Multiple Levels Research Articles

The role of developmental caregiving programming in modulating our affiliation tendency and the vulnerability to social anxiety and eating disorders.

Attachment is the evolutionarily-established process through which humans create bonds with others to receive care from them. The phenomenon is as essential to our physical survival as it is to our psychological development. An increasing number of studies demonstrates that in sensitive periods during the early years of life, our brain circuitry is programmed in the interactions with our caregivers, with the imprinting of information over multiple attachment dimensions. Adopting a basic brain-computer analogy, we can think of this knowledge as the psycho-social firmware of our mind. According to a recently proposed extension of the classical three-dimensional view, one attachment dimension - somaticity - concerns the caregiver's task of reflecting and confirming the child's (internal) states - such as sensations, emotions, and representations - to support the child's ability to identify and define those entities autonomously. Relying on multidisciplinary evidence - from neuroscientific, developmental, evolutionary, and clinical sources - we suggest that somaticity (H1) has the adaptive function to modulate our tendency to comply and affiliate with a reference group but also (H2) increases the vulnerability to developing Social Anxiety (SA) and Eating Disorders (EDs). We evaluate H1-H2, (1) indicating the evolutionary role of somaticity in modulating our affiliation tendency to optimize the ancestral threat-opportunity balance coming from infectious diseases and (2) showing the deep connection between SA-EDs and the features most closely related to somaticity - interoception and parenting style. Finally, we discuss three relevant implications of H1-H2: (A) Bringing into research focus the adaptive role of our firmware knowledge system versus the hardware (neural substrate) and software (higher cognition) ones. (B) Complementing the well-grounded Objectification and Allocentric Lock Theories, allowing us to integrate multiple levels of explanation on the etiology of psychopathology. (C) Suggesting the design of new psychological treatments. While not aiming to prove H1-H2, our analysis supports them and encourages their direct testing.

Medical student experiences of equality, diversity, and inclusion: content analysis of student feedback using Bronfenbrenner’s ecological systems theory

BackgroundIssues relating to equality, diversity, and inclusion (EDI) significantly impact on medical student achievement and wellbeing. Interventions have been introduced at curricular and organisational levels, yet progress in addressing these issues remains limited. Timely evaluation is needed to assess effectiveness of interventions, and to explore issues and interactions in learning environments impacting on student experience. We introduced an anonymous question concerning students’ experiences of EDI into routine online student feedback questionnaires, to scope the nature of ongoing issues and develop greater understanding of students’ experiences in our programme environment. Ecological systems theory, which conceptualizes learning as a function of complex social interactions, determined by characteristics of individual learners and their environment, provides a framework for understanding.MethodsFree-text responses regarding experiences of EDI gathered over 20 months from all programme years (n = 760) were pooled for analysis, providing a holistic overview of experiences in the learning environment. A counting exercise identified broad categories reported by students. Content analysis of the qualitative dataset was undertaken. Bronfenbrenner’s ecological systems theory was applied as a framework to demonstrate interdependencies between respondents’ experiences and environments, and associated impacts.ResultsThree hundred and seventy-six responses were received relating to wide-ranging EDI issues, most frequently gender or ethnicity. Responses mapped onto all areas of the ecological systems model, with frequent links between subsystems, indicating considerable complexity and interdependencies. Interpersonal interactions and associated impacts like exclusion were frequently discussed. Differential experiences of EDI-related issues in medical school compared to clinical settings were reported. Impacts of institutional leadership and wider societal norms were considered by respondents. Respondents discussed their need for awareness of EDI with reference to future professional practice.ConclusionsImplementation of a regular free-text evaluation question allowed data-gathering across cohorts and throughout several stages of the curriculum, illuminating student experience. Connections established demonstrated intersectionality, and how environment and other factors interact, impacting on student experiences. Students experience EDI-related issues on multiple levels within the educational environment, with consequent impacts on learning. Any successful approach towards tackling issues and promoting equity of opportunity for all requires multi-level actions and widespread culture change. Students can offer fresh and distinct perspectives regarding change needed, to complement and diversify perspectives provided by staff and organisational leadership. Student voice should be enabled to shape change.

Effects of altered gravity on growth and morphology in Wolffia globosa implications for bioregenerative life support systems and space-based agriculture

Understanding the response of plants to varied gravitational conditions is vital for developing effective food production in space bioregenerative life support systems. This study examines the impact of altered gravity conditions on the growth and morphological responses of Wolffia globosa (commonly known as “water lentils” or “duckweed”), assessing its potential as a space crop. Although an experiment testing the effect of simulated microgravity on Wolffia globosa has been previously conducted, for the first time, we investigated the effect of multiple gravity levels on the growth and morphological traits of Wolffia globosa plants. The plant responses to simulated microgravity, simulated partial gravity (Moon), and hypergravity environments were evaluated using random positioning machines and the large-diameter centrifuge. As hypothesized, we observed a slight reaction to different gravitational levels in the growth and morphological traits of Wolffia globosa. The relative growth rates (RGR) of plants subjected to simulated microgravity and partial gravity were reduced when compared to those in other gravity levels. The morphological analysis revealed differences in plant dimensions and frond length-to-width ratios under diverse gravity conditions. Our findings showed that Wolffia globosa is responsive to gravitational changes, with its growth and morphological adaptations being slightly influenced by varying gravitational environments. As for other crop species, growth was reduced by the microgravity conditions; however, RGR remained substantial at 0.33 a day. In conclusion, this study underscores the potential of Wolffia globosa as a space crop and its adaptability to diverse gravitational conditions, contributing to the development of sustainable food production and bioregenerative life support systems for future space exploration missions.

Reflecting on gamified learning in medical education: a systematic literature review grounded in the Structure of Observed Learning Outcomes (SOLO) taxonomy 2012—2022

BackgroundThe acquisition of in-depth medical knowledge, skills, and competencies is of utmost importance when training future medical professionals. This systematic literature review delves into the empirical connection between gamified learning in medical education and the Structure of Observed Learning Outcomes (SOLO) taxonomy.MethodsFollowing PRISMA guidelines, a systematic literature review was conducted in seven databases to identify empirical studies related to gamification and medical education. The literature search was limited to peer-reviewed articles published between January 2012 and December 2022. Articles focusing on games or learning technologies in a broader sense as well as research focusing on nursing or pharmacy education were excluded.ResultsUpon reviewing 23 qualified empirical studies that applied gamified learning strategies in medical education, 18 (78%) studies are associated with the second lowest level (uni-structural) of the SOLO taxonomy. The mid-level (multi-structural) learning outcomes are associated with three (13%) of the reviewed studies. There are five (22%) studies focusing on the second highest (relational) level of the SOLO. Only one study (4%) is associated with the highest SOLO level (extended-abstract). Finally, three (13%) studies were identified with multiple levels of the SOLO. In addition to the SOLO levels, the review found six (26%) studies emphasizing motivational and engagement support of gamified learning strategies in facilitating intended learning outcome attainment. A total of three (13%) studies, across three SOLO levels, suggested that gamified learning strategies can improve students’ soft skills in medical education programs.ConclusionThese findings collectively emphasize the need for future research and development to design gamified learning experiences capable of fostering higher SOLO taxonomy attainment in medical education. Moreover, there is potential to extend the SOLO framework to encompass motivational and affective learning outcomes, providing a comprehensive understanding of the impact of gamified learning on medical students.

Collaboration as organizational empowerment: a network perspective

ABSTRACT Inter-organizational collaboration is central to Organizational Empowerment theory. It enables organizations to strengthen their capacity and to take collective action to achieve empowerment at multiple levels. Despite the growing significance of inter-organizational collaboration in addressing complex issues in our society, little is known about what facilitates or hinders collaboration between local organizations involved with community development in a neighborhood. By applying theories of organizational networks and Institutional Collective Action Framework (ICA), and Exponential Random Graph Modeling, this study analyzes the collaboration networks between 167 local organizations in Chicago in 2016. The results suggest that these local organizations are more likely to collaborate when they are the same type of organization and have a more diverse board of directors. It was also found that organizations built a bonding structure to cope with a challenging period for neighborhood organizations in Chicago. Overall, this study contributes to the understanding of empowerment literature by extending ICA and organizational network theories and a statistical approach to network analysis to describe mechanisms of organizational empowerment.

Dynamic changes in LINC00458/HBL1 lncRNA expression during hiPSC differentiation to cardiomyocytes

Long non-coding RNAs (lncRNAs) constitute the largest and most diverse class of non-coding RNAs. They localize to the nucleus, cytoplasm, or both compartments, and regulate gene expression through various mechanisms at multiple levels. LncRNAs tend to evolve faster and present higher tissue- and developmental stage-specific expression than protein-coding genes. Initially considered byproducts of erroneous transcription without biological function, lncRNAs are now recognized for their involvement in numerous biological processes, such as immune response, apoptosis, pluripotency, reprogramming, and differentiation. In this study, we focused on Heart Brake lncRNA 1 (HBL1), a lncRNA recently reported to modulate the process of pluripotent stem cell differentiation toward cardiomyocytes. We employed RT-qPCR and high-resolution RNA FISH to monitor the expression and localization of HBL1 during the differentiation progression. Our findings indicate a significant increase in HBL1 expression during mesodermal and cardiac mesodermal stages, preceding an anticipated decrease in differentiated cells. We detected the RNA in discrete foci in both the nucleus and in the cytoplasm. In the latter compartment, we observed colocalization of HBL1 with Y-box binding protein 1 (YB-1), which likely results from an interaction between the RNA and the protein, as the two were found to be coimmunoprecipitated in RNP-IP experiments. Finally, we provide evidence that HBL1, initially reported as an independent lncRNA gene, is part of the LINC00458 (also known as lncRNA-ES3 or ES3) gene, forming the last exon of some LINC00458 splice isoforms.

‘Athletes’ participation in the National Anti-Doping Organisations of Germany and Poland: democratic governance?’

ABSTRACT Athletes’ participation in sports governance gains momentum at multiple levels and challenges the long-prevailing power relations in organised sport. At the same time, the sport-specific discourse on good governance extends to the field of anti-doping, following low levels of testing effectiveness, untransparent decision-making and ethical misconduct in leading anti-doping authorities. Adopting a case-oriented comparative approach between the National Anti-Doping Organisations (NADOs) of Germany (NADA) and Poland (POLADA), two consecutive data collection steps were applied in mixed-methods design to assess and compare the status quo, and to discuss the future development of athletes’ participation in anti-doping through the lens of democratic processes in good governance research. First, document analysis showed important similarities and differences between the organisations’ approaches to athletes’ participation. Overall, NADA implements a more democratic and transparent approach than POLADA. Second, expert interviews revealed three key issues in relation to democratic forms of athletes’ participation in the two NADOs: athletes’ and their representatives’ (limited) personal resources and engagement (individual); an adequate degree of codification and institutionalisation of athletes’ representation on NADOs’ internal bodies (organisational); and NADOs’ operational (in-)dependence (political/systemic). Researchers and practitioners are recommended to further examine how NADOs’ control functions over athletes and athletes’ participation in their decision-making can be adequately balanced as part of aspirations to foster democratic governance in these organisations.

An oncolytic system produces oxygen selectively in pancreatic tumor cells to alleviate hypoxia and improve immune activation

IntroductionHypoxia is one of the important reasons for the poor therapeutic efficacy of current pancreatic cancer treatment, and the dense stroma of pancreatic cancer restricts the diffusion of oxygen within the tumor. MethodsA responsive oxygen-self-supplying adv-miRT-CAT-KR (adv-MCK) cascade reaction system to improve hypoxia in pancreatic cancer is constructed. We utilized various experiments at multiple levels (cells, organoids, in vivo) to investigate its effect on pancreatic cancer and analyzed the role of immune microenvironment changes in it through high-throughput sequencing. ResultsThe adv-MCK system is an oncolytic adenovirus system expressing three special components of genes. The microRNA (miRNA) targets (miRTs) enable adv-MCK to selectively replicate in pancreatic cancer cells. Catalase catalyzes the overexpressed hydrogen peroxide in pancreatic cancer cells to generate endogenous oxygen, which is catalyzed by killerRed to generate singlet oxygen (1O2) and further to enhance the oncolytic effect. Meanwhile, the adv-MCK system can specifically improve hypoxia in pancreatic cancer, exert antitumor effects in combination with photodynamic therapy, and activate antitumor immunity, especially by increasing the level of γδ T cells in the tumor microenvironment. ConclusionThe responsive oxygen-self-supplying adv-MCK cascade reaction system combined with photodynamic therapy can improve the hypoxic microenvironment of pancreatic cancer and enhance antitumor immunity, which provides a promising alternative treatment strategy for pancreatic cancer.

Image-Specific Information Suppression and Implicit Local Alignment for Text-Based Person Search.

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress, and state-of-the-art (SOTA) methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, the existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint multilevel alignment network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression (ISS) module, which suppresses image background and environmental factors by relation-guided localization (RGL) and channel attention filtration (CAF), respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Second, we propose an implicit local alignment (ILA) module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. Also, a global alignment (GA) is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet.

Digital adaptation of the Standing up for Myself intervention in young people and adults with intellectual disabilities: the STORM feasibility study.

Stigma contributes to the negative social conditions persons with intellectual disabilities are exposed to, and it needs tackling at multiple levels. Standing Up for Myself is a psychosocial group intervention designed to enable individuals with intellectual disabilities to discuss stigmatising encounters in a safe and supportive setting and to increase their self-efficacy in managing and resisting stigma. To adapt Standing Up for Myself to make it suitable as a digital intervention; to evaluate the feasibility and acceptability of Digital Standing Up for Myself and online administration of outcome measures in a pilot; to describe usual practice in the context of the coronavirus disease 2019 pandemic to inform future evaluation. Adaptation work followed by a single-arm pilot of intervention delivery. Four third and education sector organisations. Individuals with mild-to-moderate intellectual disabilities, aged 16+, members of existing groups, with access to digital platforms. Digital Standing Up for Myself intervention. Adapted from face-to-face Standing Up for Myself intervention, delivered over four weekly sessions, plus a 1-month follow-up session. Acceptability and feasibility of delivering Digital Standing Up for Myself and of collecting outcome and health economic measures at baseline and 3 months post baseline. Outcomes are mental well-being, self-esteem, self-efficacy in rejecting prejudice, reactions to discrimination and sense of social power. Adaptation to the intervention required changes to session duration, group size and number of videos; otherwise, the content remained largely the same. Guidance was aligned with digital delivery methods and a new group member booklet was produced. Twenty-two participants provided baseline data. The intervention was started by 21 participants (four groups), all of whom were retained at 3 months. Group facilitators reported delivering the intervention as feasible and suggested some refinements. Fidelity of the intervention was good, with over 90% of key components observed as implemented by facilitators. Both facilitators and group members reported the intervention to be acceptable. Group members reported subjective benefits, including increased confidence, pride and knowing how to deal with difficult situations. Digital collection of all outcome measures was feasible and acceptable, with data completeness ≥ 95% for all measures at both time points. Finally, a picture of usual practice has been developed as an intervention comparator for a future trial. The pilot sample was small. It remains unclear whether participants would be willing to be randomised to a treatment as usual arm or whether they could be retained for 12 months follow-up. The target number of groups and participants were recruited, and retention was good. It is feasible and acceptable for group facilitators with some training and supervision to deliver Digital Standing Up for Myself. Further optimisation of the intervention is warranted. To maximise the acceptability and reach of the intervention, a future trial could offer the adapted Digital Standing Up for Myself, potentially alongside the original face-to-face version of the intervention. This study was registered as ISRCTN16056848. This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/149/03) and is published in full in Public Health Research; Vol. 12, No. 1. See the NIHR Funding and Awards website for further award information.

Barriers to and facilitators of successful implementation of a palliative approach to care in primary care practices: a mixed methods study

ObjectiveIntegrating a palliative approach to care into primary care is an emerging evidence-based practice. Despite the evidence, this type of care has not been widely adopted into primary care settings....

Deciphering the relationship between temperature and immunity.

Fever is a hallmark symptom of disease across the animal kingdom. Yet, despite the evidence linking temperature fluctuation and immune response, much remains to be discovered about the molecular mechanisms governing these interactions. In patients with rheumatoid arthritis, for instance, it is clinically accepted that joint temperature can predict disease progression. But it was only recently demonstrated that the mitochondria of stimulated T cells can rise to an extreme 50°C, potentially indicating a cellular source of these localized 'fevers'. A challenge to dissecting these mechanisms is a bidirectional interplay between temperature and immunity. Heat shock response is found in virtually all organisms, activating protective pathways when cells are exposed to elevated temperatures. However, the temperature threshold that activates these pathways can vary within the same organism, with human immune cells, in particular, demonstrating differential sensitivity to heat. Such inter-cellular variation may be clinically relevant given the small but significant temperature differences seen between tissues, ages, and sexes. Greater understanding of how such small temperature perturbations mediate immune responses may provide new explanations for persistent questions in disease such as sex disparity in disease prevalence. Notably, the prevalence and severity of many maladies are rising with climate change, suggesting temperature fluctuations can interact with disease on multiple levels. As global temperatures are rising, and our body temperatures are falling, questions regarding temperature-immune interactions are increasingly critical. Here, we review this aspect of environmental interplay to better understand temperature's role in immune variation and subsequent risk of disease.

Multi-Level Object-Aware Guidance Network for Biomedical Image Segmentation

Most state-of-the-art models for biomedical image segmentation are developed based on U-shape architecture, which has two renowned, yet mutually affected, shortcomings: 1) difficulties in capturing global long-range dependencies, and 2) semantic information dilution in the decoding process. In this paper, we propose a novel network with a new object-aware module (OAM) to effectively establish global dependencies at multiple levels within the network and compensate high-level semantic information dilution when fusing the extracted multi-level features; we call the network MOG-Net. Specifically, the OAM is designed to figure out the relations between each pixel and targeting object region and recalibrate class-level semantic information according to the relations. Compared with non-local models, which construct pixel-wise global dependencies, our OAM is more efficient and target-specific, enabling us to achieve satisfactory results with less extra computational overhead. In addition, we embed a pyramid context encoder module (PCEM) in the proposed OAM to alleviate semantic information dilution; this scheme is able to bridge the spatial-semantic gap when fusing features extracted from different levels. We extensively evaluate the proposed MOG-Net on four diverse biomedical image segmentation tasks with different imaging modalities, achieving segmentation performance with 88.19%, 90.95% and 66.03% in Dice on three one-class datasets, as well as 88.83% and 87.11% in Dice for two classes on a multi-class dataset, respectively. Experimental results demonstrate the effectiveness of the proposed method, consistently outperforming state-of-the-art methods in most evaluation metrics. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Semantic segmentation of biomedical images is a critical prerequisite for subsequent diagnosis, treatment, and quantitative tasks in clinical practice. This article proposes a novel biomedical image segmentation network, namely MOG-Net, with a new object-aware module (OAM) to model global context dependencies from a category perspective and a pyramid context encoder module (PCEM) to enhance feature representation capabilities of spatial and channel dimensions. We experimentally demonstrate the effectiveness and generalization capability of proposed MOG-Net on diverse biomedical image segmentation tasks with different imaging modalities. We believe that our proposed method can serve as a practical clinical tool and has the potential to be applied to existing computer-aided medical systems and clinical measurement.

Virtual-Reality-Based Supermarket for Intellectual Disability Classification, Diagnostics, and Assessment

Possible symptoms of intellectual disability (ID) include delayed physical development that becomes more pronounced as the disability progresses, delayed development of gross and fine motor skills, sensory perception problems, and difficulty grasping the integrity of objects. Although there is no cure or reversal, research has shown that extensive training and learning can lead to easier social integration, but the human demands of diagnosis and the cost of training often result in overburdened families of origin, an unmanageable workload for teachers, and high social costs. Therefore, it is important to conduct efficient, effective, and economical assessments in a safe and reproducible training environment. Currently, the assessment of intellectual disability relies on intelligence tests such as the Wechsler Intelligence Scale (WIS) and the Vineland Adaptive Behavior Scale (VABS). With the rapid development of virtual reality (VR) and machine learning (ML), we created a virtual supermarket and then collected data in three areas, including eye movements, brain waves, and behaviors. We also propose an intelligent executive function evaluation using ML to develop a more objective and automatic evaluation model based on real data through physiological data obtained from user reflections. Statistical analysis of the obtained data showed that some data metrics derived from behavioral information differed significantly between ID patients and healthy participants. This shows that it is possible to perform classification through neural networks, even at multiple levels, which may prove effective for vocational training through VR.

‘I really feel like I am ageing early’: an interpretative phenomenological analysis of the lived experience of young adults with life-limiting conditions (YALLC)

ObjectivesThis study aims to explore the lived experience of young adults with life-limiting conditions (YALLC) and how they perceive and navigate adulthood, to identify challenges and opportunities for improved care...

Context-Aware Attentive Multilevel Feature Fusion for Named Entity Recognition.

In the era of information explosion, named entity recognition (NER) has attracted widespread attention in the field of natural language processing, as it is fundamental to information extraction. Recently, methods of NER based on representation learning, e.g., character embedding and word embedding, have demonstrated promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information, e.g., capitalization, inter-word relations, keywords, and lexical phrases, from multilevel perspectives. Intuitively, multilevel features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel attentive multilevel feature fusion (AMFF) model for NER, which captures the multilevel features in the current context from various perspectives. It consists of four components to, respectively, capture the local character-level (CL), global character-level (CG), local word-level (WL), and global word-level (WG) features in the current context. In addition, we further define document-level features crafted from other sentences to enhance the representation learning of the current context. To this end, we introduce a novel context-aware attentive multilevel feature fusion (CAMFF) model based on AMFF, to fully leverage document-level features from all the previous inputs. The obtained multilevel features are then fused and fed into a bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) network for the final sequence labeling. Extensive experiments on four benchmark datasets demonstrate that our proposed AMFF and CAMFF models outperform a set of state-of-the-art baseline methods and the features learned from multiple levels are complementary.

Clinical practice guideline of the Spanish society of oral surgery for dental care in patients with oral cancer.

Oral cancer is the sixteenth most common malignant neoplasm worldwide, with a high mortality rate, greater than 50% at five years, and high morbidity. The effect of oncological treatment in the oral cavity is broad and has multiple levels, therefore knowing these effects and preventing them is essential for avoiding an increase in the oral pathology related with oncological therapy, maintaining the quality of life of the patient, and improving the efficacy of the treatment itself. A group of experts belonging to the fields of Dentistry, Maxillofacial Surgery and Oncology of the University of Seville and the Virgen del Rocío University Hospital of Seville in collaboration with the University of Valencia, University of Barcelona, and University of the Basque Country, developed this Clinical Practice Guideline for the proper clinical management of patients diagnosed with oral cancer. The clinical questions were formulated in PICO format. The databases consulted were Medline/PubMed and Embase/Elsevier. The systematic reviews published on the topic were identified on Tripdatabase, Cochrane Library and CRD (Centre for Reviews and Dissemination). The recommendations were prepared based on the GRADE methodology. Various recommendations were defined, derived from the 21 PICO questions, referring to prevention, treatment and care for alterations arising from the pathology of oral cancer itself and its treatment. The preparation of this clinical practice guideline allows recommendations to be generated based on the scientific evidence available, on dentistry actions in patients with oral cancer and undergoing oncological treatment, which may be of use to the multidisciplinary team treating this type of patient.

Process improvement in automated blood cultures: Our road to better patient care

BackgroundBlood cultures (BC) are a complex multistep process susceptible to errors at multiple levels. Our study was conducted to monitor the compliance to standard operating procedures, design an intervention plan for improvement and monitor the impact of the intervention. The study design focuses on the intervention. MethodsA hospital-based prospective interventional study was carried out, monitoring various pre-analytical, analytical, and post-analytical factors in two phases. Our interventions focused on communication and training for the deficiencies noted in completeness of test request form, contamination rates, and sample loading interval. ResultsComparison between both phases showed a statistically significant improvement (p < 0.05, Chi square test) following our interventions in the completeness of test request form, contamination rates and sample loading interval. There was an improvement in the intimation of critical Gram stain from positive flagged bottles, which was not statistically significant. ConclusionOur study highlights how educational interventions can influence quality of BC. Continuous monitoring and interventions are key for quality improvement in BC processes.

Establishing a Pregnancy Lyme Disease Biobank.

Availability of relevant biological samples supports both basic science research and patient-centered clinical studies. Establishing a biorepository faces challenges at multiple levels. These tasks include defining mission definition and scope; selection of subjects and sample types; recruitment strategies; timing of collection in the patient's journey; sample logistics and processing; determining what clinical data to collect; ensuring sample integrity on transport, processing, and storage; defining governance structures and oversight responsibilities; clarifying sample provenance and ownership; establishing procedures for sample and data access; selecting testing to be performed routinely versus upon request, and management of results; data security; funding sources; and regulatory compliance. Establishing and maintaining a biorepository therefore requires careful planning, diligent and sustained execution, technical and financial resources, stakeholder support, and flexible and resilient management to respond to changing environments and needs.

Reliable uncertainties of tests and surveys – a data-driven approach

Policy decisions are often motivated by results attained by a cohort of responders to a survey or a test. However, erroneous identification of the reliability or the complimentary uncertainty of the test/survey instrument, will distort the data that such policy decisions are based upon. Thus, robust learning of the uncertainty of such an instrument is sought. This uncertainty is parametrised by the departure from reproducibility of the data comprising responses to questions of this instrument, given the responders. Such departure is best modelled using the distance between the data on responses to questions that comprise the two similar subtests that the given test/survey can be split into. The paper presents three fast and robust ways for learning the optimal-subtests that a given test/survey instrument can be spilt into, to allow for reliable uncertainty of the given instrument, where the response to a question is either binary, or categorical − taking values at multiple levels − and the test/survey instrument is realistically heterogeneous in the correlation structure of the questions (or items); prone to measuring multiple traits; and built of small to a very large number of items. Our methods work in the presence of such messiness of real tests and surveys that typically violate applicability of conventional methods. We illustrate our new methods, by computing uncertainty of three real tests and surveys that are large to very-large in size, subsequent to learning the optimal subtests.