Search
Full database 2,041 resources
-
The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.
-
Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.
Explore
USJ Theses and Dissertations
- Doctorate Theses (58)
- Master Dissertations (1,048)
Academic Units
- Domingos Lam Centre for Research in Education (1)
- Faculty of Arts and Humanities (262)
- Faculty of Business and Law (194)
- Faculty of Health Sciences (40)
- Faculty of Religious Studies and Philosophy (92)
- Institute for Data Engineering and Sciences (29)
- Institute of Science and Environment (128)
- Library (3)
- Macau Ricci Institute (17)
- School of Education (184)
Resource type
- Blog Post (3)
- Book (67)
- Book Section (124)
- Conference Paper (133)
- Document (4)
- Encyclopedia Article (1)
- Film (1)
- Journal Article (419)
- Magazine Article (19)
- Manuscript (1)
- Newspaper Article (34)
- Preprint (4)
- Presentation (59)
- Radio Broadcast (5)
- Report (62)
- Thesis (1,102)
- TV Broadcast (1)
- Web Page (2)
United Nations SDGs
- 01 - No Poverty (1)
- 02 - Zero Hunger (1)
- 03 - Good Health and Well-being (33)
- 04 - Quality Education (17)
- 05 - Gender Equality (1)
- 07 - Affordable and Clean Energy (3)
- 08 - Decent Work and Economic Growth (6)
- 09 - Industry, Innovation and Infrastructure (23)
- 10 - Reduced Inequalities (1)
- 11 - Sustainable Cities and Communities (9)
- 12 - Responsable Consumption and Production (4)
- 13 - Climate Action (5)
- 14 - Life Below Water (19)
- 15 - Life on Land (4)
- 16 - Peace, Justice and Strong Institutions (2)
Cooperation
Student Research and Output
Publication year
- Between 1900 and 1999 (13)
- Between 2000 and 2024 (2,013)
- Unknown (15)