Sickle cell disease: Its signs, symptoms and varied manifestations

Seventy-five sickle cell patients, age 3-36 years from Houston, Texas, participated in the research study to investigate sickle cell manifestations, conducted between November 1989 and August 1990. All the participants were blacks. There were 35 females and 39 males among the participants in this research study. One of the participants did not document the gender.^ The sickle cell history questionnaire was administered to the participants. Data collected from this study were statistically analyzed using frequencies, percentages, crosstabulations and chi-squares.^ Regular source of health care influences the time of diagnosis of sickle cell disease. Early diagnosis of sickle cell disease with proper care and management will reduce the morbidity and mortality rate of the disease.^ Fevers, bacterial infection, pneumoniae, anemiae, pains, ulcers and cardiovascular problems are common causes of hospitalizations. The average length of stay in the hospital on admission were higher among the sickle cell patients than their family members who themselves did not have sickle cell disease. ^

A systematic review of clostridium difficile infection in patients with iatrogenic immunesuppression

Background: Incidence of C. difficile infection (CDI) has increased dramatically in the past decade and is the most frequent cause of nosocomial infectious diarrhea. The outcome of infection may range from mild diarrhea to life-threatening pseudomembranous colitis depending on the immunological response of the host, which is highly compromised in this special population that includes bone marrow transplant (BMT), solid organ transplant (SOT) and cancer patients on cytotoxic chemotherapy. ^ Objectives: We conducted a meta-analysis to assess the incidence rates of CDI and the time to onset of infection in patients with iatrogenic immune suppression. ^ Methods: Original studies were identified through an extensive search of electronic databases including PubMed, Ovid Medline (R), RefWorks and Biological Abstracts and their references. The overall incidence rate of CDI in the immune suppressed population was calculated using random effects model and their 95% confidence interval was derived. Differences in the incidence of CDI and time to onset of infection were calculated between the groups and within the groups. Publication bias was assessed using a funnel plot. Results: Twenty nine published articles involving 7,424 patients met the eligibility requirements. The overall incidence of CDI in the immune suppressed population is 11.1% (95% Confidence Interval (CI): 9.2–13.4%). The incidence of CDI was higher in SOT patients (14.2%, 95% CI: 6.8–21.5%); (p-value-0.022) and in cancer patients on cytotoxic chemotherapy (11.4%, 95% CI: 8.4–15.4%); (p = 0.042) than in BMT patients (10.5%, 95% CI: 7.9–13.1%). In a subgroup analysis of BMT population, the incidence of CDI is significantly higher in patients who received allogeneic BMT (15.1%, 95% CI: 11.2–20.0%; p value <0.0001). Similarly, in the SOT population, the incidence of CDI was higher in patients who underwent liver transplantation (11.0%, 95% CI: 5.6–20.3%); (p= 0.0672). The median time to onset of infection was shorter in BMT patients (p=0.0025). ^ Conclusions: It is evident from the combined analysis of these 29 published studies that the incidence of CDI in the immune suppressed population is higher. However, early diagnosis and treatment of CDI will help reduce the morbidity and mortality due to CDI in this special population.^

Identification of micro-RNAs targeting genes of PI3K/AKT pathway in ovarian cancer

Ovarian cancer is the leading cause of cancer-related death for females due to lack of specific early detection method. It is of great interest to find molecular-based biomarkers which are sensitive and specific to ovarian cancer for early diagnosis, prognosis and therapeutics. miRNAs have been proposed to be potential biomarkers that could be used in cancer prevention and therapeutics. The current study analyzed the miRNA and mRNA expression data extracted from the Cancer Genome Atlas (TCGA) database. Using simple linear regression and multiple regression models, we found 71 miRNA-mRNA pairs which were negatively associated between 56 miRNAs and 24 genes of PI3K/AKT pathway. Among these miRNA and mRNA target pairs, 9 of them were in agreement with the predictions from the most commonly used target prediction programs including miRGen, miRDB, miRTarbase and miR2Disease. These shared miRNA-mRNA pairs were considered to be the most potential genes that were involved in ovarian cancer. Furthermore, 4 of the 9 target genes encode cell cycle or apoptosis related proteins including Cyclin D1, p21, FOXO1 and Bcl2, suggesting that their regulator miRNAs including miR-16, miR-96 and miR-21 most likely played important roles in promoting tumor growth through dysregulated cell cycle or apoptosis. miR-96 was also found to directly target IRS-1. In addition, the results showed that miR-17 and miR-9 may be involved in ovarian cancer through targeting JAK1. This study might provide evidence for using miRNA or miRNA profile as biomarker.^

Angiosarcoma en linfedema crónico posmastectomía : Síndrome de Stewart-Treves

Objetivo: comunicar un caso de angiosarcoma en linfedema crónico posmastectomía, revisar los diagnósticos diferenciales, destacando que el diagnóstico temprano de esta entidad es la única alternativa para poder modificar la evolución tórpida de esta enfermedad. Caso clínico: presentamos una mujer de 78 años con el antecedente de mastectomía izquierda y cobaltoterapia realizadas en 1990, presentando en el año 2000 edema progresivo en miembro homolateral y pared torácica, con la aparición de placas y nódulos rojo-violáceos, indurados, dolorosos desde noviembre de 2006. La extensión y progresión de su enfermedad motivó su internación para corroborar diagnóstico presuntivo de síndrome de Stewart-Treves con biopsias, establecer diagnósticos diferenciales, estudio clínico-oncológico, control del dolor y evaluar posibles tratamientos. Comentarios: 1). El intervalo entre el diagnóstico del carcinoma de mama y el de este cuadro es de 10-20 años. 2). La supervivencia de los pacientes es < 5%. 3). Las posibilidades terapéuticas son agresivas e infructuosas en la mayoría de los casos. 4). El pronóstico depende de la alta sospecha precoz de las lesiones y su extirpación quirúrgica.

Los adolescentes y los estigmas

En este trabajo se desarrolla el tema de los diagnósticos tempranos en los que se rotula a un niño como deficitario y de las dificultades de los adolescentes que llegan a esa etapa crítica de la vida portando algún “cartel" que los señala como un “sindrome" o un “trastorno de...". Este estigma suele dificultarles el rearmado narcisista, la relación con el grupo de pares y la salida exogámica. Se habla de la crisis adolescente y de los avatares particulares de la sexualidad en estos casos.

Enfermedad celíaca y su relación con la cavidad bucal

El objetivo del siguiente trabajo es describir las características de la enfermedad celíaca, y sus expresiones más frecuentes en la cavidad bucal. Las manifestaciones de esta patología pueden ser divididas en formas típicas y atípicas. Las formas atípicas saben mostrar alteraciones extra-intestinales entre las cuales los signos orales suelen constituir las señales iniciales de la enfermedad y podrían orientar al diagnóstico precoz de la misma, favoreciendo a implementar las medidas preventivas adecuadas.

Historias de hormonas en Argentina : La obesidad de los niños y el diagnóstico precoz del embarazo en las décadas de 1930 y 1940

Este artículo aborda, desde una perspectiva de género, la producción de saberes sobre hormonas en Argentina, en las décadas de 1930 y 1940. Entendemos que un análisis histórico de las ideas científicas y las prácticas clínicas permite indagar en cómo éstas se entraman tanto con el escenario social del que forman parte como con los contextos institucionales donde se elaboran. Focalizaremos en las miradas de los endocrinólogos del Hospital de Niños de Buenos Aires sobre la obesidad en la infancia y el síndrome adiposo genital, y los debates en la Sociedad de Obstetricia y Ginecología de Buenos Aires sobre las técnicas de detección precoz del embarazo y la hormona gonadotropina coriónica

Historias de hormonas en Argentina : La obesidad de los niños y el diagnóstico precoz del embarazo en las décadas de 1930 y 1940

Este artículo aborda, desde una perspectiva de género, la producción de saberes sobre hormonas en Argentina, en las décadas de 1930 y 1940. Entendemos que un análisis histórico de las ideas científicas y las prácticas clínicas permite indagar en cómo éstas se entraman tanto con el escenario social del que forman parte como con los contextos institucionales donde se elaboran. Focalizaremos en las miradas de los endocrinólogos del Hospital de Niños de Buenos Aires sobre la obesidad en la infancia y el síndrome adiposo genital, y los debates en la Sociedad de Obstetricia y Ginecología de Buenos Aires sobre las técnicas de detección precoz del embarazo y la hormona gonadotropina coriónica

Historias de hormonas en Argentina : La obesidad de los niños y el diagnóstico precoz del embarazo en las décadas de 1930 y 1940

Este artículo aborda, desde una perspectiva de género, la producción de saberes sobre hormonas en Argentina, en las décadas de 1930 y 1940. Entendemos que un análisis histórico de las ideas científicas y las prácticas clínicas permite indagar en cómo éstas se entraman tanto con el escenario social del que forman parte como con los contextos institucionales donde se elaboran. Focalizaremos en las miradas de los endocrinólogos del Hospital de Niños de Buenos Aires sobre la obesidad en la infancia y el síndrome adiposo genital, y los debates en la Sociedad de Obstetricia y Ginecología de Buenos Aires sobre las técnicas de detección precoz del embarazo y la hormona gonadotropina coriónica

Brain dynamics of mild cognitive impairment during face encoding

Brain oscillations are closely correlated with human information processing and fundamental aspects of cognition. Previous literature shows that due to the relation between brain oscillations and memory processes, spectral dynamics during such tasks are good candidates to study and characterize memory related pathologies. Mild cognitive impairment (MCI), defined as a clinical condition characterized by memory impairment and/ or deterioration of additional cognitive domains, is considered a preliminary stage in the dementia process. In consequence, the study of its brain patterns could help to achieve an early diagnosis of Alzheimer Disease.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

QuantiDOPA: A Quantification Software for Dopaminergic Neurotransmission SPECT

Quantification of neurotransmission Single-Photon Emission Computed Tomography (SPECT) studies of the dopaminergic system can be used to track, stage and facilitate early diagnosis of the disease. The aim of this study was to implement QuantiDOPA, a semi-automatic quantification software of application in clinical routine to reconstruct and quantify neurotransmission SPECT studies using radioligands which bind the dopamine transporter (DAT). To this end, a workflow oriented framework for the biomedical imaging (GIMIAS) was employed. QuantiDOPA allows the user to perform a semiautomatic quantification of striatal uptake by following three stages: reconstruction, normalization and quantification. QuantiDOPA is a useful tool for semi-automatic quantification inDAT SPECT imaging and it has revealed simple and flexible

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Evaluating a web-based clinical decision support system for language disorders screening in a nursery school

Background: Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder. Objective: This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care. Methods: This paper details the evaluation results of the ?Gades? CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child?s language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results. Results: The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic abilities. The refinement of the KB will address these issues and include the requested improvements, with the support of the experts who took part in the original KB development. Conclusions: This research demonstrated the benefit of a Web-based CDSS to monitor children?s neurodevelopment via the early detection of language delays at a nursery school. Current next steps focus on the design of a model that includes pseudo auto-learning capacity, supervised by experts.

Sistemas de ayuda al diagnóstico y reconocimiento de microcalcificaciones en mamografía mediante descriptores de escala y redes jerárquicas

La presente Tesis investiga el campo del reconocimiento automático de imágenes mediante ordenador aplicado al análisis de imágenes médicas en mamografía digital. Hay un interés por desarrollar sistemas de aprendizaje que asistan a los radiólogos en el reconocimiento de las microcalcificaciones para apoyarles en los programas de cribado y prevención del cáncer de mama. Para ello el análisis de las microcalcificaciones se ha revelado como técnica clave de diagnóstico precoz, pero sin embargo el diseño de sistemas automáticos para reconocerlas es complejo por la variabilidad y condiciones de las imágenes mamográficas. En este trabajo se analizan los planteamientos teóricos de diseño de sistemas de reconocimiento de imágenes, con énfasis en los problemas específicos de detección y clasificación de microcalcificaciones. Se ha realizado un estudio que incluye desde las técnicas de operadores morfológicos, redes neuronales, máquinas de vectores soporte, hasta las más recientes de aprendizaje profundo mediante redes neuronales convolucionales, contemplando la importancia de los conceptos de escala y jerarquía a la hora del diseño y sus implicaciones en la búsqueda de la arquitectura de conexiones y capas de la red. Con estos fundamentos teóricos y elementos de diseño procedentes de otros trabajos en este área realizados por el autor, se implementan tres sistemas de reconocimiento de mamografías que reflejan una evolución tecnológica, culminando en un sistema basado en Redes Neuronales Convolucionales (CNN) cuya arquitectura se diseña gracias al análisis teórico anterior y a los resultados prácticos de análisis de escalas llevados a cabo en nuestra base de datos de imágenes. Los tres sistemas se entrenan y validan con la base de datos de mamografías DDSM, con un total de 100 muestras de entrenamiento y 100 de prueba escogidas para evitar sesgos y reflejar fielmente un programa de cribado. La validez de las CNN para el problema que nos ocupa queda demostrada y se propone un camino de investigación para el diseño de su arquitectura. ABSTRACT This Dissertation investigates the field of computer image recognition applied to medical imaging in mammography. There is an interest in developing learning systems to assist radiologists in recognition of microcalcifications to help them in screening programs for prevention of breast cancer. Analysis of microcalcifications has emerged as a key technique for early diagnosis of breast cancer, but the design of automatic systems to recognize them is complicated by the variability and conditions of mammographic images. In this Thesis the theoretical approaches to design image recognition systems are discussed, with emphasis on the specific problems of detection and classification of microcalcifications. Our study includes techniques ranging from morphological operators, neural networks and support vector machines, to the most recent deep convolutional neural networks. We deal with learning theory by analyzing the importance of the concepts of scale and hierarchy at the design stage and its implications in the search for the architecture of connections and network layers. With these theoretical facts and design elements coming from other works in this area done by the author, three mammogram recognition systems which reflect technological developments are implemented, culminating in a system based on Convolutional Neural Networks (CNN), whose architecture is designed thanks to the previously mentioned theoretical study and practical results of analysis conducted on scales in our image database. All three systems are trained and validated against the DDSM mammographic database, with a total of 100 training samples and 100 test samples chosen to avoid bias and stand for a real screening program. The validity of the CNN approach to the problem is demonstrated and a research way to help in designing the architecture of these networks is proposed.