A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease

Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients and healthy subjects is increased using the proposed ratio.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Multifunctional ligands for application in Alzheimer’s Disease: molecular modelling and biological evaluation

Projecte de recerca elaborat a partir d’una estada a la University of British Columbia, Canadà, entre 2010 i 2012 La malaltia d'Alzheimer (MA) representa avui la forma més comuna de demència en la població envellida. Malgrat fa 100 anys que va ser descoberta, encara avui no existeix cap tractament preventiu i/o curatiu ni cap agent de diagnòstic que permeti valorar quantitativament l'evolució d'aquesta malaltia. L'objectiu en el que s'emmarca aquest treball és contribuir a aportar solucions al problema de la manca d'agents terapèutics i de diagnosi, unívocs i rigorosos, per a la MA. Des del camp de la química bioinorgànica és fàcil fixar-se en l'excessiva concentració d'ions Zn(II) i Cu(II) en els cervells de malalts de MA, plantejar-se la seva utilització com a dianes terapèutica i, en conseqüència, cercar agents quelants que evitin la formació de plaques senils o contribueixin a la seva dissolució. Si bé aquest va ser el punt de partida d’aquest projecte, els múltiples factors implicats en la patogènesi de la MA fan que el clàssic paradigma d’ ¨una molècula, una diana¨ limiti la capacitat de la molècula de combatre aquesta malaltia tan complexa. Per tant, un esforç considerable s’ha dedicat al disseny d’agentsmultifuncionals que combatin els múltiples factors que caracteritzen el desenvolupament de la MA. En el present treball s’han dissenyat agents multifuncionals inspirats en dos esquelets moleculars ben establers i coneguts en el camp de la química medicinal: la tioflavina-T (ThT) i la deferiprona (DFP). La utilització de tècniques in silico que inclouen càlculs farmacocinètics i modelatge molecular ha estat un procés cabdal per a l’avaluació dels millors candidats en base als següents requeriments: (a) compliment de determinades propietats farmacocinètiques que estableixin el seu possible ús com a fàrmac (b) hidrofobicitat adequada per travessar la BBB i (c) interacció amb el pèptid Aen solució.

The Three Horsemen of Growth: Plague, War and Urbanization in Early Modern Europe

How did Europe overtake China? We construct a simple Malthusian model with two sectors, and use it to explain how European per capita incomes and urbanization rates could surge ahead of Chinese ones. That living standards could exceed subsistence levels at all in a Malthusian setting should be surprising. Rising fertility and falling mortality ought to have reversed any gains. We show that productivity growth in Europe can only explain a small fraction of rising living standards. Population dynamics - changes of the birth and death schedules - were far more important drivers of the longrun Malthusian equilibrium. The Black Death raised wages substantially, creating important knock-on effects. Because of Engel's Law, demand for urban products increased, raising urban wages and attracting migrants from rural areas. European cities were unhealthy, especially compared to Far Eastern ones. Urbanization pushed up aggregate death rates. This effect was reinforced by more frequent wars (fed by city wealth) and disease spread by trade. Thus, higher wages themselves reduced population pressure. Without technological change, our model can account for the sharp rise in European urbanization as well as permanently higher per capita incomes. We complement our calibration exercise with a detailed analysis of intra-European growth in the early modern period. Using a panel of European states in the period 1300-1700, we show that war frequency can explain a good share of the divergent fortunes within Europe.

Pretreatment with statins improves early outcome in patients with first-ever ischaemic stroke: a pleiotropic effect of statins or a beneficial effect of hypercholesterolemia?

Background: Data from different studies suggest a favourable association between pretreatment with statins or hypercholesterolemia and outcome after ischaemic stroke. We examined whether there were differences in in-hospital mortality according to the presence or absence of statin therapy in a large population of first-ever ischaemic stroke patients and assessed the influence of statins upon early death and spontaneous neurological recovery. Methods: In 2,082 consecutive patients with first-ever ischaemic stroke collected from a prospective hospital-based stroke registry during a period of 19 years (1986-2004), statin use or hypercholesterolemia before stroke was documented in 381 patients. On the other hand, favourable outcome defined as grades 0-2 in the modified Rankin scale was recorded in 382 patients. Results: Early outcome was better in the presence of statin therapy or hypercholesterolemia (cholesterol levels were not measured) with significant differences between the groups with and without pretreatment with statins in in-hospital mortality (6% vs 13.3%, P = 0.001) and symptom-free (22% vs 17.5%, P = 0.025) and severe functional limitation (6.6% vs 11.5%, P = 0.002) at hospital discharge, as well as lower rates of infectious respiratory complications during hospitalization. In the logistic regression model, statin therapy was the only variable inversely associated with in-hospital death (odds ratio 0.57) and directly associated with favourable outcome (odds ratio 1.32).

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Metabolic alterations and increased liver mTOR expression precede the development of autoimmune disease in a murine model of lupus erythematosus

Although metabolic syndrome (MS) and systemic lupus erythematosus (SLE) are often associated, a common link has not been identified. Using the BWF1 mouse, which develops MS and SLE, we sought a molecular connection to explain the prevalence of these two diseases in the same individuals. We determined SLE- markers (plasma anti-ds-DNA antibodies, splenic regulatory T cells (Tregs) and cytokines, proteinuria and renal histology) and MS-markers (plasma glucose, non-esterified fatty acids, triglycerides, insulin and leptin, liver triglycerides, visceral adipose tissue, liver and adipose tissue expression of 86 insulin signaling-related genes) in 8-, 16-, 24-, and 36-week old BWF1 and control New-Zealand-White female mice. Up to week 16, BWF1 mice showed MS-markers (hyperleptinemia, hyperinsulinemia, fatty liver and visceral adipose tissue) that disappeared at week 36, when plasma anti-dsDNA antibodies, lupus nephritis and a pro-autoimmune cytokine profile were detected. BWF1 mice had hyperleptinemia and high splenic Tregs till week 16, thereby pointing to leptin resistance, as confirmed by the lack of increased liver P-Tyr-STAT-3. Hyperinsulinemia was associated with a down-regulation of insulin related-genes only in adipose tissue, whereas expression of liver mammalian target of rapamicyn (mTOR) was increased. Although leptin resistance presented early in BWF1 mice can slow-down the progression of autoimmunity, our results suggest that sustained insulin stimulation of organs, such as liver and probably kidneys, facilitates the over-expression and activity of mTOR and the development of SLE.

Early diagnosis of osteoporosis by means of orthopantomograms and oral x-rays: a systematic review

Osteoporosis is a systemic bone disease that is characterized by a generalized reduction of the bone mass. It is the main cause of fractures in elderly women. Bone densitometry is used in the lumbar spine and hip in order to detect osteoporosis in its early stages. Different studies have observed a correlation between the bone mineral density of the jaw (BMD) and that of the lumbar spine and/or hip. On the other hand, there are studies that evaluate the findings in the orthopantomograms and perapical X-rays, correlating them with the early diagnosis of osteoporosis and highlighting the role of the dentist in the early diagnosis of this disease. Materials and methods: A search was carried out in the Medline-Pubmed database in order to identify those articles that deal with the association between the X-ray findings observed in the orthopantomograms and the diagnosis of the osteoporosis, as well as those that deal with the bone mineral density of the jaw. Results: There were 406 articles, and with the limits established, this number was reduced to 21. Almost all of the articles indicate that when examining oral X-rays, it is possible to detect signs indicative of osteoporosis. Discussion: The radiomorphometric indices use measurements in orthopantomograms and evaluate possible loss of bone mineral density. They can be analyzed alone or along with the visual indices. In the periapical X-rays, the photodensimetric analyses and the trabecular pattern appear to be the most useful. There are seven studies that analyze the densitometry of the jaw, but only three do so independently of the photodensitometric analysis. Conclusions: The combination of mandibular indices, along with surveys on the risk of fracture, can be useful as indicators of early diagnosis of osteoporosis. Visual and morphometric indices appear to be especially important in the orthopantomograms. Photodensitometry indices and the trabecular pattern are used in periapical X-rays. Studies on mandibular dual-energy X-ray absorptiometry are inconclusive

Parkinson"s disease DJ-1 L166P alters rRNA biogenesis by exclusion of TTRAP from the nucleolus and sequestration into cytoplasmic aggregates via TRAF6

Mutations in PARK7/DJ-1 gene are associated to autosomal recessive early onset forms of Parkinson"s disease (PD). Although large gene deletions have been linked to a loss-of-function phenotype, the pathogenic mechanism of missense mutations is less clear. The L166P mutation causes misfolding of DJ-1 protein and its degradation. L166P protein may also accumulate into insoluble cytoplasmic aggregates with a mechanism facilitated by the E3 ligase TNF receptor associated factor 6 (TRAF6). Upon proteasome impairment L166P activates the JNK/p38 MAPK apoptotic pathway by its interaction with TRAF and TNF Receptor Associated Protein (TTRAP). When proteasome activity is blocked in the presence of wild-type DJ-1, TTRAP forms aggregates that are localized to the cytoplasm or associated to nucleolar cavities, where it is required for a correct rRNA biogenesis. In this study we show that in post-mortem brains of sporadic PD patients TTRAP is associated to the nucleolus and to Lewy Bodies, cytoplasmic aggregates considered the hallmark of the disease. In SH-SY5Y neuroblastoma cells, misfolded mutant DJ-1 L166P alters rRNA biogenesis inhibiting TTRAP localization to the nucleolus and enhancing its recruitment into cytoplasmic aggregates with a mechanism that depends in part on TRAF6 activity. This work suggests that TTRAP plays a role in the molecular mechanisms of both sporadic and familial PD. Furthermore, it unveils the existence of an interplay between cytoplasmic and nucleolar aggregates that impacts rRNA biogenesis and involves TRAF6

Sustained CTL activation by murine pulmonary epithelial cells promotes the development of COPD-like disease

Chronic obstructive pulmonary disease (COPD) is a lethal progressive lung disease culminating in permanent airway obstruction and alveolar enlargement. Previous studies suggest CTL involvement in COPD progression; however, their precise role remains unknown. Here, we investigated whether the CTL activation receptor NK cell group 2D (NKG2D) contributes to the development of COPD. Using primary murine lung epithelium isolated from mice chronically exposed to cigarette smoke and cultured epithelial cells exposed to cigarette smoke extract in vitro, we demonstrated induced expression of the NKG2D ligand retinoic acid early tran - script 1 (RAET1)as well as NKG2D-mediated cytotoxicity. Furthermore, a genetic model of inducible RAET1 expression on mouse pulmonary epithelial cells yielded a severe emphysematous phenotype characterized by epithelial apoptosis and increased CTL activation, which was reversed by blocking NKG2D activation. We also assessed whether NKG2D ligand expression corresponded with pulmonary disease in human patients by staining airway and peripheral lung tissues from never smokers, smokers with normal lung function, and current and former smokers with COPD. NKG2D ligand expression was independent of NKG2D receptor expression in COPD patients, demonstrating that ligand expression is the limiting factor in CTL activation. These results demonstrate that aberrant, persistent NKG2D ligand expression in the pulmonary epithelium contributes to the development of COPD pathologies.