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70. Symptom clusters in patients with hypertensive cerebral haemorrhage during the acute phase and their correlation with prognostic quality of life

Author

LIU Yujing*, MA Qing, LIANG Yajing, LIU Ying, ZHANG Jing, CHENG Jie, ZHOU Ran, GUO Zonghai

Subjects
cerebral haemorrhage, stroke, symptoms cluster, symptom management, quality of life, exploratory factor analysis, Medicine
Abstract

Objective To explore the composition of symptom clusters in the acute phase of patients with hypertensive cerebral haemorrhage and to analyse their correlation with prognostic quality of life. Methods From July 2021 to June 2022, 396 patients with hypertensive cerebral haemorrhage hospitalised in neurosurgery departments of two tertiary care hospitals in Tangshan City were selected and surveyed using the General Information Questionnaire, the Memory Symptom Assessment Scale and the Stroke-Specific Quality of Life Scale. An exploratory factor method was used to extract symptom clusters and Biased correlation analysis was applied to analyse the correlation between symptom clusters and quality of life. Results Symptoms with a higher incidence in the acute phase in patients with hypertensive cerebral hemorrhage included loss of appetite (77.3%), low energy/weakness (74.0%), pain (72.9%), weight loss (71.5%), and feeling nervous (70.7%); symptoms with a higher degree of severity included loss of appetite (2.01±1.22), low energy/weakness (2.00±1.32), pain (1.97±1.30), vomiting/nausea (1.83±1.21), difficulty concentrating (1.81±1.30); symptoms with higher levels of distress included pain (2.27±1.53), vomiting/nausea (2.20±1.48), vomiting (1.98±1.59), low energy/weakness (1.96±1.30), dizziness (1.81±1.40). A total of six symptom clusters were extracted by the exploratory factor method, namely digestive symptom cluster, psychological symptom cluster, pain-fatigue related symptom cluster, physical-cognitive impairment symptom cluster, speech-swallowing disorder symptom cluster and pneumonia related symptom cluster, all of which were negatively correlated with prognostic quality of life with correlation coefficients of-0.261,-0.388,-0.335,-0.513,-0.304,-0.273. Conclusion The presence of six symptom clusters in the acute phase of hypertensive cerebral haemorrhage patients, all of which were negatively associated with prognosis of quality of life, suggests that medical staff should identify or predict symptom clusters in a timely and accurate manner and optimise symptom management strategies to improve patients ‘ prognosis of quality of life.

Published
2024
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72. Tbx5 maintains atrial identity in postnatal cardiomyocytes by regulating an atrial-specific enhancer network

Author

Sweat, Mason E., Cao, Yangpo, Zhang, Xiaoran, Burnicka-Turek, Ozanna, Perez-Cervantes, Carlos, Kulandaisamy, Arulsamy, Lu, Fujian, Keating, Erin M., Akerberg, Brynn N., Ma, Qing, Wakimoto, Hiroko, Gorham, Joshua M., Hill, Lauren D., Song, Mi Kyoung, Trembley, Michael A., Wang, Peizhe, Gianeselli, Matteo, Prondzynski, Maksymilian, Bortolin, Raul H., Bezzerides, Vassilios J., Chen, Kaifu, Seidman, Jonathan G., Seidman, Christine E., Moskowitz, Ivan P., and Pu, William T.

Published
2023
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84. The association between benzodiazepine use and greater risk of neurocognitive impairment is moderated by medical burden in people with HIV

Author

Sundermann, Erin E, Saloner, Rowan, Rubtsova, Anna, Nguyen, Annie L, Letendre, Scott, Moore, Raeanne C, Cherner, Mariana, Ma, Qing, and Marquine, María J

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Aging, HIV/AIDS, Clinical Research, Behavioral and Social Science, Mental Health, Infectious Diseases, Benzodiazepines, Cognition Disorders, HIV Infections, Humans, Neuropsychological Tests, Retrospective Studies, HIV, Neurocognitive impairment, Comorbidities, Medical burden, Virology, Clinical sciences, Medical microbiology
Abstract

Benzodiazepine use is linked to neurocognitive impairment (NCI) in the general population and people with HIV (PWH); however, this relationship may depend on age-related factors such as medical comorbidities, which occur at an elevated rate and manifest earlier in PWH. We retrospectively examined whether chronological age or medical burden, a clinical marker for aging, moderated the relationship between benzodiazepine use and NCI in PWH. Participants were 435 PWH on antiretroviral therapy who underwent neurocognitive and medical evaluations, including self-reported current benzodiazepine use. A medical burden index score (proportion of accumulated multisystem deficits) was calculated from 28 medical deficits. Demographically corrected cognitive deficit scores from 15 neuropsychological tests were used to calculate global and domain-specific NCI based on established cut-offs. Logistic regressions separately modeled global and domain-specific NCI as a function of benzodiazepine x age and benzodiazepine x medical burden interactions, adjusting for current affective symptoms and HIV disease characteristics. A statistically significant benzodiazepine x medical burden interaction (p = .006) revealed that current benzodiazepine use increased odds of global NCI only among those who had a high medical burden (index score > 0.3 as indicated by the Johnson-Neyman analysis), which was driven by the domains of processing speed, motor, and verbal fluency. No age x benzodiazepine interactive effects on NCI were present. Findings suggest that the relationship between BZD use and NCI among PWH is specific to those with greater medical burden, which may be a greater risk factor for BZD-related NCI than chronological age.

Published
2022

85. Teachers' Perceived Corpus Literacy and Their Intention to Integrate Corpora into Classroom Teaching: A Survey Study

Author

Ma, Qing, Chiu, Ming Ming, Lin, Shanru, and Mendoza, Norman B.

Abstract

Given the importance of corpus linguistics in language learning, there have been calls for the integration of corpus training into teacher education programmes. However, the question of what knowledge and skills the training should target remains unclear. Hence, we advance our understanding of measures and outcomes of teacher corpus training by proposing and testing a five-component theoretical framework for measuring teachers' perceived corpus literacy (CL) and its subskills: understanding, search, analysis, and the advantages and limitations of corpora. Also, we hypothesised that teacher CL is linked to their intention to use corpora in classroom teaching. Specifically, 183 teachers and student teachers received corpus training to develop their CL and then completed a survey to measure their CL and intention to use corpora in teaching in Likert-scale items together with open-ended questions. Confirmatory factor analysis indicated that a hierarchical factor structure for CL using the aforementioned five subfactors best fitted the data. Moreover, structural equation modelling indicated that CL is positively linked to the participants' intention to integrate corpora into classroom teaching. While all five subskills are important for teachers, greater effort should be made to develop their corpus search and analysis skills, which can be viewed as the "bread and butter" of corpus training.

Published
2023
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86. Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution

Author

Ma, Qing, Jiang, Junjun, Liu, Xianming, and Ma, Jiayi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

To solve the ill-posed problem of hyperspectral image super-resolution (HSISR), an usually method is to use the prior information of the hyperspectral images (HSIs) as a regularization term to constrain the objective function. Model-based methods using hand-crafted priors cannot fully characterize the properties of HSIs. Learning-based methods usually use a convolutional neural network (CNN) to learn the implicit priors of HSIs. However, the learning ability of CNN is limited, it only considers the spatial characteristics of the HSIs and ignores the spectral characteristics, and convolution is not effective for long-range dependency modeling. There is still a lot of room for improvement. In this paper, we propose a novel HSISR method that uses Transformer instead of CNN to learn the prior of HSIs. Specifically, we first use the proximal gradient algorithm to solve the HSISR model, and then use an unfolding network to simulate the iterative solution processes. The self-attention layer of Transformer makes it have the ability of spatial global interaction. In addition, we add 3D-CNN behind the Transformer layers to better explore the spatio-spectral correlation of HSIs. Both quantitative and visual results on two widely used HSI datasets and the real-world dataset demonstrate that the proposed method achieves a considerable gain compared to all the mainstream algorithms including the most competitive conventional methods and the recently proposed deep learning-based methods., Comment: 10 pages, 5 figures

Published
2021

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