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Revista Da Escola de Enfermagem Da U S P 2024To characterize the perceptions and feelings of parents diagnosed with cancer in relation to communication with their children between 3 and 12 years old.
OBJECTIVE
To characterize the perceptions and feelings of parents diagnosed with cancer in relation to communication with their children between 3 and 12 years old.
METHOD
A cross-sectional, multicenter, with data triangulation, through structured and semi-structured interviews, with a question with a Semantic Differential Scale, carried out with the father or mother with cancer undergoing outpatient treatment in two hospital institutions in the city of São Paulo, São Paulo, Brazil. Data were analyzed using descriptive statistics, content analysis, using the ATLAS.ti 8.0R software and the Social Representation Theory.
RESULTS
Forty-three respondents participated, 37 (86.0%) were female, 23 (53.5%) aged between 31 and 50 years old, 29 (67.5%) with only children between 7 and 12 years old. The experience was considered painful (73.1%), stressful (53.6%), clear (53.7%) and safe (51.2%). The feelings experienced generated two categories: Trial by fire; and Grateful rewards. Children's reactions from parents' perspective generated the categories: Sadness and suffering; Trust and support; Change of behavior; and Denial or insensitivity.
CONCLUSION
Communication was assessed as negative and conflicting, positive and welcoming, and causing changes in children's behaviors.
Topics: Child; Humans; Female; Adult; Middle Aged; Child, Preschool; Male; Cross-Sectional Studies; Brazil; Parents; Neoplasms; Communication
PubMed: 38373186
DOI: 10.1590/1980-220X-REEUSP-2023-0079en -
European Journal of Ageing Aug 2023
Correction: The role of semantic assessment in the differential diagnosis between late‑life depression and Alzheimer's disease or amnestic mild cognitive impairment: systematic review and meta‑analysis.
PubMed: 37612530
DOI: 10.1007/s10433-023-00783-w -
Scientific Reports Jul 2023MRI images used in breast cancer diagnosis are taken in a lying position and therefore are inappropriate for reconstructing the natural breast shape in a standing...
MRI images used in breast cancer diagnosis are taken in a lying position and therefore are inappropriate for reconstructing the natural breast shape in a standing position. Some studies have proposed methods to present the breast shape in a standing position using an ordinary differential equation of the finite element method. However, it is difficult to obtain meaningful results because breast tissues have different elastic moduli. This study proposed a multi-class semantic segmentation method for breast tissues to reconstruct breast shapes using U-Net based on Haar wavelet pooling. First, a dataset was constructed by labeling the skin, fat, and fibro-glandular tissues and the background from MRI images taken in a lying position. Next, multi-class semantic segmentation was performed using U-Net based on Haar wavelet pooling to improve the segmentation accuracy for breast tissues. The U-Net effectively extracted breast tissue features while reducing image information loss in a subsampling stage using multiple sub-bands. In addition, the proposed network is robust to overfitting. The proposed network showed a mIOU of 87.48 for segmenting breast tissues. The proposed networks demonstrated high-accuracy segmentation for breast tissue with different elastic moduli to reconstruct the natural breast shape.
Topics: Humans; Semantics; Elastic Modulus; Fibromyalgia; Magnetic Resonance Imaging; Product Labeling; Image Processing, Computer-Assisted
PubMed: 37474633
DOI: 10.1038/s41598-023-38557-0 -
Explore (New York, N.Y.) 2023This study aimed to investigate the effect of short-term inhalation of fir essential oil on autonomic nervous activity in middle-aged women. Twenty-six women (mean age,... (Clinical Trial)
Clinical Trial
This study aimed to investigate the effect of short-term inhalation of fir essential oil on autonomic nervous activity in middle-aged women. Twenty-six women (mean age, 51.0 ± 2.9 years) participated in this study. The participants sat on a chair, closed their eyes, and inhaled fir essential oil and room air (control) for 3 min. A crossover trial was performed to eliminate the effect of the order of olfactory stimulation. Approximately half of the participants were administered stimuli in the following order: exposure to fir essential oil, then control. The remaining participants were administered control, followed by fir essential oil. Heart rate variability, heart rate, blood pressure, and pulse rate were used as indicators of the autonomic nervous system activity. The Semantic Differential method and Profile of Mood States were used as psychological indicators. The High Frequency (HF) value, an indicator of parasympathetic nerve activity reflecting a relaxed state, was significantly higher during stimulation with fir essential oil than during the control condition. The Low Frequency (LF)/(LF+HF) value, an indicator of sympathetic nerve activity reflecting awake state, was marginally lower during stimulation with fir essential oil than during the control condition. No significant differences were found in heart rate, blood pressure, and pulse rate. After inhaling fir essential oil, "comfortable," "relaxed," and "natural" feelings improved, negative moods decreased, and positive moods increased. In conclusion, inhalation of fir essential oil can help menopausal women in their physiological and psychological relaxation.
Topics: Female; Humans; Middle Aged; Affect; Autonomic Nervous System; Emotions; Heart Rate; Oils, Volatile; Parasympathetic Nervous System; Cross-Over Studies
PubMed: 37120331
DOI: 10.1016/j.explore.2023.04.006 -
Frontiers in Neuroscience 2024Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical,...
BACKGROUND
Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).
METHODS
Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.
RESULTS
The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.
CONCLUSION
In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.
PubMed: 38384484
DOI: 10.3389/fnins.2024.1331677 -
Diagnostics (Basel, Switzerland) Sep 2023Convolutional neural network (CNN) models have been extensively applied to skin lesions segmentation due to their information discrimination capabilities. However, CNNs'...
Convolutional neural network (CNN) models have been extensively applied to skin lesions segmentation due to their information discrimination capabilities. However, CNNs' struggle to capture the connection between long-range contexts when extracting deep semantic features from lesion images, resulting in a semantic gap that causes segmentation distortion in skin lesions. Therefore, detecting the presence of differential structures such as pigment networks, globules, streaks, negative networks, and milia-like cysts becomes difficult. To resolve these issues, we have proposed an approach based on semantic-based segmentation (Dermo-Seg) to detect differential structures of lesions using a UNet model with a transfer-learning-based ResNet-50 architecture and a hybrid loss function. The Dermo-Seg model uses ResNet-50 backbone architecture as an encoder in the UNet model. We have applied a combination of focal Tversky loss and IOU loss functions to handle the dataset's highly imbalanced class ratio. The obtained results prove that the intended model performs well compared to the existing models. The dataset was acquired from various sources, such as ISIC18, ISBI17, and HAM10000, to evaluate the Dermo-Seg model. We have dealt with the data imbalance present within each class at the pixel level using our hybrid loss function. The proposed model achieves a mean IOU score of 0.53 for streaks, 0.67 for pigment networks, 0.66 for globules, 0.58 for negative networks, and 0.53 for milia-like-cysts. Overall, the Dermo-Seg model is efficient in detecting different skin lesion structures and achieved 96.4% on the IOU index. Our Dermo-Seg system improves the IOU index compared to the most recent network.
PubMed: 37761291
DOI: 10.3390/diagnostics13182924 -
Language and Speech Nov 2023Constructed languages, frequently invented to support world-building in fantasy and science fiction genres, are often intended to sound similar to the characteristics of...
Constructed languages, frequently invented to support world-building in fantasy and science fiction genres, are often intended to sound similar to the characteristics of the people who speak them. The aims of this study are (1) to investigate whether some fictional languages, such as Orkish whose speakers are portrayed as villainous, are rated more negatively by listeners than, for example, the Elvish languages, even when they are all produced without emotional involvement in the voice; and (2) to investigate whether the rating results can be related to the sound structure of the languages under investigation. An online rating experiment with three 7-point semantic differential scales was conducted, in which three sentences from each of 12 fictional languages (Neo-Orkish, Quenya, Sindarin, Khuzdul, Adûnaic, Klingon, Vulcan, Atlantean, Dothraki, Na'vi, Kesh, ʕuiʕuid) were rated, spoken by a female and a male speaker. The results from 129 participants indicate that Klingon and Dothraki do indeed sound more unpleasant, evil, and aggressive than the Elvish languages Sindarin and Quenya. Furthermore, this difference in rating is predicted by certain characteristics of the sound structure, such as the percentage of non-German sounds and the percentage of voicing. The implications of these results are discussed in relation to theories of language attitude.
PubMed: 38018568
DOI: 10.1177/00238309231202944 -
Journal of Neurology May 2024Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of...
Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of this study was to investigate the volumetric alterations of the amygdala subregions in FTD phenotypes and how they evolve with disease progression. Patients clinically diagnosed with behavioral variant FTD (bvFTD) (n = 20), semantic dementia (SD) (n = 20), primary nonfluent aphasia (PNFA) (n = 20), Alzheimer's disease (AD) (n = 20), and 20 matched healthy controls underwent whole brain structural MRI. The patient groups were followed up annually for up to 3.5 years. Amygdala nuclei were segmented using FreeSurfer, corrected by total intracranial volumes, and grouped into the basolateral, superficial, and centromedial subregions. Linear mixed effects models were applied to identify changes in amygdala subregional volumes over time. At baseline, bvFTD, SD, and AD displayed global amygdala volume reduction, whereas amygdala volume appeared to be preserved in PNFA. Asymmetrical amygdala atrophy (left > right) was most pronounced in SD. Longitudinally, SD and PNFA showed greater rates of annual decline in the right basolateral and superficial subregions compared to bvFTD and AD. The findings provide comprehensive insights into the differential impact of FTD pathology on amygdala subregions, revealing distinct atrophy patterns that evolve over disease progression. The characterization of amygdala subregional involvement in FTD and their potential role as biomarkers carry substantial clinical implications.
Topics: Amygdala; Frontotemporal Dementia; Female; Middle Aged; Aged; Organ Size; Time Factors; Longitudinal Studies; Cross-Sectional Studies; Magnetic Resonance Imaging; Disease Progression; Atrophy; Primary Progressive Nonfluent Aphasia; Alzheimer Disease
PubMed: 38265470
DOI: 10.1007/s00415-023-12172-5 -
Intensive & Critical Care Nursing Jun 2024This study aimed to investigate the voice use of nurses working in intensive care units (ICUs) and their perception of acoustic environments.
OBJECTIVE
This study aimed to investigate the voice use of nurses working in intensive care units (ICUs) and their perception of acoustic environments.
SETTING AND SAMPLE
The research was conducted in four different hospitals in China during the COVID-19 pandemic. A total of 60 ICU nurses were recruited for their voice use monitoring and 100 nurses participated in the survey.
RESEARCH METHODOLOGY
Firstly, voice-related parameters such as voice level (SPL, dB), fundamental frequency (F0, Hz), and voicing time percentage (Dt, %) were measured using a vocal monitor. To collect data, a non-invasive accelerometer was attached to the participants' necks during their working hours. Secondly, the perception of the ICU acoustic environment was assessed using semantic differential.
RESULTS
The results showed that nurses spoke approximately 0.9-4 dB louder to patients and colleagues in ICUs compared to quiet rooms, and their fundamental frequency (F0) significantly increased during work. The voice levels of nurses were influenced by background noise levels, with a significant correlation coefficient of 0.44 (p < 0.01). Furthermore, the background noise levels ranged from 58.1 to 73.9 dBA, exceeding the guideline values set by the World Health Organisation (WHO). The semantic differential analysis identified 'Stress' and 'Irritation' as the two main components, indicating the prevalence of negative experiences within ICUs.
IMPLICATIONS FOR CLINICAL PRACTICE
This study highlights the potential risk of voice disorders among ICU nurses. The findings also underscore the importance of implementing strategies to reduce noise levels in ICUs to reduce voice disorders among nurses.
Topics: Humans; Phonation; COVID-19; Pandemics; Voice Disorders; Intensive Care Units; Nurses
PubMed: 38232571
DOI: 10.1016/j.iccn.2023.103620 -
A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops.Frontiers in Plant Science 2024Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing...
Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant . On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene and the polyphenotype gene were verified via a literature search. Furthermore, the wheat gene was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.
PubMed: 38571713
DOI: 10.3389/fpls.2024.1361716