Enhanced adsorption dynamics and thermal stability of radioactive Sr(II) by lamellar Nb-doped sodium vanadosilicate via self-assembly and conditional natroxalate intercalation.

To promote the environmentally friendly and sustainable development of nuclear energy, it is imperative to address the treatment of wastewater generated by the nuclear industry. This necessitates the enhancement of fission product reclamation efficiency post-treatment. This study aims to combine defect control and confined self-assembly strategies for the precise design of interlayer spacing (14.6 Å to 15.1 Å), leading to the fabrication of conditional natroxalate-functionalized vanadosilicate, and its potential application in the efficient adsorption and reclamation of 90Sr. Na0.03Natroxalate2.47Si1.44Nb0.08V1.92O5·1.2 H2O (Nb4-NxSiVO), with a layer spacing of 14.9 Å, exhibits the highest Sr(II) adsorption capacity (248.76 mg/g), enabling effective separation with Cs+. The natroxalate embedded within the confined interlayers demonstrates excellent stability, offering rapid (within 10 min) and stable adsorption sites for Sr(II). Furthermore, Nb4-NxSiVO exhibits a wide band gap and exceptional thermal stability before and after adsorption, rendering hard desorption of 90Sr. The findings highlight the potential of Nb4-NxSiVO as a promising adsorbent for rapid and selective purification of 90Sr-containing wastewater and further application in nuclear batteries.

Pb isotopic fingerprinting of uranium pollution: New insight on uranium transport in stream-river sediments.

Uranium mill tailings (UMT) present a significant environmental concern due to high levels of radioactive and toxic elements, including uranium (U), thorium (Th), and lead (Pb), which can pose serious health risks to aquatic ecosystems. While Pb isotopic tracers have been widely utilized in environmental studies to identify elemental sources and geological processes, their application in U geochemistry remains relatively limited. In this study, we investigate the distribution and migration of U in stream-river sediments surrounding a decommissioned U hydrometallurgical area, employing Pb isotopes as tracers. Our findings reveal significant enrichment and ecological risk of U, Pb, and Th in the sediments. Uranium predominantly associates with quartz and silicate minerals, and its dispersion process is influenced by continuous leaching and precipitation cycles of typical U-bearing minerals. Furthermore, we establish a compelling positive relationship (r2 = 0.97) between 208Pb/207Pb and 206Pb/207Pb in the stream-river sediments and sediment derived from UMT. Application of a binary Pb mixing model indicates that anthropogenic hydrometallurgical activities contribute to 2.5-62.7% of the stream-river sediments. Notably, these values are lower than the 6.6-89.6% recorded about 10 years ago, prior to the decommissioning of the U hydrometallurgical activity. Our results underscore the continued risk of U pollution dispersion even after decommission, highlighting the long-term environmental impact of UMT.

Discovery of novel HER2 targeting peptide-camptothecin conjugates with effective suppression for selective cancer treatment.

Due to the strong selectivity and permeability of tumor tissue, anti-cancer peptide-drug conjugates (PDCs) can accumulate high concentration of toxic payloads at the target, effectively killing tumor cells. This approach holds great promise for tumor-targeted treatment. In our previous study, we identified the optimal peptide P1 (NPNWGRSWYNQRFK) targeting HER2 from pertuzumab, a monoclonal antibody that blocks the HER2 signaling pathway. Here, a series of PDCs were constructed through connecting P1 and CPT with different linkers. Among these, Z8 emerged as the optimal compound, demonstrating good antitumor activity and targeting ability in biological activity tests. Z8 exhibited IC50 values of 1.04 ± 0.24 µM and 1.91 ± 0.71 µM against HER2-positive SK-BR-3 and NCI-N87 cells, respectively. Moreover, superior antitumor activity and higher biosafety of Z8 were observed compared to the positive control CPT in vivo, suggesting a novel idea for the construction of PDCs.

Decreasing exciton dissociation rates for reduced voltage losses in organic solar cells.

Enhancing the device electroluminescence quantum efficiency (EQEEL) is a critical factor in mitigating non-radiative voltage losses (VNR) and further improving the performance of organic solar cells (OSCs). While the common understanding attributes EQEEL in OSCs to the dynamics of charge transfer (CT) states, persistent efforts to manipulate these decay dynamics have yielded limited results, with the EQEEL of high-efficiency OSCs typically remaining below 10-2%. This value is considerably lower than that observed in high efficiency inorganic photovoltaic devices. Here, we report that EQEEL is also influenced by the dissociation rate constant of singlet states (kDS). Importantly, in contrast to the traditional belief that advocates maximizing kDS for superior photovoltaic quantum efficiency (EQEPV), a controlled reduction in kDS is shown to enhance EQEEL without compromising EQEPV. Consequently, a promising experimental approach to address the VNR challenge is proposed, resulting in a significant improvement in the performance of OSCs.

Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq.

Despite recent advances in single-cell genomics, the lack of maps for single-cell candidate cis-regulatory elements (cCREs) in non-mammal species has limited our exploration of conserved regulatory programs across vertebrates and invertebrates. Here, we developed a combinatorial-hybridization-based method for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) named CH-ATAC-seq, enabling the construction of single-cell accessible chromatin landscapes for zebrafish, Drosophila, and earthworms (Eisenia andrei). By integrating scATAC censuses of humans, monkeys, and mice, we systematically identified 152 distinct main cell types and around 0.8 million cell-type-specific cCREs. Our analysis provided insights into the conservation of neural, muscle, and immune lineages across species, while epithelial cells exhibited a higher organ-origin heterogeneity. Additionally, a large-scale gene regulatory network (GRN) was constructed in four vertebrates by integrating scRNA-seq censuses. Overall, our study provides a valuable resource for comparative epigenomics, identifying the evolutionary conservation and divergence of gene regulation across different species.

MnFe2O4-biochar decreases bioavailable fractions of thallium in highly acidic soils from pyrite mining area.

The prevalence of toxic element thallium (Tl) in soils is of increasing concern as a hidden hazard in agricultural systems and food chains. In the present work, pure biochar (as a comparison) and jacobsite (MnFe2O4)-biochar composite (MFBC) were evaluated for their immobilization effects in Tl-polluted agricultural soils (Tl: ∼10 mg/kg). Overall, MFBC exhibited an efficient effect on Tl immobilization, and the effect was strengthened with the increase of amendment ratio. After being amended by MFBC for 15 and 30 days, the labile fraction of Tl in soil decreased from 1.55 to 0.97 mg/kg, and from 1.51 to 0.88 mg/kg, respectively. In addition, pH (3.05) of the highly acidic soil increased to a maximum of 3.97 after the immobilization process. Since the weak acid extractable and oxidizable Tl were the preponderantly mitigated fractions and displayed a negative correlation with pH, it can be inferred that pH may serve as one of the most critical factors in regulating the Tl immobilization process in MFBC-amended acidic soils. This study indicated a great potential of jacobsite-biochar amendment in stabilization and immobilization of Tl in highly acidic and Tl-polluted agricultural soils; and it would bring considerable environmental benefit to these Tl-contaminated sites whose occurrence has significantly increased in recent decades near the pyrite or other sulfide ore mining and smelting area elsewhere.

Boosting Phosphoric Acid Retention in Polymer Electrolyte Membranes by Zwitterions: Insights from DFT Calculations and MD Simulations.

Effective retention of phosphoric acid (PA) is crucial for the efficient operation of fuel cells based on PA-doped polymeric membranes, which is highly challenging due to the moisture-induced loss of PA. Therefore, a comprehensive understanding of the interplay among PA, functional groups, and water is essential for designing membrane materials. Using density functional theory (DFT) calculations and molecular dynamics (MD) simulations, we unveil the remarkable capability of zwitterions to effectively sequester PA, thereby unlocking the potential for fuel cell optimization. Our DFT calculations show that zwitterions, termed "charged proton-accepting bases", exhibit stronger interactions with PA compared to the traditional neutral proton-accepting bases. Furthermore, the presence of water amplifies such a discrepancy, with the zwitterion-PA interactions playing a dominant role in the zwitterion-PA-water cluster due to the strongest affinity of zwitterions to PA. Conversely, the ability of neutral bases to retain PA is significantly attenuated by moisture as the interactions between water and PA surpass those between neutral bases and PA. The strong zwitterion-PA associations arise primarily from the formation of multiple hydrogen bonds. Furthermore, MD simulations reveal the uniform distribution of zwitterions in aqueous environments and their pronounced affinities for both PA and water. In contrast, neutral bases tend to aggregate, interacting limitedly with PA. These findings underscore the effectiveness of zwitterions in boosting PA retention in fuel cells.

The identification and expression of an interleukin-21 receptor in large yellow croaker (Larimichthys crocea).

BACKGROUND: We identified a homologue of IL-21R (LcIL-21R) in large yellow croaker (Larimichthys crocea, Lc). Our investigation focused on understanding the molecular structural features and immune function of LcIL-21R. METHODS: We cloned the LcIL-21R gene from the genome of Larimichthys crocea by RT‒PCR, and the molecular and structural characteristics of LcIL-21R were analyzed by a series of protein analysis tools. We used real-time PCR to investigate the tissue distribution of LcIL-21R, and LcIL-21R gene expression regulation was also measured in head kidney leukocytes under trivalent bacterial vaccine or poly (I:C) stimulation. RESULTS: The open reading frame (ORF) of the LcIL-21R gene is 1629 bp long and encodes a precursor protein of 542 amino acids (aa), with a 23-aa signal peptide and a 519-aa mature peptide containing four putative N-glycosylation sites. LcIL-21R has two fibronectin type III (FNIII)-like domains (D1 and D2), a transmembrane domain, and a cytoplasmic region. A conserved WSXWS motif was also found in the D2 domain. The predicted structure of the extracellular region of LcIL-21R (LcIL-21R-Ex) is highly similar to that of human IL-21R. LcIL-21R was constitutively expressed in all tissues examined, and LcIL-21R mRNA levels were increased in the head kidney and spleen upon inactivated trivalent bacterial vaccine or poly(I:C) stimulation. CONCLUSIONS: Our results suggest that LcIL-21R shares structural and functional properties with IL-21Rs found in other vertebrates, indicating its potential involvement in the IL-21-mediated immune response to pathogenic infections. These findings contribute to our understanding of the evolutionary conservation of IL-21 signaling and its role in the immune system.

Synthesis and fabrication of segregative and durable MnO2@chitosan composite aerogel beads for uranium(VI) removal from wastewater.

To address the imperative need for efficient removal of uranium-containing wastewater and mitigate radioactive contamination risks associated with nuclear energy, the development of materials with high removal efficiency and facile separation is crucial. This study designed and synthesised MnO2@chitosan (CTS) composite aerogel beads by in-situ growing δ-MnO2 on porous CTS aerogel beads. This approach not only mitigates the agglomeration of MnO2 nanospheres but also significantly enhances the porous structure and surface area of MnO2@CTS. These cost-effective and eco-friendly millimeter-scale spherical aerogels exhibited convenient separation properties after adsorption. These characteristics help mitigate the risk of equipment seam blockage and secondary pollution that are often associated with powdered adsorbents. Additionally, MnO2@CTS exhibited remarkable mechanical strength (stress approximately 0.55 MPa at 60 % strain), enabling rapid separation and easy regeneration while maintaining high adsorption performance even after five cycles. Significantly, MnO2@CTS exhibited a maximum adsorption capacity of 410.7 mg/g at pH 6 and 298 K, surpassing reported values for most CTS/MnO2-based adsorbents. The chemisorption process of U(VI) on MnO2@CTS followed the pseudo-second-order kinetic and Dubinin-Radushkevish models. X-ray photoelectron spectroscopy analysis further confirmed the reduction of U(VI) to U(V/IV). These findings highlight the substantial potential of MnO2@CTS aerogel beads for U(VI) removal from aqueous solutions, positioning them as a promising solution for addressing U(VI) contamination in wastewater.

The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model.

PURPOSE: By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations. METHODS: Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed. RESULTS: In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score. CONCLUSION: The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.

Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population.

BACKGROUND: Although the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data. RESULTS: We used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN. CONCLUSIONS: The SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Modelling porcine NAFLD by deletion of leptin and defining the role of AMPK in hepatic fibrosis.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic hepatic disease and results in non-alcoholic steatohepatitis (NASH), which progresses to fibrosis and cirrhosis. Although the Leptin deficient rodent models are widely used in study of metabolic syndrome and obesity, they fail to develop liver injuries as in patients. METHODS: Due to the high similarity with humans, we generated Leptin-deficient (Leptin-/-) pigs to investigate the mechanisms and clinical trials of obesity and NAFLD caused by Leptin. RESULTS: The Leptin-/- pigs showed increased body fat and significant insulin resistance at the age of 12 months. Moreover, Leptin-/- pig developed fatty liver, non-alcoholic steatohepatitis and hepatic fibrosis with age. Absence of Leptin in pig reduced the phosphorylation of JAK2-STAT3 and AMPK. The inactivation of JAK2-STAT3 and AMPK enhanced fatty acid ß-oxidation and leaded to mitochondrial autophagy respectively, and both contributed to increased oxidative stress in liver cells. In contrast with Leptin-/- pig, although Leptin deletion in rat liver inhibited JAK2-STAT3 phosphorylation, the activation of AMPK pathway might prevent liver injury. Therefore, ß-oxidation, mitochondrial autophagy and hepatic fibrosis did not occurred in Leptin-/- rat livers. CONCLUSIONS: The Leptin-deficient pigs presents an ideal model to illustrate the full spectrum of human NAFLD. The activity of AMPK signaling pathway suggests a potential target to develop new strategy for the diagnosis and treatment of NAFLD.

Interfacial Engineering of Block Copolymer Nanostructures: Morphology and Solvent Stability.

Interfacial engineering is a critical pathway for modulating the self-assembled nanostructures of block copolymers (BCPs) during solvent exchange. Herein, we demonstrated the generation of different stacked lamellae of polystyrene-block-poly(2-vinyl pyridine) (PS-b-P2VP) nanostructures during solvent exchange by using phosphotungstic acid (PTA) or PTA/NaCl aqueous solution as the nonsolvent. The participation of PTA in the confined microphase separation of PS-b-P2VP in droplets increases the volume fraction of P2VP and decreases the tension at the oil/water interface. Moreover, the addition of NaCl to the PTA solution can further increase the surface coverage of P2VP/PTA on droplets. All factors impact the morphology of assembled BCP nanostructures. In the presence of PTA, ellipsoidal particles composed of alternatively stacked lamellae of PS and P2VP were formed (named BP), whereas, in the coexistence of PTA and NaCl, they changed to stacked disks with PS-core-P2VP-shell (called BPN). The different structures of assembled particles induce their different stabilities in solvents and different dissociation conditions as well. The dissociation of BP particles was easy because PS chains were only entangled together which can be swollen in toluene or chloroform. However, the dissociation of BPN was hard, requiring an organic base in hot ethanol. The structural difference in BP and BPN particles further extended to their dissociated disks, which makes the cargo (like R6G) loaded on these disks to show a different stability in acetone. This study demonstrated that a subtle structural change can greatly affect their properties.

Serum metabolic profile and metabolome genome-wide association study in chicken.

BACKGROUND: Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied. RESULTS: Here, comprehensive metabolome detection was performed using non-targeted LC-MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids. CONCLUSIONS: We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding.

Loss of MuRF1 in Duroc pigs promotes skeletal muscle hypertrophy.

Muscle mass development depends on increased protein synthesis and reduced muscle protein degradation. Muscle ring-finger protein-1 (MuRF1) plays a key role in controlling muscle atrophy. Its E3 ubiquitin ligase activity recognizes and degrades skeletal muscle proteins through the ubiquitin-proteasome system. The loss of Murf1, which encodes MuRF1, in mice leads to the accumulation of skeletal muscle proteins and alleviation of muscle atrophy. However, the function of Murf1 in agricultural animals remains unclear. Herein, we bred F1 generation Murf1+/- and F2 generation Murf1-/- Duroc pigs from F0 Murf1-/- pigs to investigate the effect of Murf1 knockout on skeletal muscle development. We found that the Murf1+/- pigs retained normal levels of muscle growth and reproduction, and their percentage of lean meat increased by 6% compared to that of the wild type (WT) pigs. Furthermore, the meat color, pH, water-holding capacity, and tenderness of the Murf1+/- pigs were similar to those of the WT pigs. The drip loss rate and intramuscular fat decreased slightly in the Murf1+/- pigs. However, the cross-sectional area of the myofibers in the longissimus dorsi increased in the adult Murf1+/- pigs. The skeletal muscle proteins MYBPC3 and actin, which are targeted by MuRF1, accumulated in the Murf1+/- and Murf1-/- pigs. Our findings show that inhibiting muscle protein degradation in MuRF1-deficient Duroc pigs increases the size of their myofibers and their percentage of lean meat without influencing their growth or pork quality. Our study demonstrates that Murf1 is a target gene for promoting skeletal muscle hypertrophy in pig breeding.

Exploration of Janus Kinase (JAK) and Histone Deacetylase (HDAC) Bispecific Inhibitors Based on the Moiety of Fedratinib for Treatment of Both Hematologic Malignancies and Solid Cancers.

The feedback activation of the Janus kinase (JAK)-STAT pathway leads to the fact that solid cancers are not sensitive to histone deacetylase (HDAC) inhibitors. Herein, a series of novel 2-amino-4-phenylaminopyrimidine JAK/HDAC dual-target inhibitors based on the moiety of fedratinib were designed and synthesized. Among them, 21 and 30 potently inhibited HDAC3/6 and JAK1/2 at nanomolar levels and exhibited splendid selectivity for the JAK2 against a panel of 76 kinases. 21 and 30 presented remarkable antiproliferative activity in both hematological malignancies and solid cancers, which was endorsed by JAK-STAT and HDAC pathway blockade and proapoptotic activity. On the basis of great plasma stability and oral bioavailability, 21 and 30 effectively suppressed the tumor growth of HEL and A549 xenograft models. Collectively, the above results validate that JAK/HDAC dual-target inhibitors provide valuable clues for targeted treatment of hematological malignancies and solid cancers.

Interactions between micaceous minerals weathering and cesium adsorption.

The environmental behavior of radioactive cesium (RCs) in contaminated areas is generally governed by soil and sediment components and natural weathering conditions. In this study, desorption tests and spectroscopic approaches were used to explore the interaction between the weathering of micaceous minerals (i.e., biotite and phlogopite) and the adsorption of Cs+ and the critical role of weathering in the environmental behavior of RCs. Results showed that the reaction sequence between weathering and Cs+ adsorption significantly affected the surface species of Cs+ and the structure of biotite and phlogopite. Regardless of whether it occurred before, after, or during Cs+ adsorption, weathering generated more high-affinity adsorption sites, namely, interlayer sites (ITs) and frayed edge sites (FESs), to different extents, and then facilitated the uptake of Cs+ at FESs and ITs on micaceous minerals in a poorly exchangeable state. Cs+ stabilized the micaceous mineral structure once it was absorbed within collapsed interlayers by hindering cation exchange and preventing further destruction during weathering. As important weathering factors, high temperature and Ca2+ content promoted the binding of Cs+ in the interlayers of biotite and phlogopite by enhancing interlayer cation exchange. These findings are beneficial for a better understanding of the environmental behaviors of RCs in the hydrosphere and pedosphere.

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models.

BACKGROUND: To evaluate the diagnostic value of combinations of tumor markers carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, and CA19-9 in identifying malignant pleural effusion (MPE) from non-malignant pleural effusion (non-MPE) using machine learning, and compare the performance of popular machine learning methods. METHODS: A total of 319 samples were collected from patients with pleural effusion in Beijing and Wuhan, China, from January 2018 to June 2020. Five machine learning methods including Logistic regression, extreme gradient boosting (XGBoost), Bayesian additive regression tree, random forest, and support vector machine were applied to evaluate the diagnostic performance. Sensitivity, specificity, Youden's index, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of different diagnostic models. RESULTS: For diagnostic models with a single tumor marker, the model using CEA, constructed by XGBoost, performed best (AUC = 0.895, sensitivity = 0.80), and the model with CA153, also by XGBoost, showed the largest specificity 0.98. Among all combinations of tumor markers, the combination of CEA and CA153 achieved the best performance (AUC = 0.921, sensitivity = 0.85) in identifying MPE under the diagnostic model constructed by XGBoost. CONCLUSIONS: Diagnostic models for MPE with a combination of multiple tumor markers outperformed the models with a single tumor marker, particularly in sensitivity. Using machine learning methods, especially XGBoost, could comprehensively improve the diagnostic accuracy of MPE.