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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.

This paper focuses on automatically assessing language proficiency levels according to linguistic complexity in learner English. We implement a supervised learning approach as part of an automatic essay scoring system. The objective is to uncover Common European Framework of Reference for Languages (CEFR) criterial features in writings by learners of English as a foreign language. Our method relies on the concept of microsystems with features related to learner-specific linguistic systems in which several forms operate paradigmatically. Results on internal data show that different microsystems help classify writings from A1 to C2 levels (82% balanced accuracy). Overall results on external data show that a combination of lexical, syntactic, cohesive and accuracy features yields the most efficient classification across several corpora (59.2% balanced accuracy).

Goal: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15th August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 from cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. The proposed methods (with some time and appropriate clinical testing) could prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in the human body.

Although experimental protein-structure determination usually targets known proteins, chains of unknown sequence are often encountered. They can be purified from natural sources, appear as an unexpected fragment of a well characterized protein or appear as a contaminant. Regardless of the source of the problem, the unknown protein always requires characterization. Here, an automated pipeline is presented for the identification of protein sequences from cryo-EM reconstructions and crystallographic data. The method's application to characterize the crystal structure of an unknown protein purified from a snake venom is presented. It is also shown that the approach can be successfully applied to the identification of protein sequences and validation of sequence assignments in cryo-EM protein structures.

Zoonotic spillover of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to humans in December 2019 caused the coronavirus disease 2019 (COVID-19) pandemic. Serological monitoring is critical for detailed understanding of individual immune responses to infection and protection to guide clinical therapeutic and vaccine strategies. We developed a high throughput multiplexed SARS-CoV-2 antigen microarray incorporating spike (S) and nucleocapsid protein (NP) and fragments expressed in various hosts which allowed simultaneous assessment of serum IgG, IgA, and IgM responses. Antigen glycosylation influenced antibody binding, with S glycosylation generally increasing and NP glycosylation decreasing binding. Purified antibody isotypes demonstrated a binding pattern and intensity different from the same isotype in whole serum, probably due to competition from the other isotypes present. Using purified antibody isotypes from naïve Irish COVID-19 patients, we correlated antibody isotype binding to different panels of antigens with disease severity, with binding to the S region S1 expressed in insect cells (S1 Sf21) significant for IgG, IgA, and IgM. Assessing longitudinal response for constant concentrations of purified antibody isotypes for a patient subset demonstrated that the relative proportion of antigen-specific IgGs decreased over time for severe disease, but the relative proportion of antigen-specific IgA binding remained at the same magnitude at 5 and 9 months post-first symptom onset. Further, the relative proportion of IgM binding decreased for S antigens but remained the same for NP antigens. This may support antigen-specific serum IgA and IgM playing a role in maintaining longer-term protection, important for developing and assessing vaccine strategies. Overall, these data demonstrate the multiplexed platform as a sensitive and useful platform for expanded humoral immunity studies, allowing detailed elucidation of antibody isotypes response against multiple antigens. This approach will be useful for monoclonal antibody therapeutic studies and screening of donor polyclonal antibodies for patient infusions.

ObjectivesThere has been a steady increase in the number of studies of the complex relationship between glucose and electrical cardiac activity which use simultaneous continuous glucose monitors (CGM) and continuous electrocardiogram (ECG). However, data collected on the same individual tend to be similar (yielding correlated or dependent data) and require analyses that take into account that correlation. Many opt for simplified techniques such as calculating one measure from the data collected and analyse one observation per subject. These simplified methods may yield inconsistent and biased results in some instances. In this systematic review, we aim to examine the adequacy of the statistical analyses performed in such studies and make recommendations for future studies.Research questionsWhat are the objectives of studies collecting simultaneous CGM and ECG data? Do methods used in analysing CGM and continuous ECG data fully optimise the data collected?DesignSystematic review.Data sourcesPubMed and Web of Science.MethodsA comprehensive search of the PubMed and Web of Science databases to June 2022 was performed. Studies utilising CGM and continuous ECG simultaneously in people with diabetes were included. We extracted information about study objectives, technologies used to collect data and statistical analysis methods used for analysis. Reporting was done following PRISMA guidelines.ResultsOut of 118 publications screened, a total of 31 studies met the inclusion criteria. There was a diverse array of study objectives, with only two studies exploring the same exposure-outcome relationship, allowing only qualitative analysis. Only seven studies (23%) incorporated methods which fully utilised the study data using methods that yield the correct power and minimize type I error rate. The rest (77%) used analyses that summarise the data first before analysis and/or totally ignored data dependency. Of those who applied more advanced methods, one study performed both simple and correct analyses and found that ignoring data structure resulted in no association whilst controlling for repeated measures yielded a significant relationship.ConclusionMost studies underutilised statistical methods suitable for analysis of dynamic continuous data, potentially attenuating their statistical power and overall conclusions. We recommend that aggregated data be used only as exploratory analysis, while primary analysis should use methods applied to the raw data such as mixed models or functional data analyses. These methods are widely available in many free, open source software applications.

Purpose: To assess objective strain and subjective muscle soreness in "Bigs" (offensive and defensive line), "Combos" (tight ends, quarterbacks, line backers, and running backs), and "Skills" (wide receivers and defensive backs) in American college football players during off-season, fall camp, and in-season phases. Methods: Twenty-three male players were assessed once weekly (3-wk off-season, 4-wk fall camp, and 3-wk in-season) for hydroperoxides (free oxygen radical test [FORT]), antioxidant capacity (free oxygen radical defense test [FORD]), oxidative stress index (OSI), countermovement-jump flight time, Reactive Strength Index (RSI) modified, and subjective soreness. Linear mixed models analyzed the effect of a 2-within-subject-SD change between predictor and dependent variables. Results: Compared to fall camp and in-season phases, off-season FORT (P ≤.001 and <.001), FORD (P ≤.001 and <.001), OSI (P ≤.001 and <.001), flight time (P ≤.001 and <.001), RSI modified (P ≤.001 and <.001), and soreness (P ≤.001 and <.001) were higher for "Bigs," whereas FORT (P ≤.001 and <.001) and OSI (P =.02 and <.001) were lower for "Combos." FORT was higher for "Bigs" compared to "Combos" in all phases (P ≤.001,.02, and.01). FORD was higher for "Skills" compared with "Bigs" in off-season (P =.02) and "Combos" in-season (P =.01). OSI was higher for "Bigs" compared with "Combos" (P ≤.001) and "Skills" (P =.01) during off-season and to "Combos" in-season (P ≤.001). Flight time was higher for "Skills" in fall camp compared with "Bigs" (P =.04) and to "Combos" in-season (P =.01). RSI modified was higher for "Skills" during off-season compared with "Bigs" (P =.02) and "Combos" during fall camp (P =.03), and in-season (P =.03). Conclusion: Off-season American college football training resulted in higher objective strain and subjective muscle soreness in "Bigs" compared with fall camp and during in-season compared with "Combos" and "Skills" players. [ABSTRACT FROM AUTHOR]