Search

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

Abstract One of the great diagnostic challenges for children with pulmonary arterial hypertension is the need for general anesthesia (GA) to enable successful right heart catheterization. Here, for the first time, we describe how echocardiographic estimates of right ventricular function and pulmonary pressures change in pediatric patients during GA.

Summary: Addressing the mononuclear phagocyte system (MPS) and macrophage M1/M2 activation is important in diagnosing hematological disorders and inflammatory pathologies and designing therapeutic tools. CSF1R is a reliable marker to identify all circulating MPS cells and tissue macrophages in humans using a single surface protein. CSF1R permits the quantification and isolation of monocyte and dendritic cell (DC) subsets in conjunction with CD14, CD16, and CD1c and is stable across the lifespan and sexes in the absence of overt pathology. Beyond cell detection, measuring M1/M2 activation in humans poses challenges due to response heterogeneity, transient signaling, and multiple regulation steps for transcripts and proteins. MPS cells respond in a conserved manner to M1/M2 pathways such as interleukin-4 (IL-4), steroids, interferon-γ (IFNγ), and lipopolysaccharide (LPS), for which we propose an ad hoc modular gene expression tool. Signature analysis highlights macrophage activation mosaicism in experimental samples, an emerging concept that points to mixed macrophage activation states in pathology.

Artificial intelligence (AI) technology in professional practice is regarded as the latest disruption to challenge ethical, societal, economic, and educational paradigms. It is becoming a contemporary narrative in our healthcare and educational discourse as it is thought to improve decision-making, education, patient care, and service delivery. If such benefits are to be realized it is important to ensure that our understanding of AI is underpinned with curricula to educate healthcare professionals about AI. Practitioners can then be better informed to participate fully in digital initiatives in their field. Few if any studies articulate a clear process in curricula design for professional practice and healthcare education. Therefore, an understanding of real-world applications of AI education for most professions is limited. As a result, there is an imperative to establish a sustainable learning space for professional healthcare practice education. This paper presents novel experiences, within a virtual lab environment, for the application of AI in healthcare. We identify and problematize insights into the AI competencies required for AI in professional healthcare practice education. With reference to our novel living lab titled, the Home Health Project, we present an interconnected case study model that suggest theoretical underpinning and opportunities to develop practitioner education pedagogy using AI concepts, concluding with a three-step proposal when delivering AI curriculum for this context.

We report here an assessment of the model refinement category of the 14th round of Critical Assessment of Structure Prediction (CASP14). As before, predictors submitted up to five ranked refinements, along with associated residue-level error estimates, for targets that had a wide range of starting quality. The ability of groups to accurately rank their submissions and to predict coordinate error varied widely. Overall, only four groups out-performed a "naïve predictor" corresponding to the resubmission of the starting model. Among the top groups, there are interesting differences of approach and in the spread of improvements seen: some methods are more conservative, others more adventurous. Some targets were "double-barreled" for which predictors were offered a high-quality AlphaFold 2 (AF2)-derived prediction alongside another of lower quality. The AF2-derived models were largely unimprovable, many of their apparent errors being found to reside at domain and, especially, crystal lattice contacts. Refinement is shown to have a mixed impact overall on structure-based function annotation methods to predict nucleic acid binding, spot catalytic sites, and dock protein structures.

Abstract Background Clinical psychomotor skills training is a core component of undergraduate nursing education. Performing technical skills competently involves the use of cognitive and motor function. The training of these technical skills is typically carried out in clinical simulation laboratories. Peripheral intravenous catheter/cannula insertion is an example of a technical skill. It is the most common invasive procedure performed in the healthcare environment. Owing to unacceptable clinical risk and complications to patients, it is imperative that practitioners performing these skills are trained effectively to provide patients with best practice and high-quality care. Technologies identified as innovative teaching methods to help train students in this skill and in the skill of venepuncture include virtual reality, hypermedia and simulators. However, little high-quality evidence exists to confirm such educational approaches are effective. Methods/design This study is a single-centre, non-blinded, two-group, pre-test and post-test randomised control trial. The randomised control trial will investigate whether a formal structured self-evaluation of videoed performance (experimental group) has an impact on nursing students’ peripheral intravenous cannulation knowledge, performance and confidence. The control group will also be videoed performing the skill but they will not view or self-evaluate their videoed performance. The peripheral intravenous cannulation procedures will be carried out in a clinical simulation laboratory using a task trainer. The data collection tools will be completed online using survey forms. Students will be randomised into the experimental group or into the control group using simple random sampling. The primary outcome measures the nursing students’ knowledge level of the skill of peripheral intravenous cannulation insertion. Secondary outcomes evaluate procedural competence and self-reported confidence and practices in the clinical environment. Discussion This randomised control trial will investigate whether this pedagogical approach, using video modelling and self-evaluation, will positively influence students’ knowledge, confidence and performance in the skill of peripheral intravenous cannulation. Evaluating such teaching strategies using stringent methodologies may be impactful in influencing the training provided to healthcare practitioners. Trial registration The randomised control trial detailed in this article is an educational research study and so does not fall under the ICMJE definition of a clinical trial as “any research project that prospectively assigns people or a group of people to an intervention, with or without concurrent comparison or control groups, to study the relationship between a health-related intervention and a health outcome”.

Abstract In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Abstract Background Acute kidney injury (AKI) is a frequent and severe complication of both COVID-19-related acute respiratory distress syndrome (ARDS) and non-COVID-19-related ARDS. The COVID-19 Critical Care Consortium (CCCC) has generated a global data set on the demographics, management and outcomes of critically ill COVID-19 patients. The LUNG-SAFE study was an international prospective cohort study of patients with severe respiratory failure, including ARDS, which pre-dated the pandemic. Methods The incidence, demographic profile, management and outcomes of early AKI in patients undergoing invasive mechanical ventilation for COVID-19-related ARDS were described and compared with AKI in a non-COVID-19-related ARDS cohort. Results Of 18,964 patients in the CCCC data set, 1699 patients with COVID-19-related ARDS required invasive ventilation and had relevant outcome data. Of these, 110 (6.5%) had stage 1, 94 (5.5%) had stage 2, 151 (8.9%) had stage 3 AKI, while 1214 (79.1%) had no AKI within 48 h of initiating invasive mechanical ventilation. Patients developing AKI were older and more likely to have hypertension or chronic cardiac disease. There were geo-economic differences in the incidence of AKI, with lower incidence of stage 3 AKI in European high-income countries and a higher incidence in patients from middle-income countries. Both 28-day and 90-day mortality risk was increased for patients with stage 2 (HR 2.00, p

DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilizing precious or limited DNA sources. We used a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. We found that data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; and that low DNA input is associated with a reduction in power to detect EWAS associations, requiring larger sample sizes. We conclude that DNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

Biomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We analysed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modelling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions. Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant. Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds.