Validation of an on-screen application-based measurement of shoulder range of motion over telehealth medium

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不同年龄阿尔茨海默病患者脑核团ADC值与年龄的相关性

目的 探讨不同年龄阿尔茨海默病（AD）患者ADC值与年龄的相关性。方法 选取30例AD患者（AD组）和年龄与之相匹配的30名志愿者（对照组），按年龄段各分为6个亚组[55~59岁（n=3）、60~64岁（n=4）、65~70岁（n=9）、71~74岁（n=5）、75~80岁（n=6）、>80岁（n=3）]，测量双侧海马、红核、尾状核、杏仁体、壳核ADC值，并进行配对t检验、独立样本t检验、单因素方差分析及Pearson相关分析。结果 AD组红核左、右侧ADC值有统计学差异（P=0.022）。AD组不同年龄亚组右侧海马、双侧尾状核、右侧壳核ADC值差异有统计学意义（P均<0.05）；2组不同年龄亚组双侧海马、壳核、尾状核ADC值差异有统计学意义（P均<0.05）。AD组右侧海马（r=0.615，P<0.001）、右侧壳核（r=0.653，P=0.001）及双侧尾状核（左侧：r=0.397，P=0.030；右侧：r=0.429，P=0.020）ADC值与年龄呈正相关。结论 AD患者右侧海马和壳核、双侧尾状核ADC值随年龄增加而增大。ADC值可为临床预测和早期诊断AD脑内右侧海马和壳核、双侧尾状核神经退行性病变提供参考。     

Polymerase Chain Reaction and Goldmann-Witmer Coefficient Testing in the Diagnosis of Infectious Uveitis in HIV-Positive and HIV-Negative Patients in South Africa

Purpose: To use polymerase chain reaction (PCR) and Goldmann-Witmer coefficient (GWC) calculation to diagnose infectious uveitis.

Methods: Prospective cross-sectional study.

Results: Twenty-seven of 106 patients had positive PCR and/or GWC results on aqueous humor (AH) sampling and 15 of 27 (55.6%) were HIV-positive. Patients with non-anterior uveitis (NAU) were more likely to be HIV+ (p = 0.005). More than 1 possible pathogen was identified in 9 of 27 patients of whom 7 were HIV+. The final clinical diagnosis was discordant with AH findings in 9 of 27 cases. A positive EBV PCR result was associated with a discordant diagnosis (p = 0.001). All cases of herpetic anterior uveitis (42.9% HIV+) tested PCR-/GWC+ while all cases of herpetic NAU tested PCR+/GWC- (83.3% HIV+). All rubella virus cases were PCR+/GWC+.

Conclusion: PCR is useful to diagnose herpetic NAU in HIV+ patients while GWC is useful to diagnose herpetic anterior uveitis.     

Unconditional Positivity-Preserving and Energy Stable Schemes for a Reduced Poisson-Nernst-Planck System

The Poisson-Nernst-Planck (PNP) system is a widely accepted model for simulation of ionic channels. In this paper, we design, analyze, and numerically validate a second order unconditional positivity-preserving scheme for solving a reduced PNP system, which can well approximate the three dimensional ion channel problem. Positivity of numerical solutions is proven to hold true independent of the size of time steps and the choice of the Poisson solver. The scheme is easy to implement without resorting to any iteration method. Several numerical examples further confirm the positivity-preserving property, and demonstrate the accuracy, efficiency, and robustness of the proposed scheme, as well as the fast approach to steady states.     

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.     

Moving Knowledge to Action: Aware,Adopt, Adapt (A3)

This article begins with an overview of the knowledge translation (KT) process, introduces commonly used KT terms and the Aware-Adopt-Adapt (A³) KT map. The A³ was created by a nurse practitioner (NP) for practitioners and NP students to provide a map for those who wish to move existing knowledge to practice, yet do not know where to start or do not have the time to take a deeper dive into specific KT theories.     

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.