碳纤维复合材料人工颅骨板急性期生物相容性的研究

目的 评价碳纤维复合材料人工颅骨板在急性期的生物相容性。方法 制作碳纤维复合材料、含重组人骨形态发生蛋白-2（rhBMP-2）的碳纤维复合材料、钛网、MEM培养基、含有0.64%苯酚的MEM培养基的浸出液，用于测试。采用L929小鼠成纤维细胞进行细胞毒性试验，采用健康ICR小鼠进行急性毒性试验，采用新西兰大白兔耳静脉血进行急性溶血试验，采用白化豚鼠进行皮肤致敏试验。结果 碳纤维复合材料、含rhBMP-2的碳纤维复合材料、钛网浸提液对L929细胞的相对增殖率均在75%以上，细胞毒性1级，无明显细胞毒性；小鼠腹腔注射相应材料的浸提液后，均活动良好，均无不良反应，均无明显急性毒性；碳纤维复合材料、含rhBMP-2的碳纤维复合材料、钛网浸提液溶血率均小于5%，均不引起溶血反应；碳纤维复合材料、含rhBMP-2的碳纤维复合材料、钛网浸提液不引起致敏性。结论 碳纤维复合人工颅骨板急性期生物相容性良好。     

The influence of processed total non-forward and non-motile sperm count on the outcome of artificial insemination with the husband’s semen

BackgroundArtificial insemination with the husband’s semen (AIH) is an economical and noninvasive method of infertility treatment. However, AIH’s pregnancy rate is much lower than in vitro fertilization (IVF) as its multiple and complex uncertainty factors. Semen quality has been one of the main factors which affect the pregnancy outcome of AIH.MethodsThe relevant parameters of 1,142 AIH cycles were retrospectively studied, including the general parameters and the semen quality parameters among clinical pregnancy, biochemical pregnancy, non-pregnancy group, age, infertility duration, infertility type, body mass index (BMI), cycle count, morphology in previously semen examination, and semen quality parameters on the day of AIH.ResultsThe statistically significant difference was only found on processed total non-forward and non-motile sperm count (N-TFMSC). The mean processed N-TFMSC in the biochemical pregnancy group was 6.37±4.27 million, significantly higher than the other two groups (vs. 4.40±3.15 million or vs. 4.48±3.60 million, P<0.05). The study was then divided into two groups according to processed N-TFMSC, Group 1 ≤5.0 million, and Group 2 >5.0 million. A statistical increase in biochemical pregnancy rate was observed when the processed N-TFMSC was >5.0 million (2.72% vs. 0.90%).ConclusionsProcessed N-TFMSC may be one of the independent factors on AIH’s outcome; it should be given equal attention the same as processed total forward motile sperm count (TFMSC).     

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.     

儿童永久起搏器电池更换术后切口愈合不良两种治疗方案的对比研究

目的对比儿童永久起搏器电池更换术后切口愈合不良两种不同治疗方式的疗效。方法回顾性分析2001年1月至2019年11月行起搏器电池更换后出现切口愈合不良的11例患儿病历资料,其中男童7例,女童4例,平均年龄(7.3±2.5)岁,电池更换距离上一次起搏器电池植入时间(4.1±1.5)年,7例切口有脓性分泌物,4例有清亮分泌物。8例于术后3周内再次入院,3例分别于术后45 d、40 d、32 d再次入院。5例有低热症状,6例无发热症状。依据不同治疗方式分为手术组(n=6)和常规换药组(n=5)。手术组1例予囊袋扩大后清创缝合,5例予原囊袋切除清创缝合。常规换药组5例均予以头孢菌素或万古霉素抗感染,治疗碘伏纱布每日换药2次,其中2例予持续负压吸引2周。结果手术组6例患儿均在术后2周内痊愈出院,平均住院时间(11±3)d,均在术后3 d内拔除引流管,平均引流量(25.6±10.1)mL,术后电话随访,未再发生切口愈合不良。手术组6例患儿均未发生全身感染及感染性心内膜炎,伤口愈合良好,疗效满意。常规换药组平均住院时间(30±5)d,1例出院2个月后再次入院,拆除原腹部起搏器,更换为心内膜起搏器。两组患儿住院时间具有统计学差异(t=-9.6,P<0.01)。出院前手术组和常规换药组C反应蛋白测量值分别为(8.2±2.5)mg/L和(25.0±15.0)mg/L,差异有统计学意义(t=-2.8,P<0.05)。结论儿童永久起搏器电池更换术后切口愈合不良发生后,将原起搏器囊袋切除,减少异物植入,可吸收线间断缝合,适当游离皮片减少吻合口张力,可取得满意疗效。此方法与常规换药相比可明显缩短住院时间,减轻病人痛苦,降低再次感染的发生率。     

人工智能在医学领域应用浅析

医疗与人工智能相结合,是医学发展的重要方向。作者综述了人工智能在诊断、治疗、预防、科研、教学等医学领域的应用情况,分析了人工智能发展面临的问题和挑战,对人工智能的应用前景进行了展望。     

Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy

PurposeThe primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the “Workflow Box” software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to European Society of Therapeutic Radiation Oncology (ESTRO) guidelines; review and comparison with other cardiac atlas-based autosegmentation algorithms published to date.Materials and MethodsOf an atlas of data set from 20 breast cancer patients’ CT scans with recontoured cardiac substructures creation according to the ESTRO guidelines. Performance evaluation on a validation data set consisting of 20 others CT scans acquired in the same treatment position: cardiac substructure were automatically contoured by the Mirada system, using the implemented cardiac atlas, and simultaneously manually contoured by a radiation oncologist. The Dice similarity coefficient was used to evaluate the concordance level between the manual and the automatic segmentations.ResultsDice similarity coefficient value was 0.95 for the whole heart and 0.80 for the four cardiac chambers. Average Dice similarity coefficient value for the left ventricle walls was 0.50, ranging between 0.34 for the apical wall and 0.70 for the lateral wall. Compared to manual contours, autosegmented substructure volumes were significantly smaller, with the exception of the left ventricle. Coronary artery segmentation was unsuccessful. Performances were overall similar to other published cardiac atlas-based autosegmentation algorithms.ConclusionThe evaluated cardiac atlas-based autosegmentation technique, using the Mirada software, demonstrated acceptable performance for cardiac cavities delineation. However, algorithm improvement is still needed in order to develop efficient and trusted cardiac autosegmentation working tools for daily practice.     

人工智能在内镜诊断上消化道早癌领域的研究进展

随着深度学习算法的不断开发,人工智能在内镜辅助诊断中的应用也备受关注。上消化道肿瘤因其早期不易被发现的特点成为内镜医师镜下诊断的难题,而计算机辅助诊断等新技术有望实现对上消化道早期恶性病变及其相关危险因素的识别及诊断。本文阐述了目前人工智能在内镜诊断上消化道早癌领域的应用情况和发展前景。