活性炭在过氧化氢氧化脱除二苯并噻吩中的催化作用

研究了几种活性炭在过氧化氢氧化二苯并噻吩(DBT)中的催化性能、氧化脱除机理以及碳表面化学对催化性能的影响。考察了活性炭的吸附性能与催化性能以及两者之间的关系、DBT在活性炭上的动态反应活性以及水相pH对其吸附性能和催化性能的影响;并将一种木质活性炭经过3种表面处理,研究了活性炭的表面化学对其催化性能的影响。结果表明:活性炭在过氧化氢氧化DBT中具有较高的催化活性,正辛烷中的DBT转化率可达到81%以上;木质粉末活性炭对DBT的吸附性能优于果壳炭和煤质颗粒活性炭;吸附容量大的活性炭,其催化性能也好;DBT在催化性能高的活性炭作用下的动态反应活性也高;水相pH在低于2的条件下有利于DBT在活性炭上的吸附和催化氧化。DBT的氧化脱除强烈依赖于表面羰基量,是由于表面羰基能加速过氧化氢产生自由基。     

神经外科术中急性脑膨出的原因分析及处理方法

目的：总结分析脑外科手术中急性脑膨出的发生原因和防治措施。方法：对术中出现急性脑膨出的74例病人进行回顾性分析，并对不同处理方法导致的结果进行比较。结果：本组应用综合措施处理术中急性脑膨出，术后3个月GOS评分，良好32例，中残21例，重残7例，植物生存4例，死亡7例。结论：尽早发现并去除致膨出因素，综合运用降颅压措施，尽量扩大颅腔容积，可以避免脑疝或膨出脑组织切除，明显改善患者预后。     

单孔腹腔镜与多孔腹腔镜下行卵巢囊肿剔除术的应用效果对比

目的探究单孔腹腔镜与多孔腹腔镜下行卵巢囊肿剔除术的应用效果。方法86例卵巢囊肿患者,按治疗方法不同分为单孔组和多孔组,每组43例。单孔组行单孔腹腔镜下卵巢囊肿剔除术,多孔组行多孔腹腔镜下卵巢囊肿剔除术。观察比较两组患者术中出血量、手术时间、术后排气时间、住院时间及术后24 h视觉模拟评分法(VAS)评分,术后体象量表(BIS)评分、美容评分(CS),并发症发生率。结果单孔组术中出血量、手术时间、术后排气时间、住院时间及术后24 hVAS评分与多孔组比较,差异无统计学意义(P>0.05)。单孔组与多孔组BIS评分比较,差异无统计学意义(P>0.05);单孔组CS评分(22.55±3.98)分高于多孔组的(18.65±6.23)分,差异有统计学意义(P<0.05)。两组术后并发症发生率比较,差异无统计学意义(P>0.05)。结论单孔腹腔镜卵巢囊肿剔除术较多孔腹腔镜并无减轻切口疼痛感的优势,但在切口美观程度上存在优势。     

What would you do? Challenges in shoulder surgery

Dr. Evan Flatow moderates a panel of experts and discusses complex shoulder arthroplasty cases dealing with bone loss, malunions, arthroplasty revision, implant failure, and more. Panel includes Dr. Jon P. Warner, Dr. Gerald Williams, Dr. William Levine, Dr. William Seitz, and Dr. Evan Lederman.     

Guidelines for laser hair removal

Requests for removal of unwanted body hair are common in dermatologic and surgical practices. Technology continues to improve the achievement of a more permanent reduction through the use of lasers. Despite the increased use of lasers, to date, few guidelines exist in terms of how to approach laser hair removal. Specifically, one must understand the mechanism of hair growth and how lasers work to target the hair follicle. There is significant variation among practitioners in pre‐and post‐laser recommendations to patients as well as intervals between treatment sessions. We performed a thorough review of the literature in order to determine evidence for the ideal interval between treatment sessions and the ideal number of sessions. We also sought to establish, based on published reports, the recommendations for shaving, plucking, waxing or other hair removal methods prior to laser hair removal and the guidelines for sun exposure before and after laser treatments. Finally, we searched the literature to find out whether there are areas that should not be treated with laser hair removal. The evidence and recommendations in this article aim to help guide practitioners in their approach to laser hair removal.     

Accuracy and Efficiency of 3-dimensional Dynamic Navigation System for Removal of Fiber Post from Root Canal–Treated Teeth

IntroductionThe purpose of this study was to investigate the accuracy and efficiency of the 3-dimensional dynamic navigation system (DNS) compared with the freehand technique (FH) when removing fiber posts from root canal–treated teeth.MethodsTwenty-six maxillary teeth were included. Teeth were root canal treated and restored with Parapost Taper Lux (Coltene/Whaledent, Altstätten, Switzerland) luted with RelyX Unicem (3M ESPE, St Paul, MN). A core buildup was then performed using Paracore (Coltene/Whaledent). Teeth were mounted in tissue-denuded cadaver maxillae. Teeth were divided into 2 groups: the DNS group (n = 13) and the FH group (n = 13). Cone-beam computed tomographic scans were taken pre- and postoperatively. The drilling path and depth were planned virtually using X-guide software (X-Nav Technologies, Lansdale, PA) in both groups. For the DNS group, drilling was guided with X-Nav software and the FH group under a dental operating microscope. Global coronal and apical deviations, angular deflection, operation time, and the number of mishaps were compared between the groups to determine the accuracy and efficiency. The 3-dimensional volume (mm³) of all teeth was calculated before and after post removal using the Mimics Innovation Suite (Materialise NV, Leuven, Belgium). The Shapiro-Wilk, 1-way analysis of variance, and Fisher exact tests were used (P < .05).ResultsThe DNS group showed significantly less global coronal and apical deviations and angular deflection than the FH group (P < .05). DNS required less operation time than FH. Moreover, the DNS technique had significantly less volumetric loss of tooth structure than the FH technique (P < .05).ConclusionsThe DNS was more accurate and efficient in removing fiber posts from root canal–treated teeth than the FH technique.     

Predicting protein-binding RNA nucleotides using the feature-based removal of data redundancy and the interaction propensity of nucleotide triplets

Several learning approaches have been used to predict RNA-binding amino acids in a protein sequence, but there has been little attempt to predict protein-binding nucleotides in an RNA sequence. One of the reasons is that the differences between nucleotides in their interaction propensity are much smaller than those between amino acids. Another reason is that RNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding RNA nucleotides is much harder than predicting RNA-binding amino acids. We developed a new method that removes data redundancy in a training set of sequences based on their features. The new method constructs a larger and more informative training set than the standard redundancy removal method based on sequence similarity, and the constructed dataset is guaranteed to be redundancy-free. We computed the interaction propensity (IP) of nucleotide triplets by applying a new definition of IP to an extensive dataset of protein-RNA complexes, and developed a support vector machine (SVM) model to predict protein binding sites in RNA sequences. In a 5-fold cross-validation with 812 RNA sequences, the SVM model predicted protein-binding nucleotides with an accuracy of 86.4%, an F-measure of 84.8%, and a Matthews correlation coefficient of 0.66. With an independent dataset of 56 RNA sequences that were not used in training, the resulting accuracy was 68.1% with an F-measure of 71.7% and a Matthews correlation coefficient of 0.35. To the best of our knowledge, this is the first attempt to predict protein-binding RNA nucleotides in a given RNA sequence from the sequence data alone. The SVM model and datasets are freely available for academics at http://bclab.inha.ac.kr/primer.