牙支持3D导板辅助穿刺卵圆孔射频热凝治疗三叉神经痛的疗效分析

目的:探讨牙支持式3D打印导板辅助穿刺卵圆孔射频热凝治疗三叉神经痛术中术后的疗效分析.方法:对2019年1月～2021年5月收治的三叉神经痛患者40人,根据患者需要随机分成2组:A组(对照组),常规按照Hartel前入路法穿刺卵圆孔,进入卵圆孔后,耳-床线定位确定入颅深度后行射频治疗.B组(导板组),采用3D导板,给患者戴上导板固定后,完成穿刺,耳-床线定位确定入颅深度后行射频治疗.分析2组的术前疼痛评分(VAS)术后即刻及术后1月疼痛消失的有效率,手术时间,穿刺次数,并发症,穿刺的偏移程度.结果:与对照组A组比较在术后即刻有效率、术后1月随访有效率比较无统计学差异(P>0.05).在穿刺次数、穿刺偏移率、手术时间、并发症两组间比较有统计学差异(P<0.05).导板与牙齿紧密贴合率100％.结论:3D打印导板应用于穿刺卵圆孔具有明显的优势,可以减少患者创伤,减少X线辐射,减少穿刺偏移,术后疗效肯定,值得广泛应用.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

八段锦治疗神经根型颈椎病的系统评价与Meta分析

[目的] 系统评价八段锦治疗神经根型颈椎病的临床疗效。[方法] 计算机检索中国知网数据库（CNKI）、维普资讯中文科技期刊数据库（VIP）、万方数据库（Wan-fang）、中国生物医学文献数据库（SinoMed）、Web of science、Clinical Trials gov、Pubmed、EMBASE、CENTRAL、Cochrane Library，查找出八段锦治疗神经根型颈椎病的随机对照试验（RCT），检索时限均从建库至2019年7月1日。两名研究者按照纳入与排除标准独立进行文献筛选、资料提取和偏倚风险评估，应用RevMan 5.3软件对最后纳入的研究进行Meta分析。[结果] 最终纳入10个RCT，共843例患者。Meta分析结果显示：八段锦+常规治疗与常规治疗比较，其临床疗效的总有效率差异具有统计学意义[OR=4.19，95% CI（2.42，7.26），P<0.000 01]；八段锦+常规治疗与常规治疗+颈部“米”字操比较，其临床疗效的总有效率差异具有统计学意义[OR=3.41，95% CI（1.56，7.46），P=0.002]；八段锦+常规治疗与常规治疗比较，其疼痛视觉模拟评分法（VAS）评分差异具有统计学意义[MD=-1.90，95% CI（-1.97，-1.83），P<0.000 01]。[结论] 基于现有的研究，该系统评价显示，与常规治疗相比，八段锦+常规治疗在临床疗效总有效率、VAS评分方面有一定的治疗优势。与常规治疗+颈部“米”字操比较，八段锦+常规治疗在临床疗效的总有效率方面有一定的治疗优势。但由于纳入研究的质量不高，仍需要大样本、多中心、随机、双盲试验及试验参照CONSORT标准和STRICTA声明进行RCT研究。     

某设备所致脉冲X射线辐射场测量与防护评价

目的 了解某设备实验条件下不同位置脉冲X射线电离辐射水平，提出适当的防护建议。方法 采用热释光测量方法，分别在设备舱周围不同方向不同距离布放热释光剂量计，累积一定数量脉冲辐射后进行测量；采用电离室型X、γ剂量率仪（FJ-347A）实时测量工作状态下不同距离处电离辐射剂量率水平。依据《电离辐射防护与辐射源安全基本标准》（GB18871—2002）规定的职业照射人员和公众个人剂量限值提出不同工作位辐射防护建议。结果 热释光剂量计累计接收3 000个脉冲辐射，设备舱外壁0.01～8.98 mGy，顶部0.01～15.67 mGy，距外壁1～12 m之间0.01～2.18 mGy，工作位0 mGy。工作状态下，X射线剂量率仪测得距设备舱外侧壁1～20 m之间空气比释动能率范围0.26～16 mGy/h。结论 热释光剂量计、电离室型剂量率仪测量结果基本一致，说明两种方法均可用于脉冲X射线测量；工作状态下设备舱外近距离处辐射剂量率较高，可通过采取防护措施或者限制人员工作量以满足辐射防护要求。     

连续护理干预模式对脊柱骨折伴脊髓损伤患者术后康复效果的研究

目的研究分析连续护理干预模式对脊柱骨折伴脊髓损伤患者术后康复效果。方法本项研究回顾了2017年2月—2018年7月间在某院采取了脊柱骨折伴脊髓损伤治疗的52例患者整个治疗过程的资料,将所有的患者使用数字双盲法将其分为了对照组(n=26)和观察组(n=26)。对照组患者使用护理措施为常规护理措施,观察组使用的护理措施为连续护理干预模式,在患者治疗完成后对比两个不同组别患者在围手术期期间各类指标和出现并发症概率。结果对照组患者手术下床活动时间、住院时间、住院所需费用以及患者并发症发生率均显著高于观察组,组别间数据对比差异具有统计学意义(P<0.05)。结论医护人员在进行脊柱骨折伴脊髓损伤患者的护理工作当中,对其采取连续护理模式可以降低患者并发症发生率,缩短患者住院时间,对患者身体的恢复具有显著意义。     

99mTc-DTPA核素肾动态显像评价主动脉夹层患者术后肾功能受损研究

目的:应用99mTc-DTPA肾动脉显像评价Stanford不同分型主动脉夹层患者术后左、右侧肾功能及总体肾功能受损程度,帮助临床制定进一步的治疗方案,改善患者预后。方法:回顾性分析2018年3月8日至2019年7月19日,在本院核医学科行99mTc-DTPA肾动态显像的主动脉夹层术后患者48例,评价患者双肾血流灌注、总肾小球滤过率(GFR)和分肾的GFR,比较Stanford主动脉夹层A型(简称A型)患者和主动脉夹层B型(简称B型)患者之间总肾功能及分肾功能,血肌酐、血尿素氮及血尿酸水平的差异。结果:B型患者术后总GFR低于A型患者(67.5 vs.80.6 m L/min,P<0.05),其中以左肾功能受损为著(30.9 vs.40.3 m L/min,P<0.05),差异有统计学意义。结论:肾动态显像对主动脉夹层术后患者早期评价肾功能有重要价值。主动脉夹层B型患者GFR较A型减低,且左侧肾GFR减低更明显,临床可以早期采取干预措施,改善主动脉夹层患者预后。     

血液系统恶性肿瘤患者化疗后合并中性粒细胞减少性肠炎的诊治研究

目的分析成人血液系统恶性肿瘤患者接受强烈化疗后中性粒细胞减少性肠炎（NE）的发生率、危险因素及预后情况。方法收集2004至2013年接受化疗的1804例血液系统恶性肿瘤患者，记录患者血常规、凝血检测和血液生化检测结果，并记录患者年龄、性别、原发病、既往化疗次数、既往化疗方案中是否使用阿糖胞苷、临床症状、肠壁厚度、中性粒细胞最低计数、中性粒细胞缺乏持续时间、NE的治疗方法和预后等，探讨NE起病诱因、临床特征、腹部B超特点、症状的预后意义及化疗药物对发病的影响等。结果1804例患者中226例（12.5%）化疗后合并NE，化疗后10~19d起病，中位起病时间为化疗后第14天。发生NE后26例患者死亡，病死率11.5%。化疗药物包括阿糖胞苷、临床症状≥4项、中性粒细胞缺乏持续超过7d以及B超下肠壁厚度≥10mm的患者病死率相对较高。结论NE是接受强烈化疗的血液系统肿瘤患者的严重的并发症，发生NE后患者病死率较高。     

丙泊酚麻醉对老龄大鼠认知功能和海马神经元γ-氨基丁酸A受体表达的影响

目的观察丙泊酚麻醉对老龄大鼠认知功能及海马神经元γ-氨基丁酸A(GABA)受体表达的影响。方法50只SD老年大鼠随机分为丙泊酚组和对照组,每组25只。丙泊酚组大鼠腹腔注射1%丙泊酚中/长链脂肪乳注射液6 mL/kg,对照组腹腔注射等体积的生理盐水。麻醉后1 d进行Morris水迷宫实验,分别采用HE染色和尼氏体染色观察海马区神经细胞及尼氏体(Nissl体)形态学变化,Western Blot检测GABA蛋白量表达。结果麻醉后,2组大鼠肛温、心率、呼吸频率、血氧饱和度比较,差异均无统计学意义(P>0.05)。随着实验时间的延长,2组大鼠逃逸潜伏期、总里程数逐渐减小,丙泊酚组大鼠各时间点逃逸潜伏期、总里程均高于对照组,差异有统计学意义(P<0.05)。丙泊酚组大鼠穿越平台区域次数、时间小于、短于对照组,差异有统计学意义(P<0.05)。对照组海马区Nissl体存在于细胞浆及树突,染色较深,神经细胞排列整齐,形态规则;丙泊酚组Nissl体消失,神经细胞数量减少,细胞核破裂、丢失。丙泊酚组大鼠海马GABA蛋白表达量低于对照组,差异有统计学意义(P<0.05)。结论丙泊酚对大鼠认知功能有影响,并与海马区GABA的表达相关。     

电子病历系统中检验数据信息抽取研究

抽取电子病历中的检验检查数据,可以为临床科研人员对于某些疾病的定量分析研究提供数据支持;分析了基于规则、正则表达式抽取方法的缺陷,在此基础之上,结合相似度匹配方法,提出了一种混合算法。实验结果表明,该混合算法比单独使用某一种算法,其查全率和查准率均有了较大的提高。