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1.
Journal of Osteopathic Medicine ; 14:14, 2022.
Article in English | MEDLINE | ID: covidwho-2029810

ABSTRACT

CONTEXT: Previous studies have examined the changes in the dietary habits of general populations during the COVID-19 pandemic but have not focused on specific populations such as those with chronic medical conditions (CMCs). Prior to major vaccination efforts, 96.1% of deaths were attributed to patients with preexisting CMCs, thus it is important to examine how this population has endured changes. OBJECTIVES: The purpose of this study was to identify differences in dietary habits, lifestyle habits, and food attitudes between those with CMCs compared to the populations without chronic medical conditions (non-CMCs) since the beginning of the COVID-19 pandemic. METHODS: An online cross-sectional study was conducted from May 2021 to July 2021. Participants (n=299) responded to a 58-item questionnaire regarding demographics (n=9), health information (n=8), lifestyle habits (n=7), dietary habits (n=28), and food attitudes (n=6). Frequency counts and percentages were tabulated, and t-test sampling and ANOVA testing were conducted to examine the associations utilizing SPSS V28 at a statistical significance level of p<0.05. RESULTS: When compared to non-CMC participants, with CMCs had a less frequent change in their diet and had better food attitudes when it came to consumption habits. Non-CMC and CMC participants had no statistically significant differences in overall dietary habits;however, an examination of specific food items reviews significant findings. Compared to non-CMC participants, those with CMCs reported significantly decreased consumption of energy-dense food such as French fries, white pasta, sweets, and salty snacks, with notable exceptions in increased consumption of energy-dense foods, starchy veggies, and vegetable/tomato juice. CONCLUSIONS: These findings indicate that participants with CMCs indicated that fewer changes occurred in participants with a CMC;however, when these participants made changes, they were beneficial to their consumption habits. Future studies should aim to develop interventions for the demographics with poor dietary habits so that those that are most vulnerable may have their needs met.

2.
Frontiers in Medicine ; 9:924979, 2022.
Article in English | MEDLINE | ID: covidwho-2022768

ABSTRACT

Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

3.
Breast Cancer Res Treat ; 190(2): 287-293, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1404658

ABSTRACT

PURPOSE: Older cancer survivors required medical care during the COVID-19 pandemic, but there are limited data on medical care in this age group. METHODS: We evaluated care disruptions in a longitudinal cohort of non-metastatic breast cancer survivors aged 60-98 from five US regions (n = 321). Survivors completed a web-based or telephone survey from May 27, 2020 to September 11, 2020. Care disruptions included interruptions in seeing or speaking to doctors, receiving medical treatment or supportive therapies, or filling prescriptions since the pandemic began. Logistic regression models evaluated associations between care disruptions and education, medical, psychosocial, and COVID-19-related factors. Multivariate models included age, county COVID-19 death rates, comorbidity, and post-diagnosis time. RESULTS: There was a high response rate (n = 262, 81.6%). Survivors were 32.2 months post-diagnosis (SD 17.5, range 4-73). Nearly half (48%) reported a medical disruption. The unadjusted odds of care disruptions were higher with each year of education (OR 1.22, 95% CI 1.08-1.37, p = < 0.001) and increased depression by CES-D score (OR 1.04, CI 1.003-1.08, p = 0.033) while increased tangible support decreased the odds of disruptions (OR 0.99, 95% CI 0.97-0.99, p = 0.012). There was a trend between disruptions and comorbidities (unadjusted OR 1.13 per comorbidity, 95% CI 0.99-1.29, p = 0.07). Adjusting for covariates, higher education years (OR1.23, 95% CI 1.09-1.39, p = 0.001) and tangible social support (OR 0.98 95% CI 0.97-1.00, p = 0.006) remained significantly associated with having care disruptions. CONCLUSION: Older breast cancer survivors reported high rates of medical care disruptions during the COVID-19 pandemic and psychosocial factors were associated with care disruptions. CLINICALTRIALS. GOV IDENTIFIER: NCT03451383.


Subject(s)
Breast Neoplasms , COVID-19 , Cancer Survivors , Aged , Aged, 80 and over , Breast Neoplasms/epidemiology , Breast Neoplasms/therapy , Female , Humans , Middle Aged , Pandemics , SARS-CoV-2
5.
5th International Conference on Computing Methodologies and Communication, ICCMC 2021 ; : 947-951, 2021.
Article in English | Scopus | ID: covidwho-1247042

ABSTRACT

The various techniques and algorithms of ML DL are becoming popular for prediction with different level of accuracy. This paper includes performance comparison of few machine learning algorithms in the reference of student social engagement during covid-19 pandemic period. In this study, the comparison of Naïve Bayes, J48 tree, REPTree and Random forest algorithm is carried on structured dataset of 1200+ instance. In this paper, study proposes scrutinizes commonly used social app platform. Further, it compares them with the various ML approach. The objective of this study is to foreseeing the correlation between student social engagement for one the most popular social engagement platform during covid-19 pandemic. This paper focusses on accuracy, F-measure and time to summarize comparison result. The findings of the study and dynamic analysis indicate ML/Deep learning algorithm can lead better accuracy and other factor for preprocessed student social engagement dataset. The finding can predict engagement of students for most popular social media platform with performance comparison of ML algorithm. © 2021 IEEE.

6.
Journal of Hypertension ; 39(SUPPL 1):e205, 2021.
Article in English | EMBASE | ID: covidwho-1240912

ABSTRACT

Objective: Australia is experiencing ever more frequent/provocative weather and environmental challenges, including more extreme heatwaves and catastrophic bushfires. Concurrently, the annual challenge of wintry conditions to a population adapted to warmer conditions persists. Remarkably, however, there are no proven interventions to reduce seasonal challenges to the cardiovascular health of vulnerable individuals. In a world-first, the REsilience to Seasonal ILlness and Increased Emergency admissioNs CarE (RESILIENCE) Trial will test the hypothesis that an individually tailored, intervention program will reduce the risk of re-hospitalisation and mortality in vulnerable individuals. Design and method: 300 adult patients admitted to the Austin Hospital in Melbourne, Australia with heart disease and multimorbidity will be recruited and randomised (1:1) to standard care (SC) or the RESILIENCE program (RP) over 12-months. Applying a COVID-19 adapted protocol, the RP group will have their bio-behavioural profile and home environment assessed post-discharge, to determine their vulnerability to seasonal events. An individualised case-management program, including a virtual clinic review with a dedicated RP cardiac nurse and physician, will be applied to promote seasonal resilience. The primary end-point is all-cause days alive out of hospital (DAOH) during 12-month follow-up. Results: With study recruitment delayed due to COVID-19 restrictions, virtual screening of medical in-patients has confirmed the need and potential for the RP. Of 630 potential participants identified over a 6 week period, 196 patients (31%) met eligibility criteria-85 women and 79 men, mean (±SD) age 79 ± 11 years. Non-eligibility was largely due to non-chronic form of heart disease (34%), no comorbidity (23 %), and inability to give informed consent (15%). Conclusions: Preliminary data suggest that once commenced, we will rapidly recruit the requisite number of trial participants and depending on the results, we will be able to determine the cost-effectiveness of the RP to reduce seasonallyinduced admissions and mortality.

7.
Res Sq ; 2021 Apr 14.
Article in English | MEDLINE | ID: covidwho-1200427

ABSTRACT

PurposeOlder cancer survivors required medical care during the COVID-19 pandemic despite infection risks, but there are limited data on medical care in this age group. METHODS: We evaluated care disruptions in a longitudinal cohort of non-metastatic breast cancer survivors ages 60-98 from five US regions (n=321). Survivors completed a web-based or telephone survey from May 27, 2020 to September 11, 2020. Care disruptions included self-reported interruptions in ability to see doctors, receive treatment or supportive therapies, or fill prescriptions. Logistic regression models evaluated bivariate and multivariate associations between care disruptions and education, medical, psychosocial and COVID-19-related factors. Multivariate models included age, county COVID-19 rates, comorbidity and post-diagnosis time. RESULTS: There was a high response rate (n=262, 81.6%). Survivors were 32.2 months post-diagnosis (SD 17.5, range 4-73). Nearly half (48%) reported a medical disruption. The unadjusted odds of care disruptions were significantly higher with more education (OR 1.23 per one-year increase, 95% CI 1.09-1.39, p =0.001) and greater depression (OR 1.04 per one-point increase in CES-D score, CI 1.003-1.08, p=0.033); tangible support decreased the odds of disruptions (OR 0.99, 95% CI 0.97-0.99 per one-point increase, p=0.012). There was a trend for associations between disruptions and comorbidity (unadjusted OR 1.13 per 1 added comorbidity, 95% CI 0.99-1.29, p=0.07). Adjusting for covariates, only higher education (p=0.001) and tangible social support (p=0.006) remained significantly associated with having care disruptions. CONCLUSIONS: Older breast cancer survivors reported high rates of medical care disruptions during the COVID-19 pandemic and psychosocial factors were associated with care disruptions.

8.
Journal of Pure and Applied Microbiology ; 14(3):1663-1674, 2020.
Article in English | EMBASE | ID: covidwho-891731

ABSTRACT

Severe acute respiratory syndrome coronavirus – 2 (SARS-CoV-2), an emerging novel coronavirus causing coronavirus disease 2019 (COVID-19) pandemic, has now rapidly spread to more than 215 countries and has killed nearly 0.75 million people out of more than 20 million confirmed cases as of 10th August, 2020. Apart from affecting respiratory system, the virus has shown multiple manifestations with neurological affections and damaging kidneys. SARS-CoV-2 transmission mainly occurs through close contact of COVID-19 affected person, however air-borne route is also now considered as dominant route of virus spread. The virus has been implicated to have originated from animals. Apart from bats, pangolins and others being investigates to play role in transmitting SARS-CoV-2 as intermediate hosts, the recent reports of this virus infection in other animals (cats, dogs, tigers, lions, mink) suggest one health approach implementation along with adopting appropriate mitigation strategies. Researchers are pacing to develop effective vaccines and drugs, few reached to clinical trials also, however these may take time to reach the mass population, and so till then adopting appropriate prevention and control is the best option to avoid SARS-CoV-2 infection. This article presents an overview on this pandemic virus and the disease it causes, with few recent concepts and advances.

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