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牙外伤是口腔急诊常见的病症之一,及时准确的牙髓活力判断是正确治疗以利维持美学与功能的重要前提条件。文章通过对牙髓活力的生物学基础、牙髓活力测验方法及评价、外伤牙髓受损类型和牙髓活力特点分析,在国际牙外伤临床治疗指南的方向指导下,结合案例和经验,概括外伤牙活力与实施临床治疗方案的关系,为提高牙外伤的临床诊治思维、科学精准和个性化治疗设计提供支持,具有重要的应用价值。 相似文献
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IntroductionThis study was designed to assess whether a dental caries management protocol combining a single application of 38% silver diamine fluoride (SDF) with comprehensive oral health education will successfully divert high-risk children from dental treatment under dental general anaesthesia (DGA), arrest active caries in primary teeth, and improve parent-reported child oral health–related quality of life (OHRQoL).MethodsChildren aged 2 to 10 years, who attended two public dental agencies in Victoria, Australia, and were unable to tolerate restorative treatments in the clinic setting, elected to participate in either a 38% SDF intervention protocol or, alternatively, referral for DGA. Follow-up examinations were completed at 6 months to assess caries progression, decayed missing filled tooth index, PUFA index (pulpal involvement, ulceration, fistula, abscess), DGA referral rates, and OHRQoL (Early Childhood Oral Health Impact Scale [ECOHIS]).ResultsOf the total sample, 89.5% of children (n = 102) [mean (SD) age, 4.1 (1.0) years] with 401 active carious lesions elected to participate in the 38% SDF protocol; 10.5% (n = 12) of parents opted for referral for treatment under DGA. The proportion of active caries subsequently arrested at follow-up (number of arrested lesions/number of lesions treated) was 0.78 (95% CI, 0.69 to 0.87). There was an 88% reduction in referrals for DGA in eligible children over the 6-month period. The 38% SDF intervention group showed a significant improvement in ECOHIS scores at follow-up (P < .001).DiscussionAdoption of the 38% SDF intervention protocol resulted in a significant reduction in the rate of preventable dental hospitalisations. Most parents opted against referral for DGA. Parent-reported OHRQoL for children improved significantly. 相似文献
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《Journal of endodontics》2020,46(10):1455-1464
IntroductionThe aim of this study was to investigate whether mineral trioxide aggregate (MTA) can be modified with caffeic acid (CA) to form caffeic acid/mineral trioxide aggregate (CAMTA) cement and to evaluate its physicochemical and biological properties as well as its capability in immune suppression and angiogenesis.MethodsMTA was immersed in trishydroxymethyl aminomethane buffer with CA to allow coating onto MTA powders. X-ray diffractometry and tensile stress-strain tests were conducted to assess for physical characteristics of CAMTA and to evaluate for successful modification of MTA. Then, the CAMTA cement was immersed in simulated body fluid to evaluate its hydroxyapatite formation capabilities and Si release profiles. In addition, RAW 264.7 cells and human dental pulp stem cells were used to evaluate CAMTA’s immunosuppressive capabilities and cell responses, respectively. hDPSCs were also used to assess CAMTA’s angiogenic capabilities.ResultsThe X-ray diffractometry results showed that CA can be successfully coated onto MTA without disrupting or losing MTA’s original structural properties, thus allowing us to retain the initial advantages of MTA. CAMTA was shown to have higher mechanical properties compared with MTA and had rougher pitted surfaces, which were hypothesized to lead to enhanced adhesion, proliferation, and secretion of angiogenic- and odontogenic-related proteins. In addition, it was found that CAMTA was able to enhance hydroxyapatite formation and immunosuppressive capabilities compared with MTA.ConclusionsCAMTA cements were found to have improved physicochemical and biological characteristics compared with their counterpart. In addition, CAMTA cements had enhanced odontogenic, angiogenic, and immunosuppressive properties compared with MTA. All of the results of this study proved that CAMTA cements could be a biomaterial for future clinical applications and tissue engineering use. 相似文献
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《Diagnostic and interventional imaging》2020,101(12):821-830
PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10−3 mm2/s [range: (0.91 - 1.3) × 10−3 mm2/s] vs. [1.4 ± 0.2 (SD)] × 10−3 mm2/s [range: (1.1 - 1.6) × 10−3 mm2/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI. 相似文献
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《Diagnostic and interventional imaging》2020,101(9):555-564
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
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