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Updates every hour. Last Updated: 23-Mar-2026 12:16 ET (23-Mar-2026 16:16 GMT/UTC)
A DNA nanomachine strategy to reverse tumor stemness and overcome chemoresistance in small cell lung cancer
ResearchProfessor Chao Zhang’s team at Zhujiang Hospital, Southern Medical University, has developed a novel DNA nanomachine–based drug delivery and release strategy aimed at overcoming chemoresistance in small cell lung cancer (SCLC). The team identified the PRMT1/SOX2 signaling axis as a key driver of chemotherapy resistance in SCLC and, based on this mechanism, designed a DNA nanomachine capable of temporally programmed drug release. By precisely targeting chemoresistant tumor cells, the nanomachine rapidly releases a stemness inhibitor followed by the sustained release of the chemotherapeutic agent cisplatin, thereby effectively reversing tumor stemness and significantly enhancing chemosensitivity. The related work, entitled “A DNA Nanomachine Modulates the Stemness-Associated Signaling Pathways for Overcoming Chemoresistance by Temporally Programming Drug Release,” was published in Research.
- Journal
- Research
- Funder
- National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Fundamental Research Funds for the Central Universities, Open Competition Mechanism to Select the Best Candidates for Key Research Projects of Ningxia Medical University
Researchers uncover how prostate cancer cells build up cholesterol and fuel tumor growth
Texas A&M University- Journal
- International Journal of Molecular Sciences
- Funder
- Cancer Prevention and Research Institute of Texas, Cancer Prevention and Research Institute of Texas, Cancer Prevention and Research Institute of Texas, Cancer Prevention and Research Institute of Texas, Cancer Prevention and Research Institute of Texas, Welch Foundation
Self-supervised learning opens a new path for neuroimaging analysis in brain disorders: a review highlights key opportunities from data scarcity to clinical translation
Health Data ScienceNeuroimaging analysis in brain disorders faces a persistent challenge: brain signals are complex and high-dimensional, while high-quality labeled datasets remain limited. This review article systematically examines how self-supervised learning can help address that gap by learning meaningful representations directly from unlabeled neuroimaging data. It covers major methodological families, including contrastive, generative, and hybrid generative-contrastive approaches, and discusses their applications in functional MRI, EEG, and multimodal brain network analysis.
The review argues that self-supervised learning offers more than annotation efficiency. It may enable more transferable and clinically useful representations for disease screening, diagnosis, and prognosis across heterogeneous datasets and disorders. At the same time, interpretability, data heterogeneity, missing modalities, and clinical validation remain major barriers. Future work will likely focus on stronger multimodal fusion, better cross-site generalization, and more clinically adaptable model design.
- Journal
- Health Data Science
A 12-year longitudinal study reveals nonlinear associations between dietary intake and outcomes in peritoneal dialysis patients
Health Data ScienceDietary management in patients with end-stage renal disease (ESRD) has long lacked robust evidence directly linking nutrient intake to clinical outcomes, particularly in peritoneal dialysis populations. This study analyzed 12 years of longitudinal data from 656 patients, combining frequent follow-up assessments with detailed dietary records to evaluate associations between nutrient intake and mortality risk using both multivariable and nonlinear modeling approaches.
The findings demonstrate that most diet–outcome relationships are nonlinear, highlighting the importance of identifying optimal intake ranges rather than relying on single thresholds. In addition, the effects of nutrient intake vary across different nutritional states, as indicated by serum albumin levels, emphasizing the need for personalized dietary strategies. This study provides evidence-based reference ranges for nutritional management and introduces a methodological framework for future research on diet and long-term outcomes in chronic disease populations.
- Journal
- Health Data Science
Brain connectivity reveals causal insights into epilepsy subtypes through mendelian randomization
Health Data ScienceThis study investigates the causal relationship between epilepsy subtypes and brain connectivity within resting-state networks using a bidirectional Mendelian randomization framework. By leveraging large-scale genetic and neuroimaging datasets, the researchers provide a more reliable assessment of causality beyond traditional observational studies.
The results demonstrate that genetic generalized epilepsy is associated with functional alterations in attention and motor-related brain networks, while brain connectivity changes do not appear to causally drive epilepsy. These findings deepen our understanding of epilepsy as a network-level disorder and suggest new avenues for targeted intervention and precision medicine.
- Journal
- Health Data Science
Enjoy the latest research for Environmental Surfaces and Interfaces
KeAi Communications Co., Ltd.- Journal
- Environmental Surfaces and Interfaces
Magnetic field technology offers new hope for organ preservation, expanding donor pools
KeAi Communications Co., Ltd.- Journal
- Magnetic Medicine
- Funder
- Key R&D Program of Shandong Province China, National Natural Science Foundation of China, International Partnership Program of CAS
Capture of TELSCs and step-wise remodeling of placental development in vitro
Higher Education Press
Researchers at Peking University has developed a "two-step" signaling switch—transitioning from Hippo-YAP/Notch to TGFβ1 pathways—to capture and stably maintain a novel stem cell state termed Trophectoderm-Like Stem Cells (TELSCs). These cells, derived from totipotent blastomere-like cells (TBLCs) or 8-cell embryos, precisely mirror the transcriptomic and epigenetic landscape of the E4.5 pre-implantation trophectoderm. Functionally, TELSCs exhibit extraordinary developmental plasticity; in chimeric assays, they contribute to all eight known trophoblast lineages at single-cell resolution. Furthermore, TELSCs efficiently assemble into 3D trophoblast organoids (TELSC-TOs) that recapitulate the coupled self-renewal and multi-lineage differentiation characteristic of the native placenta.
- Journal
- Protein & Cell
Integrated carbon capture and utilization via plasma-assisted KHCO₃ decomposition
Higher Education Press- Journal
- ENGINEERING Chemical Engineering