inflammatory markers (SIMs) are associated with patient survival in various types of cancer. This study aimed to determine the optimal cut-off values, and to evaluate the prognostic performance of SIMs for oral squamous cell carcinoma (OSCC) within the framework of the American Joint Committee of Cancer (AJCC) cancer staging manual, 8th edition. Records were collected for a total 291 patients who had had a peripheral blood test within 1 week prior to surgery and had undergone the surgical resection of OSCC in a single institution between 2005 and 2018. The cut-off values of SIMs were obtained, and the survival analyses for overall survival (OS) and disease-free survival (DFS) were performed. Multivariate analyses incorporating other clinicopathologic factors were performed to verify the independent risk factors for survival. The cut-off values of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were 2.23, 135.14 for OS and 2.16, 131.07 for DFS, respectively, demonstrating a significant association for OS and DFS in OSCC. AJCC pathologic regional lymph node category (pN) (P < 0.001), perineural invasion (PNI) (P < 0.001) and NLR (P < 0.001) were independent predictors for OS. Meanwhile, for DFS, AJCC pN (P = 0.018) and NLR (P = 0.015) were shown to be independent predictors. Before the curative surgery, NLR and PLR could be auxiliary parameters for OS and DFS in OSCC. And based on the 8th edition of AJCC staging system, elevated NLR will be a potential indicator of the worse OS or DFS along with pN or PNI in OSCC.

The Cox proportional hazards model commonly used to evaluate prognostic variables in survival of cancer patients may be too simplistic to properly predict a cancer patient's outcome since it assumes that the outcome is a linear combination of covariates. In this retrospective study including 255 patients suitable for analysis who underwent surgical treatment in our department from 2000 to 2017, we applied a deep learning-based survival prediction method in oral squamous cell carcinoma (SCC) patients and validated its performance. Survival prediction using DeepSurv, a deep learning based-survival prediction algorithm, was compared with random survival forest (RSF) and the Cox proportional hazard model (CPH). DeepSurv showed the best performance among the three models, the c-index of the training and testing sets reaching 0.810 and 0.781, respectively, followed by RSF (0.770/0.764), and CPH (0.756/0.694). The performance of DeepSurv steadily improved with added features. Thus, deep learning-based survival prediction may improve prediction accuracy and guide clinicians both in choosing treatment options for better survival and in avoiding unnecessary treatments.