Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts

International Undiagnosed Diseases Programs (UDPs): components and outcomes

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program

A trapped field of 17.6 T in melt-processed, bulk Gd-Ba-Cu-O reinforced with shrink-fit steel

The ability of large grain, REBa$_{2}$Cu$_{3}$O$_{7-\delta}$ [(RE)BCO; RE = rare earth] bulk superconductors to trap magnetic field is determined by their critical current. With high trapped fields, however, bulk samples are subject to a relatively large Lorentz force, and their performance is limited primarily by their tensile strength. Consequently, sample reinforcement is the key to performance improvement in these technologically important materials. In this work, we report a trapped field of 17.6 T, the largest reported to date, in a stack of two, silver-doped GdBCO superconducting bulk samples, each of diameter 25 mm, fabricated by top-seeded melt growth (TSMG) and reinforced with shrink-fit stainless steel. This sample preparation technique has the advantage of being relatively straightforward and inexpensive to implement and offers the prospect of easy access to portable, high magnetic fields without any requirement for a sustaining current source.The ability of large-grain (RE)Ba2Cu3O7−δ ((RE)BCO; RE = rare earth) bulk superconductors to trap magnetic fields is determined by their critical current. With high trapped fields, however, bulk samples are subject to a relatively large Lorentz force, and their performance is limited primarily by their tensile strength. Consequently, sample reinforcement is the key to performance improvement in these technologically important materials. In this work, we report a trapped field of 17.6 T, the largest reported to date, in a stack of two silver-doped GdBCO superconducting bulk samples, each 25 mm in diameter, fabricated by top-seeded melt growth and reinforced with shrink-fit stainless steel. This sample preparation technique has the advantage of being relatively straightforward and inexpensive to implement, and offers the prospect of easy access to portable, high magnetic fields without any requirement for a sustaining current source.This is the final published version, distributed under a Creative Commons Attribution License. This can also be found on the publisher's website at: http://iopscience.iop.org/0953-2048/27/8/08200

‘Advocacy groups are the connectors’: Experiences and contributions of rare disease patient organization leaders in advanced neurotherapeutics

Introduction: Biomedical progress has facilitated breakthrough advanced neurotherapeutic interventions, whose potential to improve outcomes in rare neurological diseases has increased hope among people with lived experiences and their carers. Nevertheless, gene, somatic cell and other advanced neurotherapeutic interventions carry significant risks. Rare disease patient organizations (RDPOs) may enhance patient experiences, inform expectations and promote health literacy. However, their perspectives are understudied in paediatric neurology. If advanced neurotherapeutics is to optimize RDPO contributions, it demands further insights into their roles, interactions and support needs. Methods: We used a mixed-methodology approach, interviewing 20 RDPO leaders representing paediatric rare neurological diseases and following them up with two online surveys featuring closed and open-ended questions on advanced neurotherapeutics (19/20) and negative mood states (17/20). Qualitative and quantitative data were analysed using thematic discourse analysis and basic descriptive statistics, respectively. Results: Leaders perceived their roles to be targeted at educational provision (20/20), community preparation for advanced neurotherapeutic clinical trials (19/20), information simplification (19/20) and focused research pursuits (20/20). Although most leaders perceived the benefits of collaboration between stakeholders, some cited challenges around collaborative engagement under the following subthemes: conflicts of interest, competition and logistical difficulties. Regarding neurotherapeutics, RDPO leaders identified support needs centred on information provision, valuing access to clinician experts and highlighting a demand for co-developed, centralized, high-level and understandable, resources that may improve information exchange. Leaders perceived a need for psychosocial support within themselves and their communities, proposing that this would facilitate informed decision-making, reduce associated psychological vulnerabilities and maintain hope throughout neurotherapeutic development. Conclusion: This study provides insights into RDPO research activities, interactions and resource needs. It reveals a demand for collaboration guidelines, central information resources and psychosocial supports that may address unmet needs and assist RDPOs in their advocacy. Patient or Public Contribution: In this study, RDPO leaders were interviewed and surveyed to examine their perspectives and roles in advanced neurotherapeutic development. Some participants sent researchers postinterview clarification emails regarding their responses to questions

“I am not a number!” Opinions and preferences of people with intellectual disability about genetic healthcare

There is limited research exploring the knowledge and experiences of genetic healthcare from the perspective of people with intellectual disability. This study, conducted in New South Wales (Australia), addresses this gap. Eighteen adults with intellectual disability and eight support people were interviewed in this inclusive research study. The transcribed interviews were analysed using inductive content analysis. The findings were discussed in a focus group with ten adults with intellectual disability and in three multi-stakeholder advisory workshops, contributing to the validity and trustworthiness of the findings. Five main themes emerged: (i) access to genetic healthcare services is inequitable, with several barriers to the informed consent process; (ii) the experiences and opinions of people with intellectual disability are variable, including frustration, exclusion and fear; (iii) genetic counselling and diagnoses can be profoundly impactful, but translating a genetic diagnosis into tailored healthcare, appropriate support, peer connections and reproductive planning faces barriers; (iv) people with intellectual disability have a high incidence of exposure to trauma and some reported that their genetic healthcare experiences were associated with further trauma; (v) recommendations for a more respectful and inclusive model of genetic healthcare. Co-designed point-of-care educational and consent resources, accompanied by tailored professional education for healthcare providers, are required to improve the equity and appropriateness of genetic healthcare for people with intellectual disability

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

Bottom mixed layer oxygen dynamics in the Celtic Sea

The seasonally stratified continental shelf seas are highly productive, economically important environments which are under considerable pressure from human activity. Global dissolved oxygen concentrations have shown rapid reductions in response to anthropogenic forcing since at least the middle of the twentieth century. Oxygen consumption is at the same time linked to the cycling of atmospheric carbon, with oxygen being a proxy for carbon remineralisation and the release of CO2. In the seasonally stratified seas the bottom mixed layer (BML) is partially isolated from the atmosphere and is thus controlled by interplay between oxygen consumption processes, vertical and horizontal advection. Oxygen consumption rates can be both spatially and temporally dynamic, but these dynamics are often missed with incubation based techniques. Here we adopt a Bayesian approach to determining total BML oxygen consumption rates from a high resolution oxygen time-series. This incorporates both our knowledge and our uncertainty of the various processes which control the oxygen inventory. Total BML rates integrate both processes in the water column and at the sediment interface. These observations span the stratified period of the Celtic Sea and across both sandy and muddy sediment types. We show how horizontal advection, tidal forcing and vertical mixing together control the bottom mixed layer oxygen concentrations at various times over the stratified period. Our muddy-sand site shows cyclic spring-neap mediated changes in oxygen consumption driven by the frequent resuspension or ventilation of the seabed. We see evidence for prolonged periods of increased vertical mixing which provide the ventilation necessary to support the high rates of consumption observed

Intrauterine exposures, pregnancy estrogens and breast cancer risk: where do we currently stand?

Since 1990, when a hypothesis on intrauterine influences on breast cancer risk was published, several studies have provided supportive, indirect evidence by documenting associations of birth weight and other correlates of the prenatal environment with breast cancer risk in offspring. Recent results from a unique cohort of women with documented exposure to diethylstilbestrol in utero have provided direct evidence in support of a potential role of pregnancy oestrogens on breast cancer risk in offspring

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.</jats:p