8 research outputs found
The First Case of COVID-19 Reinfection in Duhok City, Kurdistan Region of Iraq: A Case Report
No full text
The Effect of COVID-19 Pandemic on the General and Mental Health of Medical Residency in Kurdistan Region, Iraq; A Cross-Sectional Study
No full textCOVID-19 Reinfection in a Nurse Working in Emergency Hospital in Duhok City, Kurdistan Region of Iraq
No full textNovel coronavirus disease (COVID-19) or SARS-CoV-2 infection was discovered in December 2019 in Wuhan City, China. The infection became a global pandemic over a period of few months. Post-infection immunity and susceptibility for reinfection is still under investigation. In this report, we present a case of a COVID-19 reinfection in patient who had recovered from an initial infection.
Case Report: 41-year-old male, nurse working in emergency hospital, presented in August 2020 with two days history of fever, sore throat, myalgia, lower back pain, shortness of breath. Then, his oxygen saturation dropped to 80%. COVID-19 infection was proved by a positive RT-PCR for SARS-CoV-2 and CT scan of the chest demonstrated bilateral ground glass opacities. After clinical improvement, the patient was discharged from hospital. On 26th of October, he developed fever, and fatiguability. RT-PCR for SARS-CoV-2 resulted as positive twice. The infection was mild and no specific treatment was administered to the patient during the second infection. On 6th of November, the patient was asymptomatic. On 7-8th of November, he consecutively tested negative for SARS-CoV-2 twice.
Conclusions: Mild SARS-CoV-2 reinfection may occur rarely due to repeated exposed to the virus in hospital setting. If the occurrence of reinfections is demonstrated to be true, it may change the strategy of infection prevention. Further studies are needed to confirm the possibility of COVID-19 reinfection
Non-conducting interfaces of LaAlO3/SrTiO3 produced in sputter deposition: The role of stoichiometry
No full textWe have investigated the properties of interfaces between LaAlO3 films grown on SrTiO3 substrates singly terminated by TiO2. We used RF sputtering in a high-pressure oxygen atmosphere. The films are smooth, with flat surfaces. Transmission electron microscopy shows sharp and continuous interfaces with some slight intermixing. The elemental ratio of La to Al, measured by the energy dispersive X-ray technique, is found to be 1.07. Importantly, we find these interfaces to be non-conducting, indicating that the sputtered interface is not electronically reconstructed in the way reported for films grown by pulsed laser deposition because of the different interplays among stoichiometry, mixing, and oxygen vacancies.QN/Quantum NanoscienceApplied Science
Growing LaAlO3/SrTiO3 interfaces by sputter deposition
No full textSputter deposition of oxide materials in a high-pressure oxygen atmosphere is a well-known technique to produce thin films of perovskite oxides in particular. Also interfaces can be fabricated, which we demonstrated recently by growing LaAlO3 on SrTiO3 substrates and showing that the interface showed the same high degree of epitaxy and atomic order as is made by pulsed laser deposition. However, the high pressure sputtering of oxides is not trivial and number of parameters are needed to be optimized for epitaxial growth. Here we elaborate on the earlier work to show that only a relatively small parameter window exists with respect to oxygen pressure, growth temperature, radiofrequency power supply and target to substrate distance. In particular the sensitivity to oxygen pressure makes it more difficult to vary the oxygen stoichiometry at the interface, yielding it insulating rather than conducting.QN/Quantum NanoscienceApplied Science
A Cross-sectional Study of Clinical Characteristics and Outcomes among Adults with Laboratory-confirmed SARS-CoV-2 Infection with Omicron Variant
No full textThe emergence of the SARS-CoV-2 Omicron variant has raised concerns due to its increased transmissibility and potential implications on clinical characteristics and outcomes in infected individuals. The aims of this report were to study the profile of SARS-CoV-2 infection with omicron variant, investigate the infection outcome, reinfection rates with associated factors, antibody levels, and explore the associations between biochemical markers and disease severity. This prospective cohort study was conducted in Duhok city in the Northern of Iraq. All volunteers with confirmed SARS-CoV-2 RT–PCR and confirmed Omicron infection who were older than 18 years old and agreed to participate were recruited for this study. The study was carried out from January to April 2022. There were 234 cases of confirmed SARS-CoV-2 RT–PCR Omicron infection. The mean age was 48.12±17.3 years, 43.2% were vaccinated, and 40.2% were male. Among the recruited patients, 99.1% recovered and did not need hospitalization. In this study, (38.9%) had a history of previously confirmed COVID-19 infection. Reinfection was significantly higher in females than males (p=0.04; OR= 0.56). It was found that the IgG antibody levels were higher in patients who received Pfizer-BioNTech than in those who received other vaccines (p=0.001). The levels of IgG were also significantly higher in patients with mild infection (p=0.046), whereas the levels of D-dimer were significantly higher in patients with severe cases of the infection compared to those with mild or moderate cases (p=0.001). Additionally, the levels of C-reactive protein (CRP) were observed to be higher in individuals with moderate cases of infection than in mild and severe cases (0.001). Individuals who contracted the Omicron strain generally had positive outcomes. Reinfection with the Omicron variant was relatively high. IgG levels were higher in patients with mild disease, implying that they were associated with decreased disease severity. We found significant associations between D-dimer levels and the severity of the disease. Additional research is required to investigate the long-term effects of Omicron infection
Potencial de la inteligencia artificial para la detección temprana del melanoma maligno en Colombia
No full textLa Inteligencia Artificial (IA) en el campo médico de Colombia, especialmente en la detección temprana del melanoma, ofrece perspectivas transformadoras. La aplicación del Aprendizaje Automático y el Aprendizaje Profundo ha abierto nuevas posibilidades para el análisis detallado y complejo de datos. Dentro de estas técnicas, las Redes Neuronales Convolucionales (CNN) destacan por su potencial en la interpretación precisa de imágenes dermatológicas. Pero, a pesar de su alta precisión, estos sistemas podrían no abarcar completamente las sutilezas clínicas, desafiando el paradigma tradicional del diagnóstico basado en la experiencia y la empatía humanas.
Este estudio tiene como objetivo compilar información sobre el potencial de la Inteligencia Artificial para la detección temprana del melanoma maligno en Colombia, identificando los desafíos y oportunidades que enfrenta la implementación de estas herramientas avanzadas en la práctica clínica.
Esta monografía de compilación se relaciona con la aplicación de IA en la detección de melanoma en el contexto médico colombiano. Se recopiló información de diversas fuentes bibliográficas, incluyendo estudios académicos, páginas web y datos sobre el uso de herramientas de IA en el sector médico de Colombia.
Finalmente, con la biodiversidad y datos en Colombia, existe una oportunidad para mejorar y adaptar los modelos de Aprendizaje Automático y otras formas de IA para lograr un diagnóstico más preciso y efectivo del melanoma. Si bien existen desafíos, el interés del sector privado es un motivo para ser optimistas sobre el futuro de la implementación de la IA en el campo médico colombiano, especialmente en la detección temprana del melanoma.Artificial Intelligence (AI) in Colombia's medical field, especially in the early detection of melanoma, offers transformative perspectives. The application of Machine Learning and Deep Learning has opened new possibilities for detailed and complex data analysis. Within these techniques, Convolutional Neural Networks (CNN) stand out for their potential in the precise interpretation of dermatological images. But despite their high accuracy, these systems may not fully encompass clinical subtleties, challenging the traditional paradigm of diagnosis based on human experience and empathy.
This study aims to compile information on the potential of Artificial Intelligence for the early detection of malignant melanoma in Colombia, identifying the challenges and opportunities faced by the implementation of these advanced tools in clinical practice.
This compilation monograph is related to the application of AI in the detection of melanoma in the Colombian medical context. Information was collected from various bibliographic sources, including academic studies, web pages and data on the use of AI tools in the Colombian medical sector.
Finally, with the biodiversity and data in Colombia, there is an opportunity to improve and adapt Machine Learning models and other forms of AI to achieve more accurate and effective diagnosis of melanoma. While there are challenges, the interest of the private sector is a reason to be optimistic about the future of AI implementation in the Colombian medical field, especially in the early detection of melanoma.1. PLANTEAMIENTO DEL PROBLEMA.............................................. 121.1. Descripción del problema.......................................................... 122. JUSTIFICACIÓN............................................................................. 143. OBJETIVOS...................................................................................... 163.1. Objetivo General......................................................................... 163.2. Objetivos Específicos...................................................................... 164. METODOLOGÍA............................................................................ 174.1. Tipo de Estudio............................................................................. 174.2. Población..................................................................................... 174.3. Muestra......................................................................................... 174.4. Unidad de Análisis...................................................................... 184.5. Organización de la monografía.................................................. 184.6. Presentación de la Información................................................. 184.7. Aspectos éticos............................................................................. 184.8. Aspectos de propiedad intelectual y derechos de autor....... 195. MONOGRAFÍA.............................................................................. 205.1. RAMAS DE LA INTELIGENCIA ARTIFICIAL UTILIZADAS INTERNACIONALMENTE PARA LA DETECCIÓN TEMPRANA DEL MELANOMA MALIGNO........ 205.1.1. Aprendizaje Automático (Machine Learning-ML) como Rama Central de la IA ...................................................215.1.2. Aprendizaje Profundo (Depp Learning-DL)............................................................... 235.1.3. Redes Neuronales Convolucionales (CNNs)............................................................ 255.1.4. Aprendizaje por Refuerzo........................................................ 265.2. BARRERAS Y FACILITADORES PARA LA IMPLEMENTACIÓN DE INTELIGENCIA ARTIFICIAL EN LA DETECCIÓN TEMPRANA DEL MELANOMA MALIGNO EN COLOMBIA..... 305.2.1. IMPORTANCIA DE LA IMPLEMENTACIÓN DE LA INTELIGENCIA ARTIFICIAL EN LA DETECCIÓN TEMPRANA DEL MELANOMA MALIGNO EN EL SISTEMA DE SALUD COLOMBIANO...... 376. CONSIDERACIONES FINALES............................................................ 427. CONCLUSIONES.......................................................................... 458. RECOMENDACIONES.................................................................... 489. BIBLIOGRAFÍA.................................................................................. 51EspecializaciónEspecialista en Auditoria de la Calidad en SaludMonografía
