Short-term and long-term measures of cortisol in saliva and hair in atypical and non-atypical depression.

BACKGROUND: Atypical depression may show lowered rather than raised short-term cortisol levels. Atypical major depressive episodes (A-MDE) may also be more closely linked to environmental factors and show overlap with somatic symptom disorders. Hair specimens allow measuring long-term cortisol levels. METHODS: Twenty-seven A-MDE and 44 NA-MDE patients and 40 matched controls were tested. Measures of hair cortisol concentration [HCC] covering the previous 3 months and short-term cortisol parameters (six saliva specimens to assess the cortisol awakening response [CAR] and total daily cortisol output calculated as the area under the curve [AUCg]) were taken alongside measures of environmental factors and clinical variables. RESULTS: There were no differences in HCC between the three groups (P = 0.8), and no difference in the CAR (P = 0.95). However, A-MDE showed lowered short-term cortisol output (AUCg) compared to controls (P = 0.04). A-MDE patients also reported a higher number of daily hassles, and higher levels of fatigue and impaired concentration than NA-MDE. CONCLUSIONS: Normal long-term (HCC) and reduced short-term (AUCg) cortisol levels in A-MDE could suggest a disrupted long-term cortisol rhythm, perhaps affected by environmental factors or by certain symptoms, such as mid-nocturnal insomnia. However, other underlying explanations for these findings should also be investigated in the future.

Automatic identification of epileptic electroencephalography signals using higher-order spectra.

Epilepsy is a pathological condition characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behaviour of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on electroencephalography (EEG) recordings. The use of non-linear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal), and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model classifier and a support vector machine classifier. Results show that the classifiers were able to achieve 93.11 per cent and 92.67 per cent classification accuracy respectively, with selected HOS-based features. About 2 h of EEG recordings from ten patients were used in this study.

Enhanced biological phosphorus removal in the retrofitting from an anoxic selector to an anaerobic selector in a full-scale activated sludge process in Singapore.

This paper presents the investigation results of retrofitting an anoxic selector to an anaerobic selector through stepwise reduction of air supply in a full-scale activated sludge process with a focus on enhanced biological phosphorus removal (EBPR). The process experienced gradual shift from a Ludzack-Ettinger (LE) to an anaerobic-anoxic-oxic (A(2)O) process and subsequently, an anaerobic-oxic (A/O) process. The major findings are: (i) the average influent-based PO(4) (3-)-P release in the anaerobic selector compartment was 16.3 mg P l(-1) and that in the secondary clarifier was 1.7 mg P l(-1). 75% of the SCOD and 93% of the acetic acid in the primary effluent were taken up in the anaerobic selector compartment, respectively; (ii) PO(4) (3-)-P uptake contributed by both aerobic and denitrifying phosphorus accumulating organisms (DPAOs) occurred mainly in the first and second aerobic lanes together with simultaneous nitrification and denitrification (SND) while there was not much contribution from the last aerobic lane; (iii) The average PO(4) (3-)-P concentration of the final effluent was 2.4 mg P l(-1) corresponding to a removal efficiency of 85%; (iv) the SVI was satisfactory after retrofitting; and (v) the increase of NH(4) (+)-N in the final effluent from the commencement to the completion of the retrofitting resulted in an approximate 40-50% reduction in oxygen demand and a significant aeration energy saving was achieved.

Cardiac state diagnosis using higher order spectra of heart rate variability.

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Relationship between Mastery and Caregiving Competence in Protecting against Burden, Anxiety and Depression among Caregivers of Frail Older Adults.

OBJECTIVE: Studies suggest the protective effect of mastery and caregiving competence against psychological stressors of caregiving in the context of dementia, although the interplay between the two with caregiver outcomes is not well understood. This study examines the independent and moderating impact of mastery and caregiving competence on burden, anxiety and depression among caregivers of older adults with frailty-related care needs. DESIGN, SETTING AND PARTICIPANTS: This is a cross-sectional study of 274 older adults-family caregiver dyads from a hospital in Singapore. Mean ages of the older adults and their caregivers were 85 and 59 years respectively. MEASUREMENTS: We performed hierarchical linear regression models to examine the independent influence of mastery and caregiving competence on caregiver burden, anxiety and depression. We also examined the interaction effect between mastery and caregiving competence for each outcome. RESULTS: Mastery and caregiving competence were independently negatively associated with caregiver burden, anxiety and depression. Mastery explained more variance than caregiving competence and had a stronger correlation with all outcomes. There was a statistically significant interaction between mastery and caregiving competence for depression (interaction term beta=.14, p<0.01), but not burden and anxiety. High levels of mastery are associated with less depression. particularly among caregivers with below-average levels of caregiving competence. Likewise, high levels of caregiving competence are associated with less depression. particularly among caregivers with below-average levels of mastery. CONCLUSION: Our findings suggest potential benefits adressing targeted interventions for mastery and caregiving competence of caregivers to older adults as they independently influence caregiver outcomes and moderate each other's effect on depression. Mastery-based interventions should be incorporated into current caregiver training which traditionally has focused on caregiver competence alone.

Genetic advances uncover mechanisms of chemotherapy-induced peripheral neuropathy.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common dose-limiting toxicity experienced in 30-40% of patients undergoing treatment with various chemotherapeutics, including taxanes, vinca alkaloids, epothilones, proteasome inhibitors, and thalidomide. Importantly, CIPN significantly affects a patient's quality of life. Recent genetic association studies are enhancing our understanding of CIPN pathophysiology and serve as a foundation for identification of genetic biomarkers to predict toxicity risk and for the development of novel strategies for prevention and treatment.

Development of DEMQOL-U and DEMQOL-PROXY-U: generation of preference-based indices from DEMQOL and DEMQOL-PROXY for use in economic evaluation.

BACKGROUND: Dementia is one of the most common and serious disorders in later life and the economic and personal cost of caring for people with dementia is immense. There is a need to be able to evaluate interventions in dementia using cost-effectiveness analyses, but the generic preference-based measures typically used to measure effectiveness do not work well in dementia. Existing dementia-specific measures can effectively measure health-related quality of life but in their current form cannot be used directly to inform cost-effectiveness analysis using quality-adjusted life-years as the measure of effectiveness. OBJECTIVES: The aim was to develop two brief health-state classifications, one from DEMQOL and one from DEMQOL-Proxy, to generate health states amenable to valuation. These classification systems consisted of items taken from DEMQOL and DEMQOL-Proxy so they can be derived from any study that has used these instruments. DATA SOURCES: In the first stage of the study we used a large, clinically representative sample aggregated from two sources: a sample of patients and carers attending a memory service in south London and a sample of patients and carers from other community services in south London. This included 644 people with a diagnosis of mild/moderate dementia and 689 carers of those with mild/moderate dementia. For the valuation study, the general population sample of 600 respondents was drawn to be representative of the UK general population. Households were sampled in urban and rural areas in northern England and balanced to the UK population according to geodemographic profiles. In the patient/carer valuation study we interviewed a sample of 71 people with mild dementia and 71 family carers drawn from a memory service in south London. Finally, the instruments derived were applied to data from the HTA-SADD (Study of Antidepressants for Depression in Dementia) trial. REVIEW METHODS: This was a complex multiphase study with four linked phases: phase 1 - derivation of the health-state classification system; phase 2 - general population valuation survey and modelling to produce values for every health state; phase 3 - patient/carer valuation survey; and phase 4 - application of measures to trial data. RESULTS: All four phases were successful and this report details this development process leading to the first condition-specific preference-based measures in dementia, an important new development in this field. LIMITATIONS: The first limitation relates to the lack of an external data set to validate the DEMQOL-U and DEMQOL-Proxy-U classification systems. Throughout the development process we have made decisions about which methodology to use. There are other valid techniques that could be used and it is possible to criticise the choices that we have made. It is also possible that the use of a mild to moderate dementia sample has resulted in classification systems that do not fully reflect the challenges of severe dementia. CONCLUSION: The results presented are sufficiently encouraging to recommend that the DEMQOL instruments be used alongside a generic measure such as the European Quality of Life-5 Dimensions (EQ-5D) in future studies of interventions in dementia as there was evidence that they can be more sensitive for patients at the milder end of disease and some limited evidence that the person with dementia measure may be able to reflect deterioration. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Analysis of epileptic EEG signals using higher order spectra.

The unpredictability of the occurrence of epileptic seizures contributes to the burden of the disease to a major degree. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into these phenomena, thereby revealing important clinical information. Thus, various methods have been proposed to predict the onset of seizures based on EEG recordings. A seemingly promising approach involves nonlinear features motivated by the higher order spectra (HOS). The goal in this paper is to find the different HOS features for normal, pre-ictal (background) and epileptic EEG signals. This may help in the detection of seizure onset as early as possible with maximal accuracy. In this work, 300 EEG data, each belonging to the three classes, are studied. Our results show that the HOS based measures show unique ranges for the different classes with high confidence level (p = 0.002).

Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry.

Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities.

Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study.

Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into these phenomena. The use of non-linear features motivated by the higher order spectra (HOS) had been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, the features are extracted from the power spectrum and the bispectrum. Their performance is studied by feeding them to a Gaussian mixture model (GMM) classifier. Results show that with selected HOS based features, we were able to achieve 93.11% compared to classification accuracy of 88.78% as that of features derived from PSD.