Feng Pan | PubMed Health

PubMed2026

Decoupled Synthesis Pathway via Precursor Functionalization Stabilizes High-Voltage Nickel-Based Cathodes.

Nickel-based layered cathodes are promising candidates for high-performance, high-energy lithium-ion batteries, yet their high-voltage application is jointly limited by synthesis-inherited structural defects and an unstable lattice oxygen framework. Here, we show that both limitations can be overcome by decoupled synthesis pathway (DSP) via La/Nb oxalate functionalization of the Ni0.6Co0.1Mn0.3(OH)2 precursor. Unlike the conventional coupled synthesis pathway (CSP) where precursor dehydration and Li2CO3 decomposition overlap in temperature, the DSP introduces a low‑temperature decomposition of La/Nb oxalates at 200°C, which effectively avoids localized contact between the precursor and Li2CO3 and shifts Li2CO3-related reactions to high temperatures. This allows sequential precursor dehydroxylation, rock‑salt (RS) intermediate formation, and layered‑phase transformation over a broad temperature window. The resulting LiNi0.6Co0.1Mn0.3O2 cathode with La/Nb functionalization (NCM-LN) features a uniform surface LaNiO3 perovskite heterostructure and a Nb‑doped layered bulk with suppressed RS and spinel defects. Consequently, under 4.5 V operation (vs. Li+/Li), NCM-LN exhibits homogeneous Li+ (de)intercalation, and a stabilized oxygen framework. In graphite||NCM-LN full cells, NCM-LN retains 80.1% of its capacity after 2000 cycles at 1C, substantially outperforming the pristine cathode. This decoupling strategy is broadly effective across various Ni‑based systems, providing a generalizable route toward high‑energy, long‑life cathode materials.

decoupled synthesis pathway↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗