A dynamic prediction model for intraoperative somatosensory evoked potential monitoring

This study proposed a support vector regression model applied in prediction of intraoperative somatosensory evoked potential changes associated with physiological and anesthetic changes. This model was developed from probability distribution and support vector machines. The predicted results showed that observed and predicted SEP has similar variation trend with different values, with acceptable errors. With this prediction model, changes of SEP in correlation with non-surgical factors were estimated. Not only the prediction accuracy of SEP has been improved, but also provides the reliability of the classification. It will be helpful to develop an intelligent monitor model based expert system that can make a reliable decision for the potential spinal injury.published_or_final_versio

Electrically controllable saturable absorption in hybrid graphene-silicon waveguides

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Photodynamic inactivation of Candida albicans by hematoporphyrin monomethyl ether

AIM: To evaluate the capacity of hematoporphyrin monomethyl ether (HMME) in the presence of light to cause photodynamic inactivation (PDI) of Candida albicans. MATERIALS & METHODS: HMME photoactivity was evaluated against azole-susceptible and -resistant C. albicans. The mechanisms by which PDI of C. albicans occurred were also investigated. RESULTS: HMME-mediated PACT caused a dose-dependent inactivation of azole-susceptible and -resistant C. albicans. Incubation with 10 μM HMME and irradiation with 72 J cm(-2) light decreased the viability of C. albicans by 7 log10, induced damage of genomic DNA, led to loss of cellular proteins and damaged the cell wall, membrane and intracellular targets. CONCLUSION: Candida albicans can be effectively inactivated by HMME in the presence of light, and HMME-mediated PACT shows its potential as an antifungal treatment

Trial-to-trial latency variability of somatosensory evoked potentials as a prognostic indicator for surgical management of cervical spondylotic myelopathy

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The effect of clozapine on mRNA expression for genes encoding G protein-coupled receptors and the protein components of clathrin-mediated endocytosis.

Clathrin-mediated endocytosis (CME) is an intracellular trafficking mechanism for packaging cargo, including G protein-coupled receptors (GPCRs), into clathrin-coated vesicles (CCVs). The antipsychotic chlorpromazine inhibits CCV assembly of adaptor protein AP2 whereas clozapine increases serotonin2A receptor internalization. We hypothesized that clozapine alters the expression of CME genes modulating vesicle turnover and GPCR internalization

Hepatitis B among Pacific Islanders in Southern California: how is health information associated with screening and vaccination?

We measured Hepatitis B virus (HBV) transmission knowledge and self-reported screening/testing behavior among Pacific Islanders (Guamanians/Chamorros, Samoans, and Tongans) in Southern California. We also examined access and trust by Pacific Islanders of varying health information sources. We administered and analyzed survey data (N = 297), using a convenience sample in Los Angeles, Orange, and San Diego Counties in spring 2009. We found that while Pacific Islander respondents reported that they receive health information from physicians, and largely trust this source, information from and trust in physicians were not statistically significant in explaining whether respondents sought HBV screening or vaccination

Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound

Endoscopic ultrasound (EUS) is a minimally-invasive imaging technique that can be technically difficult to perform due to the small field of view and uncertainty in the endoscope position. Electromagnetic (EM) tracking is emerging as an important technology in guiding endoscopic interventions and for training in endotherapy by providing information on endoscope location by fusion with pre-operative images. However, the accuracy of EM tracking could be compromised by the endoscopic ultrasound transducer. In this work, we quantify the precision and accuracy of EM tracking sensors inserted into the working channel of a flexible endoscope, with the ultrasound transducer turned on and off. The EUS device was found to have little (no significant) effect on static tracking accuracy although jitter increased significantly. A significant change in the measured distance between sensors arranged in a fixed geometry was found during a dynamic acquisition. In conclusion, EM tracking accuracy was not found to be significantly affected by the flexible endoscope

Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations

Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks

Segmentation of anatomy on abdominal CT enables patient-specific image guidance in clinical endoscopic procedures and in endoscopy training. Because robust interpatient registration of abdominal images is necessary for existing multi-atlas- and statistical-shape-model-based segmentations, but remains challenging, there is a need for automated multi-organ segmentation that does not rely on registration. We present a deep-learning-based algorithm for segmenting the liver, pancreas, stomach, and esophagus using dilated convolution units with dense skip connections and a new spatial prior. The algorithm was evaluated with an 8-fold cross-validation and compared to a joint-label-fusion-based segmentation based on Dice scores and boundary distances. The proposed algorithm yielded more accurate segmentations than the joint-label-fusion-ba sed algorithm for the pancreas (median Dice scores 66 vs 37), stomach (83 vs 72) and esophagus (73 vs 54) and marginally less accurate segmentation for the liver (92 vs 93). We conclude that dilated convolutional networks with dense skip connections can segment the liver, pancreas, stomach and esophagus from abdominal CT without image registration and have the potential to support image-guided navigation in gastrointestinal endoscopy procedures

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures

PURPOSE: Navigation of endoscopic ultrasound (EUS)-guided procedures of the upper gastrointestinal (GI) system can be technically challenging due to the small fields-of-view of ultrasound and optical devices, as well as the anatomical variability and limited number of orienting landmarks during navigation. Co-registration of an EUS device and a pre-procedure 3D image can enhance the ability to navigate. However, the fidelity of this contextual information depends on the accuracy of registration. The purpose of this study was to develop and test the feasibility of a simulation-based planning method for pre-selecting patient-specific EUS-visible anatomical landmark locations to maximise the accuracy and robustness of a feature-based multimodality registration method. METHODS: A registration approach was adopted in which landmarks are registered to anatomical structures segmented from the pre-procedure volume. The predicted target registration errors (TREs) of EUS-CT registration were estimated using simulated visible anatomical landmarks and a Monte Carlo simulation of landmark localisation error. The optimal planes were selected based on the 90th percentile of TREs, which provide a robust and more accurate EUS-CT registration initialisation. The method was evaluated by comparing the accuracy and robustness of registrations initialised using optimised planes versus non-optimised planes using manually segmented CT images and simulated ([Formula: see text]) or retrospective clinical ([Formula: see text]) EUS landmarks. RESULTS: The results show a lower 90th percentile TRE when registration is initialised using the optimised planes compared with a non-optimised initialisation approach (p value [Formula: see text]). CONCLUSIONS: The proposed simulation-based method to find optimised EUS planes and landmarks for EUS-guided procedures may have the potential to improve registration accuracy. Further work will investigate applying the technique in a clinical setting