Issues in Delivering Morbidity Management for Lymphatic Filariasis Elimination: A Study in Pondicherry, South India

Lymphatic filariasis is a vector borne parasitic disease causing long term disability. The Global Programme to Eliminate Lymphatic Filariasis aims to achieve its objective through two strategies; Mass Drug Administration (MDA) to interrupt transmission and Morbidity Management (MM) to manage disability for those already affected. MDA is going on in full swing in endemic areas; but MM is lagging behind. An exploratory study was conducted in Pondicherry through focus group discussions to find out whether there are delivery issues if any, in the MM programme and get suggestions from end users. The study results show that MM has not received the same attention as MDA and there are shortcomings in the delivery mechanism of the programme. The importance of these findings are discussed and suggestions given for improving the programme

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Pseudo cholinesterase-diagnostic and prognostic value in organophosphorus poisoning

Background: Organophosphorus compound poisoning is a common clinical situation encountered in emergency department. The estimation of pseudo cholinesterase levels in plasma help to identify OP poisoning. Aim of this study was the levels of pseudo cholinesterase in plasma as a diagnostic aid in patients with suspected organophosphorus poisoning and to predict the prognosis and mortality based on pseudo cholinesterase activity.Methods: 390 cases of suspected organophosphorus compound poisoning were selected above 18 years of age. Detailed history and clinical examination were done. The serum levels of enzyme pseudo cholinesterase were estimated on 1st and 3rd and 6th day.Results: 61% were males, 39% were females. Age range 18-55 years. Most of the patients were admitted within 4 hours of consumption. 50% cases were mild, 26% were moderate and 24% were severe cases, Commonest clinical features were vomiting, diarrhea and abdominal cramps. Common signs were miosis (46%), difficulty in breathing (42%), cold calmmy skin, bradycardia. Patients who survived had raising values of enzyme levels and in patients who expired did not show much increase in enzyme values.Conclusions: In initial stages of poisoning, determining pseudo cholinesterase activity forms a reliable test. In patients who survived had values above 4300 U/L and showed increasing levels on successive days indicating better prognosis. Low values of enzymes in initial stages of poisoning as well as decreasing values on the third day indicate increased mortality

Ethyl 4-(4-bromo­phen­yl)-6-r-phenyl-2-oxocyclo­hex-3-ene-1-t-carboxyl­ate

In the title compound, C21H19BrO3, the cyclo­hexene ring adopts an envelope conformation, with all substituents equatorial. The plane through its five coplanar atoms makes dihedral angles of 28.88 (10) and 71.94 (10)° with the bromo­benzene and phenyl rings, respectively. The dihedral angle between the latter two rings is 51.49 (15)°. Inter­molecular C—H⋯O hydrogen bonds are found in the crystal structure; a C—H⋯π inter­action is also present

Cardiac computed tomography radiomics for the non-invasive assessment of coronary inflammation

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.Kevin Cheng, Andrew Lin, Jeremy Yuvaraj, Stephen J. Nicholls and Dennis T.L. Won

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities

The Effect of Lateralization of Motor Onset and Emotional Recognition in PD Patients Using EEG

The objective of this research was to investigate the relationship between emotion recognition and lateralization of motor onset in Parkinson’s disease (PD) patients using electroencephalogram (EEG) signals. The subject pool consisted of twenty PD patients [ten with predominantly leftsided (LPD) and ten with predominantly rightsided (RPD) motor symptoms] and 20 healthy controls (HC) that were matched for age and gender. Multimodal stimuli were used to evoke simple emotions, such as happiness, sadness, fear, anger, surprise, and disgust. Artifactfree emotion EEG signals were processed using the auto regressive spectral method and then subjected to repeated ANOVA measures. No group differences were observed across behavioral measures? however, a significant reduction in EEG spectral power was observed at alpha, beta and gamma frequency oscillations in LPD, compared to RPD and HC participants, suggesting that LPD patients (inferred righthemisphere pathology) are impaired compared to RPD patients in emotional processing. We also found that PD related emotional processing deficits may be selective to the perception of negative emotions. Previous findings have suggested a hemispheric effect on emotion processing that could be related to emotional response impairment in a subgroup of PD patients. This study may help in clinical practice to uncover potential neurophysiologic abnormalities of emotional changes with respect to PD patient’s motor onset