心脏瓣膜置换术后患者的出院准备度现状及其影响因素分析

目的:调查心脏瓣膜置换术后患者的出院准备度现状,并分析其影响因素。方法:于2018年9月至2019年3月用一般资料、出院准备度量表、出院指导质量量表对139例瓣膜置换术后患者进行调查。结果:心脏瓣膜置换术后患者的出院准备度总分为(89.51±8.53)分,与出院指导质量呈正相关;多元回归分析显示,主要照顾者、婚姻状况、工作状态、服药种类、出院指导质量为出院准备度影响因素。结论:心脏瓣膜置换术后患者的出院准备度有待提高,患者感知的个人状态得分不理想;护士应鼓励患者配合进行早期康复,强化出院指导,采取有效措施提供康复信息,满足患者及其家属需求,提高出院准备度。     

醛糖还原酶和晚期糖基化终末产物受体对糖尿病视网膜病变神经元凋亡的影响

目的　探究醛糖还原酶和晚期糖基化终末产物受体对糖尿病视网膜病变神经元凋亡的影响。方法　Wistar大鼠36只，随机分为对照组、模型组、转染组，后两组建立糖尿病大鼠模型。模型建立成功后，构建含有晚期糖基化终末产物受体siRNA的质粒并利用慢病毒转染入转染组大鼠体内。造模后4周、8周、12周，记录各组大鼠体质量及空腹血糖。造模后9周，禁食6 h，测定口服葡萄糖耐量。造模后12周，处死全部大鼠后，TUNEL法检测各组大鼠视网膜神经元凋亡情况，荧光分光光度计测定醛糖还原酶活性，Western blotting法测定晚期糖基化终末产物受体的表达，RT-PCR检测视网膜中Bcl-2和Bax mRNA相对表达量。结果　造模后4周、8周、12周，转染组和模型组的大鼠体质量均低于对照组（均为P<0.05）；造模后12周，转染组大鼠体质量高于模型组（P<0.05）。造模后4周、8周、12周，各组内大鼠空腹血糖水平均无明显变化（均为P>0.05），转染组和模型组大鼠的空腹血糖水平均高于对照组（均为P<0.05）。模型组和转染组大鼠在口服葡萄糖后30 min时，血糖水平均高于对照组（均为P<0.05）；在120 min时分别下降至最低，但仍高于对照组（均为P<0.05）。模型组和转染组的视网膜神经元凋亡指数、醛糖还原酶活性、晚期糖基化终末产物受体和Bax mRNA相对表达量均高于对照组（均为P<0.05），且转染组均高于模型组（均为P<0.05）。模型组和转染组的Bcl-2 mRNA相对表达量均低于对照组（均为P<0.05），转染组低于模型组（P<0.05）。结论　晚期糖基化终末产物结合受体后产生大量的氧自由基损伤，可能是导致糖尿病视网膜神经元凋亡，进而导致糖尿病视网膜病变发生的机制之一。     

A systematic review of antiglycation medicinal plants

Background and objectivesThe present review shows a list of anti-glycation plants with their anti-glycation activity mechanisms that can attract the attention of pharmacologist for further scientific research towards finding better remedy for diabetic complications.MaterialsGoogle scholar, Pubmed, Web of Science and Scopus were searched. The terms were advanced glycation end products (AGEs), medicinal plants, antiglycation products.Resultsplants that studied in this review inhibit glycation in several possible mechanisms. Some of these plants inhibit the production of shiff base and amadori products. The others inhibit the generation of amadori products in the advanced phase. Some others blocked the aggregation of AGEs and some plants have antioxidant activity and reduce AGEs formation by preventing oxidation of amadori product and metal-catalyzed glucoxidation.ConclusionThis review can help pharmacologist to find antiglycation natural substance that can be useful in treatment of diabetic complications.     

Cleaning,Shaping, and Disinfecting Abilities of 2 Instrument Systems as Evaluated by a Correlative Micro–computed Tomographic and Histobacteriologic Approach

IntroductionThis study assessed the cleaning, shaping, and disinfection abilities of 2 instrumentation systems in molar root canals using a novel correlative analytical approach.MethodsThe root canals from extracted mandibular and maxillary molars with apical periodontitis were pair matched according to anatomic similarities as determined by micro–computed tomographic analysis and prepared with either XP-endo Shaper (FKG Dentaire, La Chaux-de-Fonds, Switzerland) (n = 16) or Reciproc Blue (VDW, Munich, Germany) (n = 16) instruments and 2.5% sodium hypochlorite irrigation. Pre- and postpreparation micro–computed tomographic scans were used to identify and calculate the unprepared surface areas (shaping), which were histobacteriologically evaluated for the presence of residual bacteria (disinfection) and pulp tissue remnants (cleaning) in each canal third.ResultsUnprepared canal surface areas for XP-endo Shaper and Reciproc Blue in the full canal length were approximately 26% and 19% (P < .05), respectively (30% and 23% in the apical part of the canal, P > .05). Preparation with Reciproc Blue resulted in 37.5% canals free of bacteria in all sections examined and 56% in the apical sections only. XP-endo Shaper resulted in 44% canals free of bacteria in all sections, and 56% in the apical part of the canal only. Pulp tissue remnants were not observed in 31% (all canal sections) and 50% (apical canal sections) of specimens from both instrument systems. No significant differences were observed between instruments when comparing the amount of pulp tissue remnants and the number of cases negative for bacteria and tissue remnants (P > .05).ConclusionsAlthough the Reciproc Blue instrument had superior shaping ability in comparison with XP-endo Shaper, both systems performed similarly in cleaning and disinfecting root canals. Irregular canals and difficult-to-reach areas were not thoroughly cleaned and disinfected by any of the tested systems.     

Retrograde Instrumentation of Surgically Resected Roots Using Controlled Memory Files: A Human Cadaver Study

IntroductionThe purpose of this study is to evaluate the amount of residual obturation material of retroinstrumented surgically resected roots using controlled memory files and to evaluate the incidence of adverse treatment outcomes.MethodsThirty maxillary anterior teeth in human cadavers were selected, and nonsurgical root canal treatment was performed on these teeth. A standardized 4-mm osteotomy and a 3-mm root resection with as close to 0° bevel as possible were made on each tooth. A microsurgical diamond tip was used to create a 1- to 2-mm starting point for each retropreparation. A 25/06 and 30/06 VTaper 2H were bent at about 90° angle to mimic the clinical and anatomic restrictions and used to create a retropreparation to a depth of 14 mm. Micro–computed tomography scans were taken and analyzed for volume and percentage of residual obturation material at 5 and 10 mm. In addition, the incidences of instrument separation and crack and ledge formation in the teeth were recorded.ResultsThe median volume of residual obturation at 5 and 10 mm was 0.18 mm³ (interquartile range, 0.36 mm³) and 1.97 mm³ (interquartile range, 1.99 mm³), respectively. The overall incidence of file separation during retropreparation was 13.33% (4/30). Among the cases analyzed with micro–computed tomography, none showed crack or ledge formation.ConclusionsRetroinstrumentation of surgically resected roots using controlled memory files cleans the canal effectively with relatively low adverse treatment outcomes. Although this novel technique is limited in application, it is a safe and effective way to achieve a deep, clean retropreparation.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

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%.