Role of technetium-99m diphosphonate and gallium-67 citrate bone scanning in the early diagnosis of infectious spondylodiscitis: a comparative study

A comparative study of the parts played by technetium-99m diphosphonate and gallium-67 citrate bone scanning in the early diagnosis of infectious spondylodiscitis is presented. Nineteen patients were included in the study. All patients (11 men aged 19-70 years and eight women aged 18-72 years) had a history of back pain varying in duration from one to 15 weeks. A 99mTc diphosphonate bone scan was positive in 17 patients. The two patients with negative results had less than two weeks of back pain. The 67Ga citrate bone scan showed uptake in all patients.

Role of technetium-99m diphosphonate and gallium-67 citrate bone scanning in the early diagnosis of infectious spondylodiscitis: a comparative study

A comparative study of the parts played by technetium-99m diphosphonate and gallium-67 citrate bone scanning in the early diagnosis of infectious spondylodiscitis is presented. Nineteen patients were included in the study. All patients (11 men aged 19-70 years and eight women aged 18-72 years) had a history of back pain varying in duration from one to 15 weeks. A 99mTc diphosphonate bone scan was positive in 17 patients. The two patients with negative results had less than two weeks of back pain. The 67Ga citrate bone scan showed uptake in all patients.

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

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.

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.

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.

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 improvement 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.

Myofascial pain associated to trigger points: A literature review. Part 2: Differential diagnosis and treatment

During the last decades the advance in knowledge of myofascial pain has been constant in the medical and dental community. However, although several aspects have been clarified in relation to its epidemiology, clinical characteristics and etiopathogenesis, many uncertainties remain. Many clinical conditions are included in the differential diagnosis of myofascial pain associated to trigger points. A good anamnesis and clinical exploration is thus required in order to ensure correct diagnosis and treatment. Among the numerous treatments used in application to trigger points, the spray-and-stretch technique and direct injection targeted to such trigger points have been found to be the most effective options. In chronic cases, psychosocial intervention is required, due to the high incidence of mood disorders and/or anxiety observed in these patients, who in turn present a poorer prognosis. This underscores the importance of early diagnosis and treatment.

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Ciencia, industria e ideología en la Cataluña del siglo XX: el Instituto Ravetllat-Pla en Sudamérica entre 1924 y 1936

En 1923, Ramón Plá i Armengol (1880-1958) fundó el Instituto Ravetllat-Pla para la comercialización y producción de dos productos antituberculosos (Hemo-Antitoxina y Suero Ravetllat-Pla) fundamentados en una teoría heterodoxa postulada por el veterinario Joaquim Ravetllat i Estech (1871-1923). A través del instituto creó una gran red internacional científico-comercial principalmente en Latinoamérica. Plá i Armengol fue doctor en medicina y participó activamente en la lucha antituberculosa en Cataluña sin dejar de lado su militancia socialista. A través de estos dos productos, logró crear un mercado que se sustentaba en una teoría heterodoxa que integraban sus principios e ideología.

TDA-H más que un simple trastorno

El TDA-H es un trastorno que no solo afecta y dificulta al niño su aprendizaje, sino que sus relaciones sociales, su entorno se ven trastornados. Lo que provoca al niño con TDA-H un sentimiento de soledad y de inseguridad que afecta negativa y directamente a su autoestima, lo que acrecienta aun más su problemática. Por ello es importante que familia y escuela se alíen a la hora de luchar contra este trastorno, haciendo partícipe al niño en esta lucha, donde el trió familia-escuela-niñoTDA-H han de trabajar de manera simbiótica, para que el niño pueda superar todas las dificultades que este trastorno arrastra con él. El diagnostico temprano es imprescindible, pero se debe empezar a trabajar a partir de las primeras alarmas que se despierten y que suelen despertarse en la escuela. Esto aliviará e incluso esquivará algunos de los golpes que este niño TDA-H tendrá que ir superando, hasta iniciar su correcto tratamiento.

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.

Estimation of the viscoelastic properties of vessel walls using a computational model and Doppler ultrasound

Human arteries affected by atherosclerosis are characterized by altered wall viscoelastic properties. The possibility of noninvasively assessing arterial viscoelasticity in vivo would significantly contribute to the early diagnosis and prevention of this disease. This paper presents a noniterative technique to estimate the viscoelastic parameters of a vascular wall Zener model. The approach requires the simultaneous measurement of flow variations and wall displacements, which can be provided by suitable ultrasound Doppler instruments. Viscoelastic parameters are estimated by fitting the theoretical constitutive equations to the experimental measurements using an ARMA parameter approach. The accuracy and sensitivity of the proposed method are tested using reference data generated by numerical simulations of arterial pulsation in which the physiological conditions and the viscoelastic parameters of the model can be suitably varied. The estimated values quantitatively agree with the reference values, showing that the only parameter affected by changing the physiological conditions is viscosity, whose relative error was about 27% even when a poor signal-to-noise ratio is simulated. Finally, the feasibility of the method is illustrated through three measurements made at different flow regimes on a cylindrical vessel phantom, yielding a parameter mean estimation error of 25%.