Conventional Analysis Research Articles

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo</i>, and ex vivo</i> experiments. As a result, numerous in silico</i> computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

All Good Without Anything Good. Beyond Survival: Understanding the Psychosocial Experiences of Individuals With Chronic Kidney Disease and Their Caregivers in Sri Lanka.

This study aims to explore the experiences of individuals with chronic kidney disease (CKD) undergoing haemodialysis and their caregivers, focusing on the disease's impact and the treatment process. In Sri Lanka, CKD is a growing health concern, particularly affecting farming communities and contributing to the strain on the biomedical healthcare system. Despite increasing awareness of CKD's physical implications, its psychosocial impact remains underexplored. This study seeks to fill this gap, aiming to inform culturally sensitive interventions and improve the healthcare system's responsiveness to the unique needs of Sinhala Buddhist individuals with CKD and their caregivers. An exploratory qualitative study. Semistructured interviews were conducted with 10 individuals undergoing haemodialysis and 5 caregivers at a dialysis unit. The interviews were audio-recorded, transcribed and analysed using conventional qualitative content analysis. The analysis revealed three interrelated main themes: (1) impact on standard of living (quality of life), (2) coping strategies and (3) medical experience, with a notable influence of traditional beliefs and practices. The findings highlight the need for a holistic approach to CKD management that integrates physical, emotional, psychological and social aspects, considering the significant role of traditional influences. Further research is essential to develop effective interventions that can enhance the quality of life for CKD. The lived experiences of Sinhala Buddhist individuals with CKD and their caregivers served as a cornerstone, providing profound insights into the impact of the condition on their lives. Throughout the study, these participants played an instrumental role in refining the research's cultural sensitivity and relevance. Their engagement extended beyond the data collection phase to encompass feedback sessions, where they actively shared their perspectives. This ongoing collaboration ensured the study's depth and applicability to real-world experiences. By actively involving those directly affected by CKD, this collaborative approach safeguards that the study remains rooted in their voices and addresses their unique needs. This study adhered to relevant EQUATOR guidelines (the COREQ checklist). This study is not a clinical trial, and thus, registration is not applicable.

Stability and instability nature of solitons in an optical fiber with four wave mixing effect

Abstract The investigation into modulational instability (MI) within the Kundu-Eckhaus (KE) equation, governing optical solitons, involves a thorough examination of the effects of self-phase modulation, cross-phase modulation, and intermodal dispersion. Special attention is given to understanding the influence of the four-wave mixing effect. The KE equation, which models birefringent fiber and includes terms related to intermodal dispersion, cross-phase modulation, and self-phase modulation, serves as the fundamental framework for this analytical study. Employing conventional linear stability analysis, the gain within the KE equation is determined. To shed light on the role of four-wave mixing in various scenarios, the gain spectrum is utilized as a tool to analyze the behavior of the KE equation under different conditions. This methodology seeks to provide insightful information about the intricate interactions that impact the modulational instability of solitonic pulses in an optical systems. After that, we have investigated the soliton solution by implementing the Jacobian elliptical function approach. Finally, our focus here is on linear stability analysis, which employs eigenvalue spectra to study solitons' stability via direct numerical simulation.&amp;#xD;

Tree knowledge structure for better insight: Capturing biomedical science-technology knowledge linkage with MeSH

Measuring the knowledge linkage between science and technology (S&T) is crucial for understanding the interactions between S&T and assisting decision-makers in strategizing research and development investments. Conventional analyses of S&T knowledge linkage have frequently overlooked the semantic structure of knowledge elements thereby introducing biases in the measurements. To address this issue, this study introduces a novel method predicated on the tree semantic structure, which quantifies the S&T linkage by considering the hierarchy and category of knowledge elements within an ontological framework. In this method, knowledge trees are constructed to represent the core knowledge of S&T literature, incorporating hierarchically organized MeSH descriptors. These knowledge trees are subsequently utilized to measure the knowledge linkage between S&T by integrating intra-branch knowledge similarity and inter-branch knowledge distribution. An empirical analysis was conducted on a substantial corpus of scientific publications and patents within the biomedicine sector. The findings predominantly revealed a stronger knowledge linkage between S&T in recent years, relative to the early 2000 s. It was also observed that patents are more inclined to include broader concepts in their titles and abstracts, in contract to the more specific concepts found in scientific publications. S&T literatures have increasingly focused on knowledge related to diseases, equipment, and health care. To verify the reliability of the proposed method, validation was performed with alternative measurements of knowledge linkage. In comparison to single-feature-based linkage measurements and network-based approaches, our proposed method demonstrates superior adaptability in capturing S&T linkage, especially when there is a marked disparity in the sample sizes of S&T literature. This study not only enriches the measurements of S&T knowledge linkage, but also furnishes empirical insights into the evolving patterns of S&T linkage within the biomedical domain.

Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling.

Quantum Cascade Laser Infrared Spectroscopy for Glycan Analysis of Glycoprotein Solutions.

Glycans are oligosaccharides attached to proteins or lipids and affect their functions, such as drug efficacy, structural contribution, metabolism, immunogenicity, and molecular recognition. Conventional glycosylation analysis has relied on destructive, slow, system-sensitive methods, including enzymatic reactions, chromatography, fluorescence labeling, and mass spectrometry. Herein, we propose quantum cascade laser (QCL) infrared (IR) spectroscopy as a rapid, nondestructive method to quantify glycans and their monosaccharide composition. Previously, we demonstrated high-sensitivity IR spectroscopy of protein solution using solvent absorption compensation (SAC) and double-beam modulation (DBM) techniques. However, the SAC-DBM approach suffered a limited frequency scanning range (<400 cm-1) due to the light dispersion by acousto-optic modulators (AOMs). Here, we implemented a mirror-based double-pass AOM in the SAC-DBM scheme and successfully extended the frequency range to (970 to 1840 cm-1), which encompasses the vibrational fingerprint of biomolecules. The extended frequency range allowed the simultaneous observation of monosaccharide ring bands (1000 to 1200 cm-1) and protein amide bands (1500 to 1700 cm-1). We compared the IR spectra of six glycoproteins and two nonglycosylated proteins with the results from intact mass spectrometry. The IR absorbance ratios of the ring band to the amide band of glycoproteins in solutions showed a linear correlation with the ratios of glycan to protein backbone masses. Furthermore, a multivariate analysis produced monosaccharide compositions consistent with the reported database for the glycoproteins, and the monosaccharide compositions were used to improve the predictability of the glycan-protein mass ratio from the IR-absorbance ratio. This nondestructive, high-sensitivity QCL-IR spectroscopy could be used as a standard method to monitor batch-to-batch comparability during drug manufacturing and quantify the glycosylation and monosaccharide composition of new glycoproteins and other glycosylated biosystems.

Patient-delivered partner therapy for chlamydia: health practitioner views on updated guidance in Victoria, Australia.

Background Patient-delivered partner therapy (PDPT) involves providing a prescription or medication to a patient diagnosed with chlamydia to pass to their sexual partner/s. Barriers to PDPT include uncertainty about its integration into clinical practice and permissibility. In Victoria, Australia, the Department of Health provides clinical guidance for PDPT (updated in 2022). We explored health practitioner views on the usefulness of the updated guidance for providing PDPT. Methods We conducted an online survey (12 December 2022 to 2 May 2023) of health practitioners who primarily work in Victoria and can prescribe to treat chlamydia. The survey displayed excerpts from the guidance, and asked closed and free-text questions about its ability to address barriers to PDPT. Quantitative data were descriptively analysed, complemented by conventional content analysis of qualitative data. Results Of a total of 49 respondents (66.7% general practitioners), 74.5% were aware of PDPT, and 66.7% had previously offered PDPT. After viewing excerpts of the guidance, >80% agreed it could support them to identify patients eligible/ineligible for PDPT, and 66.7% indicated they would be comfortable to offer PDPT. The guidance was viewed as helpful to address some barriers, including complicated documentation (87.7%) and medico-legal concerns (66.7%). Qualitative data highlighted medico-legal concerns by a minority of respondents. Some raised concerns that the guidance recommended prescribing azithromycin, despite doxycycline being first-line chlamydia treatment. Conclusions The guidance was largely viewed as supportive for PDPT decision-making. There is scope for further refinements and clarifications, and wider dissemination of the guidance.

Spatio‐temporal differences in the diet and trophic ecology of Kelp Gulls (Larus dominicanus) in South Africa

Opportunistic species, including some gulls (Laridae), can benefit from urbanization and increased anthropogenic food resources. Knowledge of the level of exploitation of anthropogenic resources by gulls is crucial to understand how changes in food availability might affect their populations, which in turn may impact other species. The Kelp Gull Larus dominicanus is widely distributed in the southern hemisphere and consumes a wide variety of resources, from waste in landfills to seabirds. We assessed if proximity to landfills influenced the diet and trophic ecology of Kelp Gull incubating adults and chicks using a combination of conventional diet sampling (stomach contents, regurgitated pellets), and carbon and nitrogen stable isotope analysis of blood plasma in seven colonies over two consecutive years in South Africa. Kelp Gulls consumed a variety of resources, including marine (e.g. fish), coastal (e.g. molluscs), terrestrial natural (e.g. insects, mammals), or terrestrial anthropogenic (e.g. chicken, bread) items. Inter‐annual differences were most apparent among gulls breeding next to a seabird colony, whereas gulls breeding at other colonies were more consistent in diet and trophic ecology. At most colonies, chicks were fed more natural and higher trophic level food items than were consumed by incubating adults. Overall, distance to landfill did not strongly affect the diet and trophic ecology and our results suggest that the Kelp Gull's broad feeding ecology allows it to buffer changes in food availability by switching among food resources. However, such dietary changes triggered by human actions may impact other species, including threatened seabirds.

Quantitative Hydrogen-Deuterium Exchange Mass Spectrometry for Simultaneous Structural Characterization and Affinity Indexing of Single Target Drug Candidate Libraries.

Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients (KD) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict KD from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.

Anthocyanin Profiles in the Tropical Fruits Eugenia jambolana and Inga edulis: A Comparative Study Using Paper Spray Ionization (PSI-MS), Tissue Spray Ionization (TSI-MS), and Direct Infusion (DI-MS).

Paper spray ionization (PSI-MS) and tissue spray ionization (TSI-MS) mass spectrometry are simple and rapid ambient ionization mass spectrometry techniques that offer numerous advantages over conventional analysis methods. This study aims to analyze the efficiency of detecting anthocyanins from Eugenia jambolana fruit peel and Inga edulis seeds using PSI-MS, TSI-MS, and DI-MS (direct infusion). DI-MS exhibited high efficiency, detecting all compounds in abundance, with anthocyanins malvidin 3,5-O-diglucoside (1) and petunidin 3,5-O-diglucoside (2) being the most prevalent. PSI-MS, however, struggled to detect delphinidin 3-O-glucoside and showed lower abundances for compounds 1, 2, 3 (delphinidin 3,5-O-diglucoside), and 4 (petunidin 3-O-glucoside) compared to DI-MS, attributed to the technique's challenges with molecular weight and polarity. TSI-MS was least effective, detecting only compounds 1, 2, and 3 at low intensities. The overall unique compounds identified across techniques were 134, emphasizing the importance of comprehensively employing multiple methods to analyze anthocyanins in these edible plants.

Challenges and Barriers to Commercialization of Health Sciences Research Results: A Qualitative Study

Background: In today's world, due to intense competition and the rapid pace of production, exploiting knowledge and converting it into economic returns has become a critical management issue for academics and capital owners. Commercialization activities can impact the educational and research programs of universities, potentially leading to resistance against them. Objectives: The current study was conducted to identify the challenges of the commercialization process in health science research. Methods: This qualitative study was conducted between June and December 2022 in Tehran province, Iran. Data were collected using semi-structured interviews with 22 key individuals, including university management and technology experts, as well as CEOs and experts from knowledge-based companies, selected through purposive sampling. The interview guide was designed based on four in-depth interviews, theoretical foundations, and comparative study findings. The obtained data were analyzed using the conventional content analysis method in MAXQDA 10 software. Results: The challenges of the commercialization process for health sciences research results were categorized into six main themes and 16 sub-themes. The main themes identified in this study were rules and regulations, societal culture, university management and infrastructure, human resources, financial systems, and organizational cooperation with industry. Conclusions: Policymakers, especially senior health managers, can create a suitable platform for the optimal use of resources and the expansion of targeted relationships between universities and industries by incorporating economic insight into academic services. By compiling relevant laws and guidelines, optimizing resource management, achieving financial independence for universities, and increasing the productivity of health research, the high costs associated with the commercialization of academic research projects can be reduced.

Exploring the impact of thiol collectors system on copper sulfide flotation through machine learning-driven modeling

Collector selection is a critical step in flotation, as it has a direct impact on product quality, flotation recovery, and selectivity. Collectors can consist of different components, and their effectiveness can vary depending on the type of ore being processed. The general practice in both literature and in industry is to use a mixture of collectors rather than a single collector. However, the use of a collector mixture introduces several complex issues. It is challenging to determine the specific effects of each collector on different minerals, as well as to understand the synergistic effects of mixed collectors in flotation. This study presents a novel investigation focusing on the impact of blends of NAX, AEROPHINE® 3422, and AERO® MX 5149, in varying dosages and combinations, on the flotation performance of Kupferschiefer copper ore. Kinetics flotation tests were conducted using a mechanical flotation cell with various combinations and dosages of listed collectors. For this investigation, different predictive models such as machine-learning (ML) and conventional regression analyses were developed. For model construction, a database including the results of comprehensive experimental results was constructed. The best performing model was selected considering statistical performance indicators and their performance on unseen data. A sensitivity analysis was conducted on the model to justify contributions of collectors on the copper recovery and grade. The results showed that the ML-based models provide compatible results with the expert opinions and have higher statistical performance than conventional modelling tools. According to the experimental results and models’ findings, it has shown that AEROPHINE® 3422 (a blend of isopropyl ethyl thionocarbamate and dithiophosphinate) was the most influential collector for the copper recovery. In addition, two ternary graphs were generated from the modeled data to formulate mixtures for different grades and recovery priorities.

Assessment of panel performance in CATA and RATA experiment

AbstractCATATIS is a recently developed method for the analysis of multiple binary datasets such as Check‐All‐That‐Apply (CATA) data. CATATIS yields a homogeneity index reflecting the overall agreement among the panelists and weights associated with the panelists that highlight the extent to which they agree with the general point of view. These weights are used to compute an average group configuration, which is submitted to Correspondence Analysis to derive a perceptual product map. Here, CATATIS is extended to situations where the data are not binary. In particular, we focus on the case of data from a CATA experiment with repetitions and data from a Rate‐All‐That‐Apply (RATA) experiment. Furthermore, a hypothesis‐testing framework based on permutations is set up to assess the significance of the weights associated with the panelists. Finally, the general strategy of analysis is illustrated using data from real case studies pertaining to CATA and RATA experiments.Practical ApplicationsEvaluating panel performance is often important in sensory studies, especially in industrial contexts. While many professionals are familiar with evaluating the outputs of conventional descriptive analysis, the increasing adoption of rapid sensory methods calls for methods and tools tailored to different sensory tasks. Our paper introduces an approach (CATATIS) to enhance the interpretation of the outputs of CATA and RATA tasks, focusing on attribute consistency, overall panel agreement, and individual weights associated with panelists. The approach is demonstrated through concrete case studies. CATATIS, together with its extensions, are available in XLSTAT software and in the R package ClustBlock.

Cerebral perfusion metrics calculated directly from a hypoxia-induced step change in deoxyhemoglobin

Resting cerebral perfusion metrics can be calculated from the MRI ΔR2* signal during the first passage of an intravascular bolus of a Gadolinium-based contrast agent (GBCA), or more recently, a transient hypoxia-induced change in the concentration of deoxyhemoglobin ([dOHb]). Conventional analysis follows a proxy process that includes deconvolution of an arterial input function (AIF) in a tracer kinetic model. We hypothesized that the step reduction in magnetic susceptibility accompanying a step decrease in [dOHb] that occurs when a single breath of oxygen terminates a brief episode of lung hypoxia permits direct calculation of relative perfusion metrics. The time course of the ΔR2* signal response enables both the discrimination of blood arrival times and the time course of voxel filling. We calculated the perfusion metrics implied by this step signal change in seven healthy volunteers and compared them to those from conventional analyses of GBCA and dOHb using their AIF and indicator dilution theory. Voxel-wise maps of relative cerebral blood flow and relative cerebral blood volume had a high spatial and magnitude congruence for all three analyses (r > 0.9) and were similar in appearance to published maps. The mean (SD) transit times (s) in grey and white matter respectively for the step response (7.4 (1.1), 8.05 (1.71)) were greater than those for GBCA (2.6 (0.45), 3.54 (0.83)) attributable to the nature of their respective calculation models. In conclusion we believe these calculations of perfusion metrics derived directly from ΔR2* have superior merit to calculations via AIF by virtue of being calculated from a direct signal rather than through a proxy model which encompasses errors inherent in designating an AIF and performing deconvolution calculations.

CSF liquid biopsy by low-pass whole genome sequencing for clinical disease monitoring in pediatric embryonal tumors

Abstract Background Liquid biopsy assays that detect cell-free DNA (cfDNA) in cerebrospinal fluid (CSF) are a promising tool for disease monitoring in pediatric patients with primary central nervous system (CNS) tumors. As a compliment to tissue-derived molecular analyses, CSF liquid biopsy has the potential to transform risk stratification, prognostication, and precision medicine approaches. Methods In this pilot study, we evaluated a clinical pipeline to determine feasibility and sensitivity of low-pass whole genome sequencing (LP-WGS) of CSF-derived cfDNA from patients with CNS embryonal tumors. Thirty-two longitudinal CSF samples collected from 17 patients with molecularly characterized medulloblastoma (12), embryonal tumor with multilayered rosettes (2), CNS embryonal tumor, NEC (2), and atypical teratoid/rhabdoid tumor (1) were analyzed. Results Adequate CSF-derived cfDNA for LP-WGS analysis was obtained in 94% of samples (30/32). Copy number variants compatible with neoplasia were detected in 90% (27/30) and included key alterations, such as isodicentric ch17, monosomy 6, and MYCN amplification, among others. Compared to tissue specimens, LP-WGS detected additional aberrations in CSF not previously identified in corresponding primary tumor specimens, suggesting a more comprehensive profile of tumor heterogeneity and evolution of cfDNA profiles over time. Among the 12 CSF samples obtained at initial staging, only 2 (17%) were cytologically positive, compared to 11 (92%) that were copy number positive by LP-WGS. Conclusions LP-WGS of CSF-derived cfDNA is feasible using a clinical platform, with greater sensitivity for tumor detection compared to conventional CSF cytologic analysis. Large prospective studies are needed to further evaluate LP-WGS as a predictive biomarker.

Nursing educators' experiences of cultural competence in the nursing education program: A qualitative descriptive study

BackgroundTo meet the population's increasing diversity and the health system's needs, it is necessary to prepare nursing schools to produce culturally qualified students. PurposeThis study aimed to extract the experiences of nursing educators about the cultural competence of nursing students. MethodIn this qualitative descriptive study, data were collected through in-depth, semi-structured interviews with 15 nursing educators affiliated with three medical sciences universities in southeastern Iran. Purposive data sampling and analysis were performed using Graneheim and Lundman's conventional content analysis methods. ResultsOne main theme, 5 categories, and 18 subcategories were extracted. The main theme was “cultural equality, the essence of care and education”. Categories included “Toward culturally-based caring and education”, “Charter of cultural rights”, “The need for cultural competence facilitators”, “Cultural exposure”, and “Modifying the curriculum policies”. ConclusionThis study shows that nursing educators strive to take into account students' cultural competence, even though cultural competence is not explicitly integrated into nursing curricula. The acquisition of cultural competence requires multifaceted changes in education, in clinical centers, and in the creation of cultural infrastructure.

Point-of-care personalized rapid diagnosis of allergies using peptide epitopes and SPR multiplexed detection

Allergy is the most common chronic disease in Europe. Up to 20% of patients with allergies live with a severe debilitating form of their condition, struggling with the fear of a possible asthma attack, anaphylactic shock, or even death from an allergic reaction. Allergic reactions vary among different patients, due to the presence of different antibodies, which may also define the severity of the allergic reaction. Diagnostic tools are still suboptimal, as they require an oral food challenge for diagnostic, which is personnel, time, and cost consuming and bear the risk of severe allergic reactions for the patient. Also, there is a major gap in food allergy diagnosis to easily distinguish between sensitization and allergy.Therefore, a fast point-of-care multiplexed analytical platform for allergy assessment, supporting low-risk minimally invasive personalized diagnostics and treatment monitoring is advanced. The novel portable Surface Plasmon Resonance system integrates microfluidics and a low-fouling, multiple-ligand biosensor based on recently identified peptide epitopes for appraising allergen-specific serum antibodies. Aiming at personalized diagnosis of allergy, we successfully tested and validated the platform on hazelnuts and peanuts, two of the most threatening and frequent food allergens. Moreover, in contrast to the conventional SPR analysis and ELISA, by using an innovative analysis of the entire sensing area where the peptides are immobilized, it was even possible to discern different sera related to the same allergen.

Filling in the blanks. A method to infer the substructure membership and dynamics of 5D stars

In the solar neighbourhood, only $ of stars in the survey have a line-of-sight velocity ( contained within the RVS catalogue. These limitations restrict conventional dynamical analysis, such as finding and studying substructures in the stellar halo. We aim to present and test a method to infer a probability density function (PDF) for the missing of a star with 5D information within $2.5\ kpc $. This technique also allows us to infer the probability that a 5D star is associated with the Milky Way's stellar Disc or the stellar Halo, which can be further decomposed into known stellar substructures. We use stars from the DR3 RVS catalogue to describe the local orbital structure in action space. The method is tested on a 6D DR3 RVS sample and a 6D sample crossmatched to ground-based spectroscopic surveys, stripped of their true The stars predicted membership probabilities, and inferred structure properties are then compared to the true 6D equivalents, allowing the method's accuracy and limitations to be studied in detail. Our predicted PDFs are statistically consistent with the true with accurate uncertainties. We find that the of Disc stars can be well-constrained, with a median uncertainty of $26\ Halo stars are typically less well-constrained with a median uncertainty of $72\ but those found likely to belong to Halo substructures can be better constrained. The dynamical properties of the total sample and subgroups, such as distributions of integrals of motion and velocities, are also accurately recovered. The group membership probabilities are statistically consistent with our initial labelling, allowing high-quality sets to be selected from 5D samples by choosing a trade-off between higher expected purity and decreasing expected completeness. We have developed a method to estimate 5D stars' and substructure membership. We have demonstrated that it is possible to find likely substructure members and statistically infer the group's dynamical properties.

COVID-19 clinical trials: who is likely to participate and why?

Aim: To identify factors associated with willingness to participate in a COVID-19 clinical trial and reasons for and against participating. Materials & methods: We surveyed Massachusetts (MA, USA)residents online using the Dynata survey platform and via phone using random digit dialing between October andNovember 2021. Respondents were asked to imagine they were hospitalized with COVID-19 and invited to participate in a treatment trial. We assessed willingness to participate by asking, "Which way are you leaning" and why. We used multivariate logistic regression to model factors associated with leaning toward participation. Open-ended responses were analyzed using conventional content analysis. Results: Of 1071 respondents, 65.6% leaned toward participating. Multivariable analyses revealed college-education (OR: 1.59; 95% CI: 1.11, 2.27), trust in the healthcare system (OR: 1.32; 95% CI: 1.10, 1.58) and relying on doctors (OR: 1.77; 95% CI: 1.45, 2.17) and family or friends (OR: 1.31; 95% CI: 1.11, 1.54) to make health decisions were significantly associated with leaning toward participating. Respondents with lower health literacy (OR: 0.57; 95% CI: 0.36, 0.91) and who identify as Black (OR: 0.40; 95% CI: 0.24, 0.68), Hispanic (OR: 0.61; 95% CI: 0.38, 0.98), or republican (OR: 0.61; 95% CI: 0.38, 0.97) were significantly less likely to lean toward participating. Common reasons for participating included helping others, benefitting oneself and deeming the study low risk. Common reasons for leaning against were deeming the study high risk, disliking experimental treatments and not wanting to be a guinea pig. Conclusion: Our finding that vulnerable individuals and those with lower levels of trust in the healthcare system are less likely to be receptive to participating in a COVID-19 clinical trial highlights that work is needed to achieve a healthcare system that provides confidence to historically disadvantaged groups that their participation in research will benefit their community.

Identified Needs in Pediatric Education for Emergency Medicine Physicians: A Qualitative Analysis.

General emergency medicine (EM) physicians provide most pediatric emergency care in the United States, yet EM physicians feel underprepared to manage pediatric emergencies. Pediatric emergency medicine (PEM) education during EM residency is variable, and learner preferences regarding educational experiences have not been widely explored through a qualitative lens. We aimed to better describe EM physicians' PEM educational needs and preferred teaching methods. In 2021, as part of a survey querying senior EM residents and recent graduates from 8 diverse EM programs regarding perceived preparedness for PEM emergencies, educational needs and content delivery methods were assessed using 2 free-text questions. Qualitative analysis included deidentification and iterative coding of the data with double coding of 100% of the comments. We performed conventional content analysis of responses to identify emerging themes. The overall response rate for the survey was 53% (N = 129 out of 242 eligible participants) with 84 distinct free-text responses. Major themes included: 1) desire for education regarding neonates, infants, and critically ill children, especially airway management and 2) need for help translating lessons from PEM rotations to community EM practice. Respondents desired more autonomy during training. Their preferred PEM educational modality was simulation, and they appreciated online clinical pathways for just-in-time decision support. This qualitative study of EM physicians proximal to training adds to a prior needs assessment by describing in detail desired pediatric content and preferred delivery. The findings can be used to better inform the development of PEM curricula for this group of EM physicians.