Results 211 to 220 of about 330,630 (266)

Metarhizium anisopliae Mitigates the Phytotoxicity of Lead and Nanoplastics on Rice by Modifying Physiological, Transcriptomic, Metabolomic Activities, and Soil Microbiome

open access: yesAdvanced Science, EarlyView.
Metarhizium anisopliae alleviates the phytotoxic effects of polyethylene nanoplastics (NP) and lead (Pb) in rice by decreasing Pb uptake, restoring antioxidant and hormonal equilibrium, and promoting growth. Additionally, the fungus modifies the rhizosphere microbiota, enhancing both contaminant tolerance and plant growth, thereby effectively ...
Jing Peng   +7 more
wiley   +1 more source

Kinesin‐Induced Buckling Reveals the Limits of Microtubule Self‐Repair

open access: yesAdvanced Science, EarlyView.
This study shows that kinesin‐driven buckling induces extensive microtubule lattice damage that often exceeds intrinsic self‐repair and leads to filament failure. While curvature, motor motility, and force individually cause limited damage, their combination overwhelms repair.
Shweta Nandakumar   +9 more
wiley   +1 more source

Efficient classical sampling from Gaussian boson sampling distributions on unweighted graphs. [PDF]

open access: yesNat Commun
Zhang Y   +7 more
europepmc   +1 more source

Tumor‐Derived Exosomes Deliver Membrane‐Bound Fgl2 to Activate FcγRIIB‐Mediated Immunosuppression in Myeloid‐Derived Suppressor Cells

open access: yesAdvanced Science, EarlyView.
This study reveals that the Fgl2‐FcγRIIB signaling axis is a key mechanism by which MDSCs mediate tumor immune evasion. Tumor‐derived exosomes systemically activate MDSCs via this pathway, positioning this axis as a promising broad‐spectrum target for cancer immunotherapy.
Fenglin Lin   +12 more
wiley   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

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