C-S-H Seeds Accelerate Early Age Hydration of Carbonate-Activated Slag and the Underlying Mechanism.

The slow hardening process of carbonate-activated slag limits its application as a construction material. This paper aims to provide an acceleration method for the early age hydration of carbonate-activated slag by applying calcium silicate hydrate (C-S-H) seeds and unveil the underlying mechanism. The results show that the incorporation of C-S-H seeds significantly accelerates the early age reaction of carbonate-activated slag and shortens the setting time. With 4% of calcium silicate hydrate (C-S-H) seeds, the 1d-compressive strength of carbonate activates slag can achieve 25.4 MPa. The C-S-H seeds acts as the preferred nucleation sites for the strength-giving phase C-A-S-H gel and the carbonate-containing phases (e.g., calcite, gaylussite, hydrotalcite, etc.), and accelerates hydration. The dormant period of samples with C-S-H seeds becomes negligible, confirming that the seeding effect that controls the saturation limits of the pore solution is the major reason for the accelerated hydration.

A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study.

BACKGROUND: The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers' clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. METHODS: We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. FINDINGS: 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973-0·991), sensitivity of 94·9% (0·915-0·978), and specificity of 88·7% (0·845-0·926) on the internal validation dataset (n = 401), and an AUC of 0·935 (0·910-0·957), sensitivity of 89·6% (0·847-0·942) and specificity of 80·6% (0·757-0·853) on the external validation dataset (n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988-0·999), sensitivity of 97·4% (0·932-1·000) and specificity of 93·5% (0·882-0·979) in detecting early-stage OCSCC. On the clinical validation dataset (n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902-0·943] vs 92.4% [0·912-0·936]), sensitivity (91·0% [0·879-0·941] vs 91·7% [0·898-0·934]), and specificity (93·5% [0·909-0·960] vs 93·1% [0·914-0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855-0·885], sensitivity of 83·1% [0·807-0·854], and specificity of 90·7% [0·889-0·924]) and the average non-medical student (accuracy of 77·2% [0·757-0·787], sensitivity of 76·6% [0·743-0·788], and specificity of 77·9% [0·759-0·797]). INTERPRETATION: Automated detection of OCSCC by deep-learning-powered algorithm is a rapid, non-invasive, low-cost, and convenient method, which yielded comparable performance to that of human specialists and has the potential to be used as a clinical tool for fast screening, earlier detection, and therapeutic efficacy assessment of the cancer.

Simvastatin Improves the Jaw Bone Microstructural Defect Induced by High Cholesterol Diet in Rats by Regulating Autophagic Flux.

OBJECTIVE: The objective of this study is to evaluate the effect of simvastatin on the jaw bone microstructural defect and autophagy in rats with high cholesterol diet (HCD). METHODS: Male Sprague-Dawley rats were fed a standard rodent chow (NC group) or a high cholesterol diet for 32 weeks and the HCD-fed rats were treated with vehicle (HC group) or simvastatin (5 mg/kg orally daily for 8 weeks, HC + SIM group, and n = 10/group). The static histomorphometric changes in the jaw bone tissues in individual rats were evaluated. The relative levels of OPG, RANKL, NF-κB, LC3, and p62 in the jaw bone tissues were determined by quantitative RT-PCR and/or immunohistochemistry. RESULTS: Compared with the NC group, the HC groups had lower trabecular bone volume, trabecular thickness and trabecular number, and increased ratios of RANKL/OPG in the jaw bone, accompanied by enhanced NF-κB activation and autophagy. Simvastatin treatment inhabited these changes, including the decreased levels of serum proinflammatory cytokines and increased autophagy. CONCLUSION: Simvastatin treatment could inhibit the hyperlipidemia-induced jaw bone microstructural defect in rats by increasing autophagic flux.

Effect of Sustained Hypoxia on Autophagy of Genioglossus Muscle-Derived Stem Cells.

BACKGROUND Previous studies have demonstrated that sustained hypoxia in people with obstructive sleep apnea (OSA) impairs upper airway muscle activity, but the underlying mechanism remains poorly understood. As autophagy acts as an important regulator under hypoxia stress, we performed an in vitro investigation of the effects of sustained hypoxia on autophagy of genioglossus muscle-derived stem cells (GG MDSC), an important component of the upper airway muscle. MATERIAL AND METHODS Genioglossus MDSCs were obtained from Sprague-Dawley (SD) rats and identified by using immunofluorescence staining for CD34, Sca-1, and desmin. GG MDSCs were incubated under normoxic or sustained hypoxic conditions for different periods of time. Western blotting was used to detect LC3 and Beclin 1, which are 2 important proteins in autophagy flux, and autophagolysosomes accumulation was observed by transmission electron microscopy (TEM). The mRNA and protein levels of HIF-1α and BNIP3 were evaluated by RT-PCR and Western blot analysis, respectively. RESULTS Our study shows that sustained hypoxia promotes the expression of LC3BII and Beclin 1 in GG MDSCs in a time-dependent manner. TEM showed an increased number of autophagolysosomes in GG MDSCs under sustained hypoxia for 12 and 24 h. In addition, hypoxia activated the HIF-1α/BNIP3 signal pathway both at protein levels (shown by Western blot) and at mRNA levels (shown by RT-PCR). CONCLUSIONS Our study shows that sustained hypoxia promotes autophagy in GG MDSCs, and the HIF-1a/BNIP3 signal pathway was involved in this process.

The expression of NF-kappaB, Ki-67 and MMP-9 in CCOT, DGCT and GCOC.

Calcifying odontogenic cysts (COCs) represent a group of rare odontogenic lesions with a diversity of clinicopathological and behavioral features. According to the WHO classification of head and neck tumors in 2005, COC has been divided into calcifying cystic odontogenic tumor (CCOT), dentinogenic ghost cell tumor (DGCT) and ghost cell odontogenic carcinoma (GCOC). With few reports available on its immunohistochemical profile, this study investigated the histopathological features and the expression of nuclear factor kappaB (NF-kappaB), Ki-67 and matrix metalloproteinase-9 (MMP-9) in CCOT, DGCT and GCOC. According to the WHO classification of head and neck tumors in 2005, 26 cases of the so-called COC were diagnosed as CCOT (n=14), DGCT (n=7) and GCOC (n=5), respectively. The specimens of 26 COCs and 10 classic ameloblastomas (as control group) were examined by immunohistochemistry using anti-NF-kappaB p65, anti-Ki-67 and anti-MMP-9 antibodies and by in situ hybridization(ISH)using anti-MMP-9 mRNA probes. Immunohistochemical reactivity for NF-kappaB was mainly detected in the cytoplasm of tumor cells, and nuclear reactivity was only seen in few tumor cells in COC and classic ameloblastomas. Rate of nuclear staining was less than 1%. The expression of Ki-67 in GCOC was significantly higher than those in CCOT (p<0.001), DGCT and ameloblastoma (p<0.005). In COCs and ameloblastomas, expression of MMP-9 mRNA and protein was detected in tumor cells as well as in stromal cells. The positive staining for MMP-9 protein was detected in stromal cells of all GCOC cases and was significantly stronger than those in CCOT and DGCT groups (p<0.05). NF-kappaB may minimally affect the progression and local invasiveness of CCOT, DGCT and GCOC. GCOC show significantly higher proliferative activity than CCOT and DGCT. MMP-9 in stroma is associated with invasive ability of the CCOT, DGCT and GCOC.

[Expression of nuclear factor-kappaB, Ki-67 and matrix metalloproteinase-9 in calcifying odontogenic cyst].

OBJECTIVE: To evaluate the expression of nuclear factor-kappaB (NF-kappaB), Ki-67 and matrix metalloproteinase-9 (MMP-9) in calcifying odontogenic cyst (COC), in order to investigate the proliferation and invasion of COC. METHODS: Twenty-six cases of COC were classified into calcifying cystic odontogenic tumor (CCOT), dentinogenic ghost cell tumor (DGCT) and ghost cell odontogenic carcinoma (GCOC) based on the WHO classification of odontogenic tumors in 2005. The specimens of COC and 10 classic ameloblastoma (AB) were examined immunohistochemically to determine the expression of NF-kappaB p65, Ki-67 and MMP-9. RESULTS: NF-kappaB was mainly detected in the cytoplasm of most tumor cells, but was only detected in the nucleus of few tumor cells (rate of nuclear staining < 1%). The expression of Ki-67 was significantly higher in GCOC than in CCOT (P < 0.001), DGCT (P < 0.05) and AB (P < 0.005). MMP-9 was detected both in tumor cells and stromal cells. GCOC showed significantly higher percentage of MMP-9 positive cases in stromal cells than CCOT, DGCT and AB (P < 0.05). CONCLUSIONS: NF-kappaB may minimally affect the progression and invasion of COC. GCOC shows significantly higher proliferative activity and aggressiveness than CCOT and DGCT. MMP-9 in stroma may play a key role in the invasion of GCOC.