简介：无

简介：AbstractPurpose:Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.Methods:A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.Results:There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.Conclusion:The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

简介：摘要：传统上，国内高等教育课程文件是依靠在院校教学大纲完成的，即将踏入临床护生所学内容与临床活动内容往往存在巨大差异。随着护理学科的发展，同时，对护理人员也提出了更高的要求，实习护士如何做好自身准备也成为我们一直探讨的问题。传统背景下，护生该如何找到突破口，提高实习质量，护生面临更大挑战，菲律宾圣托马斯大学理学硕士提出高等教育的质量推动转向OBL学习方法，即护生带着“wish list{1}进入临床活动，根据自身能力制定学习计划，是护生主动将所学的理论知识与实践相结合并巩固加深的重要环节，使护生在学校学习的理论知识应用于临床实践，培养和提高临床思维分析和独立自主解决问题的能力。

简介：AbstractBackground:A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.Methods:We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.Results:We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.Conclusions:In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.

简介：AbstractBackground:Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animalorigin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.Methods:A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models.Results:The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor.Conclusions:Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

简介：无

简介：摘要m6A修饰是真核生物中最常见、最丰富的修饰形式之一，在不改变碱基序列的情况下对基因的转录后表达水平发挥调控作用。该修饰过程是动态可逆的，由甲基转移酶、去甲基化酶和相应的读取蛋白协同调控，参与m6A修饰的酶出现异常会导致肿瘤等一系列疾病的发生。本文综述了m6A修饰相关蛋白的组成与功能，以及m6A修饰在肿瘤增殖、侵袭与转移、血管生成、炎症反应、免疫反应、基因组不稳定、细胞代谢中的功能和调节机制。

简介：摘要N6-甲基腺嘌呤(m6A)甲基化修饰的生物学作用已被逐渐深入研究，在肿瘤中显示出越来越高的价值。近年来，随着对表观遗传学在RNA修饰方面的深入研究，诸多研究表明m6A甲基化修饰在肺癌的发生与发展中发挥了重要作用。m6A相关修饰蛋白具有成为肺癌临床诊治靶标的潜在应用价值。

简介：摘要急性呼吸窘迫综合征（ARDS）是临床常见危重症，表现为进行性呼吸窘迫、顽固性低氧血症、呼吸衰竭等，病死率高，目前尚缺乏有效的防治策略。近年来，间充质干细胞（MSC）用于急性肺损伤（ALI）的治疗受到高度关注，其不仅能替代受损的肺上皮细胞，还能通过分泌抗炎和抗纤维化因子来促进组织修复，减轻ARDS。现就MSC及其旁分泌因子等通过调控巨噬细胞极化平衡治疗ARDS的相关机制及信号通路进行综述。

简介：摘要目的探讨谷胱甘肽硫转移酶M1（GSTM1）和谷胱甘肽硫转移酶M2（GSTM2）在甲状腺滤泡癌（FTC）中的表达及其临床意义。方法收集基因表达综合（GEO）数据库中甲状腺滤泡性肿瘤基因芯片GSE82208数据，共52例样本，包括FTC 27例，甲状腺滤泡性腺瘤（FA）25例。提取基因矩阵数据并整理，利用R语言Limma包筛选出FTC和FA间差异表达基因。收集2000年1月至2020年12月辽宁省丹东市第一医院手术切除的FTC及FA标本各56例。采用免疫组织化学SABC法检测FTC和FA标本中GSTM1、GSTM2蛋白的表达水平，分析二者与FTC患者临床病理因素间的关系及二者间的相互关系。结果基于GEO数据库数据，共获得40个FTC和FA间差异表达基因；其中FTC中表达上调9个，分别为GSTM1、GSTM2、COL6A2、CUX2、CLUH、TSC2、OGDHL、ACADVL、SDHA；表达下调31个。免疫组织化学法检测显示，在手术切除FTC标本中，GSTM1、GSTM2阳性率分别为71.4%（40/56）、80.4%（45/56），在FA中阳性率分别为23.2%（13/56）、14.3%（8/56），差异均有统计学意义（χ2值分别为26.11、49.03，均P<0.01）。FTC中GSTM1和GSTM2蛋白表达与临床分期、浸润程度及远处转移均有关（均P<0.05），与性别、年龄和肿瘤长径均无关（均P>0.05）。FTC中GSTM1和GSTM2表达呈正相关（r=0.384，P=0.004）。结论FTC中GSTM1和GSTM2表达水平升高，二者可能相互作用，参与FTC的发生、发展。

简介：AbstractBackground:Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41 + 6 weeks were analyzed in this study. There were no pregnancy-related complications that affected fetal lung development, and all infants were born without neonatal respiratory diseases. The images were divided into three classes based on the gestational week: class I: 20 to 29 + 6 weeks, class II: 30 to 36 + 6 weeks, and class III: 37 to 41 + 6 weeks. There were 3323, 2142, and 1548 images in each class, respectively. First, we performed a pre-processing algorithm to remove irrelevant information from each image. Then, a convolutional neural network was designed to identify different categories of fetal lung ultrasound images. Finally, we used ten-fold cross-validation to validate the performance of our model. This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA. This was used to establish a grading model. The performance of the grading model was assessed using accuracy, sensitivity, specificity, and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated. The sensitivity of each class in the independent test set was 91.7%, 69.8%, and 86.4%, respectively. The specificity of each class in the independent test set was 76.8%, 90.0%, and 83.1%, respectively. The total accuracy was 83.8%. The area under the curve (AUC) of each class was 0.982, 0.907, and 0.960, respectively. The micro-average AUC was 0.957, and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs, which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy. The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.

简介：摘要目的对厚唇合并唇形不佳的美容就医者行上唇M唇修薄成形术，观察术式的效果及风险。方法2013年9月至2019年11月，北京大学第三医院成形外科为自觉上唇形态不佳美容就医者进行上唇M唇修薄成形术415例[女403例，男12例，年龄18～56(25.1±4.4)岁]，测量术前及术后唇部形态，并进行满意度调查及远期随访。结果对415例受术者进行统计，术后消肿3～21 (11.8±2.6) d；切口瘢痕软化1～24(2.6±0.8)个月；表情恢复自然1～24(3.1±1.4)个月；麻木感消失12～24个月。术后3个月对受术者标准化二维照片进行测量，上红唇中央高度较术前无明显变化，唇峰点红唇高度明显降低，术前(13.3±2.2) mm，术后(11.4±1.7) mm；穹隆点相对高度明显增加，术前(0.4±1.0) mm，术后(2.1±0.5) mm。术后6个月随访，受术者1次手术满意率72.0%，上红唇形态满意率75.4%，远期并发症包括瘢痕明显或不适(3.6%)；术区局部麻木(1.7%)；表情欠自然(0.7%)。结论上唇M唇修薄成形术对上红唇形态不佳合并过厚或正常厚度有较好的矫治效果，并发症轻微，值得临床应用。

简介：无

简介：摘要Alström综合征是一种由ALMS1基因突变导致的罕见多系统遗传病，临床上诊断和治疗均十分困难。2020年，由多国参与制定的《Alström综合征临床管理指南》在Orphanet Journal of Rare Diseases杂志发布。该指南对截至2019年10月检查到的45年文献证据进行严格审查后，提出了Alström综合征的临床管理建议。现简要介绍2020年欧洲Alström综合征指南内容，并予适当解读，以供参考。

简介：摘要Müller细胞作为视网膜中一种特殊的放射状胶质细胞，贯穿整个视网膜，与视网膜中的神经元、微血管和突起相接触，对视网膜的结构和功能具有重要的保护作用。糖尿病视网膜病变(DR)是糖尿病患者主要的眼部并发症，在DR的进展中，糖尿病黄斑水肿(DME)是患者视力下降的主要原因。在DME的发生中，Müller细胞的形态和结构发生变化、胞体肿胀、空泡化程度加深、细胞凋亡增多以及细胞因子分泌异常等，对血-视网膜屏障(BRB)具有破坏作用，不仅增加了BRB的通透性，还加速了视网膜下液体的渗出。此外，Müller细胞也可使K+和水的转运调节发生紊乱，阻碍视网膜下间隙的液体吸收，进一步促进DME发生。但在DR早期，Müller细胞分泌的神经营养因子可为视网膜提供保护，减轻视网膜水肿，保护视网膜神经节细胞，提示Müller细胞可作为DME治疗的靶点。因此探讨Müller细胞在DME形成中的作用与机制，可为DME的治疗提供新策略。本文就Müller细胞在DME中的作用机制和在DME进展中的保护作用进行综述。

简介：摘要目的对7例Alström综合征患者的 ALMS1基因进行变异分析，明确其致病原因，为临床诊断提供依据。方法提取7例患儿及其父母外周血DNA，对患儿进行全外显子组基因测序，应用Sanger测序对患儿及父母进行变异位点验证及致病性分析。结果基因测序结果显示在7例患儿中检出12个ALMS1变异位点，分别是c. 5418delC(p.Tyr1807Thrfs*23)、c. 10549C>T(p.Gln3517*)、c.9145dupC(p.Thr3049Asnfs*12)、c.10819C>T(p.Arg3607*)、c.5701_5704delGAGA(p.Glu1901Argfs*18)、c.9154_9155delCT(p.Cys3053Serfs*9)、c.9460delG(p.Val3154*)、c.9379C>T(p.Gln3127*)、c.12115C>T(p.Gln4039*)、c.1468dupA(p.Thr490Asnfs*15)、c.10825C>T(p.Arg3609*)和c.3902C>A(p.Ser1301*)；其中7个为无义变异，5个为移码变异；c．9154_9155delCT、c.9460delG、c.9379C>T和c.1468dupA是未报道过的新变异。根据美国医学遗传学与基因组学学会遗传变异分类标准与指南，c.9379C>T和c.12115C>T变异判定为可能致病性变异(PVS1＋PM2)、其余10个变异均判定为致病性变异(PVS1＋PM2＋PP3＋PP4)。结论ALMS1基因变异为这7例患儿的致病原因，基因检测可以为临床诊断提供依据，新变异的检出拓展了ALMS1变异谱。

简介：AbstractBackground:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.Methods:Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.Results:The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).Conclusions:The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registration:Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.

简介：摘要目的探讨海上300 m饱和潜水对潜水员手指震颤强度变化的影响。方法在进舱加压前、加压舱内和300 m饱和稳压各阶段，对4名潜水员手指震颤强度进行监测。结果潜水员在进舱加压至300 m稳压阶段手指震颤强度均较加压前增高，其中加压至250 m、270 m、290 m时手指震颤强度较加压前差异有统计学意义(P<0.05)；而加压至195 m、220 m、250 m、300 m以及300 m饱和停留24 h后，手指震颤强度均较加压前差异无统计学意义(P>0.05)。结论在饱和潜水加压阶段通过手指震颤强度的监测，可以客观且量化地评价高压神经综合征(HPNS)的发生和发展。将手指震颤强度监测与传统评价方式一起使用可为提高饱和潜水加压速率、保障潜水员安全提供依据。

简介：无

简介：摘要线粒体糖尿病又名母系遗传糖尿病伴耳聋（MIDD），能够影响3%的糖尿病患者，约85%的MIDD与m.3243A>G突变相关。MIDD临床表现多样，诊治困难，本文结合最新证据对m.3243A>G突变相关MIDD的特征、诊断、评估、治疗等方面进行综述，以期为MIDD的临床应对提供支持。