使用生物标志物建立机器学习模型用于菌血症的早期预测

目的 菌血症的早期识别和及时干预是降低发病率和死亡率的关键。血培养是诊断菌血症的金标准,但其敏感性低且周转时间长,难以满足临床诊断的需求。本研究的目的是通过回顾性分析,从若干与感染相关的生物标志物中筛选出最佳特征子集用于构建机器学习模型,用于菌血症的早期预测。方法 采用回顾性分析筛选出符合研究标准的菌血症和局部感染患者,并收集参与者的生物标志物数据。通过特征选择算法筛选最佳特征子集,并以此构建机器学习模型。使用精确率,召回率和准确率综合评估机器学习模型的性能。结果 本研究收集到247例菌血症患者为病例组,262例局部感染患者为对照组,并纳入12项生物标志物。通过特征选择算法,我们发现支持向量机(support vector machine, SVM)模型只须纳入5项生物标志物,其模型准确率为90.2%,AUC高达0.967。结论 通过特征选择算法与机器学习模型相结合的策略,本研究开发了3种菌血症的预测模型,其中SVM的性能最佳,为菌血症的早期诊断提供了数据支持。     

Automated Classification of 6-n-Propylthiouracil Taster Status with Machine Learning

Several studies have used taste sensitivity to 6-n-propylthiouracil (PROP) to evaluate interindividual taste variability and its impact on food preferences, nutrition, and health. We used a supervised learning (SL) approach for the automatic identification of the PROP taster categories (super taster (ST); medium taster (MT); and non-taster (NT)) of 84 subjects (aged 18–40 years). Biological features determined from subjects were included for the training system. Results showed that SL enables the automatic identification of objective PROP taster status, with high precision (97%). The biological features were classified in order of importance in facilitating learning and as prediction factors. The ratings of perceived taste intensity for PROP paper disks (50 mM) and PROP solution (3.2 mM), along with fungiform papilla density, were the most important features, and high estimated values pushed toward ST prediction, while low values leaned toward NT prediction. Furthermore, TAS2R38 genotypes were significant features (AVI/AVI, PAV/PAV, and PAV/AVI to classify NTs, STs, and MTs, respectively). These results, in showing that the SL approach enables an automatic, immediate, scalable, and high-precision classification of PROP taster status, suggest that it may represent an objective and reliable tool in taste physiology studies, with applications ranging from basic science and medicine to food sciences.     

我院聘用护士自主学习中存在的问题与干预对策

为了解医院聘用护士自主学习现状,分析存在的问题,制定有效的干预措施,提高护士自主学习的能力。采用文献计量学方法,对全院聘用护士2005年度业务学习及读书应用项目的资料来源、内容和记录方法进行统计。结果显示,学习笔记及读书应用项目,来源于各类护理专业统计源期刊比例偏低,学习笔记记录方法欠科学,不便查阅。读书应用项目总体质量较低。针对护士在自主学习中,利用信息资源和主动选择利用有效信息的能力较差,缺乏科学、有效的读书方法和积累整理资料的能力等问题,提出引导护士掌握科学、有效的学习方法和笔记记录的方法;指导护士理论联系实际,积极开展读书应用活动;充分发挥医院电子网络的作用,进行广泛阅读,提高聘用护士获取信息的能力。     

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020—March 2021) Using a Statistical Learning Strategy

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.     

高校大学生婚恋现状及应对策略

通过对高校大学生婚恋现状的调查,分析婚恋现状,针对大学生婚前性行为、性向往和性要求等现实问题提出对策。     

严肃游戏在医学专业学习中应用的研究进展

虚拟现实技术的不断发展推动了严肃游戏在医学专业教育领域的应用.笔者搜集了严肃游戏在国内外医学专业教育中应用的文献,对严肃游戏在医学专业教育中的应用情况、应用优势及挑战等进行总结.总体来说,严肃游戏可以激发学生的专业学习兴趣,在临床技能训练、临床决策、专业知识学习等方面有较好的效果,但软硬件条件、开发困难等问题可能阻碍严...     

鲜天麻对睡眠干扰诱导小鼠学习记忆障碍的改善作用

目的研究鲜天麻对睡眠干扰(sleep interruption,SI)诱导的小鼠学习记忆障碍的改善作用。方法 HPLC法测定鲜天麻中天麻素、对羟基苯甲醇的含量,苯酚硫酸法测定多糖的含量。60只ICR雄鼠随机分为对照组、睡眠干扰模型组、阳性药(莫达非尼)组和鲜天麻低(3 g/kg)、高(9 g/kg)剂量组。睡眠干扰造模14 d后,依次进行自主活动、新物体识别、水迷宫和避暗等动物行为学检测实验,并测定小鼠血清和海马组织超氧化物歧化酶(SOD)、丙二醛(MDA)水平及海马组织乙酰胆碱(Ach)、谷氨酸(Glu)和去甲肾上腺素(NE)水平。结果自主活动实验中,各组小鼠运动功能无显著性差异。与对照组比较,模型组小鼠在新物体识别实验中的相对辨别指数(DI)显著性下降,水迷宫寻台潜伏期明显延长,避暗实验错误次数增加、入暗潜伏期缩短;血清和海马组织MDA水平升高,海马组织的SOD水平降低;海马组织Ach、Glu和NE水平均显著降低。与模型组比较,莫达非尼和鲜天麻各剂量能不同程度增加小鼠新物体识别DI,提高新物体辨别能力;增强空间学习获得和保持能力,缩短水迷宫潜伏期;减少避暗错误次数、延长避暗潜伏期;提高血清和海马组织中SOD、Ach、Glu和NE水平,降低MDA水平。结论鲜天麻能改善睡眠干扰引起的学习记忆障碍,改善氧化应激和神经递质水平,是一种有潜力的改善学习记忆中药。     

Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery

Background and aimEyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery.MethodsThis study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs).ResultsThe intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (p < .001), and the bias ranged from 0.09 mm to 0.15 mm. The ICCs between two repeated automated measurements were up to 0.999 (p < .001), and the bias was no more than 0.002 mm. After surgery, MRD1 increased significantly from 0.31 ± 1.17 mm to 2.89 ± 1.06 mm, upper eyelid length from 19.94 ± 3.61 mm to 21.40 ± 2.40 mm, and corneal area from 52.72 ± 15.97 mm² to 76.31 ± 11.31mm² (all p < .001). Postoperative binocular MPLDs at different angles (from 0° to 180°) showed no significant differences in the patients.ConclusionThis technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients’ photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases.     

合成语音感知学习模型的建立和效应验证

目的: 建立合成语音的感知学习模型并验证该模型的学习效应. 方法: 编写合成语音感知学习模型的计算机程序,并在29名大学本科生中应用该程序模型. 将被试学员随机分为4组: 组1(n=6),组2(n=6),组3(n=7),组4(n=10). 采用配对t检验和方差分析验证该模型的学习效应. 结果: 依照4种不同的实验模式实施该模型,各组平均得分由学习前的(11.76±0.87)%提高到学习后的(20.69±1.59)%,且具有显著性差异(P<0.01). 其中程序模型改良组的学习效果最为显著,成绩平均提高了(14.10±2.52)%(P<0.001). 结论: 本研究所建立的英语合成语音感知学习模型有良好的学习效应,学习的过程体现出了人的概括和泛化能力. 该模型为深入研究比较复杂的认知活动奠定了基础.     

基于深度学习的冠状动脉CTA自动后处理技术诊断冠心病的价值研究

目的 探讨一种基于深度学习（deep learning, DL）的冠状动脉CT血管成像（coronary computed tomography angiography, CCTA）人工智能（artificial intelligence, AI）辅助诊断系统诊断冠心病（coronary artery disease, CAD）的临床价值。 方法 回顾性分析4周内先后完成CCTA和侵入性冠状动脉血管造影（coronary angiography, CAG）检查的69例患者,共462个冠状动脉近端血管节段。比较AI辅助诊断技术和CCTA人工分析影像数据所用时间。分别在患者水平、血管节段水平评估AI和CCTA对CAD的诊断效能;在各血管节段水平对AI、CCTA诊断结果进行误差分析。 结果 AI耗时明显低于CCTA[（5.5±0.9） min vs (14.2±1.8） min]。在患者水平和总血管节段水平,AI识别显著狭窄（狭窄程度≥50%）的灵敏度分别为97.9%、79.6%,阴性预测值分别为90.9%、93.2%,准确性分别为82.6%、80.5%;CCTA识别显著狭窄的灵敏度分别为96.6%、85.9%,阴性预测值分别为81.8%、92.9%,准确性分别为94.2%、91.6%;CCTA识别显著狭窄的准确性较高,AUC分别为0.984（95%CI： 0.9591.000）、0.960（95%CI： 0.9420.977）,与CAG差异无统计学意义（均P＞0.05）;AI识别显著狭窄有一定准确性,AUC分别为0.864（95%CI： 0.7480.979）、0.823（95%CI： 0.7800.865）,与CAG差异有统计学意义（均P＜0.05）。在各血管节段水平,AI检测显著狭窄的准确性分别为89.9%、78.3%、92.8%、69.6%、60.9%、88.4%、85.4%。对于钙化斑块、混合斑块、非钙化斑块、心肌桥及血管解剖异常,AI分别漏诊11、20、29、7、2段,误诊7、3、5、1、5段;CCTA分别漏诊2、5、6、2、2段,误诊9、3、4、4、2段。 结论 基于DL的CCTA自动后处理技术能高效完成CCTA数据分析,有较高的CAD诊断效能,具有成为临床诊断CAD辅助工具的潜力。而人机结合可能有望进一步提高CAD的诊断效能。