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The integration of large language models (LLMs) into clinical diagnostics has the potential to transform doctor–patient interactions. However, the readiness of these models for real-world clinical application remains inadequately tested. This paper introduces the Conversational Reasoning Assessment Framework for Testing in Medicine (CRAFT-MD) approach for evaluating clinical LLMs. Unlike traditional methods that rely on structured medical examinations, CRAFT-MD focuses on natural dialogues, using simulated artificial intelligence agents to interact with LLMs in a controlled environment. We applied CRAFT-MD to assess the diagnostic capabilities of GPT-4, GPT-3.5, Mistral and LLaMA-2-7b across 12 medical specialties. Our experiments revealed critical insights into the limitations of current LLMs in terms of clinical conversational reasoning, history-taking and diagnostic accuracy. These limitations also persisted when analyzing multimodal conversational and visual assessment capabilities of GPT-4V. We propose a comprehensive set of recommendations for future evaluations of clinical LLMs based on our empirical findings. These recommendations emphasize realistic doctor–patient conversations, comprehensive history-taking, open-ended questioning and using a combination of automated and expert evaluations. The introduction of CRAFT-MD marks an advancement in testing of clinical LLMs, aiming to ensure that these models augment medical practice effectively and ethically. By simulating realistic doctor–patient conversations, a framework can be applied to large language models to investigate shortcomings and bias in patient interactions, providing insight before actual clinical deployment.

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and one of the leading causes of cancer-related death worldwide. Despite the improvements in surveillance and treatment, the prognosis of HCC remains poor. Extracellular vesicles (EVs) are a heterogeneous group of phospholipid bilayer-enclosed particles circulating in the bloodstream and mediating intercellular communication. Emerging studies have shown that EVs play a crucial role in regulating the proliferation, immune escape, and metastasis of HCC. In addition, because EVs are present in the circulation at relatively early stages of disease, they are getting attention as an attractive biomarker for HCC detection. Over the past decade, dedicated efforts have been made to isolate EVs more efficiently and make them useful tools in different clinical settings. In this review article, we provide an overview of the EVs isolation methods and highlight the role of EVs as mediators in the pathogenesis and progression of HCC. Lastly, we summarize the potential applications of EVs in early-stage HCC detection.

Well‐preserved molecular cargo in circulating extracellular vesicles (EVs) offers an ideal material for detecting oncogenic gene alterations in cancer patients, providing a noninvasive diagnostic solution for detection of disease status and monitoring treatment response. Therefore, technologies that conveniently isolate EVs with sufficient efficiency are desperately needed. Here, a lipid labeling and click chemistry‐based EV capture platform (“Click Beads”), which is ideal for EV message ribonucleic acid (mRNA) assays due to its efficient, convenient, and rapid purification of EVs, enabling downstream molecular quantification using reverse transcription digital polymerase chain reaction (RT‐dPCR) is described and demonstrated. Ewing sarcoma protein (EWS) gene rearrangements and kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutation status are detected and quantified using EVs isolated by Click Beads and matched with those identified in biopsy specimens from Ewing sarcoma or pancreatic cancer patients. Moreover, the quantification of gene alterations can be used for monitoring treatment responses and disease progression. This work demonstrates an efficient and rapid extracellular vesicle (EV) capture platform (“Click Beads”), which enables downstream quantification of gene alterations in both Ewing sarcoma and pancreatic cancer using reverse transcription digital polymerase chain reaction (RT‐dPCR). The streamlined workflow that combines Click Bead‐based EV capture and RT‐dPCR exhibits potential clinical utility in disease detection and treatment response monitoring.

Graduate students face numerous demands, high stress levels, and associated challenges to intra- and inter-personal relationships. Mindfulness may help to ease such challenging experiences. The Mindfulness Ambassador Program (MAP) is a promising group-based program that has not yet been studied among graduate students. The primary objectives of this study were to: (1) explore graduate students' perceptions of stress, and their relationships with themselves and meaningful others; (2) explore graduate students' perspectives of and satisfaction with the MAP; and (3) investigate if participation in the MAP elicited changes in graduate students' perceived levels of stress, self-awareness, interpersonal skills, and/or social connectedness. In this one-group, pre/post mixed-methods pilot study, nine participants completed pre-post questionnaires and participated in a semi-structured interview post-intervention. Data were analyzed using descriptive statistics, thematic analysis, and paired t-tests. Pre-intervention, qualitative themes included participants experiencing moderate-to-high stress levels, intrapersonal conflict, interpersonal relationship challenges, and seeing oneself as a work in progress. Post-intervention themes included better stress management, increased consideration for oneself and others, feelings of connection with others, and overall satisfaction with the MAP. Statistically significant improvements were found from pre- to post-intervention in mean score differences for perceived stress (p = .043), private self-awareness (p = .006), awareness of immediate surroundings (p = .044), and social connectedness (p = .006). Participants reported several benefits from their positive experience participating in the MAP. These findings may be used to inform future mindfulness-based programming for graduate students.

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow. Early cancer detection by cell-free DNA (cfDNA) is challenged by the low amount of tumour DNA in cfDNA, tumour heterogeneity and the small patient cohorts. Here, the authors develop a method, cfMethyl-Seq, for cost-effective methylome profiling of cfDNA and for detecting and locating cancer.

BackgroundThe International Hernia Collaboration (IHC) is a closed Facebook™ group that allows international surgeons to post clinical questions and exchange transparent feedback with the intent to optimize patient outcomes. Despite the educational value of closed FB groups, CME credits have not been available to members. To determine feasibility of and user interest in earning CME credit through social media, the IHC piloted a series of expert lectures followed by an interactive Facebook Live session as a novel pathway offering CME credit.MethodsNine monthly lectures and Facebook Live sessions were presented. CME credit was offered for the final seven lectures. Participation in the form of views, comments, and likes was quantified by a Facebook analytics service and an engagement score, defined as [(the number of comments × 2) + (the number of reactions)], was calculated for each lecture and Facebook Live session. CME credit was obtained through a two-question quiz.ResultsOf 5400 + Facebook members of the IHC, an average of 1116 (20.4 ± 4.0%) viewed the live session event following each lecture (n = 9 events). The average Facebook engagement score for Facebook Live was 259 ± 75, a significant difference with the average Facebook engagement score on the IHC (40.8) over the same time period (p < 0.001). On average, 16 users [range 8–35, (n = 7 events)] claimed CME credit for each educational series.ConclusionsClosed Facebook groups can be a useful media to offer educational content and CME credit. The pilot IHC Lecture and Facebook Live series offering CME credit resulted in significantly more engagement amongst its members compared to other posts during the same time period. A small portion of participants qualified for CME credit. Future social media educational series may increase participants qualifying for CME by streamlining the interface to obtain CME credit.

The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet need to improve treatment decision-making. Epigenetic changes in cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Thus, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for prognostication in patients with active, radiographically viable HCC. Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we identified 158 HCC-related DNA methylation markers associated with overall survival (OS). This signature was further validated in 29 HCC tumor tissue samples. Subsequently, we applied the signature to an independent cohort of 52 patients with plasma cfDNA samples by calculating the cfDNA methylation-based risk score (methRisk) via random survival forest models with 10-fold cross-validation for the prognostication of OS. The cfDNA-based methRisk showed strong discriminatory power when evaluated as a single predictor for OS (3-year AUC = 0.81, 95% CI: 0.68-0.94). Integrating the methRisk with existing risk indices like Barcelona clinic liver cancer (BCLC) staging significantly improved the noninvasive prognostic assessments for OS (3-year AUC = 0.91, 95% CI: 0.80-1), and methRisk remained an independent predictor of survival in the multivariate Cox model (P = 0.007). Our study serves as a pilot study demonstrating that cfDNA methylation biomarkers assessed from a peripheral blood draw can stratify HCC patients into clinically meaningful risk groups. These findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility and laying the groundwork for broader applications.

Despite the major roles of choroid plexus epithelial cells (CPECs) in brain homeostasis and repair, their developmental lineage and diversity remain undefined. In simplified differentiations from human pluripotent stem cells, derived CPECs (dCPECs) display canonical properties and dynamic motile multiciliated phenotypes that interact with Aβ uptake. Single dCPEC transcriptomes over time correlate well with human organoid and fetal CPECs, while pseudotemporal and cell cycle analyses highlight the direct CPEC origin from neuroepithelial cells. In addition, time series analyses define metabolic (type 1) and ciliogenic dCPECs (type 2) at early timepoints, followed by type 1 diversification into anabolic-secretory (type 1a) and catabolic-absorptive subtypes (type 1b) as type 2 cells contract. These temporal patterns are then confirmed in independent derivations and mapped to prenatal stages using human tissues. In addition to defining the prenatal lineage of human CPECs, these findings suggest dynamic models of ChP support for the developing human brain. The choroid plexus is a crucial but understudied brain tissue. Here, the authors use stem cells and tissue samples to trace its origins and lineage during pregnancy and to create new models for its support of the developing human brain.

Virtual methods for conducting focus group studies are increasingly being used in many fields, including rehabilitation sciences. This is partly due to the current pandemic, and the need for social distancing, however, may also relate to factors such as convenience and practicality. Virtual research methods enable investigators to collect data at a distance from the participant(s) through the use of technology-mediated data collection methods incorporating new tools and technologies. The aim of this scoping review was to identify, synthesize, and present current evidence related to the methods for conducting virtual focus groups. A comparison of asynchronous and synchronous data collection methods was conducted. The objectives, inclusion criteria, and scoping review methods were specified in advance and documented in a protocol. The 40 articles in this review included virtual focus group research conducted in rehabilitation sciences including data collection conducted using both synchronous (22.5%) and asynchronous (77.5%) models and using a defined moderation method. Three modes of focus group discussion were reported including email, chat-based, and videoconferencing; these were facilitated through the various technology platforms reported in the review. Reported barriers and facilitators to conducting virtual focus group research were extracted and summarized. Commonly reported facilitators to virtual focus group research included the ability to recruit participants from diverse geographical locations and the participants’ ability to engage at times convenient to them. Both computer literacy and access to technology were reported as common barriers. This review highlighted the need for further research and guidance around virtual focus groups conducted using face-to-face synchronous methods and with younger participants groups.

Background Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias-correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. Results We present DeBias , a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias identifies and removes bias-associated subspaces from the feature space using control samples, enabling global correction of demographic distortions while preserving disease-specific signals. To evaluate its effectiveness, we apply DeBias to cell-free DNA methylation data for cancer detection. DeBias achieves a significant reduction in the number of features exhibiting demographic bias and outperforms existing methods in improving cancer detection performance for minority populations. Performance gains are validated in independent cohorts, highlighting the robustness of the approach. Conclusions DeBias offers an effective and generalizable strategy for correcting demographic biases in biomedical machine learning. It represents a step toward more equitable machine learning models that can deliver reliable and unbiased predictions across diverse patient populations.