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%.     

A prime-boost concept using a T-cell epitope-driven DNA vaccine followed by a whole virus vaccine effectively protected pigs in the pandemic H1N1 pig challenge model

Influenza A virus (IAV) vaccines in pigs generally provide homosubtypic protection but fail to prevent heterologous infections. In this pilot study, the efficacy of an intradermal pDNA vaccine composed of conserved SLA class I and class II T cell epitopes (EPITOPE) against a homosubtypic challenge was compared to an intramuscular commercial inactivated whole virus vaccine (INACT) and a heterologous prime boost approach using both vaccines. Thirty-nine IAV-free, 3-week-old pigs were randomly assigned to one of five groups including NEG-CONTROL (unvaccinated, sham-challenged), INACT-INACT-IAV (vaccinated with FluSure XP® at 4 and 7 weeks, pH1N1 challenged), EPITOPE-INACT-IAV (vaccinated with PigMatrix EDV at 4 and FluSure XP® at 7 weeks, pH1N1 challenged), EPITOPE-EPITOPE-IAV (vaccinated with PigMatrix EDV at 4 and 7 weeks, pH1N1 challenged), and a POS-CONTROL group (unvaccinated, pH1N1 challenged). The challenge was done at 9 weeks of age and pigs were necropsied at day post challenge (dpc) 5. At the time of challenge, all INACT-INACT-IAV pigs, and by dpc 5 all EPITOPE-INACT-IAV pigs were IAV seropositive. IFNγ secreting cells, recognizing vaccine epitope-specific peptides and pH1N1 challenge virus were highest in the EPITOPE-INACT-IAV pigs at challenge. Macroscopic lung lesion scores were reduced in all EPITOPE-INACT-IAV pigs while INACT-INACT-IAV pigs exhibited a bimodal distribution of low and high scores akin to naïve challenged animals. No IAV antigen in lung tissues was detected at necropsy in the EPITOPE-INACT-IAV group, which was similar to naïve unchallenged pigs and different from all other challenged groups. Results suggest that the heterologous prime boost approach using an epitope-driven DNA vaccine followed by an inactivated vaccine was effective against a homosubtypic challenge, and further exploration of this vaccine approach as a practical control measure against heterosubtypic IAV infections is warranted.     

寰枢椎侧块关节运动轨迹数学模型的探讨

应用最小二乘法拟合得到寰枢椎侧块关节运动轨迹方程 ,左半部是 :S(x) =-2 3 .2 42 9-8.663 1x-0 .45 91x2 ;右半部是 :S(x) =-19.683 8+ 7.15 5 4x-0 .3 5 2 6x2 。     

17β-雌二醇对子宫内膜异位症患者在位子宫内膜间质细胞β-catenin mRNA和蛋白表达的影响

目的研究17β-雌二醇(17β-E2)对子宫内膜异位症(内异症)患者在位子宫内膜间质细胞β-catenin mRNA和蛋白表达的影响,探讨Wnt/β-catenin信号通路在介导雌激素促进内异症发生发展的作用。方法体外分离培养内异症患者在位子宫内膜间质细胞。用不同浓度17β-E2处理子宫内膜间质细胞48 h;此后选用10-10mol/L 17β-E2处理子宫内膜间质细胞12、24和48 h,逆转录聚合酶链反应(RT-PCR)和免疫印迹法(Western blotting)检测17β-E2处理前后子宫内膜间质细胞β-catenin mRNA和蛋白的表达水平。同法分析雌激素受体拮抗剂ICI182,780(10-6mol/L)对17β-E2促进β-catenin mRNA和蛋白表达的影响。免疫组织化学染色观察17β-E2作用后β-catenin在子宫内膜间质细胞中的定位。结果17β-E2能明显促进内异症患者在位子宫内膜间质细胞β-catenin mRNA和蛋白的表达,并呈剂量和时间依赖性,于10-10mol/L作用48 h最明显。雌激素受体拮抗剂ICI182,780能明显抑制17β-E2对子宫内膜间质细胞β-catenin mRNA和蛋白的表达。免疫组织化学染色发现17β-E2能促进β-catenin在子宫内膜间质细胞核内的表达。结论雌激素可能通过激活Wnt/β-catenin信号通路促进内异症在位子宫内膜的异位种植。     

Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure.

The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real-time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS-EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean +/- SD) 0.18 +/- 0.84, and 0.50 +/- 0.59 mm for STN exit point, which yields a 0.30 +/- 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting-independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders.     

On the synthesis of time‐varying LQG weights and noises along optimal control and state trajectories

A general approach to control non‐linear uncertain systems is to apply a pre‐computed nominal optimal control, and use a pre‐computed LQG compensator to generate control corrections from the on‐line measured data. If the non‐linear model, on which the optimal control and LQG compensator design is based, is of sufficient quality, and when the LQG compensator is designed appropriately, the closed‐loop control system is approximately optimal. This paper contributes to the selection and computation of the time‐varying LQG weighting and noise matrices, which determine the LQG compensator design. It is argued that the noise matrices may be taken time‐invariant and diagonal. Three very important considerations concerning the selection of the time‐varying LQG weighting matrices are turned into a concrete computational scheme. Thereby, the selection of the time‐varying LQG weighting matrices is reduced to selecting three scalar design parameters, each one weighting one consideration. Although the three considerations seem straightforward they may oppose one another. Furthermore, they usually result in time‐varying weighting matrices that are indefinite, rather than positive (semi) definite as required for the LQG design. The computational scheme presented in this paper addresses and resolves both problems. By two numerical examples the benefits of our optimal closed‐loop control system design are demonstrated and evaluated using Monte Carlo simulation. Copyright © 2005 John Wiley & Sons, Ltd.     

Indoor air pollution and pulmonary performance: investigating errors in exposure assessment

We use pulmonary function measurements on pre-adolescent children and indoor air pollution measurements in the homes of these children to illustrate estimation techniques for linear regression models containing independent variables measured with error. In our data set, replicate measures of indoor air pollutant concentrations provide one method of estimating measurement error variances. Surrogate information in the form of cigarettes smoked is also available for the pollutant of interest. Several estimation procedures are presented, and we combine two estimators, one based on surrogate information and one based on replication information, using generalized least squares.     

An algorithm for the automated quantitation of metabolites in in vitro NMR signals.

The quantitation of metabolite concentrations from in vitro NMR spectra is hampered by the sensitivity of peak positions to experimental conditions. The quantitation methods currently available are generally labor intensive and cannot readily be automated. Here, an algorithm is presented for the automatic time domain analysis of high-resolution NMR spectra. The TARQUIN algorithm uses a set of basis functions obtained by quantum mechanical simulation using predetermined parameters. Each basis function is optimized by subdividing it into a set of signals from magnetically equivalent spins and varying the simulated chemical shifts of each of these groups to match the signal undergoing analysis. A novel approach to the standard multidimensional minimization problem is introduced based on evaluating the fit resulting from different permutations of possible chemical shifts, obtained from one-dimensional searches. Results are presented from the analysis of (1)H proton magic angle spinning spectra of cell lines illustrating the robustness of the method in a typical application. Simulation was used to investigate the biggest peak shifts that can be tolerated.     

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets.