Researchers successfully implanted a genetically modified pig liver into a human, proving that such an organ can function for an extended period. The graft supported essential liver processes before complications required its removal. Although the patient ultimately passed away, the experiment demonstrates both the potential and the complexity of xenotransplantation. Experts believe this could reshape the future of organ replacement.
Front Mol Biosci. 2025 Nov 18;12:1697083. doi: 10.3389/fmolb.2025.1697083. eCollection 2025.
ABSTRACT
BACKGROUND: EIF3M, a core subunit of eukaryotic translation initiation factor 3, plays a pivotal role in protein synthesis by regulating the assembly of the 43S initiation complex. However, its biological functions in cancer remain poorly understood. To further investigate the clinical translational value and underlying mechanisms of EIF3M in tumors, this study conducted comprehensive bioinformatic analysis of EIF3M across various tumor types.
METHODS: We utilized publicly available databases to perform a comprehensive bioinformatics analysis of EIF3M's biological roles in oncogenesis, aiming to elucidate its pan-cancer expression patterns and prognostic significance. Furthermore, we conducted an integrative multi-omics analysis incorporating methylation profiling, co-expressed gene networks, targeted miRNA interactions, and tumor immune microenvironment infiltration to decipher the complex regulatory architecture and biological pathways mediated by EIF3M across cancer types. Finally, we used HCC cell lines for in vitro functional validation, determining how EIF3M expression modulates malignant phenotypic behaviors in hepatocellular carcinoma.
RESULTS: EIF3M was overexpressed in multiple cancers and correlated with advanced tumor stage and poor survival. Its dysregulation was primarily driven by gene amplification and regulated by promoter methylation and miRNAs. EIF3M functioned as a hub in cell cycle and transcriptional networks and was linked to an immunosuppressive microenvironment. In hepatocellular carcinoma models, EIF3M modulated tumor proliferation, migration, and activated oncogenic pathways like Wnt/β-catenin.
CONCLUSION: This study reveals that EIF3M expression correlates with immune infiltration and poor prognosis in multiple cancers. In vitro experiments in hepatocellular carcinoma models demonstrated that EIF3M critically regulates malignant cell behaviors. Collectively, our findings highlight EIF3M's value as a promising pan-cancer biomarker worthy of further investigation for its utility in prognosis prediction and as an indicator of immunotherapeutic response.
Background: Large language models (LLMs) are increasingly applied in healthcare. However, concerns remain that their nursing care recommendations may reflect patients’ sociodemographic attributes rather than clinical needs. Objective: To investigate potential biases in nursing care plans generated by LLMs, we focused on whether outputs differ systematically based on patients’ sociodemographic characteristics and assessed the implications for equitable nursing care. Methods: We utilized a standardized clinical scenario with GPT to generate care plans for 96 sociodemographic identity combinations, drawing on 9,600 tests. We conducted statistical analyses (t-tests and ANOVA) to analyze how text length and the frequency of physiological and psychological nursing terms varied across sociodemographic factors. Additionally, we utilized Python for data processing and visualization to ensure methodological rigor throughout the study. Results: The analysis revealed significant sociodemographic biases in LLMs-generated nursing care plans. Female patients received shorter care plans (t = 4.864, P
Background: The rapid deployment of DeepSeek, an open-source large language model has sparked concerns of its impact on patient outcomes and safety. However, little is known about how DeepSeek is used and regulated in these facilities. Objective: This study aimed to 1) systematically review the characteristics of deployed DeepSeek in the top 100 hospitals in China; and 2) compare performances and risks from hospital disclosure with research evidence. Methods: We performed a scoping review of gray and white literature, collecting data from the top 100 Chinese hospitals. We extracted basic characteristics of DeepSeek, its aim, evaluation approach, performance, risk and hospital regulation. A coding framework was developedcovering LLMs application scenario, evaluation dimension and source of risk. Results: We identified a total of 58 DeepSeek models in 48 out of the top 100 Chinese hospitals as well as 27 studies. We observed deployed DeepSeek mainly intended to assist clinical decision making, such as patient diagnosis and treatment recommendation. However, only 36.2% hospital-deployed models clearly indicated a pre-deployment assessment, 22.4% presented assessment results, and 8.6% identified potential risks and countermeasures. We found poor transparency in hospital reporting, with none presenting evaluation details. Hospitals were likely to report DeepSeek’s higher performance and fewer risks. Conclusions: The irresponsible deployment of DeepSeek in Chinese leading hospitals poses potential risks to patient outcomes and safety. We highlight the urgent need that existing regulations should be expanded to the downstream developers and users and hospitals need to perform a more rigorous validation and transparent reporting.
Background: Artificial intelligence (AI) holds great promise in transforming healthcare delivery. However, successful implementation of AI projects in healthcare depends on patients' acceptance and trust. There is only limited empirical research examining public perceptions, particularly on the use of personal health data in AI applications in healthcare. Objective: To examine public knowledge and comfort levels with AI use in healthcare, including use of personal health data with and without consent, and to assess how sociodemographic factors, digital literacy, and health conditions influence these perceptions. Methods: We analyzed data from 6,904 Canadian adults who participated in the 2023 Canadian Digital Health Survey. AI-related knowledge and comfort levels were measured using ordinal scales. Sociodemographic characteristics, digital health literacy, and self-reported chronic health conditions were included as predictors. Ordinal logistic regression models were used to assess associations between these factors and AI-related attitudes. Results: 42.3% reported moderate knowledge of AI, while only 7.8% described themselves as very knowledgeable. Overall, 44.6% were comfortable with AI use in healthcare, increasing to 64.7% when personal health data were used with consent, but decreasing when used without consent (52.6% uncomfortable). Respondents were most comfortable with AI use for epidemic tracking and workflow management, and less so for clinical tasks. Fully weighted ordinal logistic regression models indicated that men (OR=1.57, p<.001 non-citizens higher-income respondents p those with graduate education higher digital health literacy and more chronic conditions exhibited greater odds of reporting ai knowledge. for comfort use in healthcare aged men .001 or comfort. lower-income white reported lower levels. using personal data consent adults were less comfortable than showed while other racial groups without contrast black conclusions: the findings point to enhancing transparent policies ethical governance as key increasing public trust ai-driven healthcare.>
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Satish Kothapalli about the transformative impact of AI and vibe coding in life sciences software development, the acceleration of drug development timelines, and the evolving roles of developers in an AI-augmented environment.
Front Mol Biosci. 2025 Nov 18;12:1697083. doi: 10.3389/fmolb.2025.1697083. eCollection 2025.
ABSTRACT
BACKGROUND: EIF3M, a core subunit of eukaryotic translation initiation factor 3, plays a pivotal role in protein synthesis by regulating the assembly of the 43S initiation complex. However, its biological functions in cancer remain poorly understood. To further investigate the clinical translational value and underlying mechanisms of EIF3M in tumors, this study conducted comprehensive bioinformatic analysis of EIF3M across various tumor types.
METHODS: We utilized publicly available databases to perform a comprehensive bioinformatics analysis of EIF3M's biological roles in oncogenesis, aiming to elucidate its pan-cancer expression patterns and prognostic significance. Furthermore, we conducted an integrative multi-omics analysis incorporating methylation profiling, co-expressed gene networks, targeted miRNA interactions, and tumor immune microenvironment infiltration to decipher the complex regulatory architecture and biological pathways mediated by EIF3M across cancer types. Finally, we used HCC cell lines for in vitro functional validation, determining how EIF3M expression modulates malignant phenotypic behaviors in hepatocellular carcinoma.
RESULTS: EIF3M was overexpressed in multiple cancers and correlated with advanced tumor stage and poor survival. Its dysregulation was primarily driven by gene amplification and regulated by promoter methylation and miRNAs. EIF3M functioned as a hub in cell cycle and transcriptional networks and was linked to an immunosuppressive microenvironment. In hepatocellular carcinoma models, EIF3M modulated tumor proliferation, migration, and activated oncogenic pathways like Wnt/β-catenin.
CONCLUSION: This study reveals that EIF3M expression correlates with immune infiltration and poor prognosis in multiple cancers. In vitro experiments in hepatocellular carcinoma models demonstrated that EIF3M critically regulates malignant cell behaviors. Collectively, our findings highlight EIF3M's value as a promising pan-cancer biomarker worthy of further investigation for its utility in prognosis prediction and as an indicator of immunotherapeutic response.
The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains.
“Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don’t adapt well to change, technology systems that don’t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.”
The central challenge, then, lies in rethinking how people, processes, and technology work together.
Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.
“It is very important that humans continue to verify the content. And that is where you’re going to see more energy being put into,” Ryan Peterson, EVP and chief product officer at Concentrix.
Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.
“My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.”
Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate.
“Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,” says Hung.
This webcast is produced in partnership with Concentrix.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
A new report is peeling back the curtain on Chinese pharmaceutical innovation, assessing which companies are best at driving the drug development that’s captured the attention of pharmaceutical executives and investors worldwide.
On Sunday, IDEA Pharma and parent company SAI MedPartners released their inaugural China Pharmaceutical Innovation and Invention Index. The report joins a global pharmaceutical ranking that IDEA produces every spring.
The rankings were chosen from a list of 50 companies around China. The report ranks the top companies for invention — namely, which companies are developing new medicines that matter and use novel science — and innovation, which assesses how these companies are developing and launching medicines. Research spending, international patent filings, and drug approvals all factor into a company’s standing.
Thorac Res Pract. 2025 Dec 1;26(Suppl 1):10-11. doi: 10.4274/ThoracResPract.2025.s004.
ABSTRACT
INTRODUCTION: Lung cancer remains a leading cause of cancer-related deaths worldwide, largely due to late diagnosis and the complexity of the tumor microenvironment (TME).1 A key factor in lung cancer is the extracellular matrix (ECM), a 3D network composed of structural proteins, glycoproteins, proteoglycans, and growth factors that together regulate cell adhesion, proliferation, and signaling. ECM architecture and its changes are closely related to cancer mechanisms.2 Thus, physiological models that recapitulate ECM composition and mechanics are essential. 2D cultures fail to replicate the organization and biochemical and mechanical signals of the TME, whereas microfluidic platforms offer dynamic, 3D cell culture systems hydrogel-integrated.3 A broad range of biomaterials (synthetic, semi-synthetic, natural) is used to recapitulate the dynamics of ECM. Natural hydrogels such as collagen, gelatin methacrylate (GelMA), Matrigel, alginate, fibrin, and decellularized ECM (dECM) are widely used due to their inherent bioactivity and ability to support cell adhesion and proliferation.4 Synthetic hydrogels, such as polyethylene glycol (PEG) and polyacrylamide, provide tunable stiffness and control over matrix composition, while semi-synthetic hybrids (e.g., PEG-GelMA, GelMA-dECM) combine biological cues with structural stability (Figure 1).5.
MATERIAL AND METHODS: Cancer cell lines A549 (adenocarcinoma), H1299 (non-small cell lung cancer), and H460 (large cell carcinoma) are frequently used in cancer modelling. Co-culture systems integrate fibroblasts, endothelial cells, and immune cells (e.g., macrophages) to simulate the TME and study cell-matrix-cell interactions. Patient-derived organoids preserve tumor heterogeneity, genetic mutations, and drug response profiles, representing a personalized in vitro cancer model. Tumor spheroids embedded in hydrogels recapitulate diffusion gradients and are used to evaluate drug penetration and metastasis. Further, they can be integrated into microfluidic platforms or well plates. As a next step, it is important to investigate ECM rheology, determine mechanical structure and cytokine expression levels, and further validate cell-matrix interactions.6,7.
RESULTS: These models have shown that tumor cells embedded in hydrogels exhibit enhanced invasive behavior and increased expression of matrix metalloproteinases (enzymes responsible for ECM degradation and remodeling). From a biomechanical perspective, rheological analyses revealed that cancer-associated hydrogels typically exhibit higher storage modulus than healthy matrices, reflecting a stiffer microenvironment and increased collagen levels.8.
CONCLUSION: Collectively, recent findings underscore the central role of the ECM and hydrogel-based systems in modeling lung cancer progression. The development of tissue-specific, mechanically tunable, and microfluidic-integrated hydrogels has transformed in vitro modeling from static 2D monolayers to dynamic, physiologically relevant 3D systems. Increased stiffness is now recognized as a key regulator of cancer cell fate, governing proliferation, EMT, and metastasis through mechanotransduction pathways such as YAP/TAZ and integrin-FAK signaling.9 Despite significant progress, variability in dECM composition and crosslinking chemistry still challenges reproducibility and bioactivity. Moreover, current hydrogel-based models often lack immune cell components and vascular complexity, limiting their ability to fully emulate the native TME. Future efforts should integrate dECM-based hydrogels with organoid and microfluidic systems, supported by multi-omics profiling, to achieve patient-specific and physiologically relevant lung cancer models.
The U.S. Food and Drug Administration today announced the Technology-Enabled Meaningful Patient Outcomes (TEMPO) for Digital Health Devices Pilot, a voluntary pilot designed to promote access to certain digital health devices while safeguarding patient safety.
BMC Bioinformatics. 2025 Dec 3;26(1):290. doi: 10.1186/s12859-025-06319-6.
ABSTRACT
Lung cancer is one of the most prevalent malignant tumors with high morbidity and mortality rates worldwide. Extensive multi-omics analyses have revealed significant intratumoral heterogeneity even within the same histopathological subtype. However, a database that systematically integrates multi-omics data for lung cancer research has long been lacking. Here, we developed LOSTdb, a molecular subtype annotation system for lung cancer that integrates multi-omics data and metadata. LOSTdb comprises 295 multi-omics datasets, including bulk RNA-seq, genomic, proteomic, methylation, and scRNA-seq data, with over 10,000 manually curated metadata entries. This resource encompasses high-quality clinical specimens, mouse models, and cell lines, totaling 34,393 samples and more than 1.2 million single cells. Each omics sample was annotated with both literature-based classical subtypes and NMF-derived meta-program (MP) subtypes. The platform supports cross-searching of omics and metadata at the gene and dataset levels, offers multiple visualization and analysis methods, and includes five tool modules, enabling functions such as integrated analysis, significance analysis between metadata as well as between genes and metadata, and target prediction for lung cancer molecular subtypes, serving as an essential tool for lung cancer precision medicine. LOSTdb is a user-friendly interactive database freely accessible at http://lostdbcancer.com:8080 .
arXiv:2512.03048v1 Announce Type: new
Abstract: I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms rather than encoding fixed human value content. The paper makes three philosophical contributions. First, I articulate the ``specification trap'' argument demonstrating why content-based value specification appears structurally unstable due to the conjunction of the is-ought gap, value pluralism, and the extended frame problem. Second, I propose syntropy -- the recursive reduction of mutual uncertainty between agents through state alignment -- as an information-theoretic framework for understanding multi-agent alignment dynamics. Third, I establish a functional distinction between genuine and simulated moral capacity grounded in compatibilist theories of guidance control, coupled with an embodied experimental paradigm and verification regime providing operational criteria independent of phenomenological claims. This paper represents the philosophical component of a broader research program whose empirical validation is being developed in a separate project currently in preparation. While the framework generates specific, falsifiable predictions about value emergence and moral agency in artificial systems, empirical validation remains pending.
arXiv:2512.03072v1 Announce Type: new
Abstract: Current AI paradigms, as "architects of experience," face fundamental challenges in explainability and value alignment. This paper introduces "Weight-Calculatism," a novel cognitive architecture grounded in first principles, and demonstrates its potential as a viable pathway toward Artificial General Intelligence (AGI). The architecture deconstructs cognition into indivisible Logical Atoms and two fundamental operations: Pointing and Comparison. Decision-making is formalized through an interpretable Weight-Calculation model (Weight = Benefit * Probability), where all values are traceable to an auditable set of Initial Weights. This atomic decomposition enables radical explainability, intrinsic generality for novel situations, and traceable value alignment. We detail its implementation via a graph-algorithm-based computational engine and a global workspace workflow, supported by a preliminary code implementation and scenario validation. Results indicate that the architecture achieves transparent, human-like reasoning and robust learning in unprecedented scenarios, establishing a practical and theoretical foundation for building trustworthy and aligned AGI.
arXiv:2505.02828v3 Announce Type: replace
Abstract: Explainable Artificial Intelligence (XAI) has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this additional information to end users. In this article, we conduct a scoping review of existing literature to elicit details on the conflict between privacy and explainability. Using the standard methodology for scoping review, we extracted 57 articles from 1,943 studies published from January 2019 to December 2024. The review addresses 3 research questions to present readers with more understanding of the topic: (1) what are the privacy risks of releasing explanations in AI systems? (2) what current methods have researchers employed to achieve privacy preservation in XAI systems? (3) what constitutes a privacy preserving explanation? Based on the knowledge synthesized from the selected studies, we categorize the privacy risks and preservation methods in XAI and propose the characteristics of privacy preserving explanations to aid researchers and practitioners in understanding the requirements of XAI that is privacy compliant. Lastly, we identify the challenges in balancing privacy with other system desiderata and provide recommendations for achieving privacy preserving XAI. We expect that this review will shed light on the complex relationship of privacy and explainability, both being the fundamental principles of Trustworthy AI.
arXiv:2510.18212v3 Announce Type: replace
Abstract: The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.
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