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Healthcare innovation, exemplified by genomic medicine, requires increasingly sophisticated understanding of the interdisciplinary-organizational context in which new innovations are implemented. Deliberative stakeholder consultations are public engagement tools that are gaining increasing traction in health care, as a means of maximizing the diversity of roles and interests vested in a particular policy or practice issue. They engage participants from different knowledge systems (“cultures”) in mutually respectful debate to enable group consensus on implementation strategies. Current deliberation analytic methods tend to overlook the cultural contexts of the deliberative process. The paper aims to discuss this issue.

This conceptual paper proposes adding ethnographic participant observation to provide a more comprehensive account of the process that gives rise to deliberative outputs. To underpin this conceptual paper, the authors draw on the authors’ experience engaging healthcare professionals during implementation of genomics in the care for pediatric oncology patients with treatment-resistant glioblastoma at two tertiary care hospitals.

Ethnography enabled a deeper understanding of deliberative outcomes by combining rhetorical and non-rhetorical analysis to identify the implementation and coordination of care barriers across professional cultures.

This paper highlights the value of ethnographic methods in enabling a more comprehensive assessment of the quality of engagement across professional cultures in implementation studies.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This chapter presents findings of ethnographic work in a neuro-oncology clinic in Israel. It is claimed that patients, close-ones and physicians engage in creating metaphorical visions of the brain and brain tumours that reaffirm Cartesian dualism. The ‘brain talk’ involved visible and spatial terms and results in a particular kind of objectification of the organ of the self. The overbearing presence of visual media (i.e., magnetic resonance imaging, computed tomography, angiographic studies) further gave rise to particular forms of interactions with patients and physicians where the ‘imageable’ (i.e., the image on the screen) became the ‘imaginable’ (i.e., the metaphor). The images mostly referred to a domain of mundane objects: a meatball in a dish of spaghetti, a topping of olives over a pizza, the surface of the moon, a stone, an egg, an animal, a dark cloud. Furthermore, conversations with family members showed that formal facts and informed compassion were substituted by concrete representations. For them, and especially for the patient, these representations redefined an ungraspable situation, where a tumour – an object – can so easily affect the organ of their subjectivity, into something comprehensible through the materialistic, often mechanistic actions of most mundane objects. This, however, also created alienated objects within the boundaries of their own embodied selves. Patients, on the one hand, did not reject their own sense of ‘own-ness’, of having a lifeworld (lebenswelt) as subjective agents, but on the other, did talk about their own interiors as being an ‘other’: an object visible, observable and imaginable from a third-person standpoint – a standpoint drawing its authority from biomedical epistemology and practice.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

Classifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model.

In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation.

In the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets.

The experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.

The incidence of prostate cancer is increasing from the past few decades. Various studies have tried to determine the survival of patients, but metastatic prostate cancer is still not extensively explored. The survival rate of metastatic prostate cancer is very less compared to the earlier stages. The study aims to investigate the survivability of metastatic prostate cancer based on the age group to which a patient belongs, and the difference between the significance of the attributes for different age groups.

Data of metastatic prostate cancer patients was collected from a cancer hospital in India. Two predictive models were built for the analysis-one for the complete dataset, and the other for separate age groups. Machine learning was applied to both the models and their accuracies were compared for the analysis. Also, information gain for each model has been evaluated to determine the significant predictors for each age group.

The ensemble approach gave the best results of 81.4% for the complete dataset, and thus was used for the age-specific models. The results concluded that the age-specific model had the direct average accuracy of 83.74% and weighted average accuracy of 79.9%, with the highest accuracy levels for age less than 60.

The study developed a model that predicts the survival of metastatic prostate cancer based on age. The study will be able to assist the clinicians in determining the best course of treatment for each patient based on ECOG, age and comorbidities.

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training.

The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.

In this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.

The proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.

The model can be used by doctors to identify the exact location of the tumorous region.

The proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable) risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors). We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders.

Owing to the toxicity, biodegradability, and cost of most corrosion inhibitors, research attention is now focused on the development of environmentally benign, biodegradable, cheap, and efficient options. In consideration of these facts, chrysin, a phytocompound of Populus tomentosa (Chinese white poplar) has been isolated and investigated for its anticorrosion abilities on carbon steel in a mixed acid and chloride system. This highlights the main purpose of the study.

Chrysin was isolated from Populus tomentosa using column chromatography and characterized using Fourier Transform Infrared Spectroscopy and Nuclear Magnetic Resonance Spectroscopy. The investigations are outlined based on theory (Fukui indices, condensed density functional theory and molecular dynamic simulation) and experiments (electrochemical, gravimetry and surface morphology examinations).

Theoretical evaluations permitted the description of the adsorption characteristics, and molecular interactions and orientations of chrysin on Fe substrate. The interaction energy for protonated and neutral chrysin on Fe (110) were −149.10 kcal/mol and −143.28 kcal/mol, respectively. Moreover, experimental investigations showed that chrysin is a potent mixed-type corrosion inhibitor for steel, whose effectiveness depends on its surrounding temperature and concentration. The optimum inhibition efficiency of 78.7% after 24 h for 1 g/L chrysin at 298 K indicates that the performance of chrysin, as a pure compound, compares favorably with other phytocompounds and plant extracts investigated under similar conditions. However, the inhibition efficiency decreased to 62.5% and 51.8% at 318 K after 48 h and 72 h, respectively.

The novelty of this study relies on the usage of a pure compound in corrosion suppression investigation, thus eliminating the unknown influences obtainable by the presence of multi-phytocompounds in plant extracts, thereby advancing the commercialization of bio-based corrosion inhibitors.