自体富血小板血浆一脂肪颗粒填充面部凹陷的临床应用及Fuzzy评价

目的评价f]体富血小板血浆一脂肪颗粒填充面部凹陷的临床疗效。方法采用自体寓血小板血浆加自体脂肪颗粒,对35例面部凹陷患者进行填充修复。3～6个月后,用Fuzzy评判法（模糊评判法）对面部凹陷部丰满度、注射次数、手术满意度进行评价。结果术后患者面部获得了一个相对对称且自然的外观,填充修复效果稳定,未见明显脂肪吸收。医患双方均对手术效果满意。综合评判结果表明有效这一隶属度最高。结论自体富血小板血浆应用于面部脂肪移植,有助于脂肪仃活和减少脂肪吸收,填充矫治面部软组织缺损畸形效果确切可靠。     

产前超声早期诊断胎儿前腹壁异常的临床意义

目的探讨早孕及中孕早期超声诊断胎儿前腹壁异常的临床意义。方法回顾性分析我院早孕及中孕早期超声诊断的21胎前腹壁异常胎儿超声表现及随访结果,分析比较前腹壁异常的超声声像图特征。结果早孕及中孕早期超声筛查出21胎胎儿前腹壁异常,均在首次超声检查时发现,均经引产直接观察或病理检查进一步证实;其中脐膨出12胎,泄殖腔外翻1胎,体蒂异常8胎。7胎接受羊水穿刺检查,1胎脐膨出为13-三体,余染色体未见异常。结论早孕及中孕早期超声检查可有效诊断及鉴别诊断前腹壁异常,具有重要临床意义。     

2种术式治疗胸腰椎严重前柱损伤后骨缺损的对比研究

目的 以一期后路短节段植骨融合内固定联合伤椎骨水泥强化术与分期前后路切开复位融合内固定术治疗胸腰椎严重前柱损伤后骨缺损的疗效对比,探讨其适应证及优缺点。方法 自2009-01-2011-01对48例AO分型A3型胸腰段单椎体骨折随机施行骨水泥强化或前后路联合手术各24例。计算2种术式的手术时间、出血量,采用疼痛视觉模拟法（VAS）评分及Oswestry功能障碍指数（ODI）评分综合评估2种术式的手术疗效。结果 联合组获得随访平均23.2个月（18-36个月）,强化组获得随访平均21.8个月（18-24个月）。2组手术时间、出血量及住院时间比较,差异有统计学意义（P〈0.05）。2组VAS和ODI评分、Cobb角术后1周、末次随访时与术前比较,差异有统计学意义（P〈0.05）;术后1周与末次随访时比较,差异均无统计学意义（P〉0.05）;联合组与强化组各数据间比较,差异均无统计学意义（P〉0.05）。结论采用一期后路短节段植骨融合内固定联合伤椎骨水泥强化术治疗胸腰椎严重前柱损伤安全、近期疗效满意,可预防骨缺损形成后前柱不稳等并发症。     

创伤性连枷胸患者电视胸腔镜辅助和传统开胸手术内固定的临床疗效比较

目的 探讨电视胸腔镜辅助和传统开胸在连枷胸手术内固定中的临床疗效.方法 回顾性分析2008年3月至2012年12月浙江省台州医院路桥院区59例连枷胸患者手术的临床资料.根据手术方式不同分为电视胸腔镜辅助手术内固定（腔镜组,28例）和传统手术内固定（传统组,31例）.比较2种治疗方法的手术时间、术中出血量、引流管拔除时间、疼痛缓解时间、术后机械通气时间、肺不张发生率及术后生活能力和劳动能力恢复等指标.结果 全组59例均临床愈合.腔镜组2例术中发现胸腔内活动性出血转开胸手术.所有患者术后肺功能恢复良好,出院3个月CT复查胸廓无明显畸形,无对内固定材料有排异不适等需取出.手术时间腔镜组（1.6±0.6）h、传统组（2.7±0.5）h,术中出血量腔镜组（166±37）ml、传统组（209±41） ml,术后引流管拔除时间腔镜组（4.8±1.6）d、传统组（8.3±3.4）d,疼痛缓解时间腔镜组（8.5±2.1）d、传统组（12.4±1.8）d,术后机械通气时间腔镜组（9±4）h、传统组（65±12）h,腔镜组肺不张发生率16.7％ （3/28）,传统组35.5％（11/31）,腔镜组各项指标均优于传统组（P＜0.05或P＜0.01）.术后1年随访,腔镜组恢复正常劳动能力18例（64.3％）、传统组4例（12.9％）;腔镜组恢复生活能力23例（82.1％）、传统组16例（51.6％）,腔镜组明显多于传统组（P＜0.05）.结论 胸腔镜辅助胸膜外内固定方法在手术时间、术中出血量、术后机械通气时间、并发症发生率及生活质量方面改善均优于传统开胸手术.     

Correlation between Defects and Electrical Performances of Ion-Irradiated 4H-SiC p–n Junctions

In this study, 4H-SiC p–n junctions were irradiated with 700 keV He⁺ ions in the fluence range 1.0 × 10¹² to 1.0 × 10¹⁵ ions/cm². The effects of irradiation were investigated by current–voltage (I–V) and capacitance–voltage (C–V) measurements, while deep-level transient spectroscopy (DLTS) was used to study the traps introduced by irradiation defects. Modifications of the device’s electrical performances were observed after irradiation, and two fluence regimes were identified. In the low fluence range (≤10¹³ ions/cm²), I–V characteristics evidenced an increase in series resistance, which can be associated with the decrease in the dopant concentration, as also denoted by C–V measurements. In addition, the pre-exponential parameter of junction generation current increased with fluence due to the increase in point defect concentration. The main produced defect states were the Z_1/2, RD_1/2, and EH_6/7 centers, whose concentrations increased with fluence. At high fluence (>10¹³ ions/cm²), I–V curves showed a strong decrease in the generation current, while DLTS evidenced a rearrangement of defects. The detailed electrical characterization of the p–n junction performed at different temperatures highlights the existence of conduction paths with peculiar electrical properties introduced by high fluence irradiation. The results suggest the formation of localized highly resistive regions (realized by agglomeration of point defects) in parallel with the main junction.     

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.     

The Size Effects of Point Defect on the Mechanical Properties of Monocrystalline Silicon: A Molecular Dynamics Study

The molecular dynamics method was used to simulate the fracture process of monocrystalline silicon with different sizes of point defect under a constant strain rate. The mechanism of the defect size on the mechanical properties of monocrystalline silicon was also investigated. The results suggested that the point defect significantly reduces the yield strength of monocrystalline silicon. The relationships between the yield strength variation and the size of point defect fitted an exponential function. By statistically analyzing the internal stress in monocrystalline silicon, it was found that the stress concentration induced by the point defect led to the decrease in the yield strength. A comparison between the theoretical strength given by the four theories of strength and actual strength proved that the Mises theory was the best theory of strength to describe the yield strength of monocrystalline silicon. The dynamic evolution process of Mises stress and dislocation showed that the fracture was caused by the concentration effect of Mises stress and dislocation slip. Finally, the fractured microstructures were similar to a kind of two-dimensional grid which distributed along the cleavage planes while visualizing the specimens. The results of this article provide a reference for evaluating the size effects of point defects on the mechanical properties of monocrystalline silicon.     

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia

BackgroundTo develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness.MethodsA total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC).ResultsThe mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913–1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757–1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906–1.000).ConclusionsA XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.     

Risk factors of chest wall invasion in non-small cell lung cancer

BackgroundThe risk factors for the development of chest wall invasion (CWI) in non-small cell lung cancer (NSCLC) patients are unclear. If the risk factors for the development of CWI can be clarified, surgical treatment might be able to be performed before CWI development, thus improving the prognosis.MethodsIn the present study, we enrolled patients who received surgery for NSCLC between January 2008 and December 2019 with available data on the maximum standardized uptake value (SUV_max) on positron emission tomography (PET) with lesions adjacent to the visceral pleura. Furthermore, the preoperative white blood cell (WBC) count, the preoperative neutrophil-to-lymphocyte ratio (NLR), platelet (Plt) count, levels of lactate dehydrogenase (LDH) and C-reactive protein (CRP) were analyzed as predictive factors of CWI.ResultsThe relationships between CWI and clinicopathological variables were analyzed, and there were significant differences between patients with and without CWI in the age (P=0.02), maximum tumor diameter on computed tomography (CT) (P<0.01), diameter of tumors adjacent to the visceral pleura (P_max) (P<0.01), SUV_max (P<0.01), maximum tumor diameter on a pathological examination (P<0.01), WBC count (P=0.03), Plt count (P=0.04), and levels of LDH (P<0.01) and CRP (P=0.01). Logistic regression analyses of the risk factors related to CWI showed that the age (P=0.02), P_max (P=0.02), SUV_max (P=0.01), and LDH (P<0.01) were significant risk factors.ConclusionsThe age, P_max, SUV_max, and LDH levels might be associated with CWI.