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Background: Renal insufficiency (RI) is a key factor affecting the prognosis of multiple myeloma (MM) patients. Because the benefit of daratumumab for treating MM patients with RI remains unclear, our objective was to evaluate the efficacy of daratumumab on MM patients with RI. Methods: We conducted a systematic search of the PubMed, EMBASE, and Cochrane Library databases as of October 24, 2023. Two independent reviewers screened the article titles, abstracts, and full text to identify the randomized controlled trials (RCTs) meeting the inclusion and exclusion criteria. Meta-analyses were performed using RevMan version 5.4. Outcomes of interest were progression-free survival (PFS), overall survival (OS), complete response or better (≥CR), and minimal residual disease (MRD) negativity, all calculated as hazard ratios (HRs) or risk ratios (RRs) with 95% confidence intervals (CIs). Results: A total of 10 RCTs with 5003 patients were included. Add-on daratumumab improved PFS and OS among newly diagnosed MM (NDMM) patients with RI (HR 0.48 [95% CI: 0.36, 0.64, I2 = 65%] and HR 0.63 [95% CI: 0.48, 0.82, I2 = 0%]) as well as relapsed/refractory MM (RRMM)-RI patients, compared with the control group (HR 0.46 [95% CI: 0.37, 0.58, I2 = 0%] and HR 0.68 [95% CI: 0.51, 0.92, I2 = 0%]). In terms of the renal status, the efficacy of add-on daratumumab for MMRI patients was similar to that for MM patients with normal renal function. A prolonged PFS benefit for add-on daratumumab treatment versus the control was evident across all RRMM-RI subgroups, and the benefits tended to increase with the follow-up time. Conclusions: Our results indicate that MM patients with RI could benefit from a daratumumab-added regimen regardless of MM status. Additional high-quality RCTs are still warranted to confirm our findings. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary productivity (GPP) is a critical part of land carbon fluxes. Accurately quantifying GPP in terrestrial ecosystems and understanding its spatiotemporal dynamics are essential for assessing the capability of vegetation to absorb carbon from the atmosphere. Nevertheless, traditional remote sensing estimation models often require complex parameters and data inputs, and they do not account for spatial effects resulting from the distribution of monitoring sites. This can lead to biased parameter estimation and unstable results. To address these challenges, we have raised a spatial autocorrelation light gradient boosting machine model (SA-LGBM) to enhance GPP estimation. SA-LGBM combines reflectance information from remote sensing observations with eigenvector spatial filtering (ESF) methods to create a set of variables that capture continuous spatiotemporal variations in plant functional types and GPP. SA-LGBM demonstrates promising results when compared to existing GPP products. With the inclusion of eigenvectors, we observed an 8.5% increase in R2 and a 20.8% decrease in RMSE. Furthermore, the residuals of the model became more random, reducing the inherent spatial effects within them. In summary, SA-LGBM represents the first attempt to quantify the impact of spatial autocorrelation and addresses the limitations of underestimation present in existing GPP products. Moreover, SA-LGBM exhibits favorable applicability across various vegetation types. [ABSTRACT FROM AUTHOR]

China's new-type urbanization has been instrumental in fostering high-quality economic and social progress. This research explores the impact and underlying mechanisms of the new-type urbanization policy on urban green total factor productivity (GTFP) by analyzing a sample of 165 Chinese prefecture-level cities from 2009 to 2020. Utilizing the difference-in-differences (DID) approach, the study uncovers several key findings: (1) compared to non-pilot areas, the new-type urbanization policy significantly boosts urban GTFP by 43.5%, supporting a shift towards more sustainable and eco-friendly urban growth; (2) the analysis of impact mechanisms indicates that these policies enhance urban GTFP through technological innovation and environmental regulation; and (3) the urban agglomeration model test reveals that being part of an urban agglomeration amplifies the effects of the new-type urbanization policy on GTFP. These results underscore the significance of these policies in driving sustainable and high-quality urban development in China, offering valuable insights for policymakers to formulate and implement effective urbanization strategies. [ABSTRACT FROM AUTHOR]

The "dry gets drier, wet gets wetter" (DGDWGW) paradigm well describes the pattern of precipitation changes over the oceans. However, it has also been usually considered as a simplified pattern of regional changes in wet/dry under global warming, although GCMs mostly do not agree this pattern over land. To examine the validity of this paradigm over land and evaluate how usage of drought indices estimated from different hydrological variables affects detection of regional wet/dry trends, we take the arid regions of central Asia as a case study area and estimate the drying and wetting trends during the period of 1950–2015 based on multiple drought indices. These indices include the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the Palmer drought severity index (PDSI) and self‐calibrating PDSI (sc_PDSI) with both the Thornthwaite (th) and Penman–Monteith (pm) equations in PDSI calculation (namely, PDSI_th, PDSI_pm, sc_PDSI_th and sc_PDSI_pm). The results show that there is an overall agreement among the indices in terms of inter‐annual variation, especially for the PDSIs. All drought indices except SPI show a drying trend over the five states of central Asia (CAS5: including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan). The four PDSIs and SPEI reveal a wetting tendency over the northwestern China (NW; including Xinjiang Uygur Autonomous Region and Hexi Corridor). The contrasting trends between CAS5 and NW can also be revealed in soil moisture (SM) variations. The nonlinear wet and dry variations are dominated by the 3–7 years oscillations for the indices. Relationships between the six indices and climate variables show the major drought drivers have regional features: with mean temperature (TMP), precipitation total (PRE) and potential evapotranspiration (PET) for CAS5, and PRE and PET for NW. Finally, our analyses indicate that the dry and wet variations are strongly correlated with the El Niño/Southern Oscillation (ENSO). Study area and its topography, including the major rivers: Syr Darya and Amu Darya, the major mountains: the Tianshan Mountain, Kunlun Mountain and Altai Mountains. [ABSTRACT FROM AUTHOR]