Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Microspheres of essential oil in polylactic acid and poly(methyl methacrylate) matrices and their blends

This study is focussed on micro-encapsulation of essential oils in polylactic acid (PLA) and a poly(methyl methacrylate) (PMMA) matrix as well as blends of the same. Microspheres were prepared by the solvent evaporation technique and characterised by scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and Fourier transform infra-red spectroscopy (FTIR). The encapsulation efficiencies and release profiles of the essential oils were studied by gas chromatography mass spectrometry (GC-MS) and head-space solid-phase microextraction GC-MS, respectively. Furthermore, the microspheres were tested for antibacterial activity against both Gram-negative and Gram-positive bacterial strains.

The results showed that the microspheres compositions (PLA/PMMA ratio) have significant effect on their characteristics. The process adopted for preparing the microspheres promoted formation of spherical particles at the sizes of 1.5–9.5?µm. The highest encapsulation efficiency of the prepared microspheres was observed in systems consisting of linalool (81.10?±?10.0?wt. % for PLA system and 76.0?±?3.3?wt. % for PMMA system). Confirmation was also made that the release rate of the microspheres was affected by the size of the same.     

角膜电刺激对糖尿病大鼠前部缺血性视神经病变模型的影响

观察并评估角膜电刺激对糖尿病大鼠前部缺血性视神经病变（AION）模型的影响。方法：实验 研究。健康雄性Sparague-Dawley大鼠40只，随机分组后抽出8只作为正常大鼠组。余下32只先予 以链脲佐菌素腹腔注射建立糖尿病大鼠模型，将造模成功的大鼠随机抽出8只作为糖尿病组，余下 24只糖尿病大鼠采用孟加拉玫瑰红联合532 nm激光方法建立AION大鼠模型。将24只造模成功的 AION大鼠随机分成3组，每组8只，分别为AION模型组，不予任何处理；电刺激组，予以角膜电刺 激（刺激参数为：电流1 mA，频率20 Hz，波宽1 ms/phase，刺激时间1 h，隔日1次，刺激2周）；假电 刺激组，电极安放位置与电刺激组相同，仅不接通电源。2周后5组大鼠进行眼底照相、光学相干断 层扫描和视觉诱发电位，然后处死，行视网膜及视神经冰冻切片，苏木精伊红染色观察。数据采用 单因素方差分析和LSD-t检验进行分析。结果：正常大鼠组视盘上半部视网膜厚度为（211±13）μm， 糖尿病大鼠组为（206±16）μm，AION模型组为（240±54）μm，假电刺激组为（216±11）μm，电刺 激组为（198±4）μm，5组视盘上半部视网膜厚度差异有统计学意义（F=2.854，P=0.038）。其中AION 模型组视盘上半部视网膜厚度高于正常组、糖尿病组、电刺激组，差异均有统计学意义（P<0.05）； 正常组与糖尿病组差异无统计学意义，AION模型组与假电刺激组未见明显差异。视觉诱发电位示 AION模型组N1潜伏期较电刺激组延长，差异有统计学意义（t=4.1，P<0.001）；AION模型组P1潜伏 期较正常组、糖尿病组、假电刺激组、电刺激组延长，差异均有统计学意义（t=4.1、2.5、2.6、3.2， P<0.05）；电刺激组N1-P1波幅大于假电刺激组，差异有统计学意义（t=4.0，P<0.001）。结论：角膜电 刺激能促进糖尿病大鼠前部缺血性视神经病变模型肿胀的视盘变薄，加速视盘水肿的消退，同时在 一定程度上改善视功能。     

空间频域成像在皮肤病定量评估中的研究

利用空间频域成像技术搭建的成像系统检测多种皮肤病组织的光学参数和生理参数信息，并对比分析讨论不同类型的皮肤病与光学参数、生理参数之间的关系。实验结果表明，病变皮肤组织与正常皮肤组织之间在光学参数、生理参数上存在较大差异，这将为临床医生对皮肤病诊治提供一种新颖、可靠、科学的评估方法。     

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

经食管实时三维超声心动图评价房性心律失常对二尖瓣结构和功能的影响

目的:应用经食管实时三维超声心动图(RT-3D-TEE)技术,探讨房性心律失常对二尖瓣结构和功能的影响.方法:选取2018年6月~2019年6月本院收治的房性心律失常拟行射频消融的患者49例纳入观察组,另外选取21例正常窦性心律者作为对照组,所有患者均行经胸超声心电图(TTE)和经食管实时三维超声心动图(RT-3D-TEE)检查,将两组患者的瓣环投影面积(A2D)、瓣环周长(C3D)、瓣环高度(H)、瓣环前后径(DAP)、瓣环前外侧至后内侧直径(DAIPm)、左室射血分数(LVEF)、左心房前后径(Lad)等参数进行比较,并分别计算对合面积和对合指数,之后采取二项Logistic回归分析或逐步线性回归对各参数及临床因素与对合指数的相关性进行分析.结果:两组患者在LVEF和对合面积方面的比较无明显差异(P>0.05),在A2D、C3D、H、DA、PDAIPm、Lad方面的比较,房性心律失常组患者明显高于正常窦性心律组患者(P<0.05),且观察组患者的对合指数明显降低(P<0.05).将49例观察组患者以不同心律失常类型分为持续性房颤、阵发性房颤、房扑、混合型房性心律失常4个亚组,采用单因素方差分析,发现在LVEF、C3D、Lad、对合指数方面的比较,各亚组间无明显差异(P>0.05).采用二项Logistic回归分析发现,女性是导致对合指数低的危险因素,另外对合指数较低、房性心律失常是导致二尖瓣返流的重要因素.结论:房性心律失常对患者二尖瓣的A2D、C3D、H、DA、PDAIPm、Lad等都会造成较大影响,从而降低对合指数,影响二尖瓣的正常功能,导致二尖瓣返流的发生.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.