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Converting tumor-associated macrophages (TAMs) from the M2 to the M1 phenotype is considered an effective strategy for cancer therapy. TRAF3 is known to regulate NF-κB signaling. However, the role of TRAF3 in TAM polarization has not yet been completely elucidated. Here, we found that ablation of TRAF3 increased M1 markers, iNOS, FGR and SLC4A7, while down-regulated M2 markers, CD206, CD36 and ABCC3, expression levels in macrophages. Moreover, TRAF3 deficiency enhanced LPS-induced M1 and abolished IL-4-induced macrophage polarization. Next, quantitative ubiquitomics assays demonstrated that among the quantitative 7618 ubiquitination modification sites on 2598 proteins, ubiquitination modification of IL-4 responding proteins was the most prominently reduced according to enrichment analysis. STAT6, a key factor of IL-4 responding protein, K450 and K129 residue ubiquitination levels were dramatically decreased in TRAF3-deficient macrophages. Ubiquitination assay and luciferase assay demonstrated that TRAF3 promotes STAT6 ubiquitination and transcriptional activity. Site mutation analysis revealed STAT6 K450 site ubiquitination played a vital role in TRAF3-mediated STAT6 activation. Finally, B16 melanoma mouse model demonstrated that myeloid TRAF3 deficiency suppressed tumor growth and lung metastasis in vivo. Taken together, TRAF3 plays a vital role in M2 polarization via regulating STAT6 K450 ubiquitination in macrophages.

Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R2 = 0.857), followed by the data-fusion method (R2 = 0.709) and curve-fitting method (R2 = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies.

Estimates of global forest area vary widely; this discrepancy is now shown to originate primarily from ambiguity in the definition of ‘forest’. Monitoring and reporting should focus on measures more directly relevant to ecosystem function. Deforestation is a major driver of climate change 1 and the major driver of biodiversity loss 1 , 2 . Yet the essential baseline for monitoring forest cover—the global area of forests—remains uncertain despite rapid technological advances and international consensus on conserving target extents of ecosystems 3 . Previous satellite-based estimates 4 , 5 of global forest area range from 32.1 × 10 6  km 2 to 41.4 × 10 6  km 2 . Here, we show that the major reason underlying this discrepancy is ambiguity in the term ‘forest’. Each of the >800 official definitions 6 that are capable of satellite measurement relies on a criterion of percentage tree cover. This criterion may range from >10% to >30% cover under the United Nations Framework Convention on Climate Change 7 . Applying the range to the first global, high-resolution map of percentage tree cover 8 reveals a discrepancy of 19.3 × 10 6  km 2 , some 13% of Earth’s land area. The discrepancy within the tropics alone involves a difference of 45.2 Gt C of biomass, valued at US$1 trillion. To more effectively link science and policy to ecosystems, we must now refine forest monitoring, reporting and verification to focus on ecological measurements that are more directly relevant to ecosystem function, to biomass and carbon, and to climate and biodiversity.

Stress granules (SGs) are cytoplasmic biomolecular condensates containing proteins and RNAs in response to stress. Ras-GTPase–activating protein binding protein 1 (G3BP1) is a core SG protein. Caprin-1 and ubiquitin specific peptidase 10 (USP10) interact with G3BP1, facilitating and suppressing SG formation, respectively. The crystal structures of the nuclear transport factor 2-like (NTF2L) domain of G3BP1 in complex with the G3BP1-interacting motif (GIM) of Caprin-1 and USP10 show that both GIMs bind to the same hydrophobic pocket of G3BP1. Moreover, both GIMs suppressed the liquid–liquid phase separation (LLPS) of G3BP1, suggesting that Caprin-1 likely facilitates SG formation via other mechanisms. Thus, we dissected various domains of Caprin-1 and investigated their role in LLPS in vitro and SG formation in cells. The C-terminal domain of Caprin-1 underwent spontaneous LLPS, whereas the N-terminal domain and GIM of Caprin-1 suppressed LLPS of G3BP1. The opposing effect of the N- and C-terminal domains of Caprin-1 on SG formation were demonstrated in cells with or without the endogenous Caprin-1. We propose that the N- and C-terminal domains of Caprin-1 regulate SG formation in a “yin and yang” fashion, mediating the dynamic and reversible assembly of SGs.

Background This study aimed to investigate the risk factors related to the failure of initial combined local methotrexate (MTX) treatment and minimally invasive surgery for late cesarean scar pregnancy (CSP). Methods This retrospective case-control study was conducted between January 2016 and December 2023, involving patients with late CSP (≥ 8 weeks) who received local MTX injection combined with either hysteroscopic or laparoscopic surgery. Cesarean scar pregnancy was classified as type I, II, or III based on the direction of growth of the gestational sac and the residual myometrial thickness as assessed by ultrasound. Binary logistic regression analysis was utilized to identify the risk factors associated with the failure of the initial combined treatment. Results Overall, 574 patients with late CSP were included in our study. Among them, 29 patients (5.1%) experienced treatment failure with the initial local MTX combined with minimally invasive surgery, while 545 patients (94.9%) achieved successful treatment outcomes. In the univariate analysis, several potential risk factors associated with the initial combined treatment failure were identified, including baseline serum β-human chorionic gonadotropin (β-hCG) levels, type of CSP, time interval between MTX and surgery, and positive fetal heart activity before surgery. Subsequent binary logistic regression analysis revealed the following independent risk factors linked to the failure of the initial combined treatment: baseline serum β-hCG levels exceeding 94,000 IU/L [odds ratio (OR) 3.060, 95% confidence interval (CI) 1.387–6.749, P  = 0.006], type III CSP (OR 3.574, 95% CI 1.147–11.135, P  = 0.028), a time interval greater than seven days between MTX and surgery (OR 3.847, 95% CI 1.725–8.581, P  = 0.001), and the presence of a fetal heartbeat before surgery (OR 4.405, 95% CI 1.014–19.128, P  = 0.048). Conclusion The findings indicate that higher baseline serum β-hCG levels, an extended time interval between MTX and surgery, type III CSP and a positive preoperative fetal heartbeat are significant risk factors for the failure of initial local MTX combined with minimally invasive surgery in patients with late CSP. Individualized treatment strategies are recommended for these high-risk patients with late CSP.

Geostationary satellite data enable frequent observations of the Earth’s surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring.

Predicting drug–drug interactions (DDI) is crucial for preventing adverse reactions in patients and plays a vital role in drug design and development. However, traditional Chinese medicine (TCM) formulations, typically composed of multiple herbal ingredients with diverse bioactive compounds, present a unique challenge in comprehensively assessing potential adverse interactions among their components. To address this challenge, we propose a novel Dual Graph Attention Network (DGAT) designed to predict TCM drug-drug interactions (TCMDDI) by extracting key structural features of active molecules within the herbal ingredients. Our approach leverages graph-based representations of chemical molecules and employs attention mechanism to extract deep structural features, enabling the effective prediction of TCMDDI by capturing spatial structural relationships among different compounds. Furthermore, we construct a comprehensive dataset encompassing three different categories of herbal ingredients, informed by traditional TCM principles. Experimental results reveal that the proposed DGAT method significantly outperforms currently advanced deep learning techniques, including Graph Convolutional Networks, Weave, and Message Passing Neural Networks. Compared to traditional rule-based two-dimensional molecular descriptors, DGAT more effectively captures the spatial structural information of molecules. Notably, DGAT exhibits robust performance and strong generalizability on unseen samples, providing valuable insights for future research on TCMDDI prediction and advancing the integration of artificial intelligence in TCM studies.

Background Chimeric antigen receptor CAR-T cell therapies have ushered in a new era of treatment for specific blood cancers, offering unparalleled efficacy in cases of treatment resistance or relapse. However, the emergence of cytokine release syndrome (CRS) as a side effect poses a challenge to the widespread application of CAR-T cell therapies. Melatonin, a natural hormone produced by the pineal gland known for its antioxidant and anti-inflammatory properties, has been explored for its potential immunomodulatory effects. Despite this, its specific role in mitigating CAR-T cell-induced CRS remains poorly understood. Methods In this study, our aim was to investigate the potential of melatonin as an immunomodulatory agent in the context of CD19-targeting CAR-T cell therapy and its impact on associated side effects. Using a mouse model, we evaluated the effects of melatonin on CAR-T cell-induced CRS and overall survival. Additionally, we assessed whether melatonin administration had any detrimental effects on the antitumor efficacy and persistence of CD19 CAR-T cells. Results Our findings demonstrate that melatonin effectively mitigated the severity of CAR-T cell-induced CRS in the mouse model, leading to improved overall survival outcomes. Remarkably, melatonin administration did not compromise the antitumor effectiveness or persistence of CD19 CAR-T cells, indicating its compatibility with therapeutic goals. These results suggest melatonin's potential as an immunomodulatory compound to alleviate CRS without compromising the therapeutic benefits of CAR-T cell therapy. Conclusion The study's outcomes shed light on melatonin's promise as a valuable addition to the existing treatment protocols for CAR-T cell therapies. By attenuating CAR-T cell-induced CRS while preserving the therapeutic impact of CAR-T cells, melatonin offers a potential strategy for optimizing and refining the safety and efficacy profile of CAR-T cell therapy. This research contributes to the evolving understanding of how to harness immunomodulatory agents to enhance the clinical application of innovative cancer treatments.

Neohelicomyces is a genus of helicosporous hyphomycetes with the potential to produce bioactive secondary metabolites. During a survey of helicosporous fungi in Guizhou and Hainan provinces, southern China, four isolates were obtained from both freshwater and terrestrial habitats. Based on combined analyses of multigene phylogenetic data (ITS, LSU, tef 1-α, and rpb 2) and morphological characteristics, two novel species, Neohelicomyces aquisubtropicus and N. wuzhishanensis , are proposed. Detailed descriptions, illustrations, and phylogenetic analyses of the new taxa are presented. Additionally, a checklist of currently accepted Neohelicomyces species supported by molecular data is provided.

Background Dementia is a prevalent non-motor manifestation among individuals with advanced Parkinson’s disease (PD). Glial fibrillary acidic protein (GFAP) is an inflammatory marker derived from astrocytes. Research has demonstrated the potential of plasma GFAP to forecast the progression to dementia in PD patients with mild cognitive impairment (PD–MCI). However, the predictive role of cerebrospinal fluid (CSF) GFAP on future cognitive transformation and alterations in Alzheimer’s disease (AD)-associated CSF biomarkers in newly diagnosed PD patients has not been investigated. Methods 210 de novo PD patients from the Parkinson’s Progression Markers Initiative were recruited. Cognitive progression in PD participants was evaluated using Cox regression. Cross-sectional and longitudinal associations between baseline CSF GFAP and cognitive function and AD-related CSF biomarkers were evaluated using multiple linear regression and generalized linear mixed model. Results At baseline, the mean age of PD participants was 60.85 ± 9.78 years, including 142 patients with normal cognition (PD–NC) and 68 PD–MCI patients. The average follow-up time was 6.42 ± 1.69 years. A positive correlation was observed between baseline CSF GFAP and age (β = 0.918, p  < 0.001). There was no statistically significant difference in baseline CSF GFAP levels between PD–NC and PD–MCI groups. Higher baseline CSF GFAP predicted greater global cognitive decline over time in early PD patients (Montreal Cognitive Assessment, β = − 0.013, p  = 0.014). Furthermore, Cox regression showed that high baseline CSF GFAP levels were associated with a high risk of developing dementia over an 8-year period in the PD–NC group (adjusted HR = 3.070, 95% CI 1.119–8.418, p  = 0.029). In addition, the baseline CSF GFAP was positively correlated with the longitudinal changes of not only CSF α-synuclein (β = 0.313, p  < 0.001), but also CSF biomarkers associated with AD, namely, amyloid-β 42 (β = 0.147, p  = 0.034), total tau (β = 0.337, p  < 0.001) and phosphorylated tau (β = 0.408, p  < 0.001). Conclusions CSF GFAP may be a valuable prognostic tool that can predict the severity and progression of cognitive deterioration, accompanied with longitudinal changes in AD-associated pathological markers in early PD.