MATERIAL AND METHODS: Somatosensory evoked magnetic fields (SEFs) were elicited in 10 patients with somatosensory tumors and in 10 control participants using electrical stimulation of the median nerve via the right and left wrists. We localized the N20m component of the SEFs using dynamic statistical parametric mapping (dSPM) and standardized low-resolution brain electromagnetic tomography (sLORETA) combined with 3D magnetic resonance imaging (MRI). The obtained coordinates were compared between groups. Finally, we statistically evaluated the N20m parameters across hemispheres using non-parametric statistical tests.
RESULTS: The N20m sources were accurately localized to Brodmann area 3b in all members of the control group and in seven of the patients; however, the sources were shifted in three patients relative to locations outside the primary somatosensory cortex (SI). Compared with the affected (tumor) hemispheres in the patient group, N20m amplitudes and the strengths of the current sources were significantly lower in the unaffected hemispheres and in both hemispheres of the control group. These results were consistent for both dSPM and sLORETA approaches.
CONCLUSION: Tumors in the sensorimotor cortex lead to cortical functional reorganization and an increase in N20m amplitude and current-source strengths. Noise-normalized approaches for MEG analysis that are integrated with MRI show accurate and reliable localization of sensorimotor function.
OBJECTIVE: The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual's emotional state. We present our findings of a user study with 50 participants.
METHODS: The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual "movie clips" and audio "music clips"). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual's emotion.
RESULTS: Overall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P
METHODS: 50 asymptomatic (subjects have remained leukemia-free since treatment cessation) CLS and 50 healthy controls were recruited in this cross-sectional study. Of 50 CLS, 44 had acute lymphoblastic leukemia and 6 had acute myeloid leukemia. G-banded karyotyping was performed on unstimulated peripheral blood leukocytes of all subjects.
RESULTS: CLS had significantly higher occurrence of karyotypic abnormalities compared to controls. Five CLS harbored six nonclonal abnormalities (mostly aneuploidy) while none were found in controls.
CONCLUSION: Subpopulations with nonclonal chromosomal aberrations were present in peripheral blood leukocytes of our cohort of childhood leukemia long-term survivors.
MATERIALS AND METHODS: This was a descriptive, retrospective study of odontogenic tumours diagnosed from January 2007 to December 2014 at this centre. The odontogenic tumours were classified using the 2005 World Health Organization classification system.
RESULTS: Among 2,733 biopsy specimens, 173 cases were diagnosed as odontogenic tumours (6.3%), of which 171 (98.8%) are benign and 2 (1.2%) are malignant. The most frequently encountered tumour was ameloblastoma (n=96, 55.5%), followed by keratocystic odontogenic tumour (KCOT) (n=38, 22.0%) and odontomas (n=16, 9.2%). Malignant tumours accounted for 1.2% of the tumours. Most ameloblastomas and KCOTs affected the mandible preferentially. The mean age was 33.5 (± 17.8) years and 64.7% of patients were in the age group of 10 to 39. Odontogenic tumours were slightly more common in males, with a male to female ratio of 1.4:1.
CONCLUSION: The findings of this study are similar to the other studies in Asia in which the most common tumour encountered is the ameloblastoma, followed by KCOT. The most common signs and symptoms are pain and swelling, while paraesthesia and root resorption are less frequently reported. Such clinical and radiographic features should alert the clinician of a possible odontogenic tumour and though rare, malignant tumours should also be included in the differential diagnoses.
METHODS: The study samples comprised 140 subjects aged 18 to 50 years old, natural and unnatural causes of sudden death brought to the Department of Forensic Medicine, Hospital Sungai Buloh (HSgB) and Hospital Sultanah Aminah Johor Bahru (HSAJB) for a period of 12 months. The subjects were categorised into 5 groups: cardiovascular disease (CVD), sudden unexplained death (SUD), thoracic trauma (TT), non-thoracic trauma (NTT) and other diseases (OD).
RESULTS: Median troponin concentration in cases of CVD, SUD, TT, NTT, and OD were 0.51 μg/L, 0.17 μg/L, 0.62 μg/L, 0.90 μg/L and 0.51 μg/L respectively. We found no significant difference of troponin T level in different causes of death (p ≥ 0.05). NTT has the highest median troponin concentration with 0.90 μg/L, SUD possessed the lowest median concentration with 0.17 μg/L.
CONCLUSION: Troponin T is neither specific nor useful as cardiac biomarker for post mortem sample. Therefore, it may not be a useful diagnostic tool at autopsy.
APPROACH: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated 'gold standard' (GS).
MAIN RESULTS: Our algorithm achieved an overall accuracy of 91.5% ± 2.9%, with a sensitivity of 94.1% ± 2.7% and a specificity of 89.7% ± 5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively.
SIGNIFICANCE: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.