Coordinated dynamics of aquaculture ponds and water eutrophication owing to policy: A case of Jiangsu province, China.

Aquaculture ponds (APs) are rapidly expanding globally and are considered crucial for guaranteeing the supply of food, population growth, and economic development. However, the rapid expansion of aquaculture not only brought benefits but also a series of eco-environmental issues, such as water eutrophication. To achieve sustainable development, it is essential to gain a profound understanding of the spatiotemporal evolution of APs, the drivers behind their dynamics, and their relationship with the aquatic environment. Jiangsu Province (JS) in China, a historically significant aquaculture region, encompasses two prominent river basins: the Huai River Basin (HRB) and the Yangtze River Basin (YRB). In light of the construction of an ecological civilization, JS serves as a demonstration and pioneering area for basin protection and development. Therefore, this study focuses on JS, aiming to elucidate the spatiotemporal dynamics of APs, the corresponding relationship with basin management policies, and the impact on water eutrophication. The results revealed that: (1) in 2022, APs in JS were unevenly distributed, with a total area of 3278.78 km2, of which 79 % was located in the HRB. (2) During 2016-2022, APs exhibited an initial growth trend before 2019, followed by a decrease. (3) Due to policy interventions, AP changes within different basins showed opposite trends, and the corresponding water eutrophic state aligned with AP dynamics. The findings of this study can serve as a typical case to provide scientific evidence for the formulation and implementation of policies to improve the water environment in eutrophic basins.

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer.

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

Characterization of a novel 3-quinuclidinone reductase possessing remarkable thermostability.

The 3-quinuclidinone reductase plays an irreplaceable role in the biopreparation of (R)-3-quinuclidinol, an intermediate vital for synthesis of various pharmaceuticals. Thermal robustness is a critical factor for enzymatic synthesis in industrial applications. This study characterized a new 3-quinuclidinone reductase, named SaQR, with significant thermal stability. The SaQR was overexpressed in a GST-fused state, and substrate and cofactor screening were conducted. Additionally, three-dimensional structure prediction using AlphaFold and analysis were performed, along with relevant thermostability tests, and the evaluation of factors influencing enzyme activity. The findings highlight the remarkable thermostability of SaQR, retaining over 90% of its activity after 72 h at 50°C, with an optimal operational temperature of 85°C. SaQR showed typical structural traits of the SDR superfamily, with its cofactor-determining residue being aspartic acid, conferring nicotinamide adenine dinucleotide (NAD(H)) preference. Moreover, K+ and Na+, at a concentration of 400 mM, could significantly enhance the activity, while Mg2+ and Mn2+ only display inhibitory effects within the tested concentration range. The findings of molecular dynamics simulations suggest that high temperatures may disrupt the binding of enzyme to substrate by increasing the flexibility of residues 205-215. In conclusion, this study reports a novel 3-quinuclidinone reductase with remarkable thermostability.

Molecular subgroup establishment and signature creation of lncRNAs associated with acetylation in lung adenocarcinoma.

BACKGROUND: The significance of long non-coding RNAs (lncRNAs) as pivotal mediators of histone acetylation and their influential role in predicting the prognosis of lung adenocarcinoma (LUAD) has been increasingly recognized. However, there remains uncertainty regarding the potential utility of acetylation-related lncRNAs (ARLs) in prognosticating the overall survival (OS) of LUAD specimens. METHODS: The RNA-Seq and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Through the differential analysis, weighted correlation network analysis (WGCNA), Pearson correlation test and univariate Cox regression, we found out the prognosis associated ARLs and divided LUAD specimens into two molecular subclasses. The ARLs were employed to construct a unique signature through the implementation of the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Subsequently, the predictive performance was evaluated using ROC analysis and Kaplan-Meier survival curve analysis. Finally, ARL expression in LUAD was confirmed by quantitative real-time PCR (qRT-PCR). RESULTS: We triumphantly built a ARLs prognostic model with excellent predictive accuracy for LUAD. Univariate and multivariate Cox analysis illustrated that risk model served as an independent predictor for influencing the overall survival OS of LUAD. Furthermore, a nomogram exhibited strong prognostic validity. Additionally, variations were observed among subgroups in the field of immunity, biological functions, drug sensitivity and gene mutations within the field. CONCLUSIONS: Nine ARLs were identified as promising indicators of personalized prognosis and drug selection for people suffering with LUAD.

Characterization of a Novel Thermostable 7α-hydroxysteroid Dehydrogenase.

BACKGROUND: 7α-Hydroxysteroid dehydrogenase (7α-HSDH) plays a pivotal role in vivo in the biotransformation of secondary bile acids and has great potential in industrial biosynthesis due to its broad substrate specificity. In this study, we expressed and characterized a novel thermostable 7α-HSDH (named Sa 7α-HSDH). METHODS: The DNA sequence was derived from the black bear gut microbiome metagenomic sequencing data, and the coding sequence of Sa 7α-HSDH was chemically synthesized. The heterologous expression of the enzyme was carried out using the pGEX-6p-1 vector. Subsequently, the activity of the purified enzyme was studied by measuring the absorbance change at 340 nm. Finally, the three-dimensional structure was predicted with AlphaFold2. RESULTS: Coenzyme screening results confirmed it to be NAD(H) dependent. Substrate specificity test revealed that Sa 7α-HSDH could catalyze taurochenodeoxycholic acid (TCDCA) with catalytic efficiency (kcat/Km) 3.81 S-1 mM-1. The optimum temperature of Sa 7α-HSDH was measured to be 75°C, confirming that it belongs to thermophilic enzymes. Additionally, its thermostability was assessed using an accelerated stability test over 32 hours. The catalytic activity of Sa 7α-HSDH remained largely unchanged for the first 24 hours and retained over 90% of its functionality after 32 hours at 50°C. Sa 7α-HSDH exhibited maximal activity at pH 10. The effect of metal ions-K+, Na+, Mg2+ and Cu2+-on the enzymatic activity of Sa 7α-HSDH was investigated. Only Mg2+ was observed to enhance the enzyme's activity by 27% at a concentration of 300 mM. Neither K+ nor Na+ had a significant influence on activity. Only Cu2+ was found to reduce enzyme activity. CONCLUSION: We characterized the thermostable 7α-HSDH, which provides a promising biocatalyst for bioconversion of steroids at high reaction temperatures.

Network pharmacology and in vitro experimental verification unveil glycyrrhizin from glycyrrhiza glabra alleviates acute pancreatitis via modulation of MAPK and STAT3 signaling pathways.

Acute pancreatitis (AP) is a severe gastrointestinal inflammatory disease with increasing mortality and morbidity. Glycyrrhiza glabra, commonly known as Liquorice, is a widely used plant containing bioactive compounds like Glycyrrhizin, which possesses diverse medicinal properties such as anti-inflammatory, antioxidant, antiviral, and anticancer activities. The objective of this study is to investigate the active components, relevant targets, and underlying mechanisms of the traditional Chinese medicine Glycyrrhiza glabra in the treatment of AP. Utilizing various computational biology methods, we explored the potential targets and molecular mechanisms through Glycyrrhizin supplementation. Computational results indicated that Glycyrrhizin shows promising pharmacological potential, particularly with mitogen-activated protein kinase 3 (MAPK3) protein (degree: 70), forming stable complexes with Glycyrrhizin through ionic and hydrogen bonding interactions, with a binding free energy (ΔGbind) of -33.01 ± 0.08 kcal/mol. Through in vitro experiments, we validated that Glycyrrhizin improves primary pancreatic acinar cell injury by inhibiting the MAPK/STAT3/AKT signaling pathway. Overall, MAPK3 emerges as a reliable target for Glycyrrhizin's therapeutic effects in AP treatment. This study provides novel insights into the active components and potential targets and molecular mechanisms of natural products.

Targeting METTL3 reprograms the tumor microenvironment to improve cancer immunotherapy.

The tumor microenvironment (TME) is a heterogeneous ecosystem containing cancer cells, immune cells, stromal cells, cytokines, and chemokines which together govern tumor progression and response to immunotherapies. Methyltransferase-like 3 (METTL3), a core catalytic subunit for RNA N6-methyladenosine (m6A) modification, plays a crucial role in regulating various physiological and pathological processes. Whether and how METTL3 regulates the TME and anti-tumor immunity in non-small-cell lung cancer (NSCLC) remain poorly understood. Here, we report that METTL3 elevates expression of pro-tumorigenic chemokines including CXCL1, CXCL5, and CCL20, and destabilizes PD-L1 mRNA in an m6A-dependent manner, thereby shaping a non-inflamed TME. Thus, inhibiting METTL3 reprograms a more inflamed TME that renders anti-PD-1 therapy more effective in several murine lung tumor models. Clinically, NSCLC patients who exhibit low-METTL3 expression have a better prognosis when receiving anti-PD-1 therapy. Collectively, our study highlights targeting METTL3 as a promising strategy to improve immunotherapy in NSCLC patients.

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery.

Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr-1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56-76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data.

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation.

Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016-2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery.

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.

Computational biology-based study of the molecular mechanism of spermidine amelioration of acute pancreatitis.

Acute pancreatitis (AP) is an acute inflammatory gastrointestinal disease, the mortality and morbility of which has been on the increase in the past years. Spermidine, a natural polyamine, has a wide range of pharmacological effects including anti-inflammation, antioxidation, anti-aging, and anti-tumorigenic. This study aimed to investigate the reliable targets and molecular mechanisms of spermidine in treating AP. By employing computational biology methods including network pharmacology, molecular docking, and molecular dynamics (MD) simulations, we explored the potential targets of spermidine in improving AP with dietary supplementation. The computational biology results revealed that spermidine had high degrees (degree: 18, betweenness: 38.91; degree: 18, betweenness: 206.41) and stable binding free energy (ΔGbind: - 12.81 ± 0.55 kcal/mol, - 15.00 ± 1.00 kcal/mol) with acetylcholinesterase (AchE) and serotonin transporter (5-HTT). Experimental validation demonstrates that spermidine treatment could reduce the necrosis and AchE activity in pancreatic acinar cells. Cellular thermal shift assay (CETSA) results revealed that spermidine could bind to and stabilize the 5-HTT protein in acinar cells. Moreover, spermidine treatment impeded the rise of the expression of 5-HTT in pancreatic tissues of caerulein induced acute pancreatitis mice. In conclusion, serotonin transporter might be a reliable target of spermidine in treating AP. This study provides new idea for the exploration of potential targets of natural compounds.

Management actions mitigate the risk of carbon dioxide emissions from urban lakes.

Lakes are recognized as important sources of carbon dioxide (CO2) emissions, which vary greatly across land use type. However, CO2 emissions from lakes in urban landscapes are generally overlooked despite their daily connections to human activity. Furthermore, the role of management actions in CO2 emissions remained unclear mostly because of the lack of long-term observations. Here, the CO2 partial pressure (pCO2) from two urban lakes (Lake Wuli and Lake Donghu) in eastern China were investigated based on 16-year (2002-2017) field measurements. This long-term measurements showed the annual mean pCO2 were 1150 ± 612 µatm for Lake Wuli and 1143 ± 887 µatm for Lake Donghu, with corresponding estimated flux of 21.12 ± 19.60 mmol m-2 d-1 and 16.42 ± 20.39 mmol m-2 d-1, respectively. This indicates significant CO2 evasion into the atmosphere. Strong links between CO2 and human-derived nutrients (e.g., ammonium) and dissolved organic carbon, dissolved oxygen, and trophic state index were found. Although pCO2 was relatively uniform across sites and seasons in each lake, substantial inter-annual variability with significant decreasing trends were found. The decrease in annual CO2 can be partly explained by the reduction of pollutant loadings with management actions, which held the hypotheses that management actions mitigated the CO2 emission risks. Overall, management actions (e.g., ecological restoration and municipal engineering) should be considered for better understanding the roles of anthropogenic aquatic ecosystems in carbon cycle.

A multicenter, single-arm, open study of neoadjuvant or conversion atezolizumab in combination with chemotherapy in resectable small cell lung cancer (Cohort Study).

BACKGROUND: This study aimed to investigate the prospects of using chemotherapy in combination with atezolizumab in the neoadjuvant or conversion treatment of small cell lung cancer (SCLC). METHODS: Prior to surgery, untreated patients with limited-stage SCLC received three cycles of neoadjuvant or conversion atezolizumab combined with chemotherapy of etoposide and platinum. The primary endpoint of the trial was pathological complete response (pCR) in the per-protocol (PP) cohort. In addition, safety was assessed based on treatment-related adverse events (AEs) and postoperative complications. RESULTS: Overall, 13 of 17 patients (including 14 males and 3 females) underwent surgery. In the PP cohort, pCR and major pathological response were observed in 8 (8/13, 61.5%) and 12 (12/13, 92.3%) patients, respectively. According to the intention-to-treat (ITT) analysis, the pCR and major pathological response in the ITT cohort were 47.1% (8/17) and 70.6% (12/17), respectively. In addition, an overall response rate of 100% was recorded in the PP cohort. Moreover, 15 (15/17, 88.2%) patients and 1 (1/17, 5.9%) in the ITT cohort attained partial remission (PR), and complete remission, respectively, with an overall response rate of 94.1%. The median overall survival of the patients of pCR and the median event-free survival of the patients on surgery had not achieved. However, the median overall survival of the patients of non-pCR was 18.2 months and the median event-free survival of the nonsurgical patients was 9.5 months. During the neoadjuvant treatment, the incidence of grade 3 or higher AEs was 58.8% (10/17). Additionally, three patients (17.6%) developed immune-related adverse event (grades 1-2). CONCLUSION: In patients with SCLC, neoadjuvant or conversion atezolizumab combined with chemotherapy significantly improved pCR with manageable AEs. Therefore, this regimen may be considered a safe and effective treatment for SCLC.

Development and validation of a MUC16 mutation-associated immune prognostic model for lung adenocarcinoma.

Mucin 16 (MUC16) mutation ranks third among all common mutations in lung adenocarcinoma (LUAD), and it has a certain effect on LUAD development and prognostic outcome. This research aimed to analyze the effects of MUC16 mutation on LUAD immunophenotype regulation and determine the prognostic outcome using an immune prognostic model (IPM) built with immune-related genes. The MUC16 mutation status and mRNA expression profiles were analyzed using diverse platforms and among several LUAD patients (n = 691). An IPM was then constructed using differentially expressed immune-related genes (DEIRGs) in MUC16MUT LUAD cases, and the data were compared with those of MUC16WT LUAD cases. The IPM's performance in distinguishing high-risk cases from low-risk ones among 691 LUAD cases was verified. Additionally, a nomogram was built and applied in the clinical setting. Furthermore, a comprehensive IPM-based analysis of how MUC16 mutation affected the tumor immune microenvironment (TIME) of LUAD was performed. MUC16 mutation decreased the immune response in LUAD. As revealed by functional annotation, the DEIRGs in the IPM were most significantly enriched in the humoral immune response function and the immune system disease pathway. Moreover, high-risk cases were associated with increased proportions of immature dendritic cells, neutrophils, and B-cells; enhanced type I interferon T-cell response; and increased expression of PD-1, CTLA-4, TIM-3, and LAG3 when compared with low-risk cases. MUC16 mutation shows potent association with TIME of LUAD. The as-constructed IPM displays high sensitivity to MUC16 mutation status and can be applied to discriminate high-risk LUAD cases from low-risk ones.

An improved nomogram including elastography for the prediction of non-sentinel lymph node metastasis in breast cancer patients with 1 or 2 sentinel lymph node metastases.

Background: The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. Methods: A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram. Results: The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA. Conclusions: We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Eutrophication and salinization elevate the dissolved organic matter content in arid lakes.

Dissolved organic matter (DOM) plays an essential role in the global lake carbon cycle. Understanding DOM composition and monitoring its spatiotemporal dynamics are of great significance for understanding the lake carbon cycle, controlling water pollution, and protecting water resources. However, previous studies have focused mainly on eutrophic freshwater lakes, with limited attention given to saline lakes. Based on in situ data collected in ten lakes in northwestern China, this study reported the changes in DOM components in different lake types. Parallel factor analysis (PARAFAC) was used to analyze the three-dimensional excitation emission matrix (EEMs) to obtain the DOM fluorescence components. The contributions of different environmental factors to the changes in DOM components were quantified by the generalized linear model (GLM). The results showed that the eutrophication index was significantly positively related to dissolved organic carbon (DOC) (R2 = 0.95, p < 0.01) and colored DOM (CDOM) (R2 = 0.96, p < 0.01) concentrations. Terrestrial humic-like and tryptophan-like components, which are highly correlated with human activities, explained 62% and 64% of the variations in DOC and CDOM, respectively. In sum, the contributions of human activities to the DOC and CDOM variations were 61% and 57%, respectively. Salinity also showed significant positive correlations with both DOC (R2 = 0.88, p < 0.01) and CDOM (R2 = 0.87, p < 0.01). Lake salinization led to increases in DOM concentration, and salinity contributed 20% and 16% to the DOC and CDOM variations, respectively. Therefore, human activities and salinity codetermined the DOM concentration and its composition in the western arid lakes. Based on these findings, this study proposed a feasible flowchart for remotely estimating DOM in saline lakes using satellite data. This study is significant for the long-term monitoring of the carbon cycle and the effective protection of lake water resources in saline lakes.

Can the establishment of a protected area improve the lacustrine environment? A case study of Lake Chaohu, China.

Eutrophication of lacustrine environments with frequent cyanobacterial blooms has become a serious problem. Overpopulation and runoff into groundwater and lakes from fertilizers with too much nitrogen and phosphorous have caused most of the problems. Here, we first devised a land use and cover classification system based on the local characteristics of the first-level protected area of Lake Chaohu (FPALC). Lake Chaohu is the fifth largest freshwater lake in China. The land use and cover change (LUCC) products were produced using sub-meter resolution satellite data from 2019 to 2021 in the FPALC. This study presents an evaluation of the effectiveness of the established protected areas. The results show the most impactful type was the reduction in cropland with an area that declined from 7446.4 hm2 to 6433.3 hm2 from 2019 to 2021. Most of the reduced cropland was converted into wetlands, with 460.2 hm2 and 152.0 hm2 of cropland restored to wetlands from 2019 to 2020 and 2020-2021, respectively. The area of cyanobacterial blooms in Lake Chaohu showed a downward trend, and the lacustrine environment was greatly improved after the establishment of the FPALC. These quantified data can inform decision-making related to Lake Chaohu conservation and provide a reference for managing the aquatic environment in other watersheds.