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This article presents findings from a research project that aimed to replace physical books with an adaptive learning platform, Area9 Rhapsode™, for nursing students' class preparation. As the project expanded, it became evident that students employed diverse preparation practices, prompting an investigation into their preparation habits. This is done through the following research questions: (1) how do students feel about class preparation? (2) what learning resources do students choose? (3) when and where do they prepare for class? This is conducted through a mixed methods design that draws on empirical data from interviews, mobile ethnography, surveys, and log data. The results highlight the wide range of resources students utilise, including digital and analogue tools, the various temporal patterns of preparation, and students' spectrum of feelings in their preparation. The findings suggest the importance of understanding individual students' preparation habits to facilitate targeted teaching approaches that cater to diverse learning needs. The article contributes to the existing literature on study habits and strategies, emphasising the need for educators to recognise and adapt to students' unique preparation behaviours in order to enhance their academic success.

RNA structural switches are key regulators of gene expression in bacteria, but their characterization in Metazoa remains limited. Here, we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3' untranslated region of the RORC (RAR-related orphan receptor C) transcript. In vivo dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated messenger RNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

Polysubstance use is associated with myriad short- and long-term health outcomes. Although prior research has documented differences in polysubstance use between lesbian, gay, bisexual, transgender, queer/questioning, and other sexual and gender minoritized (LGBTQ +) youth and their heterosexual/cisgender counterparts, as well as between subgroups of LGBTQ + youth, it is unknown how personal, family, and school factors are associated with substance use patterns among LGBTQ + youth. Using a large, national sample of 9646 LGBTQ + youth ages 13-17, we used latent class analysis to examine patterns of alcohol, tobacco, and marijuana use and to determine whether personal, family, and school factors predict class membership. We identified five classes of substance use: polysubstance use, polysubstance experimentation, dual alcohol and cannabis, alcohol, and no use. Greater depression and LGBTQ + victimization, and an ability to be oneself at school, were associated with greater odds of membership in the polysubstance use class, while higher levels of family connection and having a Gender Sexuality Alliance (GSA) at school were associated with lower odds of membership in the polysubstance use class. Our analysis also revealed sociodemographic differences in class membership. These findings highlight potential mechanisms for intervention to reduce polysubstance use among LGBTQ + youth.

RationaleTreatments with the serotonergic psychedelic psilocybin are being investigated for multiple neuropsychiatric disorders. Because many patients with these disorders use selective serotonin reuptake inhibitors (SSRIs), understanding interactions between psilocybin and SSRIs is critical for evaluating the safety, efficacy, and scalability of psilocybin-based treatments. Current knowledge about these interactions is limited, as most clinical psilocybin research has prohibited concomittant SSRI use.ObjectivesWe aimed to explore potential interactions between psilocybin and SSRIs by characterizing peoples' real-world experiences using psilocybin mushrooms and SSRIs together.MethodsWe conducted a systematic search of Reddit for posts describing psilocybin mushroom and SSRI coadministration. We identified 443 eligible posts and applied qualitative content analysis to each.Results8% of posts reported negative physical or psychological effects resulting from coadministration. These included 13 reports that may reflect serotonin toxicity, and 1 concerning for a psychotic/manic episode. 54% of posts described reduced intensity of the acute psilocybin experience, but 39% reported unchanged intensity with SSRI coadministration.ConclusionsPsilocybin's interactions with SSRIs are likely complex and may depend on multiple factors. Prospective studies are needed to evaluate whether psilocybin treatments are reliably safe and effective in the setting of SSRI use.

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

Despite significant social and legal progress, LGBTQ+ (lesbian, gay, bisexual, transgender, and other sexual and gender minority) populations continue to experience higher rates of mental health and substance use disorders than their heterosexual and cisgender counterparts. Effective LGBTQ+ affirmative mental health care is essential for addressing these disparities but is often limited and difficult to access. The shortage of LGBTQ+ affirmative mental health care providers results from the absence of required and accessible LGBTQ+ -focused training and technical assistance opportunities for mental health care professionals. This study evaluates the implementation of our COVID-19 adapted, completely virtual, organization- and therapist-focused training program to improve the mental health workforce's cultural competence in working with the LGBTQ+ community: the "Sexual and Gender Diversity Learning Community" (SGDLC). Guided by an expanded RE-AIM model, we used administrator and therapist feedback to assess SGDLC implementation factors to understand how it may be best translated for scaled-up promotion and widespread adoption. Assessment of the initial reach, adoption, and implementation of the SGDLC indicated that it had strong feasibility; reports on satisfaction and relevance support the SGDLC's acceptability. Maintenance could not be fully assessed from the short study follow-up period. Still, administrators and therapists expressed an intent to continue their newfound practices, a desire for continued training and technical assistance in this area, but also concerns about finding additional opportunities for this education.

This research paper highlights and addresses the lack of a systematic review of the methods used to evaluate Learning Analytics (LA) and Learning Analytics Dashboards (LAD) of Adaptive Learning Platforms (ALPs) in the current literature. Addressing this gap, the authors built upon the work of Tretow-Fish and Khalid (2022) and analyzed 32 papers, which were grouped into six categories (C1-6) based on their themes. The categories include C1) the evaluation of LA and LAD design and framework, C2) the evaluation of user performance with LA and LAD, C3) the evaluation of adaptivity, C4) the evaluation of ALPs through perceived value, C5) the evaluation of Multimodal methods, and C6) the evaluation of the pedagogical implementation of ALP's LA and LAD. The results include a tabular summary of the papers including the categories, evaluation unit(s), methods, variables and purpose. While there are numerous studies in categories C1-4 that focus on the design, development, and impact assessment of ALP's LA and LAD, there are only a few studies in categories C5 and C6. For the category of C5), very few studies applied any evaluation methods assessing the multimodal features of LA and LADs on ALPs. Especially for C6), evaluating the pedagogical implementation of ALP's LA and LAD, the three dimensions of signature pedagogy are used to assess the level of pedagogy evaluation. Findings showed that no studies focus on evaluating the deep or implicit structure of ALP's LA. All studies examine the structural surface dimension of learning activities and interactions between students, teachers, and ALP's LA and LAD, as examined in categories C2-C5. No studies were exclusively categorized as a C6 category, indicating that all studies evaluate ALP's LA and LAD on the surface structure dimension of signature pedagogy. This review highlights the lack of pedagogical methodology and theory in ALP's LA and LAD, which are recommended to be emphasized in future research and ALP development and implementation.

Over the last two decades, U.S. courts have convicted hundreds of thousands of Latin American defendants for misdemeanor immigration crimes. This has mostly happened through a federal program called Operation Streamline. In that program, immigrants are convicted without any semblance of due process. They are charged with the crime of entering the United States, have a brief conversation with a defense lawyer, plead guilty in a mass plea hearing with up to one hundred defendants at once, and receive their sentence—all in a single court appearance. In 2018, this program encountered its first organized resistance. In that year, the Trump Administration tried to bring Operation Streamline to California for the first time. There, immigrant defendants and their lawyers did not acquiesce to a norm of immediate guilty pleas. Instead, they fought their cases by securing release on bond, raising objections, taking their cases to trial, and appealing their convictions. This unexpected resistance prevented federal prosecutors from processing dozens of cases per day. In 2021, something similar happened in Texas. Governor Greg Abbott created a state-law version of Operation Streamline called Operation Lone Star. Immigrant defendants and their lawyers have resisted this program as well, securing release on bond and fighting through motions, writs, and trials. This Article documents, analyzes, and draws lessons from these immigrants’ defiance. It does so using court records, transcripts, and firsthand accounts. In the process, this Article uncovers the institutional logic of these mass immigrant prosecution systems, which have become a major feature of U.S. immigration policy. It shows how these systems prioritize efficiency above all else, resulting in inferior jail conditions, summary court proceedings, and coerced guilty pleas. In particular, it critiques the role defense lawyers typically play in these systems. Defense lawyers are expected to facilitate these prosecutions by coaching their clients to plead guilty quickly. Their presence gives the proceedings a false legitimacy, as these systems are designed to prevent lawyers from providing competent counsel. As this Article argues, defense lawyers should instead undermine these systems by helping defendants assert their rights and litigate. Indeed, immigrant defendants have powerful incentives to fight their cases if their lawyers will help them. The battles in California and Texas reveal several effective legal strategies for immigrant defendants to resist mass criminalization. They also illustrate how criminal defense lawyers can pursue systemic litigation while honoring their duties to individual clients. The keys are to seek out situations where a defendant can safely assert their procedural rights, and to only waive those rights when doing so benefits the defendant.

Global trends of ocean warming, deoxygenation, and acidification are not easily extrapolated to coastal environments. Local factors, including intricate hydrodynamics, high primary productivity, freshwater inputs, and pollution, can exacerbate or attenuate global trends and produce complex mosaics of physiologically stressful or favorable conditions for organisms. In the California Current System (CCS), coastal oceanographic monitoring programs document some of this complexity; however, data fragmentation and limited data availability constrain our understanding of when and where intersecting stressful temperatures, carbonate system conditions, and reduced oxygen availability manifest. Here, we undertake a large data synthesis to compile, format, and quality-control publicly available oceanographic data from the US West Coast to create an accessible database for coastal CCS climate risk mapping, available from the National Centers for Environmental Information (accession 0277984) at 10.25921/2vve-fh39 (Kennedy et al., 2023). With this synthesis, we combine publicly available observations and data contributed by the author team from synoptic oceanographic cruises, autonomous sensors, and shore samples with relevance to coastal ocean acidification and hypoxia (OAH) risk. This large-scale compilation includes 13.7 million observations from 66 sources and spans 1949 to 2020. Here, we discuss the quality and composition of the synthesized dataset, the spatial and temporal distribution of available data, and examples of potential analyses. This dataset will provide a valuable tool for scientists supporting policy- and management-relevant investigations including assessing regional and local climate risk, evaluating the efficacy and completeness of CCS monitoring efforts, and elucidating spatiotemporal scales of coastal oceanographic variability.

Abstract Cancers evolve not only through the acquisition and clonal transmission of somatic mutations but also by epigenetic mechanisms that modify cell phenotype. Here, we use histology-guided and spatial transcriptomics to characterize hepatoblastoma, a childhood liver cancer that exhibits significant histologic and proliferative heterogeneity despite clonal activating mutations in the Wnt/β-catenin pathway. Highly proliferative regions with embryonal histology show high expression of Wnt target genes, the embryonic biliary transcription factor SOX4, and striking focal expression of the growth factor FGF19. In patient-derived tumoroids with constitutive Wnt activation, FGF19 is a required growth signal for FGF19-negative cells. Indeed, some tumoroids contain subsets of cells that endogenously express FGF19, downstream of Wnt/β-catenin and SOX4. Thus, the embryonic biliary lineage program cooperates with stabilized nuclear β-catenin, inducing FGF19 as a paracrine growth signal that promotes tumor cell proliferation, together with active Wnt signaling. In this pediatric cancer presumed to originate from a multipotent hepatobiliary progenitor, lineage-driven heterogeneity results in a functional growth advantage, a non-genetic mechanism whereby developmental lineage programs influence tumor evolution.

Abstract Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%.

Abstract Introduction To ensure that pre-final year medical students at Stellenbosch University were able to resume clinical training during the COVID-19 pandemic, a 12-week integrated rotation was introduced, during which students were distributed across a widespread training platform in two provinces of South Africa, utilizing a range of health care facilities in both rural and urban areas, rather than the central academic hospital (CAH) in which they would have been doing clerkships. Called the Integrated Distributed Engagement to Advance Learning (IDEAL) rotation, this clerkship was based on supervised engagement in healthcare services, focusing on patient-based clinical training, self-regulated learning and student participation as integral members of clinical teams. The success of this emergency intervention has led to its formal incorporation into the medical curriculum. This study aimed to understand the factors that influenced learning among students undertaking the IDEAL rotation at multiple sites on a distributed training platform. Methods Using an interpretive paradigm, we sought to conduct focus group interviews with students who completed the first iteration of the IDEAL rotation in the year after they had undertaken it to understand their experiences. All 252 students who were eligible were invited to participate by email on several occasions. Ultimately three focus group discussions and two individual interviews were undertaken, based on volunteers. Using a semi-structured interview guide, these explored student perceptions of their learning and growth through the rotation. Inductive and deductive analysis was carried out to identify themes. Findings Student descriptions of their learning experiences coalesced in 6 themes. The rotation was an enabling learning experience, which was more practically focused and assisted students in developing confidence in their clinical skills. It was seen to be a humanizing learning experience with greater opportunities for the development of relationships with patients and families, as well as with health professionals, who made them feel part of the team, so it was also a more collegial learning experience. At the same time, it was a variable learning experience with a lack of standardization on a number of levels and challenges being experienced at particular sites regarding both logistics and the nature of the exposure. Students perceived it to be a very different learning experience from what they had encountered in the CAH in terms of relationships, the kinds of patients and problems they saw, and their active participation. Through this, they also learned more about themselves and their roles, making it a personal learning journey. The findings confirm the importance of the dimensions of person, participation and place for being and becoming a doctor in a clinical environment. Conclusions Student learning experiences in the IDEAL rotation emphasize the importance of context, reinforcing the value of a distributed training platform in developing health professionals who are responsive to their environment. They emphasize the vital role of active participation in learning and the centrality of relationships in medical training, helping to develop graduates who are human beings and not only human doings.

Abstract Localized delivery of inflammasome inhibitors in phagocytic macrophages could be promising for psoriasis treatment. The present work demonstrates the development of non-spherical lipid nanoparticles, mimicking pathogen-like shapes, consisting of an anti-inflammatory inflammasome inhibiting lipid (pyridoxine dipalmitate) as a trojan horse. The nanorods inhibit inflammasome by 3.8- and 4.5-fold compared with nanoellipses and nanospheres, respectively. Nanorods reduce apoptosis-associated speck-like protein and lysosomal rupture, restrain calcium influx, and mitochondrial reactive oxygen species. Dual inflammasome inhibitor (NLRP3/AIM-2-IN-3) loaded nanorods cause synergistic inhibition by 21.5- and 59-folds compared with nanorods and free drug, respectively alongside caspase-1 inhibition. The NLRP3/AIM-2-IN-3 nanorod when transformed into a polymeric scaffold, simultaneously and effectively inhibits RNA levels of NLRP3, AIM2, caspase-1, chemokine ligand-2, gasdermin-D, interleukin-1β, toll-like receptor 7/ 8, and IL-17A by 6.4-, 1.6-, 2.0-, 13.0-, 4.2-, 24.4-, 4.3-, and 1.82-fold, respectively in psoriatic skin in comparison to Imiquimod positive control group in an in-vivo psoriasis-like mice model.

Abstract Background Paroxysmal Nocturnal Haemoglobinuria (PNH) is an ultra-rare, acquired disorder that is challenging to diagnose due to varied symptoms, heterogeneous patient presentations, and lack of awareness among healthcare professionals. This leads to frequent misdiagnosis and delays in diagnosis. This study evaluated the feasibility of a machine learning model to identify undiagnosed PNH patients using structured electronic health records. Methods The study used data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. PNH patients were identified by the presence, and control patients by the absence of a PNH diagnosis code in their records. Clinical features (symptoms, diagnoses, healthcare utilisation) from 131 patients in the PNH group and 593,838 patients in the control group, were inputted to a tree-based XGBoost machine learning model to classify patients as either “positive” or “negative” for PNH suspicion. The algorithm was finalised after additional exclusions and inclusions applied. Performance was assessed using positive predictive value (PPV), recall and specificity. As the sample used to develop the algorithm was not representative of the true population prevalence, PPV was additionally adjusted to reflect performance in the wider population. Results Of all the patients in the PNH group, 27% were classified as positive (recall). 99.99% of the control group were classified as negative (specificity). Of all the patients classified as positive, 60.4% had a diagnosis of PNH in their record (PPV). The PPV adjusted for the population prevalence of PNH was 19.59 suggesting nearly 1 in 5 patients flagged may warrant further PNH investigation. The key clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari syndrome. Conclusion This is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed.

Abstract Severe febrile illnesses in children encompass life-threatening organ dysfunction caused by diverse pathogens and other severe inflammatory syndromes. A comparative approach to these illnesses may identify shared and distinct features of host immune dysfunction amenable to immunomodulation. Here, using immunophenotyping with mass cytometry and cell stimulation experiments, we illustrate trajectories of immune dysfunction in 74 children with multi-system inflammatory syndrome in children (MIS-C) associated with SARS-CoV-2, 30 with bacterial infection, 16 with viral infection, 8 with Kawasaki disease, and 42 controls. We explore these findings in a secondary cohort of 500 children with these illnesses and 134 controls. We show that neutrophil activation and apoptosis are prominent in multi-system inflammatory syndrome, and that this is partially shared with bacterial infection. We show that memory T cells from patients with multi-system inflammatory syndrome and bacterial infection are exhausted. In contrast, we show viral infection to be characterized by a distinct signature of decreased interferon signaling and lower interferon receptor gene expression. Improved understanding of immune dysfunction may improve approaches to immunomodulator therapy in severe febrile illnesses in children.

Abstract The gut microbiome of worms from composting facilities potentially harbors organisms that are beneficial to plant growth and development. In this experiment, we sought to examine the potential impacts of rhizosphere microbiomes derived from Eisenia fetida worm castings (i.e. vermicompost) on tomato (Solanum lycopersicum, L.) plant growth and physiology. Our experiment consisted of a greenhouse trial lasting 17 weeks total in which tomato plants were grown with one of three inoculant treatments: a microbial inoculant created from vermicompost (V), a microbial inoculant created from sterilized vermicompost (SV), and a no-compost control inoculant (C). We hypothesized that living microbiomes from the vermicompost inoculant treatment would enhance host plant growth and gene expression profiles compared to plants grown in sterile and control treatments. Our data showed that bacterial community composition was significantly altered in tomato rhizospheres, but fungal community composition was highly variable in each treatment. Plant phenotypes that were significantly enhanced in the vermicompost and sterile vermicompost treatments, compared to the control, included aboveground biomass and foliar δ15N nitrogen. RNA sequencing revealed distinct gene expression changes in the vermicompost treatment, including upregulation of nutrient transporter genes such as Solyc06g074995 (high affinity nitrate transporter), which exhibited a 250.2-fold increase in expression in the vermicompost treatment compared to both the sterile vermicompost and control treatments. The plant transcriptome data suggest that rhizosphere microbiomes derived from vermicompost can influence tomato gene expression and growth-related regulatory pathways, which highlights the value of RNA sequencing in uncovering molecular responses in plant microbiome studies.