OBJECTMultilevel long-segment lumbar fusion poses a high risk for future development of adjacent-segment degeneration (ASD). Creating a dynamic transition zone with an interspinous process device (IPD) proximal to the fusion has recently been applied as a method to reduce the occurrence of ASD. The authors report their experience with the Device for Intervertebral Assisted Motion (DIAM) implanted proximal to multilevel posterior lumbar interbody fusion (PLIF) in reducing the development of proximal ASD.METHODSThis retrospective study reviewed 91 cases involving patients who underwent 2-level (L4–S1), 3-level (L3–S1), or 4-level (L2–S1) PLIF. In Group A (42 cases), the patients received PLIF only, while in Group B (49 cases), an interspinous process device, a DIAM implant, was put at the adjacent level proximal to the PLIF construct. Bone resection at the uppermost segment of the PLIF was equally limited in the 2 groups, with preservation of the upper portion of the spinous process/lamina and the attached supraspinous ligament. Outcome measures included a visual analog scale (VAS) for low-back pain and leg pain and the Oswestry Disability Index (ODI) for functional impairment. Anteroposterior and lateral flexion/extension radiographs were used to evaluate the fusion status, presence and patterns of ASD, and mobility of the DIAM-implanted segment.RESULTSSolid interbody fusion without implant failure was observed in all cases. Radiographic ASD occurred in 20 (48%) of Group A cases and 3 (6%) of Group B cases (p < 0.001). Among the patients in whom ASD was identified, 9 in Group A and 3 in Group B were symptomatic; of these patients, 3 in Group A and 1 in Group B underwent a second surgery for severe symptomatic ASD. At 24 months after surgery, Group A patients fared worse than Group B, showing higher mean VAS and ODI scores due to symptoms related to ASD. At the final follow-up evaluations, as reoperations had been performed to treat symptomatic ASD in some patients, significant differences no longer existed between the 2 groups. In Group B, flexion/extension mobility at the DIAM-implanted segment was maintained in 35 patients and restricted or lost in 14 patients, 5 of whom had already lost segmental flexion/extension mobility before surgery. No patient in Group B developed ASD at the segment proximal to the DIAM implant.CONCLUSIONSProviding a dynamic transition zone with a DIAM implant placed immediately proximal to a multilevel PLIF construct was associated with a significant reduction in the occurrence of radiographic ASD, compared with PLIF alone. Given the relatively old age and more advanced degeneration in patients undergoing multilevel PLIF, this strategy appears to be effective in lowering the risk of clinical ASD and a second surgery subsequent to PLIF.

Nurses could use the two-hour personal suicidal education programme to increase one's ability to care for their relatives who had attempted suicide and promote one's positive attitudes towards attempted suicide.

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We study the maximum likelihood model in emission tomography and propose a new family of algorithms for its solution, called string-averaging expectationmaximization (SAEM). In the string-averaging algorithmic regime, the index set of all underlying equations is split into subsets, called 'strings', and the algorithm separately proceeds along each string, possibly in parallel. Then, the end-points of all strings are averaged to form the next iterate. SAEM algorithms with several strings present better practical merits than the classical row-action maximum-likelihood algorithm. We present numerical experiments showing the effectiveness of the algorithmic scheme, using data of image reconstruction problems.

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

Single photon emission computed tomography (SPECT) has been employed to detect Parkinson’s disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models’ performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

The segmentation of cDNA microarray spots is essential in analyzing the intensities of microarray images for biological and medical investigation. In this work, nonparametric methods using kernel density estimation are applied to segment two-channel cDNA microarray images. This approach groups pixels into both a foreground and a background. The segmentation performance of this model is tested and evaluated with reference to 16 microarray data. In particular, spike genes with various contents are spotted in a microarray to examine and evaluate the accuracy of the segmentation results. Duplicated design is implemented to evaluate the accuracy of the model. The results of this study demonstrate that this method can cluster pixels and estimate statistics regarding spots with high accuracy.