Nature, Published online: 31 December 2025; doi:10.1038/s41586-025-09819-w
An integrated systems engineering framework based on life-cycle inventories is used to quantify the global eco-footprint of wearable healthcare electronics and identify effective mitigation strategies.
Nature, Published online: 31 December 2025; doi:10.1038/d41586-025-03982-w
A model quantifies the environmental footprint of wearable health-care electronics and identifies strategies to reduce their environmental toll.
Gut Liver. 2025 Dec 31. doi: 10.5009/gnl250268. Online ahead of print.
ABSTRACT
The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semiannual ultrasonography and the measurement of Ξ±-fetoprotein levels, offer suboptimal sensitivity for early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies. In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.
PMID:41472345 | DOI:10.5009/gnl250268
Medicina (Kaunas). 2025 Dec 11;61(12):2196. doi: 10.3390/medicina61122196.
ABSTRACT
Background and Objectives: Although beaded filament structural protein 1 (BFSP1) may be involved in oncogenic mechanisms, its clinical relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. This study examined the prognostic significance, regulatory mechanisms, and potential therapeutic implications of BFSP1 in LIHC. Materials and Methods: Comprehensive bioinformatics analysis was performed across multiple platforms using datasets derived from The Cancer Genome Atlas. Differential gene expression, DNA methylation, copy number variation, immune cell infiltration, drug sensitivity, and co-expression networks were systematically examined. Functional enrichment analyses of protein-protein and gene-gene interaction networks were conducted using STRING and GeneMANIA. Additionally, short interfering RNA-mediated knockdown and wound-healing assays were performed in HepG2 cells to evaluate BFSP1 function in vitro. Results: The results showed that BFSP1 mRNA expression was significantly upregulated in tissues from LIHC patients. Elevated BFSP1 levels were associated with poorer prognostic patterns, which were further supported by detailed clinicopathological subgroup analyses. Furthermore, BFSP1 expression was correlated with promoter hypomethylation and associated with patterns of tumor-infiltrating immune cells, including specific immune cell subtypes such as M1 and M2 macrophages. Integrative analyses revealed strong associations between BFSP1 and drug sensitivity, as well as a regulatory network encompassing genes involved in the cell cycle, DNA repair, and metabolic processes. Functional knockdown of BFSP1 significantly reduced HepG2 cell migration in vitro, as assessed by wound healing assay, with decreased wound closure at 24 h (11.0% vs. 16.5%) and 48 h (7.4% vs. 12.5%) compared with the control (p < 0.05, n = 6 biological replicates). Conclusions: In conclusion, these findings suggest that BFSP1 functions as a multifaceted prognostic biomarker and a potential therapeutic target for LIHC.
PMID:41470198 | PMC:PMC12735119 | DOI:10.3390/medicina61122196
Gut Liver. 2025 Dec 31. doi: 10.5009/gnl250268. Online ahead of print.
ABSTRACT
The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semiannual ultrasonography and the measurement of Ξ±-fetoprotein levels, offer suboptimal sensitivity for early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies. In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.
PMID:41472345 | DOI:10.5009/gnl250268
Medicina (Kaunas). 2025 Dec 11;61(12):2196. doi: 10.3390/medicina61122196.
ABSTRACT
Background and Objectives: Although beaded filament structural protein 1 (BFSP1) may be involved in oncogenic mechanisms, its clinical relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. This study examined the prognostic significance, regulatory mechanisms, and potential therapeutic implications of BFSP1 in LIHC. Materials and Methods: Comprehensive bioinformatics analysis was performed across multiple platforms using datasets derived from The Cancer Genome Atlas. Differential gene expression, DNA methylation, copy number variation, immune cell infiltration, drug sensitivity, and co-expression networks were systematically examined. Functional enrichment analyses of protein-protein and gene-gene interaction networks were conducted using STRING and GeneMANIA. Additionally, short interfering RNA-mediated knockdown and wound-healing assays were performed in HepG2 cells to evaluate BFSP1 function in vitro. Results: The results showed that BFSP1 mRNA expression was significantly upregulated in tissues from LIHC patients. Elevated BFSP1 levels were associated with poorer prognostic patterns, which were further supported by detailed clinicopathological subgroup analyses. Furthermore, BFSP1 expression was correlated with promoter hypomethylation and associated with patterns of tumor-infiltrating immune cells, including specific immune cell subtypes such as M1 and M2 macrophages. Integrative analyses revealed strong associations between BFSP1 and drug sensitivity, as well as a regulatory network encompassing genes involved in the cell cycle, DNA repair, and metabolic processes. Functional knockdown of BFSP1 significantly reduced HepG2 cell migration in vitro, as assessed by wound healing assay, with decreased wound closure at 24 h (11.0% vs. 16.5%) and 48 h (7.4% vs. 12.5%) compared with the control (p < 0.05, n = 6 biological replicates). Conclusions: In conclusion, these findings suggest that BFSP1 functions as a multifaceted prognostic biomarker and a potential therapeutic target for LIHC.
PMID:41470198 | PMC:PMC12735119 | DOI:10.3390/medicina61122196
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