Information Today (United States)

Accurate lung cancer prediction from CT scans using advanced deep learning methods is crucial for improving early diagnosis and treatment outcomes, as it harnesses innovative algorithms to enhance the detection and classification of malignant lesions in imaging data. The comprehensive approach for accurate lung cancer prediction from CT scans using advanced deep learning methods. Lung cancer remains one of the leading causes of cancer-related deaths globally, emphasizing the need for early and precise diagnosis. They propose a multistage framework that integrates state-of-the-art techniques, including hybrid Graph Convolutional Networks (GCNs) and Conditional Random Fields (CRFs) for image segmentation, followed by an innovative feature extraction pipeline utilizing Capsule Networks (CapsNets), Siamese Neural Networks, and Hybrid Deep Autoencoders. This combination allows for the effective identification of lung regions and the detection of potential lesions, ensuring high segmentation accuracy and robustness against noise. The feature extraction implements a refined classification strategy that merges a Hybrid CNN-Transformer Model with Graph Neural Networks (GNNs). This dual approach leverages CNNs for capturing local patterns and transformers for modelling long-range dependencies, enhancing the ability to recognize subtle features indicative of malignancies. GNNs further contribute by modelling spatial and relational information among extracted features, facilitating a deeper understanding of the lung's complex anatomic structures. The proposed technique also leads with 91%, compared with LSTM's 80%, FNN's 70%, and RNN's 70%, highlighting its ability to minimize false positives, implemented using Python software. The future scope for accurate lung cancer prediction from CT scans using advanced deep learning methods includes the development of more sophisticated algorithms that integrate multimodal imaging data, enhancing diagnostic precision, and personalization of treatment plans.

Nursing informatics and generative artificial intelligence are emerging as key enablers of competency-based education in nursing, supporting educators in delivering personalized support while maintaining quality, flexibility, and efficiency. The Joint Artificial Intelligence Model in Education, a custom generative pretrained transformer, was developed to assist doctoral nursing students with academic writing, assignment clarification, and practicum guidance. To ensure pedagogical and contextual relevance, its design was grounded in the Technological Pedagogical Content Knowledge framework and the Purpose, Integration, Curriculum/Replacement, Amplification, Transformation model, and aligned with the American Association of Colleges of Nursing Essentials. The tool was trained on course-specific resources, including rubrics, policies, and instructional materials, then piloted in a graduate nursing informatics course. Evaluation measures included data from the learning management system, student performance, and qualitative feedback. Findings indicated that the tool promoted learner autonomy and was used frequently outside business hours and on weekends; however, usage showed limited correlation with academic performance, underscoring its supplemental role. Faculty reported fewer frequently asked questions and an improvement in instructional efficiency. Students valued its accessibility, nonjudgmental tone, and 24/7 availability. This article strengthens the evidence base, demonstrates feasibility, and offers a replicable model for integrating artificial intelligence into nursing education.

As we head into 2026, artificial intelligence (AI), blockchain, and other emerging technologies are moving from experiments into core healthcare systems. That shift promises tangible benefits: fewer people left untreated, faster discovery of lifesaving treatments, and simpler, lower‑cost ways to move money and data across borders. It also brings real risks-speculative hype, erosion of institutional trust, and rushed rollouts that fail patients-so adoption must be disciplined and values-driven. This annual predictions article, informed by ConV2X Symposium speakers, highlights practical advances likely to matter at the bedside and beyond: programmable stablecoins that lower cross‑border payment friction; AI that surfaces pediatric risks earlier; verifiable digital credentials that ease clinician mobility; post‑quantum cryptography to safeguard sensitive records; domain‑specific AI designed for regulatory compliance; consumer apps that put usable health tools in people's pockets; and the rise of Decentralized Science (DeSci) to restore transparency and funding momentum to stalled research. Realizing these possibilities will require deliberate choices, commitment, and coordinated stewardship across innovators, clinicians, and policymakers. With that effort, these tools can help build a more verifiable, equitable, and resilient global healthcare system-technology shaped to serve people, not the other way around; aspirations for healing, dignity, and universal well-being. While uncertainties persist, the path forward is clear: responsible innovation today will shape a healthier, more inclusive tomorrow.

Pemphigus vulgaris (PV) is a CD4+ T-cell-dependent autoantibody-mediated blistering disease associated with human leucocyte antigen (HLA) class II molecules. IgG autoantibodies against the primary autoantigen desmoglein 3 (Dsg3), a desmosomal adhesion protein on epidermal keratinocytes, cause loss of epidermal cell adhesion. To assess the clinical applicability of an innovative nanoparticle platform for the induction of immune tolerance exploiting the natural tolerance potential of liver sinusoidal endothelial cells. An open-label first-in-human study was conducted with TPM203, a mixture of four nanoparticle-coupled immunodominant Dsg3 T-cell peptides. The efficacy and mechanism of action of TPM203 were first tested in a humanized HLA-DRB1*0402-transgenic PV mouse model. In the clinical phase I trial, TPM203 was administered intravenously in patients with PV with no-to-moderate disease activity in single ascending and multiple doses (three doses of TPM203 two weeks apart). Primary endpoints included safety and tolerability. As a secondary endpoint, pharmacokinetics were assessed. Exploratory endpoints comprised changes in Dsg3-specific and bulk T- and B-cell frequencies, anti-Dsg3 IgG levels and autoantibody-induced keratinocyte dissociation. The trial was registered with EudraCT (2019-001727-12). In the PV mouse model, two administrations of TPM203 significantly reduced anti-Dsg3 IgG. On the cellular level, TPM203 led to a significant decrease in CD4+ T cells in the spleen, accompanied by increased frequencies of regulatory T (Treg) cells. In the clinical trial, the 17 patients with PV enrolled across single- and multiple-dose groups did not experience any serious or severe adverse events, or treatment-related PV worsening. Pharmacokinetics confirmed rapid TPM203 clearance from the circulation. Significant TPM203-induced modulations in bulk lymphocyte subsets included an increase in Treg cells, and reductions in T helper 17.1 and CD27+ memory B cells, when dose groups were combined for analysis. Dsg3-specific T cells were found to be significantly reduced at week 8 following single administration of TPM203. Anti-Dsg3 IgG levels trended downward in the three lower single ascending dose groups, while IgG-induced keratinocyte-dissociating capacity was significantly reduced after multiple doses. Administered for the first time in humans, TPM203 was shown to be a safe and well-tolerated nanoparticle-based therapeutic approach with the potential to promote tolerance induction in PV, justifying further clinical development in this and other autoimmune diseases. An author video to accompany this article is available online.

V souvislosti s tvorbou středoškolské učebnice fyziky, která v současné době probíhá na katedře didaktiky fyziky Matematicko-fyzikální fakulty Univerzity Karlovy, je cílem této studie odpovědět na otázku, jaké jsou požadavky kladené na učebnice fyziky nebo obecněji na učebnice přírodních věd, které se opakovaně objevují ve studiích publikovaných v mezinárodním prostředí. K řešení této otázky byla využita systematická rešerše v databázi Web of Science, obsahová analýza nalezených studií a jejich komparace. Zejména na základě volby různých kombinací klíčových slov bylo nalezeno šest relevantních studií. V nich bylo identifikováno 25 opakujících se požadavků kladených na fyzikální nebo přírodovědné učebnice. Významově podobné požadavky byly sdruženy do tří základních skupin (a několika podskupin): i) obsahové požadavky (oborový obsah nezávislý na žákovi, oborový obsah orientovaný na žáka, kognice žáků, motivace žáků, různorodost); ii) formální požadavky (verbální stránka, grafická stránka); iii) požadavky kladené na strukturu učebnice (rozložení ilustrací a textu, uspořádanost učebnice). V rozsáhlé příloze článku jsou uvedena původní (přesná) znění požadavků kladených na učebnice. Na základě výsledků studie budou zkonstruovány nástroje, prostřednictvím nichž budou vznikající učebnici fyziky reflektovat žáci, učitelé a didaktikové fyziky. Výsledky studie a vzniklé nástroje budou využitelné také k reflexi dalších přírodovědných učebnic.

The highest-achieving figures in politics, business, academia, and the media dominate public discourse and wield great influence in society. Education—perhaps especially at “elite” colleges and universities—may lie at the heart of the divide between the general public and these top achievers. In this paper, we build a new data set for the American “elite” and systematically examine the link between selective schools and outstanding achievements. In Study 1, across 30 different achievement groups totaling 26,198 people, we document patterns of attendance at a set of 34 “Elite” 34 schools, the 8 Ivy League schools, and Harvard University in particular. In Study 2, we surveyed 1810 laypeople to estimate how well they are aware of the key empirical facts from Study 1. We found that exceptional achievement is surprisingly strongly associated with “elite” education, especially obtaining a degree from Harvard, and the general public tends to underestimate the size of this effect. Attending one of just 34 institutions of higher education out of the roughly 4000 in the U.S. appears to be a critical and surprising factor separating extraordinary achievers from others in their fields.